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MultiTabQA-tapex-Salesforce-codet5-base-markdown

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select count ( * ) where represents = 'chiriqui occidente' and represents = 'chiriqui' col : represents | contestant | age | height | hometown row 1 : bocas del toro | | | 0.0 m (0 in) | row 2 : chiriqui | | | 0.0 m (0 in) | row 3 : chiriqui occidente | | | 0.0 m (0 in) | row 4 : cocle | | | 0.0 m (0 in) | row 5 : colon | | | 0.0 m (0 in) | row 6 : darien | | | 0.0 m (0 in) | row 7 : comarcas | | | 0.0 m (0 in) | row 8 : herrera | | | 0.0 m (0 in) | row 9 : los santos | | | 0.0 m (0 in) | row 10 : panama centro | | | 0.0 m (0 in) | row 11 : panama este | | | 0.0 m (0 in) | row 12 : panama oeste | | | 0.0 m (0 in) | row 13 : veraguas | | | 0.0 m (0 in) |

col : count ( "*" ) row 1 : 0

select count ( * ) where represents = 'chiriqui occidente' and represents = 'chiriqui' <table_name> : table \begin{tabular}{llllll} \toprule & represents & contestant & age & height & hometown \\ \midrule 0 & bocas del toro & & & 0.0 m (0 in) & \\ 1 & chiriqui & & & 0.0 m (0 in) & \\ 2 & chiriqui occidente & & & 0.0 m (0 in) & \\ 3 & cocle & & & 0.0 m (0 in) & \\ 4 & colon & & & 0.0 m (0 in) & \\ 5 & darien & & & 0.0 m (0 in) & \\ 6 & comarcas & & & 0.0 m (0 in) & \\ 7 & herrera & & & 0.0 m (0 in) & \\ 8 & los santos & & & 0.0 m (0 in) & \\ 9 & panama centro & & & 0.0 m (0 in) & \\ 10 & panama este & & & 0.0 m (0 in) & \\ 11 & panama oeste & & & 0.0 m (0 in) & \\ 12 & veraguas & & & 0.0 m (0 in) | & NaN \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 0 \\ \bottomrule \end{tabular}

select count ( * ) where represents = 'chiriqui occidente' and represents = 'chiriqui' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>represents</th> <th>contestant</th> <th>age</th> <th>height</th> <th>hometown</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>bocas del toro</td> <td></td> <td></td> <td>0.0 m (0 in)</td> <td></td> </tr> <tr> <th>1</th> <td>chiriqui</td> <td></td> <td></td> <td>0.0 m (0 in)</td> <td></td> </tr> <tr> <th>2</th> <td>chiriqui occidente</td> <td></td> <td></td> <td>0.0 m (0 in)</td> <td></td> </tr> <tr> <th>3</th> <td>cocle</td> <td></td> <td></td> <td>0.0 m (0 in)</td> <td></td> </tr> <tr> <th>4</th> <td>colon</td> <td></td> <td></td> <td>0.0 m (0 in)</td> <td></td> </tr> <tr> <th>5</th> <td>darien</td> <td></td> <td></td> <td>0.0 m (0 in)</td> <td></td> </tr> <tr> <th>6</th> <td>comarcas</td> <td></td> <td></td> <td>0.0 m (0 in)</td> <td></td> </tr> <tr> <th>7</th> <td>herrera</td> <td></td> <td></td> <td>0.0 m (0 in)</td> <td></td> </tr> <tr> <th>8</th> <td>los santos</td> <td></td> <td></td> <td>0.0 m (0 in)</td> <td></td> </tr> <tr> <th>9</th> <td>panama centro</td> <td></td> <td></td> <td>0.0 m (0 in)</td> <td></td> </tr> <tr> <th>10</th> <td>panama este</td> <td></td> <td></td> <td>0.0 m (0 in)</td> <td></td> </tr> <tr> <th>11</th> <td>panama oeste</td> <td></td> <td></td> <td>0.0 m (0 in)</td> <td></td> </tr> <tr> <th>12</th> <td>veraguas</td> <td></td> <td></td> <td>0.0 m (0 in) |</td> <td>None</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "*" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>0</td> </tr> </tbody> </table>

select count ( * ) where represents = 'chiriqui occidente' and represents = 'chiriqui' <table_name> : table | | represents | contestant | age | height | hometown | |---:|:-------------------|:-------------|:------|:---------------|:-----------| | 0 | bocas del toro | | | 0.0 m (0 in) | | | 1 | chiriqui | | | 0.0 m (0 in) | | | 2 | chiriqui occidente | | | 0.0 m (0 in) | | | 3 | cocle | | | 0.0 m (0 in) | | | 4 | colon | | | 0.0 m (0 in) | | | 5 | darien | | | 0.0 m (0 in) | | | 6 | comarcas | | | 0.0 m (0 in) | | | 7 | herrera | | | 0.0 m (0 in) | | | 8 | los santos | | | 0.0 m (0 in) | | | 9 | panama centro | | | 0.0 m (0 in) | | | 10 | panama este | | | 0.0 m (0 in) | | | 11 | panama oeste | | | 0.0 m (0 in) | | | 12 | veraguas | | | 0.0 m (0 in) | | |

| | count ( "*" ) | |---:|----------------:| | 0 | 0 |

select count ( * ) where player = 'todor yanchev' col : player | league | cup | europa league | total row 1 : spas delev | 13 | 7 | 2 | 22 row 2 : marquinhos | 9 | 1 | 3 | 13 row 3 : michel platini | 10 | 0 | 0 | 10 row 4 : cillian sheridan | 4 | 2 | 1 | 7 row 5 : giuseppe aquaro | 3 | 0 | 2 | 5 row 6 : gregory nelson | 3 | 0 | 1 | 4 row 7 : rumen trifonov | 2 | 0 | 1 | 3 row 8 : aleksandar tonev | 2 | 0 | 0 | 2 row 9 : apostol popov | 2 | 0 | 0 | 2 row 10 : emil gargorov | 2 | 0 | 0 | 2 row 11 : todor yanchev | 0 | 1 | 1 | 2 row 12 : stanislav kostov | 1 | 0 | 0 | 1 row 13 : kostadin stoyanov | 1 | 0 | 0 | 1 row 14 : boris galchev | 1 | 0 | 0 | 1 row 15 : pavel vidanov | 0 | 0 | 1 | 1 row 16 : tomislav kostadinov | 0 | 0 | 1 | 1 row 17 : christian tiboni | 0 | 0 | 1 | 1 row 18 : total | 53 | 11 | 14 | 78

col : count ( "*" ) row 1 : 1

select count ( * ) where player = 'todor yanchev' <table_name> : table \begin{tabular}{llllll} \toprule & player & league & cup & europa league & total \\ \midrule 0 & spas delev & 13 & 7 & 2 & 22 \\ 1 & marquinhos & 9 & 1 & 3 & 13 \\ 2 & michel platini & 10 & 0 & 0 & 10 \\ 3 & cillian sheridan & 4 & 2 & 1 & 7 \\ 4 & giuseppe aquaro & 3 & 0 & 2 & 5 \\ 5 & gregory nelson & 3 & 0 & 1 & 4 \\ 6 & rumen trifonov & 2 & 0 & 1 & 3 \\ 7 & aleksandar tonev & 2 & 0 & 0 & 2 \\ 8 & apostol popov & 2 & 0 & 0 & 2 \\ 9 & emil gargorov & 2 & 0 & 0 & 2 \\ 10 & todor yanchev & 0 & 1 & 1 & 2 \\ 11 & stanislav kostov & 1 & 0 & 0 & 1 \\ 12 & kostadin stoyanov & 1 & 0 & 0 & 1 \\ 13 & boris galchev & 1 & 0 & 0 & 1 \\ 14 & pavel vidanov & 0 & 0 & 1 & 1 \\ 15 & tomislav kostadinov & 0 & 0 & 1 & 1 \\ 16 & christian tiboni & 0 & 0 & 1 & 1 \\ 17 & total & 53 & 11 & 14 & 78 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 1 \\ \bottomrule \end{tabular}

select count ( * ) where player = 'todor yanchev' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>player</th> <th>league</th> <th>cup</th> <th>europa league</th> <th>total</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>spas delev</td> <td>13</td> <td>7</td> <td>2</td> <td>22</td> </tr> <tr> <th>1</th> <td>marquinhos</td> <td>9</td> <td>1</td> <td>3</td> <td>13</td> </tr> <tr> <th>2</th> <td>michel platini</td> <td>10</td> <td>0</td> <td>0</td> <td>10</td> </tr> <tr> <th>3</th> <td>cillian sheridan</td> <td>4</td> <td>2</td> <td>1</td> <td>7</td> </tr> <tr> <th>4</th> <td>giuseppe aquaro</td> <td>3</td> <td>0</td> <td>2</td> <td>5</td> </tr> <tr> <th>5</th> <td>gregory nelson</td> <td>3</td> <td>0</td> <td>1</td> <td>4</td> </tr> <tr> <th>6</th> <td>rumen trifonov</td> <td>2</td> <td>0</td> <td>1</td> <td>3</td> </tr> <tr> <th>7</th> <td>aleksandar tonev</td> <td>2</td> <td>0</td> <td>0</td> <td>2</td> </tr> <tr> <th>8</th> <td>apostol popov</td> <td>2</td> <td>0</td> <td>0</td> <td>2</td> </tr> <tr> <th>9</th> <td>emil gargorov</td> <td>2</td> <td>0</td> <td>0</td> <td>2</td> </tr> <tr> <th>10</th> <td>todor yanchev</td> <td>0</td> <td>1</td> <td>1</td> <td>2</td> </tr> <tr> <th>11</th> <td>stanislav kostov</td> <td>1</td> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <th>12</th> <td>kostadin stoyanov</td> <td>1</td> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <th>13</th> <td>boris galchev</td> <td>1</td> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <th>14</th> <td>pavel vidanov</td> <td>0</td> <td>0</td> <td>1</td> <td>1</td> </tr> <tr> <th>15</th> <td>tomislav kostadinov</td> <td>0</td> <td>0</td> <td>1</td> <td>1</td> </tr> <tr> <th>16</th> <td>christian tiboni</td> <td>0</td> <td>0</td> <td>1</td> <td>1</td> </tr> <tr> <th>17</th> <td>total</td> <td>53</td> <td>11</td> <td>14</td> <td>78</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "*" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> </tr> </tbody> </table>

select count ( * ) where player = 'todor yanchev' <table_name> : table | | player | league | cup | europa league | total | |---:|:--------------------|---------:|------:|----------------:|--------:| | 0 | spas delev | 13 | 7 | 2 | 22 | | 1 | marquinhos | 9 | 1 | 3 | 13 | | 2 | michel platini | 10 | 0 | 0 | 10 | | 3 | cillian sheridan | 4 | 2 | 1 | 7 | | 4 | giuseppe aquaro | 3 | 0 | 2 | 5 | | 5 | gregory nelson | 3 | 0 | 1 | 4 | | 6 | rumen trifonov | 2 | 0 | 1 | 3 | | 7 | aleksandar tonev | 2 | 0 | 0 | 2 | | 8 | apostol popov | 2 | 0 | 0 | 2 | | 9 | emil gargorov | 2 | 0 | 0 | 2 | | 10 | todor yanchev | 0 | 1 | 1 | 2 | | 11 | stanislav kostov | 1 | 0 | 0 | 1 | | 12 | kostadin stoyanov | 1 | 0 | 0 | 1 | | 13 | boris galchev | 1 | 0 | 0 | 1 | | 14 | pavel vidanov | 0 | 0 | 1 | 1 | | 15 | tomislav kostadinov | 0 | 0 | 1 | 1 | | 16 | christian tiboni | 0 | 0 | 1 | 1 | | 17 | total | 53 | 11 | 14 | 78 |

| | count ( "*" ) | |---:|----------------:| | 0 | 1 |

select max ( nation ) where nation = 'argentina' col : rank | nation | gold | silver | bronze | total row 1 : 1 | brazil | 9 | 0 | 0 | 9 row 2 : 2 | argentina | 0 | 7 | 1 | 8 row 3 : 3 | uruguay | 0 | 1 | 4 | 5 row 4 : 4 | dominican republic | 0 | 1 | 0 | 1 row 5 : 5 | chile | 0 | 0 | 1 | 1 row 6 : 5 | greenland | 0 | 0 | 1 | 1 row 7 : 5 | paraguay | 0 | 0 | 1 | 1 row 8 : 5 | puerto rico | 0 | 0 | 1 | 1 row 9 : total | total | 9 | 9 | 9 | 27

col : max ( "nation" ) row 1 : argentina

select max ( nation ) where nation = 'argentina' <table_name> : table \begin{tabular}{lllllll} \toprule & rank & nation & gold & silver & bronze & total \\ \midrule 0 & 1 & brazil & 9 & 0 & 0 & 9 \\ 1 & 2 & argentina & 0 & 7 & 1 & 8 \\ 2 & 3 & uruguay & 0 & 1 & 4 & 5 \\ 3 & 4 & dominican republic & 0 & 1 & 0 & 1 \\ 4 & 5 & chile & 0 & 0 & 1 & 1 \\ 5 & 5 & greenland & 0 & 0 & 1 & 1 \\ 6 & 5 & paraguay & 0 & 0 & 1 & 1 \\ 7 & 5 & puerto rico & 0 & 0 & 1 & 1 \\ 8 & total & total & 9 & 9 & 9 & 27 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & max ( "nation" ) \\ \midrule 0 & argentina \\ \bottomrule \end{tabular}

select max ( nation ) where nation = 'argentina' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>rank</th> <th>nation</th> <th>gold</th> <th>silver</th> <th>bronze</th> <th>total</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>brazil</td> <td>9</td> <td>0</td> <td>0</td> <td>9</td> </tr> <tr> <th>1</th> <td>2</td> <td>argentina</td> <td>0</td> <td>7</td> <td>1</td> <td>8</td> </tr> <tr> <th>2</th> <td>3</td> <td>uruguay</td> <td>0</td> <td>1</td> <td>4</td> <td>5</td> </tr> <tr> <th>3</th> <td>4</td> <td>dominican republic</td> <td>0</td> <td>1</td> <td>0</td> <td>1</td> </tr> <tr> <th>4</th> <td>5</td> <td>chile</td> <td>0</td> <td>0</td> <td>1</td> <td>1</td> </tr> <tr> <th>5</th> <td>5</td> <td>greenland</td> <td>0</td> <td>0</td> <td>1</td> <td>1</td> </tr> <tr> <th>6</th> <td>5</td> <td>paraguay</td> <td>0</td> <td>0</td> <td>1</td> <td>1</td> </tr> <tr> <th>7</th> <td>5</td> <td>puerto rico</td> <td>0</td> <td>0</td> <td>1</td> <td>1</td> </tr> <tr> <th>8</th> <td>total</td> <td>total</td> <td>9</td> <td>9</td> <td>9</td> <td>27</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>max ( "nation" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>argentina</td> </tr> </tbody> </table>

select max ( nation ) where nation = 'argentina' <table_name> : table | | rank | nation | gold | silver | bronze | total | |---:|:-------|:-------------------|-------:|---------:|---------:|--------:| | 0 | 1 | brazil | 9 | 0 | 0 | 9 | | 1 | 2 | argentina | 0 | 7 | 1 | 8 | | 2 | 3 | uruguay | 0 | 1 | 4 | 5 | | 3 | 4 | dominican republic | 0 | 1 | 0 | 1 | | 4 | 5 | chile | 0 | 0 | 1 | 1 | | 5 | 5 | greenland | 0 | 0 | 1 | 1 | | 6 | 5 | paraguay | 0 | 0 | 1 | 1 | | 7 | 5 | puerto rico | 0 | 0 | 1 | 1 | | 8 | total | total | 9 | 9 | 9 | 27 |

| | max ( "nation" ) | |---:|:-------------------| | 0 | argentina |

select count ( * ) where position = 'fw' col : name | nation | position | league apps | league goals | fa cup apps | fa cup goals | total apps | total goals row 1 : john battye | england | df | 22 | 0 | 0 | 0 | 22 | 0 row 2 : eddie boot | england | df | 38 | 0 | 1 | 0 | 39 | 0 row 3 : tom briggs | england | df | 4 | 0 | 0 | 0 | 4 | 0 row 4 : ronnie burke | england | fw | 12 | 5 | 0 | 0 | 12 | 5 row 5 : ian duthie | scotland | mf | 1 | 0 | 0 | 0 | 1 | 0 row 6 : charlie gallogly | northern ireland | df | 15 | 0 | 0 | 0 | 15 | 0 row 7 : jimmy glazzard | england | fw | 21 | 5 | 1 | 0 | 22 | 5 row 8 : harold hassall | england | fw | 10 | 4 | 1 | 0 | 11 | 4 row 9 : bill hayes | republic of ireland | df | 17 | 0 | 1 | 0 | 18 | 0 row 10 : george hepplewhite | england | df | 36 | 0 | 1 | 0 | 37 | 0 row 11 : bob hesford | england | gk | 6 | 0 | 0 | 0 | 6 | 0 row 12 : george howe | england | df | 5 | 0 | 0 | 0 | 5 | 0 row 13 : jack howe | england | df | 20 | 1 | 1 | 0 | 21 | 1 row 14 : donald hunter | england | df | 7 | 0 | 0 | 0 | 7 | 0 row 15 : joe lynn | england | mf | 5 | 0 | 0 | 0 | 5 | 0 row 16 : don mcevoy | england | df | 5 | 2 | 0 | 0 | 5 | 2 row 17 : johnny mckenna | northern ireland | mf | 40 | 3 | 1 | 0 | 41 | 3 row 18 : vic metcalfe | england | mf | 41 | 11 | 1 | 0 | 42 | 11 row 19 : harry mills | england | gk | 34 | 0 | 1 | 0 | 35 | 0 row 20 : lol morgan | england | df | 6 | 0 | 0 | 0 | 6 | 0 row 21 : albert nightingale | england | mf | 39 | 7 | 1 | 0 | 40 | 7 row 22 : jack percival | england | df | 7 | 0 | 0 | 0 | 7 | 0 row 23 : arnold rodgers | england | fw | 4 | 2 | 0 | 0 | 4 | 2 row 24 : conway smith | england | mf | 10 | 0 | 0 | 0 | 10 | 0 row 25 : henry stewart | england | df | 15 | 0 | 0 | 0 | 15 | 0 row 26 : jeff taylor | england | fw | 21 | 11 | 0 | 0 | 21 | 11 row 27 : ray taylor | england | mf | 2 | 0 | 0 | 0 | 2 | 0 row 28 : jack wheeler | england | gk | 2 | 0 | 0 | 0 | 2 | 0 row 29 : bill whittaker | england | df | 16 | 0 | 0 | 0 | 16 | 0 row 30 : harry yates | england | mf | 1 | 0 | 0 | 0 | 1 | 0

col : count ( "*" ) row 1 : 5

select count ( * ) where position = 'fw' <table_name> : table \begin{tabular}{llllllllll} \toprule & name & nation & position & league apps & league goals & fa cup apps & fa cup goals & total apps & total goals \\ \midrule 0 & john battye & england & df & 22 & 0 & 0 & 0 & 22 & 0 \\ 1 & eddie boot & england & df & 38 & 0 & 1 & 0 & 39 & 0 \\ 2 & tom briggs & england & df & 4 & 0 & 0 & 0 & 4 & 0 \\ 3 & ronnie burke & england & fw & 12 & 5 & 0 & 0 & 12 & 5 \\ 4 & ian duthie & scotland & mf & 1 & 0 & 0 & 0 & 1 & 0 \\ 5 & charlie gallogly & northern ireland & df & 15 & 0 & 0 & 0 & 15 & 0 \\ 6 & jimmy glazzard & england & fw & 21 & 5 & 1 & 0 & 22 & 5 \\ 7 & harold hassall & england & fw & 10 & 4 & 1 & 0 & 11 & 4 \\ 8 & bill hayes & republic of ireland & df & 17 & 0 & 1 & 0 & 18 & 0 \\ 9 & george hepplewhite & england & df & 36 & 0 & 1 & 0 & 37 & 0 \\ 10 & bob hesford & england & gk & 6 & 0 & 0 & 0 & 6 & 0 \\ 11 & george howe & england & df & 5 & 0 & 0 & 0 & 5 & 0 \\ 12 & jack howe & england & df & 20 & 1 & 1 & 0 & 21 & 1 \\ 13 & donald hunter & england & df & 7 & 0 & 0 & 0 & 7 & 0 \\ 14 & joe lynn & england & mf & 5 & 0 & 0 & 0 & 5 & 0 \\ 15 & don mcevoy & england & df & 5 & 2 & 0 & 0 & 5 & 2 \\ 16 & johnny mckenna & northern ireland & mf & 40 & 3 & 1 & 0 & 41 & 3 \\ 17 & vic metcalfe & england & mf & 41 & 11 & 1 & 0 & 42 & 11 \\ 18 & harry mills & england & gk & 34 & 0 & 1 & 0 & 35 & 0 \\ 19 & lol morgan & england & df & 6 & 0 & 0 & 0 & 6 & 0 \\ 20 & albert nightingale & england & mf & 39 & 7 & 1 & 0 & 40 & 7 \\ 21 & jack percival & england & df & 7 & 0 & 0 & 0 & 7 & 0 \\ 22 & arnold rodgers & england & fw & 4 & 2 & 0 & 0 & 4 & 2 \\ 23 & conway smith & england & mf & 10 & 0 & 0 & 0 & 10 & 0 \\ 24 & henry stewart & england & df & 15 & 0 & 0 & 0 & 15 & 0 \\ 25 & jeff taylor & england & fw & 21 & 11 & 0 & 0 & 21 & 11 \\ 26 & ray taylor & england & mf & 2 & 0 & 0 & 0 & 2 & 0 \\ 27 & jack wheeler & england & gk & 2 & 0 & 0 & 0 & 2 & 0 \\ 28 & bill whittaker & england & df & 16 & 0 & 0 & 0 & 16 & 0 \\ 29 & harry yates & england & mf & 1 & 0 & 0 & 0 & 1 & 0 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 5 \\ \bottomrule \end{tabular}

select count ( * ) where position = 'fw' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>name</th> <th>nation</th> <th>position</th> <th>league apps</th> <th>league goals</th> <th>fa cup apps</th> <th>fa cup goals</th> <th>total apps</th> <th>total goals</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>john battye</td> <td>england</td> <td>df</td> <td>22</td> <td>0</td> <td>0</td> <td>0</td> <td>22</td> <td>0</td> </tr> <tr> <th>1</th> <td>eddie boot</td> <td>england</td> <td>df</td> <td>38</td> <td>0</td> <td>1</td> <td>0</td> <td>39</td> <td>0</td> </tr> <tr> <th>2</th> <td>tom briggs</td> <td>england</td> <td>df</td> <td>4</td> <td>0</td> <td>0</td> <td>0</td> <td>4</td> <td>0</td> </tr> <tr> <th>3</th> <td>ronnie burke</td> <td>england</td> <td>fw</td> <td>12</td> <td>5</td> <td>0</td> <td>0</td> <td>12</td> <td>5</td> </tr> <tr> <th>4</th> <td>ian duthie</td> <td>scotland</td> <td>mf</td> <td>1</td> <td>0</td> <td>0</td> <td>0</td> <td>1</td> <td>0</td> </tr> <tr> <th>5</th> <td>charlie gallogly</td> <td>northern ireland</td> <td>df</td> <td>15</td> <td>0</td> <td>0</td> <td>0</td> <td>15</td> <td>0</td> </tr> <tr> <th>6</th> <td>jimmy glazzard</td> <td>england</td> <td>fw</td> <td>21</td> <td>5</td> <td>1</td> <td>0</td> <td>22</td> <td>5</td> </tr> <tr> <th>7</th> <td>harold hassall</td> <td>england</td> <td>fw</td> <td>10</td> <td>4</td> <td>1</td> <td>0</td> <td>11</td> <td>4</td> </tr> <tr> <th>8</th> <td>bill hayes</td> <td>republic of ireland</td> <td>df</td> <td>17</td> <td>0</td> <td>1</td> <td>0</td> <td>18</td> <td>0</td> </tr> <tr> <th>9</th> <td>george hepplewhite</td> <td>england</td> <td>df</td> <td>36</td> <td>0</td> <td>1</td> <td>0</td> <td>37</td> <td>0</td> </tr> <tr> <th>10</th> <td>bob hesford</td> <td>england</td> <td>gk</td> <td>6</td> <td>0</td> <td>0</td> <td>0</td> <td>6</td> <td>0</td> </tr> <tr> <th>11</th> <td>george howe</td> <td>england</td> <td>df</td> <td>5</td> <td>0</td> <td>0</td> <td>0</td> <td>5</td> <td>0</td> </tr> <tr> <th>12</th> <td>jack howe</td> <td>england</td> <td>df</td> <td>20</td> <td>1</td> <td>1</td> <td>0</td> <td>21</td> <td>1</td> </tr> <tr> <th>13</th> <td>donald hunter</td> <td>england</td> <td>df</td> <td>7</td> <td>0</td> <td>0</td> <td>0</td> <td>7</td> <td>0</td> </tr> <tr> <th>14</th> <td>joe lynn</td> <td>england</td> <td>mf</td> <td>5</td> <td>0</td> <td>0</td> <td>0</td> <td>5</td> <td>0</td> </tr> <tr> <th>15</th> <td>don mcevoy</td> <td>england</td> <td>df</td> <td>5</td> <td>2</td> <td>0</td> <td>0</td> <td>5</td> <td>2</td> </tr> <tr> <th>16</th> <td>johnny mckenna</td> <td>northern ireland</td> <td>mf</td> <td>40</td> <td>3</td> <td>1</td> <td>0</td> <td>41</td> <td>3</td> </tr> <tr> <th>17</th> <td>vic metcalfe</td> <td>england</td> <td>mf</td> <td>41</td> <td>11</td> <td>1</td> <td>0</td> <td>42</td> <td>11</td> </tr> <tr> <th>18</th> <td>harry mills</td> <td>england</td> <td>gk</td> <td>34</td> <td>0</td> <td>1</td> <td>0</td> <td>35</td> <td>0</td> </tr> <tr> <th>19</th> <td>lol morgan</td> <td>england</td> <td>df</td> <td>6</td> <td>0</td> <td>0</td> <td>0</td> <td>6</td> <td>0</td> </tr> <tr> <th>20</th> <td>albert nightingale</td> <td>england</td> <td>mf</td> <td>39</td> <td>7</td> <td>1</td> <td>0</td> <td>40</td> <td>7</td> </tr> <tr> <th>21</th> <td>jack percival</td> <td>england</td> <td>df</td> <td>7</td> <td>0</td> <td>0</td> <td>0</td> <td>7</td> <td>0</td> </tr> <tr> <th>22</th> <td>arnold rodgers</td> <td>england</td> <td>fw</td> <td>4</td> <td>2</td> <td>0</td> <td>0</td> <td>4</td> <td>2</td> </tr> <tr> <th>23</th> <td>conway smith</td> <td>england</td> <td>mf</td> <td>10</td> <td>0</td> <td>0</td> <td>0</td> <td>10</td> <td>0</td> </tr> <tr> <th>24</th> <td>henry stewart</td> <td>england</td> <td>df</td> <td>15</td> <td>0</td> <td>0</td> <td>0</td> <td>15</td> <td>0</td> </tr> <tr> <th>25</th> <td>jeff taylor</td> <td>england</td> <td>fw</td> <td>21</td> <td>11</td> <td>0</td> <td>0</td> <td>21</td> <td>11</td> </tr> <tr> <th>26</th> <td>ray taylor</td> <td>england</td> <td>mf</td> <td>2</td> <td>0</td> <td>0</td> <td>0</td> <td>2</td> <td>0</td> </tr> <tr> <th>27</th> <td>jack wheeler</td> <td>england</td> <td>gk</td> <td>2</td> <td>0</td> <td>0</td> <td>0</td> <td>2</td> <td>0</td> </tr> <tr> <th>28</th> <td>bill whittaker</td> <td>england</td> <td>df</td> <td>16</td> <td>0</td> <td>0</td> <td>0</td> <td>16</td> <td>0</td> </tr> <tr> <th>29</th> <td>harry yates</td> <td>england</td> <td>mf</td> <td>1</td> <td>0</td> <td>0</td> <td>0</td> <td>1</td> <td>0</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "*" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>5</td> </tr> </tbody> </table>

select count ( * ) where position = 'fw' <table_name> : table | | name | nation | position | league apps | league goals | fa cup apps | fa cup goals | total apps | total goals | |---:|:-------------------|:--------------------|:-----------|--------------:|---------------:|--------------:|---------------:|-------------:|--------------:| | 0 | john battye | england | df | 22 | 0 | 0 | 0 | 22 | 0 | | 1 | eddie boot | england | df | 38 | 0 | 1 | 0 | 39 | 0 | | 2 | tom briggs | england | df | 4 | 0 | 0 | 0 | 4 | 0 | | 3 | ronnie burke | england | fw | 12 | 5 | 0 | 0 | 12 | 5 | | 4 | ian duthie | scotland | mf | 1 | 0 | 0 | 0 | 1 | 0 | | 5 | charlie gallogly | northern ireland | df | 15 | 0 | 0 | 0 | 15 | 0 | | 6 | jimmy glazzard | england | fw | 21 | 5 | 1 | 0 | 22 | 5 | | 7 | harold hassall | england | fw | 10 | 4 | 1 | 0 | 11 | 4 | | 8 | bill hayes | republic of ireland | df | 17 | 0 | 1 | 0 | 18 | 0 | | 9 | george hepplewhite | england | df | 36 | 0 | 1 | 0 | 37 | 0 | | 10 | bob hesford | england | gk | 6 | 0 | 0 | 0 | 6 | 0 | | 11 | george howe | england | df | 5 | 0 | 0 | 0 | 5 | 0 | | 12 | jack howe | england | df | 20 | 1 | 1 | 0 | 21 | 1 | | 13 | donald hunter | england | df | 7 | 0 | 0 | 0 | 7 | 0 | | 14 | joe lynn | england | mf | 5 | 0 | 0 | 0 | 5 | 0 | | 15 | don mcevoy | england | df | 5 | 2 | 0 | 0 | 5 | 2 | | 16 | johnny mckenna | northern ireland | mf | 40 | 3 | 1 | 0 | 41 | 3 | | 17 | vic metcalfe | england | mf | 41 | 11 | 1 | 0 | 42 | 11 | | 18 | harry mills | england | gk | 34 | 0 | 1 | 0 | 35 | 0 | | 19 | lol morgan | england | df | 6 | 0 | 0 | 0 | 6 | 0 | | 20 | albert nightingale | england | mf | 39 | 7 | 1 | 0 | 40 | 7 | | 21 | jack percival | england | df | 7 | 0 | 0 | 0 | 7 | 0 | | 22 | arnold rodgers | england | fw | 4 | 2 | 0 | 0 | 4 | 2 | | 23 | conway smith | england | mf | 10 | 0 | 0 | 0 | 10 | 0 | | 24 | henry stewart | england | df | 15 | 0 | 0 | 0 | 15 | 0 | | 25 | jeff taylor | england | fw | 21 | 11 | 0 | 0 | 21 | 11 | | 26 | ray taylor | england | mf | 2 | 0 | 0 | 0 | 2 | 0 | | 27 | jack wheeler | england | gk | 2 | 0 | 0 | 0 | 2 | 0 | | 28 | bill whittaker | england | df | 16 | 0 | 0 | 0 | 16 | 0 | | 29 | harry yates | england | mf | 1 | 0 | 0 | 0 | 1 | 0 |

| | count ( "*" ) | |---:|----------------:| | 0 | 5 |

select count ( * ) where from = '17 march 1516' col : name | from | until | relationship with predecessor row 1 : cosimo de' medici (pater patriae) | 1434 | 1 august 1464 | son of giovanni di bicci de' medici who was not as prominently involved in florentine politics, rather more involved in the financial area row 2 : piero i de' medici (piero the gouty) | 1 august 1464 | 2 december 1469 | eldest son of cosimo de' medici row 3 : lorenzo i de' medici (lorenzo the magnificent) | 2 december 1469 | 9 april 1492 | eldest son of piero i de' medici row 4 : piero ii de' medici (piero the unfortunate) | 9 april 1492 | 8 november 1494 | eldest son of lorenzo the magnificent. overthrown when charles viii of france invaded as a full republic was restored, first under the theocracy of girolamo savonarola and then statesman piero soderini row 5 : cardinal giovanni de' medici | 31 august 1512 | 9 march 1513 | brother of piero the unfortunate, second son of lorenzo the magnificent. elected to the papacy, becoming pope leo x row 6 : giuliano de' medici, duke of nemours | 9 march 1513 | 17 march 1516 | brother of cardinal giovanni de' medici, third son of lorenzo the magnificent row 7 : lorenzo ii de' medici, duke of urbino | 17 march 1516 | 4 may 1519 | nephew of giuliano de' medici, duke of nemours, son of piero the unfortunate row 8 : cardinal giulio de' medici | 4 may 1519 | 19 november 1523 | cousin of lorenzo ii de' medici, duke of urbino, son of giuliano de' medici who was the brother of lorenzo the magnificent. elected to the papacy, becoming pope clement vii row 9 : cardinal ippolito de' medici | 19 november 1523 | 24 october 1529 | cousin of cardinal giulio de' medici, illegitimate son of giuliano de' medici, duke of nemours

col : count ( "*" ) row 1 : 1

select count ( * ) where from = '17 march 1516' <table_name> : table \begin{tabular}{lllll} \toprule & name & from & until & relationship with predecessor \\ \midrule 0 & cosimo de' medici (pater patriae) & 1434 & 1 august 1464 & son of giovanni di bicci de' medici who was not as prominently involved in florentine politics, rather more involved in the financial area \\ 1 & piero i de' medici (piero the gouty) & 1 august 1464 & 2 december 1469 & eldest son of cosimo de' medici \\ 2 & lorenzo i de' medici (lorenzo the magnificent) & 2 december 1469 & 9 april 1492 & eldest son of piero i de' medici \\ 3 & piero ii de' medici (piero the unfortunate) & 9 april 1492 & 8 november 1494 & eldest son of lorenzo the magnificent. overthrown when charles viii of france invaded as a full republic was restored, first under the theocracy of girolamo savonarola and then statesman piero soderini \\ 4 & cardinal giovanni de' medici & 31 august 1512 & 9 march 1513 & brother of piero the unfortunate, second son of lorenzo the magnificent. elected to the papacy, becoming pope leo x \\ 5 & giuliano de' medici, duke of nemours & 9 march 1513 & 17 march 1516 & brother of cardinal giovanni de' medici, third son of lorenzo the magnificent \\ 6 & lorenzo ii de' medici, duke of urbino & 17 march 1516 & 4 may 1519 & nephew of giuliano de' medici, duke of nemours, son of piero the unfortunate \\ 7 & cardinal giulio de' medici & 4 may 1519 & 19 november 1523 & cousin of lorenzo ii de' medici, duke of urbino, son of giuliano de' medici who was the brother of lorenzo the magnificent. elected to the papacy, becoming pope clement vii \\ 8 & cardinal ippolito de' medici & 19 november 1523 & 24 october 1529 & cousin of cardinal giulio de' medici, illegitimate son of giuliano de' medici, duke of nemours \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 1 \\ \bottomrule \end{tabular}

select count ( * ) where from = '17 march 1516' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>name</th> <th>from</th> <th>until</th> <th>relationship with predecessor</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>cosimo de' medici (pater patriae)</td> <td>1434</td> <td>1 august 1464</td> <td>son of giovanni di bicci de' medici who was not as prominently involved in florentine politics, rather more involved in the financial area</td> </tr> <tr> <th>1</th> <td>piero i de' medici (piero the gouty)</td> <td>1 august 1464</td> <td>2 december 1469</td> <td>eldest son of cosimo de' medici</td> </tr> <tr> <th>2</th> <td>lorenzo i de' medici (lorenzo the magnificent)</td> <td>2 december 1469</td> <td>9 april 1492</td> <td>eldest son of piero i de' medici</td> </tr> <tr> <th>3</th> <td>piero ii de' medici (piero the unfortunate)</td> <td>9 april 1492</td> <td>8 november 1494</td> <td>eldest son of lorenzo the magnificent. overthrown when charles viii of france invaded as a full republic was restored, first under the theocracy of girolamo savonarola and then statesman piero soderini</td> </tr> <tr> <th>4</th> <td>cardinal giovanni de' medici</td> <td>31 august 1512</td> <td>9 march 1513</td> <td>brother of piero the unfortunate, second son of lorenzo the magnificent. elected to the papacy, becoming pope leo x</td> </tr> <tr> <th>5</th> <td>giuliano de' medici, duke of nemours</td> <td>9 march 1513</td> <td>17 march 1516</td> <td>brother of cardinal giovanni de' medici, third son of lorenzo the magnificent</td> </tr> <tr> <th>6</th> <td>lorenzo ii de' medici, duke of urbino</td> <td>17 march 1516</td> <td>4 may 1519</td> <td>nephew of giuliano de' medici, duke of nemours, son of piero the unfortunate</td> </tr> <tr> <th>7</th> <td>cardinal giulio de' medici</td> <td>4 may 1519</td> <td>19 november 1523</td> <td>cousin of lorenzo ii de' medici, duke of urbino, son of giuliano de' medici who was the brother of lorenzo the magnificent. elected to the papacy, becoming pope clement vii</td> </tr> <tr> <th>8</th> <td>cardinal ippolito de' medici</td> <td>19 november 1523</td> <td>24 october 1529</td> <td>cousin of cardinal giulio de' medici, illegitimate son of giuliano de' medici, duke of nemours</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "*" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> </tr> </tbody> </table>

select count ( * ) where from = '17 march 1516' <table_name> : table | | name | from | until | relationship with predecessor | |---:|:-----------------------------------------------|:-----------------|:-----------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | cosimo de' medici (pater patriae) | 1434 | 1 august 1464 | son of giovanni di bicci de' medici who was not as prominently involved in florentine politics, rather more involved in the financial area | | 1 | piero i de' medici (piero the gouty) | 1 august 1464 | 2 december 1469 | eldest son of cosimo de' medici | | 2 | lorenzo i de' medici (lorenzo the magnificent) | 2 december 1469 | 9 april 1492 | eldest son of piero i de' medici | | 3 | piero ii de' medici (piero the unfortunate) | 9 april 1492 | 8 november 1494 | eldest son of lorenzo the magnificent. overthrown when charles viii of france invaded as a full republic was restored, first under the theocracy of girolamo savonarola and then statesman piero soderini | | 4 | cardinal giovanni de' medici | 31 august 1512 | 9 march 1513 | brother of piero the unfortunate, second son of lorenzo the magnificent. elected to the papacy, becoming pope leo x | | 5 | giuliano de' medici, duke of nemours | 9 march 1513 | 17 march 1516 | brother of cardinal giovanni de' medici, third son of lorenzo the magnificent | | 6 | lorenzo ii de' medici, duke of urbino | 17 march 1516 | 4 may 1519 | nephew of giuliano de' medici, duke of nemours, son of piero the unfortunate | | 7 | cardinal giulio de' medici | 4 may 1519 | 19 november 1523 | cousin of lorenzo ii de' medici, duke of urbino, son of giuliano de' medici who was the brother of lorenzo the magnificent. elected to the papacy, becoming pope clement vii | | 8 | cardinal ippolito de' medici | 19 november 1523 | 24 october 1529 | cousin of cardinal giulio de' medici, illegitimate son of giuliano de' medici, duke of nemours |

| | count ( "*" ) | |---:|----------------:| | 0 | 1 |

select count ( * ) where bike = rider col : pos | no | rider | bike | laps | time | grid | points row 1 : 1 | 21 | troy bayliss | ducati 999 f07 | 22 | 34:11.276 | 1 | 25 row 2 : 2 | 52 | james toseland | honda cbr1000rr | 22 | 2.096 | 2 | 20 row 3 : 3 | 3 | max biaggi | suzuki gsx-r1000 k7 | 22 | 10.143 | 5 | 16 row 4 : 4 | 41 | noriyuki haga | yamaha yzf-r1 | 22 | 18.923 | 4 | 13 row 5 : 5 | 11 | troy corser | yamaha yzf-r1 | 22 | 19.742 | 3 | 11 row 6 : 6 | 57 | lorenzo lanzi | ducati 999 f07 | 22 | 24.765 | 10 | 10 row 7 : 7 | 111 | ruben xaus | ducati 999 f06 | 22 | 27.404 | 8 | 9 row 8 : 8 | 76 | max neukirchner | suzuki gsx-r1000 k6 | 22 | 34.614 | 7 | 8 row 9 : 9 | 10 | fonsi nieto | kawasaki zx-10r | 22 | 35.339 | 6 | 7 row 10 : 10 | 99 | steve martin | honda cbr1000rr | 22 | 36.238 | 11 | 6 row 11 : 11 | 44 | roberto rolfo | honda cbr1000rr | 22 | 38.067 | 15 | 5 row 12 : 12 | 25 | josh brookes | honda cbr1000rr | 22 | 47.078 | 12 | 4 row 13 : 13 | 38 | shinichi nakatomi | yamaha yzf-r1 | 22 | 58.571 | 17 | 3 row 14 : 14 | 96 | jakub smrz | ducati 999 f05 | 22 | +1:08.000 | 14 | 2 row 15 : 15 | 53 | alex polita | suzuki gsx-r1000 k6 | 22 | +1:22.584 | 18 | 1 row 16 : 16 | 73 | christian zaiser | mv agusta f4 1000r | 22 | +1:28.374 | 19 | none row 17 : 17 | 42 | dean ellison | ducati 999rs | 22 | +1:30.184 | 21 | none row 18 : 18 | 36 | jiri drazdak | yamaha yzf-r1 | 21 | +1 lap | 20 | none row 19 : ret | 55 | regis laconi | kawasaki zx-10r | 16 | retirement | 9 | none row 20 : ret | 84 | michel fabrizio | honda cbr1000rr | 5 | retirement | 13 | none

col : count ( "*" ) row 1 : 0

select count ( * ) where bike = rider <table_name> : table \begin{tabular}{lllllllll} \toprule & pos & no & rider & bike & laps & time & grid & points \\ \midrule 0 & 1 & 21 & troy bayliss & ducati 999 f07 & 22 & 34:11.276 & 1 & 25 \\ 1 & 2 & 52 & james toseland & honda cbr1000rr & 22 & 2.096 & 2 & 20 \\ 2 & 3 & 3 & max biaggi & suzuki gsx-r1000 k7 & 22 & 10.143 & 5 & 16 \\ 3 & 4 & 41 & noriyuki haga & yamaha yzf-r1 & 22 & 18.923 & 4 & 13 \\ 4 & 5 & 11 & troy corser & yamaha yzf-r1 & 22 & 19.742 & 3 & 11 \\ 5 & 6 & 57 & lorenzo lanzi & ducati 999 f07 & 22 & 24.765 & 10 & 10 \\ 6 & 7 & 111 & ruben xaus & ducati 999 f06 & 22 & 27.404 & 8 & 9 \\ 7 & 8 & 76 & max neukirchner & suzuki gsx-r1000 k6 & 22 & 34.614 & 7 & 8 \\ 8 & 9 & 10 & fonsi nieto & kawasaki zx-10r & 22 & 35.339 & 6 & 7 \\ 9 & 10 & 99 & steve martin & honda cbr1000rr & 22 & 36.238 & 11 & 6 \\ 10 & 11 & 44 & roberto rolfo & honda cbr1000rr & 22 & 38.067 & 15 & 5 \\ 11 & 12 & 25 & josh brookes & honda cbr1000rr & 22 & 47.078 & 12 & 4 \\ 12 & 13 & 38 & shinichi nakatomi & yamaha yzf-r1 & 22 & 58.571 & 17 & 3 \\ 13 & 14 & 96 & jakub smrz & ducati 999 f05 & 22 & +1:08.000 & 14 & 2 \\ 14 & 15 & 53 & alex polita & suzuki gsx-r1000 k6 & 22 & +1:22.584 & 18 & 1 \\ 15 & 16 & 73 & christian zaiser & mv agusta f4 1000r & 22 & +1:28.374 & 19 & none \\ 16 & 17 & 42 & dean ellison & ducati 999rs & 22 & +1:30.184 & 21 & none \\ 17 & 18 & 36 & jiri drazdak & yamaha yzf-r1 & 21 & +1 lap & 20 & none \\ 18 & ret & 55 & regis laconi & kawasaki zx-10r & 16 & retirement & 9 & none \\ 19 & ret & 84 & michel fabrizio & honda cbr1000rr & 5 & retirement & 13 & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 0 \\ \bottomrule \end{tabular}

select count ( * ) where bike = rider <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>pos</th> <th>no</th> <th>rider</th> <th>bike</th> <th>laps</th> <th>time</th> <th>grid</th> <th>points</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>21</td> <td>troy bayliss</td> <td>ducati 999 f07</td> <td>22</td> <td>34:11.276</td> <td>1</td> <td>25</td> </tr> <tr> <th>1</th> <td>2</td> <td>52</td> <td>james toseland</td> <td>honda cbr1000rr</td> <td>22</td> <td>2.096</td> <td>2</td> <td>20</td> </tr> <tr> <th>2</th> <td>3</td> <td>3</td> <td>max biaggi</td> <td>suzuki gsx-r1000 k7</td> <td>22</td> <td>10.143</td> <td>5</td> <td>16</td> </tr> <tr> <th>3</th> <td>4</td> <td>41</td> <td>noriyuki haga</td> <td>yamaha yzf-r1</td> <td>22</td> <td>18.923</td> <td>4</td> <td>13</td> </tr> <tr> <th>4</th> <td>5</td> <td>11</td> <td>troy corser</td> <td>yamaha yzf-r1</td> <td>22</td> <td>19.742</td> <td>3</td> <td>11</td> </tr> <tr> <th>5</th> <td>6</td> <td>57</td> <td>lorenzo lanzi</td> <td>ducati 999 f07</td> <td>22</td> <td>24.765</td> <td>10</td> <td>10</td> </tr> <tr> <th>6</th> <td>7</td> <td>111</td> <td>ruben xaus</td> <td>ducati 999 f06</td> <td>22</td> <td>27.404</td> <td>8</td> <td>9</td> </tr> <tr> <th>7</th> <td>8</td> <td>76</td> <td>max neukirchner</td> <td>suzuki gsx-r1000 k6</td> <td>22</td> <td>34.614</td> <td>7</td> <td>8</td> </tr> <tr> <th>8</th> <td>9</td> <td>10</td> <td>fonsi nieto</td> <td>kawasaki zx-10r</td> <td>22</td> <td>35.339</td> <td>6</td> <td>7</td> </tr> <tr> <th>9</th> <td>10</td> <td>99</td> <td>steve martin</td> <td>honda cbr1000rr</td> <td>22</td> <td>36.238</td> <td>11</td> <td>6</td> </tr> <tr> <th>10</th> <td>11</td> <td>44</td> <td>roberto rolfo</td> <td>honda cbr1000rr</td> <td>22</td> <td>38.067</td> <td>15</td> <td>5</td> </tr> <tr> <th>11</th> <td>12</td> <td>25</td> <td>josh brookes</td> <td>honda cbr1000rr</td> <td>22</td> <td>47.078</td> <td>12</td> <td>4</td> </tr> <tr> <th>12</th> <td>13</td> <td>38</td> <td>shinichi nakatomi</td> <td>yamaha yzf-r1</td> <td>22</td> <td>58.571</td> <td>17</td> <td>3</td> </tr> <tr> <th>13</th> <td>14</td> <td>96</td> <td>jakub smrz</td> <td>ducati 999 f05</td> <td>22</td> <td>+1:08.000</td> <td>14</td> <td>2</td> </tr> <tr> <th>14</th> <td>15</td> <td>53</td> <td>alex polita</td> <td>suzuki gsx-r1000 k6</td> <td>22</td> <td>+1:22.584</td> <td>18</td> <td>1</td> </tr> <tr> <th>15</th> <td>16</td> <td>73</td> <td>christian zaiser</td> <td>mv agusta f4 1000r</td> <td>22</td> <td>+1:28.374</td> <td>19</td> <td>none</td> </tr> <tr> <th>16</th> <td>17</td> <td>42</td> <td>dean ellison</td> <td>ducati 999rs</td> <td>22</td> <td>+1:30.184</td> <td>21</td> <td>none</td> </tr> <tr> <th>17</th> <td>18</td> <td>36</td> <td>jiri drazdak</td> <td>yamaha yzf-r1</td> <td>21</td> <td>+1 lap</td> <td>20</td> <td>none</td> </tr> <tr> <th>18</th> <td>ret</td> <td>55</td> <td>regis laconi</td> <td>kawasaki zx-10r</td> <td>16</td> <td>retirement</td> <td>9</td> <td>none</td> </tr> <tr> <th>19</th> <td>ret</td> <td>84</td> <td>michel fabrizio</td> <td>honda cbr1000rr</td> <td>5</td> <td>retirement</td> <td>13</td> <td>none</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "*" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>0</td> </tr> </tbody> </table>

select count ( * ) where bike = rider <table_name> : table | | pos | no | rider | bike | laps | time | grid | points | |---:|:------|-----:|:------------------|:--------------------|-------:|:-----------|-------:|:---------| | 0 | 1 | 21 | troy bayliss | ducati 999 f07 | 22 | 34:11.276 | 1 | 25 | | 1 | 2 | 52 | james toseland | honda cbr1000rr | 22 | 2.096 | 2 | 20 | | 2 | 3 | 3 | max biaggi | suzuki gsx-r1000 k7 | 22 | 10.143 | 5 | 16 | | 3 | 4 | 41 | noriyuki haga | yamaha yzf-r1 | 22 | 18.923 | 4 | 13 | | 4 | 5 | 11 | troy corser | yamaha yzf-r1 | 22 | 19.742 | 3 | 11 | | 5 | 6 | 57 | lorenzo lanzi | ducati 999 f07 | 22 | 24.765 | 10 | 10 | | 6 | 7 | 111 | ruben xaus | ducati 999 f06 | 22 | 27.404 | 8 | 9 | | 7 | 8 | 76 | max neukirchner | suzuki gsx-r1000 k6 | 22 | 34.614 | 7 | 8 | | 8 | 9 | 10 | fonsi nieto | kawasaki zx-10r | 22 | 35.339 | 6 | 7 | | 9 | 10 | 99 | steve martin | honda cbr1000rr | 22 | 36.238 | 11 | 6 | | 10 | 11 | 44 | roberto rolfo | honda cbr1000rr | 22 | 38.067 | 15 | 5 | | 11 | 12 | 25 | josh brookes | honda cbr1000rr | 22 | 47.078 | 12 | 4 | | 12 | 13 | 38 | shinichi nakatomi | yamaha yzf-r1 | 22 | 58.571 | 17 | 3 | | 13 | 14 | 96 | jakub smrz | ducati 999 f05 | 22 | +1:08.000 | 14 | 2 | | 14 | 15 | 53 | alex polita | suzuki gsx-r1000 k6 | 22 | +1:22.584 | 18 | 1 | | 15 | 16 | 73 | christian zaiser | mv agusta f4 1000r | 22 | +1:28.374 | 19 | none | | 16 | 17 | 42 | dean ellison | ducati 999rs | 22 | +1:30.184 | 21 | none | | 17 | 18 | 36 | jiri drazdak | yamaha yzf-r1 | 21 | +1 lap | 20 | none | | 18 | ret | 55 | regis laconi | kawasaki zx-10r | 16 | retirement | 9 | none | | 19 | ret | 84 | michel fabrizio | honda cbr1000rr | 5 | retirement | 13 | none |

| | count ( "*" ) | |---:|----------------:| | 0 | 0 |

select count ( band ) where disc description = 'monster magnet' and band != 'gary numan' col : band | disc/song | released | disc description | disk size | image row 1 : barnes & barnes | fish heads: barnes & barnes' greatest hits | 1982 | shaped as a fish head | 12" | none row 2 : broken english | comin on strong | 1987 | shaped as the 3 band members wearing ghostbusters outfits holding guitars | none | none row 3 : devo | beautiful world b/w nu-tra | 1981 | shaped like an astronaut head | none | none row 4 : gangrene | sawblade ep | 2010 | in the shape of a circular sawblade | none | none row 5 : gary numan | warriors | 1983 | shaped like a jet fighter | 7" | none row 6 : gary numan | berserker | 1984 | shaped like numan's head | 7" | none row 7 : guns n' roses | sweet child o' mine | 1988 | shape of the classic logo of the cross and skulls of the five band members | 7" | none row 8 : guns n' roses | paradise city | 1989 | shape of a colt "peacemaker" | 7" | none row 9 : guns n' roses | nightrain | 1989 | shape of a suitcase | 7" | none row 10 : joe strummer | love kills | none | shaped like a gun | 7" | a gun row 11 : killing joke | loose cannon | 2003 | shaped yellow evil clown head image from the eponymous 2003 album sleeve | none | none row 12 : kiss | lick it up | 1983 | shaped like an armored tank | none | none row 13 : less than jake | cheese | 1998 | shaped like a piece of swiss cheese. 1000 pressed in yellow. 500 pressed in green ("moldy version") | 7" | none row 14 : men without hats | the safety dance | 1982 | oddly shaped picture disc of a man and a woman dancing | none | none row 15 : men without hats | i got the message | 1983 | none | none | none row 16 : monster magnet | dopes to infinity | 1995 | shaped like the lead singer dave wyndorf's head | 12" | none row 17 : monster magnet | negasonic teenage warhead | none | shaped like a mushroom cloud | 12" | none row 18 : omd | la femme accident | 1985 | none | none | none row 19 : red box | lean on me b/w stinging bee | 1985 | hexagonal red vinyl. looks like a red box in 2d; flipside is a band photo | 7" | none row 20 : saxon | back on the streets again | none | shaped as an apple (as is printed on one side of the disk) | 7" | none row 21 : tangerine dream | warsaw in the sun | 1984 | the record is in the shape of poland and has several images including lech wałesa and pope john paul ii | 7" | none row 22 : the coconuts (side project of kid creole and the coconuts) | did you have to love me like you did | 1983 | in the shape of a coconut | 7" | none row 23 : the fat boys | wipe out | none | shaped like a hamburger | 7" | none row 24 : the enemy | you're not alone | 2007 | square shaped. has the single cover art on the a-side and a black and white picture of the band on the b-side with track listing | 7" | none row 25 : the mars volta | mr. muggs | 2008 | in the shape of a clear planchette | 7" | none row 26 : u2 | the unforgettable fire (single) | 1985 | shaped as letter & number "u2" with various pictures of the band from the period | 7" | u2 row 27 : yeah yeah yeahs | cheated hearts | 2006 | heart shaped | 7" | none

col : count ( "band" ) row 1 : 0

select count ( band ) where disc description = 'monster magnet' and band != 'gary numan' <table_name> : table \begin{tabular}{lllllll} \toprule & band & disc/song & released & disc description & disk size & image \\ \midrule 0 & barnes & barnes & fish heads: barnes & barnes' greatest hits & 1982 & shaped as a fish head & 12" & none \\ 1 & broken english & comin on strong & 1987 & shaped as the 3 band members wearing ghostbusters outfits holding guitars & none & none \\ 2 & devo & beautiful world b/w nu-tra & 1981 & shaped like an astronaut head & none & none \\ 3 & gangrene & sawblade ep & 2010 & in the shape of a circular sawblade & none & none \\ 4 & gary numan & warriors & 1983 & shaped like a jet fighter & 7" & none \\ 5 & gary numan & berserker & 1984 & shaped like numan's head & 7" & none \\ 6 & guns n' roses & sweet child o' mine & 1988 & shape of the classic logo of the cross and skulls of the five band members & 7" & none \\ 7 & guns n' roses & paradise city & 1989 & shape of a colt "peacemaker" & 7" & none \\ 8 & guns n' roses & nightrain & 1989 & shape of a suitcase & 7" & none \\ 9 & joe strummer & love kills & none & shaped like a gun & 7" & a gun \\ 10 & killing joke & loose cannon & 2003 & shaped yellow evil clown head image from the eponymous 2003 album sleeve & none & none \\ 11 & kiss & lick it up & 1983 & shaped like an armored tank & none & none \\ 12 & less than jake & cheese & 1998 & shaped like a piece of swiss cheese. 1000 pressed in yellow. 500 pressed in green ("moldy version") & 7" & none \\ 13 & men without hats & the safety dance & 1982 & oddly shaped picture disc of a man and a woman dancing & none & none \\ 14 & men without hats & i got the message & 1983 & none & none & none \\ 15 & monster magnet & dopes to infinity & 1995 & shaped like the lead singer dave wyndorf's head & 12" & none \\ 16 & monster magnet & negasonic teenage warhead & none & shaped like a mushroom cloud & 12" & none \\ 17 & omd & la femme accident & 1985 & none & none & none \\ 18 & red box & lean on me b/w stinging bee & 1985 & hexagonal red vinyl. looks like a red box in 2d; flipside is a band photo & 7" & none \\ 19 & saxon & back on the streets again & none & shaped as an apple (as is printed on one side of the disk) & 7" & none \\ 20 & tangerine dream & warsaw in the sun & 1984 & the record is in the shape of poland and has several images including lech wałesa and pope john paul ii & 7" & none \\ 21 & the coconuts (side project of kid creole and the coconuts) & did you have to love me like you did & 1983 & in the shape of a coconut & 7" & none \\ 22 & the fat boys & wipe out & none & shaped like a hamburger & 7" & none \\ 23 & the enemy & you're not alone & 2007 & square shaped. has the single cover art on the a-side and a black and white picture of the band on the b-side with track listing & 7" & none \\ 24 & the mars volta & mr. muggs & 2008 & in the shape of a clear planchette & 7" & none \\ 25 & u2 & the unforgettable fire (single) & 1985 & shaped as letter & number "u2" with various pictures of the band from the period & 7" & u2 \\ 26 & yeah yeah yeahs & cheated hearts & 2006 & heart shaped & 7" & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "band" ) \\ \midrule 0 & 0 \\ \bottomrule \end{tabular}

select count ( band ) where disc description = 'monster magnet' and band != 'gary numan' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>band</th> <th>disc/song</th> <th>released</th> <th>disc description</th> <th>disk size</th> <th>image</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>barnes &amp; barnes</td> <td>fish heads: barnes &amp; barnes' greatest hits</td> <td>1982</td> <td>shaped as a fish head</td> <td>12"</td> <td>none</td> </tr> <tr> <th>1</th> <td>broken english</td> <td>comin on strong</td> <td>1987</td> <td>shaped as the 3 band members wearing ghostbusters outfits holding guitars</td> <td>none</td> <td>none</td> </tr> <tr> <th>2</th> <td>devo</td> <td>beautiful world b/w nu-tra</td> <td>1981</td> <td>shaped like an astronaut head</td> <td>none</td> <td>none</td> </tr> <tr> <th>3</th> <td>gangrene</td> <td>sawblade ep</td> <td>2010</td> <td>in the shape of a circular sawblade</td> <td>none</td> <td>none</td> </tr> <tr> <th>4</th> <td>gary numan</td> <td>warriors</td> <td>1983</td> <td>shaped like a jet fighter</td> <td>7"</td> <td>none</td> </tr> <tr> <th>5</th> <td>gary numan</td> <td>berserker</td> <td>1984</td> <td>shaped like numan's head</td> <td>7"</td> <td>none</td> </tr> <tr> <th>6</th> <td>guns n' roses</td> <td>sweet child o' mine</td> <td>1988</td> <td>shape of the classic logo of the cross and skulls of the five band members</td> <td>7"</td> <td>none</td> </tr> <tr> <th>7</th> <td>guns n' roses</td> <td>paradise city</td> <td>1989</td> <td>shape of a colt "peacemaker"</td> <td>7"</td> <td>none</td> </tr> <tr> <th>8</th> <td>guns n' roses</td> <td>nightrain</td> <td>1989</td> <td>shape of a suitcase</td> <td>7"</td> <td>none</td> </tr> <tr> <th>9</th> <td>joe strummer</td> <td>love kills</td> <td>none</td> <td>shaped like a gun</td> <td>7"</td> <td>a gun</td> </tr> <tr> <th>10</th> <td>killing joke</td> <td>loose cannon</td> <td>2003</td> <td>shaped yellow evil clown head image from the eponymous 2003 album sleeve</td> <td>none</td> <td>none</td> </tr> <tr> <th>11</th> <td>kiss</td> <td>lick it up</td> <td>1983</td> <td>shaped like an armored tank</td> <td>none</td> <td>none</td> </tr> <tr> <th>12</th> <td>less than jake</td> <td>cheese</td> <td>1998</td> <td>shaped like a piece of swiss cheese. 1000 pressed in yellow. 500 pressed in green ("moldy version")</td> <td>7"</td> <td>none</td> </tr> <tr> <th>13</th> <td>men without hats</td> <td>the safety dance</td> <td>1982</td> <td>oddly shaped picture disc of a man and a woman dancing</td> <td>none</td> <td>none</td> </tr> <tr> <th>14</th> <td>men without hats</td> <td>i got the message</td> <td>1983</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>15</th> <td>monster magnet</td> <td>dopes to infinity</td> <td>1995</td> <td>shaped like the lead singer dave wyndorf's head</td> <td>12"</td> <td>none</td> </tr> <tr> <th>16</th> <td>monster magnet</td> <td>negasonic teenage warhead</td> <td>none</td> <td>shaped like a mushroom cloud</td> <td>12"</td> <td>none</td> </tr> <tr> <th>17</th> <td>omd</td> <td>la femme accident</td> <td>1985</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>18</th> <td>red box</td> <td>lean on me b/w stinging bee</td> <td>1985</td> <td>hexagonal red vinyl. looks like a red box in 2d; flipside is a band photo</td> <td>7"</td> <td>none</td> </tr> <tr> <th>19</th> <td>saxon</td> <td>back on the streets again</td> <td>none</td> <td>shaped as an apple (as is printed on one side of the disk)</td> <td>7"</td> <td>none</td> </tr> <tr> <th>20</th> <td>tangerine dream</td> <td>warsaw in the sun</td> <td>1984</td> <td>the record is in the shape of poland and has several images including lech wałesa and pope john paul ii</td> <td>7"</td> <td>none</td> </tr> <tr> <th>21</th> <td>the coconuts (side project of kid creole and the coconuts)</td> <td>did you have to love me like you did</td> <td>1983</td> <td>in the shape of a coconut</td> <td>7"</td> <td>none</td> </tr> <tr> <th>22</th> <td>the fat boys</td> <td>wipe out</td> <td>none</td> <td>shaped like a hamburger</td> <td>7"</td> <td>none</td> </tr> <tr> <th>23</th> <td>the enemy</td> <td>you're not alone</td> <td>2007</td> <td>square shaped. has the single cover art on the a-side and a black and white picture of the band on the b-side with track listing</td> <td>7"</td> <td>none</td> </tr> <tr> <th>24</th> <td>the mars volta</td> <td>mr. muggs</td> <td>2008</td> <td>in the shape of a clear planchette</td> <td>7"</td> <td>none</td> </tr> <tr> <th>25</th> <td>u2</td> <td>the unforgettable fire (single)</td> <td>1985</td> <td>shaped as letter &amp; number "u2" with various pictures of the band from the period</td> <td>7"</td> <td>u2</td> </tr> <tr> <th>26</th> <td>yeah yeah yeahs</td> <td>cheated hearts</td> <td>2006</td> <td>heart shaped</td> <td>7"</td> <td>none</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "band" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>0</td> </tr> </tbody> </table>

select count ( band ) where disc description = 'monster magnet' and band != 'gary numan' <table_name> : table | | band | disc/song | released | disc description | disk size | image | |---:|:-----------------------------------------------------------|:-------------------------------------------|:-----------|:---------------------------------------------------------------------------------------------------------------------------------|:------------|:--------| | 0 | barnes & barnes | fish heads: barnes & barnes' greatest hits | 1982 | shaped as a fish head | 12" | none | | 1 | broken english | comin on strong | 1987 | shaped as the 3 band members wearing ghostbusters outfits holding guitars | none | none | | 2 | devo | beautiful world b/w nu-tra | 1981 | shaped like an astronaut head | none | none | | 3 | gangrene | sawblade ep | 2010 | in the shape of a circular sawblade | none | none | | 4 | gary numan | warriors | 1983 | shaped like a jet fighter | 7" | none | | 5 | gary numan | berserker | 1984 | shaped like numan's head | 7" | none | | 6 | guns n' roses | sweet child o' mine | 1988 | shape of the classic logo of the cross and skulls of the five band members | 7" | none | | 7 | guns n' roses | paradise city | 1989 | shape of a colt "peacemaker" | 7" | none | | 8 | guns n' roses | nightrain | 1989 | shape of a suitcase | 7" | none | | 9 | joe strummer | love kills | none | shaped like a gun | 7" | a gun | | 10 | killing joke | loose cannon | 2003 | shaped yellow evil clown head image from the eponymous 2003 album sleeve | none | none | | 11 | kiss | lick it up | 1983 | shaped like an armored tank | none | none | | 12 | less than jake | cheese | 1998 | shaped like a piece of swiss cheese. 1000 pressed in yellow. 500 pressed in green ("moldy version") | 7" | none | | 13 | men without hats | the safety dance | 1982 | oddly shaped picture disc of a man and a woman dancing | none | none | | 14 | men without hats | i got the message | 1983 | none | none | none | | 15 | monster magnet | dopes to infinity | 1995 | shaped like the lead singer dave wyndorf's head | 12" | none | | 16 | monster magnet | negasonic teenage warhead | none | shaped like a mushroom cloud | 12" | none | | 17 | omd | la femme accident | 1985 | none | none | none | | 18 | red box | lean on me b/w stinging bee | 1985 | hexagonal red vinyl. looks like a red box in 2d; flipside is a band photo | 7" | none | | 19 | saxon | back on the streets again | none | shaped as an apple (as is printed on one side of the disk) | 7" | none | | 20 | tangerine dream | warsaw in the sun | 1984 | the record is in the shape of poland and has several images including lech wałesa and pope john paul ii | 7" | none | | 21 | the coconuts (side project of kid creole and the coconuts) | did you have to love me like you did | 1983 | in the shape of a coconut | 7" | none | | 22 | the fat boys | wipe out | none | shaped like a hamburger | 7" | none | | 23 | the enemy | you're not alone | 2007 | square shaped. has the single cover art on the a-side and a black and white picture of the band on the b-side with track listing | 7" | none | | 24 | the mars volta | mr. muggs | 2008 | in the shape of a clear planchette | 7" | none | | 25 | u2 | the unforgettable fire (single) | 1985 | shaped as letter & number "u2" with various pictures of the band from the period | 7" | u2 | | 26 | yeah yeah yeahs | cheated hearts | 2006 | heart shaped | 7" | none |

| | count ( "band" ) | |---:|-------------------:| | 0 | 0 |

select count ( * ) where name = 'craig dawson' col : date from | date to | pos. | name | from row 1 : 3 august 2012 | 31 january 2013 | fw | benik afobe | arsenal row 2 : 31 august 2012 | 30 june 2013 | mf | jay spearing | liverpool row 3 : 21 september 2012 | 22 december 2012 | df | stephen warnock | aston villa row 4 : 8 november 2012 | 5 january 2013 | mf | jacob butterfield | norwich city row 5 : 25 january 2013 | 27 april 2013 | df | craig dawson | west brom row 6 : 25 january 2013 | january 2014 | mf | jan gregus | banik ostrava row 7 : 31 january 2013 | 3 march 2013 | mf | steve de ridder | southampton row 8 : 28 march 2013 | 30 june 2013 | fw | robert hall | west ham row 9 : 28 march 2013 | 4 may 2013 | df | danny butterfield | southampton

col : count ( "*" ) row 1 : 1

select count ( * ) where name = 'craig dawson' <table_name> : table \begin{tabular}{llllll} \toprule & date from & date to & pos. & name & from \\ \midrule 0 & 3 august 2012 & 31 january 2013 & fw & benik afobe & arsenal \\ 1 & 31 august 2012 & 30 june 2013 & mf & jay spearing & liverpool \\ 2 & 21 september 2012 & 22 december 2012 & df & stephen warnock & aston villa \\ 3 & 8 november 2012 & 5 january 2013 & mf & jacob butterfield & norwich city \\ 4 & 25 january 2013 & 27 april 2013 & df & craig dawson & west brom \\ 5 & 25 january 2013 & january 2014 & mf & jan gregus & banik ostrava \\ 6 & 31 january 2013 & 3 march 2013 & mf & steve de ridder & southampton \\ 7 & 28 march 2013 & 30 june 2013 & fw & robert hall & west ham \\ 8 & 28 march 2013 & 4 may 2013 & df & danny butterfield & southampton \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 1 \\ \bottomrule \end{tabular}

select count ( * ) where name = 'craig dawson' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>date from</th> <th>date to</th> <th>pos.</th> <th>name</th> <th>from</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>3 august 2012</td> <td>31 january 2013</td> <td>fw</td> <td>benik afobe</td> <td>arsenal</td> </tr> <tr> <th>1</th> <td>31 august 2012</td> <td>30 june 2013</td> <td>mf</td> <td>jay spearing</td> <td>liverpool</td> </tr> <tr> <th>2</th> <td>21 september 2012</td> <td>22 december 2012</td> <td>df</td> <td>stephen warnock</td> <td>aston villa</td> </tr> <tr> <th>3</th> <td>8 november 2012</td> <td>5 january 2013</td> <td>mf</td> <td>jacob butterfield</td> <td>norwich city</td> </tr> <tr> <th>4</th> <td>25 january 2013</td> <td>27 april 2013</td> <td>df</td> <td>craig dawson</td> <td>west brom</td> </tr> <tr> <th>5</th> <td>25 january 2013</td> <td>january 2014</td> <td>mf</td> <td>jan gregus</td> <td>banik ostrava</td> </tr> <tr> <th>6</th> <td>31 january 2013</td> <td>3 march 2013</td> <td>mf</td> <td>steve de ridder</td> <td>southampton</td> </tr> <tr> <th>7</th> <td>28 march 2013</td> <td>30 june 2013</td> <td>fw</td> <td>robert hall</td> <td>west ham</td> </tr> <tr> <th>8</th> <td>28 march 2013</td> <td>4 may 2013</td> <td>df</td> <td>danny butterfield</td> <td>southampton</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "*" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> </tr> </tbody> </table>

select count ( * ) where name = 'craig dawson' <table_name> : table | | date from | date to | pos. | name | from | |---:|:------------------|:-----------------|:-------|:------------------|:--------------| | 0 | 3 august 2012 | 31 january 2013 | fw | benik afobe | arsenal | | 1 | 31 august 2012 | 30 june 2013 | mf | jay spearing | liverpool | | 2 | 21 september 2012 | 22 december 2012 | df | stephen warnock | aston villa | | 3 | 8 november 2012 | 5 january 2013 | mf | jacob butterfield | norwich city | | 4 | 25 january 2013 | 27 april 2013 | df | craig dawson | west brom | | 5 | 25 january 2013 | january 2014 | mf | jan gregus | banik ostrava | | 6 | 31 january 2013 | 3 march 2013 | mf | steve de ridder | southampton | | 7 | 28 march 2013 | 30 june 2013 | fw | robert hall | west ham | | 8 | 28 march 2013 | 4 may 2013 | df | danny butterfield | southampton |

| | count ( "*" ) | |---:|----------------:| | 0 | 1 |

select nation where nation = 'soviet union (urs)' col : rank | nation | gold | silver | bronze | total row 1 : 1 | netherlands (ned) | 4 | 3 | 2 | 9 row 2 : 2 | united states (usa) | 2 | 1 | 1 | 4 row 3 : 3 | west germany (frg) | 2 | 0 | 0 | 2 row 4 : 4 | norway (nor) | 0 | 2 | 2 | 4 row 5 : 5 | soviet union (urs) | 0 | 1 | 2 | 3 row 6 : 6 | sweden (swe) | 0 | 1 | 1 | 2 row 7 : none | total | 8 | 8 | 8 | 24

col : nation row 1 : soviet union (urs)

select nation where nation = 'soviet union (urs)' <table_name> : table \begin{tabular}{lllllll} \toprule & rank & nation & gold & silver & bronze & total \\ \midrule 0 & 1 & netherlands (ned) & 4 & 3 & 2 & 9 \\ 1 & 2 & united states (usa) & 2 & 1 & 1 & 4 \\ 2 & 3 & west germany (frg) & 2 & 0 & 0 & 2 \\ 3 & 4 & norway (nor) & 0 & 2 & 2 & 4 \\ 4 & 5 & soviet union (urs) & 0 & 1 & 2 & 3 \\ 5 & 6 & sweden (swe) & 0 & 1 & 1 & 2 \\ 6 & none & total & 8 & 8 & 8 & 24 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & nation \\ \midrule 0 & soviet union (urs) \\ \bottomrule \end{tabular}

select nation where nation = 'soviet union (urs)' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>rank</th> <th>nation</th> <th>gold</th> <th>silver</th> <th>bronze</th> <th>total</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>netherlands (ned)</td> <td>4</td> <td>3</td> <td>2</td> <td>9</td> </tr> <tr> <th>1</th> <td>2</td> <td>united states (usa)</td> <td>2</td> <td>1</td> <td>1</td> <td>4</td> </tr> <tr> <th>2</th> <td>3</td> <td>west germany (frg)</td> <td>2</td> <td>0</td> <td>0</td> <td>2</td> </tr> <tr> <th>3</th> <td>4</td> <td>norway (nor)</td> <td>0</td> <td>2</td> <td>2</td> <td>4</td> </tr> <tr> <th>4</th> <td>5</td> <td>soviet union (urs)</td> <td>0</td> <td>1</td> <td>2</td> <td>3</td> </tr> <tr> <th>5</th> <td>6</td> <td>sweden (swe)</td> <td>0</td> <td>1</td> <td>1</td> <td>2</td> </tr> <tr> <th>6</th> <td>none</td> <td>total</td> <td>8</td> <td>8</td> <td>8</td> <td>24</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>nation</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>soviet union (urs)</td> </tr> </tbody> </table>

select nation where nation = 'soviet union (urs)' <table_name> : table | | rank | nation | gold | silver | bronze | total | |---:|:-------|:--------------------|-------:|---------:|---------:|--------:| | 0 | 1 | netherlands (ned) | 4 | 3 | 2 | 9 | | 1 | 2 | united states (usa) | 2 | 1 | 1 | 4 | | 2 | 3 | west germany (frg) | 2 | 0 | 0 | 2 | | 3 | 4 | norway (nor) | 0 | 2 | 2 | 4 | | 4 | 5 | soviet union (urs) | 0 | 1 | 2 | 3 | | 5 | 6 | sweden (swe) | 0 | 1 | 1 | 2 | | 6 | none | total | 8 | 8 | 8 | 24 |

| | nation | |---:|:-------------------| | 0 | soviet union (urs) |

select count ( * ) where film = 1440 col : film -1 | film -2 | date row 1 : kodachrome film | 16 mm, daylight (asa 10) & type a (asa 16) | 1935-1962 row 2 : kodachrome film | 8 mm, daylight (asa 10) & type a (asa 16) | 1936-1962 row 3 : kodachrome film | 35 mm and 828, daylight & type a | 1936-1962 row 4 : kodachrome professional film (sheets) | daylight (asa 8) and type b (asa 10) | 1938-1951 row 5 : kodachrome film | 35 mm and 828, type f (asa 12) | 1955-1962 row 6 : kodachrome professional film | 35 mm, type a (asa 16) | 1956-1962 row 7 : kodak color print material | type d (slide duping film) | 1955-1957 row 8 : kodachrome ii film | 16 mm, daylight (asa 25) and type a (asa 40) | 1961-1974 row 9 : kodachrome ii film | 8 mm, daylight (asa 25) and type a (asa 40) | 1961-1974 row 10 : kodachrome ii film | s-8, type a (asa 40) | 1965-1974 row 11 : kodachrome ii film | 35 mm and 828, daylight (asa 25/early) (asa 64/late) | 1961-1974 row 12 : kodachrome ii film | professional, 35 mm, type a (asa 40) | 1962-1978 row 13 : kodachrome-x film | 35 mm (asa 64) | 1962-1974 row 14 : kodachrome-x film | 126 format | 1963-1974 row 15 : kodachrome-x film | 110 format | 1972-1974 row 16 : kodachrome 25 film | 35 mm, daylight | 1974-2001 row 17 : kodachrome 25 film | movie film, 16 mm, daylight | 1974-2002 row 18 : kodachrome 25 film | movie film, 8 mm, daylight | 1974-1992 row 19 : kodachrome 25 film | professional film, 35 mm, daylight | 1983-1999 row 20 : kodachrome 40 film | 35 mm, type a | 1978-1997 row 21 : kodachrome 40 film | movie film, 16 mm, type a | 1974-2006 row 22 : kodachrome 40 film | movie film, s-8, type a | 1974-2005 row 23 : kodachrome 40 film | sound movie film, s-8, type a | 1974-1998 row 24 : kodachrome 40 film | movie film, 8 mm, type a | 1974-1992 row 25 : kodachrome 64 | 35 mm, daylight | 1974-2009 row 26 : kodachrome 64 | 126 format, daylight | 1974-1993 row 27 : kodachrome 64 | 110 format, daylight | 1974-1987 row 28 : kodachrome 64 | professional film, 35 mm, daylight | 1983-2009 row 29 : kodachrome 64 | professional film, daylight, 120 format | 1986-1996 row 30 : kodachrome 200 | professional film, 35 mm, daylight | 1986-2004 row 31 : kodachrome 200 | 35 mm, daylight | 1988-2007 row 32 : cine-chrome 40a | double regular 8 mm, tungsten | 2003-2006

col : count ( "*" ) row 1 : 0

select count ( * ) where film = 1440 <table_name> : table \begin{tabular}{llll} \toprule & film -1 & film -2 & date \\ \midrule 0 & kodachrome film & 16 mm, daylight (asa 10) & type a (asa 16) & 1935-1962 \\ 1 & kodachrome film & 8 mm, daylight (asa 10) & type a (asa 16) & 1936-1962 \\ 2 & kodachrome film & 35 mm and 828, daylight & type a & 1936-1962 \\ 3 & kodachrome professional film (sheets) & daylight (asa 8) and type b (asa 10) & 1938-1951 \\ 4 & kodachrome film & 35 mm and 828, type f (asa 12) & 1955-1962 \\ 5 & kodachrome professional film & 35 mm, type a (asa 16) & 1956-1962 \\ 6 & kodak color print material & type d (slide duping film) & 1955-1957 \\ 7 & kodachrome ii film & 16 mm, daylight (asa 25) and type a (asa 40) & 1961-1974 \\ 8 & kodachrome ii film & 8 mm, daylight (asa 25) and type a (asa 40) & 1961-1974 \\ 9 & kodachrome ii film & s-8, type a (asa 40) & 1965-1974 \\ 10 & kodachrome ii film & 35 mm and 828, daylight (asa 25/early) (asa 64/late) & 1961-1974 \\ 11 & kodachrome ii film & professional, 35 mm, type a (asa 40) & 1962-1978 \\ 12 & kodachrome-x film & 35 mm (asa 64) & 1962-1974 \\ 13 & kodachrome-x film & 126 format & 1963-1974 \\ 14 & kodachrome-x film & 110 format & 1972-1974 \\ 15 & kodachrome 25 film & 35 mm, daylight & 1974-2001 \\ 16 & kodachrome 25 film & movie film, 16 mm, daylight & 1974-2002 \\ 17 & kodachrome 25 film & movie film, 8 mm, daylight & 1974-1992 \\ 18 & kodachrome 25 film & professional film, 35 mm, daylight & 1983-1999 \\ 19 & kodachrome 40 film & 35 mm, type a & 1978-1997 \\ 20 & kodachrome 40 film & movie film, 16 mm, type a & 1974-2006 \\ 21 & kodachrome 40 film & movie film, s-8, type a & 1974-2005 \\ 22 & kodachrome 40 film & sound movie film, s-8, type a & 1974-1998 \\ 23 & kodachrome 40 film & movie film, 8 mm, type a & 1974-1992 \\ 24 & kodachrome 64 & 35 mm, daylight & 1974-2009 \\ 25 & kodachrome 64 & 126 format, daylight & 1974-1993 \\ 26 & kodachrome 64 & 110 format, daylight & 1974-1987 \\ 27 & kodachrome 64 & professional film, 35 mm, daylight & 1983-2009 \\ 28 & kodachrome 64 & professional film, daylight, 120 format & 1986-1996 \\ 29 & kodachrome 200 & professional film, 35 mm, daylight & 1986-2004 \\ 30 & kodachrome 200 & 35 mm, daylight & 1988-2007 \\ 31 & cine-chrome 40a & double regular 8 mm, tungsten & 2003-2006 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 0 \\ \bottomrule \end{tabular}

select count ( * ) where film = 1440 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>film -1</th> <th>film -2</th> <th>date</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>kodachrome film</td> <td>16 mm, daylight (asa 10) &amp; type a (asa 16)</td> <td>1935-1962</td> </tr> <tr> <th>1</th> <td>kodachrome film</td> <td>8 mm, daylight (asa 10) &amp; type a (asa 16)</td> <td>1936-1962</td> </tr> <tr> <th>2</th> <td>kodachrome film</td> <td>35 mm and 828, daylight &amp; type a</td> <td>1936-1962</td> </tr> <tr> <th>3</th> <td>kodachrome professional film (sheets)</td> <td>daylight (asa 8) and type b (asa 10)</td> <td>1938-1951</td> </tr> <tr> <th>4</th> <td>kodachrome film</td> <td>35 mm and 828, type f (asa 12)</td> <td>1955-1962</td> </tr> <tr> <th>5</th> <td>kodachrome professional film</td> <td>35 mm, type a (asa 16)</td> <td>1956-1962</td> </tr> <tr> <th>6</th> <td>kodak color print material</td> <td>type d (slide duping film)</td> <td>1955-1957</td> </tr> <tr> <th>7</th> <td>kodachrome ii film</td> <td>16 mm, daylight (asa 25) and type a (asa 40)</td> <td>1961-1974</td> </tr> <tr> <th>8</th> <td>kodachrome ii film</td> <td>8 mm, daylight (asa 25) and type a (asa 40)</td> <td>1961-1974</td> </tr> <tr> <th>9</th> <td>kodachrome ii film</td> <td>s-8, type a (asa 40)</td> <td>1965-1974</td> </tr> <tr> <th>10</th> <td>kodachrome ii film</td> <td>35 mm and 828, daylight (asa 25/early) (asa 64/late)</td> <td>1961-1974</td> </tr> <tr> <th>11</th> <td>kodachrome ii film</td> <td>professional, 35 mm, type a (asa 40)</td> <td>1962-1978</td> </tr> <tr> <th>12</th> <td>kodachrome-x film</td> <td>35 mm (asa 64)</td> <td>1962-1974</td> </tr> <tr> <th>13</th> <td>kodachrome-x film</td> <td>126 format</td> <td>1963-1974</td> </tr> <tr> <th>14</th> <td>kodachrome-x film</td> <td>110 format</td> <td>1972-1974</td> </tr> <tr> <th>15</th> <td>kodachrome 25 film</td> <td>35 mm, daylight</td> <td>1974-2001</td> </tr> <tr> <th>16</th> <td>kodachrome 25 film</td> <td>movie film, 16 mm, daylight</td> <td>1974-2002</td> </tr> <tr> <th>17</th> <td>kodachrome 25 film</td> <td>movie film, 8 mm, daylight</td> <td>1974-1992</td> </tr> <tr> <th>18</th> <td>kodachrome 25 film</td> <td>professional film, 35 mm, daylight</td> <td>1983-1999</td> </tr> <tr> <th>19</th> <td>kodachrome 40 film</td> <td>35 mm, type a</td> <td>1978-1997</td> </tr> <tr> <th>20</th> <td>kodachrome 40 film</td> <td>movie film, 16 mm, type a</td> <td>1974-2006</td> </tr> <tr> <th>21</th> <td>kodachrome 40 film</td> <td>movie film, s-8, type a</td> <td>1974-2005</td> </tr> <tr> <th>22</th> <td>kodachrome 40 film</td> <td>sound movie film, s-8, type a</td> <td>1974-1998</td> </tr> <tr> <th>23</th> <td>kodachrome 40 film</td> <td>movie film, 8 mm, type a</td> <td>1974-1992</td> </tr> <tr> <th>24</th> <td>kodachrome 64</td> <td>35 mm, daylight</td> <td>1974-2009</td> </tr> <tr> <th>25</th> <td>kodachrome 64</td> <td>126 format, daylight</td> <td>1974-1993</td> </tr> <tr> <th>26</th> <td>kodachrome 64</td> <td>110 format, daylight</td> <td>1974-1987</td> </tr> <tr> <th>27</th> <td>kodachrome 64</td> <td>professional film, 35 mm, daylight</td> <td>1983-2009</td> </tr> <tr> <th>28</th> <td>kodachrome 64</td> <td>professional film, daylight, 120 format</td> <td>1986-1996</td> </tr> <tr> <th>29</th> <td>kodachrome 200</td> <td>professional film, 35 mm, daylight</td> <td>1986-2004</td> </tr> <tr> <th>30</th> <td>kodachrome 200</td> <td>35 mm, daylight</td> <td>1988-2007</td> </tr> <tr> <th>31</th> <td>cine-chrome 40a</td> <td>double regular 8 mm, tungsten</td> <td>2003-2006</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "*" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>0</td> </tr> </tbody> </table>

select count ( * ) where film = 1440 <table_name> : table | | film -1 | film -2 | date | |---:|:--------------------------------------|:-----------------------------------------------------|:----------| | 0 | kodachrome film | 16 mm, daylight (asa 10) & type a (asa 16) | 1935-1962 | | 1 | kodachrome film | 8 mm, daylight (asa 10) & type a (asa 16) | 1936-1962 | | 2 | kodachrome film | 35 mm and 828, daylight & type a | 1936-1962 | | 3 | kodachrome professional film (sheets) | daylight (asa 8) and type b (asa 10) | 1938-1951 | | 4 | kodachrome film | 35 mm and 828, type f (asa 12) | 1955-1962 | | 5 | kodachrome professional film | 35 mm, type a (asa 16) | 1956-1962 | | 6 | kodak color print material | type d (slide duping film) | 1955-1957 | | 7 | kodachrome ii film | 16 mm, daylight (asa 25) and type a (asa 40) | 1961-1974 | | 8 | kodachrome ii film | 8 mm, daylight (asa 25) and type a (asa 40) | 1961-1974 | | 9 | kodachrome ii film | s-8, type a (asa 40) | 1965-1974 | | 10 | kodachrome ii film | 35 mm and 828, daylight (asa 25/early) (asa 64/late) | 1961-1974 | | 11 | kodachrome ii film | professional, 35 mm, type a (asa 40) | 1962-1978 | | 12 | kodachrome-x film | 35 mm (asa 64) | 1962-1974 | | 13 | kodachrome-x film | 126 format | 1963-1974 | | 14 | kodachrome-x film | 110 format | 1972-1974 | | 15 | kodachrome 25 film | 35 mm, daylight | 1974-2001 | | 16 | kodachrome 25 film | movie film, 16 mm, daylight | 1974-2002 | | 17 | kodachrome 25 film | movie film, 8 mm, daylight | 1974-1992 | | 18 | kodachrome 25 film | professional film, 35 mm, daylight | 1983-1999 | | 19 | kodachrome 40 film | 35 mm, type a | 1978-1997 | | 20 | kodachrome 40 film | movie film, 16 mm, type a | 1974-2006 | | 21 | kodachrome 40 film | movie film, s-8, type a | 1974-2005 | | 22 | kodachrome 40 film | sound movie film, s-8, type a | 1974-1998 | | 23 | kodachrome 40 film | movie film, 8 mm, type a | 1974-1992 | | 24 | kodachrome 64 | 35 mm, daylight | 1974-2009 | | 25 | kodachrome 64 | 126 format, daylight | 1974-1993 | | 26 | kodachrome 64 | 110 format, daylight | 1974-1987 | | 27 | kodachrome 64 | professional film, 35 mm, daylight | 1983-2009 | | 28 | kodachrome 64 | professional film, daylight, 120 format | 1986-1996 | | 29 | kodachrome 200 | professional film, 35 mm, daylight | 1986-2004 | | 30 | kodachrome 200 | 35 mm, daylight | 1988-2007 | | 31 | cine-chrome 40a | double regular 8 mm, tungsten | 2003-2006 |

| | count ( "*" ) | |---:|----------------:| | 0 | 0 |

select count ( nation ) where nation = 'switzerland' col : rank | nation | gold | silver | bronze | total row 1 : 1 | germany | 17 | 9 | 14 | 40 row 2 : 2 | russia | 12 | 16 | 8 | 36 row 3 : 3 | ukraine | 6 | 11 | 9 | 26 row 4 : 4 | norway | 6 | 2 | 7 | 15 row 5 : 5 | france | 5 | 4 | 3 | 12 row 6 : 6 | switzerland | 3 | 5 | 3 | 11 row 7 : 7 | finland | 3 | 2 | 4 | 9 row 8 : 8 | japan | 2 | 2 | 1 | 5 row 9 : 9 | netherlands | 2 | 0 | 1 | 3 row 10 : 10 | sweden | 1 | 2 | 2 | 5 row 11 : 11 | denmark | 1 | 0 | 1 | 2 row 12 : 12 | unified team | 1 | 0 | 0 | 1 row 13 : 13 | austria | 0 | 4 | 0 | 4 row 14 : 14 | slovakia | 0 | 2 | 1 | 3 row 15 : 15 | italy | 0 | 0 | 1 | 1 row 16 : 15 | poland | 0 | 0 | 1 | 1 row 17 : 15 | canada | 0 | 0 | 1 | 1 row 18 : 15 | belarus | 0 | 0 | 1 | 1 row 19 : 15 | united states | 0 | 0 | 1 | 1 row 20 : total 19 nations | total 19 nations | 59 | 59 | 59 | 177

col : count ( "nation" ) row 1 : 1

select count ( nation ) where nation = 'switzerland' <table_name> : table \begin{tabular}{lllllll} \toprule & rank & nation & gold & silver & bronze & total \\ \midrule 0 & 1 & germany & 17 & 9 & 14 & 40 \\ 1 & 2 & russia & 12 & 16 & 8 & 36 \\ 2 & 3 & ukraine & 6 & 11 & 9 & 26 \\ 3 & 4 & norway & 6 & 2 & 7 & 15 \\ 4 & 5 & france & 5 & 4 & 3 & 12 \\ 5 & 6 & switzerland & 3 & 5 & 3 & 11 \\ 6 & 7 & finland & 3 & 2 & 4 & 9 \\ 7 & 8 & japan & 2 & 2 & 1 & 5 \\ 8 & 9 & netherlands & 2 & 0 & 1 & 3 \\ 9 & 10 & sweden & 1 & 2 & 2 & 5 \\ 10 & 11 & denmark & 1 & 0 & 1 & 2 \\ 11 & 12 & unified team & 1 & 0 & 0 & 1 \\ 12 & 13 & austria & 0 & 4 & 0 & 4 \\ 13 & 14 & slovakia & 0 & 2 & 1 & 3 \\ 14 & 15 & italy & 0 & 0 & 1 & 1 \\ 15 & 15 & poland & 0 & 0 & 1 & 1 \\ 16 & 15 & canada & 0 & 0 & 1 & 1 \\ 17 & 15 & belarus & 0 & 0 & 1 & 1 \\ 18 & 15 & united states & 0 & 0 & 1 & 1 \\ 19 & total 19 nations & total 19 nations & 59 & 59 & 59 & 177 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "nation" ) \\ \midrule 0 & 1 \\ \bottomrule \end{tabular}

select count ( nation ) where nation = 'switzerland' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>rank</th> <th>nation</th> <th>gold</th> <th>silver</th> <th>bronze</th> <th>total</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>germany</td> <td>17</td> <td>9</td> <td>14</td> <td>40</td> </tr> <tr> <th>1</th> <td>2</td> <td>russia</td> <td>12</td> <td>16</td> <td>8</td> <td>36</td> </tr> <tr> <th>2</th> <td>3</td> <td>ukraine</td> <td>6</td> <td>11</td> <td>9</td> <td>26</td> </tr> <tr> <th>3</th> <td>4</td> <td>norway</td> <td>6</td> <td>2</td> <td>7</td> <td>15</td> </tr> <tr> <th>4</th> <td>5</td> <td>france</td> <td>5</td> <td>4</td> <td>3</td> <td>12</td> </tr> <tr> <th>5</th> <td>6</td> <td>switzerland</td> <td>3</td> <td>5</td> <td>3</td> <td>11</td> </tr> <tr> <th>6</th> <td>7</td> <td>finland</td> <td>3</td> <td>2</td> <td>4</td> <td>9</td> </tr> <tr> <th>7</th> <td>8</td> <td>japan</td> <td>2</td> <td>2</td> <td>1</td> <td>5</td> </tr> <tr> <th>8</th> <td>9</td> <td>netherlands</td> <td>2</td> <td>0</td> <td>1</td> <td>3</td> </tr> <tr> <th>9</th> <td>10</td> <td>sweden</td> <td>1</td> <td>2</td> <td>2</td> <td>5</td> </tr> <tr> <th>10</th> <td>11</td> <td>denmark</td> <td>1</td> <td>0</td> <td>1</td> <td>2</td> </tr> <tr> <th>11</th> <td>12</td> <td>unified team</td> <td>1</td> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <th>12</th> <td>13</td> <td>austria</td> <td>0</td> <td>4</td> <td>0</td> <td>4</td> </tr> <tr> <th>13</th> <td>14</td> <td>slovakia</td> <td>0</td> <td>2</td> <td>1</td> <td>3</td> </tr> <tr> <th>14</th> <td>15</td> <td>italy</td> <td>0</td> <td>0</td> <td>1</td> <td>1</td> </tr> <tr> <th>15</th> <td>15</td> <td>poland</td> <td>0</td> <td>0</td> <td>1</td> <td>1</td> </tr> <tr> <th>16</th> <td>15</td> <td>canada</td> <td>0</td> <td>0</td> <td>1</td> <td>1</td> </tr> <tr> <th>17</th> <td>15</td> <td>belarus</td> <td>0</td> <td>0</td> <td>1</td> <td>1</td> </tr> <tr> <th>18</th> <td>15</td> <td>united states</td> <td>0</td> <td>0</td> <td>1</td> <td>1</td> </tr> <tr> <th>19</th> <td>total 19 nations</td> <td>total 19 nations</td> <td>59</td> <td>59</td> <td>59</td> <td>177</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "nation" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> </tr> </tbody> </table>

select count ( nation ) where nation = 'switzerland' <table_name> : table | | rank | nation | gold | silver | bronze | total | |---:|:-----------------|:-----------------|-------:|---------:|---------:|--------:| | 0 | 1 | germany | 17 | 9 | 14 | 40 | | 1 | 2 | russia | 12 | 16 | 8 | 36 | | 2 | 3 | ukraine | 6 | 11 | 9 | 26 | | 3 | 4 | norway | 6 | 2 | 7 | 15 | | 4 | 5 | france | 5 | 4 | 3 | 12 | | 5 | 6 | switzerland | 3 | 5 | 3 | 11 | | 6 | 7 | finland | 3 | 2 | 4 | 9 | | 7 | 8 | japan | 2 | 2 | 1 | 5 | | 8 | 9 | netherlands | 2 | 0 | 1 | 3 | | 9 | 10 | sweden | 1 | 2 | 2 | 5 | | 10 | 11 | denmark | 1 | 0 | 1 | 2 | | 11 | 12 | unified team | 1 | 0 | 0 | 1 | | 12 | 13 | austria | 0 | 4 | 0 | 4 | | 13 | 14 | slovakia | 0 | 2 | 1 | 3 | | 14 | 15 | italy | 0 | 0 | 1 | 1 | | 15 | 15 | poland | 0 | 0 | 1 | 1 | | 16 | 15 | canada | 0 | 0 | 1 | 1 | | 17 | 15 | belarus | 0 | 0 | 1 | 1 | | 18 | 15 | united states | 0 | 0 | 1 | 1 | | 19 | total 19 nations | total 19 nations | 59 | 59 | 59 | 177 |

| | count ( "nation" ) | |---:|---------------------:| | 0 | 1 |

select count ( competition ) where position = '16th' col : year | competition | venue | position | event | notes row 1 : 1999 | universiade | palma de mallorca, spain | 2nd | 20 km | 1:25:23 row 2 : 2001 | european race walking cup | dudince, slovakia | 12th | 20 km | 1:22:10 row 3 : 2001 | world championships | edmonton, canada | 16th | 20 km | 1:25:28 row 4 : 2001 | universiade | beijing, china | 1st | 20 km | 1:24:42 row 5 : 2002 | european championships | munich, germany | 9th | 20 km | 1:21:21 row 6 : 2002 | world race walking cup | turin, italy | 12th | 20 km | none row 7 : 2003 | world championships | paris, france | 11th | 20 km | 1:20:34 pb row 8 : 2005 | world championships | helsinki, finland | 14th | 20 km | none

col : count ( "competition" ) row 1 : 1

select count ( competition ) where position = '16th' <table_name> : table \begin{tabular}{lllllll} \toprule & year & competition & venue & position & event & notes \\ \midrule 0 & 1999 & universiade & palma de mallorca, spain & 2nd & 20 km & 1:25:23 \\ 1 & 2001 & european race walking cup & dudince, slovakia & 12th & 20 km & 1:22:10 \\ 2 & 2001 & world championships & edmonton, canada & 16th & 20 km & 1:25:28 \\ 3 & 2001 & universiade & beijing, china & 1st & 20 km & 1:24:42 \\ 4 & 2002 & european championships & munich, germany & 9th & 20 km & 1:21:21 \\ 5 & 2002 & world race walking cup & turin, italy & 12th & 20 km & none \\ 6 & 2003 & world championships & paris, france & 11th & 20 km & 1:20:34 pb \\ 7 & 2005 & world championships & helsinki, finland & 14th & 20 km & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "competition" ) \\ \midrule 0 & 1 \\ \bottomrule \end{tabular}

select count ( competition ) where position = '16th' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>year</th> <th>competition</th> <th>venue</th> <th>position</th> <th>event</th> <th>notes</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1999</td> <td>universiade</td> <td>palma de mallorca, spain</td> <td>2nd</td> <td>20 km</td> <td>1:25:23</td> </tr> <tr> <th>1</th> <td>2001</td> <td>european race walking cup</td> <td>dudince, slovakia</td> <td>12th</td> <td>20 km</td> <td>1:22:10</td> </tr> <tr> <th>2</th> <td>2001</td> <td>world championships</td> <td>edmonton, canada</td> <td>16th</td> <td>20 km</td> <td>1:25:28</td> </tr> <tr> <th>3</th> <td>2001</td> <td>universiade</td> <td>beijing, china</td> <td>1st</td> <td>20 km</td> <td>1:24:42</td> </tr> <tr> <th>4</th> <td>2002</td> <td>european championships</td> <td>munich, germany</td> <td>9th</td> <td>20 km</td> <td>1:21:21</td> </tr> <tr> <th>5</th> <td>2002</td> <td>world race walking cup</td> <td>turin, italy</td> <td>12th</td> <td>20 km</td> <td>none</td> </tr> <tr> <th>6</th> <td>2003</td> <td>world championships</td> <td>paris, france</td> <td>11th</td> <td>20 km</td> <td>1:20:34 pb</td> </tr> <tr> <th>7</th> <td>2005</td> <td>world championships</td> <td>helsinki, finland</td> <td>14th</td> <td>20 km</td> <td>none</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "competition" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> </tr> </tbody> </table>

select count ( competition ) where position = '16th' <table_name> : table | | year | competition | venue | position | event | notes | |---:|-------:|:--------------------------|:-------------------------|:-----------|:--------|:-----------| | 0 | 1999 | universiade | palma de mallorca, spain | 2nd | 20 km | 1:25:23 | | 1 | 2001 | european race walking cup | dudince, slovakia | 12th | 20 km | 1:22:10 | | 2 | 2001 | world championships | edmonton, canada | 16th | 20 km | 1:25:28 | | 3 | 2001 | universiade | beijing, china | 1st | 20 km | 1:24:42 | | 4 | 2002 | european championships | munich, germany | 9th | 20 km | 1:21:21 | | 5 | 2002 | world race walking cup | turin, italy | 12th | 20 km | none | | 6 | 2003 | world championships | paris, france | 11th | 20 km | 1:20:34 pb | | 7 | 2005 | world championships | helsinki, finland | 14th | 20 km | none |

| | count ( "competition" ) | |---:|--------------------------:| | 0 | 1 |

select w where team = 'sweden' col : team | p | w | t | l | gf | ga | gd | pts. row 1 : sweden | 5 | 5 | 0 | 0 | 114 | 91 | 23 | 10 row 2 : denmark | 5 | 3 | 1 | 1 | 107 | 99 | 8 | 7 row 3 : spain | 5 | 3 | 0 | 2 | 114 | 101 | 13 | 6 row 4 : hungary | 5 | 2 | 0 | 3 | 100 | 107 | -7 | 4 row 5 : slovenia | 5 | 1 | 1 | 3 | 94 | 111 | -17 | 3 row 6 : portugal | 5 | 0 | 0 | 5 | 96 | 116 | -20 | 0

col : w row 1 : 5

select w where team = 'sweden' <table_name> : table \begin{tabular}{llllllllll} \toprule & team & p & w & t & l & gf & ga & gd & pts. \\ \midrule 0 & sweden & 5 & 5 & 0 & 0 & 114 & 91 & 23 & 10 \\ 1 & denmark & 5 & 3 & 1 & 1 & 107 & 99 & 8 & 7 \\ 2 & spain & 5 & 3 & 0 & 2 & 114 & 101 & 13 & 6 \\ 3 & hungary & 5 & 2 & 0 & 3 & 100 & 107 & -7 & 4 \\ 4 & slovenia & 5 & 1 & 1 & 3 & 94 & 111 & -17 & 3 \\ 5 & portugal & 5 & 0 & 0 & 5 & 96 & 116 & -20 & 0 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & w \\ \midrule 0 & 5 \\ \bottomrule \end{tabular}

select w where team = 'sweden' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>team</th> <th>p</th> <th>w</th> <th>t</th> <th>l</th> <th>gf</th> <th>ga</th> <th>gd</th> <th>pts.</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>sweden</td> <td>5</td> <td>5</td> <td>0</td> <td>0</td> <td>114</td> <td>91</td> <td>23</td> <td>10</td> </tr> <tr> <th>1</th> <td>denmark</td> <td>5</td> <td>3</td> <td>1</td> <td>1</td> <td>107</td> <td>99</td> <td>8</td> <td>7</td> </tr> <tr> <th>2</th> <td>spain</td> <td>5</td> <td>3</td> <td>0</td> <td>2</td> <td>114</td> <td>101</td> <td>13</td> <td>6</td> </tr> <tr> <th>3</th> <td>hungary</td> <td>5</td> <td>2</td> <td>0</td> <td>3</td> <td>100</td> <td>107</td> <td>-7</td> <td>4</td> </tr> <tr> <th>4</th> <td>slovenia</td> <td>5</td> <td>1</td> <td>1</td> <td>3</td> <td>94</td> <td>111</td> <td>-17</td> <td>3</td> </tr> <tr> <th>5</th> <td>portugal</td> <td>5</td> <td>0</td> <td>0</td> <td>5</td> <td>96</td> <td>116</td> <td>-20</td> <td>0</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>w</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>5</td> </tr> </tbody> </table>

select w where team = 'sweden' <table_name> : table | | team | p | w | t | l | gf | ga | gd | pts. | |---:|:---------|----:|----:|----:|----:|-----:|-----:|-----:|-------:| | 0 | sweden | 5 | 5 | 0 | 0 | 114 | 91 | 23 | 10 | | 1 | denmark | 5 | 3 | 1 | 1 | 107 | 99 | 8 | 7 | | 2 | spain | 5 | 3 | 0 | 2 | 114 | 101 | 13 | 6 | | 3 | hungary | 5 | 2 | 0 | 3 | 100 | 107 | -7 | 4 | | 4 | slovenia | 5 | 1 | 1 | 3 | 94 | 111 | -17 | 3 | | 5 | portugal | 5 | 0 | 0 | 5 | 96 | 116 | -20 | 0 |

| | w | |---:|----:| | 0 | 5 |

select count ( * ) where nation = 'jamaica' col : event | performance | athlete | nation | place | date row 1 : 100 m | 9.72 | usain bolt | jamaica | new york city ny, usa | may 31 row 2 : 100 m | 9.69 | usain bolt | jamaica | beijing, china | august 16 row 3 : 200 m | 19.3 | usain bolt | jamaica | beijing, china | august 20 row 4 : 4 x 100 m relay | 37.1 | nesta carter michael frater usain bolt asafa powell | jamaica | beijing, china | august 22 row 5 : 110 m hurdles | 12.87 | dayron robles | cuba | ostrava, czech republic | june 12 row 6 : 50 km race walk | 3:34:14 | denis nizhegorodov | russia | cheboksary, russia | may 11 row 7 : marathon | 2:03:59 | haile gebrselassie | ethiopia | berlin, germany | september 28

col : count ( "*" ) row 1 : 4

select count ( * ) where nation = 'jamaica' <table_name> : table \begin{tabular}{lllllll} \toprule & event & performance & athlete & nation & place & date \\ \midrule 0 & 100 m & 9.72 & usain bolt & jamaica & new york city ny, usa & may 31 \\ 1 & 100 m & 9.69 & usain bolt & jamaica & beijing, china & august 16 \\ 2 & 200 m & 19.3 & usain bolt & jamaica & beijing, china & august 20 \\ 3 & 4 x 100 m relay & 37.1 & nesta carter michael frater usain bolt asafa powell & jamaica & beijing, china & august 22 \\ 4 & 110 m hurdles & 12.87 & dayron robles & cuba & ostrava, czech republic & june 12 \\ 5 & 50 km race walk & 3:34:14 & denis nizhegorodov & russia & cheboksary, russia & may 11 \\ 6 & marathon & 2:03:59 & haile gebrselassie & ethiopia & berlin, germany & september 28 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 4 \\ \bottomrule \end{tabular}

select count ( * ) where nation = 'jamaica' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>event</th> <th>performance</th> <th>athlete</th> <th>nation</th> <th>place</th> <th>date</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>100 m</td> <td>9.72</td> <td>usain bolt</td> <td>jamaica</td> <td>new york city ny, usa</td> <td>may 31</td> </tr> <tr> <th>1</th> <td>100 m</td> <td>9.69</td> <td>usain bolt</td> <td>jamaica</td> <td>beijing, china</td> <td>august 16</td> </tr> <tr> <th>2</th> <td>200 m</td> <td>19.3</td> <td>usain bolt</td> <td>jamaica</td> <td>beijing, china</td> <td>august 20</td> </tr> <tr> <th>3</th> <td>4 x 100 m relay</td> <td>37.1</td> <td>nesta carter michael frater usain bolt asafa powell</td> <td>jamaica</td> <td>beijing, china</td> <td>august 22</td> </tr> <tr> <th>4</th> <td>110 m hurdles</td> <td>12.87</td> <td>dayron robles</td> <td>cuba</td> <td>ostrava, czech republic</td> <td>june 12</td> </tr> <tr> <th>5</th> <td>50 km race walk</td> <td>3:34:14</td> <td>denis nizhegorodov</td> <td>russia</td> <td>cheboksary, russia</td> <td>may 11</td> </tr> <tr> <th>6</th> <td>marathon</td> <td>2:03:59</td> <td>haile gebrselassie</td> <td>ethiopia</td> <td>berlin, germany</td> <td>september 28</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "*" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>4</td> </tr> </tbody> </table>

select count ( * ) where nation = 'jamaica' <table_name> : table | | event | performance | athlete | nation | place | date | |---:|:----------------|:--------------|:----------------------------------------------------|:---------|:------------------------|:-------------| | 0 | 100 m | 9.72 | usain bolt | jamaica | new york city ny, usa | may 31 | | 1 | 100 m | 9.69 | usain bolt | jamaica | beijing, china | august 16 | | 2 | 200 m | 19.3 | usain bolt | jamaica | beijing, china | august 20 | | 3 | 4 x 100 m relay | 37.1 | nesta carter michael frater usain bolt asafa powell | jamaica | beijing, china | august 22 | | 4 | 110 m hurdles | 12.87 | dayron robles | cuba | ostrava, czech republic | june 12 | | 5 | 50 km race walk | 3:34:14 | denis nizhegorodov | russia | cheboksary, russia | may 11 | | 6 | marathon | 2:03:59 | haile gebrselassie | ethiopia | berlin, germany | september 28 |

| | count ( "*" ) | |---:|----------------:| | 0 | 4 |

select count ( * ) where bronze = 'boriana stoyanova' col : year | location | gold | silver | bronze row 1 : 1950 | basel | helena rakoczy | tereza kocis | stefania reindlova row 2 : 1954 | rome | tamara manina | eva bosakova | maria gorokovskaya row 3 : 1958 | moscow | eva bosakova | larisa latynina | keiko tanaka row 4 : 1962 | prague | larisa latynina | irina pervuschina | vera caslavska row 5 : 1966 | dortmund | natalia kutchinskaya | vera caslavska | zinaida drougina row 6 : 1970 | ljubljana | ludmila turicheva | olga karaseva | zinaida voronina row 7 : 1974 | varna | ludmila turicheva | olga korbut | elvira saadi rusudan siharulidze row 8 : 1978 | strasbourg | nellie kim elena mukhina | none | emilia eberle kathy johnson row 9 : 1979 | fort worth | emilia eberle | nellie kim | melita ruhn row 10 : 1981 | moscow | natalia ilienko | yelena davydova | zoja grantcharova row 11 : 1983 | budapest | ekaterina szabo | olga mostepanova | boriana stoyanova row 12 : 1985 | montreal | oksana omelianchik | elena shushunova | ulrike klotz row 13 : 1987 | rotterdam | daniela silivas elena shushunova | none | aurelia dobre row 14 : 1989 | stuttgart | daniela silivas svetlana boguinskaya | none | cristina bontas row 15 : 1991 | indianapolis | cristina bontas oksana chusovitina | none | kim zmeskal row 16 : 1992 | paris | kim zmeskal | henrietta onodi | maria neculita tatiana lysenko row 17 : 1993 | birmingham | shannon miller | gina gogean | natalia bobrova row 18 : 1994 | brisbane | dina kochetkova | lavinia milosovici | gina gogean row 19 : 1995 | sabae | gina gogean | ji liya | ludivine furnon row 20 : 1996 | san juan | kui yuan-yuan gina gogean | none | liubov sheremeta lavinia milosovici row 21 : 1997 | lausanne | gina gogean | svetlana khorkina | elena produnova row 22 : 1999 | tianjin | andreea raducan | simona amanar | svetlana khorkina row 23 : 2001 | ghent | andreea raducan | daniele hypolito | svetlana khorkina row 24 : 2002 | debrecen | elena gomez | verona van de leur | samantha sheehan row 25 : 2003 | anaheim | daiane dos santos | catalina ponor | elena gomez row 26 : 2005 | melbourne | alicia sacramone | anastasia liukin | suzanne harmes row 27 : 2006 | aarhus | cheng fei | jana bieger | vanessa ferrari row 28 : 2007 | stuttgart | shawn johnson | alicia sacramone | cassy vericel row 29 : 2009 | london | elizabeth tweddle | lauren mitchell | sui lu row 30 : 2010 | rotterdam | lauren mitchell | aliya mustafina diana chelaru | none row 31 : 2011 | tokyo | ksenia afanasyeva | sui lu | aly raisman row 32 : 2013 | antwerp | simone biles | vanessa ferrari | larisa iordache row 33 : 2014 | nanning | none | none | none row 34 : 2015 | glasgow | none | none | none

col : count ( "*" ) row 1 : 1

select count ( * ) where bronze = 'boriana stoyanova' <table_name> : table \begin{tabular}{llllll} \toprule & year & location & gold & silver & bronze \\ \midrule 0 & 1950 & basel & helena rakoczy & tereza kocis & stefania reindlova \\ 1 & 1954 & rome & tamara manina & eva bosakova & maria gorokovskaya \\ 2 & 1958 & moscow & eva bosakova & larisa latynina & keiko tanaka \\ 3 & 1962 & prague & larisa latynina & irina pervuschina & vera caslavska \\ 4 & 1966 & dortmund & natalia kutchinskaya & vera caslavska & zinaida drougina \\ 5 & 1970 & ljubljana & ludmila turicheva & olga karaseva & zinaida voronina \\ 6 & 1974 & varna & ludmila turicheva & olga korbut & elvira saadi rusudan siharulidze \\ 7 & 1978 & strasbourg & nellie kim elena mukhina & none & emilia eberle kathy johnson \\ 8 & 1979 & fort worth & emilia eberle & nellie kim & melita ruhn \\ 9 & 1981 & moscow & natalia ilienko & yelena davydova & zoja grantcharova \\ 10 & 1983 & budapest & ekaterina szabo & olga mostepanova & boriana stoyanova \\ 11 & 1985 & montreal & oksana omelianchik & elena shushunova & ulrike klotz \\ 12 & 1987 & rotterdam & daniela silivas elena shushunova & none & aurelia dobre \\ 13 & 1989 & stuttgart & daniela silivas svetlana boguinskaya & none & cristina bontas \\ 14 & 1991 & indianapolis & cristina bontas oksana chusovitina & none & kim zmeskal \\ 15 & 1992 & paris & kim zmeskal & henrietta onodi & maria neculita tatiana lysenko \\ 16 & 1993 & birmingham & shannon miller & gina gogean & natalia bobrova \\ 17 & 1994 & brisbane & dina kochetkova & lavinia milosovici & gina gogean \\ 18 & 1995 & sabae & gina gogean & ji liya & ludivine furnon \\ 19 & 1996 & san juan & kui yuan-yuan gina gogean & none & liubov sheremeta lavinia milosovici \\ 20 & 1997 & lausanne & gina gogean & svetlana khorkina & elena produnova \\ 21 & 1999 & tianjin & andreea raducan & simona amanar & svetlana khorkina \\ 22 & 2001 & ghent & andreea raducan & daniele hypolito & svetlana khorkina \\ 23 & 2002 & debrecen & elena gomez & verona van de leur & samantha sheehan \\ 24 & 2003 & anaheim & daiane dos santos & catalina ponor & elena gomez \\ 25 & 2005 & melbourne & alicia sacramone & anastasia liukin & suzanne harmes \\ 26 & 2006 & aarhus & cheng fei & jana bieger & vanessa ferrari \\ 27 & 2007 & stuttgart & shawn johnson & alicia sacramone & cassy vericel \\ 28 & 2009 & london & elizabeth tweddle & lauren mitchell & sui lu \\ 29 & 2010 & rotterdam & lauren mitchell & aliya mustafina diana chelaru & none \\ 30 & 2011 & tokyo & ksenia afanasyeva & sui lu & aly raisman \\ 31 & 2013 & antwerp & simone biles & vanessa ferrari & larisa iordache \\ 32 & 2014 & nanning & none & none & none \\ 33 & 2015 & glasgow & none & none & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 1 \\ \bottomrule \end{tabular}

select count ( * ) where bronze = 'boriana stoyanova' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>year</th> <th>location</th> <th>gold</th> <th>silver</th> <th>bronze</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1950</td> <td>basel</td> <td>helena rakoczy</td> <td>tereza kocis</td> <td>stefania reindlova</td> </tr> <tr> <th>1</th> <td>1954</td> <td>rome</td> <td>tamara manina</td> <td>eva bosakova</td> <td>maria gorokovskaya</td> </tr> <tr> <th>2</th> <td>1958</td> <td>moscow</td> <td>eva bosakova</td> <td>larisa latynina</td> <td>keiko tanaka</td> </tr> <tr> <th>3</th> <td>1962</td> <td>prague</td> <td>larisa latynina</td> <td>irina pervuschina</td> <td>vera caslavska</td> </tr> <tr> <th>4</th> <td>1966</td> <td>dortmund</td> <td>natalia kutchinskaya</td> <td>vera caslavska</td> <td>zinaida drougina</td> </tr> <tr> <th>5</th> <td>1970</td> <td>ljubljana</td> <td>ludmila turicheva</td> <td>olga karaseva</td> <td>zinaida voronina</td> </tr> <tr> <th>6</th> <td>1974</td> <td>varna</td> <td>ludmila turicheva</td> <td>olga korbut</td> <td>elvira saadi rusudan siharulidze</td> </tr> <tr> <th>7</th> <td>1978</td> <td>strasbourg</td> <td>nellie kim elena mukhina</td> <td>none</td> <td>emilia eberle kathy johnson</td> </tr> <tr> <th>8</th> <td>1979</td> <td>fort worth</td> <td>emilia eberle</td> <td>nellie kim</td> <td>melita ruhn</td> </tr> <tr> <th>9</th> <td>1981</td> <td>moscow</td> <td>natalia ilienko</td> <td>yelena davydova</td> <td>zoja grantcharova</td> </tr> <tr> <th>10</th> <td>1983</td> <td>budapest</td> <td>ekaterina szabo</td> <td>olga mostepanova</td> <td>boriana stoyanova</td> </tr> <tr> <th>11</th> <td>1985</td> <td>montreal</td> <td>oksana omelianchik</td> <td>elena shushunova</td> <td>ulrike klotz</td> </tr> <tr> <th>12</th> <td>1987</td> <td>rotterdam</td> <td>daniela silivas elena shushunova</td> <td>none</td> <td>aurelia dobre</td> </tr> <tr> <th>13</th> <td>1989</td> <td>stuttgart</td> <td>daniela silivas svetlana boguinskaya</td> <td>none</td> <td>cristina bontas</td> </tr> <tr> <th>14</th> <td>1991</td> <td>indianapolis</td> <td>cristina bontas oksana chusovitina</td> <td>none</td> <td>kim zmeskal</td> </tr> <tr> <th>15</th> <td>1992</td> <td>paris</td> <td>kim zmeskal</td> <td>henrietta onodi</td> <td>maria neculita tatiana lysenko</td> </tr> <tr> <th>16</th> <td>1993</td> <td>birmingham</td> <td>shannon miller</td> <td>gina gogean</td> <td>natalia bobrova</td> </tr> <tr> <th>17</th> <td>1994</td> <td>brisbane</td> <td>dina kochetkova</td> <td>lavinia milosovici</td> <td>gina gogean</td> </tr> <tr> <th>18</th> <td>1995</td> <td>sabae</td> <td>gina gogean</td> <td>ji liya</td> <td>ludivine furnon</td> </tr> <tr> <th>19</th> <td>1996</td> <td>san juan</td> <td>kui yuan-yuan gina gogean</td> <td>none</td> <td>liubov sheremeta lavinia milosovici</td> </tr> <tr> <th>20</th> <td>1997</td> <td>lausanne</td> <td>gina gogean</td> <td>svetlana khorkina</td> <td>elena produnova</td> </tr> <tr> <th>21</th> <td>1999</td> <td>tianjin</td> <td>andreea raducan</td> <td>simona amanar</td> <td>svetlana khorkina</td> </tr> <tr> <th>22</th> <td>2001</td> <td>ghent</td> <td>andreea raducan</td> <td>daniele hypolito</td> <td>svetlana khorkina</td> </tr> <tr> <th>23</th> <td>2002</td> <td>debrecen</td> <td>elena gomez</td> <td>verona van de leur</td> <td>samantha sheehan</td> </tr> <tr> <th>24</th> <td>2003</td> <td>anaheim</td> <td>daiane dos santos</td> <td>catalina ponor</td> <td>elena gomez</td> </tr> <tr> <th>25</th> <td>2005</td> <td>melbourne</td> <td>alicia sacramone</td> <td>anastasia liukin</td> <td>suzanne harmes</td> </tr> <tr> <th>26</th> <td>2006</td> <td>aarhus</td> <td>cheng fei</td> <td>jana bieger</td> <td>vanessa ferrari</td> </tr> <tr> <th>27</th> <td>2007</td> <td>stuttgart</td> <td>shawn johnson</td> <td>alicia sacramone</td> <td>cassy vericel</td> </tr> <tr> <th>28</th> <td>2009</td> <td>london</td> <td>elizabeth tweddle</td> <td>lauren mitchell</td> <td>sui lu</td> </tr> <tr> <th>29</th> <td>2010</td> <td>rotterdam</td> <td>lauren mitchell</td> <td>aliya mustafina diana chelaru</td> <td>none</td> </tr> <tr> <th>30</th> <td>2011</td> <td>tokyo</td> <td>ksenia afanasyeva</td> <td>sui lu</td> <td>aly raisman</td> </tr> <tr> <th>31</th> <td>2013</td> <td>antwerp</td> <td>simone biles</td> <td>vanessa ferrari</td> <td>larisa iordache</td> </tr> <tr> <th>32</th> <td>2014</td> <td>nanning</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>33</th> <td>2015</td> <td>glasgow</td> <td>none</td> <td>none</td> <td>none</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "*" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> </tr> </tbody> </table>

select count ( * ) where bronze = 'boriana stoyanova' <table_name> : table | | year | location | gold | silver | bronze | |---:|-------:|:-------------|:-------------------------------------|:------------------------------|:------------------------------------| | 0 | 1950 | basel | helena rakoczy | tereza kocis | stefania reindlova | | 1 | 1954 | rome | tamara manina | eva bosakova | maria gorokovskaya | | 2 | 1958 | moscow | eva bosakova | larisa latynina | keiko tanaka | | 3 | 1962 | prague | larisa latynina | irina pervuschina | vera caslavska | | 4 | 1966 | dortmund | natalia kutchinskaya | vera caslavska | zinaida drougina | | 5 | 1970 | ljubljana | ludmila turicheva | olga karaseva | zinaida voronina | | 6 | 1974 | varna | ludmila turicheva | olga korbut | elvira saadi rusudan siharulidze | | 7 | 1978 | strasbourg | nellie kim elena mukhina | none | emilia eberle kathy johnson | | 8 | 1979 | fort worth | emilia eberle | nellie kim | melita ruhn | | 9 | 1981 | moscow | natalia ilienko | yelena davydova | zoja grantcharova | | 10 | 1983 | budapest | ekaterina szabo | olga mostepanova | boriana stoyanova | | 11 | 1985 | montreal | oksana omelianchik | elena shushunova | ulrike klotz | | 12 | 1987 | rotterdam | daniela silivas elena shushunova | none | aurelia dobre | | 13 | 1989 | stuttgart | daniela silivas svetlana boguinskaya | none | cristina bontas | | 14 | 1991 | indianapolis | cristina bontas oksana chusovitina | none | kim zmeskal | | 15 | 1992 | paris | kim zmeskal | henrietta onodi | maria neculita tatiana lysenko | | 16 | 1993 | birmingham | shannon miller | gina gogean | natalia bobrova | | 17 | 1994 | brisbane | dina kochetkova | lavinia milosovici | gina gogean | | 18 | 1995 | sabae | gina gogean | ji liya | ludivine furnon | | 19 | 1996 | san juan | kui yuan-yuan gina gogean | none | liubov sheremeta lavinia milosovici | | 20 | 1997 | lausanne | gina gogean | svetlana khorkina | elena produnova | | 21 | 1999 | tianjin | andreea raducan | simona amanar | svetlana khorkina | | 22 | 2001 | ghent | andreea raducan | daniele hypolito | svetlana khorkina | | 23 | 2002 | debrecen | elena gomez | verona van de leur | samantha sheehan | | 24 | 2003 | anaheim | daiane dos santos | catalina ponor | elena gomez | | 25 | 2005 | melbourne | alicia sacramone | anastasia liukin | suzanne harmes | | 26 | 2006 | aarhus | cheng fei | jana bieger | vanessa ferrari | | 27 | 2007 | stuttgart | shawn johnson | alicia sacramone | cassy vericel | | 28 | 2009 | london | elizabeth tweddle | lauren mitchell | sui lu | | 29 | 2010 | rotterdam | lauren mitchell | aliya mustafina diana chelaru | none | | 30 | 2011 | tokyo | ksenia afanasyeva | sui lu | aly raisman | | 31 | 2013 | antwerp | simone biles | vanessa ferrari | larisa iordache | | 32 | 2014 | nanning | none | none | none | | 33 | 2015 | glasgow | none | none | none |

| | count ( "*" ) | |---:|----------------:| | 0 | 1 |

select min ( no. in series ) where appearance = 'only' col : no. in series | no. in season | performer | appearance | air date row 1 : 268 | 1 | tom segura | only | 21 january 2011 row 2 : 269 | 2 | michael kosta | only | 21 january 2011 row 3 : 270 | 3 | sheng wang | only | 28 january 2011 row 4 : 271 | 4 | chelsea peretti | only | 28 january 2011 row 5 : 272 | 5 | chris d'elia | only | 4 february 2011 row 6 : 273 | 6 | nate bargatze | only | 4 february 2011 row 7 : 274 | 7 | hari kondabolu | only | 11 february 2011 row 8 : 275 | 8 | jack whitehall | only | 11 february 2011 row 9 : 276 | 9 | al jackson | only | 18 february 2011 row 10 : 277 | 10 | kyle kinane | only | 25 february 2011 row 11 : 278 | 11 | matt fulchiron | only | 25 february 2011 row 12 : 279 | 12 | mike vecchione | only | 4 march 2011 row 13 : 280 | 13 | jessi klein | only | 4 march 2011 row 14 : 281 | 14 | louis katz | only | 11 march 2011 row 15 : 282 | 15 | jay larson | only | 11 march 2011 row 16 : 283 | 16 | natasha leggero | only | 25 march 2011

col : min ( "no. in series" ) row 1 : 268

select min ( no. in series ) where appearance = 'only' <table_name> : table \begin{tabular}{llllll} \toprule & no. in series & no. in season & performer & appearance & air date \\ \midrule 0 & 268 & 1 & tom segura & only & 21 january 2011 \\ 1 & 269 & 2 & michael kosta & only & 21 january 2011 \\ 2 & 270 & 3 & sheng wang & only & 28 january 2011 \\ 3 & 271 & 4 & chelsea peretti & only & 28 january 2011 \\ 4 & 272 & 5 & chris d'elia & only & 4 february 2011 \\ 5 & 273 & 6 & nate bargatze & only & 4 february 2011 \\ 6 & 274 & 7 & hari kondabolu & only & 11 february 2011 \\ 7 & 275 & 8 & jack whitehall & only & 11 february 2011 \\ 8 & 276 & 9 & al jackson & only & 18 february 2011 \\ 9 & 277 & 10 & kyle kinane & only & 25 february 2011 \\ 10 & 278 & 11 & matt fulchiron & only & 25 february 2011 \\ 11 & 279 & 12 & mike vecchione & only & 4 march 2011 \\ 12 & 280 & 13 & jessi klein & only & 4 march 2011 \\ 13 & 281 & 14 & louis katz & only & 11 march 2011 \\ 14 & 282 & 15 & jay larson & only & 11 march 2011 \\ 15 & 283 & 16 & natasha leggero & only & 25 march 2011 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & min ( "no. in series" ) \\ \midrule 0 & 268 \\ \bottomrule \end{tabular}

select min ( no. in series ) where appearance = 'only' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>no. in series</th> <th>no. in season</th> <th>performer</th> <th>appearance</th> <th>air date</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>268</td> <td>1</td> <td>tom segura</td> <td>only</td> <td>21 january 2011</td> </tr> <tr> <th>1</th> <td>269</td> <td>2</td> <td>michael kosta</td> <td>only</td> <td>21 january 2011</td> </tr> <tr> <th>2</th> <td>270</td> <td>3</td> <td>sheng wang</td> <td>only</td> <td>28 january 2011</td> </tr> <tr> <th>3</th> <td>271</td> <td>4</td> <td>chelsea peretti</td> <td>only</td> <td>28 january 2011</td> </tr> <tr> <th>4</th> <td>272</td> <td>5</td> <td>chris d'elia</td> <td>only</td> <td>4 february 2011</td> </tr> <tr> <th>5</th> <td>273</td> <td>6</td> <td>nate bargatze</td> <td>only</td> <td>4 february 2011</td> </tr> <tr> <th>6</th> <td>274</td> <td>7</td> <td>hari kondabolu</td> <td>only</td> <td>11 february 2011</td> </tr> <tr> <th>7</th> <td>275</td> <td>8</td> <td>jack whitehall</td> <td>only</td> <td>11 february 2011</td> </tr> <tr> <th>8</th> <td>276</td> <td>9</td> <td>al jackson</td> <td>only</td> <td>18 february 2011</td> </tr> <tr> <th>9</th> <td>277</td> <td>10</td> <td>kyle kinane</td> <td>only</td> <td>25 february 2011</td> </tr> <tr> <th>10</th> <td>278</td> <td>11</td> <td>matt fulchiron</td> <td>only</td> <td>25 february 2011</td> </tr> <tr> <th>11</th> <td>279</td> <td>12</td> <td>mike vecchione</td> <td>only</td> <td>4 march 2011</td> </tr> <tr> <th>12</th> <td>280</td> <td>13</td> <td>jessi klein</td> <td>only</td> <td>4 march 2011</td> </tr> <tr> <th>13</th> <td>281</td> <td>14</td> <td>louis katz</td> <td>only</td> <td>11 march 2011</td> </tr> <tr> <th>14</th> <td>282</td> <td>15</td> <td>jay larson</td> <td>only</td> <td>11 march 2011</td> </tr> <tr> <th>15</th> <td>283</td> <td>16</td> <td>natasha leggero</td> <td>only</td> <td>25 march 2011</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>min ( "no. in series" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>268</td> </tr> </tbody> </table>

select min ( no. in series ) where appearance = 'only' <table_name> : table | | no. in series | no. in season | performer | appearance | air date | |---:|----------------:|----------------:|:----------------|:-------------|:-----------------| | 0 | 268 | 1 | tom segura | only | 21 january 2011 | | 1 | 269 | 2 | michael kosta | only | 21 january 2011 | | 2 | 270 | 3 | sheng wang | only | 28 january 2011 | | 3 | 271 | 4 | chelsea peretti | only | 28 january 2011 | | 4 | 272 | 5 | chris d'elia | only | 4 february 2011 | | 5 | 273 | 6 | nate bargatze | only | 4 february 2011 | | 6 | 274 | 7 | hari kondabolu | only | 11 february 2011 | | 7 | 275 | 8 | jack whitehall | only | 11 february 2011 | | 8 | 276 | 9 | al jackson | only | 18 february 2011 | | 9 | 277 | 10 | kyle kinane | only | 25 february 2011 | | 10 | 278 | 11 | matt fulchiron | only | 25 february 2011 | | 11 | 279 | 12 | mike vecchione | only | 4 march 2011 | | 12 | 280 | 13 | jessi klein | only | 4 march 2011 | | 13 | 281 | 14 | louis katz | only | 11 march 2011 | | 14 | 282 | 15 | jay larson | only | 11 march 2011 | | 15 | 283 | 16 | natasha leggero | only | 25 march 2011 |

| | min ( "no. in series" ) | |---:|--------------------------:| | 0 | 268 |

select count ( * ) where score = 920 col : place | player | country | score | to par | money ($) row 1 : 1 | ralph guldahl | united states | 72-68-70-69=279 | -9 | 1,500 row 2 : 2 | sam snead | united states | 70-70-72-68=280 | -8 | 800 row 3 : t3 | billy burke | united states | 69-72-71-70=282 | -6 | 550 row 4 : t3 | lawson little | united states | 72-72-68-70=282 | -6 | 550 row 5 : 5 | gene sarazen | united states | 73-66-72-72=283 | -5 | 400 row 6 : 6 | craig wood | united states | 72-73-71-68=284 | -4 | 300 row 7 : 7 | byron nelson | united states | 71-69-72-75=287 | -1 | 250 row 8 : 8 | henry picard | united states | 71-71-76-71=289 | 1 | 175 row 9 : 9 | ben hogan | united states | 75-71-72-72=290 | 2 | 125 row 10 : t10 | ed dudley | united states | 75-75-69-72=291 | 3 | 100 row 11 : t10 | toney penna | united states | 72-75-72-72=291 | 3 | 100

col : count ( "*" ) row 1 : 0

select count ( * ) where score = 920 <table_name> : table \begin{tabular}{lllllll} \toprule & place & player & country & score & to par & money ($) \\ \midrule 0 & 1 & ralph guldahl & united states & 72-68-70-69=279 & -9 & 1,500 \\ 1 & 2 & sam snead & united states & 70-70-72-68=280 & -8 & 800 \\ 2 & t3 & billy burke & united states & 69-72-71-70=282 & -6 & 550 \\ 3 & t3 & lawson little & united states & 72-72-68-70=282 & -6 & 550 \\ 4 & 5 & gene sarazen & united states & 73-66-72-72=283 & -5 & 400 \\ 5 & 6 & craig wood & united states & 72-73-71-68=284 & -4 & 300 \\ 6 & 7 & byron nelson & united states & 71-69-72-75=287 & -1 & 250 \\ 7 & 8 & henry picard & united states & 71-71-76-71=289 & 1 & 175 \\ 8 & 9 & ben hogan & united states & 75-71-72-72=290 & 2 & 125 \\ 9 & t10 & ed dudley & united states & 75-75-69-72=291 & 3 & 100 \\ 10 & t10 & toney penna & united states & 72-75-72-72=291 & 3 & 100 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 0 \\ \bottomrule \end{tabular}

select count ( * ) where score = 920 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>place</th> <th>player</th> <th>country</th> <th>score</th> <th>to par</th> <th>money ($)</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>ralph guldahl</td> <td>united states</td> <td>72-68-70-69=279</td> <td>-9</td> <td>1,500</td> </tr> <tr> <th>1</th> <td>2</td> <td>sam snead</td> <td>united states</td> <td>70-70-72-68=280</td> <td>-8</td> <td>800</td> </tr> <tr> <th>2</th> <td>t3</td> <td>billy burke</td> <td>united states</td> <td>69-72-71-70=282</td> <td>-6</td> <td>550</td> </tr> <tr> <th>3</th> <td>t3</td> <td>lawson little</td> <td>united states</td> <td>72-72-68-70=282</td> <td>-6</td> <td>550</td> </tr> <tr> <th>4</th> <td>5</td> <td>gene sarazen</td> <td>united states</td> <td>73-66-72-72=283</td> <td>-5</td> <td>400</td> </tr> <tr> <th>5</th> <td>6</td> <td>craig wood</td> <td>united states</td> <td>72-73-71-68=284</td> <td>-4</td> <td>300</td> </tr> <tr> <th>6</th> <td>7</td> <td>byron nelson</td> <td>united states</td> <td>71-69-72-75=287</td> <td>-1</td> <td>250</td> </tr> <tr> <th>7</th> <td>8</td> <td>henry picard</td> <td>united states</td> <td>71-71-76-71=289</td> <td>1</td> <td>175</td> </tr> <tr> <th>8</th> <td>9</td> <td>ben hogan</td> <td>united states</td> <td>75-71-72-72=290</td> <td>2</td> <td>125</td> </tr> <tr> <th>9</th> <td>t10</td> <td>ed dudley</td> <td>united states</td> <td>75-75-69-72=291</td> <td>3</td> <td>100</td> </tr> <tr> <th>10</th> <td>t10</td> <td>toney penna</td> <td>united states</td> <td>72-75-72-72=291</td> <td>3</td> <td>100</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "*" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>0</td> </tr> </tbody> </table>

select count ( * ) where score = 920 <table_name> : table | | place | player | country | score | to par | money ($) | |---:|:--------|:--------------|:--------------|:----------------|---------:|:------------| | 0 | 1 | ralph guldahl | united states | 72-68-70-69=279 | -9 | 1,500 | | 1 | 2 | sam snead | united states | 70-70-72-68=280 | -8 | 800 | | 2 | t3 | billy burke | united states | 69-72-71-70=282 | -6 | 550 | | 3 | t3 | lawson little | united states | 72-72-68-70=282 | -6 | 550 | | 4 | 5 | gene sarazen | united states | 73-66-72-72=283 | -5 | 400 | | 5 | 6 | craig wood | united states | 72-73-71-68=284 | -4 | 300 | | 6 | 7 | byron nelson | united states | 71-69-72-75=287 | -1 | 250 | | 7 | 8 | henry picard | united states | 71-71-76-71=289 | 1 | 175 | | 8 | 9 | ben hogan | united states | 75-71-72-72=290 | 2 | 125 | | 9 | t10 | ed dudley | united states | 75-75-69-72=291 | 3 | 100 | | 10 | t10 | toney penna | united states | 72-75-72-72=291 | 3 | 100 |

| | count ( "*" ) | |---:|----------------:| | 0 | 0 |

select win% / seasons played where win-loss record = '122-115' col : team | city | years active | seasons played | win-loss record | win% | playoffs appearances row 1 : anderson packers | anderson, indiana | 1949-1950 | 1 | 37-27 | 0.578 | 1 row 2 : baa buffalo | buffalo, new york | never played | 0 | 0-0 | none | 0 row 3 : baa indianapolis | indianapolis, indiana | never played | 0 | 0-0 | none | 0 row 4 : baltimore bullets | baltimore, maryland | 1947-1954 | 8 | 158-292 | 0.351 | 3 row 5 : chicago stags | chicago, illinois | 1946-1950 | 4 | 145-92 | 0.612 | 4 row 6 : cleveland rebels | cleveland, ohio | 1946-1947 | 1 | 30-30 | 0.5 | 1 row 7 : denver nuggets | denver, colorado | 1949-1950 | 1 | 11-51 | 0.177 | 0 row 8 : detroit falcons | detroit, michigan | 1946-1947 | 1 | 20-40 | 0.333 | 0 row 9 : indianapolis jets | indianapolis, indiana | 1948-1949 | 1 | 18-42 | 0.3 | 0 row 10 : indianapolis olympians | indianapolis, indiana | 1949-1953 | 4 | 132-137 | 0.491 | 4 row 11 : pittsburgh ironmen | pittsburgh, pennsylvania | 1946-1947 | 1 | 15-45 | 0.25 | 0 row 12 : providence steamrollers | providence, rhode island | 1946-1949 | 3 | 46-122 | 0.274 | 0 row 13 : sheboygan red skins | sheboygan, wisconsin | 1949-1950 | 1 | 22-40 | 0.355 | 1 row 14 : st. louis bombers | st. louis, missouri | 1946-1950 | 4 | 122-115 | 0.515 | 3 row 15 : toronto huskies | toronto, ontario | 1946-1947 | 1 | 22-38 | 0.367 | 0 row 16 : washington capitols | washington, d.c | 1946-1951 | 5 | 157-114 | 0.579 | 4 row 17 : waterloo hawks | waterloo, iowa | 1949-1950 | 1 | 19-43 | 0.306 | 0

col : "win% / seasons played" row 1 : win% / seasons played

select win% / seasons played where win-loss record = '122-115' <table_name> : table \begin{tabular}{llllllll} \toprule & team & city & years active & seasons played & win-loss record & win% & playoffs appearances \\ \midrule 0 & anderson packers & anderson, indiana & 1949-1950 & 1 & 37-27 & 0.578 & 1 \\ 1 & baa buffalo & buffalo, new york & never played & 0 & 0-0 & none & 0 \\ 2 & baa indianapolis & indianapolis, indiana & never played & 0 & 0-0 & none & 0 \\ 3 & baltimore bullets & baltimore, maryland & 1947-1954 & 8 & 158-292 & 0.351 & 3 \\ 4 & chicago stags & chicago, illinois & 1946-1950 & 4 & 145-92 & 0.612 & 4 \\ 5 & cleveland rebels & cleveland, ohio & 1946-1947 & 1 & 30-30 & 0.5 & 1 \\ 6 & denver nuggets & denver, colorado & 1949-1950 & 1 & 11-51 & 0.177 & 0 \\ 7 & detroit falcons & detroit, michigan & 1946-1947 & 1 & 20-40 & 0.333 & 0 \\ 8 & indianapolis jets & indianapolis, indiana & 1948-1949 & 1 & 18-42 & 0.3 & 0 \\ 9 & indianapolis olympians & indianapolis, indiana & 1949-1953 & 4 & 132-137 & 0.491 & 4 \\ 10 & pittsburgh ironmen & pittsburgh, pennsylvania & 1946-1947 & 1 & 15-45 & 0.25 & 0 \\ 11 & providence steamrollers & providence, rhode island & 1946-1949 & 3 & 46-122 & 0.274 & 0 \\ 12 & sheboygan red skins & sheboygan, wisconsin & 1949-1950 & 1 & 22-40 & 0.355 & 1 \\ 13 & st. louis bombers & st. louis, missouri & 1946-1950 & 4 & 122-115 & 0.515 & 3 \\ 14 & toronto huskies & toronto, ontario & 1946-1947 & 1 & 22-38 & 0.367 & 0 \\ 15 & washington capitols & washington, d.c & 1946-1951 & 5 & 157-114 & 0.579 & 4 \\ 16 & waterloo hawks & waterloo, iowa & 1949-1950 & 1 & 19-43 & 0.306 & 0 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & "win% / seasons played" \\ \midrule 0 & win% / seasons played \\ \bottomrule \end{tabular}

select win% / seasons played where win-loss record = '122-115' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>team</th> <th>city</th> <th>years active</th> <th>seasons played</th> <th>win-loss record</th> <th>win%</th> <th>playoffs appearances</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>anderson packers</td> <td>anderson, indiana</td> <td>1949-1950</td> <td>1</td> <td>37-27</td> <td>0.578</td> <td>1</td> </tr> <tr> <th>1</th> <td>baa buffalo</td> <td>buffalo, new york</td> <td>never played</td> <td>0</td> <td>0-0</td> <td>none</td> <td>0</td> </tr> <tr> <th>2</th> <td>baa indianapolis</td> <td>indianapolis, indiana</td> <td>never played</td> <td>0</td> <td>0-0</td> <td>none</td> <td>0</td> </tr> <tr> <th>3</th> <td>baltimore bullets</td> <td>baltimore, maryland</td> <td>1947-1954</td> <td>8</td> <td>158-292</td> <td>0.351</td> <td>3</td> </tr> <tr> <th>4</th> <td>chicago stags</td> <td>chicago, illinois</td> <td>1946-1950</td> <td>4</td> <td>145-92</td> <td>0.612</td> <td>4</td> </tr> <tr> <th>5</th> <td>cleveland rebels</td> <td>cleveland, ohio</td> <td>1946-1947</td> <td>1</td> <td>30-30</td> <td>0.5</td> <td>1</td> </tr> <tr> <th>6</th> <td>denver nuggets</td> <td>denver, colorado</td> <td>1949-1950</td> <td>1</td> <td>11-51</td> <td>0.177</td> <td>0</td> </tr> <tr> <th>7</th> <td>detroit falcons</td> <td>detroit, michigan</td> <td>1946-1947</td> <td>1</td> <td>20-40</td> <td>0.333</td> <td>0</td> </tr> <tr> <th>8</th> <td>indianapolis jets</td> <td>indianapolis, indiana</td> <td>1948-1949</td> <td>1</td> <td>18-42</td> <td>0.3</td> <td>0</td> </tr> <tr> <th>9</th> <td>indianapolis olympians</td> <td>indianapolis, indiana</td> <td>1949-1953</td> <td>4</td> <td>132-137</td> <td>0.491</td> <td>4</td> </tr> <tr> <th>10</th> <td>pittsburgh ironmen</td> <td>pittsburgh, pennsylvania</td> <td>1946-1947</td> <td>1</td> <td>15-45</td> <td>0.25</td> <td>0</td> </tr> <tr> <th>11</th> <td>providence steamrollers</td> <td>providence, rhode island</td> <td>1946-1949</td> <td>3</td> <td>46-122</td> <td>0.274</td> <td>0</td> </tr> <tr> <th>12</th> <td>sheboygan red skins</td> <td>sheboygan, wisconsin</td> <td>1949-1950</td> <td>1</td> <td>22-40</td> <td>0.355</td> <td>1</td> </tr> <tr> <th>13</th> <td>st. louis bombers</td> <td>st. louis, missouri</td> <td>1946-1950</td> <td>4</td> <td>122-115</td> <td>0.515</td> <td>3</td> </tr> <tr> <th>14</th> <td>toronto huskies</td> <td>toronto, ontario</td> <td>1946-1947</td> <td>1</td> <td>22-38</td> <td>0.367</td> <td>0</td> </tr> <tr> <th>15</th> <td>washington capitols</td> <td>washington, d.c</td> <td>1946-1951</td> <td>5</td> <td>157-114</td> <td>0.579</td> <td>4</td> </tr> <tr> <th>16</th> <td>waterloo hawks</td> <td>waterloo, iowa</td> <td>1949-1950</td> <td>1</td> <td>19-43</td> <td>0.306</td> <td>0</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>"win% / seasons played"</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>win% / seasons played</td> </tr> </tbody> </table>

select win% / seasons played where win-loss record = '122-115' <table_name> : table | | team | city | years active | seasons played | win-loss record | win% | playoffs appearances | |---:|:------------------------|:-------------------------|:---------------|-----------------:|:------------------|:-------|-----------------------:| | 0 | anderson packers | anderson, indiana | 1949-1950 | 1 | 37-27 | 0.578 | 1 | | 1 | baa buffalo | buffalo, new york | never played | 0 | 0-0 | none | 0 | | 2 | baa indianapolis | indianapolis, indiana | never played | 0 | 0-0 | none | 0 | | 3 | baltimore bullets | baltimore, maryland | 1947-1954 | 8 | 158-292 | 0.351 | 3 | | 4 | chicago stags | chicago, illinois | 1946-1950 | 4 | 145-92 | 0.612 | 4 | | 5 | cleveland rebels | cleveland, ohio | 1946-1947 | 1 | 30-30 | 0.5 | 1 | | 6 | denver nuggets | denver, colorado | 1949-1950 | 1 | 11-51 | 0.177 | 0 | | 7 | detroit falcons | detroit, michigan | 1946-1947 | 1 | 20-40 | 0.333 | 0 | | 8 | indianapolis jets | indianapolis, indiana | 1948-1949 | 1 | 18-42 | 0.3 | 0 | | 9 | indianapolis olympians | indianapolis, indiana | 1949-1953 | 4 | 132-137 | 0.491 | 4 | | 10 | pittsburgh ironmen | pittsburgh, pennsylvania | 1946-1947 | 1 | 15-45 | 0.25 | 0 | | 11 | providence steamrollers | providence, rhode island | 1946-1949 | 3 | 46-122 | 0.274 | 0 | | 12 | sheboygan red skins | sheboygan, wisconsin | 1949-1950 | 1 | 22-40 | 0.355 | 1 | | 13 | st. louis bombers | st. louis, missouri | 1946-1950 | 4 | 122-115 | 0.515 | 3 | | 14 | toronto huskies | toronto, ontario | 1946-1947 | 1 | 22-38 | 0.367 | 0 | | 15 | washington capitols | washington, d.c | 1946-1951 | 5 | 157-114 | 0.579 | 4 | | 16 | waterloo hawks | waterloo, iowa | 1949-1950 | 1 | 19-43 | 0.306 | 0 |

| | "win% / seasons played" | |---:|:--------------------------| | 0 | win% / seasons played |

select count ( location ) where date = 'february 26, 2012' col : wrestler | times | date | location | notes row 1 : super delfin | 1 | 2000-01-04 00:00:00 | tokyo, japan | beat dick togo for the championship row 2 : takehiro murahama | 1 | 2000-05-07 00:00:00 | tokyo, japan | none row 3 : super delfin | 2 | 2000-06-18 00:00:00 | osaka, japan | none row 4 : gamma | 1 | 2001-06-24 00:00:00 | osaka, japan | none row 5 : super delfin | 3 | 2002-01-03 00:00:00 | osaka, japan | none row 6 : takehiro murahama | 2 | 2003-07-06 00:00:00 | osaka, japan | none row 7 : daio quallt | 1 | 2004-04-17 00:00:00 | osaka, japan | none row 8 : billyken kid | 1 | 2004-08-08 00:00:00 | osaka, japan | none row 9 : "big boss" ma-g-ma | 1 | 2004-10-02 00:00:00 | osaka, japan | none row 10 : super dolphin | 1 | 2005-02-13 00:00:00 | osaka, japan | none row 11 : super delfin | 4 | 2006-02-26 00:00:00 | osaka, japan | none row 12 : billyken kid | 2 | 2006-08-26 00:00:00 | osaka, japan | none row 13 : tigers mask | 1 | 2007-02-12 00:00:00 | osaka, japan | none row 14 : hideyoshi | 1 | 2008-07-26 00:00:00 | osaka, japan | none row 15 : billyken kid | 3 | 2009-02-15 00:00:00 | osaka, japan | none row 16 : dick togo | 1 | 2009-07-25 00:00:00 | osaka, japan | none row 17 : billyken kid | 4 | 2010-02-11 00:00:00 | osaka, japan | none row 18 : cima | 1 | 2010-06-18 00:00:00 | osaka, japan | none row 19 : tigers mask | 2 | 2010-07-29 00:00:00 | osaka, japan | none row 20 : asian cougar / kuuga | 1 | 2010-08-28 00:00:00 | osaka, japan | asian cougar renamed himself kuuga during his reign row 21 : tigers mask | 3 | 2011-04-29 00:00:00 | osaka, japan | none row 22 : billyken kid | 5 | 2011-08-14 00:00:00 | osaka, japan | none row 23 : daisuke harada | 1 | 2012-02-26 00:00:00 | osaka, japan | none row 24 : black buffalo | 1 | 2012-03-25 00:00:00 | osaka, japan | none row 25 : daisuke harada | 2 | 2012-07-22 00:00:00 | osaka, japan | none row 26 : vacated | none | 2013-03-30 00:00:00 | osaka, japan | title vacated, after harada announced that he was not re-signing with osaka pro after his contract ran out on april 29, 2013 row 27 : tigers mask | 4 | 2013-05-19 00:00:00 | osaka, japan | defeated billyken kid in the finals of a four-man tournament to win the vacant title row 28 : quiet storm | 1 | 2013-07-21 00:00:00 | osaka, japan | none row 29 : zeus | 1 | 2014-01-19 00:00:00 | osaka, japan | none

col : count ( "location" ) row 1 : 1

select count ( location ) where date = 'february 26, 2012' <table_name> : table \begin{tabular}{llllll} \toprule & wrestler & times & date & location & notes \\ \midrule 0 & super delfin & 1 & 2000-01-04 00:00:00 & tokyo, japan & beat dick togo for the championship \\ 1 & takehiro murahama & 1 & 2000-05-07 00:00:00 & tokyo, japan & none \\ 2 & super delfin & 2 & 2000-06-18 00:00:00 & osaka, japan & none \\ 3 & gamma & 1 & 2001-06-24 00:00:00 & osaka, japan & none \\ 4 & super delfin & 3 & 2002-01-03 00:00:00 & osaka, japan & none \\ 5 & takehiro murahama & 2 & 2003-07-06 00:00:00 & osaka, japan & none \\ 6 & daio quallt & 1 & 2004-04-17 00:00:00 & osaka, japan & none \\ 7 & billyken kid & 1 & 2004-08-08 00:00:00 & osaka, japan & none \\ 8 & "big boss" ma-g-ma & 1 & 2004-10-02 00:00:00 & osaka, japan & none \\ 9 & super dolphin & 1 & 2005-02-13 00:00:00 & osaka, japan & none \\ 10 & super delfin & 4 & 2006-02-26 00:00:00 & osaka, japan & none \\ 11 & billyken kid & 2 & 2006-08-26 00:00:00 & osaka, japan & none \\ 12 & tigers mask & 1 & 2007-02-12 00:00:00 & osaka, japan & none \\ 13 & hideyoshi & 1 & 2008-07-26 00:00:00 & osaka, japan & none \\ 14 & billyken kid & 3 & 2009-02-15 00:00:00 & osaka, japan & none \\ 15 & dick togo & 1 & 2009-07-25 00:00:00 & osaka, japan & none \\ 16 & billyken kid & 4 & 2010-02-11 00:00:00 & osaka, japan & none \\ 17 & cima & 1 & 2010-06-18 00:00:00 & osaka, japan & none \\ 18 & tigers mask & 2 & 2010-07-29 00:00:00 & osaka, japan & none \\ 19 & asian cougar / kuuga & 1 & 2010-08-28 00:00:00 & osaka, japan & asian cougar renamed himself kuuga during his reign \\ 20 & tigers mask & 3 & 2011-04-29 00:00:00 & osaka, japan & none \\ 21 & billyken kid & 5 & 2011-08-14 00:00:00 & osaka, japan & none \\ 22 & daisuke harada & 1 & 2012-02-26 00:00:00 & osaka, japan & none \\ 23 & black buffalo & 1 & 2012-03-25 00:00:00 & osaka, japan & none \\ 24 & daisuke harada & 2 & 2012-07-22 00:00:00 & osaka, japan & none \\ 25 & vacated & none & 2013-03-30 00:00:00 & osaka, japan & title vacated, after harada announced that he was not re-signing with osaka pro after his contract ran out on april 29, 2013 \\ 26 & tigers mask & 4 & 2013-05-19 00:00:00 & osaka, japan & defeated billyken kid in the finals of a four-man tournament to win the vacant title \\ 27 & quiet storm & 1 & 2013-07-21 00:00:00 & osaka, japan & none \\ 28 & zeus & 1 & 2014-01-19 00:00:00 & osaka, japan & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "location" ) \\ \midrule 0 & 1 \\ \bottomrule \end{tabular}

select count ( location ) where date = 'february 26, 2012' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>wrestler</th> <th>times</th> <th>date</th> <th>location</th> <th>notes</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>super delfin</td> <td>1</td> <td>2000-01-04 00:00:00</td> <td>tokyo, japan</td> <td>beat dick togo for the championship</td> </tr> <tr> <th>1</th> <td>takehiro murahama</td> <td>1</td> <td>2000-05-07 00:00:00</td> <td>tokyo, japan</td> <td>none</td> </tr> <tr> <th>2</th> <td>super delfin</td> <td>2</td> <td>2000-06-18 00:00:00</td> <td>osaka, japan</td> <td>none</td> </tr> <tr> <th>3</th> <td>gamma</td> <td>1</td> <td>2001-06-24 00:00:00</td> <td>osaka, japan</td> <td>none</td> </tr> <tr> <th>4</th> <td>super delfin</td> <td>3</td> <td>2002-01-03 00:00:00</td> <td>osaka, japan</td> <td>none</td> </tr> <tr> <th>5</th> <td>takehiro murahama</td> <td>2</td> <td>2003-07-06 00:00:00</td> <td>osaka, japan</td> <td>none</td> </tr> <tr> <th>6</th> <td>daio quallt</td> <td>1</td> <td>2004-04-17 00:00:00</td> <td>osaka, japan</td> <td>none</td> </tr> <tr> <th>7</th> <td>billyken kid</td> <td>1</td> <td>2004-08-08 00:00:00</td> <td>osaka, japan</td> <td>none</td> </tr> <tr> <th>8</th> <td>"big boss" ma-g-ma</td> <td>1</td> <td>2004-10-02 00:00:00</td> <td>osaka, japan</td> <td>none</td> </tr> <tr> <th>9</th> <td>super dolphin</td> <td>1</td> <td>2005-02-13 00:00:00</td> <td>osaka, japan</td> <td>none</td> </tr> <tr> <th>10</th> <td>super delfin</td> <td>4</td> <td>2006-02-26 00:00:00</td> <td>osaka, japan</td> <td>none</td> </tr> <tr> <th>11</th> <td>billyken kid</td> <td>2</td> <td>2006-08-26 00:00:00</td> <td>osaka, japan</td> <td>none</td> </tr> <tr> <th>12</th> <td>tigers mask</td> <td>1</td> <td>2007-02-12 00:00:00</td> <td>osaka, japan</td> <td>none</td> </tr> <tr> <th>13</th> <td>hideyoshi</td> <td>1</td> <td>2008-07-26 00:00:00</td> <td>osaka, japan</td> <td>none</td> </tr> <tr> <th>14</th> <td>billyken kid</td> <td>3</td> <td>2009-02-15 00:00:00</td> <td>osaka, japan</td> <td>none</td> </tr> <tr> <th>15</th> <td>dick togo</td> <td>1</td> <td>2009-07-25 00:00:00</td> <td>osaka, japan</td> <td>none</td> </tr> <tr> <th>16</th> <td>billyken kid</td> <td>4</td> <td>2010-02-11 00:00:00</td> <td>osaka, japan</td> <td>none</td> </tr> <tr> <th>17</th> <td>cima</td> <td>1</td> <td>2010-06-18 00:00:00</td> <td>osaka, japan</td> <td>none</td> </tr> <tr> <th>18</th> <td>tigers mask</td> <td>2</td> <td>2010-07-29 00:00:00</td> <td>osaka, japan</td> <td>none</td> </tr> <tr> <th>19</th> <td>asian cougar / kuuga</td> <td>1</td> <td>2010-08-28 00:00:00</td> <td>osaka, japan</td> <td>asian cougar renamed himself kuuga during his reign</td> </tr> <tr> <th>20</th> <td>tigers mask</td> <td>3</td> <td>2011-04-29 00:00:00</td> <td>osaka, japan</td> <td>none</td> </tr> <tr> <th>21</th> <td>billyken kid</td> <td>5</td> <td>2011-08-14 00:00:00</td> <td>osaka, japan</td> <td>none</td> </tr> <tr> <th>22</th> <td>daisuke harada</td> <td>1</td> <td>2012-02-26 00:00:00</td> <td>osaka, japan</td> <td>none</td> </tr> <tr> <th>23</th> <td>black buffalo</td> <td>1</td> <td>2012-03-25 00:00:00</td> <td>osaka, japan</td> <td>none</td> </tr> <tr> <th>24</th> <td>daisuke harada</td> <td>2</td> <td>2012-07-22 00:00:00</td> <td>osaka, japan</td> <td>none</td> </tr> <tr> <th>25</th> <td>vacated</td> <td>none</td> <td>2013-03-30 00:00:00</td> <td>osaka, japan</td> <td>title vacated, after harada announced that he was not re-signing with osaka pro after his contract ran out on april 29, 2013</td> </tr> <tr> <th>26</th> <td>tigers mask</td> <td>4</td> <td>2013-05-19 00:00:00</td> <td>osaka, japan</td> <td>defeated billyken kid in the finals of a four-man tournament to win the vacant title</td> </tr> <tr> <th>27</th> <td>quiet storm</td> <td>1</td> <td>2013-07-21 00:00:00</td> <td>osaka, japan</td> <td>none</td> </tr> <tr> <th>28</th> <td>zeus</td> <td>1</td> <td>2014-01-19 00:00:00</td> <td>osaka, japan</td> <td>none</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "location" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> </tr> </tbody> </table>

select count ( location ) where date = 'february 26, 2012' <table_name> : table | | wrestler | times | date | location | notes | |---:|:---------------------|:--------|:--------------------|:-------------|:-----------------------------------------------------------------------------------------------------------------------------| | 0 | super delfin | 1 | 2000-01-04 00:00:00 | tokyo, japan | beat dick togo for the championship | | 1 | takehiro murahama | 1 | 2000-05-07 00:00:00 | tokyo, japan | none | | 2 | super delfin | 2 | 2000-06-18 00:00:00 | osaka, japan | none | | 3 | gamma | 1 | 2001-06-24 00:00:00 | osaka, japan | none | | 4 | super delfin | 3 | 2002-01-03 00:00:00 | osaka, japan | none | | 5 | takehiro murahama | 2 | 2003-07-06 00:00:00 | osaka, japan | none | | 6 | daio quallt | 1 | 2004-04-17 00:00:00 | osaka, japan | none | | 7 | billyken kid | 1 | 2004-08-08 00:00:00 | osaka, japan | none | | 8 | "big boss" ma-g-ma | 1 | 2004-10-02 00:00:00 | osaka, japan | none | | 9 | super dolphin | 1 | 2005-02-13 00:00:00 | osaka, japan | none | | 10 | super delfin | 4 | 2006-02-26 00:00:00 | osaka, japan | none | | 11 | billyken kid | 2 | 2006-08-26 00:00:00 | osaka, japan | none | | 12 | tigers mask | 1 | 2007-02-12 00:00:00 | osaka, japan | none | | 13 | hideyoshi | 1 | 2008-07-26 00:00:00 | osaka, japan | none | | 14 | billyken kid | 3 | 2009-02-15 00:00:00 | osaka, japan | none | | 15 | dick togo | 1 | 2009-07-25 00:00:00 | osaka, japan | none | | 16 | billyken kid | 4 | 2010-02-11 00:00:00 | osaka, japan | none | | 17 | cima | 1 | 2010-06-18 00:00:00 | osaka, japan | none | | 18 | tigers mask | 2 | 2010-07-29 00:00:00 | osaka, japan | none | | 19 | asian cougar / kuuga | 1 | 2010-08-28 00:00:00 | osaka, japan | asian cougar renamed himself kuuga during his reign | | 20 | tigers mask | 3 | 2011-04-29 00:00:00 | osaka, japan | none | | 21 | billyken kid | 5 | 2011-08-14 00:00:00 | osaka, japan | none | | 22 | daisuke harada | 1 | 2012-02-26 00:00:00 | osaka, japan | none | | 23 | black buffalo | 1 | 2012-03-25 00:00:00 | osaka, japan | none | | 24 | daisuke harada | 2 | 2012-07-22 00:00:00 | osaka, japan | none | | 25 | vacated | none | 2013-03-30 00:00:00 | osaka, japan | title vacated, after harada announced that he was not re-signing with osaka pro after his contract ran out on april 29, 2013 | | 26 | tigers mask | 4 | 2013-05-19 00:00:00 | osaka, japan | defeated billyken kid in the finals of a four-man tournament to win the vacant title | | 27 | quiet storm | 1 | 2013-07-21 00:00:00 | osaka, japan | none | | 28 | zeus | 1 | 2014-01-19 00:00:00 | osaka, japan | none |

| | count ( "location" ) | |---:|-----------------------:| | 0 | 1 |

select count ( * ) where brief biography > 1065 col : image | name | birth date | death date | brief biography row 1 : | harriet churchill | october 1679 | october 1679 | died an infant row 2 : | henrietta churchill (later godolphin), 2nd duchess of marlborough in her own right | 19 july 1681 | 24 october 1733 | married the hon. francis godolphin (later viscount rialton by courtesy and 2nd earl of godolphin respectively) on 23 april 1698. their son, william, marquess of blandford died in 1731; their daughter henrietta married thomas pelham-holles, 1st duke of newcastle-upon-tyne; their daughter mary married thomas osborne, 4th duke of leeds row 3 : | anne churchill (later spencer) | 27 february 1683 | 15 april 1716 | anne married the whig politician charles spencer, 3rd earl of sunderland on 14 september 1699. after her sister henrietta's death in 1733, their son, charles became 3rd duke of marlborough. their son john is the ancestor of the earls spencer and thus of diana, princess of wales. their daughter diana nearly married frederick, prince of wales, but when the plan failed, married lord john russell, later 4th duke of bedford row 4 : | john churchill, marquess of blandford | 13 february 1686 | 20 february 1703 | john, marquess of blandford was the heir to the dukedom of marlborough. he died unmarried and without children in 1703, so the dukedom passed to marlborough's eldest daughter, henrietta, after his death in 1703 row 5 : | elizabeth churchill (later egerton) | 15 march 1687 | 22 march 1738 | married scroop egerton, 4th earl of bridgewater (from 1720, 1st duke of bridgewater) on 9 february 1703, leaving issue row 6 : | mary churchill (later montagu) | 15 july 1689 | 14 may 1729 | married john montagu, 2nd duke of montagu on 17 march 1705, leaving issue row 7 : | charles churchill | 19 august 1690 | 22 may 1692 | died an infant

col : count ( "*" ) row 1 : 7

select count ( * ) where brief biography > 1065 <table_name> : table \begin{tabular}{llllll} \toprule & image & name & birth date & death date & brief biography \\ \midrule 0 & & harriet churchill & october 1679 & october 1679 & died an infant \\ 1 & & henrietta churchill (later godolphin), 2nd duchess of marlborough in her own right & 19 july 1681 & 24 october 1733 & married the hon. francis godolphin (later viscount rialton by courtesy and 2nd earl of godolphin respectively) on 23 april 1698. their son, william, marquess of blandford died in 1731; their daughter henrietta married thomas pelham-holles, 1st duke of newcastle-upon-tyne; their daughter mary married thomas osborne, 4th duke of leeds \\ 2 & & anne churchill (later spencer) & 27 february 1683 & 15 april 1716 & anne married the whig politician charles spencer, 3rd earl of sunderland on 14 september 1699. after her sister henrietta's death in 1733, their son, charles became 3rd duke of marlborough. their son john is the ancestor of the earls spencer and thus of diana, princess of wales. their daughter diana nearly married frederick, prince of wales, but when the plan failed, married lord john russell, later 4th duke of bedford \\ 3 & & john churchill, marquess of blandford & 13 february 1686 & 20 february 1703 & john, marquess of blandford was the heir to the dukedom of marlborough. he died unmarried and without children in 1703, so the dukedom passed to marlborough's eldest daughter, henrietta, after his death in 1703 \\ 4 & & elizabeth churchill (later egerton) & 15 march 1687 & 22 march 1738 & married scroop egerton, 4th earl of bridgewater (from 1720, 1st duke of bridgewater) on 9 february 1703, leaving issue \\ 5 & & mary churchill (later montagu) & 15 july 1689 & 14 may 1729 & married john montagu, 2nd duke of montagu on 17 march 1705, leaving issue \\ 6 & & charles churchill & 19 august 1690 & 22 may 1692 & died an infant \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 7 \\ \bottomrule \end{tabular}

select count ( * ) where brief biography > 1065 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>image</th> <th>name</th> <th>birth date</th> <th>death date</th> <th>brief biography</th> </tr> </thead> <tbody> <tr> <th>0</th> <td></td> <td>harriet churchill</td> <td>october 1679</td> <td>october 1679</td> <td>died an infant</td> </tr> <tr> <th>1</th> <td></td> <td>henrietta churchill (later godolphin), 2nd duchess of marlborough in her own right</td> <td>19 july 1681</td> <td>24 october 1733</td> <td>married the hon. francis godolphin (later viscount rialton by courtesy and 2nd earl of godolphin respectively) on 23 april 1698. their son, william, marquess of blandford died in 1731; their daughter henrietta married thomas pelham-holles, 1st duke of newcastle-upon-tyne; their daughter mary married thomas osborne, 4th duke of leeds</td> </tr> <tr> <th>2</th> <td></td> <td>anne churchill (later spencer)</td> <td>27 february 1683</td> <td>15 april 1716</td> <td>anne married the whig politician charles spencer, 3rd earl of sunderland on 14 september 1699. after her sister henrietta's death in 1733, their son, charles became 3rd duke of marlborough. their son john is the ancestor of the earls spencer and thus of diana, princess of wales. their daughter diana nearly married frederick, prince of wales, but when the plan failed, married lord john russell, later 4th duke of bedford</td> </tr> <tr> <th>3</th> <td></td> <td>john churchill, marquess of blandford</td> <td>13 february 1686</td> <td>20 february 1703</td> <td>john, marquess of blandford was the heir to the dukedom of marlborough. he died unmarried and without children in 1703, so the dukedom passed to marlborough's eldest daughter, henrietta, after his death in 1703</td> </tr> <tr> <th>4</th> <td></td> <td>elizabeth churchill (later egerton)</td> <td>15 march 1687</td> <td>22 march 1738</td> <td>married scroop egerton, 4th earl of bridgewater (from 1720, 1st duke of bridgewater) on 9 february 1703, leaving issue</td> </tr> <tr> <th>5</th> <td></td> <td>mary churchill (later montagu)</td> <td>15 july 1689</td> <td>14 may 1729</td> <td>married john montagu, 2nd duke of montagu on 17 march 1705, leaving issue</td> </tr> <tr> <th>6</th> <td></td> <td>charles churchill</td> <td>19 august 1690</td> <td>22 may 1692</td> <td>died an infant</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "*" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>7</td> </tr> </tbody> </table>

select count ( * ) where brief biography > 1065 <table_name> : table | | image | name | birth date | death date | brief biography | |---:|:--------|:-----------------------------------------------------------------------------------|:-----------------|:-----------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | | harriet churchill | october 1679 | october 1679 | died an infant | | 1 | | henrietta churchill (later godolphin), 2nd duchess of marlborough in her own right | 19 july 1681 | 24 october 1733 | married the hon. francis godolphin (later viscount rialton by courtesy and 2nd earl of godolphin respectively) on 23 april 1698. their son, william, marquess of blandford died in 1731; their daughter henrietta married thomas pelham-holles, 1st duke of newcastle-upon-tyne; their daughter mary married thomas osborne, 4th duke of leeds | | 2 | | anne churchill (later spencer) | 27 february 1683 | 15 april 1716 | anne married the whig politician charles spencer, 3rd earl of sunderland on 14 september 1699. after her sister henrietta's death in 1733, their son, charles became 3rd duke of marlborough. their son john is the ancestor of the earls spencer and thus of diana, princess of wales. their daughter diana nearly married frederick, prince of wales, but when the plan failed, married lord john russell, later 4th duke of bedford | | 3 | | john churchill, marquess of blandford | 13 february 1686 | 20 february 1703 | john, marquess of blandford was the heir to the dukedom of marlborough. he died unmarried and without children in 1703, so the dukedom passed to marlborough's eldest daughter, henrietta, after his death in 1703 | | 4 | | elizabeth churchill (later egerton) | 15 march 1687 | 22 march 1738 | married scroop egerton, 4th earl of bridgewater (from 1720, 1st duke of bridgewater) on 9 february 1703, leaving issue | | 5 | | mary churchill (later montagu) | 15 july 1689 | 14 may 1729 | married john montagu, 2nd duke of montagu on 17 march 1705, leaving issue | | 6 | | charles churchill | 19 august 1690 | 22 may 1692 | died an infant |

| | count ( "*" ) | |---:|----------------:| | 0 | 7 |

select count ( distinct airport ) where rank > 0 col : rank | airport | passengers handled | change 2011/12 row 1 : 1 | london heath | | row 2 : | 828531 | 0% | row 3 : 2 | london gatwick | 607417 | 7% row 4 : 3 | london stansted | 331607 | 3% row 5 : 4 | london luton | 276488 | 0% row 6 : 5 | belfast international | 266987 | 8% row 7 : 6 | bristol | 239666 | 8% row 8 : 7 | birmingham | 208123 | 2% row 9 : 8 | southampton | 173576 | 24% row 10 : 9 | london city | 158239 | 6% row 11 : 10 | belfast city | 100003 | 4%

col : count ( "distinct airport" ) row 1 : 10

select count ( distinct airport ) where rank > 0 <table_name> : table \begin{tabular}{lllll} \toprule & rank & airport & passengers handled & change 2011/12 \\ \midrule 0 & 1 & london heath & & \\ 1 & & 828531 & 0% & \\ 2 & 2 & london gatwick & 607417 & 7% \\ 3 & 3 & london stansted & 331607 & 3% \\ 4 & 4 & london luton & 276488 & 0% \\ 5 & 5 & belfast international & 266987 & 8% \\ 6 & 6 & bristol & 239666 & 8% \\ 7 & 7 & birmingham & 208123 & 2% \\ 8 & 8 & southampton & 173576 & 24% \\ 9 & 9 & london city & 158239 & 6% \\ 10 & 10 & belfast city & 100003 & 4% \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "distinct airport" ) \\ \midrule 0 & 10 \\ \bottomrule \end{tabular}

select count ( distinct airport ) where rank > 0 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>rank</th> <th>airport</th> <th>passengers handled</th> <th>change 2011/12</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>london heath</td> <td></td> <td></td> </tr> <tr> <th>1</th> <td></td> <td>828531</td> <td>0%</td> <td></td> </tr> <tr> <th>2</th> <td>2</td> <td>london gatwick</td> <td>607417</td> <td>7%</td> </tr> <tr> <th>3</th> <td>3</td> <td>london stansted</td> <td>331607</td> <td>3%</td> </tr> <tr> <th>4</th> <td>4</td> <td>london luton</td> <td>276488</td> <td>0%</td> </tr> <tr> <th>5</th> <td>5</td> <td>belfast international</td> <td>266987</td> <td>8%</td> </tr> <tr> <th>6</th> <td>6</td> <td>bristol</td> <td>239666</td> <td>8%</td> </tr> <tr> <th>7</th> <td>7</td> <td>birmingham</td> <td>208123</td> <td>2%</td> </tr> <tr> <th>8</th> <td>8</td> <td>southampton</td> <td>173576</td> <td>24%</td> </tr> <tr> <th>9</th> <td>9</td> <td>london city</td> <td>158239</td> <td>6%</td> </tr> <tr> <th>10</th> <td>10</td> <td>belfast city</td> <td>100003</td> <td>4%</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "distinct airport" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>10</td> </tr> </tbody> </table>

select count ( distinct airport ) where rank > 0 <table_name> : table | | rank | airport | passengers handled | change 2011/12 | |---:|:-------|:----------------------|:---------------------|:-----------------| | 0 | 1 | london heath | | | | 1 | | 828531 | 0% | | | 2 | 2 | london gatwick | 607417 | 7% | | 3 | 3 | london stansted | 331607 | 3% | | 4 | 4 | london luton | 276488 | 0% | | 5 | 5 | belfast international | 266987 | 8% | | 6 | 6 | bristol | 239666 | 8% | | 7 | 7 | birmingham | 208123 | 2% | | 8 | 8 | southampton | 173576 | 24% | | 9 | 9 | london city | 158239 | 6% | | 10 | 10 | belfast city | 100003 | 4% |

| | count ( "distinct airport" ) | |---:|-------------------------------:| | 0 | 10 |

select driver where driver = 'felipe massa' col : pos | no | driver | constructor | laps | time/retired | grid | points row 1 : 1 | 15 | sebastian vettel | red bull-renault | 56 | 1:57:43.485 | 1 | 10 row 2 : 2 | 14 | mark webber | red bull-renault | 56 | 10.97 | 3 | 8 row 3 : 3 | 22 | jenson button | brawn-mercedes | 56 | 44.975 | 5 | 6 row 4 : 4 | 23 | rubens barrichello | brawn-mercedes | 56 | +1:03.704 | 4 | 5 row 5 : 5 | 2 | heikki kovalainen | mclaren-mercedes | 56 | +1:05.102 | 12 | 4 row 6 : 6 | 1 | lewis hamilton | mclaren-mercedes | 56 | +1:11.866 | 9 | 3 row 7 : 7 | 10 | timo glock | toyota | 56 | +1:14.476 | 19 | 2 row 8 : 8 | 12 | sebastien buemi | toro rosso-ferrari | 56 | +1:16.439 | 10 | 1 row 9 : 9 | 7 | fernando alonso | renault | 56 | +1:24.309 | 2 | none row 10 : 10 | 4 | kimi raikkonen | ferrari | 56 | +1:31.750 | 8 | none row 11 : 11 | 11 | sebastien bourdais | toro rosso-ferrari | 56 | +1:34.156 | 15 | none row 12 : 12 | 6 | nick heidfeld | bmw sauber | 56 | +1:35.834 | 11 | none row 13 : 13 | 5 | robert kubica | bmw sauber | 56 | +1:46.853 | 17 | none row 14 : 14 | 21 | giancarlo fisichella | force india-mercedes | 55 | +1 lap | 20 | none row 15 : 15 | 16 | nico rosberg | williams-toyota | 55 | +1 lap | 7 | none row 16 : 16 | 8 | nelson piquet, jr | renault | 54 | +2 laps | 16 | none row 17 : 17 | 20 | adrian sutil | force india-mercedes | 50 | accident | 18 | none row 18 : ret | 17 | kazuki nakajima | williams-toyota | 43 | transmission | 14 | none row 19 : ret | 3 | felipe massa | ferrari | 20 | electrical | 13 | none row 20 : ret | 9 | jarno trulli | toyota | 18 | collision damage | 6 | none

col : driver row 1 : felipe massa

select driver where driver = 'felipe massa' <table_name> : table \begin{tabular}{lllllllll} \toprule & pos & no & driver & constructor & laps & time/retired & grid & points \\ \midrule 0 & 1 & 15 & sebastian vettel & red bull-renault & 56 & 1:57:43.485 & 1 & 10 \\ 1 & 2 & 14 & mark webber & red bull-renault & 56 & 10.97 & 3 & 8 \\ 2 & 3 & 22 & jenson button & brawn-mercedes & 56 & 44.975 & 5 & 6 \\ 3 & 4 & 23 & rubens barrichello & brawn-mercedes & 56 & +1:03.704 & 4 & 5 \\ 4 & 5 & 2 & heikki kovalainen & mclaren-mercedes & 56 & +1:05.102 & 12 & 4 \\ 5 & 6 & 1 & lewis hamilton & mclaren-mercedes & 56 & +1:11.866 & 9 & 3 \\ 6 & 7 & 10 & timo glock & toyota & 56 & +1:14.476 & 19 & 2 \\ 7 & 8 & 12 & sebastien buemi & toro rosso-ferrari & 56 & +1:16.439 & 10 & 1 \\ 8 & 9 & 7 & fernando alonso & renault & 56 & +1:24.309 & 2 & none \\ 9 & 10 & 4 & kimi raikkonen & ferrari & 56 & +1:31.750 & 8 & none \\ 10 & 11 & 11 & sebastien bourdais & toro rosso-ferrari & 56 & +1:34.156 & 15 & none \\ 11 & 12 & 6 & nick heidfeld & bmw sauber & 56 & +1:35.834 & 11 & none \\ 12 & 13 & 5 & robert kubica & bmw sauber & 56 & +1:46.853 & 17 & none \\ 13 & 14 & 21 & giancarlo fisichella & force india-mercedes & 55 & +1 lap & 20 & none \\ 14 & 15 & 16 & nico rosberg & williams-toyota & 55 & +1 lap & 7 & none \\ 15 & 16 & 8 & nelson piquet, jr & renault & 54 & +2 laps & 16 & none \\ 16 & 17 & 20 & adrian sutil & force india-mercedes & 50 & accident & 18 & none \\ 17 & ret & 17 & kazuki nakajima & williams-toyota & 43 & transmission & 14 & none \\ 18 & ret & 3 & felipe massa & ferrari & 20 & electrical & 13 & none \\ 19 & ret & 9 & jarno trulli & toyota & 18 & collision damage & 6 & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & driver \\ \midrule 0 & felipe massa \\ \bottomrule \end{tabular}

select driver where driver = 'felipe massa' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>pos</th> <th>no</th> <th>driver</th> <th>constructor</th> <th>laps</th> <th>time/retired</th> <th>grid</th> <th>points</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>15</td> <td>sebastian vettel</td> <td>red bull-renault</td> <td>56</td> <td>1:57:43.485</td> <td>1</td> <td>10</td> </tr> <tr> <th>1</th> <td>2</td> <td>14</td> <td>mark webber</td> <td>red bull-renault</td> <td>56</td> <td>10.97</td> <td>3</td> <td>8</td> </tr> <tr> <th>2</th> <td>3</td> <td>22</td> <td>jenson button</td> <td>brawn-mercedes</td> <td>56</td> <td>44.975</td> <td>5</td> <td>6</td> </tr> <tr> <th>3</th> <td>4</td> <td>23</td> <td>rubens barrichello</td> <td>brawn-mercedes</td> <td>56</td> <td>+1:03.704</td> <td>4</td> <td>5</td> </tr> <tr> <th>4</th> <td>5</td> <td>2</td> <td>heikki kovalainen</td> <td>mclaren-mercedes</td> <td>56</td> <td>+1:05.102</td> <td>12</td> <td>4</td> </tr> <tr> <th>5</th> <td>6</td> <td>1</td> <td>lewis hamilton</td> <td>mclaren-mercedes</td> <td>56</td> <td>+1:11.866</td> <td>9</td> <td>3</td> </tr> <tr> <th>6</th> <td>7</td> <td>10</td> <td>timo glock</td> <td>toyota</td> <td>56</td> <td>+1:14.476</td> <td>19</td> <td>2</td> </tr> <tr> <th>7</th> <td>8</td> <td>12</td> <td>sebastien buemi</td> <td>toro rosso-ferrari</td> <td>56</td> <td>+1:16.439</td> <td>10</td> <td>1</td> </tr> <tr> <th>8</th> <td>9</td> <td>7</td> <td>fernando alonso</td> <td>renault</td> <td>56</td> <td>+1:24.309</td> <td>2</td> <td>none</td> </tr> <tr> <th>9</th> <td>10</td> <td>4</td> <td>kimi raikkonen</td> <td>ferrari</td> <td>56</td> <td>+1:31.750</td> <td>8</td> <td>none</td> </tr> <tr> <th>10</th> <td>11</td> <td>11</td> <td>sebastien bourdais</td> <td>toro rosso-ferrari</td> <td>56</td> <td>+1:34.156</td> <td>15</td> <td>none</td> </tr> <tr> <th>11</th> <td>12</td> <td>6</td> <td>nick heidfeld</td> <td>bmw sauber</td> <td>56</td> <td>+1:35.834</td> <td>11</td> <td>none</td> </tr> <tr> <th>12</th> <td>13</td> <td>5</td> <td>robert kubica</td> <td>bmw sauber</td> <td>56</td> <td>+1:46.853</td> <td>17</td> <td>none</td> </tr> <tr> <th>13</th> <td>14</td> <td>21</td> <td>giancarlo fisichella</td> <td>force india-mercedes</td> <td>55</td> <td>+1 lap</td> <td>20</td> <td>none</td> </tr> <tr> <th>14</th> <td>15</td> <td>16</td> <td>nico rosberg</td> <td>williams-toyota</td> <td>55</td> <td>+1 lap</td> <td>7</td> <td>none</td> </tr> <tr> <th>15</th> <td>16</td> <td>8</td> <td>nelson piquet, jr</td> <td>renault</td> <td>54</td> <td>+2 laps</td> <td>16</td> <td>none</td> </tr> <tr> <th>16</th> <td>17</td> <td>20</td> <td>adrian sutil</td> <td>force india-mercedes</td> <td>50</td> <td>accident</td> <td>18</td> <td>none</td> </tr> <tr> <th>17</th> <td>ret</td> <td>17</td> <td>kazuki nakajima</td> <td>williams-toyota</td> <td>43</td> <td>transmission</td> <td>14</td> <td>none</td> </tr> <tr> <th>18</th> <td>ret</td> <td>3</td> <td>felipe massa</td> <td>ferrari</td> <td>20</td> <td>electrical</td> <td>13</td> <td>none</td> </tr> <tr> <th>19</th> <td>ret</td> <td>9</td> <td>jarno trulli</td> <td>toyota</td> <td>18</td> <td>collision damage</td> <td>6</td> <td>none</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>driver</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>felipe massa</td> </tr> </tbody> </table>

select driver where driver = 'felipe massa' <table_name> : table | | pos | no | driver | constructor | laps | time/retired | grid | points | |---:|:------|-----:|:---------------------|:---------------------|-------:|:-----------------|-------:|:---------| | 0 | 1 | 15 | sebastian vettel | red bull-renault | 56 | 1:57:43.485 | 1 | 10 | | 1 | 2 | 14 | mark webber | red bull-renault | 56 | 10.97 | 3 | 8 | | 2 | 3 | 22 | jenson button | brawn-mercedes | 56 | 44.975 | 5 | 6 | | 3 | 4 | 23 | rubens barrichello | brawn-mercedes | 56 | +1:03.704 | 4 | 5 | | 4 | 5 | 2 | heikki kovalainen | mclaren-mercedes | 56 | +1:05.102 | 12 | 4 | | 5 | 6 | 1 | lewis hamilton | mclaren-mercedes | 56 | +1:11.866 | 9 | 3 | | 6 | 7 | 10 | timo glock | toyota | 56 | +1:14.476 | 19 | 2 | | 7 | 8 | 12 | sebastien buemi | toro rosso-ferrari | 56 | +1:16.439 | 10 | 1 | | 8 | 9 | 7 | fernando alonso | renault | 56 | +1:24.309 | 2 | none | | 9 | 10 | 4 | kimi raikkonen | ferrari | 56 | +1:31.750 | 8 | none | | 10 | 11 | 11 | sebastien bourdais | toro rosso-ferrari | 56 | +1:34.156 | 15 | none | | 11 | 12 | 6 | nick heidfeld | bmw sauber | 56 | +1:35.834 | 11 | none | | 12 | 13 | 5 | robert kubica | bmw sauber | 56 | +1:46.853 | 17 | none | | 13 | 14 | 21 | giancarlo fisichella | force india-mercedes | 55 | +1 lap | 20 | none | | 14 | 15 | 16 | nico rosberg | williams-toyota | 55 | +1 lap | 7 | none | | 15 | 16 | 8 | nelson piquet, jr | renault | 54 | +2 laps | 16 | none | | 16 | 17 | 20 | adrian sutil | force india-mercedes | 50 | accident | 18 | none | | 17 | ret | 17 | kazuki nakajima | williams-toyota | 43 | transmission | 14 | none | | 18 | ret | 3 | felipe massa | ferrari | 20 | electrical | 13 | none | | 19 | ret | 9 | jarno trulli | toyota | 18 | collision damage | 6 | none |

| | driver | |---:|:-------------| | 0 | felipe massa |

select tramway where tramway = 'sandia peak tramway' col : tramway | country | city | height of pylons | span width, leaning straight line | span width, horizontal measurement | height of cable over ground | year of inauguration | notes row 1 : peak 2 peak gondola | canada | whistler | 65m | 3024 m | 3019 m | 436 m | 2008 | 3s aerial tramway constructed by doppelmayr row 2 : hut of regensburg material transport aerial railway | austria | falbeson | none | none | none | 430 m | none | none row 3 : vanoise express | france | vanoise | none | 1850 m | 1800 m | 380 m | 2003 | none row 4 : aiguille du midi | france | chamonix | none | 2867 m | 2500 m | none | 1955 | 2nd section row 5 : vallee blanche aerial tramway | france | mont blanc | none | 2831 m, 1684 m | span is almost horizontal | appr. 300 m | 1958 | rock anchored support structure row 6 : 3s aerial tramway | austria | kitzbuhel | 0 m, 80m | 2507 m | none | 400 m | 2004 | none row 7 : sandia peak tramway | usa | albuquerque | 70.7 m, 21.33 m | 2353 m | none | 274 m | 1966 | none row 8 : feldmoos-chli-titlis aerial tramway | switzerland | titlis | 37.6 m | 3476,2 m | none | none | 1979 | temp. site tramway, demolished in 1986

col : tramway row 1 : sandia peak tramway

select tramway where tramway = 'sandia peak tramway' <table_name> : table \begin{tabular}{llllllllll} \toprule & tramway & country & city & height of pylons & span width, leaning straight line & span width, horizontal measurement & height of cable over ground & year of inauguration & notes \\ \midrule 0 & peak 2 peak gondola & canada & whistler & 65m & 3024 m & 3019 m & 436 m & 2008 & 3s aerial tramway constructed by doppelmayr \\ 1 & hut of regensburg material transport aerial railway & austria & falbeson & none & none & none & 430 m & none & none \\ 2 & vanoise express & france & vanoise & none & 1850 m & 1800 m & 380 m & 2003 & none \\ 3 & aiguille du midi & france & chamonix & none & 2867 m & 2500 m & none & 1955 & 2nd section \\ 4 & vallee blanche aerial tramway & france & mont blanc & none & 2831 m, 1684 m & span is almost horizontal & appr. 300 m & 1958 & rock anchored support structure \\ 5 & 3s aerial tramway & austria & kitzbuhel & 0 m, 80m & 2507 m & none & 400 m & 2004 & none \\ 6 & sandia peak tramway & usa & albuquerque & 70.7 m, 21.33 m & 2353 m & none & 274 m & 1966 & none \\ 7 & feldmoos-chli-titlis aerial tramway & switzerland & titlis & 37.6 m & 3476,2 m & none & none & 1979 & temp. site tramway, demolished in 1986 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & tramway \\ \midrule 0 & sandia peak tramway \\ \bottomrule \end{tabular}

select tramway where tramway = 'sandia peak tramway' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>tramway</th> <th>country</th> <th>city</th> <th>height of pylons</th> <th>span width, leaning straight line</th> <th>span width, horizontal measurement</th> <th>height of cable over ground</th> <th>year of inauguration</th> <th>notes</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>peak 2 peak gondola</td> <td>canada</td> <td>whistler</td> <td>65m</td> <td>3024 m</td> <td>3019 m</td> <td>436 m</td> <td>2008</td> <td>3s aerial tramway constructed by doppelmayr</td> </tr> <tr> <th>1</th> <td>hut of regensburg material transport aerial railway</td> <td>austria</td> <td>falbeson</td> <td>none</td> <td>none</td> <td>none</td> <td>430 m</td> <td>none</td> <td>none</td> </tr> <tr> <th>2</th> <td>vanoise express</td> <td>france</td> <td>vanoise</td> <td>none</td> <td>1850 m</td> <td>1800 m</td> <td>380 m</td> <td>2003</td> <td>none</td> </tr> <tr> <th>3</th> <td>aiguille du midi</td> <td>france</td> <td>chamonix</td> <td>none</td> <td>2867 m</td> <td>2500 m</td> <td>none</td> <td>1955</td> <td>2nd section</td> </tr> <tr> <th>4</th> <td>vallee blanche aerial tramway</td> <td>france</td> <td>mont blanc</td> <td>none</td> <td>2831 m, 1684 m</td> <td>span is almost horizontal</td> <td>appr. 300 m</td> <td>1958</td> <td>rock anchored support structure</td> </tr> <tr> <th>5</th> <td>3s aerial tramway</td> <td>austria</td> <td>kitzbuhel</td> <td>0 m, 80m</td> <td>2507 m</td> <td>none</td> <td>400 m</td> <td>2004</td> <td>none</td> </tr> <tr> <th>6</th> <td>sandia peak tramway</td> <td>usa</td> <td>albuquerque</td> <td>70.7 m, 21.33 m</td> <td>2353 m</td> <td>none</td> <td>274 m</td> <td>1966</td> <td>none</td> </tr> <tr> <th>7</th> <td>feldmoos-chli-titlis aerial tramway</td> <td>switzerland</td> <td>titlis</td> <td>37.6 m</td> <td>3476,2 m</td> <td>none</td> <td>none</td> <td>1979</td> <td>temp. site tramway, demolished in 1986</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>tramway</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>sandia peak tramway</td> </tr> </tbody> </table>

select tramway where tramway = 'sandia peak tramway' <table_name> : table | | tramway | country | city | height of pylons | span width, leaning straight line | span width, horizontal measurement | height of cable over ground | year of inauguration | notes | |---:|:----------------------------------------------------|:------------|:------------|:-------------------|:------------------------------------|:-------------------------------------|:------------------------------|:-----------------------|:--------------------------------------------| | 0 | peak 2 peak gondola | canada | whistler | 65m | 3024 m | 3019 m | 436 m | 2008 | 3s aerial tramway constructed by doppelmayr | | 1 | hut of regensburg material transport aerial railway | austria | falbeson | none | none | none | 430 m | none | none | | 2 | vanoise express | france | vanoise | none | 1850 m | 1800 m | 380 m | 2003 | none | | 3 | aiguille du midi | france | chamonix | none | 2867 m | 2500 m | none | 1955 | 2nd section | | 4 | vallee blanche aerial tramway | france | mont blanc | none | 2831 m, 1684 m | span is almost horizontal | appr. 300 m | 1958 | rock anchored support structure | | 5 | 3s aerial tramway | austria | kitzbuhel | 0 m, 80m | 2507 m | none | 400 m | 2004 | none | | 6 | sandia peak tramway | usa | albuquerque | 70.7 m, 21.33 m | 2353 m | none | 274 m | 1966 | none | | 7 | feldmoos-chli-titlis aerial tramway | switzerland | titlis | 37.6 m | 3476,2 m | none | none | 1979 | temp. site tramway, demolished in 1986 |

| | tramway | |---:|:--------------------| | 0 | sandia peak tramway |

select type where location = 'miyazaki maglev test track' col : speed [km/h (mph)] | train | type | location | date | comments row 1 : 504 (313.2) | ml-500 | maglev | miyazaki maglev test track | 1979-12-12 00:00:00 | none row 2 : 517 (321.2) | ml-500 | maglev | miyazaki maglev test track | 1979-12-21 00:00:00 | none row 3 : 352.4 (219.0) | mlu001 | maglev | miyazaki maglev test track | 1986-01-01 00:00:00 | three-car train set row 4 : 405.3 (251.8) | mlu001 | maglev | miyazaki maglev test track | 1987-01-01 00:00:00 | two-car train set row 5 : 431 (267.8) | mlu002n | maglev | miyazaki maglev test track | 1994-02-01 00:00:00 | single-car row 6 : 550 (341.8) | mlx01 | maglev | yamanashi maglev test line | 1997-12-24 00:00:00 | three-car train set row 7 : 548 (340.5) | mlx01 | maglev | yamanashi maglev test line | 1999-03-18 00:00:00 | five-car train set

col : type row 1 : maglev row 2 : maglev row 3 : maglev row 4 : maglev row 5 : maglev

select type where location = 'miyazaki maglev test track' <table_name> : table \begin{tabular}{lllllll} \toprule & speed [km/h (mph)] & train & type & location & date & comments \\ \midrule 0 & 504 (313.2) & ml-500 & maglev & miyazaki maglev test track & 1979-12-12 00:00:00 & none \\ 1 & 517 (321.2) & ml-500 & maglev & miyazaki maglev test track & 1979-12-21 00:00:00 & none \\ 2 & 352.4 (219.0) & mlu001 & maglev & miyazaki maglev test track & 1986-01-01 00:00:00 & three-car train set \\ 3 & 405.3 (251.8) & mlu001 & maglev & miyazaki maglev test track & 1987-01-01 00:00:00 & two-car train set \\ 4 & 431 (267.8) & mlu002n & maglev & miyazaki maglev test track & 1994-02-01 00:00:00 & single-car \\ 5 & 550 (341.8) & mlx01 & maglev & yamanashi maglev test line & 1997-12-24 00:00:00 & three-car train set \\ 6 & 548 (340.5) & mlx01 & maglev & yamanashi maglev test line & 1999-03-18 00:00:00 & five-car train set \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & type \\ \midrule 0 & maglev \\ 1 & maglev \\ 2 & maglev \\ 3 & maglev \\ 4 & maglev \\ \bottomrule \end{tabular}

select type where location = 'miyazaki maglev test track' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>speed [km/h (mph)]</th> <th>train</th> <th>type</th> <th>location</th> <th>date</th> <th>comments</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>504 (313.2)</td> <td>ml-500</td> <td>maglev</td> <td>miyazaki maglev test track</td> <td>1979-12-12 00:00:00</td> <td>none</td> </tr> <tr> <th>1</th> <td>517 (321.2)</td> <td>ml-500</td> <td>maglev</td> <td>miyazaki maglev test track</td> <td>1979-12-21 00:00:00</td> <td>none</td> </tr> <tr> <th>2</th> <td>352.4 (219.0)</td> <td>mlu001</td> <td>maglev</td> <td>miyazaki maglev test track</td> <td>1986-01-01 00:00:00</td> <td>three-car train set</td> </tr> <tr> <th>3</th> <td>405.3 (251.8)</td> <td>mlu001</td> <td>maglev</td> <td>miyazaki maglev test track</td> <td>1987-01-01 00:00:00</td> <td>two-car train set</td> </tr> <tr> <th>4</th> <td>431 (267.8)</td> <td>mlu002n</td> <td>maglev</td> <td>miyazaki maglev test track</td> <td>1994-02-01 00:00:00</td> <td>single-car</td> </tr> <tr> <th>5</th> <td>550 (341.8)</td> <td>mlx01</td> <td>maglev</td> <td>yamanashi maglev test line</td> <td>1997-12-24 00:00:00</td> <td>three-car train set</td> </tr> <tr> <th>6</th> <td>548 (340.5)</td> <td>mlx01</td> <td>maglev</td> <td>yamanashi maglev test line</td> <td>1999-03-18 00:00:00</td> <td>five-car train set</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>type</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>maglev</td> </tr> <tr> <th>1</th> <td>maglev</td> </tr> <tr> <th>2</th> <td>maglev</td> </tr> <tr> <th>3</th> <td>maglev</td> </tr> <tr> <th>4</th> <td>maglev</td> </tr> </tbody> </table>

select type where location = 'miyazaki maglev test track' <table_name> : table | | speed [km/h (mph)] | train | type | location | date | comments | |---:|:---------------------|:--------|:-------|:---------------------------|:--------------------|:--------------------| | 0 | 504 (313.2) | ml-500 | maglev | miyazaki maglev test track | 1979-12-12 00:00:00 | none | | 1 | 517 (321.2) | ml-500 | maglev | miyazaki maglev test track | 1979-12-21 00:00:00 | none | | 2 | 352.4 (219.0) | mlu001 | maglev | miyazaki maglev test track | 1986-01-01 00:00:00 | three-car train set | | 3 | 405.3 (251.8) | mlu001 | maglev | miyazaki maglev test track | 1987-01-01 00:00:00 | two-car train set | | 4 | 431 (267.8) | mlu002n | maglev | miyazaki maglev test track | 1994-02-01 00:00:00 | single-car | | 5 | 550 (341.8) | mlx01 | maglev | yamanashi maglev test line | 1997-12-24 00:00:00 | three-car train set | | 6 | 548 (340.5) | mlx01 | maglev | yamanashi maglev test line | 1999-03-18 00:00:00 | five-car train set |

| | type | |---:|:-------| | 0 | maglev | | 1 | maglev | | 2 | maglev | | 3 | maglev | | 4 | maglev |

select place where rider = 'willy bauer' col : place | rider | country | team | points | wins row 1 : 1 | sylvain geboers | belgium | suzuki | 3066 | 3 row 2 : 2 | adolf weil | germany | maico | 2331 | 2 row 3 : 3 | torlief hansen | sweden | husqvarna | 2052 | 0 row 4 : 4 | roger de coster | belgium | suzuki | 1865 | 3 row 5 : 5 | joel robert | belgium | suzuki | 1730 | 1 row 6 : 6 | heikki mikkola | finland | husqvarna | 1680 | 2 row 7 : 7 | willy bauer | germany | maico | 1276 | 0 row 8 : 8 | gaston rahier | belgium | cz | 1112 | 0 row 9 : 9 | pierre karsmakers | netherlands | husqvarna | 1110 | 0 row 10 : 10 | dave bickers | united kingdom | cz | 1076 | 0 row 11 : 11 | john banks | united kingdom | cz | 971 | 0 row 12 : 12 | andy roberton | united kingdom | husqvarna | 810 | 0 row 13 : 13 | vlastimil valek | czechoslovakia | cz | 709 | 0 row 14 : 14 | mark blackwell | united states | husqvarna | 604 | 0 row 15 : 15 | brad lackey | united states | cz | 603 | 0 row 16 : 16 | gary jones | united states | yamaha | 439 | 0 row 17 : 17 | john desoto | united states | suzuki | 425 | 0 row 18 : 18 | chris horsefield | united kingdom | cz | 416 | 0 row 19 : 19 | uno palm | sweden | husqvarna | 324 | 0 row 20 : 20 | peter lamppu | united states | montesa | 309 | 0

col : place row 1 : 7

select place where rider = 'willy bauer' <table_name> : table \begin{tabular}{lllllll} \toprule & place & rider & country & team & points & wins \\ \midrule 0 & 1 & sylvain geboers & belgium & suzuki & 3066 & 3 \\ 1 & 2 & adolf weil & germany & maico & 2331 & 2 \\ 2 & 3 & torlief hansen & sweden & husqvarna & 2052 & 0 \\ 3 & 4 & roger de coster & belgium & suzuki & 1865 & 3 \\ 4 & 5 & joel robert & belgium & suzuki & 1730 & 1 \\ 5 & 6 & heikki mikkola & finland & husqvarna & 1680 & 2 \\ 6 & 7 & willy bauer & germany & maico & 1276 & 0 \\ 7 & 8 & gaston rahier & belgium & cz & 1112 & 0 \\ 8 & 9 & pierre karsmakers & netherlands & husqvarna & 1110 & 0 \\ 9 & 10 & dave bickers & united kingdom & cz & 1076 & 0 \\ 10 & 11 & john banks & united kingdom & cz & 971 & 0 \\ 11 & 12 & andy roberton & united kingdom & husqvarna & 810 & 0 \\ 12 & 13 & vlastimil valek & czechoslovakia & cz & 709 & 0 \\ 13 & 14 & mark blackwell & united states & husqvarna & 604 & 0 \\ 14 & 15 & brad lackey & united states & cz & 603 & 0 \\ 15 & 16 & gary jones & united states & yamaha & 439 & 0 \\ 16 & 17 & john desoto & united states & suzuki & 425 & 0 \\ 17 & 18 & chris horsefield & united kingdom & cz & 416 & 0 \\ 18 & 19 & uno palm & sweden & husqvarna & 324 & 0 \\ 19 & 20 & peter lamppu & united states & montesa & 309 & 0 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & place \\ \midrule 0 & 7 \\ \bottomrule \end{tabular}

select place where rider = 'willy bauer' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>place</th> <th>rider</th> <th>country</th> <th>team</th> <th>points</th> <th>wins</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>sylvain geboers</td> <td>belgium</td> <td>suzuki</td> <td>3066</td> <td>3</td> </tr> <tr> <th>1</th> <td>2</td> <td>adolf weil</td> <td>germany</td> <td>maico</td> <td>2331</td> <td>2</td> </tr> <tr> <th>2</th> <td>3</td> <td>torlief hansen</td> <td>sweden</td> <td>husqvarna</td> <td>2052</td> <td>0</td> </tr> <tr> <th>3</th> <td>4</td> <td>roger de coster</td> <td>belgium</td> <td>suzuki</td> <td>1865</td> <td>3</td> </tr> <tr> <th>4</th> <td>5</td> <td>joel robert</td> <td>belgium</td> <td>suzuki</td> <td>1730</td> <td>1</td> </tr> <tr> <th>5</th> <td>6</td> <td>heikki mikkola</td> <td>finland</td> <td>husqvarna</td> <td>1680</td> <td>2</td> </tr> <tr> <th>6</th> <td>7</td> <td>willy bauer</td> <td>germany</td> <td>maico</td> <td>1276</td> <td>0</td> </tr> <tr> <th>7</th> <td>8</td> <td>gaston rahier</td> <td>belgium</td> <td>cz</td> <td>1112</td> <td>0</td> </tr> <tr> <th>8</th> <td>9</td> <td>pierre karsmakers</td> <td>netherlands</td> <td>husqvarna</td> <td>1110</td> <td>0</td> </tr> <tr> <th>9</th> <td>10</td> <td>dave bickers</td> <td>united kingdom</td> <td>cz</td> <td>1076</td> <td>0</td> </tr> <tr> <th>10</th> <td>11</td> <td>john banks</td> <td>united kingdom</td> <td>cz</td> <td>971</td> <td>0</td> </tr> <tr> <th>11</th> <td>12</td> <td>andy roberton</td> <td>united kingdom</td> <td>husqvarna</td> <td>810</td> <td>0</td> </tr> <tr> <th>12</th> <td>13</td> <td>vlastimil valek</td> <td>czechoslovakia</td> <td>cz</td> <td>709</td> <td>0</td> </tr> <tr> <th>13</th> <td>14</td> <td>mark blackwell</td> <td>united states</td> <td>husqvarna</td> <td>604</td> <td>0</td> </tr> <tr> <th>14</th> <td>15</td> <td>brad lackey</td> <td>united states</td> <td>cz</td> <td>603</td> <td>0</td> </tr> <tr> <th>15</th> <td>16</td> <td>gary jones</td> <td>united states</td> <td>yamaha</td> <td>439</td> <td>0</td> </tr> <tr> <th>16</th> <td>17</td> <td>john desoto</td> <td>united states</td> <td>suzuki</td> <td>425</td> <td>0</td> </tr> <tr> <th>17</th> <td>18</td> <td>chris horsefield</td> <td>united kingdom</td> <td>cz</td> <td>416</td> <td>0</td> </tr> <tr> <th>18</th> <td>19</td> <td>uno palm</td> <td>sweden</td> <td>husqvarna</td> <td>324</td> <td>0</td> </tr> <tr> <th>19</th> <td>20</td> <td>peter lamppu</td> <td>united states</td> <td>montesa</td> <td>309</td> <td>0</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>place</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>7</td> </tr> </tbody> </table>

select place where rider = 'willy bauer' <table_name> : table | | place | rider | country | team | points | wins | |---:|--------:|:------------------|:---------------|:----------|---------:|-------:| | 0 | 1 | sylvain geboers | belgium | suzuki | 3066 | 3 | | 1 | 2 | adolf weil | germany | maico | 2331 | 2 | | 2 | 3 | torlief hansen | sweden | husqvarna | 2052 | 0 | | 3 | 4 | roger de coster | belgium | suzuki | 1865 | 3 | | 4 | 5 | joel robert | belgium | suzuki | 1730 | 1 | | 5 | 6 | heikki mikkola | finland | husqvarna | 1680 | 2 | | 6 | 7 | willy bauer | germany | maico | 1276 | 0 | | 7 | 8 | gaston rahier | belgium | cz | 1112 | 0 | | 8 | 9 | pierre karsmakers | netherlands | husqvarna | 1110 | 0 | | 9 | 10 | dave bickers | united kingdom | cz | 1076 | 0 | | 10 | 11 | john banks | united kingdom | cz | 971 | 0 | | 11 | 12 | andy roberton | united kingdom | husqvarna | 810 | 0 | | 12 | 13 | vlastimil valek | czechoslovakia | cz | 709 | 0 | | 13 | 14 | mark blackwell | united states | husqvarna | 604 | 0 | | 14 | 15 | brad lackey | united states | cz | 603 | 0 | | 15 | 16 | gary jones | united states | yamaha | 439 | 0 | | 16 | 17 | john desoto | united states | suzuki | 425 | 0 | | 17 | 18 | chris horsefield | united kingdom | cz | 416 | 0 | | 18 | 19 | uno palm | sweden | husqvarna | 324 | 0 | | 19 | 20 | peter lamppu | united states | montesa | 309 | 0 |

| | place | |---:|--------:| | 0 | 7 |

select diameter - diameter where longitude = '107.4° e' col : al-khwarizmi | latitude | longitude | diameter row 1 : b | 9.0° n | 107.4° e | 62 km row 2 : g | 6.9° n | 107.1° e | 95 km row 3 : h | 6.0° n | 109.2° e | 50 km row 4 : j | 6.2° n | 107.6° e | 47 km row 5 : k | 4.6° n | 107.6° e | 26 km row 6 : l | 3.9° n | 107.4° e | 35 km row 7 : m | 3.1° n | 107.0° e | 18 km row 8 : t | 7.0° n | 104.5° e | 15 km

col : "diameter - diameter" row 1 : diameter - diameter row 2 : diameter - diameter

select diameter - diameter where longitude = '107.4° e' <table_name> : table \begin{tabular}{lllll} \toprule & al-khwarizmi & latitude & longitude & diameter \\ \midrule 0 & b & 9.0° n & 107.4° e & 62 km \\ 1 & g & 6.9° n & 107.1° e & 95 km \\ 2 & h & 6.0° n & 109.2° e & 50 km \\ 3 & j & 6.2° n & 107.6° e & 47 km \\ 4 & k & 4.6° n & 107.6° e & 26 km \\ 5 & l & 3.9° n & 107.4° e & 35 km \\ 6 & m & 3.1° n & 107.0° e & 18 km \\ 7 & t & 7.0° n & 104.5° e & 15 km \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & "diameter - diameter" \\ \midrule 0 & diameter - diameter \\ 1 & diameter - diameter \\ \bottomrule \end{tabular}

select diameter - diameter where longitude = '107.4° e' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>al-khwarizmi</th> <th>latitude</th> <th>longitude</th> <th>diameter</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>b</td> <td>9.0° n</td> <td>107.4° e</td> <td>62 km</td> </tr> <tr> <th>1</th> <td>g</td> <td>6.9° n</td> <td>107.1° e</td> <td>95 km</td> </tr> <tr> <th>2</th> <td>h</td> <td>6.0° n</td> <td>109.2° e</td> <td>50 km</td> </tr> <tr> <th>3</th> <td>j</td> <td>6.2° n</td> <td>107.6° e</td> <td>47 km</td> </tr> <tr> <th>4</th> <td>k</td> <td>4.6° n</td> <td>107.6° e</td> <td>26 km</td> </tr> <tr> <th>5</th> <td>l</td> <td>3.9° n</td> <td>107.4° e</td> <td>35 km</td> </tr> <tr> <th>6</th> <td>m</td> <td>3.1° n</td> <td>107.0° e</td> <td>18 km</td> </tr> <tr> <th>7</th> <td>t</td> <td>7.0° n</td> <td>104.5° e</td> <td>15 km</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>"diameter - diameter"</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>diameter - diameter</td> </tr> <tr> <th>1</th> <td>diameter - diameter</td> </tr> </tbody> </table>

select diameter - diameter where longitude = '107.4° e' <table_name> : table | | al-khwarizmi | latitude | longitude | diameter | |---:|:---------------|:-----------|:------------|:-----------| | 0 | b | 9.0° n | 107.4° e | 62 km | | 1 | g | 6.9° n | 107.1° e | 95 km | | 2 | h | 6.0° n | 109.2° e | 50 km | | 3 | j | 6.2° n | 107.6° e | 47 km | | 4 | k | 4.6° n | 107.6° e | 26 km | | 5 | l | 3.9° n | 107.4° e | 35 km | | 6 | m | 3.1° n | 107.0° e | 18 km | | 7 | t | 7.0° n | 104.5° e | 15 km |

| | "diameter - diameter" | |---:|:------------------------| | 0 | diameter - diameter | | 1 | diameter - diameter |

select count ( * ) where award = 'grammy award for best contemporary folk album' col : year | award | work/artist | result row 1 : 1999 | grammy award for best contemporary folk album | mermaid avenue | nominated row 2 : 2005 | grammy award for best alternative music album | a ghost is born | won row 3 : 2005 | grammy award for best recording package (awarded to the art director) | a ghost is born | won row 4 : 2008 | grammy award for best rock album | sky blue sky | nominated row 5 : 2010 | grammy award for best americana album | wilco (the album) | nominated row 6 : 2012 | grammy award for best rock album | the whole love | nominated

col : count ( "*" ) row 1 : 1

select count ( * ) where award = 'grammy award for best contemporary folk album' <table_name> : table \begin{tabular}{lllll} \toprule & year & award & work/artist & result \\ \midrule 0 & 1999 & grammy award for best contemporary folk album & mermaid avenue & nominated \\ 1 & 2005 & grammy award for best alternative music album & a ghost is born & won \\ 2 & 2005 & grammy award for best recording package (awarded to the art director) & a ghost is born & won \\ 3 & 2008 & grammy award for best rock album & sky blue sky & nominated \\ 4 & 2010 & grammy award for best americana album & wilco (the album) & nominated \\ 5 & 2012 & grammy award for best rock album & the whole love & nominated \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 1 \\ \bottomrule \end{tabular}

select count ( * ) where award = 'grammy award for best contemporary folk album' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>year</th> <th>award</th> <th>work/artist</th> <th>result</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1999</td> <td>grammy award for best contemporary folk album</td> <td>mermaid avenue</td> <td>nominated</td> </tr> <tr> <th>1</th> <td>2005</td> <td>grammy award for best alternative music album</td> <td>a ghost is born</td> <td>won</td> </tr> <tr> <th>2</th> <td>2005</td> <td>grammy award for best recording package (awarded to the art director)</td> <td>a ghost is born</td> <td>won</td> </tr> <tr> <th>3</th> <td>2008</td> <td>grammy award for best rock album</td> <td>sky blue sky</td> <td>nominated</td> </tr> <tr> <th>4</th> <td>2010</td> <td>grammy award for best americana album</td> <td>wilco (the album)</td> <td>nominated</td> </tr> <tr> <th>5</th> <td>2012</td> <td>grammy award for best rock album</td> <td>the whole love</td> <td>nominated</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "*" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> </tr> </tbody> </table>

select count ( * ) where award = 'grammy award for best contemporary folk album' <table_name> : table | | year | award | work/artist | result | |---:|-------:|:----------------------------------------------------------------------|:------------------|:----------| | 0 | 1999 | grammy award for best contemporary folk album | mermaid avenue | nominated | | 1 | 2005 | grammy award for best alternative music album | a ghost is born | won | | 2 | 2005 | grammy award for best recording package (awarded to the art director) | a ghost is born | won | | 3 | 2008 | grammy award for best rock album | sky blue sky | nominated | | 4 | 2010 | grammy award for best americana album | wilco (the album) | nominated | | 5 | 2012 | grammy award for best rock album | the whole love | nominated |

| | count ( "*" ) | |---:|----------------:| | 0 | 1 |

select count ( day ) where location = 'unisantos park' and kickoff > '7:05pm' col : game | day | date | kickoff | opponent | results score | results record | location | attendance row 1 : 1 | sunday | november 10 | 3:05pm | at las vegas legends | l 3-7 | 0-1 | orleans arena | 1836 row 2 : 2 | sunday | november 17 | 1:05pm | monterrey flash | l 6-10 | 0-2 | unisantos park | 363 row 3 : 3 | saturday | november 23 | 7:05pm | at bay area rosal | w 10-7 | 1-2 | cabernet indoor sports | 652 row 4 : 4 | sunday | december 1 | 1:05pm | ontario fury | w 18-4 | 2-2 | unisantos park | 207 row 5 : 5 | saturday | december 14 | 7:05pm | at sacramento surge | w 7-6 (ot) | 3-2 | estadio azteca soccer arena | 215 row 6 : 6 | sunday | december 15 | 6:00pm | at bay area rosal | l 8-9 (ot) | 3-3 | cabernet indoor sports | 480 row 7 : 7 | sunday | december 22 | 1:05pm | turlock express | w 16-8 | 4-3 | unisantos park | 218 row 8 : 8 | saturday | january 4 | 7:05pm | at ontario fury | l 5-12 | 4-4 | citizens business bank arena | 2653 row 9 : 9 | sunday | january 5 | 1:05pm | san diego sockers | l 7-12 | 4-5 | unisantos park | 388 row 10 : 10 | sunday | january 12 | 1:05pm | las vegas legends | w 10-7 | 5-5 | unisantos park | 343 row 11 : 11 | sunday | january 19 | 1:05pm | bay area rosal | w 17-7 | 6-5 | unisantos park | 219 row 12 : 12 | sunday | january 26 | 1:05pm | sacramento surge | w 20-6 | 7-5 | unisantos park | 224 row 13 : 13 | saturday | february 1 | 7:05pm | at san diego sockers | l 5-6 | 7-6 | valley view casino center | 4954 row 14 : 14 | friday | february 7 | 7:05pm | at turlock express | l 6-9 | 7-7 | turlock soccer complex | 673 row 15 : 15 | saturday | february 8 | 7:05pm | at sacramento surge | w 10-6 | 8-7 | estadio azteca soccer arena | 323 row 16 : 16 | saturday | february 15 | 5:05pm | bay area rosal | w 27-2 | 9-7 | unisantos park | 118

col : count ( "day" ) row 1 : 0

select count ( day ) where location = 'unisantos park' and kickoff > '7:05pm' <table_name> : table \begin{tabular}{llllllllll} \toprule & game & day & date & kickoff & opponent & results score & results record & location & attendance \\ \midrule 0 & 1 & sunday & november 10 & 3:05pm & at las vegas legends & l 3-7 & 0-1 & orleans arena & 1836 \\ 1 & 2 & sunday & november 17 & 1:05pm & monterrey flash & l 6-10 & 0-2 & unisantos park & 363 \\ 2 & 3 & saturday & november 23 & 7:05pm & at bay area rosal & w 10-7 & 1-2 & cabernet indoor sports & 652 \\ 3 & 4 & sunday & december 1 & 1:05pm & ontario fury & w 18-4 & 2-2 & unisantos park & 207 \\ 4 & 5 & saturday & december 14 & 7:05pm & at sacramento surge & w 7-6 (ot) & 3-2 & estadio azteca soccer arena & 215 \\ 5 & 6 & sunday & december 15 & 6:00pm & at bay area rosal & l 8-9 (ot) & 3-3 & cabernet indoor sports & 480 \\ 6 & 7 & sunday & december 22 & 1:05pm & turlock express & w 16-8 & 4-3 & unisantos park & 218 \\ 7 & 8 & saturday & january 4 & 7:05pm & at ontario fury & l 5-12 & 4-4 & citizens business bank arena & 2653 \\ 8 & 9 & sunday & january 5 & 1:05pm & san diego sockers & l 7-12 & 4-5 & unisantos park & 388 \\ 9 & 10 & sunday & january 12 & 1:05pm & las vegas legends & w 10-7 & 5-5 & unisantos park & 343 \\ 10 & 11 & sunday & january 19 & 1:05pm & bay area rosal & w 17-7 & 6-5 & unisantos park & 219 \\ 11 & 12 & sunday & january 26 & 1:05pm & sacramento surge & w 20-6 & 7-5 & unisantos park & 224 \\ 12 & 13 & saturday & february 1 & 7:05pm & at san diego sockers & l 5-6 & 7-6 & valley view casino center & 4954 \\ 13 & 14 & friday & february 7 & 7:05pm & at turlock express & l 6-9 & 7-7 & turlock soccer complex & 673 \\ 14 & 15 & saturday & february 8 & 7:05pm & at sacramento surge & w 10-6 & 8-7 & estadio azteca soccer arena & 323 \\ 15 & 16 & saturday & february 15 & 5:05pm & bay area rosal & w 27-2 & 9-7 & unisantos park & 118 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "day" ) \\ \midrule 0 & 0 \\ \bottomrule \end{tabular}

select count ( day ) where location = 'unisantos park' and kickoff > '7:05pm' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>game</th> <th>day</th> <th>date</th> <th>kickoff</th> <th>opponent</th> <th>results score</th> <th>results record</th> <th>location</th> <th>attendance</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>sunday</td> <td>november 10</td> <td>3:05pm</td> <td>at las vegas legends</td> <td>l 3-7</td> <td>0-1</td> <td>orleans arena</td> <td>1836</td> </tr> <tr> <th>1</th> <td>2</td> <td>sunday</td> <td>november 17</td> <td>1:05pm</td> <td>monterrey flash</td> <td>l 6-10</td> <td>0-2</td> <td>unisantos park</td> <td>363</td> </tr> <tr> <th>2</th> <td>3</td> <td>saturday</td> <td>november 23</td> <td>7:05pm</td> <td>at bay area rosal</td> <td>w 10-7</td> <td>1-2</td> <td>cabernet indoor sports</td> <td>652</td> </tr> <tr> <th>3</th> <td>4</td> <td>sunday</td> <td>december 1</td> <td>1:05pm</td> <td>ontario fury</td> <td>w 18-4</td> <td>2-2</td> <td>unisantos park</td> <td>207</td> </tr> <tr> <th>4</th> <td>5</td> <td>saturday</td> <td>december 14</td> <td>7:05pm</td> <td>at sacramento surge</td> <td>w 7-6 (ot)</td> <td>3-2</td> <td>estadio azteca soccer arena</td> <td>215</td> </tr> <tr> <th>5</th> <td>6</td> <td>sunday</td> <td>december 15</td> <td>6:00pm</td> <td>at bay area rosal</td> <td>l 8-9 (ot)</td> <td>3-3</td> <td>cabernet indoor sports</td> <td>480</td> </tr> <tr> <th>6</th> <td>7</td> <td>sunday</td> <td>december 22</td> <td>1:05pm</td> <td>turlock express</td> <td>w 16-8</td> <td>4-3</td> <td>unisantos park</td> <td>218</td> </tr> <tr> <th>7</th> <td>8</td> <td>saturday</td> <td>january 4</td> <td>7:05pm</td> <td>at ontario fury</td> <td>l 5-12</td> <td>4-4</td> <td>citizens business bank arena</td> <td>2653</td> </tr> <tr> <th>8</th> <td>9</td> <td>sunday</td> <td>january 5</td> <td>1:05pm</td> <td>san diego sockers</td> <td>l 7-12</td> <td>4-5</td> <td>unisantos park</td> <td>388</td> </tr> <tr> <th>9</th> <td>10</td> <td>sunday</td> <td>january 12</td> <td>1:05pm</td> <td>las vegas legends</td> <td>w 10-7</td> <td>5-5</td> <td>unisantos park</td> <td>343</td> </tr> <tr> <th>10</th> <td>11</td> <td>sunday</td> <td>january 19</td> <td>1:05pm</td> <td>bay area rosal</td> <td>w 17-7</td> <td>6-5</td> <td>unisantos park</td> <td>219</td> </tr> <tr> <th>11</th> <td>12</td> <td>sunday</td> <td>january 26</td> <td>1:05pm</td> <td>sacramento surge</td> <td>w 20-6</td> <td>7-5</td> <td>unisantos park</td> <td>224</td> </tr> <tr> <th>12</th> <td>13</td> <td>saturday</td> <td>february 1</td> <td>7:05pm</td> <td>at san diego sockers</td> <td>l 5-6</td> <td>7-6</td> <td>valley view casino center</td> <td>4954</td> </tr> <tr> <th>13</th> <td>14</td> <td>friday</td> <td>february 7</td> <td>7:05pm</td> <td>at turlock express</td> <td>l 6-9</td> <td>7-7</td> <td>turlock soccer complex</td> <td>673</td> </tr> <tr> <th>14</th> <td>15</td> <td>saturday</td> <td>february 8</td> <td>7:05pm</td> <td>at sacramento surge</td> <td>w 10-6</td> <td>8-7</td> <td>estadio azteca soccer arena</td> <td>323</td> </tr> <tr> <th>15</th> <td>16</td> <td>saturday</td> <td>february 15</td> <td>5:05pm</td> <td>bay area rosal</td> <td>w 27-2</td> <td>9-7</td> <td>unisantos park</td> <td>118</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "day" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>0</td> </tr> </tbody> </table>

select count ( day ) where location = 'unisantos park' and kickoff > '7:05pm' <table_name> : table | | game | day | date | kickoff | opponent | results score | results record | location | attendance | |---:|-------:|:---------|:------------|:----------|:---------------------|:----------------|:-----------------|:-----------------------------|-------------:| | 0 | 1 | sunday | november 10 | 3:05pm | at las vegas legends | l 3-7 | 0-1 | orleans arena | 1836 | | 1 | 2 | sunday | november 17 | 1:05pm | monterrey flash | l 6-10 | 0-2 | unisantos park | 363 | | 2 | 3 | saturday | november 23 | 7:05pm | at bay area rosal | w 10-7 | 1-2 | cabernet indoor sports | 652 | | 3 | 4 | sunday | december 1 | 1:05pm | ontario fury | w 18-4 | 2-2 | unisantos park | 207 | | 4 | 5 | saturday | december 14 | 7:05pm | at sacramento surge | w 7-6 (ot) | 3-2 | estadio azteca soccer arena | 215 | | 5 | 6 | sunday | december 15 | 6:00pm | at bay area rosal | l 8-9 (ot) | 3-3 | cabernet indoor sports | 480 | | 6 | 7 | sunday | december 22 | 1:05pm | turlock express | w 16-8 | 4-3 | unisantos park | 218 | | 7 | 8 | saturday | january 4 | 7:05pm | at ontario fury | l 5-12 | 4-4 | citizens business bank arena | 2653 | | 8 | 9 | sunday | january 5 | 1:05pm | san diego sockers | l 7-12 | 4-5 | unisantos park | 388 | | 9 | 10 | sunday | january 12 | 1:05pm | las vegas legends | w 10-7 | 5-5 | unisantos park | 343 | | 10 | 11 | sunday | january 19 | 1:05pm | bay area rosal | w 17-7 | 6-5 | unisantos park | 219 | | 11 | 12 | sunday | january 26 | 1:05pm | sacramento surge | w 20-6 | 7-5 | unisantos park | 224 | | 12 | 13 | saturday | february 1 | 7:05pm | at san diego sockers | l 5-6 | 7-6 | valley view casino center | 4954 | | 13 | 14 | friday | february 7 | 7:05pm | at turlock express | l 6-9 | 7-7 | turlock soccer complex | 673 | | 14 | 15 | saturday | february 8 | 7:05pm | at sacramento surge | w 10-6 | 8-7 | estadio azteca soccer arena | 323 | | 15 | 16 | saturday | february 15 | 5:05pm | bay area rosal | w 27-2 | 9-7 | unisantos park | 118 |

| | count ( "day" ) | |---:|------------------:| | 0 | 0 |

select count ( name ) where name = 'luther blissett' col : name | year inducted | position | apps | goals row 1 : luther blissett | 2003 | forward | 503 | 186 row 2 : tony coton | 2004 | goalkeeper | 291 | 0 row 3 : john mcclelland | 2005 | defender | 234 | 3 row 4 : tommy mooney | 2006 | forward | 287 | 64 row 5 : les taylor | 2007 | midfielder | 211 | 20 row 6 : david james | 2008 | goalkeeper | 98 | 0 row 7 : ian bolton | 2009 | defender | 287 | 36 row 8 : nigel gibbs | 2010 | defender | 491 | 7 row 9 : duncan welbourne | 2011 | defender | 457 | 25 row 10 : ross jenkins | 2012 | forward | 398 | 142 row 11 : robert page | 2013 | defender | 218 | 2

col : count ( "name" ) row 1 : 1

select count ( name ) where name = 'luther blissett' <table_name> : table \begin{tabular}{llllll} \toprule & name & year inducted & position & apps & goals \\ \midrule 0 & luther blissett & 2003 & forward & 503 & 186 \\ 1 & tony coton & 2004 & goalkeeper & 291 & 0 \\ 2 & john mcclelland & 2005 & defender & 234 & 3 \\ 3 & tommy mooney & 2006 & forward & 287 & 64 \\ 4 & les taylor & 2007 & midfielder & 211 & 20 \\ 5 & david james & 2008 & goalkeeper & 98 & 0 \\ 6 & ian bolton & 2009 & defender & 287 & 36 \\ 7 & nigel gibbs & 2010 & defender & 491 & 7 \\ 8 & duncan welbourne & 2011 & defender & 457 & 25 \\ 9 & ross jenkins & 2012 & forward & 398 & 142 \\ 10 & robert page & 2013 & defender & 218 & 2 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "name" ) \\ \midrule 0 & 1 \\ \bottomrule \end{tabular}

select count ( name ) where name = 'luther blissett' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>name</th> <th>year inducted</th> <th>position</th> <th>apps</th> <th>goals</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>luther blissett</td> <td>2003</td> <td>forward</td> <td>503</td> <td>186</td> </tr> <tr> <th>1</th> <td>tony coton</td> <td>2004</td> <td>goalkeeper</td> <td>291</td> <td>0</td> </tr> <tr> <th>2</th> <td>john mcclelland</td> <td>2005</td> <td>defender</td> <td>234</td> <td>3</td> </tr> <tr> <th>3</th> <td>tommy mooney</td> <td>2006</td> <td>forward</td> <td>287</td> <td>64</td> </tr> <tr> <th>4</th> <td>les taylor</td> <td>2007</td> <td>midfielder</td> <td>211</td> <td>20</td> </tr> <tr> <th>5</th> <td>david james</td> <td>2008</td> <td>goalkeeper</td> <td>98</td> <td>0</td> </tr> <tr> <th>6</th> <td>ian bolton</td> <td>2009</td> <td>defender</td> <td>287</td> <td>36</td> </tr> <tr> <th>7</th> <td>nigel gibbs</td> <td>2010</td> <td>defender</td> <td>491</td> <td>7</td> </tr> <tr> <th>8</th> <td>duncan welbourne</td> <td>2011</td> <td>defender</td> <td>457</td> <td>25</td> </tr> <tr> <th>9</th> <td>ross jenkins</td> <td>2012</td> <td>forward</td> <td>398</td> <td>142</td> </tr> <tr> <th>10</th> <td>robert page</td> <td>2013</td> <td>defender</td> <td>218</td> <td>2</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "name" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> </tr> </tbody> </table>

select count ( name ) where name = 'luther blissett' <table_name> : table | | name | year inducted | position | apps | goals | |---:|:-----------------|----------------:|:-----------|-------:|--------:| | 0 | luther blissett | 2003 | forward | 503 | 186 | | 1 | tony coton | 2004 | goalkeeper | 291 | 0 | | 2 | john mcclelland | 2005 | defender | 234 | 3 | | 3 | tommy mooney | 2006 | forward | 287 | 64 | | 4 | les taylor | 2007 | midfielder | 211 | 20 | | 5 | david james | 2008 | goalkeeper | 98 | 0 | | 6 | ian bolton | 2009 | defender | 287 | 36 | | 7 | nigel gibbs | 2010 | defender | 491 | 7 | | 8 | duncan welbourne | 2011 | defender | 457 | 25 | | 9 | ross jenkins | 2012 | forward | 398 | 142 | | 10 | robert page | 2013 | defender | 218 | 2 |

| | count ( "name" ) | |---:|-------------------:| | 0 | 1 |

select year where label = 'emi' col : year | title | peak chart positions ger | label | artist row 1 : 1981 | savoir faire | none | capitol | mink deville row 2 : 1993 | spanish stroll 1977-1987 | none | disky, raven | mink deville row 3 : 1995 | les inoubliables de willy deville | none | wagram | willy deville row 4 : 1996 | love & emotion: the atlantic years | 54 | atlantic | mink deville row 5 : 1996 | mink/willy deville greatest hits | none | emi | mink deville row 6 : 1996 | spanish stroll | none | disky | mink deville row 7 : 1996 | best of willy deville | none | arcade | willy deville row 8 : 1997 | his greatest hits | none | arcade | mink deville row 9 : 1997 | premium gold collection | none | emi | mink deville row 10 : 1997 | the best of mink deville | none | emi | mink deville row 11 : 1999 | collection legende | none | wagram | willy deville row 12 : 2001 | cadillac walk: the mink deville collection | none | capitol | mink deville row 13 : 2003 | greatest hits | none | emi | mink deville row 14 : 2005 | mink deville | none | disky | mink deville row 15 : 2009 | introducing willy deville | none | edel | willy deville

col : year row 1 : 1996 row 2 : 1997 row 3 : 1997 row 4 : 2003

select year where label = 'emi' <table_name> : table \begin{tabular}{llllll} \toprule & year & title & peak chart positions ger & label & artist \\ \midrule 0 & 1981 & savoir faire & none & capitol & mink deville \\ 1 & 1993 & spanish stroll 1977-1987 & none & disky, raven & mink deville \\ 2 & 1995 & les inoubliables de willy deville & none & wagram & willy deville \\ 3 & 1996 & love & emotion: the atlantic years & 54 & atlantic & mink deville \\ 4 & 1996 & mink/willy deville greatest hits & none & emi & mink deville \\ 5 & 1996 & spanish stroll & none & disky & mink deville \\ 6 & 1996 & best of willy deville & none & arcade & willy deville \\ 7 & 1997 & his greatest hits & none & arcade & mink deville \\ 8 & 1997 & premium gold collection & none & emi & mink deville \\ 9 & 1997 & the best of mink deville & none & emi & mink deville \\ 10 & 1999 & collection legende & none & wagram & willy deville \\ 11 & 2001 & cadillac walk: the mink deville collection & none & capitol & mink deville \\ 12 & 2003 & greatest hits & none & emi & mink deville \\ 13 & 2005 & mink deville & none & disky & mink deville \\ 14 & 2009 & introducing willy deville & none & edel & willy deville \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & year \\ \midrule 0 & 1996 \\ 1 & 1997 \\ 2 & 1997 \\ 3 & 2003 \\ \bottomrule \end{tabular}

select year where label = 'emi' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>year</th> <th>title</th> <th>peak chart positions ger</th> <th>label</th> <th>artist</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1981</td> <td>savoir faire</td> <td>none</td> <td>capitol</td> <td>mink deville</td> </tr> <tr> <th>1</th> <td>1993</td> <td>spanish stroll 1977-1987</td> <td>none</td> <td>disky, raven</td> <td>mink deville</td> </tr> <tr> <th>2</th> <td>1995</td> <td>les inoubliables de willy deville</td> <td>none</td> <td>wagram</td> <td>willy deville</td> </tr> <tr> <th>3</th> <td>1996</td> <td>love &amp; emotion: the atlantic years</td> <td>54</td> <td>atlantic</td> <td>mink deville</td> </tr> <tr> <th>4</th> <td>1996</td> <td>mink/willy deville greatest hits</td> <td>none</td> <td>emi</td> <td>mink deville</td> </tr> <tr> <th>5</th> <td>1996</td> <td>spanish stroll</td> <td>none</td> <td>disky</td> <td>mink deville</td> </tr> <tr> <th>6</th> <td>1996</td> <td>best of willy deville</td> <td>none</td> <td>arcade</td> <td>willy deville</td> </tr> <tr> <th>7</th> <td>1997</td> <td>his greatest hits</td> <td>none</td> <td>arcade</td> <td>mink deville</td> </tr> <tr> <th>8</th> <td>1997</td> <td>premium gold collection</td> <td>none</td> <td>emi</td> <td>mink deville</td> </tr> <tr> <th>9</th> <td>1997</td> <td>the best of mink deville</td> <td>none</td> <td>emi</td> <td>mink deville</td> </tr> <tr> <th>10</th> <td>1999</td> <td>collection legende</td> <td>none</td> <td>wagram</td> <td>willy deville</td> </tr> <tr> <th>11</th> <td>2001</td> <td>cadillac walk: the mink deville collection</td> <td>none</td> <td>capitol</td> <td>mink deville</td> </tr> <tr> <th>12</th> <td>2003</td> <td>greatest hits</td> <td>none</td> <td>emi</td> <td>mink deville</td> </tr> <tr> <th>13</th> <td>2005</td> <td>mink deville</td> <td>none</td> <td>disky</td> <td>mink deville</td> </tr> <tr> <th>14</th> <td>2009</td> <td>introducing willy deville</td> <td>none</td> <td>edel</td> <td>willy deville</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>year</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1996</td> </tr> <tr> <th>1</th> <td>1997</td> </tr> <tr> <th>2</th> <td>1997</td> </tr> <tr> <th>3</th> <td>2003</td> </tr> </tbody> </table>

select year where label = 'emi' <table_name> : table | | year | title | peak chart positions ger | label | artist | |---:|-------:|:-------------------------------------------|:---------------------------|:-------------|:--------------| | 0 | 1981 | savoir faire | none | capitol | mink deville | | 1 | 1993 | spanish stroll 1977-1987 | none | disky, raven | mink deville | | 2 | 1995 | les inoubliables de willy deville | none | wagram | willy deville | | 3 | 1996 | love & emotion: the atlantic years | 54 | atlantic | mink deville | | 4 | 1996 | mink/willy deville greatest hits | none | emi | mink deville | | 5 | 1996 | spanish stroll | none | disky | mink deville | | 6 | 1996 | best of willy deville | none | arcade | willy deville | | 7 | 1997 | his greatest hits | none | arcade | mink deville | | 8 | 1997 | premium gold collection | none | emi | mink deville | | 9 | 1997 | the best of mink deville | none | emi | mink deville | | 10 | 1999 | collection legende | none | wagram | willy deville | | 11 | 2001 | cadillac walk: the mink deville collection | none | capitol | mink deville | | 12 | 2003 | greatest hits | none | emi | mink deville | | 13 | 2005 | mink deville | none | disky | mink deville | | 14 | 2009 | introducing willy deville | none | edel | willy deville |

| | year | |---:|-------:| | 0 | 1996 | | 1 | 1997 | | 2 | 1997 | | 3 | 2003 |

select count ( * ) where hanzi = '海林市' col : # | name | hanzi | hanyu pinyin | population (2003 est.) | area (km2) | density (/km2) row 1 : 1 | aimin district | 爱民区 | aimin qu | 230000 | 359 | 641 row 2 : 2 | dong'an district | 东安区 | dong'an qu | 180000 | 566 | 318 row 3 : 3 | yangming district | 阳明区 | yangming qu | 160000 | 358 | 447 row 4 : 4 | xi'an district | 西安区 | xi'an qu | 210000 | 325 | 646 row 5 : 5 | muling city | 穆棱市 | muling shi | 330000 | 6094 | 54 row 6 : 6 | suifenhe city | 绥芬河市 | suifenhe shi | 60000 | 427 | 141 row 7 : 7 | hailin city | 海林市 | hailin shi | 440000 | 9877 | 45 row 8 : 8 | ning'an city | 宁安市 | ning'an shi | 440000 | 7870 | 56 row 9 : 9 | dongning county | 东宁县 | dongning xian | 210000 | 7368 | 29 row 10 : 10 | linkou county | 林口县 | linkou xian | 450000 | 7191 | 63

col : count ( "*" ) row 1 : 1

select count ( * ) where hanzi = '海林市' <table_name> : table \begin{tabular}{llllllll} \toprule & # & name & hanzi & hanyu pinyin & population (2003 est.) & area (km2) & density (/km2) \\ \midrule 0 & 1 & aimin district & 爱民区 & aimin qu & 230000 & 359 & 641 \\ 1 & 2 & dong'an district & 东安区 & dong'an qu & 180000 & 566 & 318 \\ 2 & 3 & yangming district & 阳明区 & yangming qu & 160000 & 358 & 447 \\ 3 & 4 & xi'an district & 西安区 & xi'an qu & 210000 & 325 & 646 \\ 4 & 5 & muling city & 穆棱市 & muling shi & 330000 & 6094 & 54 \\ 5 & 6 & suifenhe city & 绥芬河市 & suifenhe shi & 60000 & 427 & 141 \\ 6 & 7 & hailin city & 海林市 & hailin shi & 440000 & 9877 & 45 \\ 7 & 8 & ning'an city & 宁安市 & ning'an shi & 440000 & 7870 & 56 \\ 8 & 9 & dongning county & 东宁县 & dongning xian & 210000 & 7368 & 29 \\ 9 & 10 & linkou county & 林口县 & linkou xian & 450000 & 7191 & 63 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 1 \\ \bottomrule \end{tabular}

select count ( * ) where hanzi = '海林市' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>#</th> <th>name</th> <th>hanzi</th> <th>hanyu pinyin</th> <th>population (2003 est.)</th> <th>area (km2)</th> <th>density (/km2)</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>aimin district</td> <td>爱民区</td> <td>aimin qu</td> <td>230000</td> <td>359</td> <td>641</td> </tr> <tr> <th>1</th> <td>2</td> <td>dong'an district</td> <td>东安区</td> <td>dong'an qu</td> <td>180000</td> <td>566</td> <td>318</td> </tr> <tr> <th>2</th> <td>3</td> <td>yangming district</td> <td>阳明区</td> <td>yangming qu</td> <td>160000</td> <td>358</td> <td>447</td> </tr> <tr> <th>3</th> <td>4</td> <td>xi'an district</td> <td>西安区</td> <td>xi'an qu</td> <td>210000</td> <td>325</td> <td>646</td> </tr> <tr> <th>4</th> <td>5</td> <td>muling city</td> <td>穆棱市</td> <td>muling shi</td> <td>330000</td> <td>6094</td> <td>54</td> </tr> <tr> <th>5</th> <td>6</td> <td>suifenhe city</td> <td>绥芬河市</td> <td>suifenhe shi</td> <td>60000</td> <td>427</td> <td>141</td> </tr> <tr> <th>6</th> <td>7</td> <td>hailin city</td> <td>海林市</td> <td>hailin shi</td> <td>440000</td> <td>9877</td> <td>45</td> </tr> <tr> <th>7</th> <td>8</td> <td>ning'an city</td> <td>宁安市</td> <td>ning'an shi</td> <td>440000</td> <td>7870</td> <td>56</td> </tr> <tr> <th>8</th> <td>9</td> <td>dongning county</td> <td>东宁县</td> <td>dongning xian</td> <td>210000</td> <td>7368</td> <td>29</td> </tr> <tr> <th>9</th> <td>10</td> <td>linkou county</td> <td>林口县</td> <td>linkou xian</td> <td>450000</td> <td>7191</td> <td>63</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "*" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> </tr> </tbody> </table>

select count ( * ) where hanzi = '海林市' <table_name> : table | | # | name | hanzi | hanyu pinyin | population (2003 est.) | area (km2) | density (/km2) | |---:|----:|:------------------|:---------|:---------------|-------------------------:|-------------:|-----------------:| | 0 | 1 | aimin district | 爱民区 | aimin qu | 230000 | 359 | 641 | | 1 | 2 | dong'an district | 东安区 | dong'an qu | 180000 | 566 | 318 | | 2 | 3 | yangming district | 阳明区 | yangming qu | 160000 | 358 | 447 | | 3 | 4 | xi'an district | 西安区 | xi'an qu | 210000 | 325 | 646 | | 4 | 5 | muling city | 穆棱市 | muling shi | 330000 | 6094 | 54 | | 5 | 6 | suifenhe city | 绥芬河市 | suifenhe shi | 60000 | 427 | 141 | | 6 | 7 | hailin city | 海林市 | hailin shi | 440000 | 9877 | 45 | | 7 | 8 | ning'an city | 宁安市 | ning'an shi | 440000 | 7870 | 56 | | 8 | 9 | dongning county | 东宁县 | dongning xian | 210000 | 7368 | 29 | | 9 | 10 | linkou county | 林口县 | linkou xian | 450000 | 7191 | 63 |

| | count ( "*" ) | |---:|----------------:| | 0 | 1 |

select performer(s) where # = 2 col : # | title | producer(s) | performer(s) | length row 1 : 1 | "ova here" | da beatminerz | krs-one | 2:56 row 2 : 2 | "things is about to change" | creative minds combined | krs-one | 2:34 row 3 : 3 | "splash" | a-sharp, pleasure king | krs-one | 2:24 row 4 : 4 | "my people" | alumni | krs-one | 0:19 row 5 : 5 | "kreditz" | milann miles, rick long | krs-one | 2:02 row 6 : 6 | "i remember" | j-roc, mad lion | krs-one | 4:03 row 7 : 7 | "down the charts" | milann miles, rick long | krs-one | 2:10 row 8 : 8 | "you really don't want it" | jim bean | none | 3:29 row 9 : 9 | "womanology" | soul supreme | krs-one | 3:54 row 10 : 10 | "2nd kreditz" | none | krs-one | 0:46 row 11 : 11 | "stop it" | krs-one, mad lion | none | 1:38 row 12 : 12 | "problemz" | vanguard | krs-one | 3:15 row 13 : 13 | "believe it!" | soul supreme | krs-one | 3:59

col : performer(s) row 1 : krs-one

select performer(s) where # = 2 <table_name> : table \begin{tabular}{llllll} \toprule & # & title & producer(s) & performer(s) & length \\ \midrule 0 & 1 & "ova here" & da beatminerz & krs-one & 2:56 \\ 1 & 2 & "things is about to change" & creative minds combined & krs-one & 2:34 \\ 2 & 3 & "splash" & a-sharp, pleasure king & krs-one & 2:24 \\ 3 & 4 & "my people" & alumni & krs-one & 0:19 \\ 4 & 5 & "kreditz" & milann miles, rick long & krs-one & 2:02 \\ 5 & 6 & "i remember" & j-roc, mad lion & krs-one & 4:03 \\ 6 & 7 & "down the charts" & milann miles, rick long & krs-one & 2:10 \\ 7 & 8 & "you really don't want it" & jim bean & none & 3:29 \\ 8 & 9 & "womanology" & soul supreme & krs-one & 3:54 \\ 9 & 10 & "2nd kreditz" & none & krs-one & 0:46 \\ 10 & 11 & "stop it" & krs-one, mad lion & none & 1:38 \\ 11 & 12 & "problemz" & vanguard & krs-one & 3:15 \\ 12 & 13 & "believe it!" & soul supreme & krs-one & 3:59 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & performer(s) \\ \midrule 0 & krs-one \\ \bottomrule \end{tabular}

select performer(s) where # = 2 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>#</th> <th>title</th> <th>producer(s)</th> <th>performer(s)</th> <th>length</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>"ova here"</td> <td>da beatminerz</td> <td>krs-one</td> <td>2:56</td> </tr> <tr> <th>1</th> <td>2</td> <td>"things is about to change"</td> <td>creative minds combined</td> <td>krs-one</td> <td>2:34</td> </tr> <tr> <th>2</th> <td>3</td> <td>"splash"</td> <td>a-sharp, pleasure king</td> <td>krs-one</td> <td>2:24</td> </tr> <tr> <th>3</th> <td>4</td> <td>"my people"</td> <td>alumni</td> <td>krs-one</td> <td>0:19</td> </tr> <tr> <th>4</th> <td>5</td> <td>"kreditz"</td> <td>milann miles, rick long</td> <td>krs-one</td> <td>2:02</td> </tr> <tr> <th>5</th> <td>6</td> <td>"i remember"</td> <td>j-roc, mad lion</td> <td>krs-one</td> <td>4:03</td> </tr> <tr> <th>6</th> <td>7</td> <td>"down the charts"</td> <td>milann miles, rick long</td> <td>krs-one</td> <td>2:10</td> </tr> <tr> <th>7</th> <td>8</td> <td>"you really don't want it"</td> <td>jim bean</td> <td>none</td> <td>3:29</td> </tr> <tr> <th>8</th> <td>9</td> <td>"womanology"</td> <td>soul supreme</td> <td>krs-one</td> <td>3:54</td> </tr> <tr> <th>9</th> <td>10</td> <td>"2nd kreditz"</td> <td>none</td> <td>krs-one</td> <td>0:46</td> </tr> <tr> <th>10</th> <td>11</td> <td>"stop it"</td> <td>krs-one, mad lion</td> <td>none</td> <td>1:38</td> </tr> <tr> <th>11</th> <td>12</td> <td>"problemz"</td> <td>vanguard</td> <td>krs-one</td> <td>3:15</td> </tr> <tr> <th>12</th> <td>13</td> <td>"believe it!"</td> <td>soul supreme</td> <td>krs-one</td> <td>3:59</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>performer(s)</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>krs-one</td> </tr> </tbody> </table>

select performer(s) where # = 2 <table_name> : table | | # | title | producer(s) | performer(s) | length | |---:|----:|:----------------------------|:------------------------|:---------------|:---------| | 0 | 1 | "ova here" | da beatminerz | krs-one | 2:56 | | 1 | 2 | "things is about to change" | creative minds combined | krs-one | 2:34 | | 2 | 3 | "splash" | a-sharp, pleasure king | krs-one | 2:24 | | 3 | 4 | "my people" | alumni | krs-one | 0:19 | | 4 | 5 | "kreditz" | milann miles, rick long | krs-one | 2:02 | | 5 | 6 | "i remember" | j-roc, mad lion | krs-one | 4:03 | | 6 | 7 | "down the charts" | milann miles, rick long | krs-one | 2:10 | | 7 | 8 | "you really don't want it" | jim bean | none | 3:29 | | 8 | 9 | "womanology" | soul supreme | krs-one | 3:54 | | 9 | 10 | "2nd kreditz" | none | krs-one | 0:46 | | 10 | 11 | "stop it" | krs-one, mad lion | none | 1:38 | | 11 | 12 | "problemz" | vanguard | krs-one | 3:15 | | 12 | 13 | "believe it!" | soul supreme | krs-one | 3:59 |

| | performer(s) | |---:|:---------------| | 0 | krs-one |

select nationality where result < '14.21' order by result desc limit 1 col : rank | name | nationality | result | notes row 1 : none | huang qiuyan | china | 14.39 | sb row 2 : none | anastasiya juravleva | uzbekistan | 14.21 | #name? row 3 : none | zhang hao | china | 13.63 | none row 4 : 4 | wacharee ritthiwat | thailand | 13.54 | sb row 5 : 5 | tatyana bocharova | kazakhstan | 13.5 | none row 6 : 6 | wang kuo-huei | chinese taipei | 13.25 | sb row 7 : 7 | fumiyo yoshida | japan | 13.09 | none row 8 : 8 | ngew sin mei | malaysia | 12.93 | none row 9 : 9 | manisha dey | india | 12.83 | none row 10 : 10 | thitima muangjan | thailand | 12.79 | none row 11 : 11 | phan thi thu lan | vietnam | 12.75 | none row 12 : 12 | lo yu-hsin | chinese taipei | 12.69 | none row 13 : none | elena bobrovskaya | kyrgyzstan | dns | none

col : nationality row 1 : china

select nationality where result < '14.21' order by result desc limit 1 <table_name> : table \begin{tabular}{llllll} \toprule & rank & name & nationality & result & notes \\ \midrule 0 & none & huang qiuyan & china & 14.39 & sb \\ 1 & none & anastasiya juravleva & uzbekistan & 14.21 & #name? \\ 2 & none & zhang hao & china & 13.63 & none \\ 3 & 4 & wacharee ritthiwat & thailand & 13.54 & sb \\ 4 & 5 & tatyana bocharova & kazakhstan & 13.5 & none \\ 5 & 6 & wang kuo-huei & chinese taipei & 13.25 & sb \\ 6 & 7 & fumiyo yoshida & japan & 13.09 & none \\ 7 & 8 & ngew sin mei & malaysia & 12.93 & none \\ 8 & 9 & manisha dey & india & 12.83 & none \\ 9 & 10 & thitima muangjan & thailand & 12.79 & none \\ 10 & 11 & phan thi thu lan & vietnam & 12.75 & none \\ 11 & 12 & lo yu-hsin & chinese taipei & 12.69 & none \\ 12 & none & elena bobrovskaya & kyrgyzstan & dns & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & nationality \\ \midrule 0 & china \\ \bottomrule \end{tabular}

select nationality where result < '14.21' order by result desc limit 1 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>rank</th> <th>name</th> <th>nationality</th> <th>result</th> <th>notes</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>none</td> <td>huang qiuyan</td> <td>china</td> <td>14.39</td> <td>sb</td> </tr> <tr> <th>1</th> <td>none</td> <td>anastasiya juravleva</td> <td>uzbekistan</td> <td>14.21</td> <td>#name?</td> </tr> <tr> <th>2</th> <td>none</td> <td>zhang hao</td> <td>china</td> <td>13.63</td> <td>none</td> </tr> <tr> <th>3</th> <td>4</td> <td>wacharee ritthiwat</td> <td>thailand</td> <td>13.54</td> <td>sb</td> </tr> <tr> <th>4</th> <td>5</td> <td>tatyana bocharova</td> <td>kazakhstan</td> <td>13.5</td> <td>none</td> </tr> <tr> <th>5</th> <td>6</td> <td>wang kuo-huei</td> <td>chinese taipei</td> <td>13.25</td> <td>sb</td> </tr> <tr> <th>6</th> <td>7</td> <td>fumiyo yoshida</td> <td>japan</td> <td>13.09</td> <td>none</td> </tr> <tr> <th>7</th> <td>8</td> <td>ngew sin mei</td> <td>malaysia</td> <td>12.93</td> <td>none</td> </tr> <tr> <th>8</th> <td>9</td> <td>manisha dey</td> <td>india</td> <td>12.83</td> <td>none</td> </tr> <tr> <th>9</th> <td>10</td> <td>thitima muangjan</td> <td>thailand</td> <td>12.79</td> <td>none</td> </tr> <tr> <th>10</th> <td>11</td> <td>phan thi thu lan</td> <td>vietnam</td> <td>12.75</td> <td>none</td> </tr> <tr> <th>11</th> <td>12</td> <td>lo yu-hsin</td> <td>chinese taipei</td> <td>12.69</td> <td>none</td> </tr> <tr> <th>12</th> <td>none</td> <td>elena bobrovskaya</td> <td>kyrgyzstan</td> <td>dns</td> <td>none</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>nationality</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>china</td> </tr> </tbody> </table>

select nationality where result < '14.21' order by result desc limit 1 <table_name> : table | | rank | name | nationality | result | notes | |---:|:-------|:---------------------|:---------------|:---------|:--------| | 0 | none | huang qiuyan | china | 14.39 | sb | | 1 | none | anastasiya juravleva | uzbekistan | 14.21 | #name? | | 2 | none | zhang hao | china | 13.63 | none | | 3 | 4 | wacharee ritthiwat | thailand | 13.54 | sb | | 4 | 5 | tatyana bocharova | kazakhstan | 13.5 | none | | 5 | 6 | wang kuo-huei | chinese taipei | 13.25 | sb | | 6 | 7 | fumiyo yoshida | japan | 13.09 | none | | 7 | 8 | ngew sin mei | malaysia | 12.93 | none | | 8 | 9 | manisha dey | india | 12.83 | none | | 9 | 10 | thitima muangjan | thailand | 12.79 | none | | 10 | 11 | phan thi thu lan | vietnam | 12.75 | none | | 11 | 12 | lo yu-hsin | chinese taipei | 12.69 | none | | 12 | none | elena bobrovskaya | kyrgyzstan | dns | none |

| | nationality | |---:|:--------------| | 0 | china |

select league where division = 2 col : year | division | league | regular season | playoffs | open cup | avg. attendance row 1 : 2001 | 2 | usl a-league | 4th, western | quarterfinals | did not qualify | 7169 row 2 : 2002 | 2 | usl a-league | 2nd, pacific | 1st round | did not qualify | 6260 row 3 : 2003 | 2 | usl a-league | 3rd, pacific | did not qualify | did not qualify | 5871 row 4 : 2004 | 2 | usl a-league | 1st, western | quarterfinals | 4th round | 5628 row 5 : 2005 | 2 | usl first division | 5th | quarterfinals | 4th round | 6028 row 6 : 2006 | 2 | usl first division | 11th | did not qualify | 3rd round | 5575 row 7 : 2007 | 2 | usl first division | 2nd | semifinals | 2nd round | 6851 row 8 : 2008 | 2 | usl first division | 11th | did not qualify | 1st round | 8567 row 9 : 2009 | 2 | usl first division | 1st | semifinals | 3rd round | 9734 row 10 : 2010 | 2 | ussf d-2 pro league | 3rd, usl (3rd) | quarterfinals | 3rd round | 10727

col : league row 1 : usl a-league row 2 : usl a-league row 3 : usl a-league row 4 : usl a-league row 5 : usl first division row 6 : usl first division row 7 : usl first division row 8 : usl first division row 9 : usl first division row 10 : ussf d-2 pro league

select league where division = 2 <table_name> : table \begin{tabular}{llllllll} \toprule & year & division & league & regular season & playoffs & open cup & avg. attendance \\ \midrule 0 & 2001 & 2 & usl a-league & 4th, western & quarterfinals & did not qualify & 7169 \\ 1 & 2002 & 2 & usl a-league & 2nd, pacific & 1st round & did not qualify & 6260 \\ 2 & 2003 & 2 & usl a-league & 3rd, pacific & did not qualify & did not qualify & 5871 \\ 3 & 2004 & 2 & usl a-league & 1st, western & quarterfinals & 4th round & 5628 \\ 4 & 2005 & 2 & usl first division & 5th & quarterfinals & 4th round & 6028 \\ 5 & 2006 & 2 & usl first division & 11th & did not qualify & 3rd round & 5575 \\ 6 & 2007 & 2 & usl first division & 2nd & semifinals & 2nd round & 6851 \\ 7 & 2008 & 2 & usl first division & 11th & did not qualify & 1st round & 8567 \\ 8 & 2009 & 2 & usl first division & 1st & semifinals & 3rd round & 9734 \\ 9 & 2010 & 2 & ussf d-2 pro league & 3rd, usl (3rd) & quarterfinals & 3rd round & 10727 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & league \\ \midrule 0 & usl a-league \\ 1 & usl a-league \\ 2 & usl a-league \\ 3 & usl a-league \\ 4 & usl first division \\ 5 & usl first division \\ 6 & usl first division \\ 7 & usl first division \\ 8 & usl first division \\ 9 & ussf d-2 pro league \\ \bottomrule \end{tabular}

select league where division = 2 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>year</th> <th>division</th> <th>league</th> <th>regular season</th> <th>playoffs</th> <th>open cup</th> <th>avg. attendance</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>2001</td> <td>2</td> <td>usl a-league</td> <td>4th, western</td> <td>quarterfinals</td> <td>did not qualify</td> <td>7169</td> </tr> <tr> <th>1</th> <td>2002</td> <td>2</td> <td>usl a-league</td> <td>2nd, pacific</td> <td>1st round</td> <td>did not qualify</td> <td>6260</td> </tr> <tr> <th>2</th> <td>2003</td> <td>2</td> <td>usl a-league</td> <td>3rd, pacific</td> <td>did not qualify</td> <td>did not qualify</td> <td>5871</td> </tr> <tr> <th>3</th> <td>2004</td> <td>2</td> <td>usl a-league</td> <td>1st, western</td> <td>quarterfinals</td> <td>4th round</td> <td>5628</td> </tr> <tr> <th>4</th> <td>2005</td> <td>2</td> <td>usl first division</td> <td>5th</td> <td>quarterfinals</td> <td>4th round</td> <td>6028</td> </tr> <tr> <th>5</th> <td>2006</td> <td>2</td> <td>usl first division</td> <td>11th</td> <td>did not qualify</td> <td>3rd round</td> <td>5575</td> </tr> <tr> <th>6</th> <td>2007</td> <td>2</td> <td>usl first division</td> <td>2nd</td> <td>semifinals</td> <td>2nd round</td> <td>6851</td> </tr> <tr> <th>7</th> <td>2008</td> <td>2</td> <td>usl first division</td> <td>11th</td> <td>did not qualify</td> <td>1st round</td> <td>8567</td> </tr> <tr> <th>8</th> <td>2009</td> <td>2</td> <td>usl first division</td> <td>1st</td> <td>semifinals</td> <td>3rd round</td> <td>9734</td> </tr> <tr> <th>9</th> <td>2010</td> <td>2</td> <td>ussf d-2 pro league</td> <td>3rd, usl (3rd)</td> <td>quarterfinals</td> <td>3rd round</td> <td>10727</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>league</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>usl a-league</td> </tr> <tr> <th>1</th> <td>usl a-league</td> </tr> <tr> <th>2</th> <td>usl a-league</td> </tr> <tr> <th>3</th> <td>usl a-league</td> </tr> <tr> <th>4</th> <td>usl first division</td> </tr> <tr> <th>5</th> <td>usl first division</td> </tr> <tr> <th>6</th> <td>usl first division</td> </tr> <tr> <th>7</th> <td>usl first division</td> </tr> <tr> <th>8</th> <td>usl first division</td> </tr> <tr> <th>9</th> <td>ussf d-2 pro league</td> </tr> </tbody> </table>

select league where division = 2 <table_name> : table | | year | division | league | regular season | playoffs | open cup | avg. attendance | |---:|-------:|-----------:|:--------------------|:-----------------|:----------------|:----------------|------------------:| | 0 | 2001 | 2 | usl a-league | 4th, western | quarterfinals | did not qualify | 7169 | | 1 | 2002 | 2 | usl a-league | 2nd, pacific | 1st round | did not qualify | 6260 | | 2 | 2003 | 2 | usl a-league | 3rd, pacific | did not qualify | did not qualify | 5871 | | 3 | 2004 | 2 | usl a-league | 1st, western | quarterfinals | 4th round | 5628 | | 4 | 2005 | 2 | usl first division | 5th | quarterfinals | 4th round | 6028 | | 5 | 2006 | 2 | usl first division | 11th | did not qualify | 3rd round | 5575 | | 6 | 2007 | 2 | usl first division | 2nd | semifinals | 2nd round | 6851 | | 7 | 2008 | 2 | usl first division | 11th | did not qualify | 1st round | 8567 | | 8 | 2009 | 2 | usl first division | 1st | semifinals | 3rd round | 9734 | | 9 | 2010 | 2 | ussf d-2 pro league | 3rd, usl (3rd) | quarterfinals | 3rd round | 10727 |

| | league | |---:|:--------------------| | 0 | usl a-league | | 1 | usl a-league | | 2 | usl a-league | | 3 | usl a-league | | 4 | usl first division | | 5 | usl first division | | 6 | usl first division | | 7 | usl first division | | 8 | usl first division | | 9 | ussf d-2 pro league |

select most spoken language where code = 91103 col : place | code | area (km2) | population | most spoken language row 1 : backer | 91101 | 0.34 | 1217 | northern sotho row 2 : bochum | 91102 | 11.64 | 4142 | northern sotho row 3 : dendron | 91103 | 2.98 | 1885 | northern sotho row 4 : ga-ramokgopha | 91104 | 11.22 | 15806 | northern sotho row 5 : manthata | 91105 | 12.24 | 22121 | northern sotho row 6 : moletji | 91107 | 11.66 | 4989 | northern sotho row 7 : sekgosese | 91108 | 349.99 | 46749 | northern sotho row 8 : sekhokho | 91109 | 1.24 | 1852 | northern sotho row 9 : soekmekaar | 91110 | 1.06 | 217 | northern sotho row 10 : remainder of the municipality | 91106 | 2944.04 | 10463 | northern sotho

col : most spoken language row 1 : northern sotho

select most spoken language where code = 91103 <table_name> : table \begin{tabular}{llllll} \toprule & place & code & area (km2) & population & most spoken language \\ \midrule 0 & backer & 91101 & 0.34 & 1217 & northern sotho \\ 1 & bochum & 91102 & 11.64 & 4142 & northern sotho \\ 2 & dendron & 91103 & 2.98 & 1885 & northern sotho \\ 3 & ga-ramokgopha & 91104 & 11.22 & 15806 & northern sotho \\ 4 & manthata & 91105 & 12.24 & 22121 & northern sotho \\ 5 & moletji & 91107 & 11.66 & 4989 & northern sotho \\ 6 & sekgosese & 91108 & 349.99 & 46749 & northern sotho \\ 7 & sekhokho & 91109 & 1.24 & 1852 & northern sotho \\ 8 & soekmekaar & 91110 & 1.06 & 217 & northern sotho \\ 9 & remainder of the municipality & 91106 & 2944.04 & 10463 & northern sotho \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & most spoken language \\ \midrule 0 & northern sotho \\ \bottomrule \end{tabular}

select most spoken language where code = 91103 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>place</th> <th>code</th> <th>area (km2)</th> <th>population</th> <th>most spoken language</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>backer</td> <td>91101</td> <td>0.34</td> <td>1217</td> <td>northern sotho</td> </tr> <tr> <th>1</th> <td>bochum</td> <td>91102</td> <td>11.64</td> <td>4142</td> <td>northern sotho</td> </tr> <tr> <th>2</th> <td>dendron</td> <td>91103</td> <td>2.98</td> <td>1885</td> <td>northern sotho</td> </tr> <tr> <th>3</th> <td>ga-ramokgopha</td> <td>91104</td> <td>11.22</td> <td>15806</td> <td>northern sotho</td> </tr> <tr> <th>4</th> <td>manthata</td> <td>91105</td> <td>12.24</td> <td>22121</td> <td>northern sotho</td> </tr> <tr> <th>5</th> <td>moletji</td> <td>91107</td> <td>11.66</td> <td>4989</td> <td>northern sotho</td> </tr> <tr> <th>6</th> <td>sekgosese</td> <td>91108</td> <td>349.99</td> <td>46749</td> <td>northern sotho</td> </tr> <tr> <th>7</th> <td>sekhokho</td> <td>91109</td> <td>1.24</td> <td>1852</td> <td>northern sotho</td> </tr> <tr> <th>8</th> <td>soekmekaar</td> <td>91110</td> <td>1.06</td> <td>217</td> <td>northern sotho</td> </tr> <tr> <th>9</th> <td>remainder of the municipality</td> <td>91106</td> <td>2944.04</td> <td>10463</td> <td>northern sotho</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>most spoken language</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>northern sotho</td> </tr> </tbody> </table>

select most spoken language where code = 91103 <table_name> : table | | place | code | area (km2) | population | most spoken language | |---:|:------------------------------|-------:|-------------:|-------------:|:-----------------------| | 0 | backer | 91101 | 0.34 | 1217 | northern sotho | | 1 | bochum | 91102 | 11.64 | 4142 | northern sotho | | 2 | dendron | 91103 | 2.98 | 1885 | northern sotho | | 3 | ga-ramokgopha | 91104 | 11.22 | 15806 | northern sotho | | 4 | manthata | 91105 | 12.24 | 22121 | northern sotho | | 5 | moletji | 91107 | 11.66 | 4989 | northern sotho | | 6 | sekgosese | 91108 | 349.99 | 46749 | northern sotho | | 7 | sekhokho | 91109 | 1.24 | 1852 | northern sotho | | 8 | soekmekaar | 91110 | 1.06 | 217 | northern sotho | | 9 | remainder of the municipality | 91106 | 2944.04 | 10463 | northern sotho |

| | most spoken language | |---:|:-----------------------| | 0 | northern sotho |

select count ( * ) where opponent = 'frankfurt galaxy' col : week | date | kickoff | opponent | results final score | results team record | game site | attendance row 1 : 1 | saturday, april 14 | 7:00 p.m | at rhein fire | w 15-3 | 1-0 | ltu arena | 30355 row 2 : 2 | sunday, april 22 | 4:00 p.m | hamburg sea devils | l 7-16 | 1-1 | olympic stadium | 30657 row 3 : 3 | saturday, april 28 | 6:00 p.m | amsterdam admirals | l 10-14 | 1-2 | olympic stadium | 11942 row 4 : 4 | saturday, may 5 | 6:00 p.m | at cologne centurions | w 31-28 | 2-2 | rheinenergiestadion | 10084 row 5 : 5 | sunday, may 13 | 4:00 p.m | cologne centurions | l 10-24 | 2-3 | olympic stadium | 11995 row 6 : 6 | sunday, may 20 | 5:00 p.m | at frankfurt galaxy | l 7-35 | 2-4 | commerzbank-arena | 30125 row 7 : 7 | saturday, may 26 | 6:00 p.m | at hamburg sea devils | l 7-17 | 2-5 | aol arena | 18337 row 8 : 8 | saturday, june 2 | 6:00 p.m | frankfurt galaxy | l 22-25 | 2-6 | olympic stadium | 11882 row 9 : 9 | friday, june 8 | 7:00 p.m | rhein fire | l 17-24 | 2-7 | olympic stadium | 12114 row 10 : 10 | friday, june 15 | 8:00 p.m | at amsterdam admirals | l 20-21 | 2-8 | olympic stadium | 11893

col : count ( "*" ) row 1 : 1

select count ( * ) where opponent = 'frankfurt galaxy' <table_name> : table \begin{tabular}{lllllllll} \toprule & week & date & kickoff & opponent & results final score & results team record & game site & attendance \\ \midrule 0 & 1 & saturday, april 14 & 7:00 p.m & at rhein fire & w 15-3 & 1-0 & ltu arena & 30355 \\ 1 & 2 & sunday, april 22 & 4:00 p.m & hamburg sea devils & l 7-16 & 1-1 & olympic stadium & 30657 \\ 2 & 3 & saturday, april 28 & 6:00 p.m & amsterdam admirals & l 10-14 & 1-2 & olympic stadium & 11942 \\ 3 & 4 & saturday, may 5 & 6:00 p.m & at cologne centurions & w 31-28 & 2-2 & rheinenergiestadion & 10084 \\ 4 & 5 & sunday, may 13 & 4:00 p.m & cologne centurions & l 10-24 & 2-3 & olympic stadium & 11995 \\ 5 & 6 & sunday, may 20 & 5:00 p.m & at frankfurt galaxy & l 7-35 & 2-4 & commerzbank-arena & 30125 \\ 6 & 7 & saturday, may 26 & 6:00 p.m & at hamburg sea devils & l 7-17 & 2-5 & aol arena & 18337 \\ 7 & 8 & saturday, june 2 & 6:00 p.m & frankfurt galaxy & l 22-25 & 2-6 & olympic stadium & 11882 \\ 8 & 9 & friday, june 8 & 7:00 p.m & rhein fire & l 17-24 & 2-7 & olympic stadium & 12114 \\ 9 & 10 & friday, june 15 & 8:00 p.m & at amsterdam admirals & l 20-21 & 2-8 & olympic stadium & 11893 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 1 \\ \bottomrule \end{tabular}

select count ( * ) where opponent = 'frankfurt galaxy' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>week</th> <th>date</th> <th>kickoff</th> <th>opponent</th> <th>results final score</th> <th>results team record</th> <th>game site</th> <th>attendance</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>saturday, april 14</td> <td>7:00 p.m</td> <td>at rhein fire</td> <td>w 15-3</td> <td>1-0</td> <td>ltu arena</td> <td>30355</td> </tr> <tr> <th>1</th> <td>2</td> <td>sunday, april 22</td> <td>4:00 p.m</td> <td>hamburg sea devils</td> <td>l 7-16</td> <td>1-1</td> <td>olympic stadium</td> <td>30657</td> </tr> <tr> <th>2</th> <td>3</td> <td>saturday, april 28</td> <td>6:00 p.m</td> <td>amsterdam admirals</td> <td>l 10-14</td> <td>1-2</td> <td>olympic stadium</td> <td>11942</td> </tr> <tr> <th>3</th> <td>4</td> <td>saturday, may 5</td> <td>6:00 p.m</td> <td>at cologne centurions</td> <td>w 31-28</td> <td>2-2</td> <td>rheinenergiestadion</td> <td>10084</td> </tr> <tr> <th>4</th> <td>5</td> <td>sunday, may 13</td> <td>4:00 p.m</td> <td>cologne centurions</td> <td>l 10-24</td> <td>2-3</td> <td>olympic stadium</td> <td>11995</td> </tr> <tr> <th>5</th> <td>6</td> <td>sunday, may 20</td> <td>5:00 p.m</td> <td>at frankfurt galaxy</td> <td>l 7-35</td> <td>2-4</td> <td>commerzbank-arena</td> <td>30125</td> </tr> <tr> <th>6</th> <td>7</td> <td>saturday, may 26</td> <td>6:00 p.m</td> <td>at hamburg sea devils</td> <td>l 7-17</td> <td>2-5</td> <td>aol arena</td> <td>18337</td> </tr> <tr> <th>7</th> <td>8</td> <td>saturday, june 2</td> <td>6:00 p.m</td> <td>frankfurt galaxy</td> <td>l 22-25</td> <td>2-6</td> <td>olympic stadium</td> <td>11882</td> </tr> <tr> <th>8</th> <td>9</td> <td>friday, june 8</td> <td>7:00 p.m</td> <td>rhein fire</td> <td>l 17-24</td> <td>2-7</td> <td>olympic stadium</td> <td>12114</td> </tr> <tr> <th>9</th> <td>10</td> <td>friday, june 15</td> <td>8:00 p.m</td> <td>at amsterdam admirals</td> <td>l 20-21</td> <td>2-8</td> <td>olympic stadium</td> <td>11893</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "*" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> </tr> </tbody> </table>

select count ( * ) where opponent = 'frankfurt galaxy' <table_name> : table | | week | date | kickoff | opponent | results final score | results team record | game site | attendance | |---:|-------:|:-------------------|:----------|:----------------------|:----------------------|:----------------------|:--------------------|-------------:| | 0 | 1 | saturday, april 14 | 7:00 p.m | at rhein fire | w 15-3 | 1-0 | ltu arena | 30355 | | 1 | 2 | sunday, april 22 | 4:00 p.m | hamburg sea devils | l 7-16 | 1-1 | olympic stadium | 30657 | | 2 | 3 | saturday, april 28 | 6:00 p.m | amsterdam admirals | l 10-14 | 1-2 | olympic stadium | 11942 | | 3 | 4 | saturday, may 5 | 6:00 p.m | at cologne centurions | w 31-28 | 2-2 | rheinenergiestadion | 10084 | | 4 | 5 | sunday, may 13 | 4:00 p.m | cologne centurions | l 10-24 | 2-3 | olympic stadium | 11995 | | 5 | 6 | sunday, may 20 | 5:00 p.m | at frankfurt galaxy | l 7-35 | 2-4 | commerzbank-arena | 30125 | | 6 | 7 | saturday, may 26 | 6:00 p.m | at hamburg sea devils | l 7-17 | 2-5 | aol arena | 18337 | | 7 | 8 | saturday, june 2 | 6:00 p.m | frankfurt galaxy | l 22-25 | 2-6 | olympic stadium | 11882 | | 8 | 9 | friday, june 8 | 7:00 p.m | rhein fire | l 17-24 | 2-7 | olympic stadium | 12114 | | 9 | 10 | friday, june 15 | 8:00 p.m | at amsterdam admirals | l 20-21 | 2-8 | olympic stadium | 11893 |

| | count ( "*" ) | |---:|----------------:| | 0 | 1 |

select margin where venue = 'st lawrence ground, canterbury' col : margin | teams | venue | season row 1 : 192 runs | kent (205 & 512-9 d) beat essex (370 & 155) | county ground, chelmsford | 2009 row 2 : 72 runs | kent (117 & 332) beat surrey (301-8 d & 76) | woodbridge road, guildford | 1992 row 3 : 57 runs | kent (151 & 348) beat middlesex (305-5 d & 137) | hesketh park, dartford | 1976 row 4 : 36 runs | kent (127 & 198) beat australia (229 & 60) | st lawrence ground, canterbury | 1893 row 5 : 24 runs | kent (115 & 389) beat gloucestershire (351 & 129) | mote park, maidstone | 1925 row 6 : 16 runs | kent (120 & 176) beat cambridgeshire (206 & 74) | mote park, maidstone | 1863

col : margin row 1 : 36 runs

select margin where venue = 'st lawrence ground, canterbury' <table_name> : table \begin{tabular}{lllll} \toprule & margin & teams & venue & season \\ \midrule 0 & 192 runs & kent (205 & 512-9 d) beat essex (370 & 155) & county ground, chelmsford & 2009 \\ 1 & 72 runs & kent (117 & 332) beat surrey (301-8 d & 76) & woodbridge road, guildford & 1992 \\ 2 & 57 runs & kent (151 & 348) beat middlesex (305-5 d & 137) & hesketh park, dartford & 1976 \\ 3 & 36 runs & kent (127 & 198) beat australia (229 & 60) & st lawrence ground, canterbury & 1893 \\ 4 & 24 runs & kent (115 & 389) beat gloucestershire (351 & 129) & mote park, maidstone & 1925 \\ 5 & 16 runs & kent (120 & 176) beat cambridgeshire (206 & 74) & mote park, maidstone & 1863 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & margin \\ \midrule 0 & 36 runs \\ \bottomrule \end{tabular}

select margin where venue = 'st lawrence ground, canterbury' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>margin</th> <th>teams</th> <th>venue</th> <th>season</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>192 runs</td> <td>kent (205 &amp; 512-9 d) beat essex (370 &amp; 155)</td> <td>county ground, chelmsford</td> <td>2009</td> </tr> <tr> <th>1</th> <td>72 runs</td> <td>kent (117 &amp; 332) beat surrey (301-8 d &amp; 76)</td> <td>woodbridge road, guildford</td> <td>1992</td> </tr> <tr> <th>2</th> <td>57 runs</td> <td>kent (151 &amp; 348) beat middlesex (305-5 d &amp; 137)</td> <td>hesketh park, dartford</td> <td>1976</td> </tr> <tr> <th>3</th> <td>36 runs</td> <td>kent (127 &amp; 198) beat australia (229 &amp; 60)</td> <td>st lawrence ground, canterbury</td> <td>1893</td> </tr> <tr> <th>4</th> <td>24 runs</td> <td>kent (115 &amp; 389) beat gloucestershire (351 &amp; 129)</td> <td>mote park, maidstone</td> <td>1925</td> </tr> <tr> <th>5</th> <td>16 runs</td> <td>kent (120 &amp; 176) beat cambridgeshire (206 &amp; 74)</td> <td>mote park, maidstone</td> <td>1863</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>margin</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>36 runs</td> </tr> </tbody> </table>

select margin where venue = 'st lawrence ground, canterbury' <table_name> : table | | margin | teams | venue | season | |---:|:---------|:--------------------------------------------------|:-------------------------------|---------:| | 0 | 192 runs | kent (205 & 512-9 d) beat essex (370 & 155) | county ground, chelmsford | 2009 | | 1 | 72 runs | kent (117 & 332) beat surrey (301-8 d & 76) | woodbridge road, guildford | 1992 | | 2 | 57 runs | kent (151 & 348) beat middlesex (305-5 d & 137) | hesketh park, dartford | 1976 | | 3 | 36 runs | kent (127 & 198) beat australia (229 & 60) | st lawrence ground, canterbury | 1893 | | 4 | 24 runs | kent (115 & 389) beat gloucestershire (351 & 129) | mote park, maidstone | 1925 | | 5 | 16 runs | kent (120 & 176) beat cambridgeshire (206 & 74) | mote park, maidstone | 1863 |

| | margin | |---:|:---------| | 0 | 36 runs |

select regular season finish where team = 'sea' col : team | year | regular season won | regular season lost | regular season win % | regular season finish | postseason won | postseason lost | postseason win % | postseason result row 1 : cle | 1991 | 32 | 53 | 0.376 | 7th in al east | none | none | none | none row 2 : cle | 1992 | 76 | 86 | 0.469 | 5th in al east | none | none | none | none row 3 : cle | 1993 | 76 | 86 | 0.469 | 6th in al east | none | none | none | none row 4 : cle | 1994 | 66 | 47 | 0.584 | 2nd in al central | none | none | none | no mlb postseason row 5 : cle | 1995 | 100 | 44 | 0.694 | 1st in al central | 9 | 6 | 0.6 | lost ws to atl row 6 : cle | 1996 | 99 | 62 | 0.615 | 1st in al central | 1 | 3 | 0.25 | lost lds to bal row 7 : cle | 1997 | 86 | 75 | 0.534 | 1st in al central | 10 | 8 | 0.556 | lost ws to fla row 8 : cle | 1998 | 89 | 73 | 0.549 | 1st in al central | 5 | 5 | 0.5 | lost lcs to nyy row 9 : cle | 1999 | 97 | 65 | 0.599 | 1st in al central | 2 | 3 | 0.4 | lost lds to bos row 10 : bal | 2000 | 74 | 88 | 0.457 | 4th in al east | none | none | none | none row 11 : bal | 2001 | 63 | 98 | 0.391 | 4th in al east | none | none | none | none row 12 : bal | 2002 | 67 | 95 | 0.414 | 4th in al east | none | none | none | none row 13 : bal | 2003 | 71 | 91 | 0.438 | 4th in al east | none | none | none | none row 14 : sea | 2005 | 69 | 93 | 0.426 | 4th in al west | none | none | none | none row 15 : sea | 2006 | 78 | 84 | 0.481 | 4th in al west | none | none | none | none row 16 : sea | 2007 | 45 | 33 | 0.571 | 2nd in al west | none | none | none | none row 17 : cle total | cle total | 721 | 591 | 0.55 | none | 27 | 25 | 0.519 | none row 18 : bal total | bal total | 275 | 372 | 0.425 | none | 0 | 0 | 0 | none row 19 : sea total | sea total | 192 | 210 | 0.478 | none | 0 | 0 | 0 | none row 20 : total | total | 1188 | 1173 | 0.503 | none | 27 | 25 | 0.519 | none

col : regular season finish row 1 : 4th in al west row 2 : 4th in al west row 3 : 2nd in al west

select regular season finish where team = 'sea' <table_name> : table \begin{tabular}{lllllllllll} \toprule & team & year & regular season won & regular season lost & regular season win % & regular season finish & postseason won & postseason lost & postseason win % & postseason result \\ \midrule 0 & cle & 1991 & 32 & 53 & 0.376 & 7th in al east & none & none & none & none \\ 1 & cle & 1992 & 76 & 86 & 0.469 & 5th in al east & none & none & none & none \\ 2 & cle & 1993 & 76 & 86 & 0.469 & 6th in al east & none & none & none & none \\ 3 & cle & 1994 & 66 & 47 & 0.584 & 2nd in al central & none & none & none & no mlb postseason \\ 4 & cle & 1995 & 100 & 44 & 0.694 & 1st in al central & 9 & 6 & 0.6 & lost ws to atl \\ 5 & cle & 1996 & 99 & 62 & 0.615 & 1st in al central & 1 & 3 & 0.25 & lost lds to bal \\ 6 & cle & 1997 & 86 & 75 & 0.534 & 1st in al central & 10 & 8 & 0.556 & lost ws to fla \\ 7 & cle & 1998 & 89 & 73 & 0.549 & 1st in al central & 5 & 5 & 0.5 & lost lcs to nyy \\ 8 & cle & 1999 & 97 & 65 & 0.599 & 1st in al central & 2 & 3 & 0.4 & lost lds to bos \\ 9 & bal & 2000 & 74 & 88 & 0.457 & 4th in al east & none & none & none & none \\ 10 & bal & 2001 & 63 & 98 & 0.391 & 4th in al east & none & none & none & none \\ 11 & bal & 2002 & 67 & 95 & 0.414 & 4th in al east & none & none & none & none \\ 12 & bal & 2003 & 71 & 91 & 0.438 & 4th in al east & none & none & none & none \\ 13 & sea & 2005 & 69 & 93 & 0.426 & 4th in al west & none & none & none & none \\ 14 & sea & 2006 & 78 & 84 & 0.481 & 4th in al west & none & none & none & none \\ 15 & sea & 2007 & 45 & 33 & 0.571 & 2nd in al west & none & none & none & none \\ 16 & cle total & cle total & 721 & 591 & 0.55 & none & 27 & 25 & 0.519 & none \\ 17 & bal total & bal total & 275 & 372 & 0.425 & none & 0 & 0 & 0 & none \\ 18 & sea total & sea total & 192 & 210 & 0.478 & none & 0 & 0 & 0 & none \\ 19 & total & total & 1188 & 1173 & 0.503 & none & 27 & 25 & 0.519 & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & regular season finish \\ \midrule 0 & 4th in al west \\ 1 & 4th in al west \\ 2 & 2nd in al west \\ \bottomrule \end{tabular}

select regular season finish where team = 'sea' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>team</th> <th>year</th> <th>regular season won</th> <th>regular season lost</th> <th>regular season win %</th> <th>regular season finish</th> <th>postseason won</th> <th>postseason lost</th> <th>postseason win %</th> <th>postseason result</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>cle</td> <td>1991</td> <td>32</td> <td>53</td> <td>0.376</td> <td>7th in al east</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>1</th> <td>cle</td> <td>1992</td> <td>76</td> <td>86</td> <td>0.469</td> <td>5th in al east</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>2</th> <td>cle</td> <td>1993</td> <td>76</td> <td>86</td> <td>0.469</td> <td>6th in al east</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>3</th> <td>cle</td> <td>1994</td> <td>66</td> <td>47</td> <td>0.584</td> <td>2nd in al central</td> <td>none</td> <td>none</td> <td>none</td> <td>no mlb postseason</td> </tr> <tr> <th>4</th> <td>cle</td> <td>1995</td> <td>100</td> <td>44</td> <td>0.694</td> <td>1st in al central</td> <td>9</td> <td>6</td> <td>0.6</td> <td>lost ws to atl</td> </tr> <tr> <th>5</th> <td>cle</td> <td>1996</td> <td>99</td> <td>62</td> <td>0.615</td> <td>1st in al central</td> <td>1</td> <td>3</td> <td>0.25</td> <td>lost lds to bal</td> </tr> <tr> <th>6</th> <td>cle</td> <td>1997</td> <td>86</td> <td>75</td> <td>0.534</td> <td>1st in al central</td> <td>10</td> <td>8</td> <td>0.556</td> <td>lost ws to fla</td> </tr> <tr> <th>7</th> <td>cle</td> <td>1998</td> <td>89</td> <td>73</td> <td>0.549</td> <td>1st in al central</td> <td>5</td> <td>5</td> <td>0.5</td> <td>lost lcs to nyy</td> </tr> <tr> <th>8</th> <td>cle</td> <td>1999</td> <td>97</td> <td>65</td> <td>0.599</td> <td>1st in al central</td> <td>2</td> <td>3</td> <td>0.4</td> <td>lost lds to bos</td> </tr> <tr> <th>9</th> <td>bal</td> <td>2000</td> <td>74</td> <td>88</td> <td>0.457</td> <td>4th in al east</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>10</th> <td>bal</td> <td>2001</td> <td>63</td> <td>98</td> <td>0.391</td> <td>4th in al east</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>11</th> <td>bal</td> <td>2002</td> <td>67</td> <td>95</td> <td>0.414</td> <td>4th in al east</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>12</th> <td>bal</td> <td>2003</td> <td>71</td> <td>91</td> <td>0.438</td> <td>4th in al east</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>13</th> <td>sea</td> <td>2005</td> <td>69</td> <td>93</td> <td>0.426</td> <td>4th in al west</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>14</th> <td>sea</td> <td>2006</td> <td>78</td> <td>84</td> <td>0.481</td> <td>4th in al west</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>15</th> <td>sea</td> <td>2007</td> <td>45</td> <td>33</td> <td>0.571</td> <td>2nd in al west</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>16</th> <td>cle total</td> <td>cle total</td> <td>721</td> <td>591</td> <td>0.55</td> <td>none</td> <td>27</td> <td>25</td> <td>0.519</td> <td>none</td> </tr> <tr> <th>17</th> <td>bal total</td> <td>bal total</td> <td>275</td> <td>372</td> <td>0.425</td> <td>none</td> <td>0</td> <td>0</td> <td>0</td> <td>none</td> </tr> <tr> <th>18</th> <td>sea total</td> <td>sea total</td> <td>192</td> <td>210</td> <td>0.478</td> <td>none</td> <td>0</td> <td>0</td> <td>0</td> <td>none</td> </tr> <tr> <th>19</th> <td>total</td> <td>total</td> <td>1188</td> <td>1173</td> <td>0.503</td> <td>none</td> <td>27</td> <td>25</td> <td>0.519</td> <td>none</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>regular season finish</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>4th in al west</td> </tr> <tr> <th>1</th> <td>4th in al west</td> </tr> <tr> <th>2</th> <td>2nd in al west</td> </tr> </tbody> </table>

select regular season finish where team = 'sea' <table_name> : table | | team | year | regular season won | regular season lost | regular season win % | regular season finish | postseason won | postseason lost | postseason win % | postseason result | |---:|:----------|:----------|---------------------:|----------------------:|-----------------------:|:------------------------|:-----------------|:------------------|:-------------------|:--------------------| | 0 | cle | 1991 | 32 | 53 | 0.376 | 7th in al east | none | none | none | none | | 1 | cle | 1992 | 76 | 86 | 0.469 | 5th in al east | none | none | none | none | | 2 | cle | 1993 | 76 | 86 | 0.469 | 6th in al east | none | none | none | none | | 3 | cle | 1994 | 66 | 47 | 0.584 | 2nd in al central | none | none | none | no mlb postseason | | 4 | cle | 1995 | 100 | 44 | 0.694 | 1st in al central | 9 | 6 | 0.6 | lost ws to atl | | 5 | cle | 1996 | 99 | 62 | 0.615 | 1st in al central | 1 | 3 | 0.25 | lost lds to bal | | 6 | cle | 1997 | 86 | 75 | 0.534 | 1st in al central | 10 | 8 | 0.556 | lost ws to fla | | 7 | cle | 1998 | 89 | 73 | 0.549 | 1st in al central | 5 | 5 | 0.5 | lost lcs to nyy | | 8 | cle | 1999 | 97 | 65 | 0.599 | 1st in al central | 2 | 3 | 0.4 | lost lds to bos | | 9 | bal | 2000 | 74 | 88 | 0.457 | 4th in al east | none | none | none | none | | 10 | bal | 2001 | 63 | 98 | 0.391 | 4th in al east | none | none | none | none | | 11 | bal | 2002 | 67 | 95 | 0.414 | 4th in al east | none | none | none | none | | 12 | bal | 2003 | 71 | 91 | 0.438 | 4th in al east | none | none | none | none | | 13 | sea | 2005 | 69 | 93 | 0.426 | 4th in al west | none | none | none | none | | 14 | sea | 2006 | 78 | 84 | 0.481 | 4th in al west | none | none | none | none | | 15 | sea | 2007 | 45 | 33 | 0.571 | 2nd in al west | none | none | none | none | | 16 | cle total | cle total | 721 | 591 | 0.55 | none | 27 | 25 | 0.519 | none | | 17 | bal total | bal total | 275 | 372 | 0.425 | none | 0 | 0 | 0 | none | | 18 | sea total | sea total | 192 | 210 | 0.478 | none | 0 | 0 | 0 | none | | 19 | total | total | 1188 | 1173 | 0.503 | none | 27 | 25 | 0.519 | none |

| | regular season finish | |---:|:------------------------| | 0 | 4th in al west | | 1 | 4th in al west | | 2 | 2nd in al west |

select count ( locality ) where closed = 0 col : parish | locality | parish priest | founded | closed row 1 : st mary | bacup | fr frank thorpe | 1852 | none row 2 : our immaculate mother & st anselm | whitworth | fr frank thorpe | 1860 | none row 3 : st joseph | stacksteads | none | 1947 | 2005 row 4 : st joseph & st peter | newchurch-in-rossendale | fr philip boast | 1915 | none row 5 : the immaculate conception | haslingden | fr canon john mackie | 1854 | none row 6 : st veronica (chapel of ease) | helmshore | served from the immaculate conception | 1959 | none row 7 : st james the less | rawtenstall | fr david lupton, rural dean | 1828 | none

col : count ( "locality" ) row 1 : 0

select count ( locality ) where closed = 0 <table_name> : table \begin{tabular}{llllll} \toprule & parish & locality & parish priest & founded & closed \\ \midrule 0 & st mary & bacup & fr frank thorpe & 1852 & none \\ 1 & our immaculate mother & st anselm & whitworth & fr frank thorpe & 1860 & none \\ 2 & st joseph & stacksteads & none & 1947 & 2005 \\ 3 & st joseph & st peter & newchurch-in-rossendale & fr philip boast & 1915 & none \\ 4 & the immaculate conception & haslingden & fr canon john mackie & 1854 & none \\ 5 & st veronica (chapel of ease) & helmshore & served from the immaculate conception & 1959 & none \\ 6 & st james the less & rawtenstall & fr david lupton, rural dean & 1828 & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "locality" ) \\ \midrule 0 & 0 \\ \bottomrule \end{tabular}

select count ( locality ) where closed = 0 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>parish</th> <th>locality</th> <th>parish priest</th> <th>founded</th> <th>closed</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>st mary</td> <td>bacup</td> <td>fr frank thorpe</td> <td>1852</td> <td>none</td> </tr> <tr> <th>1</th> <td>our immaculate mother &amp; st anselm</td> <td>whitworth</td> <td>fr frank thorpe</td> <td>1860</td> <td>none</td> </tr> <tr> <th>2</th> <td>st joseph</td> <td>stacksteads</td> <td>none</td> <td>1947</td> <td>2005</td> </tr> <tr> <th>3</th> <td>st joseph &amp; st peter</td> <td>newchurch-in-rossendale</td> <td>fr philip boast</td> <td>1915</td> <td>none</td> </tr> <tr> <th>4</th> <td>the immaculate conception</td> <td>haslingden</td> <td>fr canon john mackie</td> <td>1854</td> <td>none</td> </tr> <tr> <th>5</th> <td>st veronica (chapel of ease)</td> <td>helmshore</td> <td>served from the immaculate conception</td> <td>1959</td> <td>none</td> </tr> <tr> <th>6</th> <td>st james the less</td> <td>rawtenstall</td> <td>fr david lupton, rural dean</td> <td>1828</td> <td>none</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "locality" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>0</td> </tr> </tbody> </table>

select count ( locality ) where closed = 0 <table_name> : table | | parish | locality | parish priest | founded | closed | |---:|:----------------------------------|:------------------------|:--------------------------------------|----------:|:---------| | 0 | st mary | bacup | fr frank thorpe | 1852 | none | | 1 | our immaculate mother & st anselm | whitworth | fr frank thorpe | 1860 | none | | 2 | st joseph | stacksteads | none | 1947 | 2005 | | 3 | st joseph & st peter | newchurch-in-rossendale | fr philip boast | 1915 | none | | 4 | the immaculate conception | haslingden | fr canon john mackie | 1854 | none | | 5 | st veronica (chapel of ease) | helmshore | served from the immaculate conception | 1959 | none | | 6 | st james the less | rawtenstall | fr david lupton, rural dean | 1828 | none |

| | count ( "locality" ) | |---:|-----------------------:| | 0 | 0 |

select count ( 2001 ) where census year = '1.4%' col : census year -1 | census year -2 | 2001 | 2006 | 2011 row 1 : population | estimated residents on census night | 58,319 | 63,605 | 67,356 row 2 : population | lga rank in terms of size within new south wales | none | 19 | none row 3 : population | % of new south wales population | none | none | 0.97% row 4 : population | % of australian population | 0.31% | 0.32% | 0.31% row 5 : cultural and language diversity | cultural and language diversity | none | none | none row 6 : ancestry, top responses | english | none | none | 20.5% row 7 : ancestry, top responses | australian | none | none | 18.3% row 8 : ancestry, top responses | chinese | none | none | 14.5% row 9 : ancestry, top responses | irish | none | none | 7.4% row 10 : ancestry, top responses | scottish | none | none | 5.6% row 11 : language, top responses (other than english) | cantonese | 6.7% | 7.6% | 7.5% row 12 : language, top responses (other than english) | mandarin | 3.6% | 5.5% | 7.2% row 13 : language, top responses (other than english) | korean | 1.7% | 2.7% | 3.6% row 14 : language, top responses (other than english) | japanese | 2.4% | 2.4% | 2.3% row 15 : language, top responses (other than english) | armenian | n/c | 1.4% | 1.3% row 16 : religious affiliation | religious affiliation | none | none | none row 17 : religious affiliation, top responses | no religion | 18.6% | 22.0% | 27.6% row 18 : religious affiliation, top responses | catholic | 26.1% | 25.5% | 25.0% row 19 : religious affiliation, top responses | anglican | 19.5% | 17.6% | 15.6% row 20 : religious affiliation, top responses | buddhism | 3.7% | 4.4% | 4.6% row 21 : religious affiliation, top responses | presbyterian and reformed | n/c | n/c | 3.1% row 22 : median weekly incomes | median weekly incomes | none | none | none row 23 : personal income | median weekly personal income | none | a$728 | a$858 row 24 : personal income | % of australian median income | none | 156.2% | 148.7% row 25 : family income | median weekly family income | none | a$1,667 | a$2,479 row 26 : family income | % of australian median income | none | 162.3% | 167.4% row 27 : household income | median weekly household income | none | a$2,066 | a$1,996 row 28 : household income | % of australian median income | none | 176.4% | 161.8%

col : count ( "2001" ) row 1 : 0

select count ( 2001 ) where census year = '1.4%' <table_name> : table \begin{tabular}{llllll} \toprule & census year -1 & census year -2 & 2001 & 2006 & 2011 \\ \midrule 0 & population & estimated residents on census night & 58,319 & 63,605 & 67,356 \\ 1 & population & lga rank in terms of size within new south wales & none & 19 & none \\ 2 & population & % of new south wales population & none & none & 0.97% \\ 3 & population & % of australian population & 0.31% & 0.32% & 0.31% \\ 4 & cultural and language diversity & cultural and language diversity & none & none & none \\ 5 & ancestry, top responses & english & none & none & 20.5% \\ 6 & ancestry, top responses & australian & none & none & 18.3% \\ 7 & ancestry, top responses & chinese & none & none & 14.5% \\ 8 & ancestry, top responses & irish & none & none & 7.4% \\ 9 & ancestry, top responses & scottish & none & none & 5.6% \\ 10 & language, top responses (other than english) & cantonese & 6.7% & 7.6% & 7.5% \\ 11 & language, top responses (other than english) & mandarin & 3.6% & 5.5% & 7.2% \\ 12 & language, top responses (other than english) & korean & 1.7% & 2.7% & 3.6% \\ 13 & language, top responses (other than english) & japanese & 2.4% & 2.4% & 2.3% \\ 14 & language, top responses (other than english) & armenian & n/c & 1.4% & 1.3% \\ 15 & religious affiliation & religious affiliation & none & none & none \\ 16 & religious affiliation, top responses & no religion & 18.6% & 22.0% & 27.6% \\ 17 & religious affiliation, top responses & catholic & 26.1% & 25.5% & 25.0% \\ 18 & religious affiliation, top responses & anglican & 19.5% & 17.6% & 15.6% \\ 19 & religious affiliation, top responses & buddhism & 3.7% & 4.4% & 4.6% \\ 20 & religious affiliation, top responses & presbyterian and reformed & n/c & n/c & 3.1% \\ 21 & median weekly incomes & median weekly incomes & none & none & none \\ 22 & personal income & median weekly personal income & none & a$728 & a$858 \\ 23 & personal income & % of australian median income & none & 156.2% & 148.7% \\ 24 & family income & median weekly family income & none & a$1,667 & a$2,479 \\ 25 & family income & % of australian median income & none & 162.3% & 167.4% \\ 26 & household income & median weekly household income & none & a$2,066 & a$1,996 \\ 27 & household income & % of australian median income & none & 176.4% & 161.8% \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "2001" ) \\ \midrule 0 & 0 \\ \bottomrule \end{tabular}

select count ( 2001 ) where census year = '1.4%' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>census year -1</th> <th>census year -2</th> <th>2001</th> <th>2006</th> <th>2011</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>population</td> <td>estimated residents on census night</td> <td>58,319</td> <td>63,605</td> <td>67,356</td> </tr> <tr> <th>1</th> <td>population</td> <td>lga rank in terms of size within new south wales</td> <td>none</td> <td>19</td> <td>none</td> </tr> <tr> <th>2</th> <td>population</td> <td>% of new south wales population</td> <td>none</td> <td>none</td> <td>0.97%</td> </tr> <tr> <th>3</th> <td>population</td> <td>% of australian population</td> <td>0.31%</td> <td>0.32%</td> <td>0.31%</td> </tr> <tr> <th>4</th> <td>cultural and language diversity</td> <td>cultural and language diversity</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>5</th> <td>ancestry, top responses</td> <td>english</td> <td>none</td> <td>none</td> <td>20.5%</td> </tr> <tr> <th>6</th> <td>ancestry, top responses</td> <td>australian</td> <td>none</td> <td>none</td> <td>18.3%</td> </tr> <tr> <th>7</th> <td>ancestry, top responses</td> <td>chinese</td> <td>none</td> <td>none</td> <td>14.5%</td> </tr> <tr> <th>8</th> <td>ancestry, top responses</td> <td>irish</td> <td>none</td> <td>none</td> <td>7.4%</td> </tr> <tr> <th>9</th> <td>ancestry, top responses</td> <td>scottish</td> <td>none</td> <td>none</td> <td>5.6%</td> </tr> <tr> <th>10</th> <td>language, top responses (other than english)</td> <td>cantonese</td> <td>6.7%</td> <td>7.6%</td> <td>7.5%</td> </tr> <tr> <th>11</th> <td>language, top responses (other than english)</td> <td>mandarin</td> <td>3.6%</td> <td>5.5%</td> <td>7.2%</td> </tr> <tr> <th>12</th> <td>language, top responses (other than english)</td> <td>korean</td> <td>1.7%</td> <td>2.7%</td> <td>3.6%</td> </tr> <tr> <th>13</th> <td>language, top responses (other than english)</td> <td>japanese</td> <td>2.4%</td> <td>2.4%</td> <td>2.3%</td> </tr> <tr> <th>14</th> <td>language, top responses (other than english)</td> <td>armenian</td> <td>n/c</td> <td>1.4%</td> <td>1.3%</td> </tr> <tr> <th>15</th> <td>religious affiliation</td> <td>religious affiliation</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>16</th> <td>religious affiliation, top responses</td> <td>no religion</td> <td>18.6%</td> <td>22.0%</td> <td>27.6%</td> </tr> <tr> <th>17</th> <td>religious affiliation, top responses</td> <td>catholic</td> <td>26.1%</td> <td>25.5%</td> <td>25.0%</td> </tr> <tr> <th>18</th> <td>religious affiliation, top responses</td> <td>anglican</td> <td>19.5%</td> <td>17.6%</td> <td>15.6%</td> </tr> <tr> <th>19</th> <td>religious affiliation, top responses</td> <td>buddhism</td> <td>3.7%</td> <td>4.4%</td> <td>4.6%</td> </tr> <tr> <th>20</th> <td>religious affiliation, top responses</td> <td>presbyterian and reformed</td> <td>n/c</td> <td>n/c</td> <td>3.1%</td> </tr> <tr> <th>21</th> <td>median weekly incomes</td> <td>median weekly incomes</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>22</th> <td>personal income</td> <td>median weekly personal income</td> <td>none</td> <td>a$728</td> <td>a$858</td> </tr> <tr> <th>23</th> <td>personal income</td> <td>% of australian median income</td> <td>none</td> <td>156.2%</td> <td>148.7%</td> </tr> <tr> <th>24</th> <td>family income</td> <td>median weekly family income</td> <td>none</td> <td>a$1,667</td> <td>a$2,479</td> </tr> <tr> <th>25</th> <td>family income</td> <td>% of australian median income</td> <td>none</td> <td>162.3%</td> <td>167.4%</td> </tr> <tr> <th>26</th> <td>household income</td> <td>median weekly household income</td> <td>none</td> <td>a$2,066</td> <td>a$1,996</td> </tr> <tr> <th>27</th> <td>household income</td> <td>% of australian median income</td> <td>none</td> <td>176.4%</td> <td>161.8%</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "2001" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>0</td> </tr> </tbody> </table>

select count ( 2001 ) where census year = '1.4%' <table_name> : table | | census year -1 | census year -2 | 2001 | 2006 | 2011 | |---:|:---------------------------------------------|:-------------------------------------------------|:-------|:--------|:--------| | 0 | population | estimated residents on census night | 58,319 | 63,605 | 67,356 | | 1 | population | lga rank in terms of size within new south wales | none | 19 | none | | 2 | population | % of new south wales population | none | none | 0.97% | | 3 | population | % of australian population | 0.31% | 0.32% | 0.31% | | 4 | cultural and language diversity | cultural and language diversity | none | none | none | | 5 | ancestry, top responses | english | none | none | 20.5% | | 6 | ancestry, top responses | australian | none | none | 18.3% | | 7 | ancestry, top responses | chinese | none | none | 14.5% | | 8 | ancestry, top responses | irish | none | none | 7.4% | | 9 | ancestry, top responses | scottish | none | none | 5.6% | | 10 | language, top responses (other than english) | cantonese | 6.7% | 7.6% | 7.5% | | 11 | language, top responses (other than english) | mandarin | 3.6% | 5.5% | 7.2% | | 12 | language, top responses (other than english) | korean | 1.7% | 2.7% | 3.6% | | 13 | language, top responses (other than english) | japanese | 2.4% | 2.4% | 2.3% | | 14 | language, top responses (other than english) | armenian | n/c | 1.4% | 1.3% | | 15 | religious affiliation | religious affiliation | none | none | none | | 16 | religious affiliation, top responses | no religion | 18.6% | 22.0% | 27.6% | | 17 | religious affiliation, top responses | catholic | 26.1% | 25.5% | 25.0% | | 18 | religious affiliation, top responses | anglican | 19.5% | 17.6% | 15.6% | | 19 | religious affiliation, top responses | buddhism | 3.7% | 4.4% | 4.6% | | 20 | religious affiliation, top responses | presbyterian and reformed | n/c | n/c | 3.1% | | 21 | median weekly incomes | median weekly incomes | none | none | none | | 22 | personal income | median weekly personal income | none | a$728 | a$858 | | 23 | personal income | % of australian median income | none | 156.2% | 148.7% | | 24 | family income | median weekly family income | none | a$1,667 | a$2,479 | | 25 | family income | % of australian median income | none | 162.3% | 167.4% | | 26 | household income | median weekly household income | none | a$2,066 | a$1,996 | | 27 | household income | % of australian median income | none | 176.4% | 161.8% |

| | count ( "2001" ) | |---:|-------------------:| | 0 | 0 |

select club where league = 'rfpl' col : season | club | league | apps | goals row 1 : 2003-04 | podgrmec | fbih first league | 23 | 16 row 2 : 2004-05 | nk zagreb | prva hnl | 26 | 3 row 3 : 2005-06 | nk zagreb | prva hnl | 25 | 3 row 4 : 2006-07 | nk zagreb | prva hnl | 17 | 4 row 5 : 2007-08 | nk zagreb | prva hnl | 31 | 12 row 6 : 2008-09 | hajduk split | prva hnl | 30 | 12 row 7 : 2009-10 | hajduk split | prva hnl | 29 | 17 row 8 : 2010-11 | hajduk split | prva hnl | 17 | 6 row 9 : 2011-12 | lokomotiv | rfpl | 28 | 4 row 10 : 2012-13 | gaziantepspor | super lig | 1 | 1

col : club row 1 : lokomotiv

select club where league = 'rfpl' <table_name> : table \begin{tabular}{llllll} \toprule & season & club & league & apps & goals \\ \midrule 0 & 2003-04 & podgrmec & fbih first league & 23 & 16 \\ 1 & 2004-05 & nk zagreb & prva hnl & 26 & 3 \\ 2 & 2005-06 & nk zagreb & prva hnl & 25 & 3 \\ 3 & 2006-07 & nk zagreb & prva hnl & 17 & 4 \\ 4 & 2007-08 & nk zagreb & prva hnl & 31 & 12 \\ 5 & 2008-09 & hajduk split & prva hnl & 30 & 12 \\ 6 & 2009-10 & hajduk split & prva hnl & 29 & 17 \\ 7 & 2010-11 & hajduk split & prva hnl & 17 & 6 \\ 8 & 2011-12 & lokomotiv & rfpl & 28 & 4 \\ 9 & 2012-13 & gaziantepspor & super lig & 1 & 1 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & club \\ \midrule 0 & lokomotiv \\ \bottomrule \end{tabular}

select club where league = 'rfpl' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>season</th> <th>club</th> <th>league</th> <th>apps</th> <th>goals</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>2003-04</td> <td>podgrmec</td> <td>fbih first league</td> <td>23</td> <td>16</td> </tr> <tr> <th>1</th> <td>2004-05</td> <td>nk zagreb</td> <td>prva hnl</td> <td>26</td> <td>3</td> </tr> <tr> <th>2</th> <td>2005-06</td> <td>nk zagreb</td> <td>prva hnl</td> <td>25</td> <td>3</td> </tr> <tr> <th>3</th> <td>2006-07</td> <td>nk zagreb</td> <td>prva hnl</td> <td>17</td> <td>4</td> </tr> <tr> <th>4</th> <td>2007-08</td> <td>nk zagreb</td> <td>prva hnl</td> <td>31</td> <td>12</td> </tr> <tr> <th>5</th> <td>2008-09</td> <td>hajduk split</td> <td>prva hnl</td> <td>30</td> <td>12</td> </tr> <tr> <th>6</th> <td>2009-10</td> <td>hajduk split</td> <td>prva hnl</td> <td>29</td> <td>17</td> </tr> <tr> <th>7</th> <td>2010-11</td> <td>hajduk split</td> <td>prva hnl</td> <td>17</td> <td>6</td> </tr> <tr> <th>8</th> <td>2011-12</td> <td>lokomotiv</td> <td>rfpl</td> <td>28</td> <td>4</td> </tr> <tr> <th>9</th> <td>2012-13</td> <td>gaziantepspor</td> <td>super lig</td> <td>1</td> <td>1</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>club</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>lokomotiv</td> </tr> </tbody> </table>

select club where league = 'rfpl' <table_name> : table | | season | club | league | apps | goals | |---:|:---------|:--------------|:------------------|-------:|--------:| | 0 | 2003-04 | podgrmec | fbih first league | 23 | 16 | | 1 | 2004-05 | nk zagreb | prva hnl | 26 | 3 | | 2 | 2005-06 | nk zagreb | prva hnl | 25 | 3 | | 3 | 2006-07 | nk zagreb | prva hnl | 17 | 4 | | 4 | 2007-08 | nk zagreb | prva hnl | 31 | 12 | | 5 | 2008-09 | hajduk split | prva hnl | 30 | 12 | | 6 | 2009-10 | hajduk split | prva hnl | 29 | 17 | | 7 | 2010-11 | hajduk split | prva hnl | 17 | 6 | | 8 | 2011-12 | lokomotiv | rfpl | 28 | 4 | | 9 | 2012-13 | gaziantepspor | super lig | 1 | 1 |

| | club | |---:|:----------| | 0 | lokomotiv |

select count ( * ) where film = 'cinderella' col : # | title | performer(s) | film | length row 1 : 1 | "once upon a dream" | emily osment | sleeping beauty | 3:32 row 2 : 2 | "that's how you know" | demi lovato | enchanted | 3:12 row 3 : 3 | "some day my prince will come" | ashley tisdale | snow white and the seven dwarfs | 3:30 row 4 : 4 | "colors of the wind" | vanessa hudgens | pocahontas | 3:58 row 5 : 5 | "reflection" | christina aguilera | mulan | 3:33 row 6 : 6 | "so this is love" | the cheetah girls | cinderella | 3:40 row 7 : 7 | "kiss the girl" | colbie caillat | the little mermaid | 3:16 row 8 : 8 | "it's not just make believe" | kari kimmel | ella enchanted | 3:06 row 9 : 9 | "under the sea" | raven-symone | the little mermaid | 3:15 row 10 : 10 | "ever ever after" | jordan pruitt | enchanted | 3:12 row 11 : 11 | "true to your heart" | keke palmer | mulan | 3:22 row 12 : 12 | "happy working song" | amy adams | enchanted | 2:09 row 13 : 13 | "part of your world" | original broadway cast | the little mermaid | 3:23 row 14 : 14 | "a dream is a wish your heart makes" | disney channel stars | cinderella | 3:46

col : count ( "*" ) row 1 : 2

select count ( * ) where film = 'cinderella' <table_name> : table \begin{tabular}{llllll} \toprule & # & title & performer(s) & film & length \\ \midrule 0 & 1 & "once upon a dream" & emily osment & sleeping beauty & 3:32 \\ 1 & 2 & "that's how you know" & demi lovato & enchanted & 3:12 \\ 2 & 3 & "some day my prince will come" & ashley tisdale & snow white and the seven dwarfs & 3:30 \\ 3 & 4 & "colors of the wind" & vanessa hudgens & pocahontas & 3:58 \\ 4 & 5 & "reflection" & christina aguilera & mulan & 3:33 \\ 5 & 6 & "so this is love" & the cheetah girls & cinderella & 3:40 \\ 6 & 7 & "kiss the girl" & colbie caillat & the little mermaid & 3:16 \\ 7 & 8 & "it's not just make believe" & kari kimmel & ella enchanted & 3:06 \\ 8 & 9 & "under the sea" & raven-symone & the little mermaid & 3:15 \\ 9 & 10 & "ever ever after" & jordan pruitt & enchanted & 3:12 \\ 10 & 11 & "true to your heart" & keke palmer & mulan & 3:22 \\ 11 & 12 & "happy working song" & amy adams & enchanted & 2:09 \\ 12 & 13 & "part of your world" & original broadway cast & the little mermaid & 3:23 \\ 13 & 14 & "a dream is a wish your heart makes" & disney channel stars & cinderella & 3:46 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 2 \\ \bottomrule \end{tabular}

select count ( * ) where film = 'cinderella' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>#</th> <th>title</th> <th>performer(s)</th> <th>film</th> <th>length</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>"once upon a dream"</td> <td>emily osment</td> <td>sleeping beauty</td> <td>3:32</td> </tr> <tr> <th>1</th> <td>2</td> <td>"that's how you know"</td> <td>demi lovato</td> <td>enchanted</td> <td>3:12</td> </tr> <tr> <th>2</th> <td>3</td> <td>"some day my prince will come"</td> <td>ashley tisdale</td> <td>snow white and the seven dwarfs</td> <td>3:30</td> </tr> <tr> <th>3</th> <td>4</td> <td>"colors of the wind"</td> <td>vanessa hudgens</td> <td>pocahontas</td> <td>3:58</td> </tr> <tr> <th>4</th> <td>5</td> <td>"reflection"</td> <td>christina aguilera</td> <td>mulan</td> <td>3:33</td> </tr> <tr> <th>5</th> <td>6</td> <td>"so this is love"</td> <td>the cheetah girls</td> <td>cinderella</td> <td>3:40</td> </tr> <tr> <th>6</th> <td>7</td> <td>"kiss the girl"</td> <td>colbie caillat</td> <td>the little mermaid</td> <td>3:16</td> </tr> <tr> <th>7</th> <td>8</td> <td>"it's not just make believe"</td> <td>kari kimmel</td> <td>ella enchanted</td> <td>3:06</td> </tr> <tr> <th>8</th> <td>9</td> <td>"under the sea"</td> <td>raven-symone</td> <td>the little mermaid</td> <td>3:15</td> </tr> <tr> <th>9</th> <td>10</td> <td>"ever ever after"</td> <td>jordan pruitt</td> <td>enchanted</td> <td>3:12</td> </tr> <tr> <th>10</th> <td>11</td> <td>"true to your heart"</td> <td>keke palmer</td> <td>mulan</td> <td>3:22</td> </tr> <tr> <th>11</th> <td>12</td> <td>"happy working song"</td> <td>amy adams</td> <td>enchanted</td> <td>2:09</td> </tr> <tr> <th>12</th> <td>13</td> <td>"part of your world"</td> <td>original broadway cast</td> <td>the little mermaid</td> <td>3:23</td> </tr> <tr> <th>13</th> <td>14</td> <td>"a dream is a wish your heart makes"</td> <td>disney channel stars</td> <td>cinderella</td> <td>3:46</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "*" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>2</td> </tr> </tbody> </table>

select count ( * ) where film = 'cinderella' <table_name> : table | | # | title | performer(s) | film | length | |---:|----:|:-------------------------------------|:-----------------------|:--------------------------------|:---------| | 0 | 1 | "once upon a dream" | emily osment | sleeping beauty | 3:32 | | 1 | 2 | "that's how you know" | demi lovato | enchanted | 3:12 | | 2 | 3 | "some day my prince will come" | ashley tisdale | snow white and the seven dwarfs | 3:30 | | 3 | 4 | "colors of the wind" | vanessa hudgens | pocahontas | 3:58 | | 4 | 5 | "reflection" | christina aguilera | mulan | 3:33 | | 5 | 6 | "so this is love" | the cheetah girls | cinderella | 3:40 | | 6 | 7 | "kiss the girl" | colbie caillat | the little mermaid | 3:16 | | 7 | 8 | "it's not just make believe" | kari kimmel | ella enchanted | 3:06 | | 8 | 9 | "under the sea" | raven-symone | the little mermaid | 3:15 | | 9 | 10 | "ever ever after" | jordan pruitt | enchanted | 3:12 | | 10 | 11 | "true to your heart" | keke palmer | mulan | 3:22 | | 11 | 12 | "happy working song" | amy adams | enchanted | 2:09 | | 12 | 13 | "part of your world" | original broadway cast | the little mermaid | 3:23 | | 13 | 14 | "a dream is a wish your heart makes" | disney channel stars | cinderella | 3:46 |

| | count ( "*" ) | |---:|----------------:| | 0 | 2 |

select high school where from = 'county c league' col : high school | join year | from | left | league left to row 1 : ainsworth | 1954 | none | 1961 | big nine conference row 2 : atherton | 1960 | none | 1976 | mid-eastern eight conference row 3 : beecher | 1950 | none | 1960 | big nine conference row 4 : bendle | 1950 | none | 1975 | mid-eastern eight conference row 5 : bentley | 1960 | none | 1978 | genesee eight row 6 : brandon | 1977 | none | 1978 | five county 5 league row 7 : clio | 1950 | none | 1960 | big nine conference row 8 : davison | 1950 | none | 1960 | big nine conference row 9 : durand | 1964 | none | 1978 | flint metro league row 10 : fenton | 1940 | none | 1968 | flint metro league row 11 : flushing | 1950 | none | 1960 | big nine conference row 12 : grand blanc | 1950 | none | 1960 | big nine conference row 13 : hartland | 1976 | none | 1978 | five county 5 row 14 : kearsley | 1950 | none | 1960 | big nine conference row 15 : lake fenton | 1976 | none | 1978 | genesee eight row 16 : lakeville | 1956 1964 | none | 1962 1976 | independent flint metro league row 17 : linden | 1974 | none | 1978 | genesee eight row 18 : mt. morris | 1950 | none | 1960 | big nine conference row 19 : swartz creek | 1960 | county c league | 1968 | flint metro league

col : high school row 1 : swartz creek

select high school where from = 'county c league' <table_name> : table \begin{tabular}{llllll} \toprule & high school & join year & from & left & league left to \\ \midrule 0 & ainsworth & 1954 & none & 1961 & big nine conference \\ 1 & atherton & 1960 & none & 1976 & mid-eastern eight conference \\ 2 & beecher & 1950 & none & 1960 & big nine conference \\ 3 & bendle & 1950 & none & 1975 & mid-eastern eight conference \\ 4 & bentley & 1960 & none & 1978 & genesee eight \\ 5 & brandon & 1977 & none & 1978 & five county 5 league \\ 6 & clio & 1950 & none & 1960 & big nine conference \\ 7 & davison & 1950 & none & 1960 & big nine conference \\ 8 & durand & 1964 & none & 1978 & flint metro league \\ 9 & fenton & 1940 & none & 1968 & flint metro league \\ 10 & flushing & 1950 & none & 1960 & big nine conference \\ 11 & grand blanc & 1950 & none & 1960 & big nine conference \\ 12 & hartland & 1976 & none & 1978 & five county 5 \\ 13 & kearsley & 1950 & none & 1960 & big nine conference \\ 14 & lake fenton & 1976 & none & 1978 & genesee eight \\ 15 & lakeville & 1956 1964 & none & 1962 1976 & independent flint metro league \\ 16 & linden & 1974 & none & 1978 & genesee eight \\ 17 & mt. morris & 1950 & none & 1960 & big nine conference \\ 18 & swartz creek & 1960 & county c league & 1968 & flint metro league \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & high school \\ \midrule 0 & swartz creek \\ \bottomrule \end{tabular}

select high school where from = 'county c league' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>high school</th> <th>join year</th> <th>from</th> <th>left</th> <th>league left to</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>ainsworth</td> <td>1954</td> <td>none</td> <td>1961</td> <td>big nine conference</td> </tr> <tr> <th>1</th> <td>atherton</td> <td>1960</td> <td>none</td> <td>1976</td> <td>mid-eastern eight conference</td> </tr> <tr> <th>2</th> <td>beecher</td> <td>1950</td> <td>none</td> <td>1960</td> <td>big nine conference</td> </tr> <tr> <th>3</th> <td>bendle</td> <td>1950</td> <td>none</td> <td>1975</td> <td>mid-eastern eight conference</td> </tr> <tr> <th>4</th> <td>bentley</td> <td>1960</td> <td>none</td> <td>1978</td> <td>genesee eight</td> </tr> <tr> <th>5</th> <td>brandon</td> <td>1977</td> <td>none</td> <td>1978</td> <td>five county 5 league</td> </tr> <tr> <th>6</th> <td>clio</td> <td>1950</td> <td>none</td> <td>1960</td> <td>big nine conference</td> </tr> <tr> <th>7</th> <td>davison</td> <td>1950</td> <td>none</td> <td>1960</td> <td>big nine conference</td> </tr> <tr> <th>8</th> <td>durand</td> <td>1964</td> <td>none</td> <td>1978</td> <td>flint metro league</td> </tr> <tr> <th>9</th> <td>fenton</td> <td>1940</td> <td>none</td> <td>1968</td> <td>flint metro league</td> </tr> <tr> <th>10</th> <td>flushing</td> <td>1950</td> <td>none</td> <td>1960</td> <td>big nine conference</td> </tr> <tr> <th>11</th> <td>grand blanc</td> <td>1950</td> <td>none</td> <td>1960</td> <td>big nine conference</td> </tr> <tr> <th>12</th> <td>hartland</td> <td>1976</td> <td>none</td> <td>1978</td> <td>five county 5</td> </tr> <tr> <th>13</th> <td>kearsley</td> <td>1950</td> <td>none</td> <td>1960</td> <td>big nine conference</td> </tr> <tr> <th>14</th> <td>lake fenton</td> <td>1976</td> <td>none</td> <td>1978</td> <td>genesee eight</td> </tr> <tr> <th>15</th> <td>lakeville</td> <td>1956 1964</td> <td>none</td> <td>1962 1976</td> <td>independent flint metro league</td> </tr> <tr> <th>16</th> <td>linden</td> <td>1974</td> <td>none</td> <td>1978</td> <td>genesee eight</td> </tr> <tr> <th>17</th> <td>mt. morris</td> <td>1950</td> <td>none</td> <td>1960</td> <td>big nine conference</td> </tr> <tr> <th>18</th> <td>swartz creek</td> <td>1960</td> <td>county c league</td> <td>1968</td> <td>flint metro league</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>high school</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>swartz creek</td> </tr> </tbody> </table>

select high school where from = 'county c league' <table_name> : table | | high school | join year | from | left | league left to | |---:|:--------------|:------------|:----------------|:----------|:-------------------------------| | 0 | ainsworth | 1954 | none | 1961 | big nine conference | | 1 | atherton | 1960 | none | 1976 | mid-eastern eight conference | | 2 | beecher | 1950 | none | 1960 | big nine conference | | 3 | bendle | 1950 | none | 1975 | mid-eastern eight conference | | 4 | bentley | 1960 | none | 1978 | genesee eight | | 5 | brandon | 1977 | none | 1978 | five county 5 league | | 6 | clio | 1950 | none | 1960 | big nine conference | | 7 | davison | 1950 | none | 1960 | big nine conference | | 8 | durand | 1964 | none | 1978 | flint metro league | | 9 | fenton | 1940 | none | 1968 | flint metro league | | 10 | flushing | 1950 | none | 1960 | big nine conference | | 11 | grand blanc | 1950 | none | 1960 | big nine conference | | 12 | hartland | 1976 | none | 1978 | five county 5 | | 13 | kearsley | 1950 | none | 1960 | big nine conference | | 14 | lake fenton | 1976 | none | 1978 | genesee eight | | 15 | lakeville | 1956 1964 | none | 1962 1976 | independent flint metro league | | 16 | linden | 1974 | none | 1978 | genesee eight | | 17 | mt. morris | 1950 | none | 1960 | big nine conference | | 18 | swartz creek | 1960 | county c league | 1968 | flint metro league |

| | high school | |---:|:--------------| | 0 | swartz creek |

select 55 °c (e.g. heated timber floor) where pump type and source = 'theoretical carnot cycle limit, source 10 °c' col : pump type and source | typical use | 35 °c (e.g. heated screed floor) | 45 °c (e.g. heated screed floor) | 55 °c (e.g. heated timber floor) | 65 °c (e.g. radiator or dhw) | 75 °c (e.g. radiator and dhw) | 85 °c (e.g. radiator and dhw) row 1 : high-efficiency air source heat pump (ashp), air at -20 °c | none | 2.2 | 2 | none | none | none | none row 2 : two-stage ashp, air at -20 °c | low source temperature | 2.4 | 2.2 | 1.9 | none | none | none row 3 : high efficiency ashp, air at 0 °c | low output temperature | 3.8 | 2.8 | 2.2 | 2 | none | none row 4 : prototype transcritical co 2 (r744) heat pump with tripartite gas cooler, source at 0 °c | high output temperature | 3.3 | none | none | 4.2 | none | 3 row 5 : ground source heat pump (gshp), water at 0 °c | none | 5.0 | 3.7 | 2.9 | 2.4 | none | none row 6 : gshp, ground at 10 °c | low output temperature | 7.2 | 5 | 3.7 | 2.9 | 2.4 | none row 7 : theoretical carnot cycle limit, source -20 °c | none | 5.6 | 4.9 | 4.4 | 4 | 3.7 | 3.4 row 8 : theoretical carnot cycle limit, source 0 °c | none | 8.8 | 7.1 | 6 | 5.2 | 4.6 | 4.2 row 9 : theoretical lorentzen cycle limit (co 2 pump), return fluid 25 °c, source 0 °c | none | 10.1 | 8.8 | 7.9 | 7.1 | 6.5 | 6.1 row 10 : theoretical carnot cycle limit, source 10 °c | none | 12.3 | 9.1 | 7.3 | 6.1 | 5.4 | 4.8

col : 55 °c (e.g. heated timber floor) row 1 : 7.3

select 55 °c (e.g. heated timber floor) where pump type and source = 'theoretical carnot cycle limit, source 10 °c' <table_name> : table \begin{tabular}{lllllllll} \toprule & pump type and source & typical use & 35 °c (e.g. heated screed floor) & 45 °c (e.g. heated screed floor) & 55 °c (e.g. heated timber floor) & 65 °c (e.g. radiator or dhw) & 75 °c (e.g. radiator and dhw) & 85 °c (e.g. radiator and dhw) \\ \midrule 0 & high-efficiency air source heat pump (ashp), air at -20 °c & none & 2.2 & 2 & none & none & none & none \\ 1 & two-stage ashp, air at -20 °c & low source temperature & 2.4 & 2.2 & 1.9 & none & none & none \\ 2 & high efficiency ashp, air at 0 °c & low output temperature & 3.8 & 2.8 & 2.2 & 2 & none & none \\ 3 & prototype transcritical co 2 (r744) heat pump with tripartite gas cooler, source at 0 °c & high output temperature & 3.3 & none & none & 4.2 & none & 3 \\ 4 & ground source heat pump (gshp), water at 0 °c & none & 5.0 & 3.7 & 2.9 & 2.4 & none & none \\ 5 & gshp, ground at 10 °c & low output temperature & 7.2 & 5 & 3.7 & 2.9 & 2.4 & none \\ 6 & theoretical carnot cycle limit, source -20 °c & none & 5.6 & 4.9 & 4.4 & 4 & 3.7 & 3.4 \\ 7 & theoretical carnot cycle limit, source 0 °c & none & 8.8 & 7.1 & 6 & 5.2 & 4.6 & 4.2 \\ 8 & theoretical lorentzen cycle limit (co 2 pump), return fluid 25 °c, source 0 °c & none & 10.1 & 8.8 & 7.9 & 7.1 & 6.5 & 6.1 \\ 9 & theoretical carnot cycle limit, source 10 °c & none & 12.3 & 9.1 & 7.3 & 6.1 & 5.4 & 4.8 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & 55 °c (e.g. heated timber floor) \\ \midrule 0 & 7.3 \\ \bottomrule \end{tabular}

select 55 °c (e.g. heated timber floor) where pump type and source = 'theoretical carnot cycle limit, source 10 °c' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>pump type and source</th> <th>typical use</th> <th>35 °c (e.g. heated screed floor)</th> <th>45 °c (e.g. heated screed floor)</th> <th>55 °c (e.g. heated timber floor)</th> <th>65 °c (e.g. radiator or dhw)</th> <th>75 °c (e.g. radiator and dhw)</th> <th>85 °c (e.g. radiator and dhw)</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>high-efficiency air source heat pump (ashp), air at -20 °c</td> <td>none</td> <td>2.2</td> <td>2</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>1</th> <td>two-stage ashp, air at -20 °c</td> <td>low source temperature</td> <td>2.4</td> <td>2.2</td> <td>1.9</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>2</th> <td>high efficiency ashp, air at 0 °c</td> <td>low output temperature</td> <td>3.8</td> <td>2.8</td> <td>2.2</td> <td>2</td> <td>none</td> <td>none</td> </tr> <tr> <th>3</th> <td>prototype transcritical co 2 (r744) heat pump with tripartite gas cooler, source at 0 °c</td> <td>high output temperature</td> <td>3.3</td> <td>none</td> <td>none</td> <td>4.2</td> <td>none</td> <td>3</td> </tr> <tr> <th>4</th> <td>ground source heat pump (gshp), water at 0 °c</td> <td>none</td> <td>5.0</td> <td>3.7</td> <td>2.9</td> <td>2.4</td> <td>none</td> <td>none</td> </tr> <tr> <th>5</th> <td>gshp, ground at 10 °c</td> <td>low output temperature</td> <td>7.2</td> <td>5</td> <td>3.7</td> <td>2.9</td> <td>2.4</td> <td>none</td> </tr> <tr> <th>6</th> <td>theoretical carnot cycle limit, source -20 °c</td> <td>none</td> <td>5.6</td> <td>4.9</td> <td>4.4</td> <td>4</td> <td>3.7</td> <td>3.4</td> </tr> <tr> <th>7</th> <td>theoretical carnot cycle limit, source 0 °c</td> <td>none</td> <td>8.8</td> <td>7.1</td> <td>6</td> <td>5.2</td> <td>4.6</td> <td>4.2</td> </tr> <tr> <th>8</th> <td>theoretical lorentzen cycle limit (co 2 pump), return fluid 25 °c, source 0 °c</td> <td>none</td> <td>10.1</td> <td>8.8</td> <td>7.9</td> <td>7.1</td> <td>6.5</td> <td>6.1</td> </tr> <tr> <th>9</th> <td>theoretical carnot cycle limit, source 10 °c</td> <td>none</td> <td>12.3</td> <td>9.1</td> <td>7.3</td> <td>6.1</td> <td>5.4</td> <td>4.8</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>55 °c (e.g. heated timber floor)</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>7.3</td> </tr> </tbody> </table>

select 55 °c (e.g. heated timber floor) where pump type and source = 'theoretical carnot cycle limit, source 10 °c' <table_name> : table | | pump type and source | typical use | 35 °c (e.g. heated screed floor) | 45 °c (e.g. heated screed floor) | 55 °c (e.g. heated timber floor) | 65 °c (e.g. radiator or dhw) | 75 °c (e.g. radiator and dhw) | 85 °c (e.g. radiator and dhw) | |---:|:-----------------------------------------------------------------------------------------|:------------------------|-----------------------------------:|:-----------------------------------|:-----------------------------------|:-------------------------------|:--------------------------------|:--------------------------------| | 0 | high-efficiency air source heat pump (ashp), air at -20 °c | none | 2.2 | 2 | none | none | none | none | | 1 | two-stage ashp, air at -20 °c | low source temperature | 2.4 | 2.2 | 1.9 | none | none | none | | 2 | high efficiency ashp, air at 0 °c | low output temperature | 3.8 | 2.8 | 2.2 | 2 | none | none | | 3 | prototype transcritical co 2 (r744) heat pump with tripartite gas cooler, source at 0 °c | high output temperature | 3.3 | none | none | 4.2 | none | 3 | | 4 | ground source heat pump (gshp), water at 0 °c | none | 5 | 3.7 | 2.9 | 2.4 | none | none | | 5 | gshp, ground at 10 °c | low output temperature | 7.2 | 5 | 3.7 | 2.9 | 2.4 | none | | 6 | theoretical carnot cycle limit, source -20 °c | none | 5.6 | 4.9 | 4.4 | 4 | 3.7 | 3.4 | | 7 | theoretical carnot cycle limit, source 0 °c | none | 8.8 | 7.1 | 6 | 5.2 | 4.6 | 4.2 | | 8 | theoretical lorentzen cycle limit (co 2 pump), return fluid 25 °c, source 0 °c | none | 10.1 | 8.8 | 7.9 | 7.1 | 6.5 | 6.1 | | 9 | theoretical carnot cycle limit, source 10 °c | none | 12.3 | 9.1 | 7.3 | 6.1 | 5.4 | 4.8 |

| | 55 °c (e.g. heated timber floor) | |---:|-----------------------------------:| | 0 | 7.3 |

select count ( artist ) where draw > 9 col : draw | country | language | artist | song | english translation | national final | place | points row 1 : 1 | malta | english | claudia faniello | "caravaggio" | none | 2nd | 13 | 53 row 2 : 2 | israel | hebrew | bo'az ma'uda & oshrat papin | "parparim" | butterflies | 3rd | 8 | 90 row 3 : 3 | russia | english | sergey lazarev | "flyer" | none | 4th | 7 | 119 row 4 : 4 | estonia | english | rolf junior | "one on one" | none | 4th | 21 | 0 row 5 : 5 | belgium | english | sandrine | "i feel the same way" | none | 2nd | 10 | 64 row 6 : 6 | macedonia | macedonian | risto samardziev | "dojdi do mene" | come to me | 3rd | 19 | 7 row 7 : 7 | poland | english | man meadow | "viva la musica" | hail to the music | 3rd | 3 | 155 row 8 : 8 | finland | finnish | mikael konttinen | "milloin" | when | none | 14 | 46 row 9 : 9 | germany | german | carolin fortenbacher | "hinterm ozean" | beyond the ocean | 2nd | 6 | 129 row 10 : 10 | denmark | english | kendra lou | "until we're satisfied" | none | 4th | 18 | 21 row 11 : 11 | greece | english | kostas martakis | "always and forever" | none | 2nd | 4 | 140 row 12 : 12 | serbia | serbian | beauty queens | "zavet" | a pledge | 3rd | 9 | 68 row 13 : 13 | croatia | croatian | emilija kokic | "andjeo" | angel | 6th | 15 | 31 row 14 : 14 | lithuania | english | aiste pilvelyte | "troy on fire" | none | 2nd | 17 | 26 row 15 : 15 | ireland | english | leona daly | "not crazy after all" | none | none | 12 | 56 row 16 : 16 | united kingdom | english | the revelations | "it's you" | none | 3rd | 16 | 29 row 17 : 17 | portugal | portuguese | lisboa nao sejas francesa | "porto de encontro" | meeting harbour | 9th | 20 | 6 row 18 : 18 | sweden | english | sanna nielsen | "empty room" | none | 2nd | 1 | 269 row 19 : 19 | norway | english | veronica akselsen | "am i supposed to love again" | none | 4th | 5 | 139 row 20 : 20 | slovenia | slovene | brigita suler | "samara" | none | 3rd | 11 | 57 row 21 : 21 | spain | spanish | coral | "todo esta en tu mente" | everything is in your mind | 2nd | 2 | 178

col : count ( "artist" ) row 1 : 0

select count ( artist ) where draw > 9 <table_name> : table \begin{tabular}{llllllllll} \toprule & draw & country & language & artist & song & english translation & national final & place & points \\ \midrule 0 & 1 & malta & english & claudia faniello & "caravaggio" & none & 2nd & 13 & 53 \\ 1 & 2 & israel & hebrew & bo'az ma'uda & oshrat papin & "parparim" & butterflies & 3rd & 8 & 90 \\ 2 & 3 & russia & english & sergey lazarev & "flyer" & none & 4th & 7 & 119 \\ 3 & 4 & estonia & english & rolf junior & "one on one" & none & 4th & 21 & 0 \\ 4 & 5 & belgium & english & sandrine & "i feel the same way" & none & 2nd & 10 & 64 \\ 5 & 6 & macedonia & macedonian & risto samardziev & "dojdi do mene" & come to me & 3rd & 19 & 7 \\ 6 & 7 & poland & english & man meadow & "viva la musica" & hail to the music & 3rd & 3 & 155 \\ 7 & 8 & finland & finnish & mikael konttinen & "milloin" & when & none & 14 & 46 \\ 8 & 9 & germany & german & carolin fortenbacher & "hinterm ozean" & beyond the ocean & 2nd & 6 & 129 \\ 9 & 10 & denmark & english & kendra lou & "until we're satisfied" & none & 4th & 18 & 21 \\ 10 & 11 & greece & english & kostas martakis & "always and forever" & none & 2nd & 4 & 140 \\ 11 & 12 & serbia & serbian & beauty queens & "zavet" & a pledge & 3rd & 9 & 68 \\ 12 & 13 & croatia & croatian & emilija kokic & "andjeo" & angel & 6th & 15 & 31 \\ 13 & 14 & lithuania & english & aiste pilvelyte & "troy on fire" & none & 2nd & 17 & 26 \\ 14 & 15 & ireland & english & leona daly & "not crazy after all" & none & none & 12 & 56 \\ 15 & 16 & united kingdom & english & the revelations & "it's you" & none & 3rd & 16 & 29 \\ 16 & 17 & portugal & portuguese & lisboa nao sejas francesa & "porto de encontro" & meeting harbour & 9th & 20 & 6 \\ 17 & 18 & sweden & english & sanna nielsen & "empty room" & none & 2nd & 1 & 269 \\ 18 & 19 & norway & english & veronica akselsen & "am i supposed to love again" & none & 4th & 5 & 139 \\ 19 & 20 & slovenia & slovene & brigita suler & "samara" & none & 3rd & 11 & 57 \\ 20 & 21 & spain & spanish & coral & "todo esta en tu mente" & everything is in your mind & 2nd & 2 & 178 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "artist" ) \\ \midrule 0 & 0 \\ \bottomrule \end{tabular}

select count ( artist ) where draw > 9 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>draw</th> <th>country</th> <th>language</th> <th>artist</th> <th>song</th> <th>english translation</th> <th>national final</th> <th>place</th> <th>points</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>malta</td> <td>english</td> <td>claudia faniello</td> <td>"caravaggio"</td> <td>none</td> <td>2nd</td> <td>13</td> <td>53</td> </tr> <tr> <th>1</th> <td>2</td> <td>israel</td> <td>hebrew</td> <td>bo'az ma'uda &amp; oshrat papin</td> <td>"parparim"</td> <td>butterflies</td> <td>3rd</td> <td>8</td> <td>90</td> </tr> <tr> <th>2</th> <td>3</td> <td>russia</td> <td>english</td> <td>sergey lazarev</td> <td>"flyer"</td> <td>none</td> <td>4th</td> <td>7</td> <td>119</td> </tr> <tr> <th>3</th> <td>4</td> <td>estonia</td> <td>english</td> <td>rolf junior</td> <td>"one on one"</td> <td>none</td> <td>4th</td> <td>21</td> <td>0</td> </tr> <tr> <th>4</th> <td>5</td> <td>belgium</td> <td>english</td> <td>sandrine</td> <td>"i feel the same way"</td> <td>none</td> <td>2nd</td> <td>10</td> <td>64</td> </tr> <tr> <th>5</th> <td>6</td> <td>macedonia</td> <td>macedonian</td> <td>risto samardziev</td> <td>"dojdi do mene"</td> <td>come to me</td> <td>3rd</td> <td>19</td> <td>7</td> </tr> <tr> <th>6</th> <td>7</td> <td>poland</td> <td>english</td> <td>man meadow</td> <td>"viva la musica"</td> <td>hail to the music</td> <td>3rd</td> <td>3</td> <td>155</td> </tr> <tr> <th>7</th> <td>8</td> <td>finland</td> <td>finnish</td> <td>mikael konttinen</td> <td>"milloin"</td> <td>when</td> <td>none</td> <td>14</td> <td>46</td> </tr> <tr> <th>8</th> <td>9</td> <td>germany</td> <td>german</td> <td>carolin fortenbacher</td> <td>"hinterm ozean"</td> <td>beyond the ocean</td> <td>2nd</td> <td>6</td> <td>129</td> </tr> <tr> <th>9</th> <td>10</td> <td>denmark</td> <td>english</td> <td>kendra lou</td> <td>"until we're satisfied"</td> <td>none</td> <td>4th</td> <td>18</td> <td>21</td> </tr> <tr> <th>10</th> <td>11</td> <td>greece</td> <td>english</td> <td>kostas martakis</td> <td>"always and forever"</td> <td>none</td> <td>2nd</td> <td>4</td> <td>140</td> </tr> <tr> <th>11</th> <td>12</td> <td>serbia</td> <td>serbian</td> <td>beauty queens</td> <td>"zavet"</td> <td>a pledge</td> <td>3rd</td> <td>9</td> <td>68</td> </tr> <tr> <th>12</th> <td>13</td> <td>croatia</td> <td>croatian</td> <td>emilija kokic</td> <td>"andjeo"</td> <td>angel</td> <td>6th</td> <td>15</td> <td>31</td> </tr> <tr> <th>13</th> <td>14</td> <td>lithuania</td> <td>english</td> <td>aiste pilvelyte</td> <td>"troy on fire"</td> <td>none</td> <td>2nd</td> <td>17</td> <td>26</td> </tr> <tr> <th>14</th> <td>15</td> <td>ireland</td> <td>english</td> <td>leona daly</td> <td>"not crazy after all"</td> <td>none</td> <td>none</td> <td>12</td> <td>56</td> </tr> <tr> <th>15</th> <td>16</td> <td>united kingdom</td> <td>english</td> <td>the revelations</td> <td>"it's you"</td> <td>none</td> <td>3rd</td> <td>16</td> <td>29</td> </tr> <tr> <th>16</th> <td>17</td> <td>portugal</td> <td>portuguese</td> <td>lisboa nao sejas francesa</td> <td>"porto de encontro"</td> <td>meeting harbour</td> <td>9th</td> <td>20</td> <td>6</td> </tr> <tr> <th>17</th> <td>18</td> <td>sweden</td> <td>english</td> <td>sanna nielsen</td> <td>"empty room"</td> <td>none</td> <td>2nd</td> <td>1</td> <td>269</td> </tr> <tr> <th>18</th> <td>19</td> <td>norway</td> <td>english</td> <td>veronica akselsen</td> <td>"am i supposed to love again"</td> <td>none</td> <td>4th</td> <td>5</td> <td>139</td> </tr> <tr> <th>19</th> <td>20</td> <td>slovenia</td> <td>slovene</td> <td>brigita suler</td> <td>"samara"</td> <td>none</td> <td>3rd</td> <td>11</td> <td>57</td> </tr> <tr> <th>20</th> <td>21</td> <td>spain</td> <td>spanish</td> <td>coral</td> <td>"todo esta en tu mente"</td> <td>everything is in your mind</td> <td>2nd</td> <td>2</td> <td>178</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "artist" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>0</td> </tr> </tbody> </table>

select count ( artist ) where draw > 9 <table_name> : table | | draw | country | language | artist | song | english translation | national final | place | points | |---:|-------:|:---------------|:-----------|:----------------------------|:------------------------------|:---------------------------|:-----------------|--------:|---------:| | 0 | 1 | malta | english | claudia faniello | "caravaggio" | none | 2nd | 13 | 53 | | 1 | 2 | israel | hebrew | bo'az ma'uda & oshrat papin | "parparim" | butterflies | 3rd | 8 | 90 | | 2 | 3 | russia | english | sergey lazarev | "flyer" | none | 4th | 7 | 119 | | 3 | 4 | estonia | english | rolf junior | "one on one" | none | 4th | 21 | 0 | | 4 | 5 | belgium | english | sandrine | "i feel the same way" | none | 2nd | 10 | 64 | | 5 | 6 | macedonia | macedonian | risto samardziev | "dojdi do mene" | come to me | 3rd | 19 | 7 | | 6 | 7 | poland | english | man meadow | "viva la musica" | hail to the music | 3rd | 3 | 155 | | 7 | 8 | finland | finnish | mikael konttinen | "milloin" | when | none | 14 | 46 | | 8 | 9 | germany | german | carolin fortenbacher | "hinterm ozean" | beyond the ocean | 2nd | 6 | 129 | | 9 | 10 | denmark | english | kendra lou | "until we're satisfied" | none | 4th | 18 | 21 | | 10 | 11 | greece | english | kostas martakis | "always and forever" | none | 2nd | 4 | 140 | | 11 | 12 | serbia | serbian | beauty queens | "zavet" | a pledge | 3rd | 9 | 68 | | 12 | 13 | croatia | croatian | emilija kokic | "andjeo" | angel | 6th | 15 | 31 | | 13 | 14 | lithuania | english | aiste pilvelyte | "troy on fire" | none | 2nd | 17 | 26 | | 14 | 15 | ireland | english | leona daly | "not crazy after all" | none | none | 12 | 56 | | 15 | 16 | united kingdom | english | the revelations | "it's you" | none | 3rd | 16 | 29 | | 16 | 17 | portugal | portuguese | lisboa nao sejas francesa | "porto de encontro" | meeting harbour | 9th | 20 | 6 | | 17 | 18 | sweden | english | sanna nielsen | "empty room" | none | 2nd | 1 | 269 | | 18 | 19 | norway | english | veronica akselsen | "am i supposed to love again" | none | 4th | 5 | 139 | | 19 | 20 | slovenia | slovene | brigita suler | "samara" | none | 3rd | 11 | 57 | | 20 | 21 | spain | spanish | coral | "todo esta en tu mente" | everything is in your mind | 2nd | 2 | 178 |

| | count ( "artist" ) | |---:|---------------------:| | 0 | 0 |

select name where rank = 7 col : rank | name | nationality | time | notes row 1 : none | chelsey gotell | canada | 2:28.15 | wr row 2 : none | kirby cote | canada | 2:28.65 | none row 3 : none | valerie grand maison | canada | 2:29.29 | none row 4 : 4 | kelley becherer | united states | 2:32.21 | none row 5 : 5 | prue watt | australia | 2:32.87 | none row 6 : 6 | teigan van roosmalen | australia | 2:39.20 | none row 7 : 7 | lidia marta banos | spain | 2:48.80 | none row 8 : 8 | akari kasamoto | japan | 2:55.16 | none

col : name row 1 : lidia marta banos

select name where rank = 7 <table_name> : table \begin{tabular}{llllll} \toprule & rank & name & nationality & time & notes \\ \midrule 0 & none & chelsey gotell & canada & 2:28.15 & wr \\ 1 & none & kirby cote & canada & 2:28.65 & none \\ 2 & none & valerie grand maison & canada & 2:29.29 & none \\ 3 & 4 & kelley becherer & united states & 2:32.21 & none \\ 4 & 5 & prue watt & australia & 2:32.87 & none \\ 5 & 6 & teigan van roosmalen & australia & 2:39.20 & none \\ 6 & 7 & lidia marta banos & spain & 2:48.80 & none \\ 7 & 8 & akari kasamoto & japan & 2:55.16 & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & name \\ \midrule 0 & lidia marta banos \\ \bottomrule \end{tabular}

select name where rank = 7 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>rank</th> <th>name</th> <th>nationality</th> <th>time</th> <th>notes</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>none</td> <td>chelsey gotell</td> <td>canada</td> <td>2:28.15</td> <td>wr</td> </tr> <tr> <th>1</th> <td>none</td> <td>kirby cote</td> <td>canada</td> <td>2:28.65</td> <td>none</td> </tr> <tr> <th>2</th> <td>none</td> <td>valerie grand maison</td> <td>canada</td> <td>2:29.29</td> <td>none</td> </tr> <tr> <th>3</th> <td>4</td> <td>kelley becherer</td> <td>united states</td> <td>2:32.21</td> <td>none</td> </tr> <tr> <th>4</th> <td>5</td> <td>prue watt</td> <td>australia</td> <td>2:32.87</td> <td>none</td> </tr> <tr> <th>5</th> <td>6</td> <td>teigan van roosmalen</td> <td>australia</td> <td>2:39.20</td> <td>none</td> </tr> <tr> <th>6</th> <td>7</td> <td>lidia marta banos</td> <td>spain</td> <td>2:48.80</td> <td>none</td> </tr> <tr> <th>7</th> <td>8</td> <td>akari kasamoto</td> <td>japan</td> <td>2:55.16</td> <td>none</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>name</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>lidia marta banos</td> </tr> </tbody> </table>

select name where rank = 7 <table_name> : table | | rank | name | nationality | time | notes | |---:|:-------|:---------------------|:--------------|:--------|:--------| | 0 | none | chelsey gotell | canada | 2:28.15 | wr | | 1 | none | kirby cote | canada | 2:28.65 | none | | 2 | none | valerie grand maison | canada | 2:29.29 | none | | 3 | 4 | kelley becherer | united states | 2:32.21 | none | | 4 | 5 | prue watt | australia | 2:32.87 | none | | 5 | 6 | teigan van roosmalen | australia | 2:39.20 | none | | 6 | 7 | lidia marta banos | spain | 2:48.80 | none | | 7 | 8 | akari kasamoto | japan | 2:55.16 | none |

| | name | |---:|:------------------| | 0 | lidia marta banos |

select count ( * ) where rank < 842 col : rank | airport | passengers handled | change 2011/12 row 1 : 1 | london heath | | row 2 : | 828531 | 0% | row 3 : 2 | london gatwick | 607417 | 7% row 4 : 3 | london stansted | 331607 | 3% row 5 : 4 | london luton | 276488 | 0% row 6 : 5 | belfast international | 266987 | 8% row 7 : 6 | bristol | 239666 | 8% row 8 : 7 | birmingham | 208123 | 2% row 9 : 8 | southampton | 173576 | 24% row 10 : 9 | london city | 158239 | 6% row 11 : 10 | belfast city | 100003 | 4%

col : count ( "*" ) row 1 : 10

select count ( * ) where rank < 842 <table_name> : table \begin{tabular}{lllll} \toprule & rank & airport & passengers handled & change 2011/12 \\ \midrule 0 & 1 & london heath & & \\ 1 & & 828531 & 0% & \\ 2 & 2 & london gatwick & 607417 & 7% \\ 3 & 3 & london stansted & 331607 & 3% \\ 4 & 4 & london luton & 276488 & 0% \\ 5 & 5 & belfast international & 266987 & 8% \\ 6 & 6 & bristol & 239666 & 8% \\ 7 & 7 & birmingham & 208123 & 2% \\ 8 & 8 & southampton & 173576 & 24% \\ 9 & 9 & london city & 158239 & 6% \\ 10 & 10 & belfast city & 100003 & 4% \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 10 \\ \bottomrule \end{tabular}

select count ( * ) where rank < 842 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>rank</th> <th>airport</th> <th>passengers handled</th> <th>change 2011/12</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>london heath</td> <td></td> <td></td> </tr> <tr> <th>1</th> <td></td> <td>828531</td> <td>0%</td> <td></td> </tr> <tr> <th>2</th> <td>2</td> <td>london gatwick</td> <td>607417</td> <td>7%</td> </tr> <tr> <th>3</th> <td>3</td> <td>london stansted</td> <td>331607</td> <td>3%</td> </tr> <tr> <th>4</th> <td>4</td> <td>london luton</td> <td>276488</td> <td>0%</td> </tr> <tr> <th>5</th> <td>5</td> <td>belfast international</td> <td>266987</td> <td>8%</td> </tr> <tr> <th>6</th> <td>6</td> <td>bristol</td> <td>239666</td> <td>8%</td> </tr> <tr> <th>7</th> <td>7</td> <td>birmingham</td> <td>208123</td> <td>2%</td> </tr> <tr> <th>8</th> <td>8</td> <td>southampton</td> <td>173576</td> <td>24%</td> </tr> <tr> <th>9</th> <td>9</td> <td>london city</td> <td>158239</td> <td>6%</td> </tr> <tr> <th>10</th> <td>10</td> <td>belfast city</td> <td>100003</td> <td>4%</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "*" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>10</td> </tr> </tbody> </table>

select count ( * ) where rank < 842 <table_name> : table | | rank | airport | passengers handled | change 2011/12 | |---:|:-------|:----------------------|:---------------------|:-----------------| | 0 | 1 | london heath | | | | 1 | | 828531 | 0% | | | 2 | 2 | london gatwick | 607417 | 7% | | 3 | 3 | london stansted | 331607 | 3% | | 4 | 4 | london luton | 276488 | 0% | | 5 | 5 | belfast international | 266987 | 8% | | 6 | 6 | bristol | 239666 | 8% | | 7 | 7 | birmingham | 208123 | 2% | | 8 | 8 | southampton | 173576 | 24% | | 9 | 9 | london city | 158239 | 6% | | 10 | 10 | belfast city | 100003 | 4% |

| | count ( "*" ) | |---:|----------------:| | 0 | 10 |

select count ( agg. ) where team 1 = 'deportivo saprissa' col : team 1 | agg. | team 2 | 1st leg | 2nd leg row 1 : tauro f.c | 5 - 0 | america managua | 5 - 0 | 0 - 0 row 2 : deportivo saprissa | 8 - 2 | real esteli | 4 - 1 | 4 - 1 row 3 : l.d. alajuelense | 11 - 1 | c.d. plaza amador | 4 - 1 | 7 - 0 row 4 : duurly's | 0 - 8 | c.d. luis angel firpo | 0 - 4 | 0 - 4 row 5 : real c.d. espana | 6 - 0 | acros real verdes | 5 - 0 | 1 - 0 row 6 : csd municipal | 3 - 6 | alianza f.c | 2 - 5 | 1 - 1 row 7 : c.d. motagua | 2 - 2 | csd comunicaciones | 1 - 1 | 1 - 1 row 8 : c.d. motagua | 0 - 1 (replay) | csd comunicaciones | none | none

col : count ( "agg." ) row 1 : 1

select count ( agg. ) where team 1 = 'deportivo saprissa' <table_name> : table \begin{tabular}{llllll} \toprule & team 1 & agg. & team 2 & 1st leg & 2nd leg \\ \midrule 0 & tauro f.c & 5 - 0 & america managua & 5 - 0 & 0 - 0 \\ 1 & deportivo saprissa & 8 - 2 & real esteli & 4 - 1 & 4 - 1 \\ 2 & l.d. alajuelense & 11 - 1 & c.d. plaza amador & 4 - 1 & 7 - 0 \\ 3 & duurly's & 0 - 8 & c.d. luis angel firpo & 0 - 4 & 0 - 4 \\ 4 & real c.d. espana & 6 - 0 & acros real verdes & 5 - 0 & 1 - 0 \\ 5 & csd municipal & 3 - 6 & alianza f.c & 2 - 5 & 1 - 1 \\ 6 & c.d. motagua & 2 - 2 & csd comunicaciones & 1 - 1 & 1 - 1 \\ 7 & c.d. motagua & 0 - 1 (replay) & csd comunicaciones & none & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "agg." ) \\ \midrule 0 & 1 \\ \bottomrule \end{tabular}

select count ( agg. ) where team 1 = 'deportivo saprissa' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>team 1</th> <th>agg.</th> <th>team 2</th> <th>1st leg</th> <th>2nd leg</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>tauro f.c</td> <td>5 - 0</td> <td>america managua</td> <td>5 - 0</td> <td>0 - 0</td> </tr> <tr> <th>1</th> <td>deportivo saprissa</td> <td>8 - 2</td> <td>real esteli</td> <td>4 - 1</td> <td>4 - 1</td> </tr> <tr> <th>2</th> <td>l.d. alajuelense</td> <td>11 - 1</td> <td>c.d. plaza amador</td> <td>4 - 1</td> <td>7 - 0</td> </tr> <tr> <th>3</th> <td>duurly's</td> <td>0 - 8</td> <td>c.d. luis angel firpo</td> <td>0 - 4</td> <td>0 - 4</td> </tr> <tr> <th>4</th> <td>real c.d. espana</td> <td>6 - 0</td> <td>acros real verdes</td> <td>5 - 0</td> <td>1 - 0</td> </tr> <tr> <th>5</th> <td>csd municipal</td> <td>3 - 6</td> <td>alianza f.c</td> <td>2 - 5</td> <td>1 - 1</td> </tr> <tr> <th>6</th> <td>c.d. motagua</td> <td>2 - 2</td> <td>csd comunicaciones</td> <td>1 - 1</td> <td>1 - 1</td> </tr> <tr> <th>7</th> <td>c.d. motagua</td> <td>0 - 1 (replay)</td> <td>csd comunicaciones</td> <td>none</td> <td>none</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "agg." )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> </tr> </tbody> </table>

select count ( agg. ) where team 1 = 'deportivo saprissa' <table_name> : table | | team 1 | agg. | team 2 | 1st leg | 2nd leg | |---:|:-------------------|:---------------|:----------------------|:----------|:----------| | 0 | tauro f.c | 5 - 0 | america managua | 5 - 0 | 0 - 0 | | 1 | deportivo saprissa | 8 - 2 | real esteli | 4 - 1 | 4 - 1 | | 2 | l.d. alajuelense | 11 - 1 | c.d. plaza amador | 4 - 1 | 7 - 0 | | 3 | duurly's | 0 - 8 | c.d. luis angel firpo | 0 - 4 | 0 - 4 | | 4 | real c.d. espana | 6 - 0 | acros real verdes | 5 - 0 | 1 - 0 | | 5 | csd municipal | 3 - 6 | alianza f.c | 2 - 5 | 1 - 1 | | 6 | c.d. motagua | 2 - 2 | csd comunicaciones | 1 - 1 | 1 - 1 | | 7 | c.d. motagua | 0 - 1 (replay) | csd comunicaciones | none | none |

| | count ( "agg." ) | |---:|-------------------:| | 0 | 1 |

select venue where position = '3rd' and year = 1998 col : year | competition | venue | position | notes row 1 : 1997 | european u23 championships | turku, finland | 1st | 200 m row 2 : 1997 | world championships | athens, greece | 3rd | 4x100m relay row 3 : 1998 | european championships | budapest, hungary | 3rd | 200 metres row 4 : 1998 | european championships | budapest, hungary | 1st | 4x100m relay row 5 : 1998 | commonwealth games | kuala lumpur, malaysia | 1st | 200 metres row 6 : 1998 | commonwealth games | kuala lumpur, malaysia | 1st | 4x100m relay row 7 : 2000 | european indoor championships | ghent, belgium | 3rd | 200 metres

col : venue row 1 : budapest, hungary

select venue where position = '3rd' and year = 1998 <table_name> : table \begin{tabular}{llllll} \toprule & year & competition & venue & position & notes \\ \midrule 0 & 1997 & european u23 championships & turku, finland & 1st & 200 m \\ 1 & 1997 & world championships & athens, greece & 3rd & 4x100m relay \\ 2 & 1998 & european championships & budapest, hungary & 3rd & 200 metres \\ 3 & 1998 & european championships & budapest, hungary & 1st & 4x100m relay \\ 4 & 1998 & commonwealth games & kuala lumpur, malaysia & 1st & 200 metres \\ 5 & 1998 & commonwealth games & kuala lumpur, malaysia & 1st & 4x100m relay \\ 6 & 2000 & european indoor championships & ghent, belgium & 3rd & 200 metres \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & venue \\ \midrule 0 & budapest, hungary \\ \bottomrule \end{tabular}

select venue where position = '3rd' and year = 1998 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>year</th> <th>competition</th> <th>venue</th> <th>position</th> <th>notes</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1997</td> <td>european u23 championships</td> <td>turku, finland</td> <td>1st</td> <td>200 m</td> </tr> <tr> <th>1</th> <td>1997</td> <td>world championships</td> <td>athens, greece</td> <td>3rd</td> <td>4x100m relay</td> </tr> <tr> <th>2</th> <td>1998</td> <td>european championships</td> <td>budapest, hungary</td> <td>3rd</td> <td>200 metres</td> </tr> <tr> <th>3</th> <td>1998</td> <td>european championships</td> <td>budapest, hungary</td> <td>1st</td> <td>4x100m relay</td> </tr> <tr> <th>4</th> <td>1998</td> <td>commonwealth games</td> <td>kuala lumpur, malaysia</td> <td>1st</td> <td>200 metres</td> </tr> <tr> <th>5</th> <td>1998</td> <td>commonwealth games</td> <td>kuala lumpur, malaysia</td> <td>1st</td> <td>4x100m relay</td> </tr> <tr> <th>6</th> <td>2000</td> <td>european indoor championships</td> <td>ghent, belgium</td> <td>3rd</td> <td>200 metres</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>venue</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>budapest, hungary</td> </tr> </tbody> </table>

select venue where position = '3rd' and year = 1998 <table_name> : table | | year | competition | venue | position | notes | |---:|-------:|:------------------------------|:-----------------------|:-----------|:-------------| | 0 | 1997 | european u23 championships | turku, finland | 1st | 200 m | | 1 | 1997 | world championships | athens, greece | 3rd | 4x100m relay | | 2 | 1998 | european championships | budapest, hungary | 3rd | 200 metres | | 3 | 1998 | european championships | budapest, hungary | 1st | 4x100m relay | | 4 | 1998 | commonwealth games | kuala lumpur, malaysia | 1st | 200 metres | | 5 | 1998 | commonwealth games | kuala lumpur, malaysia | 1st | 4x100m relay | | 6 | 2000 | european indoor championships | ghent, belgium | 3rd | 200 metres |

| | venue | |---:|:------------------| | 0 | budapest, hungary |

select votes where party = 'conservative hold' col : party | candidate | votes | % | ±% row 1 : conservative | patrick mcloughlin | 24280 | 48 | 5.9 row 2 : labour | stephen clamp | 16910 | 33.4 | -0.1 row 3 : liberal democrat | jeremy beckett | 7922 | 15.7 | -1.8 row 4 : ukip | stuart bavester | 672 | 1.3 | 0.5 row 5 : monster raving loony | "the flying brick" delves | 472 | 0.9 | 0.4 row 6 : independent | robert goodall | 333 | 0.7 | 0.5 row 7 : majority | majority | 7370 | 14.6 | none row 8 : turnout | turnout | 50589 | 67.8 | -10.5 row 9 : conservative hold | conservative hold | swing | none | none

col : votes row 1 : swing

select votes where party = 'conservative hold' <table_name> : table \begin{tabular}{llllll} \toprule & party & candidate & votes & % & ±% \\ \midrule 0 & conservative & patrick mcloughlin & 24280 & 48 & 5.9 \\ 1 & labour & stephen clamp & 16910 & 33.4 & -0.1 \\ 2 & liberal democrat & jeremy beckett & 7922 & 15.7 & -1.8 \\ 3 & ukip & stuart bavester & 672 & 1.3 & 0.5 \\ 4 & monster raving loony & "the flying brick" delves & 472 & 0.9 & 0.4 \\ 5 & independent & robert goodall & 333 & 0.7 & 0.5 \\ 6 & majority & majority & 7370 & 14.6 & none \\ 7 & turnout & turnout & 50589 & 67.8 & -10.5 \\ 8 & conservative hold & conservative hold & swing & none & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & votes \\ \midrule 0 & swing \\ \bottomrule \end{tabular}

select votes where party = 'conservative hold' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>party</th> <th>candidate</th> <th>votes</th> <th>%</th> <th>±%</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>conservative</td> <td>patrick mcloughlin</td> <td>24280</td> <td>48</td> <td>5.9</td> </tr> <tr> <th>1</th> <td>labour</td> <td>stephen clamp</td> <td>16910</td> <td>33.4</td> <td>-0.1</td> </tr> <tr> <th>2</th> <td>liberal democrat</td> <td>jeremy beckett</td> <td>7922</td> <td>15.7</td> <td>-1.8</td> </tr> <tr> <th>3</th> <td>ukip</td> <td>stuart bavester</td> <td>672</td> <td>1.3</td> <td>0.5</td> </tr> <tr> <th>4</th> <td>monster raving loony</td> <td>"the flying brick" delves</td> <td>472</td> <td>0.9</td> <td>0.4</td> </tr> <tr> <th>5</th> <td>independent</td> <td>robert goodall</td> <td>333</td> <td>0.7</td> <td>0.5</td> </tr> <tr> <th>6</th> <td>majority</td> <td>majority</td> <td>7370</td> <td>14.6</td> <td>none</td> </tr> <tr> <th>7</th> <td>turnout</td> <td>turnout</td> <td>50589</td> <td>67.8</td> <td>-10.5</td> </tr> <tr> <th>8</th> <td>conservative hold</td> <td>conservative hold</td> <td>swing</td> <td>none</td> <td>none</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>votes</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>swing</td> </tr> </tbody> </table>

select votes where party = 'conservative hold' <table_name> : table | | party | candidate | votes | % | ±% | |---:|:---------------------|:--------------------------|:--------|:-----|:------| | 0 | conservative | patrick mcloughlin | 24280 | 48 | 5.9 | | 1 | labour | stephen clamp | 16910 | 33.4 | -0.1 | | 2 | liberal democrat | jeremy beckett | 7922 | 15.7 | -1.8 | | 3 | ukip | stuart bavester | 672 | 1.3 | 0.5 | | 4 | monster raving loony | "the flying brick" delves | 472 | 0.9 | 0.4 | | 5 | independent | robert goodall | 333 | 0.7 | 0.5 | | 6 | majority | majority | 7370 | 14.6 | none | | 7 | turnout | turnout | 50589 | 67.8 | -10.5 | | 8 | conservative hold | conservative hold | swing | none | none |

| | votes | |---:|:--------| | 0 | swing |

select venue where # > 9 col : # | date | venue | opponent | score | result | competition row 1 : 1 | 2013-06-13 00:00:00 | zhejiang dragon sports centre, hangzhou | russia | 1 - 0 | 5 - 1 | friendly row 2 : 2 | 2013-06-13 00:00:00 | zhejiang dragon sports centre, hangzhou | russia | 3 - 0 | 5 - 1 | friendly row 3 : 3 | 2013-06-14 00:00:00 | zhejiang dragon sports centre, hangzhou | netherlands | none | 4 - 3 | friendly row 4 : 4 | 2013-06-14 00:00:00 | zhejiang dragon sports centre, hangzhou | netherlands | none | 4 - 3 | friendly row 5 : 5 | 2013-06-15 00:00:00 | zhejiang dragon sports centre, hangzhou | china | none | 5 - 2 | friendly row 6 : 6 | 2013-07-01 00:00:00 | songdo global university campus, incheon | iraq | 3 - 1 | 12 - 3 | 2013 asian indoor-martial arts games row 7 : 7 | 2013-07-01 00:00:00 | songdo global university campus, incheon | iraq | 4 - 1 | 12 - 3 | 2013 asian indoor-martial arts games row 8 : 8 | 2013-07-06 00:00:00 | songdo global university campus, incheon | japan | 5 - 2 | 5 - 2 | 2013 asian indoor-martial arts games row 9 : none | 2013-10-19 00:00:00 | sao keatano, brazil | bank | 3 - 0 | 3 - 0 | unofficial friendly row 10 : 9 | 2013-10-21 00:00:00 | maringa's futsal stadium, brazil | guatemala | none | 7 - 0 | friendly row 11 : 10 | 2013-10-23 00:00:00 | ginasio chico neto, maringa | argentina | 2 - 1 | 2 - 2 | 2013 grand prix de futsal

col : venue row 1 : sao keatano, brazil

select venue where # > 9 <table_name> : table \begin{tabular}{llllllll} \toprule & # & date & venue & opponent & score & result & competition \\ \midrule 0 & 1 & 2013-06-13 00:00:00 & zhejiang dragon sports centre, hangzhou & russia & 1 - 0 & 5 - 1 & friendly \\ 1 & 2 & 2013-06-13 00:00:00 & zhejiang dragon sports centre, hangzhou & russia & 3 - 0 & 5 - 1 & friendly \\ 2 & 3 & 2013-06-14 00:00:00 & zhejiang dragon sports centre, hangzhou & netherlands & none & 4 - 3 & friendly \\ 3 & 4 & 2013-06-14 00:00:00 & zhejiang dragon sports centre, hangzhou & netherlands & none & 4 - 3 & friendly \\ 4 & 5 & 2013-06-15 00:00:00 & zhejiang dragon sports centre, hangzhou & china & none & 5 - 2 & friendly \\ 5 & 6 & 2013-07-01 00:00:00 & songdo global university campus, incheon & iraq & 3 - 1 & 12 - 3 & 2013 asian indoor-martial arts games \\ 6 & 7 & 2013-07-01 00:00:00 & songdo global university campus, incheon & iraq & 4 - 1 & 12 - 3 & 2013 asian indoor-martial arts games \\ 7 & 8 & 2013-07-06 00:00:00 & songdo global university campus, incheon & japan & 5 - 2 & 5 - 2 & 2013 asian indoor-martial arts games \\ 8 & none & 2013-10-19 00:00:00 & sao keatano, brazil & bank & 3 - 0 & 3 - 0 & unofficial friendly \\ 9 & 9 & 2013-10-21 00:00:00 & maringa's futsal stadium, brazil & guatemala & none & 7 - 0 & friendly \\ 10 & 10 & 2013-10-23 00:00:00 & ginasio chico neto, maringa & argentina & 2 - 1 & 2 - 2 & 2013 grand prix de futsal \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & venue \\ \midrule 0 & sao keatano, brazil \\ \bottomrule \end{tabular}

select venue where # > 9 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>#</th> <th>date</th> <th>venue</th> <th>opponent</th> <th>score</th> <th>result</th> <th>competition</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>2013-06-13 00:00:00</td> <td>zhejiang dragon sports centre, hangzhou</td> <td>russia</td> <td>1 - 0</td> <td>5 - 1</td> <td>friendly</td> </tr> <tr> <th>1</th> <td>2</td> <td>2013-06-13 00:00:00</td> <td>zhejiang dragon sports centre, hangzhou</td> <td>russia</td> <td>3 - 0</td> <td>5 - 1</td> <td>friendly</td> </tr> <tr> <th>2</th> <td>3</td> <td>2013-06-14 00:00:00</td> <td>zhejiang dragon sports centre, hangzhou</td> <td>netherlands</td> <td>none</td> <td>4 - 3</td> <td>friendly</td> </tr> <tr> <th>3</th> <td>4</td> <td>2013-06-14 00:00:00</td> <td>zhejiang dragon sports centre, hangzhou</td> <td>netherlands</td> <td>none</td> <td>4 - 3</td> <td>friendly</td> </tr> <tr> <th>4</th> <td>5</td> <td>2013-06-15 00:00:00</td> <td>zhejiang dragon sports centre, hangzhou</td> <td>china</td> <td>none</td> <td>5 - 2</td> <td>friendly</td> </tr> <tr> <th>5</th> <td>6</td> <td>2013-07-01 00:00:00</td> <td>songdo global university campus, incheon</td> <td>iraq</td> <td>3 - 1</td> <td>12 - 3</td> <td>2013 asian indoor-martial arts games</td> </tr> <tr> <th>6</th> <td>7</td> <td>2013-07-01 00:00:00</td> <td>songdo global university campus, incheon</td> <td>iraq</td> <td>4 - 1</td> <td>12 - 3</td> <td>2013 asian indoor-martial arts games</td> </tr> <tr> <th>7</th> <td>8</td> <td>2013-07-06 00:00:00</td> <td>songdo global university campus, incheon</td> <td>japan</td> <td>5 - 2</td> <td>5 - 2</td> <td>2013 asian indoor-martial arts games</td> </tr> <tr> <th>8</th> <td>none</td> <td>2013-10-19 00:00:00</td> <td>sao keatano, brazil</td> <td>bank</td> <td>3 - 0</td> <td>3 - 0</td> <td>unofficial friendly</td> </tr> <tr> <th>9</th> <td>9</td> <td>2013-10-21 00:00:00</td> <td>maringa's futsal stadium, brazil</td> <td>guatemala</td> <td>none</td> <td>7 - 0</td> <td>friendly</td> </tr> <tr> <th>10</th> <td>10</td> <td>2013-10-23 00:00:00</td> <td>ginasio chico neto, maringa</td> <td>argentina</td> <td>2 - 1</td> <td>2 - 2</td> <td>2013 grand prix de futsal</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>venue</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>sao keatano, brazil</td> </tr> </tbody> </table>

select venue where # > 9 <table_name> : table | | # | date | venue | opponent | score | result | competition | |---:|:-----|:--------------------|:-----------------------------------------|:------------|:--------|:---------|:-------------------------------------| | 0 | 1 | 2013-06-13 00:00:00 | zhejiang dragon sports centre, hangzhou | russia | 1 - 0 | 5 - 1 | friendly | | 1 | 2 | 2013-06-13 00:00:00 | zhejiang dragon sports centre, hangzhou | russia | 3 - 0 | 5 - 1 | friendly | | 2 | 3 | 2013-06-14 00:00:00 | zhejiang dragon sports centre, hangzhou | netherlands | none | 4 - 3 | friendly | | 3 | 4 | 2013-06-14 00:00:00 | zhejiang dragon sports centre, hangzhou | netherlands | none | 4 - 3 | friendly | | 4 | 5 | 2013-06-15 00:00:00 | zhejiang dragon sports centre, hangzhou | china | none | 5 - 2 | friendly | | 5 | 6 | 2013-07-01 00:00:00 | songdo global university campus, incheon | iraq | 3 - 1 | 12 - 3 | 2013 asian indoor-martial arts games | | 6 | 7 | 2013-07-01 00:00:00 | songdo global university campus, incheon | iraq | 4 - 1 | 12 - 3 | 2013 asian indoor-martial arts games | | 7 | 8 | 2013-07-06 00:00:00 | songdo global university campus, incheon | japan | 5 - 2 | 5 - 2 | 2013 asian indoor-martial arts games | | 8 | none | 2013-10-19 00:00:00 | sao keatano, brazil | bank | 3 - 0 | 3 - 0 | unofficial friendly | | 9 | 9 | 2013-10-21 00:00:00 | maringa's futsal stadium, brazil | guatemala | none | 7 - 0 | friendly | | 10 | 10 | 2013-10-23 00:00:00 | ginasio chico neto, maringa | argentina | 2 - 1 | 2 - 2 | 2013 grand prix de futsal |

| | venue | |---:|:--------------------| | 0 | sao keatano, brazil |

select count ( builder ) where notes = 'became inspection car in 1932 retired 1941' col : number | builder | type | date | length | notes row 1 : 51 | american car company | motor | 1904 | 47'9" | retired 1933 row 2 : 53 | american car company | motor | 1904 | 47'9" | retired 1933 row 3 : 55 | american car company | motor | 1904 | 47'9" | became inspection car in 1932 retired 1941 row 4 : 57 | american car company | motor | 1904 | 47'9" | became inspection car in 1932 retired 1941 row 5 : 59 | w. l. holman car company | motor | 1904 | 44' | retired 1932 row 6 : 61 | w. l. holman car company | motor | 1904 | 44' | retired 1932 row 7 : 63 | w. l. holman car company | motor | 1904 | 44' | retired 1932 preserved western railway museum row 8 : 65 | w. l. holman car company | motor | 1904 | 44' | retired 1932 row 9 : 67 | w. l. holman car company | motor | 1904 | 44' | retired 1932 row 10 : 69 | w. l. holman car company | motor | 1904 | 44' | converted to express trailer in 1919 retired 1932 row 11 : 71 | w. l. holman car company | trailer | 1905 | 44' | retired 1929 row 12 : 73 | w. l. holman car company | trailer | 1905 | 44' | retired 1929 row 13 : 1 | w. l. holman car company | express trailer | 1916 | none | built as #8 express motor demotorized 1917 designated express trailer 1920 retired 1933

col : count ( "builder" ) row 1 : 2

select count ( builder ) where notes = 'became inspection car in 1932 retired 1941' <table_name> : table \begin{tabular}{lllllll} \toprule & number & builder & type & date & length & notes \\ \midrule 0 & 51 & american car company & motor & 1904 & 47'9" & retired 1933 \\ 1 & 53 & american car company & motor & 1904 & 47'9" & retired 1933 \\ 2 & 55 & american car company & motor & 1904 & 47'9" & became inspection car in 1932 retired 1941 \\ 3 & 57 & american car company & motor & 1904 & 47'9" & became inspection car in 1932 retired 1941 \\ 4 & 59 & w. l. holman car company & motor & 1904 & 44' & retired 1932 \\ 5 & 61 & w. l. holman car company & motor & 1904 & 44' & retired 1932 \\ 6 & 63 & w. l. holman car company & motor & 1904 & 44' & retired 1932 preserved western railway museum \\ 7 & 65 & w. l. holman car company & motor & 1904 & 44' & retired 1932 \\ 8 & 67 & w. l. holman car company & motor & 1904 & 44' & retired 1932 \\ 9 & 69 & w. l. holman car company & motor & 1904 & 44' & converted to express trailer in 1919 retired 1932 \\ 10 & 71 & w. l. holman car company & trailer & 1905 & 44' & retired 1929 \\ 11 & 73 & w. l. holman car company & trailer & 1905 & 44' & retired 1929 \\ 12 & 1 & w. l. holman car company & express trailer & 1916 & none & built as #8 express motor demotorized 1917 designated express trailer 1920 retired 1933 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "builder" ) \\ \midrule 0 & 2 \\ \bottomrule \end{tabular}

select count ( builder ) where notes = 'became inspection car in 1932 retired 1941' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>number</th> <th>builder</th> <th>type</th> <th>date</th> <th>length</th> <th>notes</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>51</td> <td>american car company</td> <td>motor</td> <td>1904</td> <td>47'9"</td> <td>retired 1933</td> </tr> <tr> <th>1</th> <td>53</td> <td>american car company</td> <td>motor</td> <td>1904</td> <td>47'9"</td> <td>retired 1933</td> </tr> <tr> <th>2</th> <td>55</td> <td>american car company</td> <td>motor</td> <td>1904</td> <td>47'9"</td> <td>became inspection car in 1932 retired 1941</td> </tr> <tr> <th>3</th> <td>57</td> <td>american car company</td> <td>motor</td> <td>1904</td> <td>47'9"</td> <td>became inspection car in 1932 retired 1941</td> </tr> <tr> <th>4</th> <td>59</td> <td>w. l. holman car company</td> <td>motor</td> <td>1904</td> <td>44'</td> <td>retired 1932</td> </tr> <tr> <th>5</th> <td>61</td> <td>w. l. holman car company</td> <td>motor</td> <td>1904</td> <td>44'</td> <td>retired 1932</td> </tr> <tr> <th>6</th> <td>63</td> <td>w. l. holman car company</td> <td>motor</td> <td>1904</td> <td>44'</td> <td>retired 1932 preserved western railway museum</td> </tr> <tr> <th>7</th> <td>65</td> <td>w. l. holman car company</td> <td>motor</td> <td>1904</td> <td>44'</td> <td>retired 1932</td> </tr> <tr> <th>8</th> <td>67</td> <td>w. l. holman car company</td> <td>motor</td> <td>1904</td> <td>44'</td> <td>retired 1932</td> </tr> <tr> <th>9</th> <td>69</td> <td>w. l. holman car company</td> <td>motor</td> <td>1904</td> <td>44'</td> <td>converted to express trailer in 1919 retired 1932</td> </tr> <tr> <th>10</th> <td>71</td> <td>w. l. holman car company</td> <td>trailer</td> <td>1905</td> <td>44'</td> <td>retired 1929</td> </tr> <tr> <th>11</th> <td>73</td> <td>w. l. holman car company</td> <td>trailer</td> <td>1905</td> <td>44'</td> <td>retired 1929</td> </tr> <tr> <th>12</th> <td>1</td> <td>w. l. holman car company</td> <td>express trailer</td> <td>1916</td> <td>none</td> <td>built as #8 express motor demotorized 1917 designated express trailer 1920 retired 1933</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "builder" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>2</td> </tr> </tbody> </table>

select count ( builder ) where notes = 'became inspection car in 1932 retired 1941' <table_name> : table | | number | builder | type | date | length | notes | |---:|---------:|:-------------------------|:----------------|-------:|:---------|:----------------------------------------------------------------------------------------| | 0 | 51 | american car company | motor | 1904 | 47'9" | retired 1933 | | 1 | 53 | american car company | motor | 1904 | 47'9" | retired 1933 | | 2 | 55 | american car company | motor | 1904 | 47'9" | became inspection car in 1932 retired 1941 | | 3 | 57 | american car company | motor | 1904 | 47'9" | became inspection car in 1932 retired 1941 | | 4 | 59 | w. l. holman car company | motor | 1904 | 44' | retired 1932 | | 5 | 61 | w. l. holman car company | motor | 1904 | 44' | retired 1932 | | 6 | 63 | w. l. holman car company | motor | 1904 | 44' | retired 1932 preserved western railway museum | | 7 | 65 | w. l. holman car company | motor | 1904 | 44' | retired 1932 | | 8 | 67 | w. l. holman car company | motor | 1904 | 44' | retired 1932 | | 9 | 69 | w. l. holman car company | motor | 1904 | 44' | converted to express trailer in 1919 retired 1932 | | 10 | 71 | w. l. holman car company | trailer | 1905 | 44' | retired 1929 | | 11 | 73 | w. l. holman car company | trailer | 1905 | 44' | retired 1929 | | 12 | 1 | w. l. holman car company | express trailer | 1916 | none | built as #8 express motor demotorized 1917 designated express trailer 1920 retired 1933 |

| | count ( "builder" ) | |---:|----------------------:| | 0 | 2 |

select count ( * ) where fa cup goals = 0 col : name | nation | position | league apps | league goals | fa cup apps | fa cup goals | total apps | total goals row 1 : ned barkas | england | df | 4 | 0 | 0 | 0 | 4 | 0 row 2 : harry brough | england | df | 9 | 0 | 0 | 0 | 9 | 0 row 3 : george brown | england | fw | 6 | 4 | 0 | 0 | 6 | 4 row 4 : jack byers | england | mf | 5 | 1 | 0 | 0 | 5 | 1 row 5 : harry cawthorne | england | df | 5 | 0 | 0 | 0 | 5 | 0 row 6 : ted davis | england | gk | 27 | 0 | 7 | 0 | 34 | 0 row 7 : ernie islip | england | fw | 34 | 11 | 9 | 4 | 43 | 15 row 8 : billy johnston | england | fw | 21 | 2 | 2 | 0 | 23 | 2 row 9 : robert jones | england | mf | 2 | 1 | 0 | 0 | 2 | 1 row 10 : frank mann | england | fw | 40 | 5 | 8 | 4 | 48 | 9 row 11 : len marlow | england | fw | 1 | 0 | 0 | 0 | 1 | 0 row 12 : colin mckay | scotland | df | 14 | 2 | 0 | 0 | 14 | 2 row 13 : sandy mutch | scotland | gk | 15 | 0 | 2 | 0 | 17 | 0 row 14 : stan pearson | england | mf | 1 | 0 | 1 | 0 | 2 | 0 row 15 : george richardson | england | mf | 14 | 1 | 8 | 1 | 22 | 2 row 16 : charlie slade | england | df | 25 | 0 | 9 | 0 | 34 | 0 row 17 : billy e. smith | england | fw | 1 | 0 | 0 | 0 | 1 | 0 row 18 : billy h. smith | england | mf | 40 | 8 | 9 | 4 | 49 | 12 row 19 : clem stephenson | england | fw | 39 | 9 | 9 | 6 | 48 | 15 row 20 : jack swann | england | fw | 9 | 3 | 0 | 0 | 9 | 3 row 21 : sam wadsworth | england | df | 39 | 1 | 9 | 0 | 48 | 1 row 22 : billy watson | england | df | 36 | 0 | 9 | 1 | 45 | 1 row 23 : tom wilson | england | df | 39 | 1 | 9 | 0 | 48 | 1 row 24 : james wood | england | df | 36 | 0 | 9 | 0 | 45 | 0

col : count ( "*" ) row 1 : 18

select count ( * ) where fa cup goals = 0 <table_name> : table \begin{tabular}{llllllllll} \toprule & name & nation & position & league apps & league goals & fa cup apps & fa cup goals & total apps & total goals \\ \midrule 0 & ned barkas & england & df & 4 & 0 & 0 & 0 & 4 & 0 \\ 1 & harry brough & england & df & 9 & 0 & 0 & 0 & 9 & 0 \\ 2 & george brown & england & fw & 6 & 4 & 0 & 0 & 6 & 4 \\ 3 & jack byers & england & mf & 5 & 1 & 0 & 0 & 5 & 1 \\ 4 & harry cawthorne & england & df & 5 & 0 & 0 & 0 & 5 & 0 \\ 5 & ted davis & england & gk & 27 & 0 & 7 & 0 & 34 & 0 \\ 6 & ernie islip & england & fw & 34 & 11 & 9 & 4 & 43 & 15 \\ 7 & billy johnston & england & fw & 21 & 2 & 2 & 0 & 23 & 2 \\ 8 & robert jones & england & mf & 2 & 1 & 0 & 0 & 2 & 1 \\ 9 & frank mann & england & fw & 40 & 5 & 8 & 4 & 48 & 9 \\ 10 & len marlow & england & fw & 1 & 0 & 0 & 0 & 1 & 0 \\ 11 & colin mckay & scotland & df & 14 & 2 & 0 & 0 & 14 & 2 \\ 12 & sandy mutch & scotland & gk & 15 & 0 & 2 & 0 & 17 & 0 \\ 13 & stan pearson & england & mf & 1 & 0 & 1 & 0 & 2 & 0 \\ 14 & george richardson & england & mf & 14 & 1 & 8 & 1 & 22 & 2 \\ 15 & charlie slade & england & df & 25 & 0 & 9 & 0 & 34 & 0 \\ 16 & billy e. smith & england & fw & 1 & 0 & 0 & 0 & 1 & 0 \\ 17 & billy h. smith & england & mf & 40 & 8 & 9 & 4 & 49 & 12 \\ 18 & clem stephenson & england & fw & 39 & 9 & 9 & 6 & 48 & 15 \\ 19 & jack swann & england & fw & 9 & 3 & 0 & 0 & 9 & 3 \\ 20 & sam wadsworth & england & df & 39 & 1 & 9 & 0 & 48 & 1 \\ 21 & billy watson & england & df & 36 & 0 & 9 & 1 & 45 & 1 \\ 22 & tom wilson & england & df & 39 & 1 & 9 & 0 & 48 & 1 \\ 23 & james wood & england & df & 36 & 0 & 9 & 0 & 45 & 0 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 18 \\ \bottomrule \end{tabular}

select count ( * ) where fa cup goals = 0 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>name</th> <th>nation</th> <th>position</th> <th>league apps</th> <th>league goals</th> <th>fa cup apps</th> <th>fa cup goals</th> <th>total apps</th> <th>total goals</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>ned barkas</td> <td>england</td> <td>df</td> <td>4</td> <td>0</td> <td>0</td> <td>0</td> <td>4</td> <td>0</td> </tr> <tr> <th>1</th> <td>harry brough</td> <td>england</td> <td>df</td> <td>9</td> <td>0</td> <td>0</td> <td>0</td> <td>9</td> <td>0</td> </tr> <tr> <th>2</th> <td>george brown</td> <td>england</td> <td>fw</td> <td>6</td> <td>4</td> <td>0</td> <td>0</td> <td>6</td> <td>4</td> </tr> <tr> <th>3</th> <td>jack byers</td> <td>england</td> <td>mf</td> <td>5</td> <td>1</td> <td>0</td> <td>0</td> <td>5</td> <td>1</td> </tr> <tr> <th>4</th> <td>harry cawthorne</td> <td>england</td> <td>df</td> <td>5</td> <td>0</td> <td>0</td> <td>0</td> <td>5</td> <td>0</td> </tr> <tr> <th>5</th> <td>ted davis</td> <td>england</td> <td>gk</td> <td>27</td> <td>0</td> <td>7</td> <td>0</td> <td>34</td> <td>0</td> </tr> <tr> <th>6</th> <td>ernie islip</td> <td>england</td> <td>fw</td> <td>34</td> <td>11</td> <td>9</td> <td>4</td> <td>43</td> <td>15</td> </tr> <tr> <th>7</th> <td>billy johnston</td> <td>england</td> <td>fw</td> <td>21</td> <td>2</td> <td>2</td> <td>0</td> <td>23</td> <td>2</td> </tr> <tr> <th>8</th> <td>robert jones</td> <td>england</td> <td>mf</td> <td>2</td> <td>1</td> <td>0</td> <td>0</td> <td>2</td> <td>1</td> </tr> <tr> <th>9</th> <td>frank mann</td> <td>england</td> <td>fw</td> <td>40</td> <td>5</td> <td>8</td> <td>4</td> <td>48</td> <td>9</td> </tr> <tr> <th>10</th> <td>len marlow</td> <td>england</td> <td>fw</td> <td>1</td> <td>0</td> <td>0</td> <td>0</td> <td>1</td> <td>0</td> </tr> <tr> <th>11</th> <td>colin mckay</td> <td>scotland</td> <td>df</td> <td>14</td> <td>2</td> <td>0</td> <td>0</td> <td>14</td> <td>2</td> </tr> <tr> <th>12</th> <td>sandy mutch</td> <td>scotland</td> <td>gk</td> <td>15</td> <td>0</td> <td>2</td> <td>0</td> <td>17</td> <td>0</td> </tr> <tr> <th>13</th> <td>stan pearson</td> <td>england</td> <td>mf</td> <td>1</td> <td>0</td> <td>1</td> <td>0</td> <td>2</td> <td>0</td> </tr> <tr> <th>14</th> <td>george richardson</td> <td>england</td> <td>mf</td> <td>14</td> <td>1</td> <td>8</td> <td>1</td> <td>22</td> <td>2</td> </tr> <tr> <th>15</th> <td>charlie slade</td> <td>england</td> <td>df</td> <td>25</td> <td>0</td> <td>9</td> <td>0</td> <td>34</td> <td>0</td> </tr> <tr> <th>16</th> <td>billy e. smith</td> <td>england</td> <td>fw</td> <td>1</td> <td>0</td> <td>0</td> <td>0</td> <td>1</td> <td>0</td> </tr> <tr> <th>17</th> <td>billy h. smith</td> <td>england</td> <td>mf</td> <td>40</td> <td>8</td> <td>9</td> <td>4</td> <td>49</td> <td>12</td> </tr> <tr> <th>18</th> <td>clem stephenson</td> <td>england</td> <td>fw</td> <td>39</td> <td>9</td> <td>9</td> <td>6</td> <td>48</td> <td>15</td> </tr> <tr> <th>19</th> <td>jack swann</td> <td>england</td> <td>fw</td> <td>9</td> <td>3</td> <td>0</td> <td>0</td> <td>9</td> <td>3</td> </tr> <tr> <th>20</th> <td>sam wadsworth</td> <td>england</td> <td>df</td> <td>39</td> <td>1</td> <td>9</td> <td>0</td> <td>48</td> <td>1</td> </tr> <tr> <th>21</th> <td>billy watson</td> <td>england</td> <td>df</td> <td>36</td> <td>0</td> <td>9</td> <td>1</td> <td>45</td> <td>1</td> </tr> <tr> <th>22</th> <td>tom wilson</td> <td>england</td> <td>df</td> <td>39</td> <td>1</td> <td>9</td> <td>0</td> <td>48</td> <td>1</td> </tr> <tr> <th>23</th> <td>james wood</td> <td>england</td> <td>df</td> <td>36</td> <td>0</td> <td>9</td> <td>0</td> <td>45</td> <td>0</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "*" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>18</td> </tr> </tbody> </table>

select count ( * ) where fa cup goals = 0 <table_name> : table | | name | nation | position | league apps | league goals | fa cup apps | fa cup goals | total apps | total goals | |---:|:------------------|:---------|:-----------|--------------:|---------------:|--------------:|---------------:|-------------:|--------------:| | 0 | ned barkas | england | df | 4 | 0 | 0 | 0 | 4 | 0 | | 1 | harry brough | england | df | 9 | 0 | 0 | 0 | 9 | 0 | | 2 | george brown | england | fw | 6 | 4 | 0 | 0 | 6 | 4 | | 3 | jack byers | england | mf | 5 | 1 | 0 | 0 | 5 | 1 | | 4 | harry cawthorne | england | df | 5 | 0 | 0 | 0 | 5 | 0 | | 5 | ted davis | england | gk | 27 | 0 | 7 | 0 | 34 | 0 | | 6 | ernie islip | england | fw | 34 | 11 | 9 | 4 | 43 | 15 | | 7 | billy johnston | england | fw | 21 | 2 | 2 | 0 | 23 | 2 | | 8 | robert jones | england | mf | 2 | 1 | 0 | 0 | 2 | 1 | | 9 | frank mann | england | fw | 40 | 5 | 8 | 4 | 48 | 9 | | 10 | len marlow | england | fw | 1 | 0 | 0 | 0 | 1 | 0 | | 11 | colin mckay | scotland | df | 14 | 2 | 0 | 0 | 14 | 2 | | 12 | sandy mutch | scotland | gk | 15 | 0 | 2 | 0 | 17 | 0 | | 13 | stan pearson | england | mf | 1 | 0 | 1 | 0 | 2 | 0 | | 14 | george richardson | england | mf | 14 | 1 | 8 | 1 | 22 | 2 | | 15 | charlie slade | england | df | 25 | 0 | 9 | 0 | 34 | 0 | | 16 | billy e. smith | england | fw | 1 | 0 | 0 | 0 | 1 | 0 | | 17 | billy h. smith | england | mf | 40 | 8 | 9 | 4 | 49 | 12 | | 18 | clem stephenson | england | fw | 39 | 9 | 9 | 6 | 48 | 15 | | 19 | jack swann | england | fw | 9 | 3 | 0 | 0 | 9 | 3 | | 20 | sam wadsworth | england | df | 39 | 1 | 9 | 0 | 48 | 1 | | 21 | billy watson | england | df | 36 | 0 | 9 | 1 | 45 | 1 | | 22 | tom wilson | england | df | 39 | 1 | 9 | 0 | 48 | 1 | | 23 | james wood | england | df | 36 | 0 | 9 | 0 | 45 | 0 |

| | count ( "*" ) | |---:|----------------:| | 0 | 18 |

select max ( tier ) where notes = 'promoted' col : season | tier | division | pos. | notes row 1 : 2002-03 | 3 | 1a estatal | 9th | none row 2 : 2003-04 | 3 | 1a estatal | 3rd | none row 3 : 2004-05 | 3 | 1a estatal | 3rd | none row 4 : 2005-06 | 3 | 1a estatal | 2nd | promoted row 5 : 2006-07 | 2 | honor b | 10th | none row 6 : 2007-08 | 2 | honor b | 11th | none row 7 : 2008-09 | 2 | honor b | 11th | none row 8 : 2009-10 | 2 | plata | 5th | none row 9 : 2010-11 | 2 | plata | 9th | none row 10 : 2011-12 | 2 | plata | 2nd | promoted row 11 : 2012-13 | 1 | asobal | 16th | relegated row 12 : 2013-14 | 2 | plata | none | none

col : max ( "tier" ) row 1 : 3

select max ( tier ) where notes = 'promoted' <table_name> : table \begin{tabular}{llllll} \toprule & season & tier & division & pos. & notes \\ \midrule 0 & 2002-03 & 3 & 1a estatal & 9th & none \\ 1 & 2003-04 & 3 & 1a estatal & 3rd & none \\ 2 & 2004-05 & 3 & 1a estatal & 3rd & none \\ 3 & 2005-06 & 3 & 1a estatal & 2nd & promoted \\ 4 & 2006-07 & 2 & honor b & 10th & none \\ 5 & 2007-08 & 2 & honor b & 11th & none \\ 6 & 2008-09 & 2 & honor b & 11th & none \\ 7 & 2009-10 & 2 & plata & 5th & none \\ 8 & 2010-11 & 2 & plata & 9th & none \\ 9 & 2011-12 & 2 & plata & 2nd & promoted \\ 10 & 2012-13 & 1 & asobal & 16th & relegated \\ 11 & 2013-14 & 2 & plata & none & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & max ( "tier" ) \\ \midrule 0 & 3 \\ \bottomrule \end{tabular}

select max ( tier ) where notes = 'promoted' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>season</th> <th>tier</th> <th>division</th> <th>pos.</th> <th>notes</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>2002-03</td> <td>3</td> <td>1a estatal</td> <td>9th</td> <td>none</td> </tr> <tr> <th>1</th> <td>2003-04</td> <td>3</td> <td>1a estatal</td> <td>3rd</td> <td>none</td> </tr> <tr> <th>2</th> <td>2004-05</td> <td>3</td> <td>1a estatal</td> <td>3rd</td> <td>none</td> </tr> <tr> <th>3</th> <td>2005-06</td> <td>3</td> <td>1a estatal</td> <td>2nd</td> <td>promoted</td> </tr> <tr> <th>4</th> <td>2006-07</td> <td>2</td> <td>honor b</td> <td>10th</td> <td>none</td> </tr> <tr> <th>5</th> <td>2007-08</td> <td>2</td> <td>honor b</td> <td>11th</td> <td>none</td> </tr> <tr> <th>6</th> <td>2008-09</td> <td>2</td> <td>honor b</td> <td>11th</td> <td>none</td> </tr> <tr> <th>7</th> <td>2009-10</td> <td>2</td> <td>plata</td> <td>5th</td> <td>none</td> </tr> <tr> <th>8</th> <td>2010-11</td> <td>2</td> <td>plata</td> <td>9th</td> <td>none</td> </tr> <tr> <th>9</th> <td>2011-12</td> <td>2</td> <td>plata</td> <td>2nd</td> <td>promoted</td> </tr> <tr> <th>10</th> <td>2012-13</td> <td>1</td> <td>asobal</td> <td>16th</td> <td>relegated</td> </tr> <tr> <th>11</th> <td>2013-14</td> <td>2</td> <td>plata</td> <td>none</td> <td>none</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>max ( "tier" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>3</td> </tr> </tbody> </table>

select max ( tier ) where notes = 'promoted' <table_name> : table | | season | tier | division | pos. | notes | |---:|:---------|-------:|:-----------|:-------|:----------| | 0 | 2002-03 | 3 | 1a estatal | 9th | none | | 1 | 2003-04 | 3 | 1a estatal | 3rd | none | | 2 | 2004-05 | 3 | 1a estatal | 3rd | none | | 3 | 2005-06 | 3 | 1a estatal | 2nd | promoted | | 4 | 2006-07 | 2 | honor b | 10th | none | | 5 | 2007-08 | 2 | honor b | 11th | none | | 6 | 2008-09 | 2 | honor b | 11th | none | | 7 | 2009-10 | 2 | plata | 5th | none | | 8 | 2010-11 | 2 | plata | 9th | none | | 9 | 2011-12 | 2 | plata | 2nd | promoted | | 10 | 2012-13 | 1 | asobal | 16th | relegated | | 11 | 2013-14 | 2 | plata | none | none |

| | max ( "tier" ) | |---:|-----------------:| | 0 | 3 |

select total where nation = 'jamaica' col : rank | nation | gold | silver | bronze | total row 1 : 1 | united states | 5 | 6 | 5 | 16 row 2 : 2 | jamaica | 4 | 1 | 1 | 6 row 3 : 3 | netherlands | 2 | 0 | 0 | 2 row 4 : 4 | bahamas | 1 | 1 | 0 | 2 row 5 : 5 | ukraine | 1 | 0 | 1 | 2 row 6 : 6 | east germany | 1 | 0 | 0 | 1 row 7 : 6 | greece | 1 | 0 | 0 | 1 row 8 : 6 | soviet union | 1 | 0 | 0 | 1 row 9 : 9 | ivory coast | 0 | 2 | 0 | 2 row 10 : 9 | united kingdom | 0 | 2 | 0 | 2 row 11 : 11 | belgium | 0 | 1 | 1 | 2 row 12 : 12 | bulgaria | 0 | 1 | 0 | 1 row 13 : 12 | russia | 0 | 1 | 0 | 1 row 14 : 12 | germany | 0 | 1 | 0 | 1 row 15 : 15 | canada | 0 | 0 | 2 | 2 row 16 : 15 | france | 0 | 0 | 2 | 2 row 17 : 17 | belarus | 0 | 0 | 1 | 1 row 18 : 17 | cuba | 0 | 0 | 1 | 1 row 19 : 17 | gabon | 0 | 0 | 1 | 1 row 20 : 17 | british virgin islands | 0 | 0 | 1 | 1 row 21 : total | total | 16 | 16 | 16 | 48

col : total row 1 : 6

select total where nation = 'jamaica' <table_name> : table \begin{tabular}{lllllll} \toprule & rank & nation & gold & silver & bronze & total \\ \midrule 0 & 1 & united states & 5 & 6 & 5 & 16 \\ 1 & 2 & jamaica & 4 & 1 & 1 & 6 \\ 2 & 3 & netherlands & 2 & 0 & 0 & 2 \\ 3 & 4 & bahamas & 1 & 1 & 0 & 2 \\ 4 & 5 & ukraine & 1 & 0 & 1 & 2 \\ 5 & 6 & east germany & 1 & 0 & 0 & 1 \\ 6 & 6 & greece & 1 & 0 & 0 & 1 \\ 7 & 6 & soviet union & 1 & 0 & 0 & 1 \\ 8 & 9 & ivory coast & 0 & 2 & 0 & 2 \\ 9 & 9 & united kingdom & 0 & 2 & 0 & 2 \\ 10 & 11 & belgium & 0 & 1 & 1 & 2 \\ 11 & 12 & bulgaria & 0 & 1 & 0 & 1 \\ 12 & 12 & russia & 0 & 1 & 0 & 1 \\ 13 & 12 & germany & 0 & 1 & 0 & 1 \\ 14 & 15 & canada & 0 & 0 & 2 & 2 \\ 15 & 15 & france & 0 & 0 & 2 & 2 \\ 16 & 17 & belarus & 0 & 0 & 1 & 1 \\ 17 & 17 & cuba & 0 & 0 & 1 & 1 \\ 18 & 17 & gabon & 0 & 0 & 1 & 1 \\ 19 & 17 & british virgin islands & 0 & 0 & 1 & 1 \\ 20 & total & total & 16 & 16 & 16 & 48 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & total \\ \midrule 0 & 6 \\ \bottomrule \end{tabular}

select total where nation = 'jamaica' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>rank</th> <th>nation</th> <th>gold</th> <th>silver</th> <th>bronze</th> <th>total</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>united states</td> <td>5</td> <td>6</td> <td>5</td> <td>16</td> </tr> <tr> <th>1</th> <td>2</td> <td>jamaica</td> <td>4</td> <td>1</td> <td>1</td> <td>6</td> </tr> <tr> <th>2</th> <td>3</td> <td>netherlands</td> <td>2</td> <td>0</td> <td>0</td> <td>2</td> </tr> <tr> <th>3</th> <td>4</td> <td>bahamas</td> <td>1</td> <td>1</td> <td>0</td> <td>2</td> </tr> <tr> <th>4</th> <td>5</td> <td>ukraine</td> <td>1</td> <td>0</td> <td>1</td> <td>2</td> </tr> <tr> <th>5</th> <td>6</td> <td>east germany</td> <td>1</td> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <th>6</th> <td>6</td> <td>greece</td> <td>1</td> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <th>7</th> <td>6</td> <td>soviet union</td> <td>1</td> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <th>8</th> <td>9</td> <td>ivory coast</td> <td>0</td> <td>2</td> <td>0</td> <td>2</td> </tr> <tr> <th>9</th> <td>9</td> <td>united kingdom</td> <td>0</td> <td>2</td> <td>0</td> <td>2</td> </tr> <tr> <th>10</th> <td>11</td> <td>belgium</td> <td>0</td> <td>1</td> <td>1</td> <td>2</td> </tr> <tr> <th>11</th> <td>12</td> <td>bulgaria</td> <td>0</td> <td>1</td> <td>0</td> <td>1</td> </tr> <tr> <th>12</th> <td>12</td> <td>russia</td> <td>0</td> <td>1</td> <td>0</td> <td>1</td> </tr> <tr> <th>13</th> <td>12</td> <td>germany</td> <td>0</td> <td>1</td> <td>0</td> <td>1</td> </tr> <tr> <th>14</th> <td>15</td> <td>canada</td> <td>0</td> <td>0</td> <td>2</td> <td>2</td> </tr> <tr> <th>15</th> <td>15</td> <td>france</td> <td>0</td> <td>0</td> <td>2</td> <td>2</td> </tr> <tr> <th>16</th> <td>17</td> <td>belarus</td> <td>0</td> <td>0</td> <td>1</td> <td>1</td> </tr> <tr> <th>17</th> <td>17</td> <td>cuba</td> <td>0</td> <td>0</td> <td>1</td> <td>1</td> </tr> <tr> <th>18</th> <td>17</td> <td>gabon</td> <td>0</td> <td>0</td> <td>1</td> <td>1</td> </tr> <tr> <th>19</th> <td>17</td> <td>british virgin islands</td> <td>0</td> <td>0</td> <td>1</td> <td>1</td> </tr> <tr> <th>20</th> <td>total</td> <td>total</td> <td>16</td> <td>16</td> <td>16</td> <td>48</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>total</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>6</td> </tr> </tbody> </table>

select total where nation = 'jamaica' <table_name> : table | | rank | nation | gold | silver | bronze | total | |---:|:-------|:-----------------------|-------:|---------:|---------:|--------:| | 0 | 1 | united states | 5 | 6 | 5 | 16 | | 1 | 2 | jamaica | 4 | 1 | 1 | 6 | | 2 | 3 | netherlands | 2 | 0 | 0 | 2 | | 3 | 4 | bahamas | 1 | 1 | 0 | 2 | | 4 | 5 | ukraine | 1 | 0 | 1 | 2 | | 5 | 6 | east germany | 1 | 0 | 0 | 1 | | 6 | 6 | greece | 1 | 0 | 0 | 1 | | 7 | 6 | soviet union | 1 | 0 | 0 | 1 | | 8 | 9 | ivory coast | 0 | 2 | 0 | 2 | | 9 | 9 | united kingdom | 0 | 2 | 0 | 2 | | 10 | 11 | belgium | 0 | 1 | 1 | 2 | | 11 | 12 | bulgaria | 0 | 1 | 0 | 1 | | 12 | 12 | russia | 0 | 1 | 0 | 1 | | 13 | 12 | germany | 0 | 1 | 0 | 1 | | 14 | 15 | canada | 0 | 0 | 2 | 2 | | 15 | 15 | france | 0 | 0 | 2 | 2 | | 16 | 17 | belarus | 0 | 0 | 1 | 1 | | 17 | 17 | cuba | 0 | 0 | 1 | 1 | | 18 | 17 | gabon | 0 | 0 | 1 | 1 | | 19 | 17 | british virgin islands | 0 | 0 | 1 | 1 | | 20 | total | total | 16 | 16 | 16 | 48 |

| | total | |---:|--------:| | 0 | 6 |

select count ( player ) where year < 2011 col : year | player | position | high school | college row 1 : 1987 | buster howe | rb-db-k-p | zanesville | ohio state row 2 : 1988 | robert smith | rb | euclid | ohio state row 3 : 1989 | robert smith | rb | euclid | ohio state row 4 : 1990 | bobby hoying | qb | st. henry | ohio state row 5 : 1991 | derek kidwell | qb-de | fostoria | bowling green row 6 : 1992 | marc edwards | rb-lb | norwood | notre dame row 7 : 1993 | curtis enis | rb-lb | mississinawa valley | penn state row 8 : 1994 | charles woodson | db-rb | fremont ross | michigan row 9 : 1995 | andy katzenmoyer | lb | westerville south | ohio state row 10 : 1996 | derek combs | rb-db-kr | grove city | ohio state row 11 : 1997 | tony fisher | rb | euclid | notre dame row 12 : 1998 | ryan brewer | rb | troy | south carolina row 13 : 1999 | bam childress | db-wr-kr | st. peter chanel | ohio state row 14 : 2000 | jeff backes | rb-db | upper arlington | northwestern row 15 : 2001 | maurice clarett | rb | warren g. harding | ohio state row 16 : 2002 | ben mauk | qb | kenton | wake forest cincinnati row 17 : 2003 | ray williams | rb | cleveland benedictine | west virginia row 18 : 2004 | tyrell sutton | rb | archbishop hoban | northwestern row 19 : 2005 | delone carter | rb | copley | syracuse row 20 : 2006 | brandon saine | rb | piqua | ohio state row 21 : 2007 | bart tanski | qb | mentor | bowling green row 22 : 2008 | erick howard | rb | north canton hoover | akron row 23 : 2009 | erick howard | rb | north canton hoover | akron row 24 : 2010 | akise teague | rb-db-kr | youngstown ursuline | cincinnati row 25 : 2011 | maty mauk | qb | kenton | missouri row 26 : 2012 | mitch trubisky | qb | mentor | north carolina row 27 : 2013 | dante booker jr | lb | st. vincent - st. mary | ohio state

col : count ( "player" ) row 1 : 24

select count ( player ) where year < 2011 <table_name> : table \begin{tabular}{llllll} \toprule & year & player & position & high school & college \\ \midrule 0 & 1987 & buster howe & rb-db-k-p & zanesville & ohio state \\ 1 & 1988 & robert smith & rb & euclid & ohio state \\ 2 & 1989 & robert smith & rb & euclid & ohio state \\ 3 & 1990 & bobby hoying & qb & st. henry & ohio state \\ 4 & 1991 & derek kidwell & qb-de & fostoria & bowling green \\ 5 & 1992 & marc edwards & rb-lb & norwood & notre dame \\ 6 & 1993 & curtis enis & rb-lb & mississinawa valley & penn state \\ 7 & 1994 & charles woodson & db-rb & fremont ross & michigan \\ 8 & 1995 & andy katzenmoyer & lb & westerville south & ohio state \\ 9 & 1996 & derek combs & rb-db-kr & grove city & ohio state \\ 10 & 1997 & tony fisher & rb & euclid & notre dame \\ 11 & 1998 & ryan brewer & rb & troy & south carolina \\ 12 & 1999 & bam childress & db-wr-kr & st. peter chanel & ohio state \\ 13 & 2000 & jeff backes & rb-db & upper arlington & northwestern \\ 14 & 2001 & maurice clarett & rb & warren g. harding & ohio state \\ 15 & 2002 & ben mauk & qb & kenton & wake forest cincinnati \\ 16 & 2003 & ray williams & rb & cleveland benedictine & west virginia \\ 17 & 2004 & tyrell sutton & rb & archbishop hoban & northwestern \\ 18 & 2005 & delone carter & rb & copley & syracuse \\ 19 & 2006 & brandon saine & rb & piqua & ohio state \\ 20 & 2007 & bart tanski & qb & mentor & bowling green \\ 21 & 2008 & erick howard & rb & north canton hoover & akron \\ 22 & 2009 & erick howard & rb & north canton hoover & akron \\ 23 & 2010 & akise teague & rb-db-kr & youngstown ursuline & cincinnati \\ 24 & 2011 & maty mauk & qb & kenton & missouri \\ 25 & 2012 & mitch trubisky & qb & mentor & north carolina \\ 26 & 2013 & dante booker jr & lb & st. vincent - st. mary & ohio state \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "player" ) \\ \midrule 0 & 24 \\ \bottomrule \end{tabular}

select count ( player ) where year < 2011 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>year</th> <th>player</th> <th>position</th> <th>high school</th> <th>college</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1987</td> <td>buster howe</td> <td>rb-db-k-p</td> <td>zanesville</td> <td>ohio state</td> </tr> <tr> <th>1</th> <td>1988</td> <td>robert smith</td> <td>rb</td> <td>euclid</td> <td>ohio state</td> </tr> <tr> <th>2</th> <td>1989</td> <td>robert smith</td> <td>rb</td> <td>euclid</td> <td>ohio state</td> </tr> <tr> <th>3</th> <td>1990</td> <td>bobby hoying</td> <td>qb</td> <td>st. henry</td> <td>ohio state</td> </tr> <tr> <th>4</th> <td>1991</td> <td>derek kidwell</td> <td>qb-de</td> <td>fostoria</td> <td>bowling green</td> </tr> <tr> <th>5</th> <td>1992</td> <td>marc edwards</td> <td>rb-lb</td> <td>norwood</td> <td>notre dame</td> </tr> <tr> <th>6</th> <td>1993</td> <td>curtis enis</td> <td>rb-lb</td> <td>mississinawa valley</td> <td>penn state</td> </tr> <tr> <th>7</th> <td>1994</td> <td>charles woodson</td> <td>db-rb</td> <td>fremont ross</td> <td>michigan</td> </tr> <tr> <th>8</th> <td>1995</td> <td>andy katzenmoyer</td> <td>lb</td> <td>westerville south</td> <td>ohio state</td> </tr> <tr> <th>9</th> <td>1996</td> <td>derek combs</td> <td>rb-db-kr</td> <td>grove city</td> <td>ohio state</td> </tr> <tr> <th>10</th> <td>1997</td> <td>tony fisher</td> <td>rb</td> <td>euclid</td> <td>notre dame</td> </tr> <tr> <th>11</th> <td>1998</td> <td>ryan brewer</td> <td>rb</td> <td>troy</td> <td>south carolina</td> </tr> <tr> <th>12</th> <td>1999</td> <td>bam childress</td> <td>db-wr-kr</td> <td>st. peter chanel</td> <td>ohio state</td> </tr> <tr> <th>13</th> <td>2000</td> <td>jeff backes</td> <td>rb-db</td> <td>upper arlington</td> <td>northwestern</td> </tr> <tr> <th>14</th> <td>2001</td> <td>maurice clarett</td> <td>rb</td> <td>warren g. harding</td> <td>ohio state</td> </tr> <tr> <th>15</th> <td>2002</td> <td>ben mauk</td> <td>qb</td> <td>kenton</td> <td>wake forest cincinnati</td> </tr> <tr> <th>16</th> <td>2003</td> <td>ray williams</td> <td>rb</td> <td>cleveland benedictine</td> <td>west virginia</td> </tr> <tr> <th>17</th> <td>2004</td> <td>tyrell sutton</td> <td>rb</td> <td>archbishop hoban</td> <td>northwestern</td> </tr> <tr> <th>18</th> <td>2005</td> <td>delone carter</td> <td>rb</td> <td>copley</td> <td>syracuse</td> </tr> <tr> <th>19</th> <td>2006</td> <td>brandon saine</td> <td>rb</td> <td>piqua</td> <td>ohio state</td> </tr> <tr> <th>20</th> <td>2007</td> <td>bart tanski</td> <td>qb</td> <td>mentor</td> <td>bowling green</td> </tr> <tr> <th>21</th> <td>2008</td> <td>erick howard</td> <td>rb</td> <td>north canton hoover</td> <td>akron</td> </tr> <tr> <th>22</th> <td>2009</td> <td>erick howard</td> <td>rb</td> <td>north canton hoover</td> <td>akron</td> </tr> <tr> <th>23</th> <td>2010</td> <td>akise teague</td> <td>rb-db-kr</td> <td>youngstown ursuline</td> <td>cincinnati</td> </tr> <tr> <th>24</th> <td>2011</td> <td>maty mauk</td> <td>qb</td> <td>kenton</td> <td>missouri</td> </tr> <tr> <th>25</th> <td>2012</td> <td>mitch trubisky</td> <td>qb</td> <td>mentor</td> <td>north carolina</td> </tr> <tr> <th>26</th> <td>2013</td> <td>dante booker jr</td> <td>lb</td> <td>st. vincent - st. mary</td> <td>ohio state</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "player" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>24</td> </tr> </tbody> </table>

select count ( player ) where year < 2011 <table_name> : table | | year | player | position | high school | college | |---:|-------:|:-----------------|:-----------|:-----------------------|:-----------------------| | 0 | 1987 | buster howe | rb-db-k-p | zanesville | ohio state | | 1 | 1988 | robert smith | rb | euclid | ohio state | | 2 | 1989 | robert smith | rb | euclid | ohio state | | 3 | 1990 | bobby hoying | qb | st. henry | ohio state | | 4 | 1991 | derek kidwell | qb-de | fostoria | bowling green | | 5 | 1992 | marc edwards | rb-lb | norwood | notre dame | | 6 | 1993 | curtis enis | rb-lb | mississinawa valley | penn state | | 7 | 1994 | charles woodson | db-rb | fremont ross | michigan | | 8 | 1995 | andy katzenmoyer | lb | westerville south | ohio state | | 9 | 1996 | derek combs | rb-db-kr | grove city | ohio state | | 10 | 1997 | tony fisher | rb | euclid | notre dame | | 11 | 1998 | ryan brewer | rb | troy | south carolina | | 12 | 1999 | bam childress | db-wr-kr | st. peter chanel | ohio state | | 13 | 2000 | jeff backes | rb-db | upper arlington | northwestern | | 14 | 2001 | maurice clarett | rb | warren g. harding | ohio state | | 15 | 2002 | ben mauk | qb | kenton | wake forest cincinnati | | 16 | 2003 | ray williams | rb | cleveland benedictine | west virginia | | 17 | 2004 | tyrell sutton | rb | archbishop hoban | northwestern | | 18 | 2005 | delone carter | rb | copley | syracuse | | 19 | 2006 | brandon saine | rb | piqua | ohio state | | 20 | 2007 | bart tanski | qb | mentor | bowling green | | 21 | 2008 | erick howard | rb | north canton hoover | akron | | 22 | 2009 | erick howard | rb | north canton hoover | akron | | 23 | 2010 | akise teague | rb-db-kr | youngstown ursuline | cincinnati | | 24 | 2011 | maty mauk | qb | kenton | missouri | | 25 | 2012 | mitch trubisky | qb | mentor | north carolina | | 26 | 2013 | dante booker jr | lb | st. vincent - st. mary | ohio state |

| | count ( "player" ) | |---:|---------------------:| | 0 | 24 |

select conflict where battle, siege, or offensive = 'battle of chickamauga' col : battle, siege, or offensive | conflict | dates | estimated number of americans killed row 1 : meuse-argonne offensive | world war i | september 26 to november 11, 1918 | 26,277 killed row 2 : battle of the bulge | world war ii | december 16, 1944 to january 25, 1945 | 19,276 killed row 3 : battle of normandy | world war ii | june 6 to july 24, 1944 | 16,293 killed row 4 : battle of okinawa | world war ii | april 1 to june 21, 1945 | 12,513 killed row 5 : battle of hurtgen forest | world war ii | september 19, 1944 to february 10, 1945 | over 12,000 killed row 6 : battle of gettysburg | american civil war | july 1 to july 3, 1863 | 7,863 killed (3,155 union and 4,708 confederacy) row 7 : battle of guadalcanal | world war ii | august 7, 1942 to february 9, 1943 | 7,100 killed row 8 : battle of iwo jima | world war ii | february 16 to march 26, 1945 | 7,000 killed row 9 : battle of anzio | world war ii | january 22 to june 5, 1944 | 5,538 killed row 10 : battle of pusan perimeter | korean war | august to september, 1950 | 4,599 killed row 11 : battle of spotsylvania | american civil war | may 8 to may 21, 1864 | 4,192 killed (2,725 union and 1,467 confederacy) row 12 : battle of chickamauga | american civil war | september 19 to september 20, 1863 | 3,969 killed (1,657 union and 2,312 confederacy) row 13 : battle of the wilderness | american civil war | may 5 to may 7, 1864 | 3,741 killed (2,246 union and 1,495 confederacy) row 14 : battle of antietam | american civil war | september 17, 1862 | 3,654 killed (2,108 union and 1,546 confederacy) row 15 : battle of shiloh | american civil war | april 6 to april 7, 1862 | 3,482 killed (1,754 union and 1,728 confederacy) row 16 : battle of chancellorsville | american civil war | april 30 to may 6, 1863 | 3,271 killed (1,606 union and 1,665 confederacy) row 17 : battle of saipan | world war ii | june 15 to july 9 1944 | 2,949 killed row 18 : battle of sicily | world war ii | july 9 to august 17, 1943 | 2,572 killed row 19 : pearl harbor attack | world war ii | december 7, 1941 | 2,500 killed row 20 : battle of salerno | world war ii | september 3 - september 16, 1943 | 2,000 killed row 21 : battle of belleau wood | world war i | june 1 to june 26, 1918 | 1,811 killed row 22 : battle of peleliu | world war ii | september 15 to november 27, 1944 | 1,794 killed row 23 : second battle of guam | world war ii | july 21 to august 8, 1944 | 1,747 killed row 24 : battle of tarawa | world war ii | november 20 to november 23, 1943 | 1,677 killed row 25 : d-day (first day of operation overlord) | world war ii | june 6, 1944 | 1,465 killed row 26 : battle of chosin reservoir | korean war | november 27 to december 13, 1950 | 1,029 killed row 27 : battle for manila | world war ii | february 3 to march 3, 1945 | 1,010 killed row 28 : battle of the kasserine pass | world war ii | february 19 to february 25, 1943 | over 1,000 killed row 29 : battle of taejon | korean war | july 14 to july 21, 1950 | 922 killed row 30 : battle of khe sanh | vietnam war | 21 january to 8 april 1968 | 737 killed

col : conflict row 1 : american civil war

select conflict where battle, siege, or offensive = 'battle of chickamauga' <table_name> : table \begin{tabular}{lllll} \toprule & battle, siege, or offensive & conflict & dates & estimated number of americans killed \\ \midrule 0 & meuse-argonne offensive & world war i & september 26 to november 11, 1918 & 26,277 killed \\ 1 & battle of the bulge & world war ii & december 16, 1944 to january 25, 1945 & 19,276 killed \\ 2 & battle of normandy & world war ii & june 6 to july 24, 1944 & 16,293 killed \\ 3 & battle of okinawa & world war ii & april 1 to june 21, 1945 & 12,513 killed \\ 4 & battle of hurtgen forest & world war ii & september 19, 1944 to february 10, 1945 & over 12,000 killed \\ 5 & battle of gettysburg & american civil war & july 1 to july 3, 1863 & 7,863 killed (3,155 union and 4,708 confederacy) \\ 6 & battle of guadalcanal & world war ii & august 7, 1942 to february 9, 1943 & 7,100 killed \\ 7 & battle of iwo jima & world war ii & february 16 to march 26, 1945 & 7,000 killed \\ 8 & battle of anzio & world war ii & january 22 to june 5, 1944 & 5,538 killed \\ 9 & battle of pusan perimeter & korean war & august to september, 1950 & 4,599 killed \\ 10 & battle of spotsylvania & american civil war & may 8 to may 21, 1864 & 4,192 killed (2,725 union and 1,467 confederacy) \\ 11 & battle of chickamauga & american civil war & september 19 to september 20, 1863 & 3,969 killed (1,657 union and 2,312 confederacy) \\ 12 & battle of the wilderness & american civil war & may 5 to may 7, 1864 & 3,741 killed (2,246 union and 1,495 confederacy) \\ 13 & battle of antietam & american civil war & september 17, 1862 & 3,654 killed (2,108 union and 1,546 confederacy) \\ 14 & battle of shiloh & american civil war & april 6 to april 7, 1862 & 3,482 killed (1,754 union and 1,728 confederacy) \\ 15 & battle of chancellorsville & american civil war & april 30 to may 6, 1863 & 3,271 killed (1,606 union and 1,665 confederacy) \\ 16 & battle of saipan & world war ii & june 15 to july 9 1944 & 2,949 killed \\ 17 & battle of sicily & world war ii & july 9 to august 17, 1943 & 2,572 killed \\ 18 & pearl harbor attack & world war ii & december 7, 1941 & 2,500 killed \\ 19 & battle of salerno & world war ii & september 3 - september 16, 1943 & 2,000 killed \\ 20 & battle of belleau wood & world war i & june 1 to june 26, 1918 & 1,811 killed \\ 21 & battle of peleliu & world war ii & september 15 to november 27, 1944 & 1,794 killed \\ 22 & second battle of guam & world war ii & july 21 to august 8, 1944 & 1,747 killed \\ 23 & battle of tarawa & world war ii & november 20 to november 23, 1943 & 1,677 killed \\ 24 & d-day (first day of operation overlord) & world war ii & june 6, 1944 & 1,465 killed \\ 25 & battle of chosin reservoir & korean war & november 27 to december 13, 1950 & 1,029 killed \\ 26 & battle for manila & world war ii & february 3 to march 3, 1945 & 1,010 killed \\ 27 & battle of the kasserine pass & world war ii & february 19 to february 25, 1943 & over 1,000 killed \\ 28 & battle of taejon & korean war & july 14 to july 21, 1950 & 922 killed \\ 29 & battle of khe sanh & vietnam war & 21 january to 8 april 1968 & 737 killed \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & conflict \\ \midrule 0 & american civil war \\ \bottomrule \end{tabular}

select conflict where battle, siege, or offensive = 'battle of chickamauga' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>battle, siege, or offensive</th> <th>conflict</th> <th>dates</th> <th>estimated number of americans killed</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>meuse-argonne offensive</td> <td>world war i</td> <td>september 26 to november 11, 1918</td> <td>26,277 killed</td> </tr> <tr> <th>1</th> <td>battle of the bulge</td> <td>world war ii</td> <td>december 16, 1944 to january 25, 1945</td> <td>19,276 killed</td> </tr> <tr> <th>2</th> <td>battle of normandy</td> <td>world war ii</td> <td>june 6 to july 24, 1944</td> <td>16,293 killed</td> </tr> <tr> <th>3</th> <td>battle of okinawa</td> <td>world war ii</td> <td>april 1 to june 21, 1945</td> <td>12,513 killed</td> </tr> <tr> <th>4</th> <td>battle of hurtgen forest</td> <td>world war ii</td> <td>september 19, 1944 to february 10, 1945</td> <td>over 12,000 killed</td> </tr> <tr> <th>5</th> <td>battle of gettysburg</td> <td>american civil war</td> <td>july 1 to july 3, 1863</td> <td>7,863 killed (3,155 union and 4,708 confederacy)</td> </tr> <tr> <th>6</th> <td>battle of guadalcanal</td> <td>world war ii</td> <td>august 7, 1942 to february 9, 1943</td> <td>7,100 killed</td> </tr> <tr> <th>7</th> <td>battle of iwo jima</td> <td>world war ii</td> <td>february 16 to march 26, 1945</td> <td>7,000 killed</td> </tr> <tr> <th>8</th> <td>battle of anzio</td> <td>world war ii</td> <td>january 22 to june 5, 1944</td> <td>5,538 killed</td> </tr> <tr> <th>9</th> <td>battle of pusan perimeter</td> <td>korean war</td> <td>august to september, 1950</td> <td>4,599 killed</td> </tr> <tr> <th>10</th> <td>battle of spotsylvania</td> <td>american civil war</td> <td>may 8 to may 21, 1864</td> <td>4,192 killed (2,725 union and 1,467 confederacy)</td> </tr> <tr> <th>11</th> <td>battle of chickamauga</td> <td>american civil war</td> <td>september 19 to september 20, 1863</td> <td>3,969 killed (1,657 union and 2,312 confederacy)</td> </tr> <tr> <th>12</th> <td>battle of the wilderness</td> <td>american civil war</td> <td>may 5 to may 7, 1864</td> <td>3,741 killed (2,246 union and 1,495 confederacy)</td> </tr> <tr> <th>13</th> <td>battle of antietam</td> <td>american civil war</td> <td>september 17, 1862</td> <td>3,654 killed (2,108 union and 1,546 confederacy)</td> </tr> <tr> <th>14</th> <td>battle of shiloh</td> <td>american civil war</td> <td>april 6 to april 7, 1862</td> <td>3,482 killed (1,754 union and 1,728 confederacy)</td> </tr> <tr> <th>15</th> <td>battle of chancellorsville</td> <td>american civil war</td> <td>april 30 to may 6, 1863</td> <td>3,271 killed (1,606 union and 1,665 confederacy)</td> </tr> <tr> <th>16</th> <td>battle of saipan</td> <td>world war ii</td> <td>june 15 to july 9 1944</td> <td>2,949 killed</td> </tr> <tr> <th>17</th> <td>battle of sicily</td> <td>world war ii</td> <td>july 9 to august 17, 1943</td> <td>2,572 killed</td> </tr> <tr> <th>18</th> <td>pearl harbor attack</td> <td>world war ii</td> <td>december 7, 1941</td> <td>2,500 killed</td> </tr> <tr> <th>19</th> <td>battle of salerno</td> <td>world war ii</td> <td>september 3 - september 16, 1943</td> <td>2,000 killed</td> </tr> <tr> <th>20</th> <td>battle of belleau wood</td> <td>world war i</td> <td>june 1 to june 26, 1918</td> <td>1,811 killed</td> </tr> <tr> <th>21</th> <td>battle of peleliu</td> <td>world war ii</td> <td>september 15 to november 27, 1944</td> <td>1,794 killed</td> </tr> <tr> <th>22</th> <td>second battle of guam</td> <td>world war ii</td> <td>july 21 to august 8, 1944</td> <td>1,747 killed</td> </tr> <tr> <th>23</th> <td>battle of tarawa</td> <td>world war ii</td> <td>november 20 to november 23, 1943</td> <td>1,677 killed</td> </tr> <tr> <th>24</th> <td>d-day (first day of operation overlord)</td> <td>world war ii</td> <td>june 6, 1944</td> <td>1,465 killed</td> </tr> <tr> <th>25</th> <td>battle of chosin reservoir</td> <td>korean war</td> <td>november 27 to december 13, 1950</td> <td>1,029 killed</td> </tr> <tr> <th>26</th> <td>battle for manila</td> <td>world war ii</td> <td>february 3 to march 3, 1945</td> <td>1,010 killed</td> </tr> <tr> <th>27</th> <td>battle of the kasserine pass</td> <td>world war ii</td> <td>february 19 to february 25, 1943</td> <td>over 1,000 killed</td> </tr> <tr> <th>28</th> <td>battle of taejon</td> <td>korean war</td> <td>july 14 to july 21, 1950</td> <td>922 killed</td> </tr> <tr> <th>29</th> <td>battle of khe sanh</td> <td>vietnam war</td> <td>21 january to 8 april 1968</td> <td>737 killed</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>conflict</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>american civil war</td> </tr> </tbody> </table>

select conflict where battle, siege, or offensive = 'battle of chickamauga' <table_name> : table | | battle, siege, or offensive | conflict | dates | estimated number of americans killed | |---:|:----------------------------------------|:-------------------|:----------------------------------------|:-------------------------------------------------| | 0 | meuse-argonne offensive | world war i | september 26 to november 11, 1918 | 26,277 killed | | 1 | battle of the bulge | world war ii | december 16, 1944 to january 25, 1945 | 19,276 killed | | 2 | battle of normandy | world war ii | june 6 to july 24, 1944 | 16,293 killed | | 3 | battle of okinawa | world war ii | april 1 to june 21, 1945 | 12,513 killed | | 4 | battle of hurtgen forest | world war ii | september 19, 1944 to february 10, 1945 | over 12,000 killed | | 5 | battle of gettysburg | american civil war | july 1 to july 3, 1863 | 7,863 killed (3,155 union and 4,708 confederacy) | | 6 | battle of guadalcanal | world war ii | august 7, 1942 to february 9, 1943 | 7,100 killed | | 7 | battle of iwo jima | world war ii | february 16 to march 26, 1945 | 7,000 killed | | 8 | battle of anzio | world war ii | january 22 to june 5, 1944 | 5,538 killed | | 9 | battle of pusan perimeter | korean war | august to september, 1950 | 4,599 killed | | 10 | battle of spotsylvania | american civil war | may 8 to may 21, 1864 | 4,192 killed (2,725 union and 1,467 confederacy) | | 11 | battle of chickamauga | american civil war | september 19 to september 20, 1863 | 3,969 killed (1,657 union and 2,312 confederacy) | | 12 | battle of the wilderness | american civil war | may 5 to may 7, 1864 | 3,741 killed (2,246 union and 1,495 confederacy) | | 13 | battle of antietam | american civil war | september 17, 1862 | 3,654 killed (2,108 union and 1,546 confederacy) | | 14 | battle of shiloh | american civil war | april 6 to april 7, 1862 | 3,482 killed (1,754 union and 1,728 confederacy) | | 15 | battle of chancellorsville | american civil war | april 30 to may 6, 1863 | 3,271 killed (1,606 union and 1,665 confederacy) | | 16 | battle of saipan | world war ii | june 15 to july 9 1944 | 2,949 killed | | 17 | battle of sicily | world war ii | july 9 to august 17, 1943 | 2,572 killed | | 18 | pearl harbor attack | world war ii | december 7, 1941 | 2,500 killed | | 19 | battle of salerno | world war ii | september 3 - september 16, 1943 | 2,000 killed | | 20 | battle of belleau wood | world war i | june 1 to june 26, 1918 | 1,811 killed | | 21 | battle of peleliu | world war ii | september 15 to november 27, 1944 | 1,794 killed | | 22 | second battle of guam | world war ii | july 21 to august 8, 1944 | 1,747 killed | | 23 | battle of tarawa | world war ii | november 20 to november 23, 1943 | 1,677 killed | | 24 | d-day (first day of operation overlord) | world war ii | june 6, 1944 | 1,465 killed | | 25 | battle of chosin reservoir | korean war | november 27 to december 13, 1950 | 1,029 killed | | 26 | battle for manila | world war ii | february 3 to march 3, 1945 | 1,010 killed | | 27 | battle of the kasserine pass | world war ii | february 19 to february 25, 1943 | over 1,000 killed | | 28 | battle of taejon | korean war | july 14 to july 21, 1950 | 922 killed | | 29 | battle of khe sanh | vietnam war | 21 january to 8 april 1968 | 737 killed |

| | conflict | |---:|:-------------------| | 0 | american civil war |

select season where season = 2009 col : season | level | division | section | position | movements row 1 : 1993 | tier 3 | division 2 | vastra gotaland | 1st | none row 2 : 1994 | tier 2 | division 1 | sodra | 7th | none row 3 : 1995 | tier 2 | division 1 | sodra | 5th | none row 4 : 1996 | tier 2 | division 1 | sodra | 2nd | promoted row 5 : 1997 | tier 1 | allsvenskan | none | 14th | panos ljungskile sk - relegated row 6 : 1998 | tier 2 | division 1 | sodra | 6th | panos ljungskile sk row 7 : 1999 | tier 2 | division 1 | sodra | 4th | panos ljungskile sk row 8 : 2000 | tier 2 | superettan | none | 15th | panos ljungskile sk - relegated row 9 : 2001 | tier 3 | division 2 | vastra gotaland | 1st | panos ljungskile sk - promotion playoffs row 10 : 2002 | tier 3 | division 2 | vastra gotaland | 6th | panos ljungskile sk row 11 : 2003 | tier 3 | division 2 | vastra gotaland | 2nd | none row 12 : 2004 | tier 3 | division 2 | vastra gotaland | 1st | promotion playoffs - promoted row 13 : 2005 | tier 2 | superettan | none | 4th | none row 14 : 2006 | tier 2 | superettan | none | 6th | none row 15 : 2007 | tier 2 | superettan | none | 2nd | promoted row 16 : 2008 | tier 1 | allsvenskan | none | 14th | relegation playoffs - relegated row 17 : 2009 | tier 2 | superettan | none | 9th | none row 18 : 2010 | tier 2 | superettan | none | 6th | none row 19 : 2011 | tier 2 | superettan | none | 8th | none row 20 : 2012 | tier 2 | superettan | none | 5th | none row 21 : 2013 | tier 2 | superettan | none | 9th | none

col : season row 1 : 2009

select season where season = 2009 <table_name> : table \begin{tabular}{lllllll} \toprule & season & level & division & section & position & movements \\ \midrule 0 & 1993 & tier 3 & division 2 & vastra gotaland & 1st & none \\ 1 & 1994 & tier 2 & division 1 & sodra & 7th & none \\ 2 & 1995 & tier 2 & division 1 & sodra & 5th & none \\ 3 & 1996 & tier 2 & division 1 & sodra & 2nd & promoted \\ 4 & 1997 & tier 1 & allsvenskan & none & 14th & panos ljungskile sk - relegated \\ 5 & 1998 & tier 2 & division 1 & sodra & 6th & panos ljungskile sk \\ 6 & 1999 & tier 2 & division 1 & sodra & 4th & panos ljungskile sk \\ 7 & 2000 & tier 2 & superettan & none & 15th & panos ljungskile sk - relegated \\ 8 & 2001 & tier 3 & division 2 & vastra gotaland & 1st & panos ljungskile sk - promotion playoffs \\ 9 & 2002 & tier 3 & division 2 & vastra gotaland & 6th & panos ljungskile sk \\ 10 & 2003 & tier 3 & division 2 & vastra gotaland & 2nd & none \\ 11 & 2004 & tier 3 & division 2 & vastra gotaland & 1st & promotion playoffs - promoted \\ 12 & 2005 & tier 2 & superettan & none & 4th & none \\ 13 & 2006 & tier 2 & superettan & none & 6th & none \\ 14 & 2007 & tier 2 & superettan & none & 2nd & promoted \\ 15 & 2008 & tier 1 & allsvenskan & none & 14th & relegation playoffs - relegated \\ 16 & 2009 & tier 2 & superettan & none & 9th & none \\ 17 & 2010 & tier 2 & superettan & none & 6th & none \\ 18 & 2011 & tier 2 & superettan & none & 8th & none \\ 19 & 2012 & tier 2 & superettan & none & 5th & none \\ 20 & 2013 & tier 2 & superettan & none & 9th & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & season \\ \midrule 0 & 2009 \\ \bottomrule \end{tabular}

select season where season = 2009 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>season</th> <th>level</th> <th>division</th> <th>section</th> <th>position</th> <th>movements</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1993</td> <td>tier 3</td> <td>division 2</td> <td>vastra gotaland</td> <td>1st</td> <td>none</td> </tr> <tr> <th>1</th> <td>1994</td> <td>tier 2</td> <td>division 1</td> <td>sodra</td> <td>7th</td> <td>none</td> </tr> <tr> <th>2</th> <td>1995</td> <td>tier 2</td> <td>division 1</td> <td>sodra</td> <td>5th</td> <td>none</td> </tr> <tr> <th>3</th> <td>1996</td> <td>tier 2</td> <td>division 1</td> <td>sodra</td> <td>2nd</td> <td>promoted</td> </tr> <tr> <th>4</th> <td>1997</td> <td>tier 1</td> <td>allsvenskan</td> <td>none</td> <td>14th</td> <td>panos ljungskile sk - relegated</td> </tr> <tr> <th>5</th> <td>1998</td> <td>tier 2</td> <td>division 1</td> <td>sodra</td> <td>6th</td> <td>panos ljungskile sk</td> </tr> <tr> <th>6</th> <td>1999</td> <td>tier 2</td> <td>division 1</td> <td>sodra</td> <td>4th</td> <td>panos ljungskile sk</td> </tr> <tr> <th>7</th> <td>2000</td> <td>tier 2</td> <td>superettan</td> <td>none</td> <td>15th</td> <td>panos ljungskile sk - relegated</td> </tr> <tr> <th>8</th> <td>2001</td> <td>tier 3</td> <td>division 2</td> <td>vastra gotaland</td> <td>1st</td> <td>panos ljungskile sk - promotion playoffs</td> </tr> <tr> <th>9</th> <td>2002</td> <td>tier 3</td> <td>division 2</td> <td>vastra gotaland</td> <td>6th</td> <td>panos ljungskile sk</td> </tr> <tr> <th>10</th> <td>2003</td> <td>tier 3</td> <td>division 2</td> <td>vastra gotaland</td> <td>2nd</td> <td>none</td> </tr> <tr> <th>11</th> <td>2004</td> <td>tier 3</td> <td>division 2</td> <td>vastra gotaland</td> <td>1st</td> <td>promotion playoffs - promoted</td> </tr> <tr> <th>12</th> <td>2005</td> <td>tier 2</td> <td>superettan</td> <td>none</td> <td>4th</td> <td>none</td> </tr> <tr> <th>13</th> <td>2006</td> <td>tier 2</td> <td>superettan</td> <td>none</td> <td>6th</td> <td>none</td> </tr> <tr> <th>14</th> <td>2007</td> <td>tier 2</td> <td>superettan</td> <td>none</td> <td>2nd</td> <td>promoted</td> </tr> <tr> <th>15</th> <td>2008</td> <td>tier 1</td> <td>allsvenskan</td> <td>none</td> <td>14th</td> <td>relegation playoffs - relegated</td> </tr> <tr> <th>16</th> <td>2009</td> <td>tier 2</td> <td>superettan</td> <td>none</td> <td>9th</td> <td>none</td> </tr> <tr> <th>17</th> <td>2010</td> <td>tier 2</td> <td>superettan</td> <td>none</td> <td>6th</td> <td>none</td> </tr> <tr> <th>18</th> <td>2011</td> <td>tier 2</td> <td>superettan</td> <td>none</td> <td>8th</td> <td>none</td> </tr> <tr> <th>19</th> <td>2012</td> <td>tier 2</td> <td>superettan</td> <td>none</td> <td>5th</td> <td>none</td> </tr> <tr> <th>20</th> <td>2013</td> <td>tier 2</td> <td>superettan</td> <td>none</td> <td>9th</td> <td>none</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>season</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>2009</td> </tr> </tbody> </table>

select season where season = 2009 <table_name> : table | | season | level | division | section | position | movements | |---:|---------:|:--------|:------------|:----------------|:-----------|:-----------------------------------------| | 0 | 1993 | tier 3 | division 2 | vastra gotaland | 1st | none | | 1 | 1994 | tier 2 | division 1 | sodra | 7th | none | | 2 | 1995 | tier 2 | division 1 | sodra | 5th | none | | 3 | 1996 | tier 2 | division 1 | sodra | 2nd | promoted | | 4 | 1997 | tier 1 | allsvenskan | none | 14th | panos ljungskile sk - relegated | | 5 | 1998 | tier 2 | division 1 | sodra | 6th | panos ljungskile sk | | 6 | 1999 | tier 2 | division 1 | sodra | 4th | panos ljungskile sk | | 7 | 2000 | tier 2 | superettan | none | 15th | panos ljungskile sk - relegated | | 8 | 2001 | tier 3 | division 2 | vastra gotaland | 1st | panos ljungskile sk - promotion playoffs | | 9 | 2002 | tier 3 | division 2 | vastra gotaland | 6th | panos ljungskile sk | | 10 | 2003 | tier 3 | division 2 | vastra gotaland | 2nd | none | | 11 | 2004 | tier 3 | division 2 | vastra gotaland | 1st | promotion playoffs - promoted | | 12 | 2005 | tier 2 | superettan | none | 4th | none | | 13 | 2006 | tier 2 | superettan | none | 6th | none | | 14 | 2007 | tier 2 | superettan | none | 2nd | promoted | | 15 | 2008 | tier 1 | allsvenskan | none | 14th | relegation playoffs - relegated | | 16 | 2009 | tier 2 | superettan | none | 9th | none | | 17 | 2010 | tier 2 | superettan | none | 6th | none | | 18 | 2011 | tier 2 | superettan | none | 8th | none | | 19 | 2012 | tier 2 | superettan | none | 5th | none | | 20 | 2013 | tier 2 | superettan | none | 9th | none |

| | season | |---:|---------:| | 0 | 2009 |

select year where world rank = '1st' col : year | best | location | date | world rank row 1 : 2012 | 27:02.59 | birmingham | 22 june | 3rd row 2 : 2011 | 26:43.16 | brussels | 16 september | 1st row 3 : 2009 | 26:46.31 | berlin | 17 august | 1st row 4 : 2008 | 26:25.97 | eugene, oregon | 8 june | 1st row 5 : 2007 | 26:46.19 | brussels | 14 september | 1st row 6 : 2005 | 26:17.53 | brussels | 26 august | 1st row 7 : 2004 | 26:20.31 | ostrava | 8 june | 1st row 8 : 2003 | 26:49.57 | paris | 25 august | 4th

col : year row 1 : 2011 row 2 : 2009 row 3 : 2008 row 4 : 2007 row 5 : 2005 row 6 : 2004

select year where world rank = '1st' <table_name> : table \begin{tabular}{llllll} \toprule & year & best & location & date & world rank \\ \midrule 0 & 2012 & 27:02.59 & birmingham & 22 june & 3rd \\ 1 & 2011 & 26:43.16 & brussels & 16 september & 1st \\ 2 & 2009 & 26:46.31 & berlin & 17 august & 1st \\ 3 & 2008 & 26:25.97 & eugene, oregon & 8 june & 1st \\ 4 & 2007 & 26:46.19 & brussels & 14 september & 1st \\ 5 & 2005 & 26:17.53 & brussels & 26 august & 1st \\ 6 & 2004 & 26:20.31 & ostrava & 8 june & 1st \\ 7 & 2003 & 26:49.57 & paris & 25 august & 4th \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & year \\ \midrule 0 & 2011 \\ 1 & 2009 \\ 2 & 2008 \\ 3 & 2007 \\ 4 & 2005 \\ 5 & 2004 \\ \bottomrule \end{tabular}

select year where world rank = '1st' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>year</th> <th>best</th> <th>location</th> <th>date</th> <th>world rank</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>2012</td> <td>27:02.59</td> <td>birmingham</td> <td>22 june</td> <td>3rd</td> </tr> <tr> <th>1</th> <td>2011</td> <td>26:43.16</td> <td>brussels</td> <td>16 september</td> <td>1st</td> </tr> <tr> <th>2</th> <td>2009</td> <td>26:46.31</td> <td>berlin</td> <td>17 august</td> <td>1st</td> </tr> <tr> <th>3</th> <td>2008</td> <td>26:25.97</td> <td>eugene, oregon</td> <td>8 june</td> <td>1st</td> </tr> <tr> <th>4</th> <td>2007</td> <td>26:46.19</td> <td>brussels</td> <td>14 september</td> <td>1st</td> </tr> <tr> <th>5</th> <td>2005</td> <td>26:17.53</td> <td>brussels</td> <td>26 august</td> <td>1st</td> </tr> <tr> <th>6</th> <td>2004</td> <td>26:20.31</td> <td>ostrava</td> <td>8 june</td> <td>1st</td> </tr> <tr> <th>7</th> <td>2003</td> <td>26:49.57</td> <td>paris</td> <td>25 august</td> <td>4th</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>year</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>2011</td> </tr> <tr> <th>1</th> <td>2009</td> </tr> <tr> <th>2</th> <td>2008</td> </tr> <tr> <th>3</th> <td>2007</td> </tr> <tr> <th>4</th> <td>2005</td> </tr> <tr> <th>5</th> <td>2004</td> </tr> </tbody> </table>

select year where world rank = '1st' <table_name> : table | | year | best | location | date | world rank | |---:|-------:|:---------|:---------------|:-------------|:-------------| | 0 | 2012 | 27:02.59 | birmingham | 22 june | 3rd | | 1 | 2011 | 26:43.16 | brussels | 16 september | 1st | | 2 | 2009 | 26:46.31 | berlin | 17 august | 1st | | 3 | 2008 | 26:25.97 | eugene, oregon | 8 june | 1st | | 4 | 2007 | 26:46.19 | brussels | 14 september | 1st | | 5 | 2005 | 26:17.53 | brussels | 26 august | 1st | | 6 | 2004 | 26:20.31 | ostrava | 8 june | 1st | | 7 | 2003 | 26:49.57 | paris | 25 august | 4th |

| | year | |---:|-------:| | 0 | 2011 | | 1 | 2009 | | 2 | 2008 | | 3 | 2007 | | 4 | 2005 | | 5 | 2004 |

select last mention or demise where last mention or demise < 1413 col : location | name of mill and grid reference | type | maps | first mention or built | last mention or demise row 1 : chesfield | none | post | none | 1318 | 1372 row 2 : chesfield | chesfield park | titt iron wind engine | none | 1896 | none row 3 : cheshunt | tl 317 036 | post | none | 1843 | demolished c. 1860 row 4 : cheshunt | goff's oak mill tl 317 036 | tower | none | c. 1860 | demolished 1953 row 5 : cheshunt | turner's hill mill tl 363 023 | none | 1822 | 1822 | 1861 row 6 : chipperfield | tl 037 014 | post | none | 1594 | 1594 row 7 : chipperfield | tl 037 014 | smock | 1822 | 1822 | 1881, gone by 1896 row 8 : chipping | approximately tl 350 319 | post | 1720 1728 | 1720 | 1728 row 9 : clothall | approximately tl 264 326 | post | none | 1370 | 1370 row 10 : codicote | codicote heath approximately tl 206 185 | smock | none | 1841 | 1855, gone by 1881 row 11 : colney heath | colney heath mill tl 205 055 | tower | none | 1854 | windmill world row 12 : cottered | none | post | none | 1581 | 1581 row 13 : cromer | none | none | none | 1192 | 1576 row 14 : cromer | cromer mill tl 304 286 | post | 1720 1728 1749 1766 1800 1822 | 1720 | windmill world row 15 : croxley green | croxley green mill tq 067 953 | tower | none | c. 1860 | windmill world

col : last mention or demise row 1 : 1372 row 2 : 1370

select last mention or demise where last mention or demise < 1413 <table_name> : table \begin{tabular}{lllllll} \toprule & location & name of mill and grid reference & type & maps & first mention or built & last mention or demise \\ \midrule 0 & chesfield & none & post & none & 1318 & 1372 \\ 1 & chesfield & chesfield park & titt iron wind engine & none & 1896 & none \\ 2 & cheshunt & tl 317 036 & post & none & 1843 & demolished c. 1860 \\ 3 & cheshunt & goff's oak mill tl 317 036 & tower & none & c. 1860 & demolished 1953 \\ 4 & cheshunt & turner's hill mill tl 363 023 & none & 1822 & 1822 & 1861 \\ 5 & chipperfield & tl 037 014 & post & none & 1594 & 1594 \\ 6 & chipperfield & tl 037 014 & smock & 1822 & 1822 & 1881, gone by 1896 \\ 7 & chipping & approximately tl 350 319 & post & 1720 1728 & 1720 & 1728 \\ 8 & clothall & approximately tl 264 326 & post & none & 1370 & 1370 \\ 9 & codicote & codicote heath approximately tl 206 185 & smock & none & 1841 & 1855, gone by 1881 \\ 10 & colney heath & colney heath mill tl 205 055 & tower & none & 1854 & windmill world \\ 11 & cottered & none & post & none & 1581 & 1581 \\ 12 & cromer & none & none & none & 1192 & 1576 \\ 13 & cromer & cromer mill tl 304 286 & post & 1720 1728 1749 1766 1800 1822 & 1720 & windmill world \\ 14 & croxley green & croxley green mill tq 067 953 & tower & none & c. 1860 & windmill world \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & last mention or demise \\ \midrule 0 & 1372 \\ 1 & 1370 \\ \bottomrule \end{tabular}

select last mention or demise where last mention or demise < 1413 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>location</th> <th>name of mill and grid reference</th> <th>type</th> <th>maps</th> <th>first mention or built</th> <th>last mention or demise</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>chesfield</td> <td>none</td> <td>post</td> <td>none</td> <td>1318</td> <td>1372</td> </tr> <tr> <th>1</th> <td>chesfield</td> <td>chesfield park</td> <td>titt iron wind engine</td> <td>none</td> <td>1896</td> <td>none</td> </tr> <tr> <th>2</th> <td>cheshunt</td> <td>tl 317 036</td> <td>post</td> <td>none</td> <td>1843</td> <td>demolished c. 1860</td> </tr> <tr> <th>3</th> <td>cheshunt</td> <td>goff's oak mill tl 317 036</td> <td>tower</td> <td>none</td> <td>c. 1860</td> <td>demolished 1953</td> </tr> <tr> <th>4</th> <td>cheshunt</td> <td>turner's hill mill tl 363 023</td> <td>none</td> <td>1822</td> <td>1822</td> <td>1861</td> </tr> <tr> <th>5</th> <td>chipperfield</td> <td>tl 037 014</td> <td>post</td> <td>none</td> <td>1594</td> <td>1594</td> </tr> <tr> <th>6</th> <td>chipperfield</td> <td>tl 037 014</td> <td>smock</td> <td>1822</td> <td>1822</td> <td>1881, gone by 1896</td> </tr> <tr> <th>7</th> <td>chipping</td> <td>approximately tl 350 319</td> <td>post</td> <td>1720 1728</td> <td>1720</td> <td>1728</td> </tr> <tr> <th>8</th> <td>clothall</td> <td>approximately tl 264 326</td> <td>post</td> <td>none</td> <td>1370</td> <td>1370</td> </tr> <tr> <th>9</th> <td>codicote</td> <td>codicote heath approximately tl 206 185</td> <td>smock</td> <td>none</td> <td>1841</td> <td>1855, gone by 1881</td> </tr> <tr> <th>10</th> <td>colney heath</td> <td>colney heath mill tl 205 055</td> <td>tower</td> <td>none</td> <td>1854</td> <td>windmill world</td> </tr> <tr> <th>11</th> <td>cottered</td> <td>none</td> <td>post</td> <td>none</td> <td>1581</td> <td>1581</td> </tr> <tr> <th>12</th> <td>cromer</td> <td>none</td> <td>none</td> <td>none</td> <td>1192</td> <td>1576</td> </tr> <tr> <th>13</th> <td>cromer</td> <td>cromer mill tl 304 286</td> <td>post</td> <td>1720 1728 1749 1766 1800 1822</td> <td>1720</td> <td>windmill world</td> </tr> <tr> <th>14</th> <td>croxley green</td> <td>croxley green mill tq 067 953</td> <td>tower</td> <td>none</td> <td>c. 1860</td> <td>windmill world</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>last mention or demise</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1372</td> </tr> <tr> <th>1</th> <td>1370</td> </tr> </tbody> </table>

select last mention or demise where last mention or demise < 1413 <table_name> : table | | location | name of mill and grid reference | type | maps | first mention or built | last mention or demise | |---:|:--------------|:----------------------------------------|:----------------------|:------------------------------|:-------------------------|:-------------------------| | 0 | chesfield | none | post | none | 1318 | 1372 | | 1 | chesfield | chesfield park | titt iron wind engine | none | 1896 | none | | 2 | cheshunt | tl 317 036 | post | none | 1843 | demolished c. 1860 | | 3 | cheshunt | goff's oak mill tl 317 036 | tower | none | c. 1860 | demolished 1953 | | 4 | cheshunt | turner's hill mill tl 363 023 | none | 1822 | 1822 | 1861 | | 5 | chipperfield | tl 037 014 | post | none | 1594 | 1594 | | 6 | chipperfield | tl 037 014 | smock | 1822 | 1822 | 1881, gone by 1896 | | 7 | chipping | approximately tl 350 319 | post | 1720 1728 | 1720 | 1728 | | 8 | clothall | approximately tl 264 326 | post | none | 1370 | 1370 | | 9 | codicote | codicote heath approximately tl 206 185 | smock | none | 1841 | 1855, gone by 1881 | | 10 | colney heath | colney heath mill tl 205 055 | tower | none | 1854 | windmill world | | 11 | cottered | none | post | none | 1581 | 1581 | | 12 | cromer | none | none | none | 1192 | 1576 | | 13 | cromer | cromer mill tl 304 286 | post | 1720 1728 1749 1766 1800 1822 | 1720 | windmill world | | 14 | croxley green | croxley green mill tq 067 953 | tower | none | c. 1860 | windmill world |

| | last mention or demise | |---:|-------------------------:| | 0 | 1372 | | 1 | 1370 |

select count ( crystal bicycle (best professional cyclist) ) where year > 1250 col : year | crystal bicycle (best professional cyclist) | best young rider | best manager | crystal drop of sweat (best helper) row 1 : 1992 | dirk de wolf | not awarded | not awarded | not awarded row 2 : 1993 | johan museeuw | not awarded | not awarded | not awarded row 3 : 1994 | paul herygers | kristof trouve | not awarded | not awarded row 4 : 1995 | johan museeuw | leif hoste | not awarded | not awarded row 5 : 1996 | johan museeuw | glenn d'hollander | not awarded | not awarded row 6 : 1997 | johan museeuw | sven nys | not awarded | not awarded row 7 : 1998 | tom steels | sven nys | not awarded | not awarded row 8 : 1999 | frank vandenbroucke | kevin hulsmans | not awarded | not awarded row 9 : 2000 | andrei tchmil | jurgen van goolen | patrick lefevere & johan bruyneel | not awarded row 10 : 2001 | rik verbrugghe | tom boonen | jef braeckevelt | not awarded row 11 : 2002 | johan museeuw | kevin de weert | johan bruyneel | not awarded row 12 : 2003 | peter van petegem | johan vansummeren | johan bruyneel | not awarded row 13 : 2004 | tom boonen | niels albert | patrick lefevere | not awarded row 14 : 2005 | tom boonen | niels albert | jose de cauwer | kevin hulsmans row 15 : 2006 | tom boonen | dominique cornu | patrick lefevere | gert steegmans row 16 : 2007 | sven nys | niels albert | johan bruyneel | johan vansummeren row 17 : 2008 | philippe gilbert | jan bakelants | johan bruyneel | mario aerts row 18 : 2009 | philippe gilbert | kris boeckmans | johan bruyneel | stijn vandenbergh row 19 : 2010 | philippe gilbert | yannick eijssen | marc sergeant | mario aerts row 20 : 2011 | philippe gilbert | tosh van der sande | john lelangue | jelle vanendert row 21 : 2012 | tom boonen | gijs van hoecke | carlo bomans | kevin de weert row 22 : 2013 | sven nys | igor decraene | patrick lefevere | stijn vandenbergh

col : count ( "crystal bicycle (best professional cyclist" ) row 1 : 22

select count ( crystal bicycle (best professional cyclist) ) where year > 1250 <table_name> : table \begin{tabular}{llllll} \toprule & year & crystal bicycle (best professional cyclist) & best young rider & best manager & crystal drop of sweat (best helper) \\ \midrule 0 & 1992 & dirk de wolf & not awarded & not awarded & not awarded \\ 1 & 1993 & johan museeuw & not awarded & not awarded & not awarded \\ 2 & 1994 & paul herygers & kristof trouve & not awarded & not awarded \\ 3 & 1995 & johan museeuw & leif hoste & not awarded & not awarded \\ 4 & 1996 & johan museeuw & glenn d'hollander & not awarded & not awarded \\ 5 & 1997 & johan museeuw & sven nys & not awarded & not awarded \\ 6 & 1998 & tom steels & sven nys & not awarded & not awarded \\ 7 & 1999 & frank vandenbroucke & kevin hulsmans & not awarded & not awarded \\ 8 & 2000 & andrei tchmil & jurgen van goolen & patrick lefevere & johan bruyneel & not awarded \\ 9 & 2001 & rik verbrugghe & tom boonen & jef braeckevelt & not awarded \\ 10 & 2002 & johan museeuw & kevin de weert & johan bruyneel & not awarded \\ 11 & 2003 & peter van petegem & johan vansummeren & johan bruyneel & not awarded \\ 12 & 2004 & tom boonen & niels albert & patrick lefevere & not awarded \\ 13 & 2005 & tom boonen & niels albert & jose de cauwer & kevin hulsmans \\ 14 & 2006 & tom boonen & dominique cornu & patrick lefevere & gert steegmans \\ 15 & 2007 & sven nys & niels albert & johan bruyneel & johan vansummeren \\ 16 & 2008 & philippe gilbert & jan bakelants & johan bruyneel & mario aerts \\ 17 & 2009 & philippe gilbert & kris boeckmans & johan bruyneel & stijn vandenbergh \\ 18 & 2010 & philippe gilbert & yannick eijssen & marc sergeant & mario aerts \\ 19 & 2011 & philippe gilbert & tosh van der sande & john lelangue & jelle vanendert \\ 20 & 2012 & tom boonen & gijs van hoecke & carlo bomans & kevin de weert \\ 21 & 2013 & sven nys & igor decraene & patrick lefevere & stijn vandenbergh \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "crystal bicycle (best professional cyclist" ) \\ \midrule 0 & 22 \\ \bottomrule \end{tabular}

select count ( crystal bicycle (best professional cyclist) ) where year > 1250 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>year</th> <th>crystal bicycle (best professional cyclist)</th> <th>best young rider</th> <th>best manager</th> <th>crystal drop of sweat (best helper)</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1992</td> <td>dirk de wolf</td> <td>not awarded</td> <td>not awarded</td> <td>not awarded</td> </tr> <tr> <th>1</th> <td>1993</td> <td>johan museeuw</td> <td>not awarded</td> <td>not awarded</td> <td>not awarded</td> </tr> <tr> <th>2</th> <td>1994</td> <td>paul herygers</td> <td>kristof trouve</td> <td>not awarded</td> <td>not awarded</td> </tr> <tr> <th>3</th> <td>1995</td> <td>johan museeuw</td> <td>leif hoste</td> <td>not awarded</td> <td>not awarded</td> </tr> <tr> <th>4</th> <td>1996</td> <td>johan museeuw</td> <td>glenn d'hollander</td> <td>not awarded</td> <td>not awarded</td> </tr> <tr> <th>5</th> <td>1997</td> <td>johan museeuw</td> <td>sven nys</td> <td>not awarded</td> <td>not awarded</td> </tr> <tr> <th>6</th> <td>1998</td> <td>tom steels</td> <td>sven nys</td> <td>not awarded</td> <td>not awarded</td> </tr> <tr> <th>7</th> <td>1999</td> <td>frank vandenbroucke</td> <td>kevin hulsmans</td> <td>not awarded</td> <td>not awarded</td> </tr> <tr> <th>8</th> <td>2000</td> <td>andrei tchmil</td> <td>jurgen van goolen</td> <td>patrick lefevere &amp; johan bruyneel</td> <td>not awarded</td> </tr> <tr> <th>9</th> <td>2001</td> <td>rik verbrugghe</td> <td>tom boonen</td> <td>jef braeckevelt</td> <td>not awarded</td> </tr> <tr> <th>10</th> <td>2002</td> <td>johan museeuw</td> <td>kevin de weert</td> <td>johan bruyneel</td> <td>not awarded</td> </tr> <tr> <th>11</th> <td>2003</td> <td>peter van petegem</td> <td>johan vansummeren</td> <td>johan bruyneel</td> <td>not awarded</td> </tr> <tr> <th>12</th> <td>2004</td> <td>tom boonen</td> <td>niels albert</td> <td>patrick lefevere</td> <td>not awarded</td> </tr> <tr> <th>13</th> <td>2005</td> <td>tom boonen</td> <td>niels albert</td> <td>jose de cauwer</td> <td>kevin hulsmans</td> </tr> <tr> <th>14</th> <td>2006</td> <td>tom boonen</td> <td>dominique cornu</td> <td>patrick lefevere</td> <td>gert steegmans</td> </tr> <tr> <th>15</th> <td>2007</td> <td>sven nys</td> <td>niels albert</td> <td>johan bruyneel</td> <td>johan vansummeren</td> </tr> <tr> <th>16</th> <td>2008</td> <td>philippe gilbert</td> <td>jan bakelants</td> <td>johan bruyneel</td> <td>mario aerts</td> </tr> <tr> <th>17</th> <td>2009</td> <td>philippe gilbert</td> <td>kris boeckmans</td> <td>johan bruyneel</td> <td>stijn vandenbergh</td> </tr> <tr> <th>18</th> <td>2010</td> <td>philippe gilbert</td> <td>yannick eijssen</td> <td>marc sergeant</td> <td>mario aerts</td> </tr> <tr> <th>19</th> <td>2011</td> <td>philippe gilbert</td> <td>tosh van der sande</td> <td>john lelangue</td> <td>jelle vanendert</td> </tr> <tr> <th>20</th> <td>2012</td> <td>tom boonen</td> <td>gijs van hoecke</td> <td>carlo bomans</td> <td>kevin de weert</td> </tr> <tr> <th>21</th> <td>2013</td> <td>sven nys</td> <td>igor decraene</td> <td>patrick lefevere</td> <td>stijn vandenbergh</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "crystal bicycle (best professional cyclist" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>22</td> </tr> </tbody> </table>

select count ( crystal bicycle (best professional cyclist) ) where year > 1250 <table_name> : table | | year | crystal bicycle (best professional cyclist) | best young rider | best manager | crystal drop of sweat (best helper) | |---:|-------:|:----------------------------------------------|:-------------------|:----------------------------------|:--------------------------------------| | 0 | 1992 | dirk de wolf | not awarded | not awarded | not awarded | | 1 | 1993 | johan museeuw | not awarded | not awarded | not awarded | | 2 | 1994 | paul herygers | kristof trouve | not awarded | not awarded | | 3 | 1995 | johan museeuw | leif hoste | not awarded | not awarded | | 4 | 1996 | johan museeuw | glenn d'hollander | not awarded | not awarded | | 5 | 1997 | johan museeuw | sven nys | not awarded | not awarded | | 6 | 1998 | tom steels | sven nys | not awarded | not awarded | | 7 | 1999 | frank vandenbroucke | kevin hulsmans | not awarded | not awarded | | 8 | 2000 | andrei tchmil | jurgen van goolen | patrick lefevere & johan bruyneel | not awarded | | 9 | 2001 | rik verbrugghe | tom boonen | jef braeckevelt | not awarded | | 10 | 2002 | johan museeuw | kevin de weert | johan bruyneel | not awarded | | 11 | 2003 | peter van petegem | johan vansummeren | johan bruyneel | not awarded | | 12 | 2004 | tom boonen | niels albert | patrick lefevere | not awarded | | 13 | 2005 | tom boonen | niels albert | jose de cauwer | kevin hulsmans | | 14 | 2006 | tom boonen | dominique cornu | patrick lefevere | gert steegmans | | 15 | 2007 | sven nys | niels albert | johan bruyneel | johan vansummeren | | 16 | 2008 | philippe gilbert | jan bakelants | johan bruyneel | mario aerts | | 17 | 2009 | philippe gilbert | kris boeckmans | johan bruyneel | stijn vandenbergh | | 18 | 2010 | philippe gilbert | yannick eijssen | marc sergeant | mario aerts | | 19 | 2011 | philippe gilbert | tosh van der sande | john lelangue | jelle vanendert | | 20 | 2012 | tom boonen | gijs van hoecke | carlo bomans | kevin de weert | | 21 | 2013 | sven nys | igor decraene | patrick lefevere | stijn vandenbergh |

| | count ( "crystal bicycle (best professional cyclist" ) | |---:|---------------------------------------------------------:| | 0 | 22 |

select count ( * ) where # > 6 col : # | name | alma mater | birth date | marriage date | president | tenure -1 | tenure -2 row 1 : 1 | jiang qing | shandong provincial experimental theatre | 19 march 1914 | 20 november 1938 | mao zedong | 27 september 1954 | 27 april 1959 row 2 : 2 | wang guangmei | fu jen catholic university | 26 september 1921 | 21 august 1948 | liu shaoqi | 27 april 1959 | 21 october 1968 row 3 : 3 | he lianying | none | none | none | dong biwu | 31 october 1968 | 17 january 1975 row 4 : 4 | lin jiamei | tong de medical college | 1924 | none | li xiannian | 18 june 1983 | 8 april 1988 row 5 : 5 | wang yeping | shanghai foreign language institute | february 1928 | december 1949 | jiang zemin | 27 march 1993 | 15 march 2003 row 6 : 6 | liu yongqing | tsinghua university | 1940 | february 1970 | hu jintao | 15 march 2003 | 14 march 2013 row 7 : 7 | peng liyuan | china conservatory of music | 20 november 1962 | 1 september 1987 | xi jinping | 14 march 2013 | incumbent

col : count ( "*" ) row 1 : 1

select count ( * ) where # > 6 <table_name> : table \begin{tabular}{lllllllll} \toprule & # & name & alma mater & birth date & marriage date & president & tenure -1 & tenure -2 \\ \midrule 0 & 1 & jiang qing & shandong provincial experimental theatre & 19 march 1914 & 20 november 1938 & mao zedong & 27 september 1954 & 27 april 1959 \\ 1 & 2 & wang guangmei & fu jen catholic university & 26 september 1921 & 21 august 1948 & liu shaoqi & 27 april 1959 & 21 october 1968 \\ 2 & 3 & he lianying & none & none & none & dong biwu & 31 october 1968 & 17 january 1975 \\ 3 & 4 & lin jiamei & tong de medical college & 1924 & none & li xiannian & 18 june 1983 & 8 april 1988 \\ 4 & 5 & wang yeping & shanghai foreign language institute & february 1928 & december 1949 & jiang zemin & 27 march 1993 & 15 march 2003 \\ 5 & 6 & liu yongqing & tsinghua university & 1940 & february 1970 & hu jintao & 15 march 2003 & 14 march 2013 \\ 6 & 7 & peng liyuan & china conservatory of music & 20 november 1962 & 1 september 1987 & xi jinping & 14 march 2013 & incumbent \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 1 \\ \bottomrule \end{tabular}

select count ( * ) where # > 6 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>#</th> <th>name</th> <th>alma mater</th> <th>birth date</th> <th>marriage date</th> <th>president</th> <th>tenure -1</th> <th>tenure -2</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>jiang qing</td> <td>shandong provincial experimental theatre</td> <td>19 march 1914</td> <td>20 november 1938</td> <td>mao zedong</td> <td>27 september 1954</td> <td>27 april 1959</td> </tr> <tr> <th>1</th> <td>2</td> <td>wang guangmei</td> <td>fu jen catholic university</td> <td>26 september 1921</td> <td>21 august 1948</td> <td>liu shaoqi</td> <td>27 april 1959</td> <td>21 october 1968</td> </tr> <tr> <th>2</th> <td>3</td> <td>he lianying</td> <td>none</td> <td>none</td> <td>none</td> <td>dong biwu</td> <td>31 october 1968</td> <td>17 january 1975</td> </tr> <tr> <th>3</th> <td>4</td> <td>lin jiamei</td> <td>tong de medical college</td> <td>1924</td> <td>none</td> <td>li xiannian</td> <td>18 june 1983</td> <td>8 april 1988</td> </tr> <tr> <th>4</th> <td>5</td> <td>wang yeping</td> <td>shanghai foreign language institute</td> <td>february 1928</td> <td>december 1949</td> <td>jiang zemin</td> <td>27 march 1993</td> <td>15 march 2003</td> </tr> <tr> <th>5</th> <td>6</td> <td>liu yongqing</td> <td>tsinghua university</td> <td>1940</td> <td>february 1970</td> <td>hu jintao</td> <td>15 march 2003</td> <td>14 march 2013</td> </tr> <tr> <th>6</th> <td>7</td> <td>peng liyuan</td> <td>china conservatory of music</td> <td>20 november 1962</td> <td>1 september 1987</td> <td>xi jinping</td> <td>14 march 2013</td> <td>incumbent</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "*" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> </tr> </tbody> </table>

select count ( * ) where # > 6 <table_name> : table | | # | name | alma mater | birth date | marriage date | president | tenure -1 | tenure -2 | |---:|----:|:--------------|:-----------------------------------------|:------------------|:-----------------|:------------|:------------------|:----------------| | 0 | 1 | jiang qing | shandong provincial experimental theatre | 19 march 1914 | 20 november 1938 | mao zedong | 27 september 1954 | 27 april 1959 | | 1 | 2 | wang guangmei | fu jen catholic university | 26 september 1921 | 21 august 1948 | liu shaoqi | 27 april 1959 | 21 october 1968 | | 2 | 3 | he lianying | none | none | none | dong biwu | 31 october 1968 | 17 january 1975 | | 3 | 4 | lin jiamei | tong de medical college | 1924 | none | li xiannian | 18 june 1983 | 8 april 1988 | | 4 | 5 | wang yeping | shanghai foreign language institute | february 1928 | december 1949 | jiang zemin | 27 march 1993 | 15 march 2003 | | 5 | 6 | liu yongqing | tsinghua university | 1940 | february 1970 | hu jintao | 15 march 2003 | 14 march 2013 | | 6 | 7 | peng liyuan | china conservatory of music | 20 november 1962 | 1 september 1987 | xi jinping | 14 march 2013 | incumbent |

| | count ( "*" ) | |---:|----------------:| | 0 | 1 |

select count ( * ) where college = 'michigan state' col : round | pick | player | position | nationality | team | college row 1 : t | none | wilt chamberlain | c | united states | philadelphia warriors | kansas row 2 : t | none | bob ferry | f/c | united states | st. louis hawks | saint louis row 3 : 1 | 1 | bob boozer | f | united states | cincinnati royals | kansas state row 4 : 1 | 2 | bailey howell | f | united states | detroit pistons | mississippi state row 5 : 1 | 3 | tom hawkins | f | united states | minneapolis lakers | notre dame row 6 : 1 | 4 | dick barnett | g/f | united states | syracuse nationals | tennessee state row 7 : 1 | 5 | johnny green | f/c | united states | new york knicks | michigan state row 8 : 1 | 6 | john richter | f | united states | boston celtics | nc state row 9 : 2 | 7 | tom robitaille | f/c | united states | detroit pistons (from cincinnati) | rice row 10 : 2 | 8 | don goldstein | f | united states | detroit pistons | louisville row 11 : 2 | 9 | joe ruklick | f/c | united states | philadelphia warriors | northwestern row 12 : 2 | 10 | rudy larusso | f/c | united states | minneapolis lakers | dartmouth row 13 : 2 | 11 | bumper tormohlen | f/c | united states | syracuse nationals | tennessee row 14 : 2 | 12 | al seiden | g | united states | st. louis hawks (from new york) | st. john's row 15 : 2 | 13 | cal ramsey | f | united states | st. louis hawks | nyu row 16 : 2 | 14 | gene guarilia | f | united states | boston celtics | george washington row 17 : 3 | 15 | mike mendenhall | g | united states | cincinnati royals | cincinnati row 18 : 3 | 16 | gary alcorn | c | united states | detroit pistons | fresno state row 19 : 3 | 17 | jim hockaday | f | united states | philadelphia warriors | memphis row 20 : 3 | 18 | bobby smith | g | united states | minneapolis lakers | west virginia row 21 : 3 | 19 | jon cincebox | f | united states | syracuse nationals | syracuse row 22 : 3 | 20 | bob anderegg | g/f | united states | new york knicks | michigan state row 23 : 3 | 21 | hank stein | g | united states | st. louis hawks | xavier row 24 : 3 | 22 | ralph crosthwaite | c | united states | boston celtics | western kentucky row 25 : 4 | 23 | leo byrd | g | united states | cincinnati royals | marshall row 26 : 4 | 24 | george lee | g/f | united states | detroit pistons | michigan row 27 : 4 | 25 | ron stevenson | f | united states | philadelphia warriors | tcu row 28 : 4 | 26 | wilson eison | f | united states | minneapolis lakers | purdue row 29 : 4 | 27 | paul neumann | g | united states | syracuse nationals | stanford row 30 : 4 | 28 | johnny cox | g | united states | new york knicks | kentucky row 31 : 4 | 29 | lee harman | g | united states | st. louis hawks | oregon state row 32 : 4 | 30 | ed kazakavich | f | united states | boston celtics | scranton row 33 : 5 | 31 | harry kirchner | g/f | united states | detroit pistons | tcu row 34 : 5 | 32 | tony windis | g | united states | detroit pistons | wyoming row 35 : 5 | 37 | nick mantis | g | united states | st. louis hawks | northwestern row 36 : 8 | 56 | dave gunther | f | united states | philadelphia warriors | iowa row 37 : 11 | 77 | john barnhill | g | united states | st. louis hawks | tennessee state row 38 : 14 | 83 | jack israel | g/f | united states | new york knicks | missouri state

col : count ( "*" ) row 1 : 2

select count ( * ) where college = 'michigan state' <table_name> : table \begin{tabular}{llllllll} \toprule & round & pick & player & position & nationality & team & college \\ \midrule 0 & t & none & wilt chamberlain & c & united states & philadelphia warriors & kansas \\ 1 & t & none & bob ferry & f/c & united states & st. louis hawks & saint louis \\ 2 & 1 & 1 & bob boozer & f & united states & cincinnati royals & kansas state \\ 3 & 1 & 2 & bailey howell & f & united states & detroit pistons & mississippi state \\ 4 & 1 & 3 & tom hawkins & f & united states & minneapolis lakers & notre dame \\ 5 & 1 & 4 & dick barnett & g/f & united states & syracuse nationals & tennessee state \\ 6 & 1 & 5 & johnny green & f/c & united states & new york knicks & michigan state \\ 7 & 1 & 6 & john richter & f & united states & boston celtics & nc state \\ 8 & 2 & 7 & tom robitaille & f/c & united states & detroit pistons (from cincinnati) & rice \\ 9 & 2 & 8 & don goldstein & f & united states & detroit pistons & louisville \\ 10 & 2 & 9 & joe ruklick & f/c & united states & philadelphia warriors & northwestern \\ 11 & 2 & 10 & rudy larusso & f/c & united states & minneapolis lakers & dartmouth \\ 12 & 2 & 11 & bumper tormohlen & f/c & united states & syracuse nationals & tennessee \\ 13 & 2 & 12 & al seiden & g & united states & st. louis hawks (from new york) & st. john's \\ 14 & 2 & 13 & cal ramsey & f & united states & st. louis hawks & nyu \\ 15 & 2 & 14 & gene guarilia & f & united states & boston celtics & george washington \\ 16 & 3 & 15 & mike mendenhall & g & united states & cincinnati royals & cincinnati \\ 17 & 3 & 16 & gary alcorn & c & united states & detroit pistons & fresno state \\ 18 & 3 & 17 & jim hockaday & f & united states & philadelphia warriors & memphis \\ 19 & 3 & 18 & bobby smith & g & united states & minneapolis lakers & west virginia \\ 20 & 3 & 19 & jon cincebox & f & united states & syracuse nationals & syracuse \\ 21 & 3 & 20 & bob anderegg & g/f & united states & new york knicks & michigan state \\ 22 & 3 & 21 & hank stein & g & united states & st. louis hawks & xavier \\ 23 & 3 & 22 & ralph crosthwaite & c & united states & boston celtics & western kentucky \\ 24 & 4 & 23 & leo byrd & g & united states & cincinnati royals & marshall \\ 25 & 4 & 24 & george lee & g/f & united states & detroit pistons & michigan \\ 26 & 4 & 25 & ron stevenson & f & united states & philadelphia warriors & tcu \\ 27 & 4 & 26 & wilson eison & f & united states & minneapolis lakers & purdue \\ 28 & 4 & 27 & paul neumann & g & united states & syracuse nationals & stanford \\ 29 & 4 & 28 & johnny cox & g & united states & new york knicks & kentucky \\ 30 & 4 & 29 & lee harman & g & united states & st. louis hawks & oregon state \\ 31 & 4 & 30 & ed kazakavich & f & united states & boston celtics & scranton \\ 32 & 5 & 31 & harry kirchner & g/f & united states & detroit pistons & tcu \\ 33 & 5 & 32 & tony windis & g & united states & detroit pistons & wyoming \\ 34 & 5 & 37 & nick mantis & g & united states & st. louis hawks & northwestern \\ 35 & 8 & 56 & dave gunther & f & united states & philadelphia warriors & iowa \\ 36 & 11 & 77 & john barnhill & g & united states & st. louis hawks & tennessee state \\ 37 & 14 & 83 & jack israel & g/f & united states & new york knicks & missouri state \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 2 \\ \bottomrule \end{tabular}

select count ( * ) where college = 'michigan state' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>round</th> <th>pick</th> <th>player</th> <th>position</th> <th>nationality</th> <th>team</th> <th>college</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>t</td> <td>none</td> <td>wilt chamberlain</td> <td>c</td> <td>united states</td> <td>philadelphia warriors</td> <td>kansas</td> </tr> <tr> <th>1</th> <td>t</td> <td>none</td> <td>bob ferry</td> <td>f/c</td> <td>united states</td> <td>st. louis hawks</td> <td>saint louis</td> </tr> <tr> <th>2</th> <td>1</td> <td>1</td> <td>bob boozer</td> <td>f</td> <td>united states</td> <td>cincinnati royals</td> <td>kansas state</td> </tr> <tr> <th>3</th> <td>1</td> <td>2</td> <td>bailey howell</td> <td>f</td> <td>united states</td> <td>detroit pistons</td> <td>mississippi state</td> </tr> <tr> <th>4</th> <td>1</td> <td>3</td> <td>tom hawkins</td> <td>f</td> <td>united states</td> <td>minneapolis lakers</td> <td>notre dame</td> </tr> <tr> <th>5</th> <td>1</td> <td>4</td> <td>dick barnett</td> <td>g/f</td> <td>united states</td> <td>syracuse nationals</td> <td>tennessee state</td> </tr> <tr> <th>6</th> <td>1</td> <td>5</td> <td>johnny green</td> <td>f/c</td> <td>united states</td> <td>new york knicks</td> <td>michigan state</td> </tr> <tr> <th>7</th> <td>1</td> <td>6</td> <td>john richter</td> <td>f</td> <td>united states</td> <td>boston celtics</td> <td>nc state</td> </tr> <tr> <th>8</th> <td>2</td> <td>7</td> <td>tom robitaille</td> <td>f/c</td> <td>united states</td> <td>detroit pistons (from cincinnati)</td> <td>rice</td> </tr> <tr> <th>9</th> <td>2</td> <td>8</td> <td>don goldstein</td> <td>f</td> <td>united states</td> <td>detroit pistons</td> <td>louisville</td> </tr> <tr> <th>10</th> <td>2</td> <td>9</td> <td>joe ruklick</td> <td>f/c</td> <td>united states</td> <td>philadelphia warriors</td> <td>northwestern</td> </tr> <tr> <th>11</th> <td>2</td> <td>10</td> <td>rudy larusso</td> <td>f/c</td> <td>united states</td> <td>minneapolis lakers</td> <td>dartmouth</td> </tr> <tr> <th>12</th> <td>2</td> <td>11</td> <td>bumper tormohlen</td> <td>f/c</td> <td>united states</td> <td>syracuse nationals</td> <td>tennessee</td> </tr> <tr> <th>13</th> <td>2</td> <td>12</td> <td>al seiden</td> <td>g</td> <td>united states</td> <td>st. louis hawks (from new york)</td> <td>st. john's</td> </tr> <tr> <th>14</th> <td>2</td> <td>13</td> <td>cal ramsey</td> <td>f</td> <td>united states</td> <td>st. louis hawks</td> <td>nyu</td> </tr> <tr> <th>15</th> <td>2</td> <td>14</td> <td>gene guarilia</td> <td>f</td> <td>united states</td> <td>boston celtics</td> <td>george washington</td> </tr> <tr> <th>16</th> <td>3</td> <td>15</td> <td>mike mendenhall</td> <td>g</td> <td>united states</td> <td>cincinnati royals</td> <td>cincinnati</td> </tr> <tr> <th>17</th> <td>3</td> <td>16</td> <td>gary alcorn</td> <td>c</td> <td>united states</td> <td>detroit pistons</td> <td>fresno state</td> </tr> <tr> <th>18</th> <td>3</td> <td>17</td> <td>jim hockaday</td> <td>f</td> <td>united states</td> <td>philadelphia warriors</td> <td>memphis</td> </tr> <tr> <th>19</th> <td>3</td> <td>18</td> <td>bobby smith</td> <td>g</td> <td>united states</td> <td>minneapolis lakers</td> <td>west virginia</td> </tr> <tr> <th>20</th> <td>3</td> <td>19</td> <td>jon cincebox</td> <td>f</td> <td>united states</td> <td>syracuse nationals</td> <td>syracuse</td> </tr> <tr> <th>21</th> <td>3</td> <td>20</td> <td>bob anderegg</td> <td>g/f</td> <td>united states</td> <td>new york knicks</td> <td>michigan state</td> </tr> <tr> <th>22</th> <td>3</td> <td>21</td> <td>hank stein</td> <td>g</td> <td>united states</td> <td>st. louis hawks</td> <td>xavier</td> </tr> <tr> <th>23</th> <td>3</td> <td>22</td> <td>ralph crosthwaite</td> <td>c</td> <td>united states</td> <td>boston celtics</td> <td>western kentucky</td> </tr> <tr> <th>24</th> <td>4</td> <td>23</td> <td>leo byrd</td> <td>g</td> <td>united states</td> <td>cincinnati royals</td> <td>marshall</td> </tr> <tr> <th>25</th> <td>4</td> <td>24</td> <td>george lee</td> <td>g/f</td> <td>united states</td> <td>detroit pistons</td> <td>michigan</td> </tr> <tr> <th>26</th> <td>4</td> <td>25</td> <td>ron stevenson</td> <td>f</td> <td>united states</td> <td>philadelphia warriors</td> <td>tcu</td> </tr> <tr> <th>27</th> <td>4</td> <td>26</td> <td>wilson eison</td> <td>f</td> <td>united states</td> <td>minneapolis lakers</td> <td>purdue</td> </tr> <tr> <th>28</th> <td>4</td> <td>27</td> <td>paul neumann</td> <td>g</td> <td>united states</td> <td>syracuse nationals</td> <td>stanford</td> </tr> <tr> <th>29</th> <td>4</td> <td>28</td> <td>johnny cox</td> <td>g</td> <td>united states</td> <td>new york knicks</td> <td>kentucky</td> </tr> <tr> <th>30</th> <td>4</td> <td>29</td> <td>lee harman</td> <td>g</td> <td>united states</td> <td>st. louis hawks</td> <td>oregon state</td> </tr> <tr> <th>31</th> <td>4</td> <td>30</td> <td>ed kazakavich</td> <td>f</td> <td>united states</td> <td>boston celtics</td> <td>scranton</td> </tr> <tr> <th>32</th> <td>5</td> <td>31</td> <td>harry kirchner</td> <td>g/f</td> <td>united states</td> <td>detroit pistons</td> <td>tcu</td> </tr> <tr> <th>33</th> <td>5</td> <td>32</td> <td>tony windis</td> <td>g</td> <td>united states</td> <td>detroit pistons</td> <td>wyoming</td> </tr> <tr> <th>34</th> <td>5</td> <td>37</td> <td>nick mantis</td> <td>g</td> <td>united states</td> <td>st. louis hawks</td> <td>northwestern</td> </tr> <tr> <th>35</th> <td>8</td> <td>56</td> <td>dave gunther</td> <td>f</td> <td>united states</td> <td>philadelphia warriors</td> <td>iowa</td> </tr> <tr> <th>36</th> <td>11</td> <td>77</td> <td>john barnhill</td> <td>g</td> <td>united states</td> <td>st. louis hawks</td> <td>tennessee state</td> </tr> <tr> <th>37</th> <td>14</td> <td>83</td> <td>jack israel</td> <td>g/f</td> <td>united states</td> <td>new york knicks</td> <td>missouri state</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "*" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>2</td> </tr> </tbody> </table>

select count ( * ) where college = 'michigan state' <table_name> : table | | round | pick | player | position | nationality | team | college | |---:|:--------|:-------|:------------------|:-----------|:--------------|:----------------------------------|:------------------| | 0 | t | none | wilt chamberlain | c | united states | philadelphia warriors | kansas | | 1 | t | none | bob ferry | f/c | united states | st. louis hawks | saint louis | | 2 | 1 | 1 | bob boozer | f | united states | cincinnati royals | kansas state | | 3 | 1 | 2 | bailey howell | f | united states | detroit pistons | mississippi state | | 4 | 1 | 3 | tom hawkins | f | united states | minneapolis lakers | notre dame | | 5 | 1 | 4 | dick barnett | g/f | united states | syracuse nationals | tennessee state | | 6 | 1 | 5 | johnny green | f/c | united states | new york knicks | michigan state | | 7 | 1 | 6 | john richter | f | united states | boston celtics | nc state | | 8 | 2 | 7 | tom robitaille | f/c | united states | detroit pistons (from cincinnati) | rice | | 9 | 2 | 8 | don goldstein | f | united states | detroit pistons | louisville | | 10 | 2 | 9 | joe ruklick | f/c | united states | philadelphia warriors | northwestern | | 11 | 2 | 10 | rudy larusso | f/c | united states | minneapolis lakers | dartmouth | | 12 | 2 | 11 | bumper tormohlen | f/c | united states | syracuse nationals | tennessee | | 13 | 2 | 12 | al seiden | g | united states | st. louis hawks (from new york) | st. john's | | 14 | 2 | 13 | cal ramsey | f | united states | st. louis hawks | nyu | | 15 | 2 | 14 | gene guarilia | f | united states | boston celtics | george washington | | 16 | 3 | 15 | mike mendenhall | g | united states | cincinnati royals | cincinnati | | 17 | 3 | 16 | gary alcorn | c | united states | detroit pistons | fresno state | | 18 | 3 | 17 | jim hockaday | f | united states | philadelphia warriors | memphis | | 19 | 3 | 18 | bobby smith | g | united states | minneapolis lakers | west virginia | | 20 | 3 | 19 | jon cincebox | f | united states | syracuse nationals | syracuse | | 21 | 3 | 20 | bob anderegg | g/f | united states | new york knicks | michigan state | | 22 | 3 | 21 | hank stein | g | united states | st. louis hawks | xavier | | 23 | 3 | 22 | ralph crosthwaite | c | united states | boston celtics | western kentucky | | 24 | 4 | 23 | leo byrd | g | united states | cincinnati royals | marshall | | 25 | 4 | 24 | george lee | g/f | united states | detroit pistons | michigan | | 26 | 4 | 25 | ron stevenson | f | united states | philadelphia warriors | tcu | | 27 | 4 | 26 | wilson eison | f | united states | minneapolis lakers | purdue | | 28 | 4 | 27 | paul neumann | g | united states | syracuse nationals | stanford | | 29 | 4 | 28 | johnny cox | g | united states | new york knicks | kentucky | | 30 | 4 | 29 | lee harman | g | united states | st. louis hawks | oregon state | | 31 | 4 | 30 | ed kazakavich | f | united states | boston celtics | scranton | | 32 | 5 | 31 | harry kirchner | g/f | united states | detroit pistons | tcu | | 33 | 5 | 32 | tony windis | g | united states | detroit pistons | wyoming | | 34 | 5 | 37 | nick mantis | g | united states | st. louis hawks | northwestern | | 35 | 8 | 56 | dave gunther | f | united states | philadelphia warriors | iowa | | 36 | 11 | 77 | john barnhill | g | united states | st. louis hawks | tennessee state | | 37 | 14 | 83 | jack israel | g/f | united states | new york knicks | missouri state |

| | count ( "*" ) | |---:|----------------:| | 0 | 2 |

select min ( weight ) where status = '5th voted out week 6' col : name | red vs. blue | couples team | status | weight | total votes row 1 : valerica marin 47, bucharest | none | none | eliminated week 1 | 142 kg | 0 row 2 : tatiana musat 25, bucharest | none | none | eliminated week 1 | 150 kg | 0 row 3 : gige state 47, bucharest | none | none | eliminated week 1 | 200 kg | 0 row 4 : alex ionescu returned week 5 | none | none | eliminated week 1 | 196 kg | 0 row 5 : catalin chiriac 23, bucharest | blue team | none | 1st voted out week 1 | 146, 4 kg | 6 row 6 : dana mocanu 34, ploiesti | red team | none | 2nd voted out week 2 | 111, 5 kg | 6 row 7 : daniela gheorghe 21, pitesti | red team | none | 3rd voted out week 3 | 112, 8 kg | 5 row 8 : alexandra patrascu 37, bucharest | red team | none | eliminated week 4 | 118, 4 kg | 0 row 9 : mihai florescu 35, bucharest | red team | none | 4th voted out week 4 | 175, 8 kg | 0 row 10 : alexandru briscu 26, bacau | blue team | gray team | 5th voted out week 5 | 158, 1 kg | 0 row 11 : daniel sulugiu 34, bucharest | blue team | yellow team | 5th voted out week 6 | 158, 1 kg | 0 row 12 : irina gur 39, bucharest | blue team | lightgreen team | 6th voted out week7 | 129,4 kg | 1 row 13 : dragos holeac 30, bucharest | blue team | lightgreen team | 6th voted out week7 | 129,4 kg | 1

col : min ( "weight" ) row 1 : 158, 1 kg

select min ( weight ) where status = '5th voted out week 6' <table_name> : table \begin{tabular}{lllllll} \toprule & name & red vs. blue & couples team & status & weight & total votes \\ \midrule 0 & valerica marin 47, bucharest & none & none & eliminated week 1 & 142 kg & 0 \\ 1 & tatiana musat 25, bucharest & none & none & eliminated week 1 & 150 kg & 0 \\ 2 & gige state 47, bucharest & none & none & eliminated week 1 & 200 kg & 0 \\ 3 & alex ionescu returned week 5 & none & none & eliminated week 1 & 196 kg & 0 \\ 4 & catalin chiriac 23, bucharest & blue team & none & 1st voted out week 1 & 146, 4 kg & 6 \\ 5 & dana mocanu 34, ploiesti & red team & none & 2nd voted out week 2 & 111, 5 kg & 6 \\ 6 & daniela gheorghe 21, pitesti & red team & none & 3rd voted out week 3 & 112, 8 kg & 5 \\ 7 & alexandra patrascu 37, bucharest & red team & none & eliminated week 4 & 118, 4 kg & 0 \\ 8 & mihai florescu 35, bucharest & red team & none & 4th voted out week 4 & 175, 8 kg & 0 \\ 9 & alexandru briscu 26, bacau & blue team & gray team & 5th voted out week 5 & 158, 1 kg & 0 \\ 10 & daniel sulugiu 34, bucharest & blue team & yellow team & 5th voted out week 6 & 158, 1 kg & 0 \\ 11 & irina gur 39, bucharest & blue team & lightgreen team & 6th voted out week7 & 129,4 kg & 1 \\ 12 & dragos holeac 30, bucharest & blue team & lightgreen team & 6th voted out week7 & 129,4 kg & 1 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & min ( "weight" ) \\ \midrule 0 & 158, 1 kg \\ \bottomrule \end{tabular}

select min ( weight ) where status = '5th voted out week 6' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>name</th> <th>red vs. blue</th> <th>couples team</th> <th>status</th> <th>weight</th> <th>total votes</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>valerica marin 47, bucharest</td> <td>none</td> <td>none</td> <td>eliminated week 1</td> <td>142 kg</td> <td>0</td> </tr> <tr> <th>1</th> <td>tatiana musat 25, bucharest</td> <td>none</td> <td>none</td> <td>eliminated week 1</td> <td>150 kg</td> <td>0</td> </tr> <tr> <th>2</th> <td>gige state 47, bucharest</td> <td>none</td> <td>none</td> <td>eliminated week 1</td> <td>200 kg</td> <td>0</td> </tr> <tr> <th>3</th> <td>alex ionescu returned week 5</td> <td>none</td> <td>none</td> <td>eliminated week 1</td> <td>196 kg</td> <td>0</td> </tr> <tr> <th>4</th> <td>catalin chiriac 23, bucharest</td> <td>blue team</td> <td>none</td> <td>1st voted out week 1</td> <td>146, 4 kg</td> <td>6</td> </tr> <tr> <th>5</th> <td>dana mocanu 34, ploiesti</td> <td>red team</td> <td>none</td> <td>2nd voted out week 2</td> <td>111, 5 kg</td> <td>6</td> </tr> <tr> <th>6</th> <td>daniela gheorghe 21, pitesti</td> <td>red team</td> <td>none</td> <td>3rd voted out week 3</td> <td>112, 8 kg</td> <td>5</td> </tr> <tr> <th>7</th> <td>alexandra patrascu 37, bucharest</td> <td>red team</td> <td>none</td> <td>eliminated week 4</td> <td>118, 4 kg</td> <td>0</td> </tr> <tr> <th>8</th> <td>mihai florescu 35, bucharest</td> <td>red team</td> <td>none</td> <td>4th voted out week 4</td> <td>175, 8 kg</td> <td>0</td> </tr> <tr> <th>9</th> <td>alexandru briscu 26, bacau</td> <td>blue team</td> <td>gray team</td> <td>5th voted out week 5</td> <td>158, 1 kg</td> <td>0</td> </tr> <tr> <th>10</th> <td>daniel sulugiu 34, bucharest</td> <td>blue team</td> <td>yellow team</td> <td>5th voted out week 6</td> <td>158, 1 kg</td> <td>0</td> </tr> <tr> <th>11</th> <td>irina gur 39, bucharest</td> <td>blue team</td> <td>lightgreen team</td> <td>6th voted out week7</td> <td>129,4 kg</td> <td>1</td> </tr> <tr> <th>12</th> <td>dragos holeac 30, bucharest</td> <td>blue team</td> <td>lightgreen team</td> <td>6th voted out week7</td> <td>129,4 kg</td> <td>1</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>min ( "weight" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>158, 1 kg</td> </tr> </tbody> </table>

select min ( weight ) where status = '5th voted out week 6' <table_name> : table | | name | red vs. blue | couples team | status | weight | total votes | |---:|:---------------------------------|:---------------|:----------------|:---------------------|:----------|--------------:| | 0 | valerica marin 47, bucharest | none | none | eliminated week 1 | 142 kg | 0 | | 1 | tatiana musat 25, bucharest | none | none | eliminated week 1 | 150 kg | 0 | | 2 | gige state 47, bucharest | none | none | eliminated week 1 | 200 kg | 0 | | 3 | alex ionescu returned week 5 | none | none | eliminated week 1 | 196 kg | 0 | | 4 | catalin chiriac 23, bucharest | blue team | none | 1st voted out week 1 | 146, 4 kg | 6 | | 5 | dana mocanu 34, ploiesti | red team | none | 2nd voted out week 2 | 111, 5 kg | 6 | | 6 | daniela gheorghe 21, pitesti | red team | none | 3rd voted out week 3 | 112, 8 kg | 5 | | 7 | alexandra patrascu 37, bucharest | red team | none | eliminated week 4 | 118, 4 kg | 0 | | 8 | mihai florescu 35, bucharest | red team | none | 4th voted out week 4 | 175, 8 kg | 0 | | 9 | alexandru briscu 26, bacau | blue team | gray team | 5th voted out week 5 | 158, 1 kg | 0 | | 10 | daniel sulugiu 34, bucharest | blue team | yellow team | 5th voted out week 6 | 158, 1 kg | 0 | | 11 | irina gur 39, bucharest | blue team | lightgreen team | 6th voted out week7 | 129,4 kg | 1 | | 12 | dragos holeac 30, bucharest | blue team | lightgreen team | 6th voted out week7 | 129,4 kg | 1 |

| | min ( "weight" ) | |---:|:-------------------| | 0 | 158, 1 kg |

select count ( venue ) where margin = 0 col : margin | teams | venue | season row 1 : 192 runs | kent (205 & 512-9 d) beat essex (370 & 155) | county ground, chelmsford | 2009 row 2 : 72 runs | kent (117 & 332) beat surrey (301-8 d & 76) | woodbridge road, guildford | 1992 row 3 : 57 runs | kent (151 & 348) beat middlesex (305-5 d & 137) | hesketh park, dartford | 1976 row 4 : 36 runs | kent (127 & 198) beat australia (229 & 60) | st lawrence ground, canterbury | 1893 row 5 : 24 runs | kent (115 & 389) beat gloucestershire (351 & 129) | mote park, maidstone | 1925 row 6 : 16 runs | kent (120 & 176) beat cambridgeshire (206 & 74) | mote park, maidstone | 1863

col : count ( "venue" ) row 1 : 0

select count ( venue ) where margin = 0 <table_name> : table \begin{tabular}{lllll} \toprule & margin & teams & venue & season \\ \midrule 0 & 192 runs & kent (205 & 512-9 d) beat essex (370 & 155) & county ground, chelmsford & 2009 \\ 1 & 72 runs & kent (117 & 332) beat surrey (301-8 d & 76) & woodbridge road, guildford & 1992 \\ 2 & 57 runs & kent (151 & 348) beat middlesex (305-5 d & 137) & hesketh park, dartford & 1976 \\ 3 & 36 runs & kent (127 & 198) beat australia (229 & 60) & st lawrence ground, canterbury & 1893 \\ 4 & 24 runs & kent (115 & 389) beat gloucestershire (351 & 129) & mote park, maidstone & 1925 \\ 5 & 16 runs & kent (120 & 176) beat cambridgeshire (206 & 74) & mote park, maidstone & 1863 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "venue" ) \\ \midrule 0 & 0 \\ \bottomrule \end{tabular}

select count ( venue ) where margin = 0 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>margin</th> <th>teams</th> <th>venue</th> <th>season</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>192 runs</td> <td>kent (205 &amp; 512-9 d) beat essex (370 &amp; 155)</td> <td>county ground, chelmsford</td> <td>2009</td> </tr> <tr> <th>1</th> <td>72 runs</td> <td>kent (117 &amp; 332) beat surrey (301-8 d &amp; 76)</td> <td>woodbridge road, guildford</td> <td>1992</td> </tr> <tr> <th>2</th> <td>57 runs</td> <td>kent (151 &amp; 348) beat middlesex (305-5 d &amp; 137)</td> <td>hesketh park, dartford</td> <td>1976</td> </tr> <tr> <th>3</th> <td>36 runs</td> <td>kent (127 &amp; 198) beat australia (229 &amp; 60)</td> <td>st lawrence ground, canterbury</td> <td>1893</td> </tr> <tr> <th>4</th> <td>24 runs</td> <td>kent (115 &amp; 389) beat gloucestershire (351 &amp; 129)</td> <td>mote park, maidstone</td> <td>1925</td> </tr> <tr> <th>5</th> <td>16 runs</td> <td>kent (120 &amp; 176) beat cambridgeshire (206 &amp; 74)</td> <td>mote park, maidstone</td> <td>1863</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "venue" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>0</td> </tr> </tbody> </table>

select count ( venue ) where margin = 0 <table_name> : table | | margin | teams | venue | season | |---:|:---------|:--------------------------------------------------|:-------------------------------|---------:| | 0 | 192 runs | kent (205 & 512-9 d) beat essex (370 & 155) | county ground, chelmsford | 2009 | | 1 | 72 runs | kent (117 & 332) beat surrey (301-8 d & 76) | woodbridge road, guildford | 1992 | | 2 | 57 runs | kent (151 & 348) beat middlesex (305-5 d & 137) | hesketh park, dartford | 1976 | | 3 | 36 runs | kent (127 & 198) beat australia (229 & 60) | st lawrence ground, canterbury | 1893 | | 4 | 24 runs | kent (115 & 389) beat gloucestershire (351 & 129) | mote park, maidstone | 1925 | | 5 | 16 runs | kent (120 & 176) beat cambridgeshire (206 & 74) | mote park, maidstone | 1863 |

| | count ( "venue" ) | |---:|--------------------:| | 0 | 0 |

select min ( assists ) where team = 'esteghlal' col : season | team | country | division | apps | goals | assists row 1 : 01/02 | esteghlal | iran | 1 | none | 3 | none row 2 : 02/03 | esteghlal | iran | 1 | none | 2 | none row 3 : 03/04 | esteghlal | iran | 1 | none | 6 | none row 4 : 04/05 | eseghlal | iran | 1 | 27 | 3 | none row 5 : 05/06 | esteghlal | iran | 1 | 21 | 0 | 2 row 6 : 06/07 | esteghlal | iran | 1 | 14 | 1 | 1 row 7 : 07/08 | steel azin | iran | 1 | none | 2 | none row 8 : 10/11 | gostaresh foolad | iran | 2 | 0 | 0 | 0

col : min ( "assists" ) row 1 : 1

select min ( assists ) where team = 'esteghlal' <table_name> : table \begin{tabular}{llllllll} \toprule & season & team & country & division & apps & goals & assists \\ \midrule 0 & 01/02 & esteghlal & iran & 1 & none & 3 & none \\ 1 & 02/03 & esteghlal & iran & 1 & none & 2 & none \\ 2 & 03/04 & esteghlal & iran & 1 & none & 6 & none \\ 3 & 04/05 & eseghlal & iran & 1 & 27 & 3 & none \\ 4 & 05/06 & esteghlal & iran & 1 & 21 & 0 & 2 \\ 5 & 06/07 & esteghlal & iran & 1 & 14 & 1 & 1 \\ 6 & 07/08 & steel azin & iran & 1 & none & 2 & none \\ 7 & 10/11 & gostaresh foolad & iran & 2 & 0 & 0 & 0 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & min ( "assists" ) \\ \midrule 0 & 1 \\ \bottomrule \end{tabular}

select min ( assists ) where team = 'esteghlal' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>season</th> <th>team</th> <th>country</th> <th>division</th> <th>apps</th> <th>goals</th> <th>assists</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>01/02</td> <td>esteghlal</td> <td>iran</td> <td>1</td> <td>none</td> <td>3</td> <td>none</td> </tr> <tr> <th>1</th> <td>02/03</td> <td>esteghlal</td> <td>iran</td> <td>1</td> <td>none</td> <td>2</td> <td>none</td> </tr> <tr> <th>2</th> <td>03/04</td> <td>esteghlal</td> <td>iran</td> <td>1</td> <td>none</td> <td>6</td> <td>none</td> </tr> <tr> <th>3</th> <td>04/05</td> <td>eseghlal</td> <td>iran</td> <td>1</td> <td>27</td> <td>3</td> <td>none</td> </tr> <tr> <th>4</th> <td>05/06</td> <td>esteghlal</td> <td>iran</td> <td>1</td> <td>21</td> <td>0</td> <td>2</td> </tr> <tr> <th>5</th> <td>06/07</td> <td>esteghlal</td> <td>iran</td> <td>1</td> <td>14</td> <td>1</td> <td>1</td> </tr> <tr> <th>6</th> <td>07/08</td> <td>steel azin</td> <td>iran</td> <td>1</td> <td>none</td> <td>2</td> <td>none</td> </tr> <tr> <th>7</th> <td>10/11</td> <td>gostaresh foolad</td> <td>iran</td> <td>2</td> <td>0</td> <td>0</td> <td>0</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>min ( "assists" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> </tr> </tbody> </table>

select min ( assists ) where team = 'esteghlal' <table_name> : table | | season | team | country | division | apps | goals | assists | |---:|:---------|:-----------------|:----------|-----------:|:-------|--------:|:----------| | 0 | 01/02 | esteghlal | iran | 1 | none | 3 | none | | 1 | 02/03 | esteghlal | iran | 1 | none | 2 | none | | 2 | 03/04 | esteghlal | iran | 1 | none | 6 | none | | 3 | 04/05 | eseghlal | iran | 1 | 27 | 3 | none | | 4 | 05/06 | esteghlal | iran | 1 | 21 | 0 | 2 | | 5 | 06/07 | esteghlal | iran | 1 | 14 | 1 | 1 | | 6 | 07/08 | steel azin | iran | 1 | none | 2 | none | | 7 | 10/11 | gostaresh foolad | iran | 2 | 0 | 0 | 0 |

| | min ( "assists" ) | |---:|--------------------:| | 0 | 1 |

select date where date = '26 november' col : occasion # | location | date | gold | time -1 | silver | time -2 | bronze | time -3 | report row 1 : 1 | chelyabinsk, russia | 18 november | pekka koskela finland | 35.0 | jan smeekens netherlands | 35.01 | yuya oikawa japan | 35.07 | row 2 : 1 | chelyabinsk, russia | 19 november | joji kato japan | 34.92 | mo tae-bum south korea | 35.01 | yuya oikawa japan | 35.14 | row 3 : 2 | astana, kazakhstan | 25 november | mo tae-bum south korea | 34.89 | tucker fredricks united states | 34.94 | stefan groothuis netherlands | 35.01 | row 4 : 2 | astana, kazakhstan | 26 november | jan smeekens netherlands | 35.05 | mo tae-bum south korea | 35.06 | tucker fredricks united states | 35.19 | row 5 : 3 | heerenveen, netherlands | 2 december | tucker fredricks united states | 34.98 | joji kato japan | 35.07 | mo tae-bum south korea | 35.08 | row 6 : 3 | heerenveen, netherlands | 3 december | pekka koskela finland | 35.01 | joji kato japan | 35.02 | jesper hospes netherlands | 35.06 | row 7 : 4 | salt lake city, united states | 21 january | keiichiro nagashima japan | 34.37 | jan smeekens netherlands | 34.4 | tucker fredricks united states | 34.45 | row 8 : 4 | salt lake city, united states | 22 january | dmitry lobkov russia | 34.54 | keiichiro nagashima japan | 34.57 | tucker fredricks united states | 34.6 | row 9 : 6 | heerenveen, netherlands | 2 march | dmitry lobkov russia | 35.11 | hein otterspeer netherlands | 35.14 | keiichiro nagashima japan | 35.2 | row 10 : 6 | heerenveen, netherlands | 3 march | tucker fredricks united states | 35.05 | michel mulder netherlands | 35.12 | joji kato japan | 35.16 | row 11 : 7 | berlin, germany | 9 march | jamie gregg canada | 35.06 | pekka koskela finland | 35.07 | mo tae-bum south korea | 35.17 | row 12 : 7 | berlin, germany | 10 march | michel mulder netherlands | 35.01 | mo tae-bum south korea | 35.04 | jan smeekens netherlands | 35.08 |

col : date row 1 : 26 november

select date where date = '26 november' <table_name> : table \begin{tabular}{lllllllllll} \toprule & occasion # & location & date & gold & time -1 & silver & time -2 & bronze & time -3 & report \\ \midrule 0 & 1 & chelyabinsk, russia & 18 november & pekka koskela finland & 35.0 & jan smeekens netherlands & 35.01 & yuya oikawa japan & 35.07 & \\ 1 & 1 & chelyabinsk, russia & 19 november & joji kato japan & 34.92 & mo tae-bum south korea & 35.01 & yuya oikawa japan & 35.14 & \\ 2 & 2 & astana, kazakhstan & 25 november & mo tae-bum south korea & 34.89 & tucker fredricks united states & 34.94 & stefan groothuis netherlands & 35.01 & \\ 3 & 2 & astana, kazakhstan & 26 november & jan smeekens netherlands & 35.05 & mo tae-bum south korea & 35.06 & tucker fredricks united states & 35.19 & \\ 4 & 3 & heerenveen, netherlands & 2 december & tucker fredricks united states & 34.98 & joji kato japan & 35.07 & mo tae-bum south korea & 35.08 & \\ 5 & 3 & heerenveen, netherlands & 3 december & pekka koskela finland & 35.01 & joji kato japan & 35.02 & jesper hospes netherlands & 35.06 & \\ 6 & 4 & salt lake city, united states & 21 january & keiichiro nagashima japan & 34.37 & jan smeekens netherlands & 34.4 & tucker fredricks united states & 34.45 & \\ 7 & 4 & salt lake city, united states & 22 january & dmitry lobkov russia & 34.54 & keiichiro nagashima japan & 34.57 & tucker fredricks united states & 34.6 & \\ 8 & 6 & heerenveen, netherlands & 2 march & dmitry lobkov russia & 35.11 & hein otterspeer netherlands & 35.14 & keiichiro nagashima japan & 35.2 & \\ 9 & 6 & heerenveen, netherlands & 3 march & tucker fredricks united states & 35.05 & michel mulder netherlands & 35.12 & joji kato japan & 35.16 & \\ 10 & 7 & berlin, germany & 9 march & jamie gregg canada & 35.06 & pekka koskela finland & 35.07 & mo tae-bum south korea & 35.17 & \\ 11 & 7 & berlin, germany & 10 march & michel mulder netherlands & 35.01 & mo tae-bum south korea & 35.04 & jan smeekens netherlands & 35.08 | & NaN \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & date \\ \midrule 0 & 26 november \\ \bottomrule \end{tabular}

select date where date = '26 november' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>occasion #</th> <th>location</th> <th>date</th> <th>gold</th> <th>time -1</th> <th>silver</th> <th>time -2</th> <th>bronze</th> <th>time -3</th> <th>report</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>chelyabinsk, russia</td> <td>18 november</td> <td>pekka koskela finland</td> <td>35.0</td> <td>jan smeekens netherlands</td> <td>35.01</td> <td>yuya oikawa japan</td> <td>35.07</td> <td></td> </tr> <tr> <th>1</th> <td>1</td> <td>chelyabinsk, russia</td> <td>19 november</td> <td>joji kato japan</td> <td>34.92</td> <td>mo tae-bum south korea</td> <td>35.01</td> <td>yuya oikawa japan</td> <td>35.14</td> <td></td> </tr> <tr> <th>2</th> <td>2</td> <td>astana, kazakhstan</td> <td>25 november</td> <td>mo tae-bum south korea</td> <td>34.89</td> <td>tucker fredricks united states</td> <td>34.94</td> <td>stefan groothuis netherlands</td> <td>35.01</td> <td></td> </tr> <tr> <th>3</th> <td>2</td> <td>astana, kazakhstan</td> <td>26 november</td> <td>jan smeekens netherlands</td> <td>35.05</td> <td>mo tae-bum south korea</td> <td>35.06</td> <td>tucker fredricks united states</td> <td>35.19</td> <td></td> </tr> <tr> <th>4</th> <td>3</td> <td>heerenveen, netherlands</td> <td>2 december</td> <td>tucker fredricks united states</td> <td>34.98</td> <td>joji kato japan</td> <td>35.07</td> <td>mo tae-bum south korea</td> <td>35.08</td> <td></td> </tr> <tr> <th>5</th> <td>3</td> <td>heerenveen, netherlands</td> <td>3 december</td> <td>pekka koskela finland</td> <td>35.01</td> <td>joji kato japan</td> <td>35.02</td> <td>jesper hospes netherlands</td> <td>35.06</td> <td></td> </tr> <tr> <th>6</th> <td>4</td> <td>salt lake city, united states</td> <td>21 january</td> <td>keiichiro nagashima japan</td> <td>34.37</td> <td>jan smeekens netherlands</td> <td>34.4</td> <td>tucker fredricks united states</td> <td>34.45</td> <td></td> </tr> <tr> <th>7</th> <td>4</td> <td>salt lake city, united states</td> <td>22 january</td> <td>dmitry lobkov russia</td> <td>34.54</td> <td>keiichiro nagashima japan</td> <td>34.57</td> <td>tucker fredricks united states</td> <td>34.6</td> <td></td> </tr> <tr> <th>8</th> <td>6</td> <td>heerenveen, netherlands</td> <td>2 march</td> <td>dmitry lobkov russia</td> <td>35.11</td> <td>hein otterspeer netherlands</td> <td>35.14</td> <td>keiichiro nagashima japan</td> <td>35.2</td> <td></td> </tr> <tr> <th>9</th> <td>6</td> <td>heerenveen, netherlands</td> <td>3 march</td> <td>tucker fredricks united states</td> <td>35.05</td> <td>michel mulder netherlands</td> <td>35.12</td> <td>joji kato japan</td> <td>35.16</td> <td></td> </tr> <tr> <th>10</th> <td>7</td> <td>berlin, germany</td> <td>9 march</td> <td>jamie gregg canada</td> <td>35.06</td> <td>pekka koskela finland</td> <td>35.07</td> <td>mo tae-bum south korea</td> <td>35.17</td> <td></td> </tr> <tr> <th>11</th> <td>7</td> <td>berlin, germany</td> <td>10 march</td> <td>michel mulder netherlands</td> <td>35.01</td> <td>mo tae-bum south korea</td> <td>35.04</td> <td>jan smeekens netherlands</td> <td>35.08 |</td> <td>None</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>date</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>26 november</td> </tr> </tbody> </table>

select date where date = '26 november' <table_name> : table | | occasion # | location | date | gold | time -1 | silver | time -2 | bronze | time -3 | report | |---:|-------------:|:------------------------------|:------------|:-------------------------------|----------:|:-------------------------------|----------:|:-------------------------------|:----------|:---------| | 0 | 1 | chelyabinsk, russia | 18 november | pekka koskela finland | 35 | jan smeekens netherlands | 35.01 | yuya oikawa japan | 35.07 | | | 1 | 1 | chelyabinsk, russia | 19 november | joji kato japan | 34.92 | mo tae-bum south korea | 35.01 | yuya oikawa japan | 35.14 | | | 2 | 2 | astana, kazakhstan | 25 november | mo tae-bum south korea | 34.89 | tucker fredricks united states | 34.94 | stefan groothuis netherlands | 35.01 | | | 3 | 2 | astana, kazakhstan | 26 november | jan smeekens netherlands | 35.05 | mo tae-bum south korea | 35.06 | tucker fredricks united states | 35.19 | | | 4 | 3 | heerenveen, netherlands | 2 december | tucker fredricks united states | 34.98 | joji kato japan | 35.07 | mo tae-bum south korea | 35.08 | | | 5 | 3 | heerenveen, netherlands | 3 december | pekka koskela finland | 35.01 | joji kato japan | 35.02 | jesper hospes netherlands | 35.06 | | | 6 | 4 | salt lake city, united states | 21 january | keiichiro nagashima japan | 34.37 | jan smeekens netherlands | 34.4 | tucker fredricks united states | 34.45 | | | 7 | 4 | salt lake city, united states | 22 january | dmitry lobkov russia | 34.54 | keiichiro nagashima japan | 34.57 | tucker fredricks united states | 34.6 | | | 8 | 6 | heerenveen, netherlands | 2 march | dmitry lobkov russia | 35.11 | hein otterspeer netherlands | 35.14 | keiichiro nagashima japan | 35.2 | | | 9 | 6 | heerenveen, netherlands | 3 march | tucker fredricks united states | 35.05 | michel mulder netherlands | 35.12 | joji kato japan | 35.16 | | | 10 | 7 | berlin, germany | 9 march | jamie gregg canada | 35.06 | pekka koskela finland | 35.07 | mo tae-bum south korea | 35.17 | | | 11 | 7 | berlin, germany | 10 march | michel mulder netherlands | 35.01 | mo tae-bum south korea | 35.04 | jan smeekens netherlands | 35.08 | | |

| | date | |---:|:------------| | 0 | 26 november |

select year - sacks where team = 'cin' col : year | team | games | combined tackles | tackles | assisted tackles | sacks | forced fumbles | fumble recoveries | fumble return yards | interceptions | interception return yards | yards per interception return | longest interception return | interceptions returned for touchdown | passes defended row 1 : 2001 | cin | 15 | 53 | 41 | 12 | 8.5 | 0 | 0 | 0 | 2 | 28 | 14 | 21 | 0 | 5 row 2 : 2002 | cin | 16 | 59 | 47 | 12 | 6.5 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 row 3 : 2003 | cin | 16 | 60 | 41 | 19 | 5.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 row 4 : 2004 | cin | 16 | 70 | 41 | 29 | 8.0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 row 5 : 2005 | cin | 16 | 65 | 45 | 20 | 6.0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 row 6 : 2006 | cin | 16 | 81 | 50 | 31 | 7.5 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 3 row 7 : 2007 | cin | 16 | 78 | 49 | 29 | 2.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 row 8 : 2008 | sf | 16 | 73 | 50 | 23 | 7.0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 row 9 : 2009 | sf | 16 | 55 | 40 | 15 | 6.0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 row 10 : 2010 | sf | 16 | 70 | 57 | 13 | 8.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 row 11 : 2011 | sf | 16 | 58 | 45 | 13 | 7.5 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 row 12 : 2012 | sf | 14 | 66 | 47 | 19 | 3.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 row 13 : 2013 | sf | 16 | 49 | 32 | 17 | 6.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 row 14 : career | none | 205 | 837 | 585 | 252 | 82.0 | 15 | 9 | 0 | 3 | 28 | 9 | 21 | 0 | 29

col : "year - sacks" row 1 : year - sacks row 2 : year - sacks row 3 : year - sacks row 4 : year - sacks row 5 : year - sacks row 6 : year - sacks row 7 : year - sacks

select year - sacks where team = 'cin' <table_name> : table \begin{tabular}{lllllllllllllllll} \toprule & year & team & games & combined tackles & tackles & assisted tackles & sacks & forced fumbles & fumble recoveries & fumble return yards & interceptions & interception return yards & yards per interception return & longest interception return & interceptions returned for touchdown & passes defended \\ \midrule 0 & 2001 & cin & 15 & 53 & 41 & 12 & 8.5 & 0 & 0 & 0 & 2 & 28 & 14 & 21 & 0 & 5 \\ 1 & 2002 & cin & 16 & 59 & 47 & 12 & 6.5 & 2 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 2 \\ 2 & 2003 & cin & 16 & 60 & 41 & 19 & 5.0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 3 \\ 3 & 2004 & cin & 16 & 70 & 41 & 29 & 8.0 & 2 & 2 & 0 & 0 & 0 & 0 & 0 & 0 & 2 \\ 4 & 2005 & cin & 16 & 65 & 45 & 20 & 6.0 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 2 \\ 5 & 2006 & cin & 16 & 81 & 50 & 31 & 7.5 & 1 & 2 & 0 & 0 & 0 & 0 & 0 & 0 & 3 \\ 6 & 2007 & cin & 16 & 78 & 49 & 29 & 2.0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 3 \\ 7 & 2008 & sf & 16 & 73 & 50 & 23 & 7.0 & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 3 \\ 8 & 2009 & sf & 16 & 55 & 40 & 15 & 6.0 & 2 & 2 & 0 & 0 & 0 & 0 & 0 & 0 & 1 \\ 9 & 2010 & sf & 16 & 70 & 57 & 13 & 8.5 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 \\ 10 & 2011 & sf & 16 & 58 & 45 & 13 & 7.5 & 3 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 2 \\ 11 & 2012 & sf & 14 & 66 & 47 & 19 & 3.0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 2 \\ 12 & 2013 & sf & 16 & 49 & 32 & 17 & 6.5 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ 13 & career & none & 205 & 837 & 585 & 252 & 82.0 & 15 & 9 & 0 & 3 & 28 & 9 & 21 & 0 & 29 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & "year - sacks" \\ \midrule 0 & year - sacks \\ 1 & year - sacks \\ 2 & year - sacks \\ 3 & year - sacks \\ 4 & year - sacks \\ 5 & year - sacks \\ 6 & year - sacks \\ \bottomrule \end{tabular}

select year - sacks where team = 'cin' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>year</th> <th>team</th> <th>games</th> <th>combined tackles</th> <th>tackles</th> <th>assisted tackles</th> <th>sacks</th> <th>forced fumbles</th> <th>fumble recoveries</th> <th>fumble return yards</th> <th>interceptions</th> <th>interception return yards</th> <th>yards per interception return</th> <th>longest interception return</th> <th>interceptions returned for touchdown</th> <th>passes defended</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>2001</td> <td>cin</td> <td>15</td> <td>53</td> <td>41</td> <td>12</td> <td>8.5</td> <td>0</td> <td>0</td> <td>0</td> <td>2</td> <td>28</td> <td>14</td> <td>21</td> <td>0</td> <td>5</td> </tr> <tr> <th>1</th> <td>2002</td> <td>cin</td> <td>16</td> <td>59</td> <td>47</td> <td>12</td> <td>6.5</td> <td>2</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>2</td> </tr> <tr> <th>2</th> <td>2003</td> <td>cin</td> <td>16</td> <td>60</td> <td>41</td> <td>19</td> <td>5.0</td> <td>1</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>3</td> </tr> <tr> <th>3</th> <td>2004</td> <td>cin</td> <td>16</td> <td>70</td> <td>41</td> <td>29</td> <td>8.0</td> <td>2</td> <td>2</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>2</td> </tr> <tr> <th>4</th> <td>2005</td> <td>cin</td> <td>16</td> <td>65</td> <td>45</td> <td>20</td> <td>6.0</td> <td>1</td> <td>1</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>2</td> </tr> <tr> <th>5</th> <td>2006</td> <td>cin</td> <td>16</td> <td>81</td> <td>50</td> <td>31</td> <td>7.5</td> <td>1</td> <td>2</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>3</td> </tr> <tr> <th>6</th> <td>2007</td> <td>cin</td> <td>16</td> <td>78</td> <td>49</td> <td>29</td> <td>2.0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>3</td> </tr> <tr> <th>7</th> <td>2008</td> <td>sf</td> <td>16</td> <td>73</td> <td>50</td> <td>23</td> <td>7.0</td> <td>1</td> <td>0</td> <td>0</td> <td>1</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>3</td> </tr> <tr> <th>8</th> <td>2009</td> <td>sf</td> <td>16</td> <td>55</td> <td>40</td> <td>15</td> <td>6.0</td> <td>2</td> <td>2</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <th>9</th> <td>2010</td> <td>sf</td> <td>16</td> <td>70</td> <td>57</td> <td>13</td> <td>8.5</td> <td>1</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <th>10</th> <td>2011</td> <td>sf</td> <td>16</td> <td>58</td> <td>45</td> <td>13</td> <td>7.5</td> <td>3</td> <td>1</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>2</td> </tr> <tr> <th>11</th> <td>2012</td> <td>sf</td> <td>14</td> <td>66</td> <td>47</td> <td>19</td> <td>3.0</td> <td>0</td> <td>1</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>2</td> </tr> <tr> <th>12</th> <td>2013</td> <td>sf</td> <td>16</td> <td>49</td> <td>32</td> <td>17</td> <td>6.5</td> <td>1</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> </tr> <tr> <th>13</th> <td>career</td> <td>none</td> <td>205</td> <td>837</td> <td>585</td> <td>252</td> <td>82.0</td> <td>15</td> <td>9</td> <td>0</td> <td>3</td> <td>28</td> <td>9</td> <td>21</td> <td>0</td> <td>29</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>"year - sacks"</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>year - sacks</td> </tr> <tr> <th>1</th> <td>year - sacks</td> </tr> <tr> <th>2</th> <td>year - sacks</td> </tr> <tr> <th>3</th> <td>year - sacks</td> </tr> <tr> <th>4</th> <td>year - sacks</td> </tr> <tr> <th>5</th> <td>year - sacks</td> </tr> <tr> <th>6</th> <td>year - sacks</td> </tr> </tbody> </table>

select year - sacks where team = 'cin' <table_name> : table | | year | team | games | combined tackles | tackles | assisted tackles | sacks | forced fumbles | fumble recoveries | fumble return yards | interceptions | interception return yards | yards per interception return | longest interception return | interceptions returned for touchdown | passes defended | |---:|:-------|:-------|--------:|-------------------:|----------:|-------------------:|--------:|-----------------:|--------------------:|----------------------:|----------------:|----------------------------:|--------------------------------:|------------------------------:|---------------------------------------:|------------------:| | 0 | 2001 | cin | 15 | 53 | 41 | 12 | 8.5 | 0 | 0 | 0 | 2 | 28 | 14 | 21 | 0 | 5 | | 1 | 2002 | cin | 16 | 59 | 47 | 12 | 6.5 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | | 2 | 2003 | cin | 16 | 60 | 41 | 19 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | | 3 | 2004 | cin | 16 | 70 | 41 | 29 | 8 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | | 4 | 2005 | cin | 16 | 65 | 45 | 20 | 6 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | | 5 | 2006 | cin | 16 | 81 | 50 | 31 | 7.5 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | | 6 | 2007 | cin | 16 | 78 | 49 | 29 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | | 7 | 2008 | sf | 16 | 73 | 50 | 23 | 7 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 | | 8 | 2009 | sf | 16 | 55 | 40 | 15 | 6 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | | 9 | 2010 | sf | 16 | 70 | 57 | 13 | 8.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | | 10 | 2011 | sf | 16 | 58 | 45 | 13 | 7.5 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | | 11 | 2012 | sf | 14 | 66 | 47 | 19 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | | 12 | 2013 | sf | 16 | 49 | 32 | 17 | 6.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | | 13 | career | none | 205 | 837 | 585 | 252 | 82 | 15 | 9 | 0 | 3 | 28 | 9 | 21 | 0 | 29 |

| | "year - sacks" | |---:|:-----------------| | 0 | year - sacks | | 1 | year - sacks | | 2 | year - sacks | | 3 | year - sacks | | 4 | year - sacks | | 5 | year - sacks | | 6 | year - sacks |

select count ( callsign ) where notes = 'mts tv hd ch. 417 shaw ch. 8 bell ch. 228' col : city of licence | analog channel | digital channel | virtual channel | callsign | network | notes row 1 : brandon | 4 | none | none | ckyb-tv | ctv | satellite of cky-dt winnipeg row 2 : dauphin | 12 | none | none | ckyd-tv | ctv | satellite of cky-dt winnipeg row 3 : fisher branch | 8 | none | none | ckya-tv | ctv | satellite of cky-dt winnipeg row 4 : flin flon | 13 | none | none | ckyf-tv | ctv | satellite of cky-dt winnipeg row 5 : mccreary | 13 | none | none | ckyb-tv | ctv | satellite of cky-dt winnipeg row 6 : minnedosa | 2 | none | none | cknd-tv-2 | global | none row 7 : the pas | 12 | none | none | ckyp-tv | ctv | satellite of cky-dt winnipeg row 8 : portage la prairie | none | 13 | 13.1 | chmi-dt | city | none row 9 : thompson | 9 | none | none | ckyt-tv | ctv | satellite of cky-dt winnipeg row 10 : winnipeg | none | 51 | 3.1 | cbwft-dt | r-c | none row 11 : winnipeg | none | 27 | 6.1 | cbwt-dt | cbc | none row 12 : winnipeg | none | 7 | 7.1 | cky-dt | ctv | none row 13 : winnipeg | none | 40 | 9.1 | cknd-dt | global | none row 14 : winnipeg | none | 13 | 13.1 | chmi-dt | city | mts tv hd ch. 417 shaw ch. 8 bell ch. 228 row 15 : winnipeg | none | 35 | 35.1 | ciit-dt | religious independent | none

col : count ( "callsign" ) row 1 : 1

select count ( callsign ) where notes = 'mts tv hd ch. 417 shaw ch. 8 bell ch. 228' <table_name> : table \begin{tabular}{llllllll} \toprule & city of licence & analog channel & digital channel & virtual channel & callsign & network & notes \\ \midrule 0 & brandon & 4 & none & none & ckyb-tv & ctv & satellite of cky-dt winnipeg \\ 1 & dauphin & 12 & none & none & ckyd-tv & ctv & satellite of cky-dt winnipeg \\ 2 & fisher branch & 8 & none & none & ckya-tv & ctv & satellite of cky-dt winnipeg \\ 3 & flin flon & 13 & none & none & ckyf-tv & ctv & satellite of cky-dt winnipeg \\ 4 & mccreary & 13 & none & none & ckyb-tv & ctv & satellite of cky-dt winnipeg \\ 5 & minnedosa & 2 & none & none & cknd-tv-2 & global & none \\ 6 & the pas & 12 & none & none & ckyp-tv & ctv & satellite of cky-dt winnipeg \\ 7 & portage la prairie & none & 13 & 13.1 & chmi-dt & city & none \\ 8 & thompson & 9 & none & none & ckyt-tv & ctv & satellite of cky-dt winnipeg \\ 9 & winnipeg & none & 51 & 3.1 & cbwft-dt & r-c & none \\ 10 & winnipeg & none & 27 & 6.1 & cbwt-dt & cbc & none \\ 11 & winnipeg & none & 7 & 7.1 & cky-dt & ctv & none \\ 12 & winnipeg & none & 40 & 9.1 & cknd-dt & global & none \\ 13 & winnipeg & none & 13 & 13.1 & chmi-dt & city & mts tv hd ch. 417 shaw ch. 8 bell ch. 228 \\ 14 & winnipeg & none & 35 & 35.1 & ciit-dt & religious independent & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "callsign" ) \\ \midrule 0 & 1 \\ \bottomrule \end{tabular}

select count ( callsign ) where notes = 'mts tv hd ch. 417 shaw ch. 8 bell ch. 228' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>city of licence</th> <th>analog channel</th> <th>digital channel</th> <th>virtual channel</th> <th>callsign</th> <th>network</th> <th>notes</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>brandon</td> <td>4</td> <td>none</td> <td>none</td> <td>ckyb-tv</td> <td>ctv</td> <td>satellite of cky-dt winnipeg</td> </tr> <tr> <th>1</th> <td>dauphin</td> <td>12</td> <td>none</td> <td>none</td> <td>ckyd-tv</td> <td>ctv</td> <td>satellite of cky-dt winnipeg</td> </tr> <tr> <th>2</th> <td>fisher branch</td> <td>8</td> <td>none</td> <td>none</td> <td>ckya-tv</td> <td>ctv</td> <td>satellite of cky-dt winnipeg</td> </tr> <tr> <th>3</th> <td>flin flon</td> <td>13</td> <td>none</td> <td>none</td> <td>ckyf-tv</td> <td>ctv</td> <td>satellite of cky-dt winnipeg</td> </tr> <tr> <th>4</th> <td>mccreary</td> <td>13</td> <td>none</td> <td>none</td> <td>ckyb-tv</td> <td>ctv</td> <td>satellite of cky-dt winnipeg</td> </tr> <tr> <th>5</th> <td>minnedosa</td> <td>2</td> <td>none</td> <td>none</td> <td>cknd-tv-2</td> <td>global</td> <td>none</td> </tr> <tr> <th>6</th> <td>the pas</td> <td>12</td> <td>none</td> <td>none</td> <td>ckyp-tv</td> <td>ctv</td> <td>satellite of cky-dt winnipeg</td> </tr> <tr> <th>7</th> <td>portage la prairie</td> <td>none</td> <td>13</td> <td>13.1</td> <td>chmi-dt</td> <td>city</td> <td>none</td> </tr> <tr> <th>8</th> <td>thompson</td> <td>9</td> <td>none</td> <td>none</td> <td>ckyt-tv</td> <td>ctv</td> <td>satellite of cky-dt winnipeg</td> </tr> <tr> <th>9</th> <td>winnipeg</td> <td>none</td> <td>51</td> <td>3.1</td> <td>cbwft-dt</td> <td>r-c</td> <td>none</td> </tr> <tr> <th>10</th> <td>winnipeg</td> <td>none</td> <td>27</td> <td>6.1</td> <td>cbwt-dt</td> <td>cbc</td> <td>none</td> </tr> <tr> <th>11</th> <td>winnipeg</td> <td>none</td> <td>7</td> <td>7.1</td> <td>cky-dt</td> <td>ctv</td> <td>none</td> </tr> <tr> <th>12</th> <td>winnipeg</td> <td>none</td> <td>40</td> <td>9.1</td> <td>cknd-dt</td> <td>global</td> <td>none</td> </tr> <tr> <th>13</th> <td>winnipeg</td> <td>none</td> <td>13</td> <td>13.1</td> <td>chmi-dt</td> <td>city</td> <td>mts tv hd ch. 417 shaw ch. 8 bell ch. 228</td> </tr> <tr> <th>14</th> <td>winnipeg</td> <td>none</td> <td>35</td> <td>35.1</td> <td>ciit-dt</td> <td>religious independent</td> <td>none</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "callsign" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> </tr> </tbody> </table>

select count ( callsign ) where notes = 'mts tv hd ch. 417 shaw ch. 8 bell ch. 228' <table_name> : table | | city of licence | analog channel | digital channel | virtual channel | callsign | network | notes | |---:|:-------------------|:-----------------|:------------------|:------------------|:-----------|:----------------------|:------------------------------------------| | 0 | brandon | 4 | none | none | ckyb-tv | ctv | satellite of cky-dt winnipeg | | 1 | dauphin | 12 | none | none | ckyd-tv | ctv | satellite of cky-dt winnipeg | | 2 | fisher branch | 8 | none | none | ckya-tv | ctv | satellite of cky-dt winnipeg | | 3 | flin flon | 13 | none | none | ckyf-tv | ctv | satellite of cky-dt winnipeg | | 4 | mccreary | 13 | none | none | ckyb-tv | ctv | satellite of cky-dt winnipeg | | 5 | minnedosa | 2 | none | none | cknd-tv-2 | global | none | | 6 | the pas | 12 | none | none | ckyp-tv | ctv | satellite of cky-dt winnipeg | | 7 | portage la prairie | none | 13 | 13.1 | chmi-dt | city | none | | 8 | thompson | 9 | none | none | ckyt-tv | ctv | satellite of cky-dt winnipeg | | 9 | winnipeg | none | 51 | 3.1 | cbwft-dt | r-c | none | | 10 | winnipeg | none | 27 | 6.1 | cbwt-dt | cbc | none | | 11 | winnipeg | none | 7 | 7.1 | cky-dt | ctv | none | | 12 | winnipeg | none | 40 | 9.1 | cknd-dt | global | none | | 13 | winnipeg | none | 13 | 13.1 | chmi-dt | city | mts tv hd ch. 417 shaw ch. 8 bell ch. 228 | | 14 | winnipeg | none | 35 | 35.1 | ciit-dt | religious independent | none |

| | count ( "callsign" ) | |---:|-----------------------:| | 0 | 1 |

select competition where home = '2-1' col : season | competition | round | opponent | home | away | agg. | bye row 1 : 1964-65 | inter-cities fairs cup | r1 | everton | 2-5 | 2-4 | 4-9 | 0 row 2 : 1965-66 | inter-cities fairs cup | r2 | hearts | 1-3 | 0-1 | 1-4 | 0 row 3 : 1966-67 | european cup | r1 | 17 nentori tirana | none | none | none | 1 row 4 : 1966-67 | european cup | r2 | linfield | 1-4 | 1-1 | 2-5 | 0 row 5 : 1975-76 | uefa cup | r1 | athlone town | 1-1 | 1-3 | 2-4 | 0 row 6 : 1981-82 | cup winners' cup | r1 | legia warszawa | 2-2 | 1-4 | 3-6 | 0 row 7 : 1982-83 | european cup | pr | dinamo bucuresti | 2-1 | 1-3 | 3-4 | 0 row 8 : 1984-85 | european cup | r1 | sparta prague | 3-3 | 0-2 | 3-5 | 0 row 9 : 1985-86 | european cup | r1 | zenit leningrad | 0-2 | 0-2 | 0-4 | 0 row 10 : 1986-87 | uefa cup | r1 | beveren | 0-0 | 0-1 | 0-1 | 0 row 11 : 1998-99 | cup winners' cup | r1 | rapid bucuresti | 0-0 | 2-2 | 2-2 (a) | 0 row 12 : 1998-99 | cup winners' cup | r2 | besiktas | 1-0 | 3-3 | 4-3 | 0 row 13 : 1998-99 | cup winners' cup | qf | chelsea | 2-3 | 0-3 | 2-6 | 0 row 14 : 1999-2000 | intertoto cup | r1 | fk ventspils | 1-0 | 0-2 | 1-2 | 0 row 15 : 2003-04 | uefa cup | r1 | grazer ak | 0-0 | 1-1 | 1-1 (a) | 0 row 16 : 2003-04 | uefa cup | r2 | wisła krakow | 0-0 | 0-0 | 0-0 (4-3 p) | 0 row 17 : 2003-04 | uefa cup | r3 | newcastle united | 1-1 | 1-3 | 2-4 | 0 row 18 : 2005-06 | champions league | qr2 | haka | 1-0 | 4-1 | 5-1 | 0 row 19 : 2005-06 | champions league | qr3 | club brugge | 1-0 | 0-1 | 1-1 (3-4 p) | 0 row 20 : 2005-06 | uefa cup | r1 | steaua bucuresti | 0-3 | 1-3 | 1-6 | 0 row 21 : 2006-07 | champions league | qr2 | mlada boleslav | 2-2 | 1-3 | 3-5 | 0 row 22 : 2007-08 | uefa cup | qr1 | flora tallinn | 1-0 | 1-0 | 2-0 | 0 row 23 : 2007-08 | uefa cup | qr2 | ekranas | 6-0 | 1-1 | 7-1 | 0 row 24 : 2007-08 | uefa cup | r1 | austria wien | 2-2 | 0-2 | 2-4 | 0 row 25 : 2009-10 | europa league | qr3 | paok | 1-2 | 1-0 | 2-2 (a) | 0 row 26 : 2011-12 | europa league | qr2 | mika | 1-0 | 1-0 | 2-0 | 0 row 27 : 2011-12 | europa league | qr3 | paok | 0-2 | 0-3 | 0-5 | 0

col : competition row 1 : european cup

select competition where home = '2-1' <table_name> : table \begin{tabular}{lllllllll} \toprule & season & competition & round & opponent & home & away & agg. & bye \\ \midrule 0 & 1964-65 & inter-cities fairs cup & r1 & everton & 2-5 & 2-4 & 4-9 & 0 \\ 1 & 1965-66 & inter-cities fairs cup & r2 & hearts & 1-3 & 0-1 & 1-4 & 0 \\ 2 & 1966-67 & european cup & r1 & 17 nentori tirana & none & none & none & 1 \\ 3 & 1966-67 & european cup & r2 & linfield & 1-4 & 1-1 & 2-5 & 0 \\ 4 & 1975-76 & uefa cup & r1 & athlone town & 1-1 & 1-3 & 2-4 & 0 \\ 5 & 1981-82 & cup winners' cup & r1 & legia warszawa & 2-2 & 1-4 & 3-6 & 0 \\ 6 & 1982-83 & european cup & pr & dinamo bucuresti & 2-1 & 1-3 & 3-4 & 0 \\ 7 & 1984-85 & european cup & r1 & sparta prague & 3-3 & 0-2 & 3-5 & 0 \\ 8 & 1985-86 & european cup & r1 & zenit leningrad & 0-2 & 0-2 & 0-4 & 0 \\ 9 & 1986-87 & uefa cup & r1 & beveren & 0-0 & 0-1 & 0-1 & 0 \\ 10 & 1998-99 & cup winners' cup & r1 & rapid bucuresti & 0-0 & 2-2 & 2-2 (a) & 0 \\ 11 & 1998-99 & cup winners' cup & r2 & besiktas & 1-0 & 3-3 & 4-3 & 0 \\ 12 & 1998-99 & cup winners' cup & qf & chelsea & 2-3 & 0-3 & 2-6 & 0 \\ 13 & 1999-2000 & intertoto cup & r1 & fk ventspils & 1-0 & 0-2 & 1-2 & 0 \\ 14 & 2003-04 & uefa cup & r1 & grazer ak & 0-0 & 1-1 & 1-1 (a) & 0 \\ 15 & 2003-04 & uefa cup & r2 & wisła krakow & 0-0 & 0-0 & 0-0 (4-3 p) & 0 \\ 16 & 2003-04 & uefa cup & r3 & newcastle united & 1-1 & 1-3 & 2-4 & 0 \\ 17 & 2005-06 & champions league & qr2 & haka & 1-0 & 4-1 & 5-1 & 0 \\ 18 & 2005-06 & champions league & qr3 & club brugge & 1-0 & 0-1 & 1-1 (3-4 p) & 0 \\ 19 & 2005-06 & uefa cup & r1 & steaua bucuresti & 0-3 & 1-3 & 1-6 & 0 \\ 20 & 2006-07 & champions league & qr2 & mlada boleslav & 2-2 & 1-3 & 3-5 & 0 \\ 21 & 2007-08 & uefa cup & qr1 & flora tallinn & 1-0 & 1-0 & 2-0 & 0 \\ 22 & 2007-08 & uefa cup & qr2 & ekranas & 6-0 & 1-1 & 7-1 & 0 \\ 23 & 2007-08 & uefa cup & r1 & austria wien & 2-2 & 0-2 & 2-4 & 0 \\ 24 & 2009-10 & europa league & qr3 & paok & 1-2 & 1-0 & 2-2 (a) & 0 \\ 25 & 2011-12 & europa league & qr2 & mika & 1-0 & 1-0 & 2-0 & 0 \\ 26 & 2011-12 & europa league & qr3 & paok & 0-2 & 0-3 & 0-5 & 0 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & competition \\ \midrule 0 & european cup \\ \bottomrule \end{tabular}

select competition where home = '2-1' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>season</th> <th>competition</th> <th>round</th> <th>opponent</th> <th>home</th> <th>away</th> <th>agg.</th> <th>bye</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1964-65</td> <td>inter-cities fairs cup</td> <td>r1</td> <td>everton</td> <td>2-5</td> <td>2-4</td> <td>4-9</td> <td>0</td> </tr> <tr> <th>1</th> <td>1965-66</td> <td>inter-cities fairs cup</td> <td>r2</td> <td>hearts</td> <td>1-3</td> <td>0-1</td> <td>1-4</td> <td>0</td> </tr> <tr> <th>2</th> <td>1966-67</td> <td>european cup</td> <td>r1</td> <td>17 nentori tirana</td> <td>none</td> <td>none</td> <td>none</td> <td>1</td> </tr> <tr> <th>3</th> <td>1966-67</td> <td>european cup</td> <td>r2</td> <td>linfield</td> <td>1-4</td> <td>1-1</td> <td>2-5</td> <td>0</td> </tr> <tr> <th>4</th> <td>1975-76</td> <td>uefa cup</td> <td>r1</td> <td>athlone town</td> <td>1-1</td> <td>1-3</td> <td>2-4</td> <td>0</td> </tr> <tr> <th>5</th> <td>1981-82</td> <td>cup winners' cup</td> <td>r1</td> <td>legia warszawa</td> <td>2-2</td> <td>1-4</td> <td>3-6</td> <td>0</td> </tr> <tr> <th>6</th> <td>1982-83</td> <td>european cup</td> <td>pr</td> <td>dinamo bucuresti</td> <td>2-1</td> <td>1-3</td> <td>3-4</td> <td>0</td> </tr> <tr> <th>7</th> <td>1984-85</td> <td>european cup</td> <td>r1</td> <td>sparta prague</td> <td>3-3</td> <td>0-2</td> <td>3-5</td> <td>0</td> </tr> <tr> <th>8</th> <td>1985-86</td> <td>european cup</td> <td>r1</td> <td>zenit leningrad</td> <td>0-2</td> <td>0-2</td> <td>0-4</td> <td>0</td> </tr> <tr> <th>9</th> <td>1986-87</td> <td>uefa cup</td> <td>r1</td> <td>beveren</td> <td>0-0</td> <td>0-1</td> <td>0-1</td> <td>0</td> </tr> <tr> <th>10</th> <td>1998-99</td> <td>cup winners' cup</td> <td>r1</td> <td>rapid bucuresti</td> <td>0-0</td> <td>2-2</td> <td>2-2 (a)</td> <td>0</td> </tr> <tr> <th>11</th> <td>1998-99</td> <td>cup winners' cup</td> <td>r2</td> <td>besiktas</td> <td>1-0</td> <td>3-3</td> <td>4-3</td> <td>0</td> </tr> <tr> <th>12</th> <td>1998-99</td> <td>cup winners' cup</td> <td>qf</td> <td>chelsea</td> <td>2-3</td> <td>0-3</td> <td>2-6</td> <td>0</td> </tr> <tr> <th>13</th> <td>1999-2000</td> <td>intertoto cup</td> <td>r1</td> <td>fk ventspils</td> <td>1-0</td> <td>0-2</td> <td>1-2</td> <td>0</td> </tr> <tr> <th>14</th> <td>2003-04</td> <td>uefa cup</td> <td>r1</td> <td>grazer ak</td> <td>0-0</td> <td>1-1</td> <td>1-1 (a)</td> <td>0</td> </tr> <tr> <th>15</th> <td>2003-04</td> <td>uefa cup</td> <td>r2</td> <td>wisła krakow</td> <td>0-0</td> <td>0-0</td> <td>0-0 (4-3 p)</td> <td>0</td> </tr> <tr> <th>16</th> <td>2003-04</td> <td>uefa cup</td> <td>r3</td> <td>newcastle united</td> <td>1-1</td> <td>1-3</td> <td>2-4</td> <td>0</td> </tr> <tr> <th>17</th> <td>2005-06</td> <td>champions league</td> <td>qr2</td> <td>haka</td> <td>1-0</td> <td>4-1</td> <td>5-1</td> <td>0</td> </tr> <tr> <th>18</th> <td>2005-06</td> <td>champions league</td> <td>qr3</td> <td>club brugge</td> <td>1-0</td> <td>0-1</td> <td>1-1 (3-4 p)</td> <td>0</td> </tr> <tr> <th>19</th> <td>2005-06</td> <td>uefa cup</td> <td>r1</td> <td>steaua bucuresti</td> <td>0-3</td> <td>1-3</td> <td>1-6</td> <td>0</td> </tr> <tr> <th>20</th> <td>2006-07</td> <td>champions league</td> <td>qr2</td> <td>mlada boleslav</td> <td>2-2</td> <td>1-3</td> <td>3-5</td> <td>0</td> </tr> <tr> <th>21</th> <td>2007-08</td> <td>uefa cup</td> <td>qr1</td> <td>flora tallinn</td> <td>1-0</td> <td>1-0</td> <td>2-0</td> <td>0</td> </tr> <tr> <th>22</th> <td>2007-08</td> <td>uefa cup</td> <td>qr2</td> <td>ekranas</td> <td>6-0</td> <td>1-1</td> <td>7-1</td> <td>0</td> </tr> <tr> <th>23</th> <td>2007-08</td> <td>uefa cup</td> <td>r1</td> <td>austria wien</td> <td>2-2</td> <td>0-2</td> <td>2-4</td> <td>0</td> </tr> <tr> <th>24</th> <td>2009-10</td> <td>europa league</td> <td>qr3</td> <td>paok</td> <td>1-2</td> <td>1-0</td> <td>2-2 (a)</td> <td>0</td> </tr> <tr> <th>25</th> <td>2011-12</td> <td>europa league</td> <td>qr2</td> <td>mika</td> <td>1-0</td> <td>1-0</td> <td>2-0</td> <td>0</td> </tr> <tr> <th>26</th> <td>2011-12</td> <td>europa league</td> <td>qr3</td> <td>paok</td> <td>0-2</td> <td>0-3</td> <td>0-5</td> <td>0</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>competition</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>european cup</td> </tr> </tbody> </table>

select competition where home = '2-1' <table_name> : table | | season | competition | round | opponent | home | away | agg. | bye | |---:|:----------|:-----------------------|:--------|:------------------|:-------|:-------|:------------|------:| | 0 | 1964-65 | inter-cities fairs cup | r1 | everton | 2-5 | 2-4 | 4-9 | 0 | | 1 | 1965-66 | inter-cities fairs cup | r2 | hearts | 1-3 | 0-1 | 1-4 | 0 | | 2 | 1966-67 | european cup | r1 | 17 nentori tirana | none | none | none | 1 | | 3 | 1966-67 | european cup | r2 | linfield | 1-4 | 1-1 | 2-5 | 0 | | 4 | 1975-76 | uefa cup | r1 | athlone town | 1-1 | 1-3 | 2-4 | 0 | | 5 | 1981-82 | cup winners' cup | r1 | legia warszawa | 2-2 | 1-4 | 3-6 | 0 | | 6 | 1982-83 | european cup | pr | dinamo bucuresti | 2-1 | 1-3 | 3-4 | 0 | | 7 | 1984-85 | european cup | r1 | sparta prague | 3-3 | 0-2 | 3-5 | 0 | | 8 | 1985-86 | european cup | r1 | zenit leningrad | 0-2 | 0-2 | 0-4 | 0 | | 9 | 1986-87 | uefa cup | r1 | beveren | 0-0 | 0-1 | 0-1 | 0 | | 10 | 1998-99 | cup winners' cup | r1 | rapid bucuresti | 0-0 | 2-2 | 2-2 (a) | 0 | | 11 | 1998-99 | cup winners' cup | r2 | besiktas | 1-0 | 3-3 | 4-3 | 0 | | 12 | 1998-99 | cup winners' cup | qf | chelsea | 2-3 | 0-3 | 2-6 | 0 | | 13 | 1999-2000 | intertoto cup | r1 | fk ventspils | 1-0 | 0-2 | 1-2 | 0 | | 14 | 2003-04 | uefa cup | r1 | grazer ak | 0-0 | 1-1 | 1-1 (a) | 0 | | 15 | 2003-04 | uefa cup | r2 | wisła krakow | 0-0 | 0-0 | 0-0 (4-3 p) | 0 | | 16 | 2003-04 | uefa cup | r3 | newcastle united | 1-1 | 1-3 | 2-4 | 0 | | 17 | 2005-06 | champions league | qr2 | haka | 1-0 | 4-1 | 5-1 | 0 | | 18 | 2005-06 | champions league | qr3 | club brugge | 1-0 | 0-1 | 1-1 (3-4 p) | 0 | | 19 | 2005-06 | uefa cup | r1 | steaua bucuresti | 0-3 | 1-3 | 1-6 | 0 | | 20 | 2006-07 | champions league | qr2 | mlada boleslav | 2-2 | 1-3 | 3-5 | 0 | | 21 | 2007-08 | uefa cup | qr1 | flora tallinn | 1-0 | 1-0 | 2-0 | 0 | | 22 | 2007-08 | uefa cup | qr2 | ekranas | 6-0 | 1-1 | 7-1 | 0 | | 23 | 2007-08 | uefa cup | r1 | austria wien | 2-2 | 0-2 | 2-4 | 0 | | 24 | 2009-10 | europa league | qr3 | paok | 1-2 | 1-0 | 2-2 (a) | 0 | | 25 | 2011-12 | europa league | qr2 | mika | 1-0 | 1-0 | 2-0 | 0 | | 26 | 2011-12 | europa league | qr3 | paok | 0-2 | 0-3 | 0-5 | 0 |

| | competition | |---:|:--------------| | 0 | european cup |

select drawn where team = 'santos' col : position | team | points | played | won | drawn | lost | for | against | difference row 1 : 1 | paulistano | 30 | 18 | 14 | 2 | 2 | 62 | 19 | 43 row 2 : 2 | palestra italia-sp | 29 | 18 | 14 | 1 | 3 | 59 | 21 | 38 row 3 : 3 | corinthians | 26 | 18 | 12 | 2 | 4 | 51 | 16 | 35 row 4 : 4 | ypiranga-sp | 25 | 18 | 11 | 3 | 4 | 56 | 34 | 22 row 5 : 5 | aa sao bento | 16 | 18 | 7 | 2 | 9 | 38 | 44 | -6 row 6 : 6 | santos | 13 | 18 | 6 | 1 | 11 | 36 | 43 | -7 row 7 : 7 | sc internacional de sao paulo | 11 | 15 | 3 | 5 | 7 | 26 | 44 | -18 row 8 : 8 | minas gerais | 10 | 15 | 4 | 2 | 9 | 18 | 49 | -31 row 9 : 9 | aa das palmeiras | 6 | 15 | 3 | 0 | 12 | 27 | 57 | -30 row 10 : 10 | mackenzie | 2 | 15 | 1 | 0 | 14 | 11 | 57 | -46

col : drawn row 1 : 1

select drawn where team = 'santos' <table_name> : table \begin{tabular}{lllllllllll} \toprule & position & team & points & played & won & drawn & lost & for & against & difference \\ \midrule 0 & 1 & paulistano & 30 & 18 & 14 & 2 & 2 & 62 & 19 & 43 \\ 1 & 2 & palestra italia-sp & 29 & 18 & 14 & 1 & 3 & 59 & 21 & 38 \\ 2 & 3 & corinthians & 26 & 18 & 12 & 2 & 4 & 51 & 16 & 35 \\ 3 & 4 & ypiranga-sp & 25 & 18 & 11 & 3 & 4 & 56 & 34 & 22 \\ 4 & 5 & aa sao bento & 16 & 18 & 7 & 2 & 9 & 38 & 44 & -6 \\ 5 & 6 & santos & 13 & 18 & 6 & 1 & 11 & 36 & 43 & -7 \\ 6 & 7 & sc internacional de sao paulo & 11 & 15 & 3 & 5 & 7 & 26 & 44 & -18 \\ 7 & 8 & minas gerais & 10 & 15 & 4 & 2 & 9 & 18 & 49 & -31 \\ 8 & 9 & aa das palmeiras & 6 & 15 & 3 & 0 & 12 & 27 & 57 & -30 \\ 9 & 10 & mackenzie & 2 & 15 & 1 & 0 & 14 & 11 & 57 & -46 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & drawn \\ \midrule 0 & 1 \\ \bottomrule \end{tabular}

select drawn where team = 'santos' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>position</th> <th>team</th> <th>points</th> <th>played</th> <th>won</th> <th>drawn</th> <th>lost</th> <th>for</th> <th>against</th> <th>difference</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>paulistano</td> <td>30</td> <td>18</td> <td>14</td> <td>2</td> <td>2</td> <td>62</td> <td>19</td> <td>43</td> </tr> <tr> <th>1</th> <td>2</td> <td>palestra italia-sp</td> <td>29</td> <td>18</td> <td>14</td> <td>1</td> <td>3</td> <td>59</td> <td>21</td> <td>38</td> </tr> <tr> <th>2</th> <td>3</td> <td>corinthians</td> <td>26</td> <td>18</td> <td>12</td> <td>2</td> <td>4</td> <td>51</td> <td>16</td> <td>35</td> </tr> <tr> <th>3</th> <td>4</td> <td>ypiranga-sp</td> <td>25</td> <td>18</td> <td>11</td> <td>3</td> <td>4</td> <td>56</td> <td>34</td> <td>22</td> </tr> <tr> <th>4</th> <td>5</td> <td>aa sao bento</td> <td>16</td> <td>18</td> <td>7</td> <td>2</td> <td>9</td> <td>38</td> <td>44</td> <td>-6</td> </tr> <tr> <th>5</th> <td>6</td> <td>santos</td> <td>13</td> <td>18</td> <td>6</td> <td>1</td> <td>11</td> <td>36</td> <td>43</td> <td>-7</td> </tr> <tr> <th>6</th> <td>7</td> <td>sc internacional de sao paulo</td> <td>11</td> <td>15</td> <td>3</td> <td>5</td> <td>7</td> <td>26</td> <td>44</td> <td>-18</td> </tr> <tr> <th>7</th> <td>8</td> <td>minas gerais</td> <td>10</td> <td>15</td> <td>4</td> <td>2</td> <td>9</td> <td>18</td> <td>49</td> <td>-31</td> </tr> <tr> <th>8</th> <td>9</td> <td>aa das palmeiras</td> <td>6</td> <td>15</td> <td>3</td> <td>0</td> <td>12</td> <td>27</td> <td>57</td> <td>-30</td> </tr> <tr> <th>9</th> <td>10</td> <td>mackenzie</td> <td>2</td> <td>15</td> <td>1</td> <td>0</td> <td>14</td> <td>11</td> <td>57</td> <td>-46</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>drawn</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> </tr> </tbody> </table>

select drawn where team = 'santos' <table_name> : table | | position | team | points | played | won | drawn | lost | for | against | difference | |---:|-----------:|:------------------------------|---------:|---------:|------:|--------:|-------:|------:|----------:|-------------:| | 0 | 1 | paulistano | 30 | 18 | 14 | 2 | 2 | 62 | 19 | 43 | | 1 | 2 | palestra italia-sp | 29 | 18 | 14 | 1 | 3 | 59 | 21 | 38 | | 2 | 3 | corinthians | 26 | 18 | 12 | 2 | 4 | 51 | 16 | 35 | | 3 | 4 | ypiranga-sp | 25 | 18 | 11 | 3 | 4 | 56 | 34 | 22 | | 4 | 5 | aa sao bento | 16 | 18 | 7 | 2 | 9 | 38 | 44 | -6 | | 5 | 6 | santos | 13 | 18 | 6 | 1 | 11 | 36 | 43 | -7 | | 6 | 7 | sc internacional de sao paulo | 11 | 15 | 3 | 5 | 7 | 26 | 44 | -18 | | 7 | 8 | minas gerais | 10 | 15 | 4 | 2 | 9 | 18 | 49 | -31 | | 8 | 9 | aa das palmeiras | 6 | 15 | 3 | 0 | 12 | 27 | 57 | -30 | | 9 | 10 | mackenzie | 2 | 15 | 1 | 0 | 14 | 11 | 57 | -46 |

| | drawn | |---:|--------:| | 0 | 1 |

select episode where sent home = 'ammerah, kimmy' col : episode | name | synopsis | sent home | highest seller row 1 : 1 | vegas meltdown | nine aspiring restaurateurs arrive in las vegas, where they meet chef robert irvine and the restaurant express bus. in order to secure one of the eight spots on board, they must first pitch their dream restaurant concept to robert: those with the best concepts head into lake las vegas for the first challenge. they'll have to create a restaurant from the ground up with just $500 and six hours to feed 250 hungry guests! the bottom three earners will be up for elimination | ammerah, kimmy | patrick row 2 : 2 | tantrum in temecula | the restaurant express bus arrives in temecula, calif., a small, upscale town with lots of restaurants. the contestants' market knowledge is tested when they must propose to robert and the mayor a new restaurant that they think could be successful in the town. the winner of the mini challenge will get extra money for the main challenge, where they will test their restaurant ideas on the locals at a lunch fair. the bottom three earners will be up for elimination | johnathan | eric row 3 : 3 | express: impossible | the restaurant express bus arrives in los angeles, where the contestants face their first group challenge. inspired by restaurant: impossible, the teams will be given 24 hours and $5,000 to revitalize two struggling restaurants. robert and two visiting critics will dine at each location to determine who won this round, and who will be off the bus | bianca | none row 4 : 4 | battle by the beach | the restaurant express bus arrives at the iconic balboa pier in newport beach, calif. the remaining five contestants will each have to open a concession stand with the same three dishes; a burger, a dessert and something on a stick. they also must produce a commercial for their stand. the contestant with the best commercial gets to have their ad play on the pier, but will that marketing advantage be enough to draw the most customers? | patrick | eric row 5 : 5 | going, going, gone! | the remaining four contestants are given their biggest budget challenge: create a supper club with $1,000 and an empty restaurant shell. they first attend an auction with items that could be a big benefit during dinner service, challenging them to spend wisely for an advantage, with enough money left over for food and supplies | eric | adam row 6 : 6 | the truck stops here! | the three remaining chefs are each given a food truck and are challenged on their ability to serve specific groups of diners at three iconic locations: a zoo, an upscale shopping center and a late night bar scene. the winner gets a big advantage heading into the final challenge | none | adam row 7 : 7 | vegas or bust | jan and seonkyoung battle to see who would face adam in the final challenge. seonkyoung won, therefore advancing herself to the final challenge. the two finalists then competed head-to-head against each other in the same restaurant. in the end, seonkyoung beat adam and she won a restaurant in the m, named jade | jan, adam | seonkyoung

col : episode row 1 : 1

select episode where sent home = 'ammerah, kimmy' <table_name> : table \begin{tabular}{llllll} \toprule & episode & name & synopsis & sent home & highest seller \\ \midrule 0 & 1 & vegas meltdown & nine aspiring restaurateurs arrive in las vegas, where they meet chef robert irvine and the restaurant express bus. in order to secure one of the eight spots on board, they must first pitch their dream restaurant concept to robert: those with the best concepts head into lake las vegas for the first challenge. they'll have to create a restaurant from the ground up with just $500 and six hours to feed 250 hungry guests! the bottom three earners will be up for elimination & ammerah, kimmy & patrick \\ 1 & 2 & tantrum in temecula & the restaurant express bus arrives in temecula, calif., a small, upscale town with lots of restaurants. the contestants' market knowledge is tested when they must propose to robert and the mayor a new restaurant that they think could be successful in the town. the winner of the mini challenge will get extra money for the main challenge, where they will test their restaurant ideas on the locals at a lunch fair. the bottom three earners will be up for elimination & johnathan & eric \\ 2 & 3 & express: impossible & the restaurant express bus arrives in los angeles, where the contestants face their first group challenge. inspired by restaurant: impossible, the teams will be given 24 hours and $5,000 to revitalize two struggling restaurants. robert and two visiting critics will dine at each location to determine who won this round, and who will be off the bus & bianca & none \\ 3 & 4 & battle by the beach & the restaurant express bus arrives at the iconic balboa pier in newport beach, calif. the remaining five contestants will each have to open a concession stand with the same three dishes; a burger, a dessert and something on a stick. they also must produce a commercial for their stand. the contestant with the best commercial gets to have their ad play on the pier, but will that marketing advantage be enough to draw the most customers? & patrick & eric \\ 4 & 5 & going, going, gone! & the remaining four contestants are given their biggest budget challenge: create a supper club with $1,000 and an empty restaurant shell. they first attend an auction with items that could be a big benefit during dinner service, challenging them to spend wisely for an advantage, with enough money left over for food and supplies & eric & adam \\ 5 & 6 & the truck stops here! & the three remaining chefs are each given a food truck and are challenged on their ability to serve specific groups of diners at three iconic locations: a zoo, an upscale shopping center and a late night bar scene. the winner gets a big advantage heading into the final challenge & none & adam \\ 6 & 7 & vegas or bust & jan and seonkyoung battle to see who would face adam in the final challenge. seonkyoung won, therefore advancing herself to the final challenge. the two finalists then competed head-to-head against each other in the same restaurant. in the end, seonkyoung beat adam and she won a restaurant in the m, named jade & jan, adam & seonkyoung \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & episode \\ \midrule 0 & 1 \\ \bottomrule \end{tabular}

select episode where sent home = 'ammerah, kimmy' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>episode</th> <th>name</th> <th>synopsis</th> <th>sent home</th> <th>highest seller</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>vegas meltdown</td> <td>nine aspiring restaurateurs arrive in las vegas, where they meet chef robert irvine and the restaurant express bus. in order to secure one of the eight spots on board, they must first pitch their dream restaurant concept to robert: those with the best concepts head into lake las vegas for the first challenge. they'll have to create a restaurant from the ground up with just $500 and six hours to feed 250 hungry guests! the bottom three earners will be up for elimination</td> <td>ammerah, kimmy</td> <td>patrick</td> </tr> <tr> <th>1</th> <td>2</td> <td>tantrum in temecula</td> <td>the restaurant express bus arrives in temecula, calif., a small, upscale town with lots of restaurants. the contestants' market knowledge is tested when they must propose to robert and the mayor a new restaurant that they think could be successful in the town. the winner of the mini challenge will get extra money for the main challenge, where they will test their restaurant ideas on the locals at a lunch fair. the bottom three earners will be up for elimination</td> <td>johnathan</td> <td>eric</td> </tr> <tr> <th>2</th> <td>3</td> <td>express: impossible</td> <td>the restaurant express bus arrives in los angeles, where the contestants face their first group challenge. inspired by restaurant: impossible, the teams will be given 24 hours and $5,000 to revitalize two struggling restaurants. robert and two visiting critics will dine at each location to determine who won this round, and who will be off the bus</td> <td>bianca</td> <td>none</td> </tr> <tr> <th>3</th> <td>4</td> <td>battle by the beach</td> <td>the restaurant express bus arrives at the iconic balboa pier in newport beach, calif. the remaining five contestants will each have to open a concession stand with the same three dishes; a burger, a dessert and something on a stick. they also must produce a commercial for their stand. the contestant with the best commercial gets to have their ad play on the pier, but will that marketing advantage be enough to draw the most customers?</td> <td>patrick</td> <td>eric</td> </tr> <tr> <th>4</th> <td>5</td> <td>going, going, gone!</td> <td>the remaining four contestants are given their biggest budget challenge: create a supper club with $1,000 and an empty restaurant shell. they first attend an auction with items that could be a big benefit during dinner service, challenging them to spend wisely for an advantage, with enough money left over for food and supplies</td> <td>eric</td> <td>adam</td> </tr> <tr> <th>5</th> <td>6</td> <td>the truck stops here!</td> <td>the three remaining chefs are each given a food truck and are challenged on their ability to serve specific groups of diners at three iconic locations: a zoo, an upscale shopping center and a late night bar scene. the winner gets a big advantage heading into the final challenge</td> <td>none</td> <td>adam</td> </tr> <tr> <th>6</th> <td>7</td> <td>vegas or bust</td> <td>jan and seonkyoung battle to see who would face adam in the final challenge. seonkyoung won, therefore advancing herself to the final challenge. the two finalists then competed head-to-head against each other in the same restaurant. in the end, seonkyoung beat adam and she won a restaurant in the m, named jade</td> <td>jan, adam</td> <td>seonkyoung</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>episode</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> </tr> </tbody> </table>

select episode where sent home = 'ammerah, kimmy' <table_name> : table | | episode | name | synopsis | sent home | highest seller | |---:|----------:|:----------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|:-----------------| | 0 | 1 | vegas meltdown | nine aspiring restaurateurs arrive in las vegas, where they meet chef robert irvine and the restaurant express bus. in order to secure one of the eight spots on board, they must first pitch their dream restaurant concept to robert: those with the best concepts head into lake las vegas for the first challenge. they'll have to create a restaurant from the ground up with just $500 and six hours to feed 250 hungry guests! the bottom three earners will be up for elimination | ammerah, kimmy | patrick | | 1 | 2 | tantrum in temecula | the restaurant express bus arrives in temecula, calif., a small, upscale town with lots of restaurants. the contestants' market knowledge is tested when they must propose to robert and the mayor a new restaurant that they think could be successful in the town. the winner of the mini challenge will get extra money for the main challenge, where they will test their restaurant ideas on the locals at a lunch fair. the bottom three earners will be up for elimination | johnathan | eric | | 2 | 3 | express: impossible | the restaurant express bus arrives in los angeles, where the contestants face their first group challenge. inspired by restaurant: impossible, the teams will be given 24 hours and $5,000 to revitalize two struggling restaurants. robert and two visiting critics will dine at each location to determine who won this round, and who will be off the bus | bianca | none | | 3 | 4 | battle by the beach | the restaurant express bus arrives at the iconic balboa pier in newport beach, calif. the remaining five contestants will each have to open a concession stand with the same three dishes; a burger, a dessert and something on a stick. they also must produce a commercial for their stand. the contestant with the best commercial gets to have their ad play on the pier, but will that marketing advantage be enough to draw the most customers? | patrick | eric | | 4 | 5 | going, going, gone! | the remaining four contestants are given their biggest budget challenge: create a supper club with $1,000 and an empty restaurant shell. they first attend an auction with items that could be a big benefit during dinner service, challenging them to spend wisely for an advantage, with enough money left over for food and supplies | eric | adam | | 5 | 6 | the truck stops here! | the three remaining chefs are each given a food truck and are challenged on their ability to serve specific groups of diners at three iconic locations: a zoo, an upscale shopping center and a late night bar scene. the winner gets a big advantage heading into the final challenge | none | adam | | 6 | 7 | vegas or bust | jan and seonkyoung battle to see who would face adam in the final challenge. seonkyoung won, therefore advancing herself to the final challenge. the two finalists then competed head-to-head against each other in the same restaurant. in the end, seonkyoung beat adam and she won a restaurant in the m, named jade | jan, adam | seonkyoung |

| | episode | |---:|----------:| | 0 | 1 |

select max ( post season win % ) where team = 'atl' col : team | year | regular season won | regular season lost | regular season ties | regular season win % | regular season finish | post season won | post season lost | post season win % | post season result row 1 : den | 1981 | 10 | 6 | 0 | 0.625 | 2nd in afc west | none | none | none | none row 2 : den | 1982 | 2 | 7 | 0 | 0.222 | 5th in afc west | none | none | none | none row 3 : den | 1983 | 9 | 7 | 0 | 0.563 | 2nd in afc west | 0 | 1 | 0 | lost to seattle seahawks in afc wild card game row 4 : den | 1984 | 13 | 3 | 0 | 0.813 | 1st in afc west | 0 | 1 | 0 | lost to pittsburgh steelers in afc divisional game row 5 : den | 1985 | 11 | 5 | 0 | 0.688 | 2nd in afc west | none | none | none | none row 6 : den | 1986 | 11 | 5 | 0 | 0.688 | 1st in afc west | 2 | 1 | 0.667 | lost to new york giants in super bowl xxi row 7 : den | 1987 | 10 | 4 | 1 | 0.714 | 1st in afc west | 2 | 1 | 0.667 | lost to washington redskins in super bowl xxii row 8 : den | 1988 | 8 | 8 | 0 | 0.5 | 2nd in afc west | none | none | none | none row 9 : den | 1989 | 11 | 5 | 0 | 0.688 | 1st in afc west | 2 | 1 | 0.667 | lost to san francisco 49ers in super bowl xxiv row 10 : den | 1990 | 5 | 11 | 0 | 0.313 | 5th in afc west | none | none | none | none row 11 : den | 1991 | 12 | 4 | 0 | 0.75 | 1st in afc west | 1 | 1 | 0.5 | lost to buffalo bills in afc championship game row 12 : den | 1992 | 8 | 8 | 0 | 0.5 | 3rd in afc west | none | none | none | none row 13 : den total | den total | 110 | 73 | 1 | 0.601 | none | 7 | 6 | 0.538 | none row 14 : nyg | 1993 | 11 | 5 | 0 | 0.688 | 2nd in nfc east | 1 | 1 | 0.5 | lost to san francisco 49ers in nfc divisional game row 15 : nyg | 1994 | 9 | 7 | 0 | 0.563 | 2nd in nfc east | none | none | none | none row 16 : nyg | 1995 | 5 | 11 | 0 | 0.313 | 4th in nfc east | none | none | none | none row 17 : nyg | 1996 | 6 | 10 | 0 | 0.375 | 5th in nfc east | none | none | none | none row 18 : nyg total | nyg total | 31 | 33 | 0 | 0.484 | none | 1 | 1 | 0.5 | none row 19 : atl | 1997 | 7 | 9 | 0 | 0.438 | 2nd in nfc west | none | none | none | none row 20 : atl | 1998 | 14 | 2 | 0 | 0.875 | 1st in nfc west | 2 | 1 | 0.667 | lost to denver broncos in super bowl xxxiii row 21 : atl | 1999 | 5 | 11 | 0 | 0.313 | 3rd in nfc west | none | none | none | none row 22 : atl | 2000 | 4 | 12 | 0 | 0.25 | 5th in nfc west | none | none | none | none row 23 : atl | 2001 | 7 | 9 | 0 | 0.438 | 3rd in nfc south | none | none | none | none row 24 : atl | 2002 | 9 | 6 | 1 | 0.594 | 2nd in nfc south | 1 | 1 | 0.5 | lost to philadelphia eagles in nfc divisional game row 25 : atl | 2003 | 3 | 10 | 0 | 0.231 | 4th in nfc south | none | none | none | none row 26 : atl total | atl total | 49 | 59 | 1 | 0.454 | none | 3 | 2 | 0.6 | none row 27 : total | total | 190 | 165 | 2 | 0.535 | none | 11 | 9 | 0.55 | none

col : max ( "post season win %" ) row 1 : none

select max ( post season win % ) where team = 'atl' <table_name> : table \begin{tabular}{llllllllllll} \toprule & team & year & regular season won & regular season lost & regular season ties & regular season win % & regular season finish & post season won & post season lost & post season win % & post season result \\ \midrule 0 & den & 1981 & 10 & 6 & 0 & 0.625 & 2nd in afc west & none & none & none & none \\ 1 & den & 1982 & 2 & 7 & 0 & 0.222 & 5th in afc west & none & none & none & none \\ 2 & den & 1983 & 9 & 7 & 0 & 0.563 & 2nd in afc west & 0 & 1 & 0 & lost to seattle seahawks in afc wild card game \\ 3 & den & 1984 & 13 & 3 & 0 & 0.813 & 1st in afc west & 0 & 1 & 0 & lost to pittsburgh steelers in afc divisional game \\ 4 & den & 1985 & 11 & 5 & 0 & 0.688 & 2nd in afc west & none & none & none & none \\ 5 & den & 1986 & 11 & 5 & 0 & 0.688 & 1st in afc west & 2 & 1 & 0.667 & lost to new york giants in super bowl xxi \\ 6 & den & 1987 & 10 & 4 & 1 & 0.714 & 1st in afc west & 2 & 1 & 0.667 & lost to washington redskins in super bowl xxii \\ 7 & den & 1988 & 8 & 8 & 0 & 0.5 & 2nd in afc west & none & none & none & none \\ 8 & den & 1989 & 11 & 5 & 0 & 0.688 & 1st in afc west & 2 & 1 & 0.667 & lost to san francisco 49ers in super bowl xxiv \\ 9 & den & 1990 & 5 & 11 & 0 & 0.313 & 5th in afc west & none & none & none & none \\ 10 & den & 1991 & 12 & 4 & 0 & 0.75 & 1st in afc west & 1 & 1 & 0.5 & lost to buffalo bills in afc championship game \\ 11 & den & 1992 & 8 & 8 & 0 & 0.5 & 3rd in afc west & none & none & none & none \\ 12 & den total & den total & 110 & 73 & 1 & 0.601 & none & 7 & 6 & 0.538 & none \\ 13 & nyg & 1993 & 11 & 5 & 0 & 0.688 & 2nd in nfc east & 1 & 1 & 0.5 & lost to san francisco 49ers in nfc divisional game \\ 14 & nyg & 1994 & 9 & 7 & 0 & 0.563 & 2nd in nfc east & none & none & none & none \\ 15 & nyg & 1995 & 5 & 11 & 0 & 0.313 & 4th in nfc east & none & none & none & none \\ 16 & nyg & 1996 & 6 & 10 & 0 & 0.375 & 5th in nfc east & none & none & none & none \\ 17 & nyg total & nyg total & 31 & 33 & 0 & 0.484 & none & 1 & 1 & 0.5 & none \\ 18 & atl & 1997 & 7 & 9 & 0 & 0.438 & 2nd in nfc west & none & none & none & none \\ 19 & atl & 1998 & 14 & 2 & 0 & 0.875 & 1st in nfc west & 2 & 1 & 0.667 & lost to denver broncos in super bowl xxxiii \\ 20 & atl & 1999 & 5 & 11 & 0 & 0.313 & 3rd in nfc west & none & none & none & none \\ 21 & atl & 2000 & 4 & 12 & 0 & 0.25 & 5th in nfc west & none & none & none & none \\ 22 & atl & 2001 & 7 & 9 & 0 & 0.438 & 3rd in nfc south & none & none & none & none \\ 23 & atl & 2002 & 9 & 6 & 1 & 0.594 & 2nd in nfc south & 1 & 1 & 0.5 & lost to philadelphia eagles in nfc divisional game \\ 24 & atl & 2003 & 3 & 10 & 0 & 0.231 & 4th in nfc south & none & none & none & none \\ 25 & atl total & atl total & 49 & 59 & 1 & 0.454 & none & 3 & 2 & 0.6 & none \\ 26 & total & total & 190 & 165 & 2 & 0.535 & none & 11 & 9 & 0.55 & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & max ( "post season win %" ) \\ \midrule 0 & none \\ \bottomrule \end{tabular}

select max ( post season win % ) where team = 'atl' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>team</th> <th>year</th> <th>regular season won</th> <th>regular season lost</th> <th>regular season ties</th> <th>regular season win %</th> <th>regular season finish</th> <th>post season won</th> <th>post season lost</th> <th>post season win %</th> <th>post season result</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>den</td> <td>1981</td> <td>10</td> <td>6</td> <td>0</td> <td>0.625</td> <td>2nd in afc west</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>1</th> <td>den</td> <td>1982</td> <td>2</td> <td>7</td> <td>0</td> <td>0.222</td> <td>5th in afc west</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>2</th> <td>den</td> <td>1983</td> <td>9</td> <td>7</td> <td>0</td> <td>0.563</td> <td>2nd in afc west</td> <td>0</td> <td>1</td> <td>0</td> <td>lost to seattle seahawks in afc wild card game</td> </tr> <tr> <th>3</th> <td>den</td> <td>1984</td> <td>13</td> <td>3</td> <td>0</td> <td>0.813</td> <td>1st in afc west</td> <td>0</td> <td>1</td> <td>0</td> <td>lost to pittsburgh steelers in afc divisional game</td> </tr> <tr> <th>4</th> <td>den</td> <td>1985</td> <td>11</td> <td>5</td> <td>0</td> <td>0.688</td> <td>2nd in afc west</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>5</th> <td>den</td> <td>1986</td> <td>11</td> <td>5</td> <td>0</td> <td>0.688</td> <td>1st in afc west</td> <td>2</td> <td>1</td> <td>0.667</td> <td>lost to new york giants in super bowl xxi</td> </tr> <tr> <th>6</th> <td>den</td> <td>1987</td> <td>10</td> <td>4</td> <td>1</td> <td>0.714</td> <td>1st in afc west</td> <td>2</td> <td>1</td> <td>0.667</td> <td>lost to washington redskins in super bowl xxii</td> </tr> <tr> <th>7</th> <td>den</td> <td>1988</td> <td>8</td> <td>8</td> <td>0</td> <td>0.5</td> <td>2nd in afc west</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>8</th> <td>den</td> <td>1989</td> <td>11</td> <td>5</td> <td>0</td> <td>0.688</td> <td>1st in afc west</td> <td>2</td> <td>1</td> <td>0.667</td> <td>lost to san francisco 49ers in super bowl xxiv</td> </tr> <tr> <th>9</th> <td>den</td> <td>1990</td> <td>5</td> <td>11</td> <td>0</td> <td>0.313</td> <td>5th in afc west</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>10</th> <td>den</td> <td>1991</td> <td>12</td> <td>4</td> <td>0</td> <td>0.75</td> <td>1st in afc west</td> <td>1</td> <td>1</td> <td>0.5</td> <td>lost to buffalo bills in afc championship game</td> </tr> <tr> <th>11</th> <td>den</td> <td>1992</td> <td>8</td> <td>8</td> <td>0</td> <td>0.5</td> <td>3rd in afc west</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>12</th> <td>den total</td> <td>den total</td> <td>110</td> <td>73</td> <td>1</td> <td>0.601</td> <td>none</td> <td>7</td> <td>6</td> <td>0.538</td> <td>none</td> </tr> <tr> <th>13</th> <td>nyg</td> <td>1993</td> <td>11</td> <td>5</td> <td>0</td> <td>0.688</td> <td>2nd in nfc east</td> <td>1</td> <td>1</td> <td>0.5</td> <td>lost to san francisco 49ers in nfc divisional game</td> </tr> <tr> <th>14</th> <td>nyg</td> <td>1994</td> <td>9</td> <td>7</td> <td>0</td> <td>0.563</td> <td>2nd in nfc east</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>15</th> <td>nyg</td> <td>1995</td> <td>5</td> <td>11</td> <td>0</td> <td>0.313</td> <td>4th in nfc east</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>16</th> <td>nyg</td> <td>1996</td> <td>6</td> <td>10</td> <td>0</td> <td>0.375</td> <td>5th in nfc east</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>17</th> <td>nyg total</td> <td>nyg total</td> <td>31</td> <td>33</td> <td>0</td> <td>0.484</td> <td>none</td> <td>1</td> <td>1</td> <td>0.5</td> <td>none</td> </tr> <tr> <th>18</th> <td>atl</td> <td>1997</td> <td>7</td> <td>9</td> <td>0</td> <td>0.438</td> <td>2nd in nfc west</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>19</th> <td>atl</td> <td>1998</td> <td>14</td> <td>2</td> <td>0</td> <td>0.875</td> <td>1st in nfc west</td> <td>2</td> <td>1</td> <td>0.667</td> <td>lost to denver broncos in super bowl xxxiii</td> </tr> <tr> <th>20</th> <td>atl</td> <td>1999</td> <td>5</td> <td>11</td> <td>0</td> <td>0.313</td> <td>3rd in nfc west</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>21</th> <td>atl</td> <td>2000</td> <td>4</td> <td>12</td> <td>0</td> <td>0.25</td> <td>5th in nfc west</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>22</th> <td>atl</td> <td>2001</td> <td>7</td> <td>9</td> <td>0</td> <td>0.438</td> <td>3rd in nfc south</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>23</th> <td>atl</td> <td>2002</td> <td>9</td> <td>6</td> <td>1</td> <td>0.594</td> <td>2nd in nfc south</td> <td>1</td> <td>1</td> <td>0.5</td> <td>lost to philadelphia eagles in nfc divisional game</td> </tr> <tr> <th>24</th> <td>atl</td> <td>2003</td> <td>3</td> <td>10</td> <td>0</td> <td>0.231</td> <td>4th in nfc south</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>25</th> <td>atl total</td> <td>atl total</td> <td>49</td> <td>59</td> <td>1</td> <td>0.454</td> <td>none</td> <td>3</td> <td>2</td> <td>0.6</td> <td>none</td> </tr> <tr> <th>26</th> <td>total</td> <td>total</td> <td>190</td> <td>165</td> <td>2</td> <td>0.535</td> <td>none</td> <td>11</td> <td>9</td> <td>0.55</td> <td>none</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>max ( "post season win %" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>none</td> </tr> </tbody> </table>

select max ( post season win % ) where team = 'atl' <table_name> : table | | team | year | regular season won | regular season lost | regular season ties | regular season win % | regular season finish | post season won | post season lost | post season win % | post season result | |---:|:----------|:----------|---------------------:|----------------------:|----------------------:|-----------------------:|:------------------------|:------------------|:-------------------|:--------------------|:---------------------------------------------------| | 0 | den | 1981 | 10 | 6 | 0 | 0.625 | 2nd in afc west | none | none | none | none | | 1 | den | 1982 | 2 | 7 | 0 | 0.222 | 5th in afc west | none | none | none | none | | 2 | den | 1983 | 9 | 7 | 0 | 0.563 | 2nd in afc west | 0 | 1 | 0 | lost to seattle seahawks in afc wild card game | | 3 | den | 1984 | 13 | 3 | 0 | 0.813 | 1st in afc west | 0 | 1 | 0 | lost to pittsburgh steelers in afc divisional game | | 4 | den | 1985 | 11 | 5 | 0 | 0.688 | 2nd in afc west | none | none | none | none | | 5 | den | 1986 | 11 | 5 | 0 | 0.688 | 1st in afc west | 2 | 1 | 0.667 | lost to new york giants in super bowl xxi | | 6 | den | 1987 | 10 | 4 | 1 | 0.714 | 1st in afc west | 2 | 1 | 0.667 | lost to washington redskins in super bowl xxii | | 7 | den | 1988 | 8 | 8 | 0 | 0.5 | 2nd in afc west | none | none | none | none | | 8 | den | 1989 | 11 | 5 | 0 | 0.688 | 1st in afc west | 2 | 1 | 0.667 | lost to san francisco 49ers in super bowl xxiv | | 9 | den | 1990 | 5 | 11 | 0 | 0.313 | 5th in afc west | none | none | none | none | | 10 | den | 1991 | 12 | 4 | 0 | 0.75 | 1st in afc west | 1 | 1 | 0.5 | lost to buffalo bills in afc championship game | | 11 | den | 1992 | 8 | 8 | 0 | 0.5 | 3rd in afc west | none | none | none | none | | 12 | den total | den total | 110 | 73 | 1 | 0.601 | none | 7 | 6 | 0.538 | none | | 13 | nyg | 1993 | 11 | 5 | 0 | 0.688 | 2nd in nfc east | 1 | 1 | 0.5 | lost to san francisco 49ers in nfc divisional game | | 14 | nyg | 1994 | 9 | 7 | 0 | 0.563 | 2nd in nfc east | none | none | none | none | | 15 | nyg | 1995 | 5 | 11 | 0 | 0.313 | 4th in nfc east | none | none | none | none | | 16 | nyg | 1996 | 6 | 10 | 0 | 0.375 | 5th in nfc east | none | none | none | none | | 17 | nyg total | nyg total | 31 | 33 | 0 | 0.484 | none | 1 | 1 | 0.5 | none | | 18 | atl | 1997 | 7 | 9 | 0 | 0.438 | 2nd in nfc west | none | none | none | none | | 19 | atl | 1998 | 14 | 2 | 0 | 0.875 | 1st in nfc west | 2 | 1 | 0.667 | lost to denver broncos in super bowl xxxiii | | 20 | atl | 1999 | 5 | 11 | 0 | 0.313 | 3rd in nfc west | none | none | none | none | | 21 | atl | 2000 | 4 | 12 | 0 | 0.25 | 5th in nfc west | none | none | none | none | | 22 | atl | 2001 | 7 | 9 | 0 | 0.438 | 3rd in nfc south | none | none | none | none | | 23 | atl | 2002 | 9 | 6 | 1 | 0.594 | 2nd in nfc south | 1 | 1 | 0.5 | lost to philadelphia eagles in nfc divisional game | | 24 | atl | 2003 | 3 | 10 | 0 | 0.231 | 4th in nfc south | none | none | none | none | | 25 | atl total | atl total | 49 | 59 | 1 | 0.454 | none | 3 | 2 | 0.6 | none | | 26 | total | total | 190 | 165 | 2 | 0.535 | none | 11 | 9 | 0.55 | none |

| | max ( "post season win %" ) | |---:|:------------------------------| | 0 | none |

select who? where occupation = 'early sailor' col : subject | robot's name | who? | when? | where? | occupation row 1 : solar system | cosmo-bot | copernicus | 1531 | poland | cosmonaut row 2 : olympics | rhonda robot | greeks | 776 b.c | greece | beauty queen row 3 : basketball | danny defrost-bot | james naismith | 1891 | united states | snowman row 4 : nursing | dr. bug-bot | florence nightengale | 1860 | england | doctor row 5 : scuba gear | flip the high-diving robot | jacques cousteau | 1946 | france | diver row 6 : helicopter | amelia air-bot | leonardo da vinci | 1483 | italy | pilot row 7 : corn flakes | chef boy-robot | william kellogg | 1894 | battle creek, michigan | cook row 8 : radium | miss battery-bot | marie curie | 1898 | france | battery lady row 9 : chewing gum | bubble-bot | mayans | 400 | mexico | bubble man row 10 : painting | pierro-bot | stone-age humans | 35,000 b.c | europe | clown/artist row 11 : phonograph | slide the heavy-metal robot | thomas edison | 1877 | new jersey | rock star row 12 : paper | noshi origami | ts'ai lun | 105 | china | origami maker row 13 : round earth | vasco da robot | ferdinand magellan | 1522 | spain | early sailor row 14 : dynamite | robby robot | alfred nobel | 1866 | sweden | prankster row 15 : microscope | slobot | antonie van leeuwenhoek | 1674 | the netherlands | dirty person row 16 : writing | eraser-bot | sumerians | 3,500 b.c | middle east | pencil man row 17 : sausage | sock-bot | babylonians | 3,000 b.c | middle east | sock man row 18 : bicycle | booster-bot | karl von drais | 1816 | germany | rocket man row 19 : wheel | rollin' road-bot | sumerians | 3,000 b.c | middle east | race starter row 20 : germs | roast-bot | louis pasteur | 1865 | france | firefighter row 21 : boomerang | oswald the mailman robot | aborigines | 40,000 years ago | australia | mailman row 22 : coins | verna the vend-bot | lydians | 600 b.c | turkey | vending machine row 23 : tools | hank the handyman robot | stone-age humans | 21⁄2 million years ago | africa | mechanic row 24 : saxophone | bongo-bot the six-armed robot | antoine-joseph sax | 1846 | france | six-armed drum player row 25 : toilet | brunwella the bombshell | minoans | 2000 b.c | crete | demolisher

col : who? row 1 : ferdinand magellan

select who? where occupation = 'early sailor' <table_name> : table \begin{tabular}{lllllll} \toprule & subject & robot's name & who? & when? & where? & occupation \\ \midrule 0 & solar system & cosmo-bot & copernicus & 1531 & poland & cosmonaut \\ 1 & olympics & rhonda robot & greeks & 776 b.c & greece & beauty queen \\ 2 & basketball & danny defrost-bot & james naismith & 1891 & united states & snowman \\ 3 & nursing & dr. bug-bot & florence nightengale & 1860 & england & doctor \\ 4 & scuba gear & flip the high-diving robot & jacques cousteau & 1946 & france & diver \\ 5 & helicopter & amelia air-bot & leonardo da vinci & 1483 & italy & pilot \\ 6 & corn flakes & chef boy-robot & william kellogg & 1894 & battle creek, michigan & cook \\ 7 & radium & miss battery-bot & marie curie & 1898 & france & battery lady \\ 8 & chewing gum & bubble-bot & mayans & 400 & mexico & bubble man \\ 9 & painting & pierro-bot & stone-age humans & 35,000 b.c & europe & clown/artist \\ 10 & phonograph & slide the heavy-metal robot & thomas edison & 1877 & new jersey & rock star \\ 11 & paper & noshi origami & ts'ai lun & 105 & china & origami maker \\ 12 & round earth & vasco da robot & ferdinand magellan & 1522 & spain & early sailor \\ 13 & dynamite & robby robot & alfred nobel & 1866 & sweden & prankster \\ 14 & microscope & slobot & antonie van leeuwenhoek & 1674 & the netherlands & dirty person \\ 15 & writing & eraser-bot & sumerians & 3,500 b.c & middle east & pencil man \\ 16 & sausage & sock-bot & babylonians & 3,000 b.c & middle east & sock man \\ 17 & bicycle & booster-bot & karl von drais & 1816 & germany & rocket man \\ 18 & wheel & rollin' road-bot & sumerians & 3,000 b.c & middle east & race starter \\ 19 & germs & roast-bot & louis pasteur & 1865 & france & firefighter \\ 20 & boomerang & oswald the mailman robot & aborigines & 40,000 years ago & australia & mailman \\ 21 & coins & verna the vend-bot & lydians & 600 b.c & turkey & vending machine \\ 22 & tools & hank the handyman robot & stone-age humans & 21⁄2 million years ago & africa & mechanic \\ 23 & saxophone & bongo-bot the six-armed robot & antoine-joseph sax & 1846 & france & six-armed drum player \\ 24 & toilet & brunwella the bombshell & minoans & 2000 b.c & crete & demolisher \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & who? \\ \midrule 0 & ferdinand magellan \\ \bottomrule \end{tabular}

select who? where occupation = 'early sailor' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>subject</th> <th>robot's name</th> <th>who?</th> <th>when?</th> <th>where?</th> <th>occupation</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>solar system</td> <td>cosmo-bot</td> <td>copernicus</td> <td>1531</td> <td>poland</td> <td>cosmonaut</td> </tr> <tr> <th>1</th> <td>olympics</td> <td>rhonda robot</td> <td>greeks</td> <td>776 b.c</td> <td>greece</td> <td>beauty queen</td> </tr> <tr> <th>2</th> <td>basketball</td> <td>danny defrost-bot</td> <td>james naismith</td> <td>1891</td> <td>united states</td> <td>snowman</td> </tr> <tr> <th>3</th> <td>nursing</td> <td>dr. bug-bot</td> <td>florence nightengale</td> <td>1860</td> <td>england</td> <td>doctor</td> </tr> <tr> <th>4</th> <td>scuba gear</td> <td>flip the high-diving robot</td> <td>jacques cousteau</td> <td>1946</td> <td>france</td> <td>diver</td> </tr> <tr> <th>5</th> <td>helicopter</td> <td>amelia air-bot</td> <td>leonardo da vinci</td> <td>1483</td> <td>italy</td> <td>pilot</td> </tr> <tr> <th>6</th> <td>corn flakes</td> <td>chef boy-robot</td> <td>william kellogg</td> <td>1894</td> <td>battle creek, michigan</td> <td>cook</td> </tr> <tr> <th>7</th> <td>radium</td> <td>miss battery-bot</td> <td>marie curie</td> <td>1898</td> <td>france</td> <td>battery lady</td> </tr> <tr> <th>8</th> <td>chewing gum</td> <td>bubble-bot</td> <td>mayans</td> <td>400</td> <td>mexico</td> <td>bubble man</td> </tr> <tr> <th>9</th> <td>painting</td> <td>pierro-bot</td> <td>stone-age humans</td> <td>35,000 b.c</td> <td>europe</td> <td>clown/artist</td> </tr> <tr> <th>10</th> <td>phonograph</td> <td>slide the heavy-metal robot</td> <td>thomas edison</td> <td>1877</td> <td>new jersey</td> <td>rock star</td> </tr> <tr> <th>11</th> <td>paper</td> <td>noshi origami</td> <td>ts'ai lun</td> <td>105</td> <td>china</td> <td>origami maker</td> </tr> <tr> <th>12</th> <td>round earth</td> <td>vasco da robot</td> <td>ferdinand magellan</td> <td>1522</td> <td>spain</td> <td>early sailor</td> </tr> <tr> <th>13</th> <td>dynamite</td> <td>robby robot</td> <td>alfred nobel</td> <td>1866</td> <td>sweden</td> <td>prankster</td> </tr> <tr> <th>14</th> <td>microscope</td> <td>slobot</td> <td>antonie van leeuwenhoek</td> <td>1674</td> <td>the netherlands</td> <td>dirty person</td> </tr> <tr> <th>15</th> <td>writing</td> <td>eraser-bot</td> <td>sumerians</td> <td>3,500 b.c</td> <td>middle east</td> <td>pencil man</td> </tr> <tr> <th>16</th> <td>sausage</td> <td>sock-bot</td> <td>babylonians</td> <td>3,000 b.c</td> <td>middle east</td> <td>sock man</td> </tr> <tr> <th>17</th> <td>bicycle</td> <td>booster-bot</td> <td>karl von drais</td> <td>1816</td> <td>germany</td> <td>rocket man</td> </tr> <tr> <th>18</th> <td>wheel</td> <td>rollin' road-bot</td> <td>sumerians</td> <td>3,000 b.c</td> <td>middle east</td> <td>race starter</td> </tr> <tr> <th>19</th> <td>germs</td> <td>roast-bot</td> <td>louis pasteur</td> <td>1865</td> <td>france</td> <td>firefighter</td> </tr> <tr> <th>20</th> <td>boomerang</td> <td>oswald the mailman robot</td> <td>aborigines</td> <td>40,000 years ago</td> <td>australia</td> <td>mailman</td> </tr> <tr> <th>21</th> <td>coins</td> <td>verna the vend-bot</td> <td>lydians</td> <td>600 b.c</td> <td>turkey</td> <td>vending machine</td> </tr> <tr> <th>22</th> <td>tools</td> <td>hank the handyman robot</td> <td>stone-age humans</td> <td>21⁄2 million years ago</td> <td>africa</td> <td>mechanic</td> </tr> <tr> <th>23</th> <td>saxophone</td> <td>bongo-bot the six-armed robot</td> <td>antoine-joseph sax</td> <td>1846</td> <td>france</td> <td>six-armed drum player</td> </tr> <tr> <th>24</th> <td>toilet</td> <td>brunwella the bombshell</td> <td>minoans</td> <td>2000 b.c</td> <td>crete</td> <td>demolisher</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>who?</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>ferdinand magellan</td> </tr> </tbody> </table>

select who? where occupation = 'early sailor' <table_name> : table | | subject | robot's name | who? | when? | where? | occupation | |---:|:-------------|:------------------------------|:------------------------|:-----------------------|:-----------------------|:----------------------| | 0 | solar system | cosmo-bot | copernicus | 1531 | poland | cosmonaut | | 1 | olympics | rhonda robot | greeks | 776 b.c | greece | beauty queen | | 2 | basketball | danny defrost-bot | james naismith | 1891 | united states | snowman | | 3 | nursing | dr. bug-bot | florence nightengale | 1860 | england | doctor | | 4 | scuba gear | flip the high-diving robot | jacques cousteau | 1946 | france | diver | | 5 | helicopter | amelia air-bot | leonardo da vinci | 1483 | italy | pilot | | 6 | corn flakes | chef boy-robot | william kellogg | 1894 | battle creek, michigan | cook | | 7 | radium | miss battery-bot | marie curie | 1898 | france | battery lady | | 8 | chewing gum | bubble-bot | mayans | 400 | mexico | bubble man | | 9 | painting | pierro-bot | stone-age humans | 35,000 b.c | europe | clown/artist | | 10 | phonograph | slide the heavy-metal robot | thomas edison | 1877 | new jersey | rock star | | 11 | paper | noshi origami | ts'ai lun | 105 | china | origami maker | | 12 | round earth | vasco da robot | ferdinand magellan | 1522 | spain | early sailor | | 13 | dynamite | robby robot | alfred nobel | 1866 | sweden | prankster | | 14 | microscope | slobot | antonie van leeuwenhoek | 1674 | the netherlands | dirty person | | 15 | writing | eraser-bot | sumerians | 3,500 b.c | middle east | pencil man | | 16 | sausage | sock-bot | babylonians | 3,000 b.c | middle east | sock man | | 17 | bicycle | booster-bot | karl von drais | 1816 | germany | rocket man | | 18 | wheel | rollin' road-bot | sumerians | 3,000 b.c | middle east | race starter | | 19 | germs | roast-bot | louis pasteur | 1865 | france | firefighter | | 20 | boomerang | oswald the mailman robot | aborigines | 40,000 years ago | australia | mailman | | 21 | coins | verna the vend-bot | lydians | 600 b.c | turkey | vending machine | | 22 | tools | hank the handyman robot | stone-age humans | 21⁄2 million years ago | africa | mechanic | | 23 | saxophone | bongo-bot the six-armed robot | antoine-joseph sax | 1846 | france | six-armed drum player | | 24 | toilet | brunwella the bombshell | minoans | 2000 b.c | crete | demolisher |

| | who? | |---:|:-------------------| | 0 | ferdinand magellan |

select sum ( not out ) where 50s = 1 col : player | matches | innings | not out | runs | high score | average | strike rate | 100s | 50s row 1 : anthony mcgrath | 9 | 9 | 2 | 392 | 72 | 56 | 132.43 | 0 | 4 row 2 : jacques rudolph | 9 | 8 | 1 | 191 | 56 | 27.29 | 129.05 | 0 | 1 row 3 : darren gough | 9 | 4 | 3 | 26 | 20 | 26 | 136.84 | 0 | 0 row 4 : gerard brophy | 9 | 9 | 1 | 177 | 57 | 22.13 | 113.46 | 0 | 1 row 5 : andrew gale | 9 | 9 | 2 | 106 | 45 | 15.14 | 86.18 | 0 | 0 row 6 : michael vaughan | 7 | 7 | 0 | 104 | 34 | 14.86 | 138.67 | 0 | 0 row 7 : craig white | 2 | 2 | 0 | 26 | 26 | 13 | 92.86 | 0 | 0 row 8 : tim bresnan | 9 | 8 | 3 | 58 | 15 | 11.6 | 85.29 | 0 | 0 row 9 : chris taylor | 2 | 2 | 1 | 10 | 10 | 10 | 111.11 | 0 | 0 row 10 : simon guy | 4 | 2 | 0 | 11 | 7 | 5.5 | 78.57 | 0 | 0 row 11 : richard pyrah | 9 | 5 | 2 | 16 | 8 | 5.33 | 114.29 | 0 | 0 row 12 : adil rashid | 8 | 6 | 2 | 18 | 10 | 4.5 | 94.74 | 0 | 0 row 13 : adam lyth | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 row 14 : david wainwright | 2 | 1 | 1 | 3 | 3 | none | 33.33 | 0 | 0 row 15 : matthew hoggard | 9 | 0 | 0 | 0 | 0 | none | none | 0 | 0 row 16 : azeem rafiq | 1 | 0 | 0 | 0 | 0 | none | none | 0 | 0 row 17 : total batting | 99 | 73 | 18 | 1138 | 72 | 20.69 | 116 | 0 | 6

col : sum ( "not out" ) row 1 : 2

select sum ( not out ) where 50s = 1 <table_name> : table \begin{tabular}{lllllllllll} \toprule & player & matches & innings & not out & runs & high score & average & strike rate & 100s & 50s \\ \midrule 0 & anthony mcgrath & 9 & 9 & 2 & 392 & 72 & 56 & 132.43 & 0 & 4 \\ 1 & jacques rudolph & 9 & 8 & 1 & 191 & 56 & 27.29 & 129.05 & 0 & 1 \\ 2 & darren gough & 9 & 4 & 3 & 26 & 20 & 26 & 136.84 & 0 & 0 \\ 3 & gerard brophy & 9 & 9 & 1 & 177 & 57 & 22.13 & 113.46 & 0 & 1 \\ 4 & andrew gale & 9 & 9 & 2 & 106 & 45 & 15.14 & 86.18 & 0 & 0 \\ 5 & michael vaughan & 7 & 7 & 0 & 104 & 34 & 14.86 & 138.67 & 0 & 0 \\ 6 & craig white & 2 & 2 & 0 & 26 & 26 & 13 & 92.86 & 0 & 0 \\ 7 & tim bresnan & 9 & 8 & 3 & 58 & 15 & 11.6 & 85.29 & 0 & 0 \\ 8 & chris taylor & 2 & 2 & 1 & 10 & 10 & 10 & 111.11 & 0 & 0 \\ 9 & simon guy & 4 & 2 & 0 & 11 & 7 & 5.5 & 78.57 & 0 & 0 \\ 10 & richard pyrah & 9 & 5 & 2 & 16 & 8 & 5.33 & 114.29 & 0 & 0 \\ 11 & adil rashid & 8 & 6 & 2 & 18 & 10 & 4.5 & 94.74 & 0 & 0 \\ 12 & adam lyth & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ 13 & david wainwright & 2 & 1 & 1 & 3 & 3 & none & 33.33 & 0 & 0 \\ 14 & matthew hoggard & 9 & 0 & 0 & 0 & 0 & none & none & 0 & 0 \\ 15 & azeem rafiq & 1 & 0 & 0 & 0 & 0 & none & none & 0 & 0 \\ 16 & total batting & 99 & 73 & 18 & 1138 & 72 & 20.69 & 116 & 0 & 6 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & sum ( "not out" ) \\ \midrule 0 & 2 \\ \bottomrule \end{tabular}

select sum ( not out ) where 50s = 1 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>player</th> <th>matches</th> <th>innings</th> <th>not out</th> <th>runs</th> <th>high score</th> <th>average</th> <th>strike rate</th> <th>100s</th> <th>50s</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>anthony mcgrath</td> <td>9</td> <td>9</td> <td>2</td> <td>392</td> <td>72</td> <td>56</td> <td>132.43</td> <td>0</td> <td>4</td> </tr> <tr> <th>1</th> <td>jacques rudolph</td> <td>9</td> <td>8</td> <td>1</td> <td>191</td> <td>56</td> <td>27.29</td> <td>129.05</td> <td>0</td> <td>1</td> </tr> <tr> <th>2</th> <td>darren gough</td> <td>9</td> <td>4</td> <td>3</td> <td>26</td> <td>20</td> <td>26</td> <td>136.84</td> <td>0</td> <td>0</td> </tr> <tr> <th>3</th> <td>gerard brophy</td> <td>9</td> <td>9</td> <td>1</td> <td>177</td> <td>57</td> <td>22.13</td> <td>113.46</td> <td>0</td> <td>1</td> </tr> <tr> <th>4</th> <td>andrew gale</td> <td>9</td> <td>9</td> <td>2</td> <td>106</td> <td>45</td> <td>15.14</td> <td>86.18</td> <td>0</td> <td>0</td> </tr> <tr> <th>5</th> <td>michael vaughan</td> <td>7</td> <td>7</td> <td>0</td> <td>104</td> <td>34</td> <td>14.86</td> <td>138.67</td> <td>0</td> <td>0</td> </tr> <tr> <th>6</th> <td>craig white</td> <td>2</td> <td>2</td> <td>0</td> <td>26</td> <td>26</td> <td>13</td> <td>92.86</td> <td>0</td> <td>0</td> </tr> <tr> <th>7</th> <td>tim bresnan</td> <td>9</td> <td>8</td> <td>3</td> <td>58</td> <td>15</td> <td>11.6</td> <td>85.29</td> <td>0</td> <td>0</td> </tr> <tr> <th>8</th> <td>chris taylor</td> <td>2</td> <td>2</td> <td>1</td> <td>10</td> <td>10</td> <td>10</td> <td>111.11</td> <td>0</td> <td>0</td> </tr> <tr> <th>9</th> <td>simon guy</td> <td>4</td> <td>2</td> <td>0</td> <td>11</td> <td>7</td> <td>5.5</td> <td>78.57</td> <td>0</td> <td>0</td> </tr> <tr> <th>10</th> <td>richard pyrah</td> <td>9</td> <td>5</td> <td>2</td> <td>16</td> <td>8</td> <td>5.33</td> <td>114.29</td> <td>0</td> <td>0</td> </tr> <tr> <th>11</th> <td>adil rashid</td> <td>8</td> <td>6</td> <td>2</td> <td>18</td> <td>10</td> <td>4.5</td> <td>94.74</td> <td>0</td> <td>0</td> </tr> <tr> <th>12</th> <td>adam lyth</td> <td>1</td> <td>1</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> </tr> <tr> <th>13</th> <td>david wainwright</td> <td>2</td> <td>1</td> <td>1</td> <td>3</td> <td>3</td> <td>none</td> <td>33.33</td> <td>0</td> <td>0</td> </tr> <tr> <th>14</th> <td>matthew hoggard</td> <td>9</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>none</td> <td>none</td> <td>0</td> <td>0</td> </tr> <tr> <th>15</th> <td>azeem rafiq</td> <td>1</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>none</td> <td>none</td> <td>0</td> <td>0</td> </tr> <tr> <th>16</th> <td>total batting</td> <td>99</td> <td>73</td> <td>18</td> <td>1138</td> <td>72</td> <td>20.69</td> <td>116</td> <td>0</td> <td>6</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>sum ( "not out" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>2</td> </tr> </tbody> </table>

select sum ( not out ) where 50s = 1 <table_name> : table | | player | matches | innings | not out | runs | high score | average | strike rate | 100s | 50s | |---:|:-----------------|----------:|----------:|----------:|-------:|-------------:|:----------|:--------------|-------:|------:| | 0 | anthony mcgrath | 9 | 9 | 2 | 392 | 72 | 56 | 132.43 | 0 | 4 | | 1 | jacques rudolph | 9 | 8 | 1 | 191 | 56 | 27.29 | 129.05 | 0 | 1 | | 2 | darren gough | 9 | 4 | 3 | 26 | 20 | 26 | 136.84 | 0 | 0 | | 3 | gerard brophy | 9 | 9 | 1 | 177 | 57 | 22.13 | 113.46 | 0 | 1 | | 4 | andrew gale | 9 | 9 | 2 | 106 | 45 | 15.14 | 86.18 | 0 | 0 | | 5 | michael vaughan | 7 | 7 | 0 | 104 | 34 | 14.86 | 138.67 | 0 | 0 | | 6 | craig white | 2 | 2 | 0 | 26 | 26 | 13 | 92.86 | 0 | 0 | | 7 | tim bresnan | 9 | 8 | 3 | 58 | 15 | 11.6 | 85.29 | 0 | 0 | | 8 | chris taylor | 2 | 2 | 1 | 10 | 10 | 10 | 111.11 | 0 | 0 | | 9 | simon guy | 4 | 2 | 0 | 11 | 7 | 5.5 | 78.57 | 0 | 0 | | 10 | richard pyrah | 9 | 5 | 2 | 16 | 8 | 5.33 | 114.29 | 0 | 0 | | 11 | adil rashid | 8 | 6 | 2 | 18 | 10 | 4.5 | 94.74 | 0 | 0 | | 12 | adam lyth | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | | 13 | david wainwright | 2 | 1 | 1 | 3 | 3 | none | 33.33 | 0 | 0 | | 14 | matthew hoggard | 9 | 0 | 0 | 0 | 0 | none | none | 0 | 0 | | 15 | azeem rafiq | 1 | 0 | 0 | 0 | 0 | none | none | 0 | 0 | | 16 | total batting | 99 | 73 | 18 | 1138 | 72 | 20.69 | 116 | 0 | 6 |

| | sum ( "not out" ) | |---:|--------------------:| | 0 | 2 |

select count ( * ) where votes = 60 col : party | candidate(s) | votes | percentage row 1 : uup | peter weir alan mcfarland roy bradford john shields irene cree | 9270 | 25.6 row 2 : uk unionist | robert mccartney valerie kinghan patricia watson | 7579 | 20.9 row 3 : dup | st clair mcalister ruby cooling cecil noble | 6699 | 18.5 row 4 : alliance | oliver napier brian wilson eileen bell larry thompson jane copeland | 6186 | 17.1 row 5 : sdlp | owen adams john burke | 1798 | 5.0 row 6 : pup | victor ash samuel curry neil mehaffy kenneth sterrit | 1694 | 4.7 row 7 : ulster democratic | james mccullough thomas lindsay | 651 | 1.8 row 8 : ni women's coalition | emma mcguigan ann marie foster jane morrice | 496 | 1.4 row 9 : conservative | ann thompson bruce mulligan | 444 | 1.2 row 10 : independent chambers | alan chambers james arbuthnot robert irvine | 334 | 0.9 row 11 : green (ni) | paddy mcevoy mary ringland | 283 | 0.8 row 12 : sinn fein | mairtin o mulleoir john smith | 275 | 0.8 row 13 : labour coalition | john magennis sean mcgouran | 171 | 0.5 row 14 : independent dup | thomas o'brien william baxter | 97 | 0.3 row 15 : democratic left | daryl armitage tim davis | 95 | 0.3 row 16 : workers' party | dessie o'hagan colum mullan | 60 | 0.2 row 17 : ulster independence | geoffrey watson glenn pollock | 49 | 0.1 row 18 : independent voice | christopher carter fidelma carter | 49 | 0.1 row 19 : natural law | peter mcgowan thomas mullins | 15 | 0.0

col : count ( "*" ) row 1 : 1

select count ( * ) where votes = 60 <table_name> : table \begin{tabular}{lllll} \toprule & party & candidate(s) & votes & percentage \\ \midrule 0 & uup & peter weir alan mcfarland roy bradford john shields irene cree & 9270 & 25.6 \\ 1 & uk unionist & robert mccartney valerie kinghan patricia watson & 7579 & 20.9 \\ 2 & dup & st clair mcalister ruby cooling cecil noble & 6699 & 18.5 \\ 3 & alliance & oliver napier brian wilson eileen bell larry thompson jane copeland & 6186 & 17.1 \\ 4 & sdlp & owen adams john burke & 1798 & 5.0 \\ 5 & pup & victor ash samuel curry neil mehaffy kenneth sterrit & 1694 & 4.7 \\ 6 & ulster democratic & james mccullough thomas lindsay & 651 & 1.8 \\ 7 & ni women's coalition & emma mcguigan ann marie foster jane morrice & 496 & 1.4 \\ 8 & conservative & ann thompson bruce mulligan & 444 & 1.2 \\ 9 & independent chambers & alan chambers james arbuthnot robert irvine & 334 & 0.9 \\ 10 & green (ni) & paddy mcevoy mary ringland & 283 & 0.8 \\ 11 & sinn fein & mairtin o mulleoir john smith & 275 & 0.8 \\ 12 & labour coalition & john magennis sean mcgouran & 171 & 0.5 \\ 13 & independent dup & thomas o'brien william baxter & 97 & 0.3 \\ 14 & democratic left & daryl armitage tim davis & 95 & 0.3 \\ 15 & workers' party & dessie o'hagan colum mullan & 60 & 0.2 \\ 16 & ulster independence & geoffrey watson glenn pollock & 49 & 0.1 \\ 17 & independent voice & christopher carter fidelma carter & 49 & 0.1 \\ 18 & natural law & peter mcgowan thomas mullins & 15 & 0.0 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 1 \\ \bottomrule \end{tabular}

select count ( * ) where votes = 60 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>party</th> <th>candidate(s)</th> <th>votes</th> <th>percentage</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>uup</td> <td>peter weir alan mcfarland roy bradford john shields irene cree</td> <td>9270</td> <td>25.6</td> </tr> <tr> <th>1</th> <td>uk unionist</td> <td>robert mccartney valerie kinghan patricia watson</td> <td>7579</td> <td>20.9</td> </tr> <tr> <th>2</th> <td>dup</td> <td>st clair mcalister ruby cooling cecil noble</td> <td>6699</td> <td>18.5</td> </tr> <tr> <th>3</th> <td>alliance</td> <td>oliver napier brian wilson eileen bell larry thompson jane copeland</td> <td>6186</td> <td>17.1</td> </tr> <tr> <th>4</th> <td>sdlp</td> <td>owen adams john burke</td> <td>1798</td> <td>5.0</td> </tr> <tr> <th>5</th> <td>pup</td> <td>victor ash samuel curry neil mehaffy kenneth sterrit</td> <td>1694</td> <td>4.7</td> </tr> <tr> <th>6</th> <td>ulster democratic</td> <td>james mccullough thomas lindsay</td> <td>651</td> <td>1.8</td> </tr> <tr> <th>7</th> <td>ni women's coalition</td> <td>emma mcguigan ann marie foster jane morrice</td> <td>496</td> <td>1.4</td> </tr> <tr> <th>8</th> <td>conservative</td> <td>ann thompson bruce mulligan</td> <td>444</td> <td>1.2</td> </tr> <tr> <th>9</th> <td>independent chambers</td> <td>alan chambers james arbuthnot robert irvine</td> <td>334</td> <td>0.9</td> </tr> <tr> <th>10</th> <td>green (ni)</td> <td>paddy mcevoy mary ringland</td> <td>283</td> <td>0.8</td> </tr> <tr> <th>11</th> <td>sinn fein</td> <td>mairtin o mulleoir john smith</td> <td>275</td> <td>0.8</td> </tr> <tr> <th>12</th> <td>labour coalition</td> <td>john magennis sean mcgouran</td> <td>171</td> <td>0.5</td> </tr> <tr> <th>13</th> <td>independent dup</td> <td>thomas o'brien william baxter</td> <td>97</td> <td>0.3</td> </tr> <tr> <th>14</th> <td>democratic left</td> <td>daryl armitage tim davis</td> <td>95</td> <td>0.3</td> </tr> <tr> <th>15</th> <td>workers' party</td> <td>dessie o'hagan colum mullan</td> <td>60</td> <td>0.2</td> </tr> <tr> <th>16</th> <td>ulster independence</td> <td>geoffrey watson glenn pollock</td> <td>49</td> <td>0.1</td> </tr> <tr> <th>17</th> <td>independent voice</td> <td>christopher carter fidelma carter</td> <td>49</td> <td>0.1</td> </tr> <tr> <th>18</th> <td>natural law</td> <td>peter mcgowan thomas mullins</td> <td>15</td> <td>0.0</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "*" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> </tr> </tbody> </table>

select count ( * ) where votes = 60 <table_name> : table | | party | candidate(s) | votes | percentage | |---:|:---------------------|:--------------------------------------------------------------------|--------:|-------------:| | 0 | uup | peter weir alan mcfarland roy bradford john shields irene cree | 9270 | 25.6 | | 1 | uk unionist | robert mccartney valerie kinghan patricia watson | 7579 | 20.9 | | 2 | dup | st clair mcalister ruby cooling cecil noble | 6699 | 18.5 | | 3 | alliance | oliver napier brian wilson eileen bell larry thompson jane copeland | 6186 | 17.1 | | 4 | sdlp | owen adams john burke | 1798 | 5 | | 5 | pup | victor ash samuel curry neil mehaffy kenneth sterrit | 1694 | 4.7 | | 6 | ulster democratic | james mccullough thomas lindsay | 651 | 1.8 | | 7 | ni women's coalition | emma mcguigan ann marie foster jane morrice | 496 | 1.4 | | 8 | conservative | ann thompson bruce mulligan | 444 | 1.2 | | 9 | independent chambers | alan chambers james arbuthnot robert irvine | 334 | 0.9 | | 10 | green (ni) | paddy mcevoy mary ringland | 283 | 0.8 | | 11 | sinn fein | mairtin o mulleoir john smith | 275 | 0.8 | | 12 | labour coalition | john magennis sean mcgouran | 171 | 0.5 | | 13 | independent dup | thomas o'brien william baxter | 97 | 0.3 | | 14 | democratic left | daryl armitage tim davis | 95 | 0.3 | | 15 | workers' party | dessie o'hagan colum mullan | 60 | 0.2 | | 16 | ulster independence | geoffrey watson glenn pollock | 49 | 0.1 | | 17 | independent voice | christopher carter fidelma carter | 49 | 0.1 | | 18 | natural law | peter mcgowan thomas mullins | 15 | 0 |

| | count ( "*" ) | |---:|----------------:| | 0 | 1 |

select count ( title ) where first year in this position > 1033 col : name | title | first year in this position | years at nebraska | alma mater row 1 : biff jones | head coach | 1937 | 1937-1941 | army row 2 : henry schulte | lineman coach | 1931 | 1919-1924, 1931-1937 | michigan row 3 : ed weir | freshmen coach | 1929 | 1926, 1929-1937, 1943 | nebraska row 4 : w. harold browne | assistant coach | 1930 | 1930-1940 | none row 5 : roy lyman | none | 1936 | 1936-1941 | none row 6 : adolph j. lewandowski | none | 1937 | 1937-1944 | nebraska row 7 : jerry adams | none | none | 1937 | none row 8 : charles armstrong | none | 1937 | 1937-1942, 1944 | none

col : count ( "title" ) row 1 : 8

select count ( title ) where first year in this position > 1033 <table_name> : table \begin{tabular}{llllll} \toprule & name & title & first year in this position & years at nebraska & alma mater \\ \midrule 0 & biff jones & head coach & 1937 & 1937-1941 & army \\ 1 & henry schulte & lineman coach & 1931 & 1919-1924, 1931-1937 & michigan \\ 2 & ed weir & freshmen coach & 1929 & 1926, 1929-1937, 1943 & nebraska \\ 3 & w. harold browne & assistant coach & 1930 & 1930-1940 & none \\ 4 & roy lyman & none & 1936 & 1936-1941 & none \\ 5 & adolph j. lewandowski & none & 1937 & 1937-1944 & nebraska \\ 6 & jerry adams & none & none & 1937 & none \\ 7 & charles armstrong & none & 1937 & 1937-1942, 1944 & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "title" ) \\ \midrule 0 & 8 \\ \bottomrule \end{tabular}

select count ( title ) where first year in this position > 1033 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>name</th> <th>title</th> <th>first year in this position</th> <th>years at nebraska</th> <th>alma mater</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>biff jones</td> <td>head coach</td> <td>1937</td> <td>1937-1941</td> <td>army</td> </tr> <tr> <th>1</th> <td>henry schulte</td> <td>lineman coach</td> <td>1931</td> <td>1919-1924, 1931-1937</td> <td>michigan</td> </tr> <tr> <th>2</th> <td>ed weir</td> <td>freshmen coach</td> <td>1929</td> <td>1926, 1929-1937, 1943</td> <td>nebraska</td> </tr> <tr> <th>3</th> <td>w. harold browne</td> <td>assistant coach</td> <td>1930</td> <td>1930-1940</td> <td>none</td> </tr> <tr> <th>4</th> <td>roy lyman</td> <td>none</td> <td>1936</td> <td>1936-1941</td> <td>none</td> </tr> <tr> <th>5</th> <td>adolph j. lewandowski</td> <td>none</td> <td>1937</td> <td>1937-1944</td> <td>nebraska</td> </tr> <tr> <th>6</th> <td>jerry adams</td> <td>none</td> <td>none</td> <td>1937</td> <td>none</td> </tr> <tr> <th>7</th> <td>charles armstrong</td> <td>none</td> <td>1937</td> <td>1937-1942, 1944</td> <td>none</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "title" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>8</td> </tr> </tbody> </table>

select count ( title ) where first year in this position > 1033 <table_name> : table | | name | title | first year in this position | years at nebraska | alma mater | |---:|:----------------------|:----------------|:------------------------------|:----------------------|:-------------| | 0 | biff jones | head coach | 1937 | 1937-1941 | army | | 1 | henry schulte | lineman coach | 1931 | 1919-1924, 1931-1937 | michigan | | 2 | ed weir | freshmen coach | 1929 | 1926, 1929-1937, 1943 | nebraska | | 3 | w. harold browne | assistant coach | 1930 | 1930-1940 | none | | 4 | roy lyman | none | 1936 | 1936-1941 | none | | 5 | adolph j. lewandowski | none | 1937 | 1937-1944 | nebraska | | 6 | jerry adams | none | none | 1937 | none | | 7 | charles armstrong | none | 1937 | 1937-1942, 1944 | none |

| | count ( "title" ) | |---:|--------------------:| | 0 | 8 |

select total where nation = 'yugoslavia' col : rank | nation | gold | silver | bronze | total row 1 : 1 | france | 11 | 5 | 3 | 19 row 2 : 2 | greece | 6 | 7 | 6 | 19 row 3 : 3 | yugoslavia | 3 | 2 | 1 | 6 row 4 : 4 | spain | 1 | 5 | 5 | 11 row 5 : 5 | morocco | 1 | 1 | 0 | 2 row 6 : 5 | turkey | 1 | 1 | 0 | 2 row 7 : 7 | egypt | 0 | 1 | 7 | 8 row 8 : 8 | tunisia | 0 | 1 | 0 | 1 row 9 : totaal | totaal | 23 | 23 | 22 | 68

col : total row 1 : 6

select total where nation = 'yugoslavia' <table_name> : table \begin{tabular}{lllllll} \toprule & rank & nation & gold & silver & bronze & total \\ \midrule 0 & 1 & france & 11 & 5 & 3 & 19 \\ 1 & 2 & greece & 6 & 7 & 6 & 19 \\ 2 & 3 & yugoslavia & 3 & 2 & 1 & 6 \\ 3 & 4 & spain & 1 & 5 & 5 & 11 \\ 4 & 5 & morocco & 1 & 1 & 0 & 2 \\ 5 & 5 & turkey & 1 & 1 & 0 & 2 \\ 6 & 7 & egypt & 0 & 1 & 7 & 8 \\ 7 & 8 & tunisia & 0 & 1 & 0 & 1 \\ 8 & totaal & totaal & 23 & 23 & 22 & 68 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & total \\ \midrule 0 & 6 \\ \bottomrule \end{tabular}

select total where nation = 'yugoslavia' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>rank</th> <th>nation</th> <th>gold</th> <th>silver</th> <th>bronze</th> <th>total</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>france</td> <td>11</td> <td>5</td> <td>3</td> <td>19</td> </tr> <tr> <th>1</th> <td>2</td> <td>greece</td> <td>6</td> <td>7</td> <td>6</td> <td>19</td> </tr> <tr> <th>2</th> <td>3</td> <td>yugoslavia</td> <td>3</td> <td>2</td> <td>1</td> <td>6</td> </tr> <tr> <th>3</th> <td>4</td> <td>spain</td> <td>1</td> <td>5</td> <td>5</td> <td>11</td> </tr> <tr> <th>4</th> <td>5</td> <td>morocco</td> <td>1</td> <td>1</td> <td>0</td> <td>2</td> </tr> <tr> <th>5</th> <td>5</td> <td>turkey</td> <td>1</td> <td>1</td> <td>0</td> <td>2</td> </tr> <tr> <th>6</th> <td>7</td> <td>egypt</td> <td>0</td> <td>1</td> <td>7</td> <td>8</td> </tr> <tr> <th>7</th> <td>8</td> <td>tunisia</td> <td>0</td> <td>1</td> <td>0</td> <td>1</td> </tr> <tr> <th>8</th> <td>totaal</td> <td>totaal</td> <td>23</td> <td>23</td> <td>22</td> <td>68</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>total</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>6</td> </tr> </tbody> </table>

select total where nation = 'yugoslavia' <table_name> : table | | rank | nation | gold | silver | bronze | total | |---:|:-------|:-----------|-------:|---------:|---------:|--------:| | 0 | 1 | france | 11 | 5 | 3 | 19 | | 1 | 2 | greece | 6 | 7 | 6 | 19 | | 2 | 3 | yugoslavia | 3 | 2 | 1 | 6 | | 3 | 4 | spain | 1 | 5 | 5 | 11 | | 4 | 5 | morocco | 1 | 1 | 0 | 2 | | 5 | 5 | turkey | 1 | 1 | 0 | 2 | | 6 | 7 | egypt | 0 | 1 | 7 | 8 | | 7 | 8 | tunisia | 0 | 1 | 0 | 1 | | 8 | totaal | totaal | 23 | 23 | 22 | 68 |

| | total | |---:|--------:| | 0 | 6 |

select count ( * ) where title(s) represented = 'world cup' and notes = '1954, 1974, third win was in 1990' col : national team | title(s) represented | first worn | number of stars | notes row 1 : brazil | world cup | 1971 | 5 | third win was in 1970; fourth and fifth stars added after 1994 and 2002 wins. briefly wore 2 stars on a tour of europe in 1968 row 2 : italy | world cup | 1982 | 4 | added after third win; fourth star added after the 2006 win. incorporated into the badge since 2006 row 3 : germany | world cup | 1996 | 3 | 1954, 1974, third win was in 1990 row 4 : uruguay | world cup and olympics | 1930 | 4 | represent 2 world cups (1930 & 1950) and 2 olympic football titles (1924 & 1928) recognized as fifa world championships row 5 : argentina | world cup | 2003 | 2 | 1978, second win was in 1986 row 6 : england | world cup | 2003 | 1 | title won in 1966. star added after a campaign on sky sports' soccer am programme, first worn on the sleeve row 7 : france | world cup | 1998 | 1 | title won in 1998. star added above their badge which was unveiled at their opening qualifying game for euro 2000 row 8 : spain | world cup | 2010 | 1 | title won in 2010. spain played in their away kit for the final, but upon winning the world cup they changed into their home kit, complete with a star above the badge, for the presentation ceremony

col : count ( "*" ) row 1 : 0

select count ( * ) where title(s) represented = 'world cup' and notes = '1954, 1974, third win was in 1990' <table_name> : table \begin{tabular}{llllll} \toprule & national team & title(s) represented & first worn & number of stars & notes \\ \midrule 0 & brazil & world cup & 1971 & 5 & third win was in 1970; fourth and fifth stars added after 1994 and 2002 wins. briefly wore 2 stars on a tour of europe in 1968 \\ 1 & italy & world cup & 1982 & 4 & added after third win; fourth star added after the 2006 win. incorporated into the badge since 2006 \\ 2 & germany & world cup & 1996 & 3 & 1954, 1974, third win was in 1990 \\ 3 & uruguay & world cup and olympics & 1930 & 4 & represent 2 world cups (1930 & 1950) and 2 olympic football titles (1924 & 1928) recognized as fifa world championships \\ 4 & argentina & world cup & 2003 & 2 & 1978, second win was in 1986 \\ 5 & england & world cup & 2003 & 1 & title won in 1966. star added after a campaign on sky sports' soccer am programme, first worn on the sleeve \\ 6 & france & world cup & 1998 & 1 & title won in 1998. star added above their badge which was unveiled at their opening qualifying game for euro 2000 \\ 7 & spain & world cup & 2010 & 1 & title won in 2010. spain played in their away kit for the final, but upon winning the world cup they changed into their home kit, complete with a star above the badge, for the presentation ceremony \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 0 \\ \bottomrule \end{tabular}

select count ( * ) where title(s) represented = 'world cup' and notes = '1954, 1974, third win was in 1990' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>national team</th> <th>title(s) represented</th> <th>first worn</th> <th>number of stars</th> <th>notes</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>brazil</td> <td>world cup</td> <td>1971</td> <td>5</td> <td>third win was in 1970; fourth and fifth stars added after 1994 and 2002 wins. briefly wore 2 stars on a tour of europe in 1968</td> </tr> <tr> <th>1</th> <td>italy</td> <td>world cup</td> <td>1982</td> <td>4</td> <td>added after third win; fourth star added after the 2006 win. incorporated into the badge since 2006</td> </tr> <tr> <th>2</th> <td>germany</td> <td>world cup</td> <td>1996</td> <td>3</td> <td>1954, 1974, third win was in 1990</td> </tr> <tr> <th>3</th> <td>uruguay</td> <td>world cup and olympics</td> <td>1930</td> <td>4</td> <td>represent 2 world cups (1930 &amp; 1950) and 2 olympic football titles (1924 &amp; 1928) recognized as fifa world championships</td> </tr> <tr> <th>4</th> <td>argentina</td> <td>world cup</td> <td>2003</td> <td>2</td> <td>1978, second win was in 1986</td> </tr> <tr> <th>5</th> <td>england</td> <td>world cup</td> <td>2003</td> <td>1</td> <td>title won in 1966. star added after a campaign on sky sports' soccer am programme, first worn on the sleeve</td> </tr> <tr> <th>6</th> <td>france</td> <td>world cup</td> <td>1998</td> <td>1</td> <td>title won in 1998. star added above their badge which was unveiled at their opening qualifying game for euro 2000</td> </tr> <tr> <th>7</th> <td>spain</td> <td>world cup</td> <td>2010</td> <td>1</td> <td>title won in 2010. spain played in their away kit for the final, but upon winning the world cup they changed into their home kit, complete with a star above the badge, for the presentation ceremony</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "*" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>0</td> </tr> </tbody> </table>

select count ( * ) where title(s) represented = 'world cup' and notes = '1954, 1974, third win was in 1990' <table_name> : table | | national team | title(s) represented | first worn | number of stars | notes | |---:|:----------------|:-----------------------|-------------:|------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | brazil | world cup | 1971 | 5 | third win was in 1970; fourth and fifth stars added after 1994 and 2002 wins. briefly wore 2 stars on a tour of europe in 1968 | | 1 | italy | world cup | 1982 | 4 | added after third win; fourth star added after the 2006 win. incorporated into the badge since 2006 | | 2 | germany | world cup | 1996 | 3 | 1954, 1974, third win was in 1990 | | 3 | uruguay | world cup and olympics | 1930 | 4 | represent 2 world cups (1930 & 1950) and 2 olympic football titles (1924 & 1928) recognized as fifa world championships | | 4 | argentina | world cup | 2003 | 2 | 1978, second win was in 1986 | | 5 | england | world cup | 2003 | 1 | title won in 1966. star added after a campaign on sky sports' soccer am programme, first worn on the sleeve | | 6 | france | world cup | 1998 | 1 | title won in 1998. star added above their badge which was unveiled at their opening qualifying game for euro 2000 | | 7 | spain | world cup | 2010 | 1 | title won in 2010. spain played in their away kit for the final, but upon winning the world cup they changed into their home kit, complete with a star above the badge, for the presentation ceremony |

| | count ( "*" ) | |---:|----------------:| | 0 | 0 |

select abs ( images - pages ) where names = 'ռստամ-սսհմ' col : volume | year | names | articles | pages | images | maps | number of copies row 1 : 1 | 1974 | ա-արգինա | 3442 | 720 | 1294 | 75 | 100000 row 2 : 2 | 1976 | արգիշտի-գեղերվան | 3503 | 720 | 1798 | 75 | 100000 row 3 : 3 | 1977 | գեղեցիկը-զուրաբյան | 3509 | 720 | 1726 | 53 | 100000 row 4 : 4 | 1978 | է-խաղխաղ | 3451 | 720 | 1228 | 53 | 100000 row 5 : 5 | 1979 | խաղող-կուլիսներ | 3694 | 720 | 1326 | 68 | 100000 row 6 : 6 | 1980 | կուլզոր-ձուլուկ | 3108 | 720 | 1097 | 64 | 100000 row 7 : 7 | 1981 | ձուլում-մոնոպխոնք | 3250 | 720 | 1436 | 46 | 100000 row 8 : 8 | 1982 | մոնոպոլիա-չեչերեկ | 3145 | 720 | 1274 | 70 | 100000 row 9 : 9 | 1983 | չեչքար-ռստակես | 3185 | 720 | 1046 | 60 | 100000 row 10 : 10 | 1984 | ռստամ-սսհմ | 2009 | 736 | 1065 | 61 | 100000 row 11 : 11 | 1985 | սովետական-տիեզերք | 2970 | 720 | 958 | 67 | 100000 row 12 : 12 | 1986 | տիեպոլո-ֆուքսին | 3501 | 752 | 1015 | 96 | 100000 row 13 : total | 1974-1986 | ա-ֆուքսին | 36767 | 8688 | 15263 | 858 | 1200000 row 14 : 13 | 1987 | սովետական հայաստան | none | none | none | none | none

col : "abs ( images - pages )" row 1 : abs ( images - pages )

select abs ( images - pages ) where names = 'ռստամ-սսհմ' <table_name> : table \begin{tabular}{lllllllll} \toprule & volume & year & names & articles & pages & images & maps & number of copies \\ \midrule 0 & 1 & 1974 & ա-արգինա & 3442 & 720 & 1294 & 75 & 100000 \\ 1 & 2 & 1976 & արգիշտի-գեղերվան & 3503 & 720 & 1798 & 75 & 100000 \\ 2 & 3 & 1977 & գեղեցիկը-զուրաբյան & 3509 & 720 & 1726 & 53 & 100000 \\ 3 & 4 & 1978 & է-խաղխաղ & 3451 & 720 & 1228 & 53 & 100000 \\ 4 & 5 & 1979 & խաղող-կուլիսներ & 3694 & 720 & 1326 & 68 & 100000 \\ 5 & 6 & 1980 & կուլզոր-ձուլուկ & 3108 & 720 & 1097 & 64 & 100000 \\ 6 & 7 & 1981 & ձուլում-մոնոպխոնք & 3250 & 720 & 1436 & 46 & 100000 \\ 7 & 8 & 1982 & մոնոպոլիա-չեչերեկ & 3145 & 720 & 1274 & 70 & 100000 \\ 8 & 9 & 1983 & չեչքար-ռստակես & 3185 & 720 & 1046 & 60 & 100000 \\ 9 & 10 & 1984 & ռստամ-սսհմ & 2009 & 736 & 1065 & 61 & 100000 \\ 10 & 11 & 1985 & սովետական-տիեզերք & 2970 & 720 & 958 & 67 & 100000 \\ 11 & 12 & 1986 & տիեպոլո-ֆուքսին & 3501 & 752 & 1015 & 96 & 100000 \\ 12 & total & 1974-1986 & ա-ֆուքսին & 36767 & 8688 & 15263 & 858 & 1200000 \\ 13 & 13 & 1987 & սովետական հայաստան & none & none & none & none & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & "abs ( images - pages )" \\ \midrule 0 & abs ( images - pages ) \\ \bottomrule \end{tabular}

select abs ( images - pages ) where names = 'ռստամ-սսհմ' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>volume</th> <th>year</th> <th>names</th> <th>articles</th> <th>pages</th> <th>images</th> <th>maps</th> <th>number of copies</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>1974</td> <td>ա-արգինա</td> <td>3442</td> <td>720</td> <td>1294</td> <td>75</td> <td>100000</td> </tr> <tr> <th>1</th> <td>2</td> <td>1976</td> <td>արգիշտի-գեղերվան</td> <td>3503</td> <td>720</td> <td>1798</td> <td>75</td> <td>100000</td> </tr> <tr> <th>2</th> <td>3</td> <td>1977</td> <td>գեղեցիկը-զուրաբյան</td> <td>3509</td> <td>720</td> <td>1726</td> <td>53</td> <td>100000</td> </tr> <tr> <th>3</th> <td>4</td> <td>1978</td> <td>է-խաղխաղ</td> <td>3451</td> <td>720</td> <td>1228</td> <td>53</td> <td>100000</td> </tr> <tr> <th>4</th> <td>5</td> <td>1979</td> <td>խաղող-կուլիսներ</td> <td>3694</td> <td>720</td> <td>1326</td> <td>68</td> <td>100000</td> </tr> <tr> <th>5</th> <td>6</td> <td>1980</td> <td>կուլզոր-ձուլուկ</td> <td>3108</td> <td>720</td> <td>1097</td> <td>64</td> <td>100000</td> </tr> <tr> <th>6</th> <td>7</td> <td>1981</td> <td>ձուլում-մոնոպխոնք</td> <td>3250</td> <td>720</td> <td>1436</td> <td>46</td> <td>100000</td> </tr> <tr> <th>7</th> <td>8</td> <td>1982</td> <td>մոնոպոլիա-չեչերեկ</td> <td>3145</td> <td>720</td> <td>1274</td> <td>70</td> <td>100000</td> </tr> <tr> <th>8</th> <td>9</td> <td>1983</td> <td>չեչքար-ռստակես</td> <td>3185</td> <td>720</td> <td>1046</td> <td>60</td> <td>100000</td> </tr> <tr> <th>9</th> <td>10</td> <td>1984</td> <td>ռստամ-սսհմ</td> <td>2009</td> <td>736</td> <td>1065</td> <td>61</td> <td>100000</td> </tr> <tr> <th>10</th> <td>11</td> <td>1985</td> <td>սովետական-տիեզերք</td> <td>2970</td> <td>720</td> <td>958</td> <td>67</td> <td>100000</td> </tr> <tr> <th>11</th> <td>12</td> <td>1986</td> <td>տիեպոլո-ֆուքսին</td> <td>3501</td> <td>752</td> <td>1015</td> <td>96</td> <td>100000</td> </tr> <tr> <th>12</th> <td>total</td> <td>1974-1986</td> <td>ա-ֆուքսին</td> <td>36767</td> <td>8688</td> <td>15263</td> <td>858</td> <td>1200000</td> </tr> <tr> <th>13</th> <td>13</td> <td>1987</td> <td>սովետական հայաստան</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>"abs ( images - pages )"</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>abs ( images - pages )</td> </tr> </tbody> </table>

select abs ( images - pages ) where names = 'ռստամ-սսհմ' <table_name> : table | | volume | year | names | articles | pages | images | maps | number of copies | |---:|:---------|:----------|:-------------------|:-----------|:--------|:---------|:-------|:-------------------| | 0 | 1 | 1974 | ա-արգինա | 3442 | 720 | 1294 | 75 | 100000 | | 1 | 2 | 1976 | արգիշտի-գեղերվան | 3503 | 720 | 1798 | 75 | 100000 | | 2 | 3 | 1977 | գեղեցիկը-զուրաբյան | 3509 | 720 | 1726 | 53 | 100000 | | 3 | 4 | 1978 | է-խաղխաղ | 3451 | 720 | 1228 | 53 | 100000 | | 4 | 5 | 1979 | խաղող-կուլիսներ | 3694 | 720 | 1326 | 68 | 100000 | | 5 | 6 | 1980 | կուլզոր-ձուլուկ | 3108 | 720 | 1097 | 64 | 100000 | | 6 | 7 | 1981 | ձուլում-մոնոպխոնք | 3250 | 720 | 1436 | 46 | 100000 | | 7 | 8 | 1982 | մոնոպոլիա-չեչերեկ | 3145 | 720 | 1274 | 70 | 100000 | | 8 | 9 | 1983 | չեչքար-ռստակես | 3185 | 720 | 1046 | 60 | 100000 | | 9 | 10 | 1984 | ռստամ-սսհմ | 2009 | 736 | 1065 | 61 | 100000 | | 10 | 11 | 1985 | սովետական-տիեզերք | 2970 | 720 | 958 | 67 | 100000 | | 11 | 12 | 1986 | տիեպոլո-ֆուքսին | 3501 | 752 | 1015 | 96 | 100000 | | 12 | total | 1974-1986 | ա-ֆուքսին | 36767 | 8688 | 15263 | 858 | 1200000 | | 13 | 13 | 1987 | սովետական հայաստան | none | none | none | none | none |

| | "abs ( images - pages )" | |---:|:---------------------------| | 0 | abs ( images - pages ) |

select poll company where poll company = 'ivad' col : poll company | source | publication date | psuv | opposition | undecided row 1 : gis xxi | radio nacional de venezuela | march 2010 | 32 | 22 | 36 row 2 : gis xxi | radio nacional de venezuela | may 2010 | 36 | 23 | 33 row 3 : gis xxi | radio nacional de venezuela | june 2010 | 44 | 20 | none row 4 : hinterlaces | hinterlaces el universal | july 2010 | 27 | 28 | 23 row 5 : gis xxi | correo del orinoco | august 2010 | 50 | 44 | none row 6 : ivad | el universal | august 2010 | 45 | 43 | none row 7 : keller & asociados | el universal | august 2010 | 32 | 46 | none row 8 : keller & asociados | el universal | 2 september 2010 | 43 | 57 | 0 row 9 : datanalisis | abc.es | september 2010 | 52 | 48 | none row 10 : hinterlaces | union radio descifrado | september 2010 | 41 | 37 | none row 11 : ivad | union radio descifrado | september 2010 | 54 | 46 | none

col : poll company row 1 : ivad row 2 : ivad

select poll company where poll company = 'ivad' <table_name> : table \begin{tabular}{lllllll} \toprule & poll company & source & publication date & psuv & opposition & undecided \\ \midrule 0 & gis xxi & radio nacional de venezuela & march 2010 & 32 & 22 & 36 \\ 1 & gis xxi & radio nacional de venezuela & may 2010 & 36 & 23 & 33 \\ 2 & gis xxi & radio nacional de venezuela & june 2010 & 44 & 20 & none \\ 3 & hinterlaces & hinterlaces el universal & july 2010 & 27 & 28 & 23 \\ 4 & gis xxi & correo del orinoco & august 2010 & 50 & 44 & none \\ 5 & ivad & el universal & august 2010 & 45 & 43 & none \\ 6 & keller & asociados & el universal & august 2010 & 32 & 46 & none \\ 7 & keller & asociados & el universal & 2 september 2010 & 43 & 57 & 0 \\ 8 & datanalisis & abc.es & september 2010 & 52 & 48 & none \\ 9 & hinterlaces & union radio descifrado & september 2010 & 41 & 37 & none \\ 10 & ivad & union radio descifrado & september 2010 & 54 & 46 & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & poll company \\ \midrule 0 & ivad \\ 1 & ivad \\ \bottomrule \end{tabular}

select poll company where poll company = 'ivad' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>poll company</th> <th>source</th> <th>publication date</th> <th>psuv</th> <th>opposition</th> <th>undecided</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>gis xxi</td> <td>radio nacional de venezuela</td> <td>march 2010</td> <td>32</td> <td>22</td> <td>36</td> </tr> <tr> <th>1</th> <td>gis xxi</td> <td>radio nacional de venezuela</td> <td>may 2010</td> <td>36</td> <td>23</td> <td>33</td> </tr> <tr> <th>2</th> <td>gis xxi</td> <td>radio nacional de venezuela</td> <td>june 2010</td> <td>44</td> <td>20</td> <td>none</td> </tr> <tr> <th>3</th> <td>hinterlaces</td> <td>hinterlaces el universal</td> <td>july 2010</td> <td>27</td> <td>28</td> <td>23</td> </tr> <tr> <th>4</th> <td>gis xxi</td> <td>correo del orinoco</td> <td>august 2010</td> <td>50</td> <td>44</td> <td>none</td> </tr> <tr> <th>5</th> <td>ivad</td> <td>el universal</td> <td>august 2010</td> <td>45</td> <td>43</td> <td>none</td> </tr> <tr> <th>6</th> <td>keller &amp; asociados</td> <td>el universal</td> <td>august 2010</td> <td>32</td> <td>46</td> <td>none</td> </tr> <tr> <th>7</th> <td>keller &amp; asociados</td> <td>el universal</td> <td>2 september 2010</td> <td>43</td> <td>57</td> <td>0</td> </tr> <tr> <th>8</th> <td>datanalisis</td> <td>abc.es</td> <td>september 2010</td> <td>52</td> <td>48</td> <td>none</td> </tr> <tr> <th>9</th> <td>hinterlaces</td> <td>union radio descifrado</td> <td>september 2010</td> <td>41</td> <td>37</td> <td>none</td> </tr> <tr> <th>10</th> <td>ivad</td> <td>union radio descifrado</td> <td>september 2010</td> <td>54</td> <td>46</td> <td>none</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>poll company</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>ivad</td> </tr> <tr> <th>1</th> <td>ivad</td> </tr> </tbody> </table>

select poll company where poll company = 'ivad' <table_name> : table | | poll company | source | publication date | psuv | opposition | undecided | |---:|:-------------------|:----------------------------|:-------------------|-------:|-------------:|:------------| | 0 | gis xxi | radio nacional de venezuela | march 2010 | 32 | 22 | 36 | | 1 | gis xxi | radio nacional de venezuela | may 2010 | 36 | 23 | 33 | | 2 | gis xxi | radio nacional de venezuela | june 2010 | 44 | 20 | none | | 3 | hinterlaces | hinterlaces el universal | july 2010 | 27 | 28 | 23 | | 4 | gis xxi | correo del orinoco | august 2010 | 50 | 44 | none | | 5 | ivad | el universal | august 2010 | 45 | 43 | none | | 6 | keller & asociados | el universal | august 2010 | 32 | 46 | none | | 7 | keller & asociados | el universal | 2 september 2010 | 43 | 57 | 0 | | 8 | datanalisis | abc.es | september 2010 | 52 | 48 | none | | 9 | hinterlaces | union radio descifrado | september 2010 | 41 | 37 | none | | 10 | ivad | union radio descifrado | september 2010 | 54 | 46 | none |

| | poll company | |---:|:---------------| | 0 | ivad | | 1 | ivad |

select act where act = 'mario winans' col : act | year signed | # albums released under bad boy row 1 : diddy | 1993 | 6 row 2 : the notorious b.i.g | 1993 | 5 row 3 : harve pierre | 1993 | none row 4 : the hitmen | 1993 | none row 5 : mario winans | 1995 | 1 row 6 : kalenna harper | 2004 | none row 7 : cassie | 2006 | 1 row 8 : janelle monae | 2007 | 2 row 9 : red cafe | 2009 | none row 10 : machine gun kelly | 2011 | 1 row 11 : french montana | 2012 | 1 row 12 : megan nicole | 2012 | none

col : act row 1 : mario winans

select act where act = 'mario winans' <table_name> : table \begin{tabular}{llll} \toprule & act & year signed & # albums released under bad boy \\ \midrule 0 & diddy & 1993 & 6 \\ 1 & the notorious b.i.g & 1993 & 5 \\ 2 & harve pierre & 1993 & none \\ 3 & the hitmen & 1993 & none \\ 4 & mario winans & 1995 & 1 \\ 5 & kalenna harper & 2004 & none \\ 6 & cassie & 2006 & 1 \\ 7 & janelle monae & 2007 & 2 \\ 8 & red cafe & 2009 & none \\ 9 & machine gun kelly & 2011 & 1 \\ 10 & french montana & 2012 & 1 \\ 11 & megan nicole & 2012 & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & act \\ \midrule 0 & mario winans \\ \bottomrule \end{tabular}

select act where act = 'mario winans' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>act</th> <th>year signed</th> <th># albums released under bad boy</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>diddy</td> <td>1993</td> <td>6</td> </tr> <tr> <th>1</th> <td>the notorious b.i.g</td> <td>1993</td> <td>5</td> </tr> <tr> <th>2</th> <td>harve pierre</td> <td>1993</td> <td>none</td> </tr> <tr> <th>3</th> <td>the hitmen</td> <td>1993</td> <td>none</td> </tr> <tr> <th>4</th> <td>mario winans</td> <td>1995</td> <td>1</td> </tr> <tr> <th>5</th> <td>kalenna harper</td> <td>2004</td> <td>none</td> </tr> <tr> <th>6</th> <td>cassie</td> <td>2006</td> <td>1</td> </tr> <tr> <th>7</th> <td>janelle monae</td> <td>2007</td> <td>2</td> </tr> <tr> <th>8</th> <td>red cafe</td> <td>2009</td> <td>none</td> </tr> <tr> <th>9</th> <td>machine gun kelly</td> <td>2011</td> <td>1</td> </tr> <tr> <th>10</th> <td>french montana</td> <td>2012</td> <td>1</td> </tr> <tr> <th>11</th> <td>megan nicole</td> <td>2012</td> <td>none</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>act</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>mario winans</td> </tr> </tbody> </table>

select act where act = 'mario winans' <table_name> : table | | act | year signed | # albums released under bad boy | |---:|:--------------------|--------------:|:----------------------------------| | 0 | diddy | 1993 | 6 | | 1 | the notorious b.i.g | 1993 | 5 | | 2 | harve pierre | 1993 | none | | 3 | the hitmen | 1993 | none | | 4 | mario winans | 1995 | 1 | | 5 | kalenna harper | 2004 | none | | 6 | cassie | 2006 | 1 | | 7 | janelle monae | 2007 | 2 | | 8 | red cafe | 2009 | none | | 9 | machine gun kelly | 2011 | 1 | | 10 | french montana | 2012 | 1 | | 11 | megan nicole | 2012 | none |

| | act | |---:|:-------------| | 0 | mario winans |

select count ( opponent ) where attendance < '74,532' col : week | date | result | record | opponent | points for | points against | first downs | attendance row 1 : 1 | september 18 | win | 1-0 | at minnesota vikings | 16 | 10 | 16 | 47,678 row 2 : 2 | september 25 | win | 2-0 | new york giants | 41 | 21 | 25 | 64,215 row 3 : 3 | october 2 | win | 3-0 | tampa bay buccaneers | 23 | 7 | 23 | 55,316 row 4 : 4 | october 9 | win | 4-0 | at st. louis cardinals | 30 | 24 | 22 | 50,129 row 5 : 5 | october 16 | win | 5-0 | washington redskins | 34 | 16 | 23 | 62,115 row 6 : 6 | october 23 | win | 6-0 | at philadelphia eagles | 16 | 10 | 17 | 65,507 row 7 : 7 | october 30 | win | 7-0 | detroit lions | 37 | 0 | 20 | 63,160 row 8 : 8 | november 6 | win | 8-0 | at new york giants | 24 | 10 | 13 | 74,532 row 9 : 9 | november 14 | loss | 8-1 | st. louis cardinals | 17 | 24 | 16 | 64,038 row 10 : 10 | november 20 | loss | 8-2 | at pittsburgh steelers | 13 | 28 | 20 | 49,761 row 11 : 11 | november 27 | win | 9-2 | at washington redskins | 14 | 7 | 19 | 55,031 row 12 : 12 | december 4 | win | 10-2 | philadelphia eagles | 24 | 14 | 19 | 60,289 row 13 : 13 | december 12 | win | 11-2 | at san francisco 49ers | 42 | 35 | 24 | 55,851 row 14 : 14 | december 18 | win | 12-2 | denver broncos | 14 | 6 | 15 | 63,752

col : count ( "opponent" ) row 1 : 13

select count ( opponent ) where attendance < '74,532' <table_name> : table \begin{tabular}{llllllllll} \toprule & week & date & result & record & opponent & points for & points against & first downs & attendance \\ \midrule 0 & 1 & september 18 & win & 1-0 & at minnesota vikings & 16 & 10 & 16 & 47,678 \\ 1 & 2 & september 25 & win & 2-0 & new york giants & 41 & 21 & 25 & 64,215 \\ 2 & 3 & october 2 & win & 3-0 & tampa bay buccaneers & 23 & 7 & 23 & 55,316 \\ 3 & 4 & october 9 & win & 4-0 & at st. louis cardinals & 30 & 24 & 22 & 50,129 \\ 4 & 5 & october 16 & win & 5-0 & washington redskins & 34 & 16 & 23 & 62,115 \\ 5 & 6 & october 23 & win & 6-0 & at philadelphia eagles & 16 & 10 & 17 & 65,507 \\ 6 & 7 & october 30 & win & 7-0 & detroit lions & 37 & 0 & 20 & 63,160 \\ 7 & 8 & november 6 & win & 8-0 & at new york giants & 24 & 10 & 13 & 74,532 \\ 8 & 9 & november 14 & loss & 8-1 & st. louis cardinals & 17 & 24 & 16 & 64,038 \\ 9 & 10 & november 20 & loss & 8-2 & at pittsburgh steelers & 13 & 28 & 20 & 49,761 \\ 10 & 11 & november 27 & win & 9-2 & at washington redskins & 14 & 7 & 19 & 55,031 \\ 11 & 12 & december 4 & win & 10-2 & philadelphia eagles & 24 & 14 & 19 & 60,289 \\ 12 & 13 & december 12 & win & 11-2 & at san francisco 49ers & 42 & 35 & 24 & 55,851 \\ 13 & 14 & december 18 & win & 12-2 & denver broncos & 14 & 6 & 15 & 63,752 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "opponent" ) \\ \midrule 0 & 13 \\ \bottomrule \end{tabular}

select count ( opponent ) where attendance < '74,532' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>week</th> <th>date</th> <th>result</th> <th>record</th> <th>opponent</th> <th>points for</th> <th>points against</th> <th>first downs</th> <th>attendance</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>september 18</td> <td>win</td> <td>1-0</td> <td>at minnesota vikings</td> <td>16</td> <td>10</td> <td>16</td> <td>47,678</td> </tr> <tr> <th>1</th> <td>2</td> <td>september 25</td> <td>win</td> <td>2-0</td> <td>new york giants</td> <td>41</td> <td>21</td> <td>25</td> <td>64,215</td> </tr> <tr> <th>2</th> <td>3</td> <td>october 2</td> <td>win</td> <td>3-0</td> <td>tampa bay buccaneers</td> <td>23</td> <td>7</td> <td>23</td> <td>55,316</td> </tr> <tr> <th>3</th> <td>4</td> <td>october 9</td> <td>win</td> <td>4-0</td> <td>at st. louis cardinals</td> <td>30</td> <td>24</td> <td>22</td> <td>50,129</td> </tr> <tr> <th>4</th> <td>5</td> <td>october 16</td> <td>win</td> <td>5-0</td> <td>washington redskins</td> <td>34</td> <td>16</td> <td>23</td> <td>62,115</td> </tr> <tr> <th>5</th> <td>6</td> <td>october 23</td> <td>win</td> <td>6-0</td> <td>at philadelphia eagles</td> <td>16</td> <td>10</td> <td>17</td> <td>65,507</td> </tr> <tr> <th>6</th> <td>7</td> <td>october 30</td> <td>win</td> <td>7-0</td> <td>detroit lions</td> <td>37</td> <td>0</td> <td>20</td> <td>63,160</td> </tr> <tr> <th>7</th> <td>8</td> <td>november 6</td> <td>win</td> <td>8-0</td> <td>at new york giants</td> <td>24</td> <td>10</td> <td>13</td> <td>74,532</td> </tr> <tr> <th>8</th> <td>9</td> <td>november 14</td> <td>loss</td> <td>8-1</td> <td>st. louis cardinals</td> <td>17</td> <td>24</td> <td>16</td> <td>64,038</td> </tr> <tr> <th>9</th> <td>10</td> <td>november 20</td> <td>loss</td> <td>8-2</td> <td>at pittsburgh steelers</td> <td>13</td> <td>28</td> <td>20</td> <td>49,761</td> </tr> <tr> <th>10</th> <td>11</td> <td>november 27</td> <td>win</td> <td>9-2</td> <td>at washington redskins</td> <td>14</td> <td>7</td> <td>19</td> <td>55,031</td> </tr> <tr> <th>11</th> <td>12</td> <td>december 4</td> <td>win</td> <td>10-2</td> <td>philadelphia eagles</td> <td>24</td> <td>14</td> <td>19</td> <td>60,289</td> </tr> <tr> <th>12</th> <td>13</td> <td>december 12</td> <td>win</td> <td>11-2</td> <td>at san francisco 49ers</td> <td>42</td> <td>35</td> <td>24</td> <td>55,851</td> </tr> <tr> <th>13</th> <td>14</td> <td>december 18</td> <td>win</td> <td>12-2</td> <td>denver broncos</td> <td>14</td> <td>6</td> <td>15</td> <td>63,752</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "opponent" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>13</td> </tr> </tbody> </table>

select count ( opponent ) where attendance < '74,532' <table_name> : table | | week | date | result | record | opponent | points for | points against | first downs | attendance | |---:|-------:|:-------------|:---------|:---------|:-----------------------|-------------:|-----------------:|--------------:|:-------------| | 0 | 1 | september 18 | win | 1-0 | at minnesota vikings | 16 | 10 | 16 | 47,678 | | 1 | 2 | september 25 | win | 2-0 | new york giants | 41 | 21 | 25 | 64,215 | | 2 | 3 | october 2 | win | 3-0 | tampa bay buccaneers | 23 | 7 | 23 | 55,316 | | 3 | 4 | october 9 | win | 4-0 | at st. louis cardinals | 30 | 24 | 22 | 50,129 | | 4 | 5 | october 16 | win | 5-0 | washington redskins | 34 | 16 | 23 | 62,115 | | 5 | 6 | october 23 | win | 6-0 | at philadelphia eagles | 16 | 10 | 17 | 65,507 | | 6 | 7 | october 30 | win | 7-0 | detroit lions | 37 | 0 | 20 | 63,160 | | 7 | 8 | november 6 | win | 8-0 | at new york giants | 24 | 10 | 13 | 74,532 | | 8 | 9 | november 14 | loss | 8-1 | st. louis cardinals | 17 | 24 | 16 | 64,038 | | 9 | 10 | november 20 | loss | 8-2 | at pittsburgh steelers | 13 | 28 | 20 | 49,761 | | 10 | 11 | november 27 | win | 9-2 | at washington redskins | 14 | 7 | 19 | 55,031 | | 11 | 12 | december 4 | win | 10-2 | philadelphia eagles | 24 | 14 | 19 | 60,289 | | 12 | 13 | december 12 | win | 11-2 | at san francisco 49ers | 42 | 35 | 24 | 55,851 | | 13 | 14 | december 18 | win | 12-2 | denver broncos | 14 | 6 | 15 | 63,752 |

| | count ( "opponent" ) | |---:|-----------------------:| | 0 | 13 |

select nation where gold < 562 col : rank | nation | gold | silver | bronze | total row 1 : 1 | venezuela | 9 | 8 | 6 | 23 row 2 : 2 | guatemala | 6 | 6 | 6 | 18 row 3 : 3 | peru | 5 | 8 | 9 | 22 row 4 : 4 | chile | 4 | 4 | 1 | 9 row 5 : 5 | el salvador | 4 | 0 | 2 | 6 row 6 : 6 | ecuador | 2 | 5 | 1 | 8 row 7 : 7 | bolivia | 2 | 1 | 2 | 5 row 8 : 8 | dominican republic | 1 | 0 | 2 | 3 row 9 : 9 | colombia | 0 | 1 | 3 | 4 row 10 : total | total | 33 | 33 | 32 | 98

col : nation row 1 : peru row 2 : chile row 3 : el salvador row 4 : ecuador row 5 : bolivia row 6 : dominican republic row 7 : colombia row 8 : total

select nation where gold < 562 <table_name> : table \begin{tabular}{lllllll} \toprule & rank & nation & gold & silver & bronze & total \\ \midrule 0 & 1 & venezuela & 9 & 8 & 6 & 23 \\ 1 & 2 & guatemala & 6 & 6 & 6 & 18 \\ 2 & 3 & peru & 5 & 8 & 9 & 22 \\ 3 & 4 & chile & 4 & 4 & 1 & 9 \\ 4 & 5 & el salvador & 4 & 0 & 2 & 6 \\ 5 & 6 & ecuador & 2 & 5 & 1 & 8 \\ 6 & 7 & bolivia & 2 & 1 & 2 & 5 \\ 7 & 8 & dominican republic & 1 & 0 & 2 & 3 \\ 8 & 9 & colombia & 0 & 1 & 3 & 4 \\ 9 & total & total & 33 & 33 & 32 & 98 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & nation \\ \midrule 0 & peru \\ 1 & chile \\ 2 & el salvador \\ 3 & ecuador \\ 4 & bolivia \\ 5 & dominican republic \\ 6 & colombia \\ 7 & total \\ \bottomrule \end{tabular}

select nation where gold < 562 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>rank</th> <th>nation</th> <th>gold</th> <th>silver</th> <th>bronze</th> <th>total</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>venezuela</td> <td>9</td> <td>8</td> <td>6</td> <td>23</td> </tr> <tr> <th>1</th> <td>2</td> <td>guatemala</td> <td>6</td> <td>6</td> <td>6</td> <td>18</td> </tr> <tr> <th>2</th> <td>3</td> <td>peru</td> <td>5</td> <td>8</td> <td>9</td> <td>22</td> </tr> <tr> <th>3</th> <td>4</td> <td>chile</td> <td>4</td> <td>4</td> <td>1</td> <td>9</td> </tr> <tr> <th>4</th> <td>5</td> <td>el salvador</td> <td>4</td> <td>0</td> <td>2</td> <td>6</td> </tr> <tr> <th>5</th> <td>6</td> <td>ecuador</td> <td>2</td> <td>5</td> <td>1</td> <td>8</td> </tr> <tr> <th>6</th> <td>7</td> <td>bolivia</td> <td>2</td> <td>1</td> <td>2</td> <td>5</td> </tr> <tr> <th>7</th> <td>8</td> <td>dominican republic</td> <td>1</td> <td>0</td> <td>2</td> <td>3</td> </tr> <tr> <th>8</th> <td>9</td> <td>colombia</td> <td>0</td> <td>1</td> <td>3</td> <td>4</td> </tr> <tr> <th>9</th> <td>total</td> <td>total</td> <td>33</td> <td>33</td> <td>32</td> <td>98</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>nation</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>peru</td> </tr> <tr> <th>1</th> <td>chile</td> </tr> <tr> <th>2</th> <td>el salvador</td> </tr> <tr> <th>3</th> <td>ecuador</td> </tr> <tr> <th>4</th> <td>bolivia</td> </tr> <tr> <th>5</th> <td>dominican republic</td> </tr> <tr> <th>6</th> <td>colombia</td> </tr> <tr> <th>7</th> <td>total</td> </tr> </tbody> </table>

select nation where gold < 562 <table_name> : table | | rank | nation | gold | silver | bronze | total | |---:|:-------|:-------------------|-------:|---------:|---------:|--------:| | 0 | 1 | venezuela | 9 | 8 | 6 | 23 | | 1 | 2 | guatemala | 6 | 6 | 6 | 18 | | 2 | 3 | peru | 5 | 8 | 9 | 22 | | 3 | 4 | chile | 4 | 4 | 1 | 9 | | 4 | 5 | el salvador | 4 | 0 | 2 | 6 | | 5 | 6 | ecuador | 2 | 5 | 1 | 8 | | 6 | 7 | bolivia | 2 | 1 | 2 | 5 | | 7 | 8 | dominican republic | 1 | 0 | 2 | 3 | | 8 | 9 | colombia | 0 | 1 | 3 | 4 | | 9 | total | total | 33 | 33 | 32 | 98 |

| | nation | |---:|:-------------------| | 0 | peru | | 1 | chile | | 2 | el salvador | | 3 | ecuador | | 4 | bolivia | | 5 | dominican republic | | 6 | colombia | | 7 | total |

select abs ( college - nfl team ) where pick # = 88 col : pick # | nfl team | player | position | college row 1 : 81 | philadelphia eagles | paul fanning | back | kansas state row 2 : 82 | brooklyn dodgers | ed nowogrowski | back | university of washington row 3 : 83 | chicago cardinals | dwight hafeli | back | washington (st. louis) row 4 : 84 | new york giants | dwight scheyer | end | washington state row 5 : 85 | pittsburgh pirates | matt patanelli | tackle | michigan row 6 : 86 | boston redskins | bill docherty | guard | temple row 7 : 87 | detroit lions | larry kelley | guard | yale row 8 : 88 | chicago bears | al guepe | end | marquette row 9 : 89 | green bay packers | gordon dahlgren | guard | michigan state row 10 : 90 | cleveland rams | ray johnson | tackle | denver

col : "abs ( college - nfl team )" row 1 : abs ( college - nfl team )

select abs ( college - nfl team ) where pick # = 88 <table_name> : table \begin{tabular}{llllll} \toprule & pick # & nfl team & player & position & college \\ \midrule 0 & 81 & philadelphia eagles & paul fanning & back & kansas state \\ 1 & 82 & brooklyn dodgers & ed nowogrowski & back & university of washington \\ 2 & 83 & chicago cardinals & dwight hafeli & back & washington (st. louis) \\ 3 & 84 & new york giants & dwight scheyer & end & washington state \\ 4 & 85 & pittsburgh pirates & matt patanelli & tackle & michigan \\ 5 & 86 & boston redskins & bill docherty & guard & temple \\ 6 & 87 & detroit lions & larry kelley & guard & yale \\ 7 & 88 & chicago bears & al guepe & end & marquette \\ 8 & 89 & green bay packers & gordon dahlgren & guard & michigan state \\ 9 & 90 & cleveland rams & ray johnson & tackle & denver \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & "abs ( college - nfl team )" \\ \midrule 0 & abs ( college - nfl team ) \\ \bottomrule \end{tabular}

select abs ( college - nfl team ) where pick # = 88 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>pick #</th> <th>nfl team</th> <th>player</th> <th>position</th> <th>college</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>81</td> <td>philadelphia eagles</td> <td>paul fanning</td> <td>back</td> <td>kansas state</td> </tr> <tr> <th>1</th> <td>82</td> <td>brooklyn dodgers</td> <td>ed nowogrowski</td> <td>back</td> <td>university of washington</td> </tr> <tr> <th>2</th> <td>83</td> <td>chicago cardinals</td> <td>dwight hafeli</td> <td>back</td> <td>washington (st. louis)</td> </tr> <tr> <th>3</th> <td>84</td> <td>new york giants</td> <td>dwight scheyer</td> <td>end</td> <td>washington state</td> </tr> <tr> <th>4</th> <td>85</td> <td>pittsburgh pirates</td> <td>matt patanelli</td> <td>tackle</td> <td>michigan</td> </tr> <tr> <th>5</th> <td>86</td> <td>boston redskins</td> <td>bill docherty</td> <td>guard</td> <td>temple</td> </tr> <tr> <th>6</th> <td>87</td> <td>detroit lions</td> <td>larry kelley</td> <td>guard</td> <td>yale</td> </tr> <tr> <th>7</th> <td>88</td> <td>chicago bears</td> <td>al guepe</td> <td>end</td> <td>marquette</td> </tr> <tr> <th>8</th> <td>89</td> <td>green bay packers</td> <td>gordon dahlgren</td> <td>guard</td> <td>michigan state</td> </tr> <tr> <th>9</th> <td>90</td> <td>cleveland rams</td> <td>ray johnson</td> <td>tackle</td> <td>denver</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>"abs ( college - nfl team )"</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>abs ( college - nfl team )</td> </tr> </tbody> </table>

select abs ( college - nfl team ) where pick # = 88 <table_name> : table | | pick # | nfl team | player | position | college | |---:|---------:|:--------------------|:----------------|:-----------|:-------------------------| | 0 | 81 | philadelphia eagles | paul fanning | back | kansas state | | 1 | 82 | brooklyn dodgers | ed nowogrowski | back | university of washington | | 2 | 83 | chicago cardinals | dwight hafeli | back | washington (st. louis) | | 3 | 84 | new york giants | dwight scheyer | end | washington state | | 4 | 85 | pittsburgh pirates | matt patanelli | tackle | michigan | | 5 | 86 | boston redskins | bill docherty | guard | temple | | 6 | 87 | detroit lions | larry kelley | guard | yale | | 7 | 88 | chicago bears | al guepe | end | marquette | | 8 | 89 | green bay packers | gordon dahlgren | guard | michigan state | | 9 | 90 | cleveland rams | ray johnson | tackle | denver |

| | "abs ( college - nfl team )" | |---:|:-------------------------------| | 0 | abs ( college - nfl team ) |

select count ( number ) where author = 'none' col : number | author | first line | notes row 1 : 1 | william shakespeare | "when my love swears that she is made of truth" | first publication, later appears as sonnet 138 in shakespeare's sonnets row 2 : 2 | william shakespeare | "two loves i have, of comfort and despair" | first publication, later appears as sonnet 144 in shakespeare's sonnets row 3 : 3 | william shakespeare | "did not the heavenly rhetoric of thine eye" | a version of longaville's sonnet to maria in love's labour's lost 4.3.58-71 row 4 : 4 | none | "sweet cytherea, sitting by a brook" | on the theme of venus and adonis, as is shakespeare's narrative poem row 5 : 5 | william shakespeare | "if love make me forsworn, how shall i swear to love?" | a version of berowne's sonnet to rosalind in love's labour's lost 4.2.105-18 row 6 : 6 | none | "scarce had the sun dried up the dewy morn" | on the theme of venus and adonis, as is shakespeare's narrative poem row 7 : 7 | none | "fair is my love, but not so fair as fickle" | in the same six-line stanza format as venus and adonis row 8 : 8 | richard barnfield | "if music and sweet poetry agree" | first published in poems in diverse humours (1598) row 9 : 9 | none | "fair was the morn when the fair queen of love" | on the theme of venus and adonis, as is shakespeare's narrative poem row 10 : 10 | none | "sweet rose, fair flower, untimely pluck'd, soon vaded" | in the same six-line stanza format as venus and adonis row 11 : 11 | bartholomew griffin | "venus, with young adonis sitting by her" | printed in fidessa (1596). on the theme of venus and adonis, as is shakespeare's narrative poem row 12 : 12 | thomas deloney? | "crabbed age and youth cannot live together" | was reprinted with additional stanzas in thomas deloney's the garden of good will entered into the stationer's register in march 1593. deloney died in 1600; he might be the author of 12, though collections of his verse issued after his death contain poems by other authors. critic hallett smith has identified poem 12 as the one most often favoured by readers as possibly shakespearean - "but there is nothing to support the attribution." row 13 : 13 | none | "beauty is but a vain and doubtful good" | in the same six-line stanza format as venus and adonis row 14 : 14 | none | "good-night, good rest, ah, neither be my share" | in the same six-line stanza format as venus and adonis. originally published as two poems; some scholars, therefore, consider them as 14 and 15, adding 1 to all subsequent poem numbers row 15 : 15 | none | "it was a lording's daughter, the fairest one of three" | none row 16 : 16 | william shakespeare | "on a day (alack the day)" | dumaine's poem to catherine in love's labour's lost 4.3.99-118. reprinted in england's helicon (1600) row 17 : 17 | none | "my flockes feed not, my ewes breed not" | first printed in thomas weelkes' madrigals to 3, 4, 5 and 6 voices (1597) row 18 : 18 | none | "when as thine eye hath chose the dame" | three versions of the poem exist in manuscript miscellanies row 19 : 19 | christopher marlowe & sir walter raleigh | "live with me and be my love" | an inferior text of marlowe's poem "the passionate shepherd to his love" followed by the first stanza of sir walter raleigh's "the nymph's reply to the shepherd" row 20 : 20 | richard barnfield | "as it fell upon a day" | first published in poems in divers humors (1598)

col : count ( "number" ) row 1 : 10

select count ( number ) where author = 'none' <table_name> : table \begin{tabular}{lllll} \toprule & number & author & first line & notes \\ \midrule 0 & 1 & william shakespeare & "when my love swears that she is made of truth" & first publication, later appears as sonnet 138 in shakespeare's sonnets \\ 1 & 2 & william shakespeare & "two loves i have, of comfort and despair" & first publication, later appears as sonnet 144 in shakespeare's sonnets \\ 2 & 3 & william shakespeare & "did not the heavenly rhetoric of thine eye" & a version of longaville's sonnet to maria in love's labour's lost 4.3.58-71 \\ 3 & 4 & none & "sweet cytherea, sitting by a brook" & on the theme of venus and adonis, as is shakespeare's narrative poem \\ 4 & 5 & william shakespeare & "if love make me forsworn, how shall i swear to love?" & a version of berowne's sonnet to rosalind in love's labour's lost 4.2.105-18 \\ 5 & 6 & none & "scarce had the sun dried up the dewy morn" & on the theme of venus and adonis, as is shakespeare's narrative poem \\ 6 & 7 & none & "fair is my love, but not so fair as fickle" & in the same six-line stanza format as venus and adonis \\ 7 & 8 & richard barnfield & "if music and sweet poetry agree" & first published in poems in diverse humours (1598) \\ 8 & 9 & none & "fair was the morn when the fair queen of love" & on the theme of venus and adonis, as is shakespeare's narrative poem \\ 9 & 10 & none & "sweet rose, fair flower, untimely pluck'd, soon vaded" & in the same six-line stanza format as venus and adonis \\ 10 & 11 & bartholomew griffin & "venus, with young adonis sitting by her" & printed in fidessa (1596). on the theme of venus and adonis, as is shakespeare's narrative poem \\ 11 & 12 & thomas deloney? & "crabbed age and youth cannot live together" & was reprinted with additional stanzas in thomas deloney's the garden of good will entered into the stationer's register in march 1593. deloney died in 1600; he might be the author of 12, though collections of his verse issued after his death contain poems by other authors. critic hallett smith has identified poem 12 as the one most often favoured by readers as possibly shakespearean - "but there is nothing to support the attribution." \\ 12 & 13 & none & "beauty is but a vain and doubtful good" & in the same six-line stanza format as venus and adonis \\ 13 & 14 & none & "good-night, good rest, ah, neither be my share" & in the same six-line stanza format as venus and adonis. originally published as two poems; some scholars, therefore, consider them as 14 and 15, adding 1 to all subsequent poem numbers \\ 14 & 15 & none & "it was a lording's daughter, the fairest one of three" & none \\ 15 & 16 & william shakespeare & "on a day (alack the day)" & dumaine's poem to catherine in love's labour's lost 4.3.99-118. reprinted in england's helicon (1600) \\ 16 & 17 & none & "my flockes feed not, my ewes breed not" & first printed in thomas weelkes' madrigals to 3, 4, 5 and 6 voices (1597) \\ 17 & 18 & none & "when as thine eye hath chose the dame" & three versions of the poem exist in manuscript miscellanies \\ 18 & 19 & christopher marlowe & sir walter raleigh & "live with me and be my love" & an inferior text of marlowe's poem "the passionate shepherd to his love" followed by the first stanza of sir walter raleigh's "the nymph's reply to the shepherd" \\ 19 & 20 & richard barnfield & "as it fell upon a day" & first published in poems in divers humors (1598) \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "number" ) \\ \midrule 0 & 10 \\ \bottomrule \end{tabular}

select count ( number ) where author = 'none' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>number</th> <th>author</th> <th>first line</th> <th>notes</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>william shakespeare</td> <td>"when my love swears that she is made of truth"</td> <td>first publication, later appears as sonnet 138 in shakespeare's sonnets</td> </tr> <tr> <th>1</th> <td>2</td> <td>william shakespeare</td> <td>"two loves i have, of comfort and despair"</td> <td>first publication, later appears as sonnet 144 in shakespeare's sonnets</td> </tr> <tr> <th>2</th> <td>3</td> <td>william shakespeare</td> <td>"did not the heavenly rhetoric of thine eye"</td> <td>a version of longaville's sonnet to maria in love's labour's lost 4.3.58-71</td> </tr> <tr> <th>3</th> <td>4</td> <td>none</td> <td>"sweet cytherea, sitting by a brook"</td> <td>on the theme of venus and adonis, as is shakespeare's narrative poem</td> </tr> <tr> <th>4</th> <td>5</td> <td>william shakespeare</td> <td>"if love make me forsworn, how shall i swear to love?"</td> <td>a version of berowne's sonnet to rosalind in love's labour's lost 4.2.105-18</td> </tr> <tr> <th>5</th> <td>6</td> <td>none</td> <td>"scarce had the sun dried up the dewy morn"</td> <td>on the theme of venus and adonis, as is shakespeare's narrative poem</td> </tr> <tr> <th>6</th> <td>7</td> <td>none</td> <td>"fair is my love, but not so fair as fickle"</td> <td>in the same six-line stanza format as venus and adonis</td> </tr> <tr> <th>7</th> <td>8</td> <td>richard barnfield</td> <td>"if music and sweet poetry agree"</td> <td>first published in poems in diverse humours (1598)</td> </tr> <tr> <th>8</th> <td>9</td> <td>none</td> <td>"fair was the morn when the fair queen of love"</td> <td>on the theme of venus and adonis, as is shakespeare's narrative poem</td> </tr> <tr> <th>9</th> <td>10</td> <td>none</td> <td>"sweet rose, fair flower, untimely pluck'd, soon vaded"</td> <td>in the same six-line stanza format as venus and adonis</td> </tr> <tr> <th>10</th> <td>11</td> <td>bartholomew griffin</td> <td>"venus, with young adonis sitting by her"</td> <td>printed in fidessa (1596). on the theme of venus and adonis, as is shakespeare's narrative poem</td> </tr> <tr> <th>11</th> <td>12</td> <td>thomas deloney?</td> <td>"crabbed age and youth cannot live together"</td> <td>was reprinted with additional stanzas in thomas deloney's the garden of good will entered into the stationer's register in march 1593. deloney died in 1600; he might be the author of 12, though collections of his verse issued after his death contain poems by other authors. critic hallett smith has identified poem 12 as the one most often favoured by readers as possibly shakespearean - "but there is nothing to support the attribution."</td> </tr> <tr> <th>12</th> <td>13</td> <td>none</td> <td>"beauty is but a vain and doubtful good"</td> <td>in the same six-line stanza format as venus and adonis</td> </tr> <tr> <th>13</th> <td>14</td> <td>none</td> <td>"good-night, good rest, ah, neither be my share"</td> <td>in the same six-line stanza format as venus and adonis. originally published as two poems; some scholars, therefore, consider them as 14 and 15, adding 1 to all subsequent poem numbers</td> </tr> <tr> <th>14</th> <td>15</td> <td>none</td> <td>"it was a lording's daughter, the fairest one of three"</td> <td>none</td> </tr> <tr> <th>15</th> <td>16</td> <td>william shakespeare</td> <td>"on a day (alack the day)"</td> <td>dumaine's poem to catherine in love's labour's lost 4.3.99-118. reprinted in england's helicon (1600)</td> </tr> <tr> <th>16</th> <td>17</td> <td>none</td> <td>"my flockes feed not, my ewes breed not"</td> <td>first printed in thomas weelkes' madrigals to 3, 4, 5 and 6 voices (1597)</td> </tr> <tr> <th>17</th> <td>18</td> <td>none</td> <td>"when as thine eye hath chose the dame"</td> <td>three versions of the poem exist in manuscript miscellanies</td> </tr> <tr> <th>18</th> <td>19</td> <td>christopher marlowe &amp; sir walter raleigh</td> <td>"live with me and be my love"</td> <td>an inferior text of marlowe's poem "the passionate shepherd to his love" followed by the first stanza of sir walter raleigh's "the nymph's reply to the shepherd"</td> </tr> <tr> <th>19</th> <td>20</td> <td>richard barnfield</td> <td>"as it fell upon a day"</td> <td>first published in poems in divers humors (1598)</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "number" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>10</td> </tr> </tbody> </table>

select count ( number ) where author = 'none' <table_name> : table | | number | author | first line | notes | |---:|---------:|:-----------------------------------------|:--------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 1 | william shakespeare | "when my love swears that she is made of truth" | first publication, later appears as sonnet 138 in shakespeare's sonnets | | 1 | 2 | william shakespeare | "two loves i have, of comfort and despair" | first publication, later appears as sonnet 144 in shakespeare's sonnets | | 2 | 3 | william shakespeare | "did not the heavenly rhetoric of thine eye" | a version of longaville's sonnet to maria in love's labour's lost 4.3.58-71 | | 3 | 4 | none | "sweet cytherea, sitting by a brook" | on the theme of venus and adonis, as is shakespeare's narrative poem | | 4 | 5 | william shakespeare | "if love make me forsworn, how shall i swear to love?" | a version of berowne's sonnet to rosalind in love's labour's lost 4.2.105-18 | | 5 | 6 | none | "scarce had the sun dried up the dewy morn" | on the theme of venus and adonis, as is shakespeare's narrative poem | | 6 | 7 | none | "fair is my love, but not so fair as fickle" | in the same six-line stanza format as venus and adonis | | 7 | 8 | richard barnfield | "if music and sweet poetry agree" | first published in poems in diverse humours (1598) | | 8 | 9 | none | "fair was the morn when the fair queen of love" | on the theme of venus and adonis, as is shakespeare's narrative poem | | 9 | 10 | none | "sweet rose, fair flower, untimely pluck'd, soon vaded" | in the same six-line stanza format as venus and adonis | | 10 | 11 | bartholomew griffin | "venus, with young adonis sitting by her" | printed in fidessa (1596). on the theme of venus and adonis, as is shakespeare's narrative poem | | 11 | 12 | thomas deloney? | "crabbed age and youth cannot live together" | was reprinted with additional stanzas in thomas deloney's the garden of good will entered into the stationer's register in march 1593. deloney died in 1600; he might be the author of 12, though collections of his verse issued after his death contain poems by other authors. critic hallett smith has identified poem 12 as the one most often favoured by readers as possibly shakespearean - "but there is nothing to support the attribution." | | 12 | 13 | none | "beauty is but a vain and doubtful good" | in the same six-line stanza format as venus and adonis | | 13 | 14 | none | "good-night, good rest, ah, neither be my share" | in the same six-line stanza format as venus and adonis. originally published as two poems; some scholars, therefore, consider them as 14 and 15, adding 1 to all subsequent poem numbers | | 14 | 15 | none | "it was a lording's daughter, the fairest one of three" | none | | 15 | 16 | william shakespeare | "on a day (alack the day)" | dumaine's poem to catherine in love's labour's lost 4.3.99-118. reprinted in england's helicon (1600) | | 16 | 17 | none | "my flockes feed not, my ewes breed not" | first printed in thomas weelkes' madrigals to 3, 4, 5 and 6 voices (1597) | | 17 | 18 | none | "when as thine eye hath chose the dame" | three versions of the poem exist in manuscript miscellanies | | 18 | 19 | christopher marlowe & sir walter raleigh | "live with me and be my love" | an inferior text of marlowe's poem "the passionate shepherd to his love" followed by the first stanza of sir walter raleigh's "the nymph's reply to the shepherd" | | 19 | 20 | richard barnfield | "as it fell upon a day" | first published in poems in divers humors (1598) |

| | count ( "number" ) | |---:|---------------------:| | 0 | 10 |

select count ( * ) where combined = 'not awarded' col : season | age | overall | slalom | giant slalom | super g | downhill | combined row 1 : 1967 | 16 | 27 | 19 | none | not run | none | not awarded row 2 : 1968 | 17 | 12 | 11 | 8 | not run | none | not awarded row 3 : 1969 | 18 | 7 | 4 | 11 | not run | 5 | not awarded row 4 : 1970 | 19 | 11 | 8 | 10 | not run | 12 | not awarded row 5 : 1971 | 20 | 14 | 13 | 9 | not run | 15 | not awarded row 6 : 1972 | 21 | 6 | 4 | 7 | not run | 10 | not awarded row 7 : 1973 | 22 | 4 | 2 | 8 | not run | 9 | not awarded row 8 : 1974 | 23 | 7 | 2 | 13 | not run | 11 | not awarded row 9 : 1975 | 24 | 3 | 7 | 7 | not run | 6 | not awarded row 10 : 1976 | 25 | 1 | 1 | 3 | not run | 9 | 1

col : count ( "*" ) row 1 : 9

select count ( * ) where combined = 'not awarded' <table_name> : table \begin{tabular}{lllllllll} \toprule & season & age & overall & slalom & giant slalom & super g & downhill & combined \\ \midrule 0 & 1967 & 16 & 27 & 19 & none & not run & none & not awarded \\ 1 & 1968 & 17 & 12 & 11 & 8 & not run & none & not awarded \\ 2 & 1969 & 18 & 7 & 4 & 11 & not run & 5 & not awarded \\ 3 & 1970 & 19 & 11 & 8 & 10 & not run & 12 & not awarded \\ 4 & 1971 & 20 & 14 & 13 & 9 & not run & 15 & not awarded \\ 5 & 1972 & 21 & 6 & 4 & 7 & not run & 10 & not awarded \\ 6 & 1973 & 22 & 4 & 2 & 8 & not run & 9 & not awarded \\ 7 & 1974 & 23 & 7 & 2 & 13 & not run & 11 & not awarded \\ 8 & 1975 & 24 & 3 & 7 & 7 & not run & 6 & not awarded \\ 9 & 1976 & 25 & 1 & 1 & 3 & not run & 9 & 1 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 9 \\ \bottomrule \end{tabular}

select count ( * ) where combined = 'not awarded' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>season</th> <th>age</th> <th>overall</th> <th>slalom</th> <th>giant slalom</th> <th>super g</th> <th>downhill</th> <th>combined</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1967</td> <td>16</td> <td>27</td> <td>19</td> <td>none</td> <td>not run</td> <td>none</td> <td>not awarded</td> </tr> <tr> <th>1</th> <td>1968</td> <td>17</td> <td>12</td> <td>11</td> <td>8</td> <td>not run</td> <td>none</td> <td>not awarded</td> </tr> <tr> <th>2</th> <td>1969</td> <td>18</td> <td>7</td> <td>4</td> <td>11</td> <td>not run</td> <td>5</td> <td>not awarded</td> </tr> <tr> <th>3</th> <td>1970</td> <td>19</td> <td>11</td> <td>8</td> <td>10</td> <td>not run</td> <td>12</td> <td>not awarded</td> </tr> <tr> <th>4</th> <td>1971</td> <td>20</td> <td>14</td> <td>13</td> <td>9</td> <td>not run</td> <td>15</td> <td>not awarded</td> </tr> <tr> <th>5</th> <td>1972</td> <td>21</td> <td>6</td> <td>4</td> <td>7</td> <td>not run</td> <td>10</td> <td>not awarded</td> </tr> <tr> <th>6</th> <td>1973</td> <td>22</td> <td>4</td> <td>2</td> <td>8</td> <td>not run</td> <td>9</td> <td>not awarded</td> </tr> <tr> <th>7</th> <td>1974</td> <td>23</td> <td>7</td> <td>2</td> <td>13</td> <td>not run</td> <td>11</td> <td>not awarded</td> </tr> <tr> <th>8</th> <td>1975</td> <td>24</td> <td>3</td> <td>7</td> <td>7</td> <td>not run</td> <td>6</td> <td>not awarded</td> </tr> <tr> <th>9</th> <td>1976</td> <td>25</td> <td>1</td> <td>1</td> <td>3</td> <td>not run</td> <td>9</td> <td>1</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "*" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>9</td> </tr> </tbody> </table>

select count ( * ) where combined = 'not awarded' <table_name> : table | | season | age | overall | slalom | giant slalom | super g | downhill | combined | |---:|---------:|------:|----------:|---------:|:---------------|:----------|:-----------|:------------| | 0 | 1967 | 16 | 27 | 19 | none | not run | none | not awarded | | 1 | 1968 | 17 | 12 | 11 | 8 | not run | none | not awarded | | 2 | 1969 | 18 | 7 | 4 | 11 | not run | 5 | not awarded | | 3 | 1970 | 19 | 11 | 8 | 10 | not run | 12 | not awarded | | 4 | 1971 | 20 | 14 | 13 | 9 | not run | 15 | not awarded | | 5 | 1972 | 21 | 6 | 4 | 7 | not run | 10 | not awarded | | 6 | 1973 | 22 | 4 | 2 | 8 | not run | 9 | not awarded | | 7 | 1974 | 23 | 7 | 2 | 13 | not run | 11 | not awarded | | 8 | 1975 | 24 | 3 | 7 | 7 | not run | 6 | not awarded | | 9 | 1976 | 25 | 1 | 1 | 3 | not run | 9 | 1 |

| | count ( "*" ) | |---:|----------------:| | 0 | 9 |

select power/torque where model = '735il' col : model | volume | engine | fuel | power/torque | years produced | produced row 1 : 728i | 2.8l | m52b28 i6 | petrol | 142 kw (193 ps; 190 hp) / 280 n·m (207 lb·ft) | 1996-2001 | 38947 row 2 : 728il | 2.8l | m52b28 i6 | petrol | 142 kw (193 ps; 190 hp) / 280 n·m (207 lb·ft) | 1996-2001 | 6816 row 3 : 730i | 3.0l | m60b30 v8 | petrol | 160 kw (218 ps; 215 hp) / 290 n·m (214 lb·ft) | 1994-1996 | 20876 row 4 : 730il | 3.0l | m60b30 v8 | petrol | 160 kw (218 ps; 215 hp) / 290 n·m (214 lb·ft) | 1994-1996 | 2137 row 5 : 735i | 3.5l | m62b35 v8 | petrol | 173 kw (235 ps; 232 hp) / 320 n·m (236 lb·ft) | 1994-1997 | 21481 row 6 : 735i | 3.5l | m62tub35 v8 | petrol | 174 kw (237 ps; 233 hp) / 345 n·m (254 lb·ft) | 1998-2001 | none row 7 : 735il | 3.5l | m62b35 v8 | petrol | 173 kw (235 ps; 232 hp) / 320 n·m (236 lb·ft) | 1994-1997 | 6963 row 8 : 735il | 3.5l | m62tub35 v8 | petrol | 174 kw (237 ps; 233 hp) / 345 n·m (254 lb·ft) | 1998-2001 | none row 9 : 740i | 4.0l | m60b40 v8 | petrol | 210 kw (286 ps; 282 hp) / 400 n·m (295 lb·ft) | 1994-1996 | none row 10 : 740il | 4.0l | m60b40 v8 | petrol | 210 kw (286 ps; 282 hp) / 400 n·m (295 lb·ft) | 1994-1996 | none row 11 : 740i | 4.4l | m62b44 v8 | petrol | 210 kw (286 ps; 282 hp) / 420 n·m (310 lb·ft) | 1996-1998 | 88853 row 12 : 740i | 4.4l | m62tub44 v8 | petrol | 210 kw (286 ps; 282 hp) / 440 n·m (325 lb·ft) | 1998-2001 | none row 13 : 740il | 4.4l | m62b44 v8 | petrol | 210 kw (286 ps; 282 hp) / 420 n·m (310 lb·ft) | 1996-1998 | 91431 row 14 : 740il | 4.4l | m62tub44 v8 | petrol | 210 kw (286 ps; 282 hp) / 440 n·m (325 lb·ft) | 1998-2001 | none row 15 : 750i-il | 5.4l | m73b54 v12 | petrol | 240 kw (326 ps; 322 hp) / 490 n·m (361 lb·ft) | 1995-1997 | 15759 row 16 : 750i-il | 5.4l | m73tub54 v12 | petrol | 240 kw (326 ps; 322 hp) / 490 n·m (361 lb·ft) | 1998-2001 | 1032 row 17 : 725tds | 2.5l | m51d25 turbocharged i6 | diesel | 105 kw (143 ps; 141 hp) / 280 n·m (207 lb·ft) | 1995-2001 | 9053 row 18 : 730d | 2.9l | m57d30 turbocharged i6 | diesel | 142 kw (193 ps; 190 hp) / 410 n·m (302 lb·ft) | 1998-2001 | 12336 row 19 : 740d | 3.9l | m67d40 turbocharged v8 | diesel | 180 kw (245 ps; 241 hp) / 560 n·m (413 lb·ft) | 1998-2001 | 3450

col : power/torque row 1 : 173 kw (235 ps; 232 hp) / 320 n·m (236 lb·ft) row 2 : 174 kw (237 ps; 233 hp) / 345 n·m (254 lb·ft)

select power/torque where model = '735il' <table_name> : table \begin{tabular}{llllllll} \toprule & model & volume & engine & fuel & power/torque & years produced & produced \\ \midrule 0 & 728i & 2.8l & m52b28 i6 & petrol & 142 kw (193 ps; 190 hp) / 280 n·m (207 lb·ft) & 1996-2001 & 38947 \\ 1 & 728il & 2.8l & m52b28 i6 & petrol & 142 kw (193 ps; 190 hp) / 280 n·m (207 lb·ft) & 1996-2001 & 6816 \\ 2 & 730i & 3.0l & m60b30 v8 & petrol & 160 kw (218 ps; 215 hp) / 290 n·m (214 lb·ft) & 1994-1996 & 20876 \\ 3 & 730il & 3.0l & m60b30 v8 & petrol & 160 kw (218 ps; 215 hp) / 290 n·m (214 lb·ft) & 1994-1996 & 2137 \\ 4 & 735i & 3.5l & m62b35 v8 & petrol & 173 kw (235 ps; 232 hp) / 320 n·m (236 lb·ft) & 1994-1997 & 21481 \\ 5 & 735i & 3.5l & m62tub35 v8 & petrol & 174 kw (237 ps; 233 hp) / 345 n·m (254 lb·ft) & 1998-2001 & none \\ 6 & 735il & 3.5l & m62b35 v8 & petrol & 173 kw (235 ps; 232 hp) / 320 n·m (236 lb·ft) & 1994-1997 & 6963 \\ 7 & 735il & 3.5l & m62tub35 v8 & petrol & 174 kw (237 ps; 233 hp) / 345 n·m (254 lb·ft) & 1998-2001 & none \\ 8 & 740i & 4.0l & m60b40 v8 & petrol & 210 kw (286 ps; 282 hp) / 400 n·m (295 lb·ft) & 1994-1996 & none \\ 9 & 740il & 4.0l & m60b40 v8 & petrol & 210 kw (286 ps; 282 hp) / 400 n·m (295 lb·ft) & 1994-1996 & none \\ 10 & 740i & 4.4l & m62b44 v8 & petrol & 210 kw (286 ps; 282 hp) / 420 n·m (310 lb·ft) & 1996-1998 & 88853 \\ 11 & 740i & 4.4l & m62tub44 v8 & petrol & 210 kw (286 ps; 282 hp) / 440 n·m (325 lb·ft) & 1998-2001 & none \\ 12 & 740il & 4.4l & m62b44 v8 & petrol & 210 kw (286 ps; 282 hp) / 420 n·m (310 lb·ft) & 1996-1998 & 91431 \\ 13 & 740il & 4.4l & m62tub44 v8 & petrol & 210 kw (286 ps; 282 hp) / 440 n·m (325 lb·ft) & 1998-2001 & none \\ 14 & 750i-il & 5.4l & m73b54 v12 & petrol & 240 kw (326 ps; 322 hp) / 490 n·m (361 lb·ft) & 1995-1997 & 15759 \\ 15 & 750i-il & 5.4l & m73tub54 v12 & petrol & 240 kw (326 ps; 322 hp) / 490 n·m (361 lb·ft) & 1998-2001 & 1032 \\ 16 & 725tds & 2.5l & m51d25 turbocharged i6 & diesel & 105 kw (143 ps; 141 hp) / 280 n·m (207 lb·ft) & 1995-2001 & 9053 \\ 17 & 730d & 2.9l & m57d30 turbocharged i6 & diesel & 142 kw (193 ps; 190 hp) / 410 n·m (302 lb·ft) & 1998-2001 & 12336 \\ 18 & 740d & 3.9l & m67d40 turbocharged v8 & diesel & 180 kw (245 ps; 241 hp) / 560 n·m (413 lb·ft) & 1998-2001 & 3450 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & power/torque \\ \midrule 0 & 173 kw (235 ps; 232 hp) / 320 n·m (236 lb·ft) \\ 1 & 174 kw (237 ps; 233 hp) / 345 n·m (254 lb·ft) \\ \bottomrule \end{tabular}

select power/torque where model = '735il' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>model</th> <th>volume</th> <th>engine</th> <th>fuel</th> <th>power/torque</th> <th>years produced</th> <th>produced</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>728i</td> <td>2.8l</td> <td>m52b28 i6</td> <td>petrol</td> <td>142 kw (193 ps; 190 hp) / 280 n·m (207 lb·ft)</td> <td>1996-2001</td> <td>38947</td> </tr> <tr> <th>1</th> <td>728il</td> <td>2.8l</td> <td>m52b28 i6</td> <td>petrol</td> <td>142 kw (193 ps; 190 hp) / 280 n·m (207 lb·ft)</td> <td>1996-2001</td> <td>6816</td> </tr> <tr> <th>2</th> <td>730i</td> <td>3.0l</td> <td>m60b30 v8</td> <td>petrol</td> <td>160 kw (218 ps; 215 hp) / 290 n·m (214 lb·ft)</td> <td>1994-1996</td> <td>20876</td> </tr> <tr> <th>3</th> <td>730il</td> <td>3.0l</td> <td>m60b30 v8</td> <td>petrol</td> <td>160 kw (218 ps; 215 hp) / 290 n·m (214 lb·ft)</td> <td>1994-1996</td> <td>2137</td> </tr> <tr> <th>4</th> <td>735i</td> <td>3.5l</td> <td>m62b35 v8</td> <td>petrol</td> <td>173 kw (235 ps; 232 hp) / 320 n·m (236 lb·ft)</td> <td>1994-1997</td> <td>21481</td> </tr> <tr> <th>5</th> <td>735i</td> <td>3.5l</td> <td>m62tub35 v8</td> <td>petrol</td> <td>174 kw (237 ps; 233 hp) / 345 n·m (254 lb·ft)</td> <td>1998-2001</td> <td>none</td> </tr> <tr> <th>6</th> <td>735il</td> <td>3.5l</td> <td>m62b35 v8</td> <td>petrol</td> <td>173 kw (235 ps; 232 hp) / 320 n·m (236 lb·ft)</td> <td>1994-1997</td> <td>6963</td> </tr> <tr> <th>7</th> <td>735il</td> <td>3.5l</td> <td>m62tub35 v8</td> <td>petrol</td> <td>174 kw (237 ps; 233 hp) / 345 n·m (254 lb·ft)</td> <td>1998-2001</td> <td>none</td> </tr> <tr> <th>8</th> <td>740i</td> <td>4.0l</td> <td>m60b40 v8</td> <td>petrol</td> <td>210 kw (286 ps; 282 hp) / 400 n·m (295 lb·ft)</td> <td>1994-1996</td> <td>none</td> </tr> <tr> <th>9</th> <td>740il</td> <td>4.0l</td> <td>m60b40 v8</td> <td>petrol</td> <td>210 kw (286 ps; 282 hp) / 400 n·m (295 lb·ft)</td> <td>1994-1996</td> <td>none</td> </tr> <tr> <th>10</th> <td>740i</td> <td>4.4l</td> <td>m62b44 v8</td> <td>petrol</td> <td>210 kw (286 ps; 282 hp) / 420 n·m (310 lb·ft)</td> <td>1996-1998</td> <td>88853</td> </tr> <tr> <th>11</th> <td>740i</td> <td>4.4l</td> <td>m62tub44 v8</td> <td>petrol</td> <td>210 kw (286 ps; 282 hp) / 440 n·m (325 lb·ft)</td> <td>1998-2001</td> <td>none</td> </tr> <tr> <th>12</th> <td>740il</td> <td>4.4l</td> <td>m62b44 v8</td> <td>petrol</td> <td>210 kw (286 ps; 282 hp) / 420 n·m (310 lb·ft)</td> <td>1996-1998</td> <td>91431</td> </tr> <tr> <th>13</th> <td>740il</td> <td>4.4l</td> <td>m62tub44 v8</td> <td>petrol</td> <td>210 kw (286 ps; 282 hp) / 440 n·m (325 lb·ft)</td> <td>1998-2001</td> <td>none</td> </tr> <tr> <th>14</th> <td>750i-il</td> <td>5.4l</td> <td>m73b54 v12</td> <td>petrol</td> <td>240 kw (326 ps; 322 hp) / 490 n·m (361 lb·ft)</td> <td>1995-1997</td> <td>15759</td> </tr> <tr> <th>15</th> <td>750i-il</td> <td>5.4l</td> <td>m73tub54 v12</td> <td>petrol</td> <td>240 kw (326 ps; 322 hp) / 490 n·m (361 lb·ft)</td> <td>1998-2001</td> <td>1032</td> </tr> <tr> <th>16</th> <td>725tds</td> <td>2.5l</td> <td>m51d25 turbocharged i6</td> <td>diesel</td> <td>105 kw (143 ps; 141 hp) / 280 n·m (207 lb·ft)</td> <td>1995-2001</td> <td>9053</td> </tr> <tr> <th>17</th> <td>730d</td> <td>2.9l</td> <td>m57d30 turbocharged i6</td> <td>diesel</td> <td>142 kw (193 ps; 190 hp) / 410 n·m (302 lb·ft)</td> <td>1998-2001</td> <td>12336</td> </tr> <tr> <th>18</th> <td>740d</td> <td>3.9l</td> <td>m67d40 turbocharged v8</td> <td>diesel</td> <td>180 kw (245 ps; 241 hp) / 560 n·m (413 lb·ft)</td> <td>1998-2001</td> <td>3450</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>power/torque</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>173 kw (235 ps; 232 hp) / 320 n·m (236 lb·ft)</td> </tr> <tr> <th>1</th> <td>174 kw (237 ps; 233 hp) / 345 n·m (254 lb·ft)</td> </tr> </tbody> </table>

select power/torque where model = '735il' <table_name> : table | | model | volume | engine | fuel | power/torque | years produced | produced | |---:|:--------|:---------|:-----------------------|:-------|:----------------------------------------------|:-----------------|:-----------| | 0 | 728i | 2.8l | m52b28 i6 | petrol | 142 kw (193 ps; 190 hp) / 280 n·m (207 lb·ft) | 1996-2001 | 38947 | | 1 | 728il | 2.8l | m52b28 i6 | petrol | 142 kw (193 ps; 190 hp) / 280 n·m (207 lb·ft) | 1996-2001 | 6816 | | 2 | 730i | 3.0l | m60b30 v8 | petrol | 160 kw (218 ps; 215 hp) / 290 n·m (214 lb·ft) | 1994-1996 | 20876 | | 3 | 730il | 3.0l | m60b30 v8 | petrol | 160 kw (218 ps; 215 hp) / 290 n·m (214 lb·ft) | 1994-1996 | 2137 | | 4 | 735i | 3.5l | m62b35 v8 | petrol | 173 kw (235 ps; 232 hp) / 320 n·m (236 lb·ft) | 1994-1997 | 21481 | | 5 | 735i | 3.5l | m62tub35 v8 | petrol | 174 kw (237 ps; 233 hp) / 345 n·m (254 lb·ft) | 1998-2001 | none | | 6 | 735il | 3.5l | m62b35 v8 | petrol | 173 kw (235 ps; 232 hp) / 320 n·m (236 lb·ft) | 1994-1997 | 6963 | | 7 | 735il | 3.5l | m62tub35 v8 | petrol | 174 kw (237 ps; 233 hp) / 345 n·m (254 lb·ft) | 1998-2001 | none | | 8 | 740i | 4.0l | m60b40 v8 | petrol | 210 kw (286 ps; 282 hp) / 400 n·m (295 lb·ft) | 1994-1996 | none | | 9 | 740il | 4.0l | m60b40 v8 | petrol | 210 kw (286 ps; 282 hp) / 400 n·m (295 lb·ft) | 1994-1996 | none | | 10 | 740i | 4.4l | m62b44 v8 | petrol | 210 kw (286 ps; 282 hp) / 420 n·m (310 lb·ft) | 1996-1998 | 88853 | | 11 | 740i | 4.4l | m62tub44 v8 | petrol | 210 kw (286 ps; 282 hp) / 440 n·m (325 lb·ft) | 1998-2001 | none | | 12 | 740il | 4.4l | m62b44 v8 | petrol | 210 kw (286 ps; 282 hp) / 420 n·m (310 lb·ft) | 1996-1998 | 91431 | | 13 | 740il | 4.4l | m62tub44 v8 | petrol | 210 kw (286 ps; 282 hp) / 440 n·m (325 lb·ft) | 1998-2001 | none | | 14 | 750i-il | 5.4l | m73b54 v12 | petrol | 240 kw (326 ps; 322 hp) / 490 n·m (361 lb·ft) | 1995-1997 | 15759 | | 15 | 750i-il | 5.4l | m73tub54 v12 | petrol | 240 kw (326 ps; 322 hp) / 490 n·m (361 lb·ft) | 1998-2001 | 1032 | | 16 | 725tds | 2.5l | m51d25 turbocharged i6 | diesel | 105 kw (143 ps; 141 hp) / 280 n·m (207 lb·ft) | 1995-2001 | 9053 | | 17 | 730d | 2.9l | m57d30 turbocharged i6 | diesel | 142 kw (193 ps; 190 hp) / 410 n·m (302 lb·ft) | 1998-2001 | 12336 | | 18 | 740d | 3.9l | m67d40 turbocharged v8 | diesel | 180 kw (245 ps; 241 hp) / 560 n·m (413 lb·ft) | 1998-2001 | 3450 |

| | power/torque | |---:|:----------------------------------------------| | 0 | 173 kw (235 ps; 232 hp) / 320 n·m (236 lb·ft) | | 1 | 174 kw (237 ps; 233 hp) / 345 n·m (254 lb·ft) |

select foundation where standard = 'unlimited' col : specifications | foundation | essentials | standard | datacenter row 1 : distribution | oem only | retail, volume licensing, oem | retail, volume licensing, oem | volume licensing and oem row 2 : licensing model | per server | per server | per cpu pair + cal | per cpu pair + cal row 3 : processor chip limit | 1 | 2 | 64 | 64 row 4 : memory limit | 32 gb | 64 gb | 4 tb | 4 tb row 5 : user limit | 15 | 25 | unlimited | unlimited row 6 : file services limits | 1 standalone dfs root | 1 standalone dfs root | unlimited | unlimited row 7 : network policy and access services limits | 50 rras connections and 10 ias connections | 250 rras connections, 50 ias connections, and 2 ias server groups | unlimited | unlimited row 8 : remote desktop services limits | 50 remote desktop services connections | gateway only | unlimited | unlimited row 9 : virtualization rights | none | either in 1 vm or 1 physical server, but not both at once | 2 vms | unlimited row 10 : dhcp role | yes | yes | yes | yes row 11 : dns server role | yes | yes | yes | yes row 12 : fax server role | yes | yes | yes | yes row 13 : uddi services | yes | yes | yes | yes row 14 : print and document services | yes | yes | yes | yes row 15 : web services (internet information services) | yes | yes | yes | yes row 16 : windows deployment services | yes | yes | yes | yes row 17 : windows server update services | no | yes | yes | yes row 18 : active directory lightweight directory services | yes | yes | yes | yes row 19 : active directory rights management services | yes | yes | yes | yes row 20 : application server role | yes | yes | yes | yes row 21 : server manager | yes | yes | yes | yes row 22 : windows powershell | yes | yes | yes | yes row 23 : active directory domain services | must be root of forest and domain | must be root of forest and domain | yes | yes row 24 : active directory certificate services | certificate authorities only | certificate authorities only | yes | yes row 25 : active directory federation services | yes | no | yes | yes row 26 : server core mode | no | no | yes | yes row 27 : hyper-v | no | no | yes | yes

col : foundation row 1 : 15 row 2 : 1 standalone dfs root row 3 : 50 rras connections and 10 ias connections row 4 : 50 remote desktop services connections

select foundation where standard = 'unlimited' <table_name> : table \begin{tabular}{llllll} \toprule & specifications & foundation & essentials & standard & datacenter \\ \midrule 0 & distribution & oem only & retail, volume licensing, oem & retail, volume licensing, oem & volume licensing and oem \\ 1 & licensing model & per server & per server & per cpu pair + cal & per cpu pair + cal \\ 2 & processor chip limit & 1 & 2 & 64 & 64 \\ 3 & memory limit & 32 gb & 64 gb & 4 tb & 4 tb \\ 4 & user limit & 15 & 25 & unlimited & unlimited \\ 5 & file services limits & 1 standalone dfs root & 1 standalone dfs root & unlimited & unlimited \\ 6 & network policy and access services limits & 50 rras connections and 10 ias connections & 250 rras connections, 50 ias connections, and 2 ias server groups & unlimited & unlimited \\ 7 & remote desktop services limits & 50 remote desktop services connections & gateway only & unlimited & unlimited \\ 8 & virtualization rights & none & either in 1 vm or 1 physical server, but not both at once & 2 vms & unlimited \\ 9 & dhcp role & yes & yes & yes & yes \\ 10 & dns server role & yes & yes & yes & yes \\ 11 & fax server role & yes & yes & yes & yes \\ 12 & uddi services & yes & yes & yes & yes \\ 13 & print and document services & yes & yes & yes & yes \\ 14 & web services (internet information services) & yes & yes & yes & yes \\ 15 & windows deployment services & yes & yes & yes & yes \\ 16 & windows server update services & no & yes & yes & yes \\ 17 & active directory lightweight directory services & yes & yes & yes & yes \\ 18 & active directory rights management services & yes & yes & yes & yes \\ 19 & application server role & yes & yes & yes & yes \\ 20 & server manager & yes & yes & yes & yes \\ 21 & windows powershell & yes & yes & yes & yes \\ 22 & active directory domain services & must be root of forest and domain & must be root of forest and domain & yes & yes \\ 23 & active directory certificate services & certificate authorities only & certificate authorities only & yes & yes \\ 24 & active directory federation services & yes & no & yes & yes \\ 25 & server core mode & no & no & yes & yes \\ 26 & hyper-v & no & no & yes & yes \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & foundation \\ \midrule 0 & 15 \\ 1 & 1 standalone dfs root \\ 2 & 50 rras connections and 10 ias connections \\ 3 & 50 remote desktop services connections \\ \bottomrule \end{tabular}

select foundation where standard = 'unlimited' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>specifications</th> <th>foundation</th> <th>essentials</th> <th>standard</th> <th>datacenter</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>distribution</td> <td>oem only</td> <td>retail, volume licensing, oem</td> <td>retail, volume licensing, oem</td> <td>volume licensing and oem</td> </tr> <tr> <th>1</th> <td>licensing model</td> <td>per server</td> <td>per server</td> <td>per cpu pair + cal</td> <td>per cpu pair + cal</td> </tr> <tr> <th>2</th> <td>processor chip limit</td> <td>1</td> <td>2</td> <td>64</td> <td>64</td> </tr> <tr> <th>3</th> <td>memory limit</td> <td>32 gb</td> <td>64 gb</td> <td>4 tb</td> <td>4 tb</td> </tr> <tr> <th>4</th> <td>user limit</td> <td>15</td> <td>25</td> <td>unlimited</td> <td>unlimited</td> </tr> <tr> <th>5</th> <td>file services limits</td> <td>1 standalone dfs root</td> <td>1 standalone dfs root</td> <td>unlimited</td> <td>unlimited</td> </tr> <tr> <th>6</th> <td>network policy and access services limits</td> <td>50 rras connections and 10 ias connections</td> <td>250 rras connections, 50 ias connections, and 2 ias server groups</td> <td>unlimited</td> <td>unlimited</td> </tr> <tr> <th>7</th> <td>remote desktop services limits</td> <td>50 remote desktop services connections</td> <td>gateway only</td> <td>unlimited</td> <td>unlimited</td> </tr> <tr> <th>8</th> <td>virtualization rights</td> <td>none</td> <td>either in 1 vm or 1 physical server, but not both at once</td> <td>2 vms</td> <td>unlimited</td> </tr> <tr> <th>9</th> <td>dhcp role</td> <td>yes</td> <td>yes</td> <td>yes</td> <td>yes</td> </tr> <tr> <th>10</th> <td>dns server role</td> <td>yes</td> <td>yes</td> <td>yes</td> <td>yes</td> </tr> <tr> <th>11</th> <td>fax server role</td> <td>yes</td> <td>yes</td> <td>yes</td> <td>yes</td> </tr> <tr> <th>12</th> <td>uddi services</td> <td>yes</td> <td>yes</td> <td>yes</td> <td>yes</td> </tr> <tr> <th>13</th> <td>print and document services</td> <td>yes</td> <td>yes</td> <td>yes</td> <td>yes</td> </tr> <tr> <th>14</th> <td>web services (internet information services)</td> <td>yes</td> <td>yes</td> <td>yes</td> <td>yes</td> </tr> <tr> <th>15</th> <td>windows deployment services</td> <td>yes</td> <td>yes</td> <td>yes</td> <td>yes</td> </tr> <tr> <th>16</th> <td>windows server update services</td> <td>no</td> <td>yes</td> <td>yes</td> <td>yes</td> </tr> <tr> <th>17</th> <td>active directory lightweight directory services</td> <td>yes</td> <td>yes</td> <td>yes</td> <td>yes</td> </tr> <tr> <th>18</th> <td>active directory rights management services</td> <td>yes</td> <td>yes</td> <td>yes</td> <td>yes</td> </tr> <tr> <th>19</th> <td>application server role</td> <td>yes</td> <td>yes</td> <td>yes</td> <td>yes</td> </tr> <tr> <th>20</th> <td>server manager</td> <td>yes</td> <td>yes</td> <td>yes</td> <td>yes</td> </tr> <tr> <th>21</th> <td>windows powershell</td> <td>yes</td> <td>yes</td> <td>yes</td> <td>yes</td> </tr> <tr> <th>22</th> <td>active directory domain services</td> <td>must be root of forest and domain</td> <td>must be root of forest and domain</td> <td>yes</td> <td>yes</td> </tr> <tr> <th>23</th> <td>active directory certificate services</td> <td>certificate authorities only</td> <td>certificate authorities only</td> <td>yes</td> <td>yes</td> </tr> <tr> <th>24</th> <td>active directory federation services</td> <td>yes</td> <td>no</td> <td>yes</td> <td>yes</td> </tr> <tr> <th>25</th> <td>server core mode</td> <td>no</td> <td>no</td> <td>yes</td> <td>yes</td> </tr> <tr> <th>26</th> <td>hyper-v</td> <td>no</td> <td>no</td> <td>yes</td> <td>yes</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>foundation</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>15</td> </tr> <tr> <th>1</th> <td>1 standalone dfs root</td> </tr> <tr> <th>2</th> <td>50 rras connections and 10 ias connections</td> </tr> <tr> <th>3</th> <td>50 remote desktop services connections</td> </tr> </tbody> </table>

select foundation where standard = 'unlimited' <table_name> : table | | specifications | foundation | essentials | standard | datacenter | |---:|:------------------------------------------------|:-------------------------------------------|:------------------------------------------------------------------|:------------------------------|:-------------------------| | 0 | distribution | oem only | retail, volume licensing, oem | retail, volume licensing, oem | volume licensing and oem | | 1 | licensing model | per server | per server | per cpu pair + cal | per cpu pair + cal | | 2 | processor chip limit | 1 | 2 | 64 | 64 | | 3 | memory limit | 32 gb | 64 gb | 4 tb | 4 tb | | 4 | user limit | 15 | 25 | unlimited | unlimited | | 5 | file services limits | 1 standalone dfs root | 1 standalone dfs root | unlimited | unlimited | | 6 | network policy and access services limits | 50 rras connections and 10 ias connections | 250 rras connections, 50 ias connections, and 2 ias server groups | unlimited | unlimited | | 7 | remote desktop services limits | 50 remote desktop services connections | gateway only | unlimited | unlimited | | 8 | virtualization rights | none | either in 1 vm or 1 physical server, but not both at once | 2 vms | unlimited | | 9 | dhcp role | yes | yes | yes | yes | | 10 | dns server role | yes | yes | yes | yes | | 11 | fax server role | yes | yes | yes | yes | | 12 | uddi services | yes | yes | yes | yes | | 13 | print and document services | yes | yes | yes | yes | | 14 | web services (internet information services) | yes | yes | yes | yes | | 15 | windows deployment services | yes | yes | yes | yes | | 16 | windows server update services | no | yes | yes | yes | | 17 | active directory lightweight directory services | yes | yes | yes | yes | | 18 | active directory rights management services | yes | yes | yes | yes | | 19 | application server role | yes | yes | yes | yes | | 20 | server manager | yes | yes | yes | yes | | 21 | windows powershell | yes | yes | yes | yes | | 22 | active directory domain services | must be root of forest and domain | must be root of forest and domain | yes | yes | | 23 | active directory certificate services | certificate authorities only | certificate authorities only | yes | yes | | 24 | active directory federation services | yes | no | yes | yes | | 25 | server core mode | no | no | yes | yes | | 26 | hyper-v | no | no | yes | yes |

| | foundation | |---:|:-------------------------------------------| | 0 | 15 | | 1 | 1 standalone dfs root | | 2 | 50 rras connections and 10 ias connections | | 3 | 50 remote desktop services connections |

select count ( * ) where advertising = 'yes' col : name | topic | cost | target age | advertising row 1 : ask a biologist | biology | free | 5 | none row 2 : archimedes-lab.org | mathematics | free | 10 | yes - limited row 3 : awesome library | all | free | all | yes - large row 4 : bitesize by the bbc | art & design, business studies, design & technology, dida, drama, english, english literature, french, geography, german, history, ict, irish, maths, music, physical education, religious studies, science, spanish | free | 5-16 | none row 5 : brainpop | science, social studies, english, maths, art & music, health, technology | from us$75/year | 4-17 | none row 6 : cut-the-knot | maths | free | 8 | yes - extensive row 7 : fact monster | world & news, u.s., people, english, science, math & money, sports | free | 4-14 (k-8) | yes row 8 : geometry from the land of the incas | geometry | free | 12 | yes - extensive row 9 : hackmath.net | mathematics | free | 9-18 | none row 10 : hyperphysics | physics | free | 15 | none row 11 : ixl | math | $80/year | 4-12 | none row 12 : le patron | french | free | 12 | yes row 13 : learnalberta.ca | everything (mainly aimed at teachers) | free | 5-18 | no row 14 : nafham | multidisciplinary 5-20min k-12 school video lessons for arabic students | free | 6-18 | yes row 15 : starfall.com | reading | free | 2-9 | none row 16 : smartygames.com | math games, reading, art, word scramble, spanish, puzzles, kids sudoku and more | free | 2-9 | none row 17 : watchknowlearn | all | free | 2-17 | none

col : count ( "*" ) row 1 : 3

select count ( * ) where advertising = 'yes' <table_name> : table \begin{tabular}{llllll} \toprule & name & topic & cost & target age & advertising \\ \midrule 0 & ask a biologist & biology & free & 5 & none \\ 1 & archimedes-lab.org & mathematics & free & 10 & yes - limited \\ 2 & awesome library & all & free & all & yes - large \\ 3 & bitesize by the bbc & art & design, business studies, design & technology, dida, drama, english, english literature, french, geography, german, history, ict, irish, maths, music, physical education, religious studies, science, spanish & free & 5-16 & none \\ 4 & brainpop & science, social studies, english, maths, art & music, health, technology & from us$75/year & 4-17 & none \\ 5 & cut-the-knot & maths & free & 8 & yes - extensive \\ 6 & fact monster & world & news, u.s., people, english, science, math & money, sports & free & 4-14 (k-8) & yes \\ 7 & geometry from the land of the incas & geometry & free & 12 & yes - extensive \\ 8 & hackmath.net & mathematics & free & 9-18 & none \\ 9 & hyperphysics & physics & free & 15 & none \\ 10 & ixl & math & $80/year & 4-12 & none \\ 11 & le patron & french & free & 12 & yes \\ 12 & learnalberta.ca & everything (mainly aimed at teachers) & free & 5-18 & no \\ 13 & nafham & multidisciplinary 5-20min k-12 school video lessons for arabic students & free & 6-18 & yes \\ 14 & starfall.com & reading & free & 2-9 & none \\ 15 & smartygames.com & math games, reading, art, word scramble, spanish, puzzles, kids sudoku and more & free & 2-9 & none \\ 16 & watchknowlearn & all & free & 2-17 & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 3 \\ \bottomrule \end{tabular}

select count ( * ) where advertising = 'yes' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>name</th> <th>topic</th> <th>cost</th> <th>target age</th> <th>advertising</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>ask a biologist</td> <td>biology</td> <td>free</td> <td>5</td> <td>none</td> </tr> <tr> <th>1</th> <td>archimedes-lab.org</td> <td>mathematics</td> <td>free</td> <td>10</td> <td>yes - limited</td> </tr> <tr> <th>2</th> <td>awesome library</td> <td>all</td> <td>free</td> <td>all</td> <td>yes - large</td> </tr> <tr> <th>3</th> <td>bitesize by the bbc</td> <td>art &amp; design, business studies, design &amp; technology, dida, drama, english, english literature, french, geography, german, history, ict, irish, maths, music, physical education, religious studies, science, spanish</td> <td>free</td> <td>5-16</td> <td>none</td> </tr> <tr> <th>4</th> <td>brainpop</td> <td>science, social studies, english, maths, art &amp; music, health, technology</td> <td>from us$75/year</td> <td>4-17</td> <td>none</td> </tr> <tr> <th>5</th> <td>cut-the-knot</td> <td>maths</td> <td>free</td> <td>8</td> <td>yes - extensive</td> </tr> <tr> <th>6</th> <td>fact monster</td> <td>world &amp; news, u.s., people, english, science, math &amp; money, sports</td> <td>free</td> <td>4-14 (k-8)</td> <td>yes</td> </tr> <tr> <th>7</th> <td>geometry from the land of the incas</td> <td>geometry</td> <td>free</td> <td>12</td> <td>yes - extensive</td> </tr> <tr> <th>8</th> <td>hackmath.net</td> <td>mathematics</td> <td>free</td> <td>9-18</td> <td>none</td> </tr> <tr> <th>9</th> <td>hyperphysics</td> <td>physics</td> <td>free</td> <td>15</td> <td>none</td> </tr> <tr> <th>10</th> <td>ixl</td> <td>math</td> <td>$80/year</td> <td>4-12</td> <td>none</td> </tr> <tr> <th>11</th> <td>le patron</td> <td>french</td> <td>free</td> <td>12</td> <td>yes</td> </tr> <tr> <th>12</th> <td>learnalberta.ca</td> <td>everything (mainly aimed at teachers)</td> <td>free</td> <td>5-18</td> <td>no</td> </tr> <tr> <th>13</th> <td>nafham</td> <td>multidisciplinary 5-20min k-12 school video lessons for arabic students</td> <td>free</td> <td>6-18</td> <td>yes</td> </tr> <tr> <th>14</th> <td>starfall.com</td> <td>reading</td> <td>free</td> <td>2-9</td> <td>none</td> </tr> <tr> <th>15</th> <td>smartygames.com</td> <td>math games, reading, art, word scramble, spanish, puzzles, kids sudoku and more</td> <td>free</td> <td>2-9</td> <td>none</td> </tr> <tr> <th>16</th> <td>watchknowlearn</td> <td>all</td> <td>free</td> <td>2-17</td> <td>none</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "*" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>3</td> </tr> </tbody> </table>

select count ( * ) where advertising = 'yes' <table_name> : table | | name | topic | cost | target age | advertising | |---:|:------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------|:-------------|:----------------| | 0 | ask a biologist | biology | free | 5 | none | | 1 | archimedes-lab.org | mathematics | free | 10 | yes - limited | | 2 | awesome library | all | free | all | yes - large | | 3 | bitesize by the bbc | art & design, business studies, design & technology, dida, drama, english, english literature, french, geography, german, history, ict, irish, maths, music, physical education, religious studies, science, spanish | free | 5-16 | none | | 4 | brainpop | science, social studies, english, maths, art & music, health, technology | from us$75/year | 4-17 | none | | 5 | cut-the-knot | maths | free | 8 | yes - extensive | | 6 | fact monster | world & news, u.s., people, english, science, math & money, sports | free | 4-14 (k-8) | yes | | 7 | geometry from the land of the incas | geometry | free | 12 | yes - extensive | | 8 | hackmath.net | mathematics | free | 9-18 | none | | 9 | hyperphysics | physics | free | 15 | none | | 10 | ixl | math | $80/year | 4-12 | none | | 11 | le patron | french | free | 12 | yes | | 12 | learnalberta.ca | everything (mainly aimed at teachers) | free | 5-18 | no | | 13 | nafham | multidisciplinary 5-20min k-12 school video lessons for arabic students | free | 6-18 | yes | | 14 | starfall.com | reading | free | 2-9 | none | | 15 | smartygames.com | math games, reading, art, word scramble, spanish, puzzles, kids sudoku and more | free | 2-9 | none | | 16 | watchknowlearn | all | free | 2-17 | none |

| | count ( "*" ) | |---:|----------------:| | 0 | 3 |

select rider where rider = 'cordy milne' col : pos. | rider | semi-final points | final points | final heats | total points row 1 : 1 | jack milne | 13 | 15 | (3,3,3,3,3) | 28 row 2 : 2 | wilbur lamoreaux | 12 | 13 | (3,3,3,1,3) | 25 row 3 : 3 | cordy milne | 11 | 12 | (2,3,2,3,2) | 23 row 4 : 4 | jack parker | 11 | 10 | (2,3,1,2,2) | 21 row 5 : 5 | ginger lees | 10 | 9 | (1,1,2,2,3) | 19 row 6 : 6 | frank charles | 10 | 7 | (0,0,1,3,3) | 17 row 7 : 7 | lionel van praag | 11 | 6 | (3,0,-,3,0) | 17 row 8 : 8 | bill kitchen | 7 | 9 | (1,2,2,2,2) | 16 row 9 : 9 | george newton | 11 | 5 | (3,0,0,0,2) | 16 row 10 : 10 | eric langton | 12 | 4 | (1,0,0,3,0) | 16 row 11 : 11 | morian hansen | 7 | 8 | (1,2,3,1,1) | 15 row 12 : 12 | eric chitty | 11 | 4 | (0,2,0,1,1) | 15 row 13 : 13 | joe abbott | 8 | 6 | (2,1,1,2,0) | 14 row 14 : 14 | arthur atkinson | 8 | 6 | (2,0,2,1,1) | 14 row 15 : 15 | frank varey | 8 | 3 | (0,1,1,0,1) | 11 row 16 : 16 | tommy croombs | 6 | 2 | (0,2,0,0,0) | 8 row 17 : 17 | bob harrison | 7 | none | none | 7 row 18 : 18 | alec statham | 6 | 0 | (0) | 6

col : rider row 1 : cordy milne

select rider where rider = 'cordy milne' <table_name> : table \begin{tabular}{lllllll} \toprule & pos. & rider & semi-final points & final points & final heats & total points \\ \midrule 0 & 1 & jack milne & 13 & 15 & (3,3,3,3,3) & 28 \\ 1 & 2 & wilbur lamoreaux & 12 & 13 & (3,3,3,1,3) & 25 \\ 2 & 3 & cordy milne & 11 & 12 & (2,3,2,3,2) & 23 \\ 3 & 4 & jack parker & 11 & 10 & (2,3,1,2,2) & 21 \\ 4 & 5 & ginger lees & 10 & 9 & (1,1,2,2,3) & 19 \\ 5 & 6 & frank charles & 10 & 7 & (0,0,1,3,3) & 17 \\ 6 & 7 & lionel van praag & 11 & 6 & (3,0,-,3,0) & 17 \\ 7 & 8 & bill kitchen & 7 & 9 & (1,2,2,2,2) & 16 \\ 8 & 9 & george newton & 11 & 5 & (3,0,0,0,2) & 16 \\ 9 & 10 & eric langton & 12 & 4 & (1,0,0,3,0) & 16 \\ 10 & 11 & morian hansen & 7 & 8 & (1,2,3,1,1) & 15 \\ 11 & 12 & eric chitty & 11 & 4 & (0,2,0,1,1) & 15 \\ 12 & 13 & joe abbott & 8 & 6 & (2,1,1,2,0) & 14 \\ 13 & 14 & arthur atkinson & 8 & 6 & (2,0,2,1,1) & 14 \\ 14 & 15 & frank varey & 8 & 3 & (0,1,1,0,1) & 11 \\ 15 & 16 & tommy croombs & 6 & 2 & (0,2,0,0,0) & 8 \\ 16 & 17 & bob harrison & 7 & none & none & 7 \\ 17 & 18 & alec statham & 6 & 0 & (0) & 6 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & rider \\ \midrule 0 & cordy milne \\ \bottomrule \end{tabular}

select rider where rider = 'cordy milne' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>pos.</th> <th>rider</th> <th>semi-final points</th> <th>final points</th> <th>final heats</th> <th>total points</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>jack milne</td> <td>13</td> <td>15</td> <td>(3,3,3,3,3)</td> <td>28</td> </tr> <tr> <th>1</th> <td>2</td> <td>wilbur lamoreaux</td> <td>12</td> <td>13</td> <td>(3,3,3,1,3)</td> <td>25</td> </tr> <tr> <th>2</th> <td>3</td> <td>cordy milne</td> <td>11</td> <td>12</td> <td>(2,3,2,3,2)</td> <td>23</td> </tr> <tr> <th>3</th> <td>4</td> <td>jack parker</td> <td>11</td> <td>10</td> <td>(2,3,1,2,2)</td> <td>21</td> </tr> <tr> <th>4</th> <td>5</td> <td>ginger lees</td> <td>10</td> <td>9</td> <td>(1,1,2,2,3)</td> <td>19</td> </tr> <tr> <th>5</th> <td>6</td> <td>frank charles</td> <td>10</td> <td>7</td> <td>(0,0,1,3,3)</td> <td>17</td> </tr> <tr> <th>6</th> <td>7</td> <td>lionel van praag</td> <td>11</td> <td>6</td> <td>(3,0,-,3,0)</td> <td>17</td> </tr> <tr> <th>7</th> <td>8</td> <td>bill kitchen</td> <td>7</td> <td>9</td> <td>(1,2,2,2,2)</td> <td>16</td> </tr> <tr> <th>8</th> <td>9</td> <td>george newton</td> <td>11</td> <td>5</td> <td>(3,0,0,0,2)</td> <td>16</td> </tr> <tr> <th>9</th> <td>10</td> <td>eric langton</td> <td>12</td> <td>4</td> <td>(1,0,0,3,0)</td> <td>16</td> </tr> <tr> <th>10</th> <td>11</td> <td>morian hansen</td> <td>7</td> <td>8</td> <td>(1,2,3,1,1)</td> <td>15</td> </tr> <tr> <th>11</th> <td>12</td> <td>eric chitty</td> <td>11</td> <td>4</td> <td>(0,2,0,1,1)</td> <td>15</td> </tr> <tr> <th>12</th> <td>13</td> <td>joe abbott</td> <td>8</td> <td>6</td> <td>(2,1,1,2,0)</td> <td>14</td> </tr> <tr> <th>13</th> <td>14</td> <td>arthur atkinson</td> <td>8</td> <td>6</td> <td>(2,0,2,1,1)</td> <td>14</td> </tr> <tr> <th>14</th> <td>15</td> <td>frank varey</td> <td>8</td> <td>3</td> <td>(0,1,1,0,1)</td> <td>11</td> </tr> <tr> <th>15</th> <td>16</td> <td>tommy croombs</td> <td>6</td> <td>2</td> <td>(0,2,0,0,0)</td> <td>8</td> </tr> <tr> <th>16</th> <td>17</td> <td>bob harrison</td> <td>7</td> <td>none</td> <td>none</td> <td>7</td> </tr> <tr> <th>17</th> <td>18</td> <td>alec statham</td> <td>6</td> <td>0</td> <td>(0)</td> <td>6</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>rider</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>cordy milne</td> </tr> </tbody> </table>

select rider where rider = 'cordy milne' <table_name> : table | | pos. | rider | semi-final points | final points | final heats | total points | |---:|-------:|:-----------------|--------------------:|:---------------|:--------------|---------------:| | 0 | 1 | jack milne | 13 | 15 | (3,3,3,3,3) | 28 | | 1 | 2 | wilbur lamoreaux | 12 | 13 | (3,3,3,1,3) | 25 | | 2 | 3 | cordy milne | 11 | 12 | (2,3,2,3,2) | 23 | | 3 | 4 | jack parker | 11 | 10 | (2,3,1,2,2) | 21 | | 4 | 5 | ginger lees | 10 | 9 | (1,1,2,2,3) | 19 | | 5 | 6 | frank charles | 10 | 7 | (0,0,1,3,3) | 17 | | 6 | 7 | lionel van praag | 11 | 6 | (3,0,-,3,0) | 17 | | 7 | 8 | bill kitchen | 7 | 9 | (1,2,2,2,2) | 16 | | 8 | 9 | george newton | 11 | 5 | (3,0,0,0,2) | 16 | | 9 | 10 | eric langton | 12 | 4 | (1,0,0,3,0) | 16 | | 10 | 11 | morian hansen | 7 | 8 | (1,2,3,1,1) | 15 | | 11 | 12 | eric chitty | 11 | 4 | (0,2,0,1,1) | 15 | | 12 | 13 | joe abbott | 8 | 6 | (2,1,1,2,0) | 14 | | 13 | 14 | arthur atkinson | 8 | 6 | (2,0,2,1,1) | 14 | | 14 | 15 | frank varey | 8 | 3 | (0,1,1,0,1) | 11 | | 15 | 16 | tommy croombs | 6 | 2 | (0,2,0,0,0) | 8 | | 16 | 17 | bob harrison | 7 | none | none | 7 | | 17 | 18 | alec statham | 6 | 0 | (0) | 6 |

| | rider | |---:|:------------| | 0 | cordy milne |

select count ( rocky mountains (scopulorum) ) where rocky mountains (scopulorum) = '22.9±0.63, n=17' col : common names subspecies | pacific (critchfieldiana) | columbia (ponderosa) | rocky mountains (scopulorum) | southwestern (brachyptera) | central high plains (readiana) row 1 : years needles remain green | 3.9±0.25, n=30 | 4.7±0.14, n=50 | 5.7±0.28, n=23 | 4.3±0.18, n=24 | 4.7±0.18, n=5 row 2 : foliage length on branch, cm | 25.1±2.4, n=30 | 26.2±2.2, n=50 | 21.1±1.7, n=23 | 21.8±2.7, n=24 | 42.2±6.7, n=5 row 3 : needle length, cm | 19.8±0.44, n=30 | 16.8±0.29, n=48 | 11.2±0.27, n=23 | 14.7±0.45, n=24 | 15.6±0.57, n=5 row 4 : needles per fascicle | 3.0±0.00, n=30 | 3.0±0.00, n=48 | 2.6±0.06, n=23 | 3.0±0.03, n=24 | 2.4±0.11, n=5 row 5 : needle thickness | 45.9±0.49, n=30 | 47.8±0.51, n=48 | 46.4±0.68, n=23 | 44.8±0.87, n=24 | 49.7±0.61, n=5 row 6 : branches per whorl | 4.4±0.13, n=30 | 3.7±0.11, n=50 | 3.0±0.17, n=23 | 3.4±0.25, n=23 | 2.3±0.11, n=5 row 7 : branch angle ° from vert | 56±1.8, n=30 | 51±1.7, n=50 | 50±2.3, n=23 | 48±3.1, n=24 | 36±1.9, n=5 row 8 : seed cones length, mm | 101.4±2.48, n=25 | 88.7±1.24, n=36 | 70.7±2.20, n=22 | 74.9±2.51, n=20 | 71.1±2.46, n=5 row 9 : seed cones width, mm | 77.1±1.35, n=25 | 71.6±0.73, n=36 | 61.5±1.08, n=22 | 62.6±1.77, n=20 | 63.3±2.18, n=5 row 10 : seed cone form w/l | 0.80±0.03, n=25 | 0.84±0.03, n=36 | 0.90±0.02, n=22 | 0.86±0.02, n=20 | 0.90±0.03, n=5 row 11 : seed length, mm | 7.5±0.08, n=23 | 7.6±0.16, n=14 | 6.3±0.09, n=17 | 6.4±0.18, n=16 | 7.0±0.12, n=5 row 12 : seed width, mm | 4.9±0.05, n=23 | 4.9±0.08, n=14 | 4.1±0.05, n=17 | 4.3±0.09, n=16 | 4.5±0.10, n=5 row 13 : seed + wing length, mm | 32.3±0.58, n=23 | 24.8±0.62, n=14 | 22.9±0.63, n=17 | 23.3±0.68, n=15 | 23.1±0.78, n=5 row 14 : mature cone color | apple green to yellow green | green & red-brown to dk. purple | green & red-brown to dk. purple | none | green & red-brown to dk. purple

col : count ( "rocky mountains (scopulorum" ) row 1 : 1

select count ( rocky mountains (scopulorum) ) where rocky mountains (scopulorum) = '22.9±0.63, n=17' <table_name> : table \begin{tabular}{lllllll} \toprule & common names subspecies & pacific (critchfieldiana) & columbia (ponderosa) & rocky mountains (scopulorum) & southwestern (brachyptera) & central high plains (readiana) \\ \midrule 0 & years needles remain green & 3.9±0.25, n=30 & 4.7±0.14, n=50 & 5.7±0.28, n=23 & 4.3±0.18, n=24 & 4.7±0.18, n=5 \\ 1 & foliage length on branch, cm & 25.1±2.4, n=30 & 26.2±2.2, n=50 & 21.1±1.7, n=23 & 21.8±2.7, n=24 & 42.2±6.7, n=5 \\ 2 & needle length, cm & 19.8±0.44, n=30 & 16.8±0.29, n=48 & 11.2±0.27, n=23 & 14.7±0.45, n=24 & 15.6±0.57, n=5 \\ 3 & needles per fascicle & 3.0±0.00, n=30 & 3.0±0.00, n=48 & 2.6±0.06, n=23 & 3.0±0.03, n=24 & 2.4±0.11, n=5 \\ 4 & needle thickness & 45.9±0.49, n=30 & 47.8±0.51, n=48 & 46.4±0.68, n=23 & 44.8±0.87, n=24 & 49.7±0.61, n=5 \\ 5 & branches per whorl & 4.4±0.13, n=30 & 3.7±0.11, n=50 & 3.0±0.17, n=23 & 3.4±0.25, n=23 & 2.3±0.11, n=5 \\ 6 & branch angle ° from vert & 56±1.8, n=30 & 51±1.7, n=50 & 50±2.3, n=23 & 48±3.1, n=24 & 36±1.9, n=5 \\ 7 & seed cones length, mm & 101.4±2.48, n=25 & 88.7±1.24, n=36 & 70.7±2.20, n=22 & 74.9±2.51, n=20 & 71.1±2.46, n=5 \\ 8 & seed cones width, mm & 77.1±1.35, n=25 & 71.6±0.73, n=36 & 61.5±1.08, n=22 & 62.6±1.77, n=20 & 63.3±2.18, n=5 \\ 9 & seed cone form w/l & 0.80±0.03, n=25 & 0.84±0.03, n=36 & 0.90±0.02, n=22 & 0.86±0.02, n=20 & 0.90±0.03, n=5 \\ 10 & seed length, mm & 7.5±0.08, n=23 & 7.6±0.16, n=14 & 6.3±0.09, n=17 & 6.4±0.18, n=16 & 7.0±0.12, n=5 \\ 11 & seed width, mm & 4.9±0.05, n=23 & 4.9±0.08, n=14 & 4.1±0.05, n=17 & 4.3±0.09, n=16 & 4.5±0.10, n=5 \\ 12 & seed + wing length, mm & 32.3±0.58, n=23 & 24.8±0.62, n=14 & 22.9±0.63, n=17 & 23.3±0.68, n=15 & 23.1±0.78, n=5 \\ 13 & mature cone color & apple green to yellow green & green & red-brown to dk. purple & green & red-brown to dk. purple & none & green & red-brown to dk. purple \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "rocky mountains (scopulorum" ) \\ \midrule 0 & 1 \\ \bottomrule \end{tabular}

select count ( rocky mountains (scopulorum) ) where rocky mountains (scopulorum) = '22.9±0.63, n=17' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>common names subspecies</th> <th>pacific (critchfieldiana)</th> <th>columbia (ponderosa)</th> <th>rocky mountains (scopulorum)</th> <th>southwestern (brachyptera)</th> <th>central high plains (readiana)</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>years needles remain green</td> <td>3.9±0.25, n=30</td> <td>4.7±0.14, n=50</td> <td>5.7±0.28, n=23</td> <td>4.3±0.18, n=24</td> <td>4.7±0.18, n=5</td> </tr> <tr> <th>1</th> <td>foliage length on branch, cm</td> <td>25.1±2.4, n=30</td> <td>26.2±2.2, n=50</td> <td>21.1±1.7, n=23</td> <td>21.8±2.7, n=24</td> <td>42.2±6.7, n=5</td> </tr> <tr> <th>2</th> <td>needle length, cm</td> <td>19.8±0.44, n=30</td> <td>16.8±0.29, n=48</td> <td>11.2±0.27, n=23</td> <td>14.7±0.45, n=24</td> <td>15.6±0.57, n=5</td> </tr> <tr> <th>3</th> <td>needles per fascicle</td> <td>3.0±0.00, n=30</td> <td>3.0±0.00, n=48</td> <td>2.6±0.06, n=23</td> <td>3.0±0.03, n=24</td> <td>2.4±0.11, n=5</td> </tr> <tr> <th>4</th> <td>needle thickness</td> <td>45.9±0.49, n=30</td> <td>47.8±0.51, n=48</td> <td>46.4±0.68, n=23</td> <td>44.8±0.87, n=24</td> <td>49.7±0.61, n=5</td> </tr> <tr> <th>5</th> <td>branches per whorl</td> <td>4.4±0.13, n=30</td> <td>3.7±0.11, n=50</td> <td>3.0±0.17, n=23</td> <td>3.4±0.25, n=23</td> <td>2.3±0.11, n=5</td> </tr> <tr> <th>6</th> <td>branch angle ° from vert</td> <td>56±1.8, n=30</td> <td>51±1.7, n=50</td> <td>50±2.3, n=23</td> <td>48±3.1, n=24</td> <td>36±1.9, n=5</td> </tr> <tr> <th>7</th> <td>seed cones length, mm</td> <td>101.4±2.48, n=25</td> <td>88.7±1.24, n=36</td> <td>70.7±2.20, n=22</td> <td>74.9±2.51, n=20</td> <td>71.1±2.46, n=5</td> </tr> <tr> <th>8</th> <td>seed cones width, mm</td> <td>77.1±1.35, n=25</td> <td>71.6±0.73, n=36</td> <td>61.5±1.08, n=22</td> <td>62.6±1.77, n=20</td> <td>63.3±2.18, n=5</td> </tr> <tr> <th>9</th> <td>seed cone form w/l</td> <td>0.80±0.03, n=25</td> <td>0.84±0.03, n=36</td> <td>0.90±0.02, n=22</td> <td>0.86±0.02, n=20</td> <td>0.90±0.03, n=5</td> </tr> <tr> <th>10</th> <td>seed length, mm</td> <td>7.5±0.08, n=23</td> <td>7.6±0.16, n=14</td> <td>6.3±0.09, n=17</td> <td>6.4±0.18, n=16</td> <td>7.0±0.12, n=5</td> </tr> <tr> <th>11</th> <td>seed width, mm</td> <td>4.9±0.05, n=23</td> <td>4.9±0.08, n=14</td> <td>4.1±0.05, n=17</td> <td>4.3±0.09, n=16</td> <td>4.5±0.10, n=5</td> </tr> <tr> <th>12</th> <td>seed + wing length, mm</td> <td>32.3±0.58, n=23</td> <td>24.8±0.62, n=14</td> <td>22.9±0.63, n=17</td> <td>23.3±0.68, n=15</td> <td>23.1±0.78, n=5</td> </tr> <tr> <th>13</th> <td>mature cone color</td> <td>apple green to yellow green</td> <td>green &amp; red-brown to dk. purple</td> <td>green &amp; red-brown to dk. purple</td> <td>none</td> <td>green &amp; red-brown to dk. purple</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "rocky mountains (scopulorum" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> </tr> </tbody> </table>

select count ( rocky mountains (scopulorum) ) where rocky mountains (scopulorum) = '22.9±0.63, n=17' <table_name> : table | | common names subspecies | pacific (critchfieldiana) | columbia (ponderosa) | rocky mountains (scopulorum) | southwestern (brachyptera) | central high plains (readiana) | |---:|:-----------------------------|:----------------------------|:--------------------------------|:--------------------------------|:-----------------------------|:---------------------------------| | 0 | years needles remain green | 3.9±0.25, n=30 | 4.7±0.14, n=50 | 5.7±0.28, n=23 | 4.3±0.18, n=24 | 4.7±0.18, n=5 | | 1 | foliage length on branch, cm | 25.1±2.4, n=30 | 26.2±2.2, n=50 | 21.1±1.7, n=23 | 21.8±2.7, n=24 | 42.2±6.7, n=5 | | 2 | needle length, cm | 19.8±0.44, n=30 | 16.8±0.29, n=48 | 11.2±0.27, n=23 | 14.7±0.45, n=24 | 15.6±0.57, n=5 | | 3 | needles per fascicle | 3.0±0.00, n=30 | 3.0±0.00, n=48 | 2.6±0.06, n=23 | 3.0±0.03, n=24 | 2.4±0.11, n=5 | | 4 | needle thickness | 45.9±0.49, n=30 | 47.8±0.51, n=48 | 46.4±0.68, n=23 | 44.8±0.87, n=24 | 49.7±0.61, n=5 | | 5 | branches per whorl | 4.4±0.13, n=30 | 3.7±0.11, n=50 | 3.0±0.17, n=23 | 3.4±0.25, n=23 | 2.3±0.11, n=5 | | 6 | branch angle ° from vert | 56±1.8, n=30 | 51±1.7, n=50 | 50±2.3, n=23 | 48±3.1, n=24 | 36±1.9, n=5 | | 7 | seed cones length, mm | 101.4±2.48, n=25 | 88.7±1.24, n=36 | 70.7±2.20, n=22 | 74.9±2.51, n=20 | 71.1±2.46, n=5 | | 8 | seed cones width, mm | 77.1±1.35, n=25 | 71.6±0.73, n=36 | 61.5±1.08, n=22 | 62.6±1.77, n=20 | 63.3±2.18, n=5 | | 9 | seed cone form w/l | 0.80±0.03, n=25 | 0.84±0.03, n=36 | 0.90±0.02, n=22 | 0.86±0.02, n=20 | 0.90±0.03, n=5 | | 10 | seed length, mm | 7.5±0.08, n=23 | 7.6±0.16, n=14 | 6.3±0.09, n=17 | 6.4±0.18, n=16 | 7.0±0.12, n=5 | | 11 | seed width, mm | 4.9±0.05, n=23 | 4.9±0.08, n=14 | 4.1±0.05, n=17 | 4.3±0.09, n=16 | 4.5±0.10, n=5 | | 12 | seed + wing length, mm | 32.3±0.58, n=23 | 24.8±0.62, n=14 | 22.9±0.63, n=17 | 23.3±0.68, n=15 | 23.1±0.78, n=5 | | 13 | mature cone color | apple green to yellow green | green & red-brown to dk. purple | green & red-brown to dk. purple | none | green & red-brown to dk. purple |

| | count ( "rocky mountains (scopulorum" ) | |---:|------------------------------------------:| | 0 | 1 |

select max ( week ) where score = '45-0' col : week | date | opponent | score | result | record row 1 : 1 | aug 28 | at toronto argonauts | 13-6 | loss | 0-1 row 2 : 2 | sept 4 | at montreal alouettes | 21-2 | loss | 0-2 row 3 : 2 | sept 6 | vs. montreal alouettes | 20-11 | loss | 0-3 row 4 : 3 | sept 11 | at toronto argonauts | 12-5 | win | 1-3 row 5 : 4 | sept 18 | vs. toronto argonauts | 34-6 | loss | 1-4 row 6 : 5 | sept 25 | vs. hamilton tiger-cats | 38-12 | loss | 1-5 row 7 : 6 | oct 2 | at hamilton tiger-cats | 45-0 | loss | 1-6 row 8 : 7 | oct 9 | vs. montreal alouettes | 25-11 | loss | 1-7 row 9 : 7 | oct 11 | at montreal alouettes | 24-6 | loss | 1-8 row 10 : 8 | oct 16 | vs. toronto argonauts | 27-11 | loss | 1-9 row 11 : 9 | oct 23 | at hamilton tiger-cats | 25-17 | loss | 1-10 row 12 : 10 | oct 30 | vs. hamilton tiger-cats | 30-9 | loss | 1-11 row 13 : 11 | nov 6 | at toronto argonauts | 18-12 | loss | 1-12 row 14 : 12 | nov 13 | vs. montreal alouettes | 14-12 | win | 2-12

col : max ( "week" ) row 1 : 6

select max ( week ) where score = '45-0' <table_name> : table \begin{tabular}{lllllll} \toprule & week & date & opponent & score & result & record \\ \midrule 0 & 1 & aug 28 & at toronto argonauts & 13-6 & loss & 0-1 \\ 1 & 2 & sept 4 & at montreal alouettes & 21-2 & loss & 0-2 \\ 2 & 2 & sept 6 & vs. montreal alouettes & 20-11 & loss & 0-3 \\ 3 & 3 & sept 11 & at toronto argonauts & 12-5 & win & 1-3 \\ 4 & 4 & sept 18 & vs. toronto argonauts & 34-6 & loss & 1-4 \\ 5 & 5 & sept 25 & vs. hamilton tiger-cats & 38-12 & loss & 1-5 \\ 6 & 6 & oct 2 & at hamilton tiger-cats & 45-0 & loss & 1-6 \\ 7 & 7 & oct 9 & vs. montreal alouettes & 25-11 & loss & 1-7 \\ 8 & 7 & oct 11 & at montreal alouettes & 24-6 & loss & 1-8 \\ 9 & 8 & oct 16 & vs. toronto argonauts & 27-11 & loss & 1-9 \\ 10 & 9 & oct 23 & at hamilton tiger-cats & 25-17 & loss & 1-10 \\ 11 & 10 & oct 30 & vs. hamilton tiger-cats & 30-9 & loss & 1-11 \\ 12 & 11 & nov 6 & at toronto argonauts & 18-12 & loss & 1-12 \\ 13 & 12 & nov 13 & vs. montreal alouettes & 14-12 & win & 2-12 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & max ( "week" ) \\ \midrule 0 & 6 \\ \bottomrule \end{tabular}

select max ( week ) where score = '45-0' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>week</th> <th>date</th> <th>opponent</th> <th>score</th> <th>result</th> <th>record</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>aug 28</td> <td>at toronto argonauts</td> <td>13-6</td> <td>loss</td> <td>0-1</td> </tr> <tr> <th>1</th> <td>2</td> <td>sept 4</td> <td>at montreal alouettes</td> <td>21-2</td> <td>loss</td> <td>0-2</td> </tr> <tr> <th>2</th> <td>2</td> <td>sept 6</td> <td>vs. montreal alouettes</td> <td>20-11</td> <td>loss</td> <td>0-3</td> </tr> <tr> <th>3</th> <td>3</td> <td>sept 11</td> <td>at toronto argonauts</td> <td>12-5</td> <td>win</td> <td>1-3</td> </tr> <tr> <th>4</th> <td>4</td> <td>sept 18</td> <td>vs. toronto argonauts</td> <td>34-6</td> <td>loss</td> <td>1-4</td> </tr> <tr> <th>5</th> <td>5</td> <td>sept 25</td> <td>vs. hamilton tiger-cats</td> <td>38-12</td> <td>loss</td> <td>1-5</td> </tr> <tr> <th>6</th> <td>6</td> <td>oct 2</td> <td>at hamilton tiger-cats</td> <td>45-0</td> <td>loss</td> <td>1-6</td> </tr> <tr> <th>7</th> <td>7</td> <td>oct 9</td> <td>vs. montreal alouettes</td> <td>25-11</td> <td>loss</td> <td>1-7</td> </tr> <tr> <th>8</th> <td>7</td> <td>oct 11</td> <td>at montreal alouettes</td> <td>24-6</td> <td>loss</td> <td>1-8</td> </tr> <tr> <th>9</th> <td>8</td> <td>oct 16</td> <td>vs. toronto argonauts</td> <td>27-11</td> <td>loss</td> <td>1-9</td> </tr> <tr> <th>10</th> <td>9</td> <td>oct 23</td> <td>at hamilton tiger-cats</td> <td>25-17</td> <td>loss</td> <td>1-10</td> </tr> <tr> <th>11</th> <td>10</td> <td>oct 30</td> <td>vs. hamilton tiger-cats</td> <td>30-9</td> <td>loss</td> <td>1-11</td> </tr> <tr> <th>12</th> <td>11</td> <td>nov 6</td> <td>at toronto argonauts</td> <td>18-12</td> <td>loss</td> <td>1-12</td> </tr> <tr> <th>13</th> <td>12</td> <td>nov 13</td> <td>vs. montreal alouettes</td> <td>14-12</td> <td>win</td> <td>2-12</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>max ( "week" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>6</td> </tr> </tbody> </table>

select max ( week ) where score = '45-0' <table_name> : table | | week | date | opponent | score | result | record | |---:|-------:|:--------|:------------------------|:--------|:---------|:---------| | 0 | 1 | aug 28 | at toronto argonauts | 13-6 | loss | 0-1 | | 1 | 2 | sept 4 | at montreal alouettes | 21-2 | loss | 0-2 | | 2 | 2 | sept 6 | vs. montreal alouettes | 20-11 | loss | 0-3 | | 3 | 3 | sept 11 | at toronto argonauts | 12-5 | win | 1-3 | | 4 | 4 | sept 18 | vs. toronto argonauts | 34-6 | loss | 1-4 | | 5 | 5 | sept 25 | vs. hamilton tiger-cats | 38-12 | loss | 1-5 | | 6 | 6 | oct 2 | at hamilton tiger-cats | 45-0 | loss | 1-6 | | 7 | 7 | oct 9 | vs. montreal alouettes | 25-11 | loss | 1-7 | | 8 | 7 | oct 11 | at montreal alouettes | 24-6 | loss | 1-8 | | 9 | 8 | oct 16 | vs. toronto argonauts | 27-11 | loss | 1-9 | | 10 | 9 | oct 23 | at hamilton tiger-cats | 25-17 | loss | 1-10 | | 11 | 10 | oct 30 | vs. hamilton tiger-cats | 30-9 | loss | 1-11 | | 12 | 11 | nov 6 | at toronto argonauts | 18-12 | loss | 1-12 | | 13 | 12 | nov 13 | vs. montreal alouettes | 14-12 | win | 2-12 |

| | max ( "week" ) | |---:|-----------------:| | 0 | 6 |

select location where result = '16-13' col : year | date | winner | result | loser | location row 1 : 2000 | september 10 | new york giants | 33-18 | philadelphia eagles | veterans stadium row 2 : 2000 | october 29 | new york giants | 24-7 | philadelphia eagles | giants stadium row 3 : 2001 | january 7 | new york giants | 20-10 | philadelphia eagles | giants stadium row 4 : 2001 | october 22 | philadelphia eagles | 10-9 | new york giants | giants stadium row 5 : 2001 | december 30 | philadelphia eagles | 24-21 | new york giants | veterans stadium row 6 : 2002 | october 28 | philadelphia eagles | 17-3 | new york giants | veterans stadium row 7 : 2002 | december 28 | new york giants | 10-7 | philadelphia eagles | giants stadium row 8 : 2003 | october 19 | philadelphia eagles | 14-10 | new york giants | giants stadium row 9 : 2003 | november 16 | philadelphia eagles | 28-10 | new york giants | lincoln financial field row 10 : 2004 | september 12 | philadelphia eagles | 31-17 | new york giants | lincoln financial field row 11 : 2004 | november 28 | philadelphia eagles | 27-6 | new york giants | giants stadium row 12 : 2005 | november 20 | new york giants | 27-17 | philadelphia eagles | giants stadium row 13 : 2005 | december 11 | new york giants | 26-23 (ot) | philadelphia eagles | lincoln financial field row 14 : 2006 | september 17 | new york giants | 30-24 (ot) | philadelphia eagles | lincoln financial field row 15 : 2006 | december 17 | philadelphia eagles | 36-22 | new york giants | giants stadium row 16 : 2007 | january 7 | philadelphia eagles | 23-20 | new york giants | lincoln financial field row 17 : 2007 | september 30 | new york giants | 16-3 | philadelphia eagles | giants stadium row 18 : 2007 | december 9 | new york giants | 16-13 | philadelphia eagles | lincoln financial field row 19 : 2008 | november 9 | new york giants | 36-31 | philadelphia eagles | lincoln financial field row 20 : 2008 | december 7 | philadelphia eagles | 20-14 | new york giants | giants stadium row 21 : 2009 | january 11 | philadelphia eagles | 23-11 | new york giants | giants stadium row 22 : 2009 | november 1 | philadelphia eagles | 40-17 | new york giants | lincoln financial field row 23 : 2009 | december 13 | philadelphia eagles | 45-38 | new york giants | giants stadium

col : location row 1 : lincoln financial field

select location where result = '16-13' <table_name> : table \begin{tabular}{lllllll} \toprule & year & date & winner & result & loser & location \\ \midrule 0 & 2000 & september 10 & new york giants & 33-18 & philadelphia eagles & veterans stadium \\ 1 & 2000 & october 29 & new york giants & 24-7 & philadelphia eagles & giants stadium \\ 2 & 2001 & january 7 & new york giants & 20-10 & philadelphia eagles & giants stadium \\ 3 & 2001 & october 22 & philadelphia eagles & 10-9 & new york giants & giants stadium \\ 4 & 2001 & december 30 & philadelphia eagles & 24-21 & new york giants & veterans stadium \\ 5 & 2002 & october 28 & philadelphia eagles & 17-3 & new york giants & veterans stadium \\ 6 & 2002 & december 28 & new york giants & 10-7 & philadelphia eagles & giants stadium \\ 7 & 2003 & october 19 & philadelphia eagles & 14-10 & new york giants & giants stadium \\ 8 & 2003 & november 16 & philadelphia eagles & 28-10 & new york giants & lincoln financial field \\ 9 & 2004 & september 12 & philadelphia eagles & 31-17 & new york giants & lincoln financial field \\ 10 & 2004 & november 28 & philadelphia eagles & 27-6 & new york giants & giants stadium \\ 11 & 2005 & november 20 & new york giants & 27-17 & philadelphia eagles & giants stadium \\ 12 & 2005 & december 11 & new york giants & 26-23 (ot) & philadelphia eagles & lincoln financial field \\ 13 & 2006 & september 17 & new york giants & 30-24 (ot) & philadelphia eagles & lincoln financial field \\ 14 & 2006 & december 17 & philadelphia eagles & 36-22 & new york giants & giants stadium \\ 15 & 2007 & january 7 & philadelphia eagles & 23-20 & new york giants & lincoln financial field \\ 16 & 2007 & september 30 & new york giants & 16-3 & philadelphia eagles & giants stadium \\ 17 & 2007 & december 9 & new york giants & 16-13 & philadelphia eagles & lincoln financial field \\ 18 & 2008 & november 9 & new york giants & 36-31 & philadelphia eagles & lincoln financial field \\ 19 & 2008 & december 7 & philadelphia eagles & 20-14 & new york giants & giants stadium \\ 20 & 2009 & january 11 & philadelphia eagles & 23-11 & new york giants & giants stadium \\ 21 & 2009 & november 1 & philadelphia eagles & 40-17 & new york giants & lincoln financial field \\ 22 & 2009 & december 13 & philadelphia eagles & 45-38 & new york giants & giants stadium \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & location \\ \midrule 0 & lincoln financial field \\ \bottomrule \end{tabular}

select location where result = '16-13' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>year</th> <th>date</th> <th>winner</th> <th>result</th> <th>loser</th> <th>location</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>2000</td> <td>september 10</td> <td>new york giants</td> <td>33-18</td> <td>philadelphia eagles</td> <td>veterans stadium</td> </tr> <tr> <th>1</th> <td>2000</td> <td>october 29</td> <td>new york giants</td> <td>24-7</td> <td>philadelphia eagles</td> <td>giants stadium</td> </tr> <tr> <th>2</th> <td>2001</td> <td>january 7</td> <td>new york giants</td> <td>20-10</td> <td>philadelphia eagles</td> <td>giants stadium</td> </tr> <tr> <th>3</th> <td>2001</td> <td>october 22</td> <td>philadelphia eagles</td> <td>10-9</td> <td>new york giants</td> <td>giants stadium</td> </tr> <tr> <th>4</th> <td>2001</td> <td>december 30</td> <td>philadelphia eagles</td> <td>24-21</td> <td>new york giants</td> <td>veterans stadium</td> </tr> <tr> <th>5</th> <td>2002</td> <td>october 28</td> <td>philadelphia eagles</td> <td>17-3</td> <td>new york giants</td> <td>veterans stadium</td> </tr> <tr> <th>6</th> <td>2002</td> <td>december 28</td> <td>new york giants</td> <td>10-7</td> <td>philadelphia eagles</td> <td>giants stadium</td> </tr> <tr> <th>7</th> <td>2003</td> <td>october 19</td> <td>philadelphia eagles</td> <td>14-10</td> <td>new york giants</td> <td>giants stadium</td> </tr> <tr> <th>8</th> <td>2003</td> <td>november 16</td> <td>philadelphia eagles</td> <td>28-10</td> <td>new york giants</td> <td>lincoln financial field</td> </tr> <tr> <th>9</th> <td>2004</td> <td>september 12</td> <td>philadelphia eagles</td> <td>31-17</td> <td>new york giants</td> <td>lincoln financial field</td> </tr> <tr> <th>10</th> <td>2004</td> <td>november 28</td> <td>philadelphia eagles</td> <td>27-6</td> <td>new york giants</td> <td>giants stadium</td> </tr> <tr> <th>11</th> <td>2005</td> <td>november 20</td> <td>new york giants</td> <td>27-17</td> <td>philadelphia eagles</td> <td>giants stadium</td> </tr> <tr> <th>12</th> <td>2005</td> <td>december 11</td> <td>new york giants</td> <td>26-23 (ot)</td> <td>philadelphia eagles</td> <td>lincoln financial field</td> </tr> <tr> <th>13</th> <td>2006</td> <td>september 17</td> <td>new york giants</td> <td>30-24 (ot)</td> <td>philadelphia eagles</td> <td>lincoln financial field</td> </tr> <tr> <th>14</th> <td>2006</td> <td>december 17</td> <td>philadelphia eagles</td> <td>36-22</td> <td>new york giants</td> <td>giants stadium</td> </tr> <tr> <th>15</th> <td>2007</td> <td>january 7</td> <td>philadelphia eagles</td> <td>23-20</td> <td>new york giants</td> <td>lincoln financial field</td> </tr> <tr> <th>16</th> <td>2007</td> <td>september 30</td> <td>new york giants</td> <td>16-3</td> <td>philadelphia eagles</td> <td>giants stadium</td> </tr> <tr> <th>17</th> <td>2007</td> <td>december 9</td> <td>new york giants</td> <td>16-13</td> <td>philadelphia eagles</td> <td>lincoln financial field</td> </tr> <tr> <th>18</th> <td>2008</td> <td>november 9</td> <td>new york giants</td> <td>36-31</td> <td>philadelphia eagles</td> <td>lincoln financial field</td> </tr> <tr> <th>19</th> <td>2008</td> <td>december 7</td> <td>philadelphia eagles</td> <td>20-14</td> <td>new york giants</td> <td>giants stadium</td> </tr> <tr> <th>20</th> <td>2009</td> <td>january 11</td> <td>philadelphia eagles</td> <td>23-11</td> <td>new york giants</td> <td>giants stadium</td> </tr> <tr> <th>21</th> <td>2009</td> <td>november 1</td> <td>philadelphia eagles</td> <td>40-17</td> <td>new york giants</td> <td>lincoln financial field</td> </tr> <tr> <th>22</th> <td>2009</td> <td>december 13</td> <td>philadelphia eagles</td> <td>45-38</td> <td>new york giants</td> <td>giants stadium</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>location</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>lincoln financial field</td> </tr> </tbody> </table>

select location where result = '16-13' <table_name> : table | | year | date | winner | result | loser | location | |---:|-------:|:-------------|:--------------------|:-----------|:--------------------|:------------------------| | 0 | 2000 | september 10 | new york giants | 33-18 | philadelphia eagles | veterans stadium | | 1 | 2000 | october 29 | new york giants | 24-7 | philadelphia eagles | giants stadium | | 2 | 2001 | january 7 | new york giants | 20-10 | philadelphia eagles | giants stadium | | 3 | 2001 | october 22 | philadelphia eagles | 10-9 | new york giants | giants stadium | | 4 | 2001 | december 30 | philadelphia eagles | 24-21 | new york giants | veterans stadium | | 5 | 2002 | october 28 | philadelphia eagles | 17-3 | new york giants | veterans stadium | | 6 | 2002 | december 28 | new york giants | 10-7 | philadelphia eagles | giants stadium | | 7 | 2003 | october 19 | philadelphia eagles | 14-10 | new york giants | giants stadium | | 8 | 2003 | november 16 | philadelphia eagles | 28-10 | new york giants | lincoln financial field | | 9 | 2004 | september 12 | philadelphia eagles | 31-17 | new york giants | lincoln financial field | | 10 | 2004 | november 28 | philadelphia eagles | 27-6 | new york giants | giants stadium | | 11 | 2005 | november 20 | new york giants | 27-17 | philadelphia eagles | giants stadium | | 12 | 2005 | december 11 | new york giants | 26-23 (ot) | philadelphia eagles | lincoln financial field | | 13 | 2006 | september 17 | new york giants | 30-24 (ot) | philadelphia eagles | lincoln financial field | | 14 | 2006 | december 17 | philadelphia eagles | 36-22 | new york giants | giants stadium | | 15 | 2007 | january 7 | philadelphia eagles | 23-20 | new york giants | lincoln financial field | | 16 | 2007 | september 30 | new york giants | 16-3 | philadelphia eagles | giants stadium | | 17 | 2007 | december 9 | new york giants | 16-13 | philadelphia eagles | lincoln financial field | | 18 | 2008 | november 9 | new york giants | 36-31 | philadelphia eagles | lincoln financial field | | 19 | 2008 | december 7 | philadelphia eagles | 20-14 | new york giants | giants stadium | | 20 | 2009 | january 11 | philadelphia eagles | 23-11 | new york giants | giants stadium | | 21 | 2009 | november 1 | philadelphia eagles | 40-17 | new york giants | lincoln financial field | | 22 | 2009 | december 13 | philadelphia eagles | 45-38 | new york giants | giants stadium |

| | location | |---:|:------------------------| | 0 | lincoln financial field |

select count ( * ) where language = 1003 col : language | totora municipality | pojo municipality | pocona municipality | chimore municipality | puerto villarroel municipality | entre rios municipality row 1 : quechua | 11671 | 10203 | 12482 | 9596 | 29940 | 14789 row 2 : aymara | 72 | 74 | 39 | 965 | 1590 | 907 row 3 : guarani | 7 | 9 | 3 | 19 | 39 | 41 row 4 : another native | 16 | 9 | 6 | 424 | 235 | 27 row 5 : spanish | 4967 | 4991 | 4954 | 11530 | 29377 | 17164 row 6 : foreign | 32 | 15 | 22 | 128 | 199 | 142 row 7 : only native | 7060 | 5623 | 7706 | 2518 | 8131 | 3207 row 8 : native and spanish | 4645 | 4598 | 4790 | 7811 | 22426 | 11864 row 9 : only spanish | 322 | 393 | 166 | 3726 | 6956 | 5314

col : count ( "*" ) row 1 : 0

select count ( * ) where language = 1003 <table_name> : table \begin{tabular}{llllllll} \toprule & language & totora municipality & pojo municipality & pocona municipality & chimore municipality & puerto villarroel municipality & entre rios municipality \\ \midrule 0 & quechua & 11671 & 10203 & 12482 & 9596 & 29940 & 14789 \\ 1 & aymara & 72 & 74 & 39 & 965 & 1590 & 907 \\ 2 & guarani & 7 & 9 & 3 & 19 & 39 & 41 \\ 3 & another native & 16 & 9 & 6 & 424 & 235 & 27 \\ 4 & spanish & 4967 & 4991 & 4954 & 11530 & 29377 & 17164 \\ 5 & foreign & 32 & 15 & 22 & 128 & 199 & 142 \\ 6 & only native & 7060 & 5623 & 7706 & 2518 & 8131 & 3207 \\ 7 & native and spanish & 4645 & 4598 & 4790 & 7811 & 22426 & 11864 \\ 8 & only spanish & 322 & 393 & 166 & 3726 & 6956 & 5314 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 0 \\ \bottomrule \end{tabular}

select count ( * ) where language = 1003 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>language</th> <th>totora municipality</th> <th>pojo municipality</th> <th>pocona municipality</th> <th>chimore municipality</th> <th>puerto villarroel municipality</th> <th>entre rios municipality</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>quechua</td> <td>11671</td> <td>10203</td> <td>12482</td> <td>9596</td> <td>29940</td> <td>14789</td> </tr> <tr> <th>1</th> <td>aymara</td> <td>72</td> <td>74</td> <td>39</td> <td>965</td> <td>1590</td> <td>907</td> </tr> <tr> <th>2</th> <td>guarani</td> <td>7</td> <td>9</td> <td>3</td> <td>19</td> <td>39</td> <td>41</td> </tr> <tr> <th>3</th> <td>another native</td> <td>16</td> <td>9</td> <td>6</td> <td>424</td> <td>235</td> <td>27</td> </tr> <tr> <th>4</th> <td>spanish</td> <td>4967</td> <td>4991</td> <td>4954</td> <td>11530</td> <td>29377</td> <td>17164</td> </tr> <tr> <th>5</th> <td>foreign</td> <td>32</td> <td>15</td> <td>22</td> <td>128</td> <td>199</td> <td>142</td> </tr> <tr> <th>6</th> <td>only native</td> <td>7060</td> <td>5623</td> <td>7706</td> <td>2518</td> <td>8131</td> <td>3207</td> </tr> <tr> <th>7</th> <td>native and spanish</td> <td>4645</td> <td>4598</td> <td>4790</td> <td>7811</td> <td>22426</td> <td>11864</td> </tr> <tr> <th>8</th> <td>only spanish</td> <td>322</td> <td>393</td> <td>166</td> <td>3726</td> <td>6956</td> <td>5314</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "*" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>0</td> </tr> </tbody> </table>

select count ( * ) where language = 1003 <table_name> : table | | language | totora municipality | pojo municipality | pocona municipality | chimore municipality | puerto villarroel municipality | entre rios municipality | |---:|:-------------------|----------------------:|--------------------:|----------------------:|-----------------------:|---------------------------------:|--------------------------:| | 0 | quechua | 11671 | 10203 | 12482 | 9596 | 29940 | 14789 | | 1 | aymara | 72 | 74 | 39 | 965 | 1590 | 907 | | 2 | guarani | 7 | 9 | 3 | 19 | 39 | 41 | | 3 | another native | 16 | 9 | 6 | 424 | 235 | 27 | | 4 | spanish | 4967 | 4991 | 4954 | 11530 | 29377 | 17164 | | 5 | foreign | 32 | 15 | 22 | 128 | 199 | 142 | | 6 | only native | 7060 | 5623 | 7706 | 2518 | 8131 | 3207 | | 7 | native and spanish | 4645 | 4598 | 4790 | 7811 | 22426 | 11864 | | 8 | only spanish | 322 | 393 | 166 | 3726 | 6956 | 5314 |

| | count ( "*" ) | |---:|----------------:| | 0 | 0 |

select count ( note ) where note = 'none' col : title | release | 6th gen | handheld | note row 1 : buggy grand prix: kattobi! dai-sakusen | 2003 | playstation 2 | none | none row 2 : gunbird special edition / gunbird 1&2 | 2004 | playstation 2 | none | none row 3 : psikyo shooting collection vol. 1: strikers 1945 i & ii | 2004 | playstation 2 | none | released and published in europe by play it as 1945 i & ii: the arcade games row 4 : psikyo shooting collection vol. 2: sengoku ace & sengoku blade | 2004 | playstation 2 | none | none row 5 : psikyo shooting collection vol. 3: sol divide & dragon blaze | 2004 | playstation 2 | none | none row 6 : taisen hot gimmick: cosplay mahjong | 2004 | playstation 2 | none | none row 7 : sengoku cannon | 2005 | none | psp | none row 8 : taisen hot gimmick: axes-jong | 2005 | playstation 2 | none | none

col : count ( "note" ) row 1 : 7

select count ( note ) where note = 'none' <table_name> : table \begin{tabular}{llllll} \toprule & title & release & 6th gen & handheld & note \\ \midrule 0 & buggy grand prix: kattobi! dai-sakusen & 2003 & playstation 2 & none & none \\ 1 & gunbird special edition / gunbird 1&2 & 2004 & playstation 2 & none & none \\ 2 & psikyo shooting collection vol. 1: strikers 1945 i & ii & 2004 & playstation 2 & none & released and published in europe by play it as 1945 i & ii: the arcade games \\ 3 & psikyo shooting collection vol. 2: sengoku ace & sengoku blade & 2004 & playstation 2 & none & none \\ 4 & psikyo shooting collection vol. 3: sol divide & dragon blaze & 2004 & playstation 2 & none & none \\ 5 & taisen hot gimmick: cosplay mahjong & 2004 & playstation 2 & none & none \\ 6 & sengoku cannon & 2005 & none & psp & none \\ 7 & taisen hot gimmick: axes-jong & 2005 & playstation 2 & none & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "note" ) \\ \midrule 0 & 7 \\ \bottomrule \end{tabular}

select count ( note ) where note = 'none' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>title</th> <th>release</th> <th>6th gen</th> <th>handheld</th> <th>note</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>buggy grand prix: kattobi! dai-sakusen</td> <td>2003</td> <td>playstation 2</td> <td>none</td> <td>none</td> </tr> <tr> <th>1</th> <td>gunbird special edition / gunbird 1&amp;2</td> <td>2004</td> <td>playstation 2</td> <td>none</td> <td>none</td> </tr> <tr> <th>2</th> <td>psikyo shooting collection vol. 1: strikers 1945 i &amp; ii</td> <td>2004</td> <td>playstation 2</td> <td>none</td> <td>released and published in europe by play it as 1945 i &amp; ii: the arcade games</td> </tr> <tr> <th>3</th> <td>psikyo shooting collection vol. 2: sengoku ace &amp; sengoku blade</td> <td>2004</td> <td>playstation 2</td> <td>none</td> <td>none</td> </tr> <tr> <th>4</th> <td>psikyo shooting collection vol. 3: sol divide &amp; dragon blaze</td> <td>2004</td> <td>playstation 2</td> <td>none</td> <td>none</td> </tr> <tr> <th>5</th> <td>taisen hot gimmick: cosplay mahjong</td> <td>2004</td> <td>playstation 2</td> <td>none</td> <td>none</td> </tr> <tr> <th>6</th> <td>sengoku cannon</td> <td>2005</td> <td>none</td> <td>psp</td> <td>none</td> </tr> <tr> <th>7</th> <td>taisen hot gimmick: axes-jong</td> <td>2005</td> <td>playstation 2</td> <td>none</td> <td>none</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "note" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>7</td> </tr> </tbody> </table>

select count ( note ) where note = 'none' <table_name> : table | | title | release | 6th gen | handheld | note | |---:|:---------------------------------------------------------------|----------:|:--------------|:-----------|:-----------------------------------------------------------------------------| | 0 | buggy grand prix: kattobi! dai-sakusen | 2003 | playstation 2 | none | none | | 1 | gunbird special edition / gunbird 1&2 | 2004 | playstation 2 | none | none | | 2 | psikyo shooting collection vol. 1: strikers 1945 i & ii | 2004 | playstation 2 | none | released and published in europe by play it as 1945 i & ii: the arcade games | | 3 | psikyo shooting collection vol. 2: sengoku ace & sengoku blade | 2004 | playstation 2 | none | none | | 4 | psikyo shooting collection vol. 3: sol divide & dragon blaze | 2004 | playstation 2 | none | none | | 5 | taisen hot gimmick: cosplay mahjong | 2004 | playstation 2 | none | none | | 6 | sengoku cannon | 2005 | none | psp | none | | 7 | taisen hot gimmick: axes-jong | 2005 | playstation 2 | none | none |

| | count ( "note" ) | |---:|-------------------:| | 0 | 7 |

select count ( * ) where year = '46' col : title | year | peak chart positions us | peak chart positions us dance | peak chart positions us pop | peak chart positions us r&b | peak chart positions ger | peak chart positions nl | peak chart positions nor | peak chart positions nz | peak chart positions swi | peak chart positions uk | album row 1 : 1980 | "earth can be just like heaven" | none | 2 | none | none | none | none | none | none | none | none | row 2 : 1980 | "i got the feeling" | none | 2 | none | none | none | none | none | none | none | none | row 3 : 1980 | "just us" | none | 2 | none | 29 | none | none | none | none | none | none | row 4 : 1982 | "it's raining men" | 46 | 1 | 46 | 34 | 43 | 46 | 8 | 13 | 95 | 2 | row 5 : 1985 | "no one can love you more than me" | none | 26 | none | none | none | none | none | none | none | none | row 6 : 1985 | "well-a-wiggy" | none | none | 107 | 76 | none | none | none | none | none | none | row 7 : 1993 | "can you feel it" | none | 2 | none | none | 75 | none | none | none | none | none |

col : count ( "*" ) row 1 : 0

select count ( * ) where year = '46' <table_name> : table \begin{tabular}{llllllllllllll} \toprule & title & year & peak chart positions us & peak chart positions us dance & peak chart positions us pop & peak chart positions us r&b & peak chart positions ger & peak chart positions nl & peak chart positions nor & peak chart positions nz & peak chart positions swi & peak chart positions uk & album \\ \midrule 0 & 1980 & "earth can be just like heaven" & none & 2 & none & none & none & none & none & none & none & none & \\ 1 & 1980 & "i got the feeling" & none & 2 & none & none & none & none & none & none & none & none & \\ 2 & 1980 & "just us" & none & 2 & none & 29 & none & none & none & none & none & none & \\ 3 & 1982 & "it's raining men" & 46 & 1 & 46 & 34 & 43 & 46 & 8 & 13 & 95 & 2 & \\ 4 & 1985 & "no one can love you more than me" & none & 26 & none & none & none & none & none & none & none & none & \\ 5 & 1985 & "well-a-wiggy" & none & none & 107 & 76 & none & none & none & none & none & none & \\ 6 & 1993 & "can you feel it" & none & 2 & none & none & 75 & none & none & none & none & none | & NaN \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 0 \\ \bottomrule \end{tabular}

select count ( * ) where year = '46' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>title</th> <th>year</th> <th>peak chart positions us</th> <th>peak chart positions us dance</th> <th>peak chart positions us pop</th> <th>peak chart positions us r&amp;b</th> <th>peak chart positions ger</th> <th>peak chart positions nl</th> <th>peak chart positions nor</th> <th>peak chart positions nz</th> <th>peak chart positions swi</th> <th>peak chart positions uk</th> <th>album</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1980</td> <td>"earth can be just like heaven"</td> <td>none</td> <td>2</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> <td></td> </tr> <tr> <th>1</th> <td>1980</td> <td>"i got the feeling"</td> <td>none</td> <td>2</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> <td></td> </tr> <tr> <th>2</th> <td>1980</td> <td>"just us"</td> <td>none</td> <td>2</td> <td>none</td> <td>29</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> <td></td> </tr> <tr> <th>3</th> <td>1982</td> <td>"it's raining men"</td> <td>46</td> <td>1</td> <td>46</td> <td>34</td> <td>43</td> <td>46</td> <td>8</td> <td>13</td> <td>95</td> <td>2</td> <td></td> </tr> <tr> <th>4</th> <td>1985</td> <td>"no one can love you more than me"</td> <td>none</td> <td>26</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> <td></td> </tr> <tr> <th>5</th> <td>1985</td> <td>"well-a-wiggy"</td> <td>none</td> <td>none</td> <td>107</td> <td>76</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> <td></td> </tr> <tr> <th>6</th> <td>1993</td> <td>"can you feel it"</td> <td>none</td> <td>2</td> <td>none</td> <td>none</td> <td>75</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> <td>none |</td> <td>None</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "*" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>0</td> </tr> </tbody> </table>

select count ( * ) where year = '46' <table_name> : table | | title | year | peak chart positions us | peak chart positions us dance | peak chart positions us pop | peak chart positions us r&b | peak chart positions ger | peak chart positions nl | peak chart positions nor | peak chart positions nz | peak chart positions swi | peak chart positions uk | album | |---:|--------:|:-----------------------------------|:--------------------------|:--------------------------------|:------------------------------|:------------------------------|:---------------------------|:--------------------------|:---------------------------|:--------------------------|:---------------------------|:--------------------------|:--------| | 0 | 1980 | "earth can be just like heaven" | none | 2 | none | none | none | none | none | none | none | none | | | 1 | 1980 | "i got the feeling" | none | 2 | none | none | none | none | none | none | none | none | | | 2 | 1980 | "just us" | none | 2 | none | 29 | none | none | none | none | none | none | | | 3 | 1982 | "it's raining men" | 46 | 1 | 46 | 34 | 43 | 46 | 8 | 13 | 95 | 2 | | | 4 | 1985 | "no one can love you more than me" | none | 26 | none | none | none | none | none | none | none | none | | | 5 | 1985 | "well-a-wiggy" | none | none | 107 | 76 | none | none | none | none | none | none | | | 6 | 1993 | "can you feel it" | none | 2 | none | none | 75 | none | none | none | none | none | | |

| | count ( "*" ) | |---:|----------------:| | 0 | 0 |

select count ( * ) where weight > max speed col : tops design code | electrical system | max speed | weight | brakes | route availability | notes row 1 : 08-0av | 90 v | 20 mph (32 km/h) | 49.8 te (496 kn) | vacuum | 5 | none row 2 : 08-0bx | 110 v | 20 mph (32 km/h) | 50.4 te (502 kn) | dual | 5 | none row 3 : 08-0ca | 90 v | 20 mph (32 km/h) | 49.6 te (494 kn) | air | 5 | none row 4 : 08-0dv | 90 v | 15 mph (24 km/h) | 49.8 te (496 kn) | vacuum | 5 | none row 5 : 08-0bx | 90 v | 15 mph (24 km/h) | 50.4 te (502 kn) | dual | 5 | none row 6 : 08-0fa | 90 v | 15 mph (24 km/h) | 49.6 te (494 kn) | air | 5 | none row 7 : 08-0kx | 110 v | 15 mph (24 km/h) | 50.4 te (502 kn) | dual | 5 | none row 8 : 08-0lx | 110 v | 15 mph (24 km/h) | 50.4 te (502 kn) | dual | 5 | scharfenberg adapter fitted row 9 : 08-0ma | 110 v | 15 mph (24 km/h) | 49.8 te (496 kn) | air | 5 | none row 10 : 08-0na | 90 v | 15 mph (24 km/h) | 49.8 te (496 kn) | air | 5 | fitted with buckeye couplings row 11 : 08-0pa | 90 v | 15 mph (24 km/h) | 51 te (508 kn) | air | 6 | fitted with buckeye couplings row 12 : 08-0qa | 90 v | 15 mph (24 km/h) | 51 te (508 kn) | air | 6 | fitted with buckeye couplings row 13 : 08-0ra | 110 v | 15 mph (24 km/h) | 51 te (508 kn) | air | 6 | fitted with buckeye couplings row 14 : 08-0sa | 110 v | 15 mph (24 km/h) | 49 te (488 kn) | air | 6 | none

col : count ( "*" ) row 1 : 14

select count ( * ) where weight > max speed <table_name> : table \begin{tabular}{llllllll} \toprule & tops design code & electrical system & max speed & weight & brakes & route availability & notes \\ \midrule 0 & 08-0av & 90 v & 20 mph (32 km/h) & 49.8 te (496 kn) & vacuum & 5 & none \\ 1 & 08-0bx & 110 v & 20 mph (32 km/h) & 50.4 te (502 kn) & dual & 5 & none \\ 2 & 08-0ca & 90 v & 20 mph (32 km/h) & 49.6 te (494 kn) & air & 5 & none \\ 3 & 08-0dv & 90 v & 15 mph (24 km/h) & 49.8 te (496 kn) & vacuum & 5 & none \\ 4 & 08-0bx & 90 v & 15 mph (24 km/h) & 50.4 te (502 kn) & dual & 5 & none \\ 5 & 08-0fa & 90 v & 15 mph (24 km/h) & 49.6 te (494 kn) & air & 5 & none \\ 6 & 08-0kx & 110 v & 15 mph (24 km/h) & 50.4 te (502 kn) & dual & 5 & none \\ 7 & 08-0lx & 110 v & 15 mph (24 km/h) & 50.4 te (502 kn) & dual & 5 & scharfenberg adapter fitted \\ 8 & 08-0ma & 110 v & 15 mph (24 km/h) & 49.8 te (496 kn) & air & 5 & none \\ 9 & 08-0na & 90 v & 15 mph (24 km/h) & 49.8 te (496 kn) & air & 5 & fitted with buckeye couplings \\ 10 & 08-0pa & 90 v & 15 mph (24 km/h) & 51 te (508 kn) & air & 6 & fitted with buckeye couplings \\ 11 & 08-0qa & 90 v & 15 mph (24 km/h) & 51 te (508 kn) & air & 6 & fitted with buckeye couplings \\ 12 & 08-0ra & 110 v & 15 mph (24 km/h) & 51 te (508 kn) & air & 6 & fitted with buckeye couplings \\ 13 & 08-0sa & 110 v & 15 mph (24 km/h) & 49 te (488 kn) & air & 6 & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 14 \\ \bottomrule \end{tabular}

select count ( * ) where weight > max speed <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>tops design code</th> <th>electrical system</th> <th>max speed</th> <th>weight</th> <th>brakes</th> <th>route availability</th> <th>notes</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>08-0av</td> <td>90 v</td> <td>20 mph (32 km/h)</td> <td>49.8 te (496 kn)</td> <td>vacuum</td> <td>5</td> <td>none</td> </tr> <tr> <th>1</th> <td>08-0bx</td> <td>110 v</td> <td>20 mph (32 km/h)</td> <td>50.4 te (502 kn)</td> <td>dual</td> <td>5</td> <td>none</td> </tr> <tr> <th>2</th> <td>08-0ca</td> <td>90 v</td> <td>20 mph (32 km/h)</td> <td>49.6 te (494 kn)</td> <td>air</td> <td>5</td> <td>none</td> </tr> <tr> <th>3</th> <td>08-0dv</td> <td>90 v</td> <td>15 mph (24 km/h)</td> <td>49.8 te (496 kn)</td> <td>vacuum</td> <td>5</td> <td>none</td> </tr> <tr> <th>4</th> <td>08-0bx</td> <td>90 v</td> <td>15 mph (24 km/h)</td> <td>50.4 te (502 kn)</td> <td>dual</td> <td>5</td> <td>none</td> </tr> <tr> <th>5</th> <td>08-0fa</td> <td>90 v</td> <td>15 mph (24 km/h)</td> <td>49.6 te (494 kn)</td> <td>air</td> <td>5</td> <td>none</td> </tr> <tr> <th>6</th> <td>08-0kx</td> <td>110 v</td> <td>15 mph (24 km/h)</td> <td>50.4 te (502 kn)</td> <td>dual</td> <td>5</td> <td>none</td> </tr> <tr> <th>7</th> <td>08-0lx</td> <td>110 v</td> <td>15 mph (24 km/h)</td> <td>50.4 te (502 kn)</td> <td>dual</td> <td>5</td> <td>scharfenberg adapter fitted</td> </tr> <tr> <th>8</th> <td>08-0ma</td> <td>110 v</td> <td>15 mph (24 km/h)</td> <td>49.8 te (496 kn)</td> <td>air</td> <td>5</td> <td>none</td> </tr> <tr> <th>9</th> <td>08-0na</td> <td>90 v</td> <td>15 mph (24 km/h)</td> <td>49.8 te (496 kn)</td> <td>air</td> <td>5</td> <td>fitted with buckeye couplings</td> </tr> <tr> <th>10</th> <td>08-0pa</td> <td>90 v</td> <td>15 mph (24 km/h)</td> <td>51 te (508 kn)</td> <td>air</td> <td>6</td> <td>fitted with buckeye couplings</td> </tr> <tr> <th>11</th> <td>08-0qa</td> <td>90 v</td> <td>15 mph (24 km/h)</td> <td>51 te (508 kn)</td> <td>air</td> <td>6</td> <td>fitted with buckeye couplings</td> </tr> <tr> <th>12</th> <td>08-0ra</td> <td>110 v</td> <td>15 mph (24 km/h)</td> <td>51 te (508 kn)</td> <td>air</td> <td>6</td> <td>fitted with buckeye couplings</td> </tr> <tr> <th>13</th> <td>08-0sa</td> <td>110 v</td> <td>15 mph (24 km/h)</td> <td>49 te (488 kn)</td> <td>air</td> <td>6</td> <td>none</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>count ( "*" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>14</td> </tr> </tbody> </table>

select count ( * ) where weight > max speed <table_name> : table | | tops design code | electrical system | max speed | weight | brakes | route availability | notes | |---:|:-------------------|:--------------------|:-----------------|:-----------------|:---------|---------------------:|:------------------------------| | 0 | 08-0av | 90 v | 20 mph (32 km/h) | 49.8 te (496 kn) | vacuum | 5 | none | | 1 | 08-0bx | 110 v | 20 mph (32 km/h) | 50.4 te (502 kn) | dual | 5 | none | | 2 | 08-0ca | 90 v | 20 mph (32 km/h) | 49.6 te (494 kn) | air | 5 | none | | 3 | 08-0dv | 90 v | 15 mph (24 km/h) | 49.8 te (496 kn) | vacuum | 5 | none | | 4 | 08-0bx | 90 v | 15 mph (24 km/h) | 50.4 te (502 kn) | dual | 5 | none | | 5 | 08-0fa | 90 v | 15 mph (24 km/h) | 49.6 te (494 kn) | air | 5 | none | | 6 | 08-0kx | 110 v | 15 mph (24 km/h) | 50.4 te (502 kn) | dual | 5 | none | | 7 | 08-0lx | 110 v | 15 mph (24 km/h) | 50.4 te (502 kn) | dual | 5 | scharfenberg adapter fitted | | 8 | 08-0ma | 110 v | 15 mph (24 km/h) | 49.8 te (496 kn) | air | 5 | none | | 9 | 08-0na | 90 v | 15 mph (24 km/h) | 49.8 te (496 kn) | air | 5 | fitted with buckeye couplings | | 10 | 08-0pa | 90 v | 15 mph (24 km/h) | 51 te (508 kn) | air | 6 | fitted with buckeye couplings | | 11 | 08-0qa | 90 v | 15 mph (24 km/h) | 51 te (508 kn) | air | 6 | fitted with buckeye couplings | | 12 | 08-0ra | 110 v | 15 mph (24 km/h) | 51 te (508 kn) | air | 6 | fitted with buckeye couplings | | 13 | 08-0sa | 110 v | 15 mph (24 km/h) | 49 te (488 kn) | air | 6 | none |

| | count ( "*" ) | |---:|----------------:| | 0 | 14 |

select month where may = 163 col : month | jan | feb | mar | apr | may | jun | jul | aug | sep | oct | nov | dec | year row 1 : record high °c (°f) | 38 (100) | 40 (104) | 43 (109) | 48 (118) | 46 (115) | 45 (113) | 42.5 (108.5) | 42.5 (108.5) | 41 (106) | 39 (102) | 39 (102) | 39 (102) | 48 (118) row 2 : average high °c (°f) | 20.7 (69.3) | 23.2 (73.8) | 26.9 (80.4) | 30 (86) | 32.2 (90) | 33.8 (92.8) | 34.8 (94.6) | 34.5 (94.1) | 31.5 (88.7) | 27.6 (81.7) | 24.1 (75.4) | 21.2 (70.2) | 28.4 (83.1) row 3 : daily mean °c (°f) | 14.4 (57.9) | 16.6 (61.9) | 20 (68) | 23.4 (74.1) | 26.2 (79.2) | 27.9 (82.2) | 28.6 (83.5) | 28.5 (83.3) | 26.2 (79.2) | 22.4 (72.3) | 18.4 (65.1) | 15.1 (59.2) | 22.3 (72.1) row 4 : average low °c (°f) | 8.2 (46.8) | 10 (50) | 13.2 (55.8) | 16.7 (62.1) | 20.2 (68.4) | 22 (72) | 22.3 (72.1) | 22.5 (72.5) | 20.9 (69.6) | 17.2 (63) | 12.7 (54.9) | 9.1 (48.4) | 16.3 (61.3) row 5 : record low °c (°f) | -7 (19) | -7 (19) | -1 (30) | 5 (41) | 8 (46) | 11.5 (52.7) | 11 (52) | 12.2 (54) | 2 (36) | 2 (36) | -5 (23) | -7.5 (18.5) | -7.5 (18.5) row 6 : precipitation mm (inches) | 16.6 (0.654) | 16.5 (0.65) | 19.9 (0.783) | 29.7 (1.169) | 52.3 (2.059) | 68.4 (2.693) | 43 (1.69) | 79.6 (3.134) | 150.6 (5.929) | 77.2 (3.039) | 23 (0.91) | 14.1 (0.555) | 590.9 (23.264) row 7 : avg. precipitation days (≥ 0.1 mm) | 4.2 | 3.8 | 3.4 | 4.5 | 5.7 | 5.6 | 3.9 | 6.4 | 8.2 | 6.5 | 4.1 | 3.4 | 59.7 row 8 : avg. snowy days | 0.03 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.03 row 9 : % humidity | 67 | 64 | 58 | 61 | 66 | 66 | 63 | 63 | 69 | 71 | 68 | 69 | 65 row 10 : mean monthly sunshine hours | 193 | 137 | 155 | 167 | 163 | 178 | 194 | 226 | 167 | 142 | 196 | 185 | 2,104

col : month row 1 : mean monthly sunshine hours

select month where may = 163 <table_name> : table \begin{tabular}{lllllllllllllll} \toprule & month & jan & feb & mar & apr & may & jun & jul & aug & sep & oct & nov & dec & year \\ \midrule 0 & record high °c (°f) & 38 (100) & 40 (104) & 43 (109) & 48 (118) & 46 (115) & 45 (113) & 42.5 (108.5) & 42.5 (108.5) & 41 (106) & 39 (102) & 39 (102) & 39 (102) & 48 (118) \\ 1 & average high °c (°f) & 20.7 (69.3) & 23.2 (73.8) & 26.9 (80.4) & 30 (86) & 32.2 (90) & 33.8 (92.8) & 34.8 (94.6) & 34.5 (94.1) & 31.5 (88.7) & 27.6 (81.7) & 24.1 (75.4) & 21.2 (70.2) & 28.4 (83.1) \\ 2 & daily mean °c (°f) & 14.4 (57.9) & 16.6 (61.9) & 20 (68) & 23.4 (74.1) & 26.2 (79.2) & 27.9 (82.2) & 28.6 (83.5) & 28.5 (83.3) & 26.2 (79.2) & 22.4 (72.3) & 18.4 (65.1) & 15.1 (59.2) & 22.3 (72.1) \\ 3 & average low °c (°f) & 8.2 (46.8) & 10 (50) & 13.2 (55.8) & 16.7 (62.1) & 20.2 (68.4) & 22 (72) & 22.3 (72.1) & 22.5 (72.5) & 20.9 (69.6) & 17.2 (63) & 12.7 (54.9) & 9.1 (48.4) & 16.3 (61.3) \\ 4 & record low °c (°f) & -7 (19) & -7 (19) & -1 (30) & 5 (41) & 8 (46) & 11.5 (52.7) & 11 (52) & 12.2 (54) & 2 (36) & 2 (36) & -5 (23) & -7.5 (18.5) & -7.5 (18.5) \\ 5 & precipitation mm (inches) & 16.6 (0.654) & 16.5 (0.65) & 19.9 (0.783) & 29.7 (1.169) & 52.3 (2.059) & 68.4 (2.693) & 43 (1.69) & 79.6 (3.134) & 150.6 (5.929) & 77.2 (3.039) & 23 (0.91) & 14.1 (0.555) & 590.9 (23.264) \\ 6 & avg. precipitation days (≥ 0.1 mm) & 4.2 & 3.8 & 3.4 & 4.5 & 5.7 & 5.6 & 3.9 & 6.4 & 8.2 & 6.5 & 4.1 & 3.4 & 59.7 \\ 7 & avg. snowy days & 0.03 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.03 \\ 8 & % humidity & 67 & 64 & 58 & 61 & 66 & 66 & 63 & 63 & 69 & 71 & 68 & 69 & 65 \\ 9 & mean monthly sunshine hours & 193 & 137 & 155 & 167 & 163 & 178 & 194 & 226 & 167 & 142 & 196 & 185 & 2,104 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & month \\ \midrule 0 & mean monthly sunshine hours \\ \bottomrule \end{tabular}

select month where may = 163 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>month</th> <th>jan</th> <th>feb</th> <th>mar</th> <th>apr</th> <th>may</th> <th>jun</th> <th>jul</th> <th>aug</th> <th>sep</th> <th>oct</th> <th>nov</th> <th>dec</th> <th>year</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>record high °c (°f)</td> <td>38 (100)</td> <td>40 (104)</td> <td>43 (109)</td> <td>48 (118)</td> <td>46 (115)</td> <td>45 (113)</td> <td>42.5 (108.5)</td> <td>42.5 (108.5)</td> <td>41 (106)</td> <td>39 (102)</td> <td>39 (102)</td> <td>39 (102)</td> <td>48 (118)</td> </tr> <tr> <th>1</th> <td>average high °c (°f)</td> <td>20.7 (69.3)</td> <td>23.2 (73.8)</td> <td>26.9 (80.4)</td> <td>30 (86)</td> <td>32.2 (90)</td> <td>33.8 (92.8)</td> <td>34.8 (94.6)</td> <td>34.5 (94.1)</td> <td>31.5 (88.7)</td> <td>27.6 (81.7)</td> <td>24.1 (75.4)</td> <td>21.2 (70.2)</td> <td>28.4 (83.1)</td> </tr> <tr> <th>2</th> <td>daily mean °c (°f)</td> <td>14.4 (57.9)</td> <td>16.6 (61.9)</td> <td>20 (68)</td> <td>23.4 (74.1)</td> <td>26.2 (79.2)</td> <td>27.9 (82.2)</td> <td>28.6 (83.5)</td> <td>28.5 (83.3)</td> <td>26.2 (79.2)</td> <td>22.4 (72.3)</td> <td>18.4 (65.1)</td> <td>15.1 (59.2)</td> <td>22.3 (72.1)</td> </tr> <tr> <th>3</th> <td>average low °c (°f)</td> <td>8.2 (46.8)</td> <td>10 (50)</td> <td>13.2 (55.8)</td> <td>16.7 (62.1)</td> <td>20.2 (68.4)</td> <td>22 (72)</td> <td>22.3 (72.1)</td> <td>22.5 (72.5)</td> <td>20.9 (69.6)</td> <td>17.2 (63)</td> <td>12.7 (54.9)</td> <td>9.1 (48.4)</td> <td>16.3 (61.3)</td> </tr> <tr> <th>4</th> <td>record low °c (°f)</td> <td>-7 (19)</td> <td>-7 (19)</td> <td>-1 (30)</td> <td>5 (41)</td> <td>8 (46)</td> <td>11.5 (52.7)</td> <td>11 (52)</td> <td>12.2 (54)</td> <td>2 (36)</td> <td>2 (36)</td> <td>-5 (23)</td> <td>-7.5 (18.5)</td> <td>-7.5 (18.5)</td> </tr> <tr> <th>5</th> <td>precipitation mm (inches)</td> <td>16.6 (0.654)</td> <td>16.5 (0.65)</td> <td>19.9 (0.783)</td> <td>29.7 (1.169)</td> <td>52.3 (2.059)</td> <td>68.4 (2.693)</td> <td>43 (1.69)</td> <td>79.6 (3.134)</td> <td>150.6 (5.929)</td> <td>77.2 (3.039)</td> <td>23 (0.91)</td> <td>14.1 (0.555)</td> <td>590.9 (23.264)</td> </tr> <tr> <th>6</th> <td>avg. precipitation days (≥ 0.1 mm)</td> <td>4.2</td> <td>3.8</td> <td>3.4</td> <td>4.5</td> <td>5.7</td> <td>5.6</td> <td>3.9</td> <td>6.4</td> <td>8.2</td> <td>6.5</td> <td>4.1</td> <td>3.4</td> <td>59.7</td> </tr> <tr> <th>7</th> <td>avg. snowy days</td> <td>0.03</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0.03</td> </tr> <tr> <th>8</th> <td>% humidity</td> <td>67</td> <td>64</td> <td>58</td> <td>61</td> <td>66</td> <td>66</td> <td>63</td> <td>63</td> <td>69</td> <td>71</td> <td>68</td> <td>69</td> <td>65</td> </tr> <tr> <th>9</th> <td>mean monthly sunshine hours</td> <td>193</td> <td>137</td> <td>155</td> <td>167</td> <td>163</td> <td>178</td> <td>194</td> <td>226</td> <td>167</td> <td>142</td> <td>196</td> <td>185</td> <td>2,104</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>month</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>mean monthly sunshine hours</td> </tr> </tbody> </table>

select month where may = 163 <table_name> : table | | month | jan | feb | mar | apr | may | jun | jul | aug | sep | oct | nov | dec | year | |---:|:-----------------------------------|:-------------|:------------|:-------------|:-------------|:-------------|:-------------|:-------------|:-------------|:--------------|:-------------|:------------|:-------------|:---------------| | 0 | record high °c (°f) | 38 (100) | 40 (104) | 43 (109) | 48 (118) | 46 (115) | 45 (113) | 42.5 (108.5) | 42.5 (108.5) | 41 (106) | 39 (102) | 39 (102) | 39 (102) | 48 (118) | | 1 | average high °c (°f) | 20.7 (69.3) | 23.2 (73.8) | 26.9 (80.4) | 30 (86) | 32.2 (90) | 33.8 (92.8) | 34.8 (94.6) | 34.5 (94.1) | 31.5 (88.7) | 27.6 (81.7) | 24.1 (75.4) | 21.2 (70.2) | 28.4 (83.1) | | 2 | daily mean °c (°f) | 14.4 (57.9) | 16.6 (61.9) | 20 (68) | 23.4 (74.1) | 26.2 (79.2) | 27.9 (82.2) | 28.6 (83.5) | 28.5 (83.3) | 26.2 (79.2) | 22.4 (72.3) | 18.4 (65.1) | 15.1 (59.2) | 22.3 (72.1) | | 3 | average low °c (°f) | 8.2 (46.8) | 10 (50) | 13.2 (55.8) | 16.7 (62.1) | 20.2 (68.4) | 22 (72) | 22.3 (72.1) | 22.5 (72.5) | 20.9 (69.6) | 17.2 (63) | 12.7 (54.9) | 9.1 (48.4) | 16.3 (61.3) | | 4 | record low °c (°f) | -7 (19) | -7 (19) | -1 (30) | 5 (41) | 8 (46) | 11.5 (52.7) | 11 (52) | 12.2 (54) | 2 (36) | 2 (36) | -5 (23) | -7.5 (18.5) | -7.5 (18.5) | | 5 | precipitation mm (inches) | 16.6 (0.654) | 16.5 (0.65) | 19.9 (0.783) | 29.7 (1.169) | 52.3 (2.059) | 68.4 (2.693) | 43 (1.69) | 79.6 (3.134) | 150.6 (5.929) | 77.2 (3.039) | 23 (0.91) | 14.1 (0.555) | 590.9 (23.264) | | 6 | avg. precipitation days (≥ 0.1 mm) | 4.2 | 3.8 | 3.4 | 4.5 | 5.7 | 5.6 | 3.9 | 6.4 | 8.2 | 6.5 | 4.1 | 3.4 | 59.7 | | 7 | avg. snowy days | 0.03 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.03 | | 8 | % humidity | 67 | 64 | 58 | 61 | 66 | 66 | 63 | 63 | 69 | 71 | 68 | 69 | 65 | | 9 | mean monthly sunshine hours | 193 | 137 | 155 | 167 | 163 | 178 | 194 | 226 | 167 | 142 | 196 | 185 | 2,104 |

| | month | |---:|:----------------------------| | 0 | mean monthly sunshine hours |

select name where function = 'reichspressechef of the nsdap and secretary of state in the propagandaministerium' col : name | function | sentence row 1 : ernst von weizsacker | permanent secretary of state in the auswartiges amt (foreign ministry) under ribbentrop until 1943, then ambassador to the holy see; ss-brigadefuhrer | 7 years' imprisonment; reduced to 5 years on december 12, 1949, released in october 1950 row 2 : gustav adolf steengracht von moyland | successor of von weizsacker as secretary of state in the foreign ministry (until 1945) | 7 years' imprisonment; reduced to 5 years on december 12, 1949, released 1950 row 3 : wilhelm keppler | secretary of state; hitler's advisor for economy | 10 years' imprisonment; released 1951 row 4 : ernst wilhelm bohle | ns-gauleiter, secretary of state in the foreign ministry; head of the auslandorganisation (foreign organization) of the nsdap | 5 years' imprisonment row 5 : ernst woermann (de) | secretary in the foreign ministry; head of the political division | 7 years' imprisonment; reduced to 5 years on december 12, 1949; released 1951 row 6 : karl ritter | liaison between foreign office and the high command of the german armed forces | 4 years' imprisonment incl. time already served; released after the judgment row 7 : otto von erdmannsdorff | secretary in the foreign ministry; deputy to wormann | acquitted row 8 : edmund veesenmayer | plenipotentiary in hungary | 20 years' imprisonment; reduced to 10 years in 1951 and released the same year row 9 : hans heinrich lammers | head of the reich chancellery | 20 years' imprisonment; reduced to 10 years in january 1951 and released december 16, 1951 row 10 : wilhelm stuckart | secretary of state in the interior ministry | time already served (3 years and 10 months) row 11 : richard walther darre | minister for food and agriculture | 7 years' imprisonment; released 1950 row 12 : otto meissner | head of the presidential chancellery | acquitted row 13 : otto dietrich | reichspressechef of the nsdap and secretary of state in the propagandaministerium | 7 years' imprisonment incl. time already served; released in 1950 row 14 : gottlob berger | head of the ss-hauptamt, ss-obergruppenfuhrer | 25 years' imprisonment; reduced to 10 years in 1951; released the same year row 15 : walter schellenberg | second-in-command of the gestapo, head of the sd and the abwehr, and successor of wilhelm canaris as the head of the combined secret services; ss-brigadefuhrer | 6 years' imprisonment incl. time already served row 16 : lutz graf schwerin von krosigk | minister of finance | 10 years' imprisonment; released in 1951 row 17 : emil puhl | vice-president of the reichsbank | 5 years' imprisonment incl. time already served row 18 : karl rasche | director of the dresdner bank | 7 years' imprisonment incl. time already served row 19 : paul korner (de) | secretary of state, deputy of goring | 15 years' imprisonment; reduced to 10 years in 1951; released the same year row 20 : paul pleiger | head of the reichswerke hermann goring (confiscated steel plants employing slave laborers) | 15 years' imprisonment; reduced to 10 years in 1951; released the same year row 21 : hans kehrl (de) | secretary in the ministry of armament; head of the planning office | 15 years' imprisonment; released in 1951

col : name row 1 : otto dietrich

select name where function = 'reichspressechef of the nsdap and secretary of state in the propagandaministerium' <table_name> : table \begin{tabular}{llll} \toprule & name & function & sentence \\ \midrule 0 & ernst von weizsacker & permanent secretary of state in the auswartiges amt (foreign ministry) under ribbentrop until 1943, then ambassador to the holy see; ss-brigadefuhrer & 7 years' imprisonment; reduced to 5 years on december 12, 1949, released in october 1950 \\ 1 & gustav adolf steengracht von moyland & successor of von weizsacker as secretary of state in the foreign ministry (until 1945) & 7 years' imprisonment; reduced to 5 years on december 12, 1949, released 1950 \\ 2 & wilhelm keppler & secretary of state; hitler's advisor for economy & 10 years' imprisonment; released 1951 \\ 3 & ernst wilhelm bohle & ns-gauleiter, secretary of state in the foreign ministry; head of the auslandorganisation (foreign organization) of the nsdap & 5 years' imprisonment \\ 4 & ernst woermann (de) & secretary in the foreign ministry; head of the political division & 7 years' imprisonment; reduced to 5 years on december 12, 1949; released 1951 \\ 5 & karl ritter & liaison between foreign office and the high command of the german armed forces & 4 years' imprisonment incl. time already served; released after the judgment \\ 6 & otto von erdmannsdorff & secretary in the foreign ministry; deputy to wormann & acquitted \\ 7 & edmund veesenmayer & plenipotentiary in hungary & 20 years' imprisonment; reduced to 10 years in 1951 and released the same year \\ 8 & hans heinrich lammers & head of the reich chancellery & 20 years' imprisonment; reduced to 10 years in january 1951 and released december 16, 1951 \\ 9 & wilhelm stuckart & secretary of state in the interior ministry & time already served (3 years and 10 months) \\ 10 & richard walther darre & minister for food and agriculture & 7 years' imprisonment; released 1950 \\ 11 & otto meissner & head of the presidential chancellery & acquitted \\ 12 & otto dietrich & reichspressechef of the nsdap and secretary of state in the propagandaministerium & 7 years' imprisonment incl. time already served; released in 1950 \\ 13 & gottlob berger & head of the ss-hauptamt, ss-obergruppenfuhrer & 25 years' imprisonment; reduced to 10 years in 1951; released the same year \\ 14 & walter schellenberg & second-in-command of the gestapo, head of the sd and the abwehr, and successor of wilhelm canaris as the head of the combined secret services; ss-brigadefuhrer & 6 years' imprisonment incl. time already served \\ 15 & lutz graf schwerin von krosigk & minister of finance & 10 years' imprisonment; released in 1951 \\ 16 & emil puhl & vice-president of the reichsbank & 5 years' imprisonment incl. time already served \\ 17 & karl rasche & director of the dresdner bank & 7 years' imprisonment incl. time already served \\ 18 & paul korner (de) & secretary of state, deputy of goring & 15 years' imprisonment; reduced to 10 years in 1951; released the same year \\ 19 & paul pleiger & head of the reichswerke hermann goring (confiscated steel plants employing slave laborers) & 15 years' imprisonment; reduced to 10 years in 1951; released the same year \\ 20 & hans kehrl (de) & secretary in the ministry of armament; head of the planning office & 15 years' imprisonment; released in 1951 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & name \\ \midrule 0 & otto dietrich \\ \bottomrule \end{tabular}

select name where function = 'reichspressechef of the nsdap and secretary of state in the propagandaministerium' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>name</th> <th>function</th> <th>sentence</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>ernst von weizsacker</td> <td>permanent secretary of state in the auswartiges amt (foreign ministry) under ribbentrop until 1943, then ambassador to the holy see; ss-brigadefuhrer</td> <td>7 years' imprisonment; reduced to 5 years on december 12, 1949, released in october 1950</td> </tr> <tr> <th>1</th> <td>gustav adolf steengracht von moyland</td> <td>successor of von weizsacker as secretary of state in the foreign ministry (until 1945)</td> <td>7 years' imprisonment; reduced to 5 years on december 12, 1949, released 1950</td> </tr> <tr> <th>2</th> <td>wilhelm keppler</td> <td>secretary of state; hitler's advisor for economy</td> <td>10 years' imprisonment; released 1951</td> </tr> <tr> <th>3</th> <td>ernst wilhelm bohle</td> <td>ns-gauleiter, secretary of state in the foreign ministry; head of the auslandorganisation (foreign organization) of the nsdap</td> <td>5 years' imprisonment</td> </tr> <tr> <th>4</th> <td>ernst woermann (de)</td> <td>secretary in the foreign ministry; head of the political division</td> <td>7 years' imprisonment; reduced to 5 years on december 12, 1949; released 1951</td> </tr> <tr> <th>5</th> <td>karl ritter</td> <td>liaison between foreign office and the high command of the german armed forces</td> <td>4 years' imprisonment incl. time already served; released after the judgment</td> </tr> <tr> <th>6</th> <td>otto von erdmannsdorff</td> <td>secretary in the foreign ministry; deputy to wormann</td> <td>acquitted</td> </tr> <tr> <th>7</th> <td>edmund veesenmayer</td> <td>plenipotentiary in hungary</td> <td>20 years' imprisonment; reduced to 10 years in 1951 and released the same year</td> </tr> <tr> <th>8</th> <td>hans heinrich lammers</td> <td>head of the reich chancellery</td> <td>20 years' imprisonment; reduced to 10 years in january 1951 and released december 16, 1951</td> </tr> <tr> <th>9</th> <td>wilhelm stuckart</td> <td>secretary of state in the interior ministry</td> <td>time already served (3 years and 10 months)</td> </tr> <tr> <th>10</th> <td>richard walther darre</td> <td>minister for food and agriculture</td> <td>7 years' imprisonment; released 1950</td> </tr> <tr> <th>11</th> <td>otto meissner</td> <td>head of the presidential chancellery</td> <td>acquitted</td> </tr> <tr> <th>12</th> <td>otto dietrich</td> <td>reichspressechef of the nsdap and secretary of state in the propagandaministerium</td> <td>7 years' imprisonment incl. time already served; released in 1950</td> </tr> <tr> <th>13</th> <td>gottlob berger</td> <td>head of the ss-hauptamt, ss-obergruppenfuhrer</td> <td>25 years' imprisonment; reduced to 10 years in 1951; released the same year</td> </tr> <tr> <th>14</th> <td>walter schellenberg</td> <td>second-in-command of the gestapo, head of the sd and the abwehr, and successor of wilhelm canaris as the head of the combined secret services; ss-brigadefuhrer</td> <td>6 years' imprisonment incl. time already served</td> </tr> <tr> <th>15</th> <td>lutz graf schwerin von krosigk</td> <td>minister of finance</td> <td>10 years' imprisonment; released in 1951</td> </tr> <tr> <th>16</th> <td>emil puhl</td> <td>vice-president of the reichsbank</td> <td>5 years' imprisonment incl. time already served</td> </tr> <tr> <th>17</th> <td>karl rasche</td> <td>director of the dresdner bank</td> <td>7 years' imprisonment incl. time already served</td> </tr> <tr> <th>18</th> <td>paul korner (de)</td> <td>secretary of state, deputy of goring</td> <td>15 years' imprisonment; reduced to 10 years in 1951; released the same year</td> </tr> <tr> <th>19</th> <td>paul pleiger</td> <td>head of the reichswerke hermann goring (confiscated steel plants employing slave laborers)</td> <td>15 years' imprisonment; reduced to 10 years in 1951; released the same year</td> </tr> <tr> <th>20</th> <td>hans kehrl (de)</td> <td>secretary in the ministry of armament; head of the planning office</td> <td>15 years' imprisonment; released in 1951</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>name</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>otto dietrich</td> </tr> </tbody> </table>

select name where function = 'reichspressechef of the nsdap and secretary of state in the propagandaministerium' <table_name> : table | | name | function | sentence | |---:|:-------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------| | 0 | ernst von weizsacker | permanent secretary of state in the auswartiges amt (foreign ministry) under ribbentrop until 1943, then ambassador to the holy see; ss-brigadefuhrer | 7 years' imprisonment; reduced to 5 years on december 12, 1949, released in october 1950 | | 1 | gustav adolf steengracht von moyland | successor of von weizsacker as secretary of state in the foreign ministry (until 1945) | 7 years' imprisonment; reduced to 5 years on december 12, 1949, released 1950 | | 2 | wilhelm keppler | secretary of state; hitler's advisor for economy | 10 years' imprisonment; released 1951 | | 3 | ernst wilhelm bohle | ns-gauleiter, secretary of state in the foreign ministry; head of the auslandorganisation (foreign organization) of the nsdap | 5 years' imprisonment | | 4 | ernst woermann (de) | secretary in the foreign ministry; head of the political division | 7 years' imprisonment; reduced to 5 years on december 12, 1949; released 1951 | | 5 | karl ritter | liaison between foreign office and the high command of the german armed forces | 4 years' imprisonment incl. time already served; released after the judgment | | 6 | otto von erdmannsdorff | secretary in the foreign ministry; deputy to wormann | acquitted | | 7 | edmund veesenmayer | plenipotentiary in hungary | 20 years' imprisonment; reduced to 10 years in 1951 and released the same year | | 8 | hans heinrich lammers | head of the reich chancellery | 20 years' imprisonment; reduced to 10 years in january 1951 and released december 16, 1951 | | 9 | wilhelm stuckart | secretary of state in the interior ministry | time already served (3 years and 10 months) | | 10 | richard walther darre | minister for food and agriculture | 7 years' imprisonment; released 1950 | | 11 | otto meissner | head of the presidential chancellery | acquitted | | 12 | otto dietrich | reichspressechef of the nsdap and secretary of state in the propagandaministerium | 7 years' imprisonment incl. time already served; released in 1950 | | 13 | gottlob berger | head of the ss-hauptamt, ss-obergruppenfuhrer | 25 years' imprisonment; reduced to 10 years in 1951; released the same year | | 14 | walter schellenberg | second-in-command of the gestapo, head of the sd and the abwehr, and successor of wilhelm canaris as the head of the combined secret services; ss-brigadefuhrer | 6 years' imprisonment incl. time already served | | 15 | lutz graf schwerin von krosigk | minister of finance | 10 years' imprisonment; released in 1951 | | 16 | emil puhl | vice-president of the reichsbank | 5 years' imprisonment incl. time already served | | 17 | karl rasche | director of the dresdner bank | 7 years' imprisonment incl. time already served | | 18 | paul korner (de) | secretary of state, deputy of goring | 15 years' imprisonment; reduced to 10 years in 1951; released the same year | | 19 | paul pleiger | head of the reichswerke hermann goring (confiscated steel plants employing slave laborers) | 15 years' imprisonment; reduced to 10 years in 1951; released the same year | | 20 | hans kehrl (de) | secretary in the ministry of armament; head of the planning office | 15 years' imprisonment; released in 1951 |

| | name | |---:|:--------------| | 0 | otto dietrich |