Datasets:

kpriyanshu256

/

MultiTabQA-tapex-Salesforce-codet5-base-latex

like 1

select count ( air date ) where performer = 907 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 : count ( "air date" ) row 1 : 0

select count ( air date ) where performer = 907 <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 & count ( "air date" ) \\ \midrule 0 & 0 \\ \bottomrule \end{tabular}

select count ( air date ) where performer = 907 <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>count ( "air date" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>0</td> </tr> </tbody> </table>

select count ( air date ) where performer = 907 <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 |

| | count ( "air date" ) | |---:|-----------------------:| | 0 | 0 |

select function where function = 'ns-gauleiter, secretary of state in the foreign ministry; head of the auslandorganisation (foreign organization) of the nsdap' 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 : function row 1 : ns-gauleiter, secretary of state in the foreign ministry; head of the auslandorganisation (foreign organization) of the nsdap

select function where function = 'ns-gauleiter, secretary of state in the foreign ministry; head of the auslandorganisation (foreign organization) of the nsdap' <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 & function \\ \midrule 0 & ns-gauleiter, secretary of state in the foreign ministry; head of the auslandorganisation (foreign organization) of the nsdap \\ \bottomrule \end{tabular}

select function where function = 'ns-gauleiter, secretary of state in the foreign ministry; head of the auslandorganisation (foreign organization) of the nsdap' <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>function</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>ns-gauleiter, secretary of state in the foreign ministry; head of the auslandorganisation (foreign organization) of the nsdap</td> </tr> </tbody> </table>

select function where function = 'ns-gauleiter, secretary of state in the foreign ministry; head of the auslandorganisation (foreign organization) of the nsdap' <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 |

| | function | |---:|:------------------------------------------------------------------------------------------------------------------------------| | 0 | ns-gauleiter, secretary of state in the foreign ministry; head of the auslandorganisation (foreign organization) of the nsdap |

select count ( * ) where tv = 'fsn' col : date | time | opponent# | site | tv | result | attendance row 1 : august 29 | 7:30 pm | appalachian state | groves stadium • winston-salem, nc | none | w 19-13 | 21129 row 2 : september 7 | 6:30 pm | #13 northwestern | groves stadium • winston-salem, nc | fsn | w 28-27 | 21749 row 3 : september 14 | 7:00 pm | at georgia tech | bobby dodd stadium • atlanta, ga | none | l 10-30 | 45750 row 4 : september 21 | 12:00 pm | #20 virginia | groves stadium • winston-salem, nc | jps | l 7-42 | 23220 row 5 : september 28 | 12:00 pm | at clemson | memorial stadium • clemson, sc | jps | l 10-21 | 63263 row 6 : october 5 | 6:30 pm | #15 north carolina | groves stadium • winston-salem, nc | none | l 6-45 | 25681 row 7 : october 19 | 2:00 pm | at maryland | byrd stadium • college park, md | none | l 0-52 | 30212 row 8 : october 26 | 1:00 pm | navy | groves stadium • winston-salem, nc | none | l 18-47 | 17307 row 9 : november 9 | 12:00 pm | vs. #3 florida state | citrus bowl • orlando, fl | jps | l 7-44 | 34974 row 10 : november 16 | 1:00 pm | duke | groves stadium • winston-salem, nc | none | w 17-16 | 17842 row 11 : november 23 | 1:00 pm | at nc state | carter-finley stadium • raleigh, nc | none | l 22-37 | 40500

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

select count ( * ) where tv = 'fsn' <table_name> : table \begin{tabular}{llllllll} \toprule & date & time & opponent# & site & tv & result & attendance \\ \midrule 0 & august 29 & 7:30 pm & appalachian state & groves stadium • winston-salem, nc & none & w 19-13 & 21129 \\ 1 & september 7 & 6:30 pm & #13 northwestern & groves stadium • winston-salem, nc & fsn & w 28-27 & 21749 \\ 2 & september 14 & 7:00 pm & at georgia tech & bobby dodd stadium • atlanta, ga & none & l 10-30 & 45750 \\ 3 & september 21 & 12:00 pm & #20 virginia & groves stadium • winston-salem, nc & jps & l 7-42 & 23220 \\ 4 & september 28 & 12:00 pm & at clemson & memorial stadium • clemson, sc & jps & l 10-21 & 63263 \\ 5 & october 5 & 6:30 pm & #15 north carolina & groves stadium • winston-salem, nc & none & l 6-45 & 25681 \\ 6 & october 19 & 2:00 pm & at maryland & byrd stadium • college park, md & none & l 0-52 & 30212 \\ 7 & october 26 & 1:00 pm & navy & groves stadium • winston-salem, nc & none & l 18-47 & 17307 \\ 8 & november 9 & 12:00 pm & vs. #3 florida state & citrus bowl • orlando, fl & jps & l 7-44 & 34974 \\ 9 & november 16 & 1:00 pm & duke & groves stadium • winston-salem, nc & none & w 17-16 & 17842 \\ 10 & november 23 & 1:00 pm & at nc state & carter-finley stadium • raleigh, nc & none & l 22-37 & 40500 \\ \bottomrule \end{tabular}

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

select count ( * ) where tv = 'fsn' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>date</th> <th>time</th> <th>opponent#</th> <th>site</th> <th>tv</th> <th>result</th> <th>attendance</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>august 29</td> <td>7:30 pm</td> <td>appalachian state</td> <td>groves stadium • winston-salem, nc</td> <td>none</td> <td>w 19-13</td> <td>21129</td> </tr> <tr> <th>1</th> <td>september 7</td> <td>6:30 pm</td> <td>#13 northwestern</td> <td>groves stadium • winston-salem, nc</td> <td>fsn</td> <td>w 28-27</td> <td>21749</td> </tr> <tr> <th>2</th> <td>september 14</td> <td>7:00 pm</td> <td>at georgia tech</td> <td>bobby dodd stadium • atlanta, ga</td> <td>none</td> <td>l 10-30</td> <td>45750</td> </tr> <tr> <th>3</th> <td>september 21</td> <td>12:00 pm</td> <td>#20 virginia</td> <td>groves stadium • winston-salem, nc</td> <td>jps</td> <td>l 7-42</td> <td>23220</td> </tr> <tr> <th>4</th> <td>september 28</td> <td>12:00 pm</td> <td>at clemson</td> <td>memorial stadium • clemson, sc</td> <td>jps</td> <td>l 10-21</td> <td>63263</td> </tr> <tr> <th>5</th> <td>october 5</td> <td>6:30 pm</td> <td>#15 north carolina</td> <td>groves stadium • winston-salem, nc</td> <td>none</td> <td>l 6-45</td> <td>25681</td> </tr> <tr> <th>6</th> <td>october 19</td> <td>2:00 pm</td> <td>at maryland</td> <td>byrd stadium • college park, md</td> <td>none</td> <td>l 0-52</td> <td>30212</td> </tr> <tr> <th>7</th> <td>october 26</td> <td>1:00 pm</td> <td>navy</td> <td>groves stadium • winston-salem, nc</td> <td>none</td> <td>l 18-47</td> <td>17307</td> </tr> <tr> <th>8</th> <td>november 9</td> <td>12:00 pm</td> <td>vs. #3 florida state</td> <td>citrus bowl • orlando, fl</td> <td>jps</td> <td>l 7-44</td> <td>34974</td> </tr> <tr> <th>9</th> <td>november 16</td> <td>1:00 pm</td> <td>duke</td> <td>groves stadium • winston-salem, nc</td> <td>none</td> <td>w 17-16</td> <td>17842</td> </tr> <tr> <th>10</th> <td>november 23</td> <td>1:00 pm</td> <td>at nc state</td> <td>carter-finley stadium • raleigh, nc</td> <td>none</td> <td>l 22-37</td> <td>40500</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 tv = 'fsn' <table_name> : table | | date | time | opponent# | site | tv | result | attendance | |---:|:-------------|:---------|:---------------------|:------------------------------------|:-----|:---------|-------------:| | 0 | august 29 | 7:30 pm | appalachian state | groves stadium • winston-salem, nc | none | w 19-13 | 21129 | | 1 | september 7 | 6:30 pm | #13 northwestern | groves stadium • winston-salem, nc | fsn | w 28-27 | 21749 | | 2 | september 14 | 7:00 pm | at georgia tech | bobby dodd stadium • atlanta, ga | none | l 10-30 | 45750 | | 3 | september 21 | 12:00 pm | #20 virginia | groves stadium • winston-salem, nc | jps | l 7-42 | 23220 | | 4 | september 28 | 12:00 pm | at clemson | memorial stadium • clemson, sc | jps | l 10-21 | 63263 | | 5 | october 5 | 6:30 pm | #15 north carolina | groves stadium • winston-salem, nc | none | l 6-45 | 25681 | | 6 | october 19 | 2:00 pm | at maryland | byrd stadium • college park, md | none | l 0-52 | 30212 | | 7 | october 26 | 1:00 pm | navy | groves stadium • winston-salem, nc | none | l 18-47 | 17307 | | 8 | november 9 | 12:00 pm | vs. #3 florida state | citrus bowl • orlando, fl | jps | l 7-44 | 34974 | | 9 | november 16 | 1:00 pm | duke | groves stadium • winston-salem, nc | none | w 17-16 | 17842 | | 10 | november 23 | 1:00 pm | at nc state | carter-finley stadium • raleigh, nc | none | l 22-37 | 40500 |

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

select name where floors = 13 col : rank | name | height | floors | year | notes row 1 : 1 | zions bank building zions bank building in downtown boise, idaho | 323 ft (98 m) | 20 | 2013 | tallest building in state since 2013 row 2 : 2 | boise airport air traffic control tower | 295 ft (90 m) | none | 2009 | tallest atc tower in the northwest row 3 : 3 | us bank plaza us bank building in downtown boise | 267 ft (81 m) | 19 | 1978 | tallest building in state from 1978 to 2013. "idaho first plaza" until 1989 row 4 : 3 | one capital center one capital center in downtown boise, idaho | 206 ft (63 m) | 14 | 1975 | tallest building in boise from 1975 to 1978 row 5 : 4 | idaho state capitol idaho state capitol; boise, idaho | 198 ft (60 m) | 4 | 1920 | tallest building in boise from 1920 to 1975 row 6 : 5 | the grove hotel grove hotel in downtown boise, idaho | 196 ft (60 m) | 16 | 2000 | none row 7 : 6 | the aspen aspen loft building in downtown boise, idaho | 194 ft (59 m) | 17 | 2009 | none row 8 : 7 | wells fargo building wells fargo building in downtown boise, idaho | 182 ft (55 m) | 11 | 1988 | first interstate until 1996 row 9 : 8 | banner bank building banner bank building in downtown boise, idaho | 181 ft (55 m) | 11 | 2006 | none row 10 : 9 | key tower key bank building in downtown boise, idaho | 174 ft (53 m) | 13 | 1963 | bank of idaho until 1981, first interstate until 1988 row 11 : 10 | bronco stadium bronco stadium in boise, idaho | 170 ft (52 m) | none | 1970 | none row 12 : 11 | hoff building hoff building in downtown boise, idaho | 165 ft (50 m) | 14 | 1930 | none row 13 : 12 | chase tower plaza chase building in downtown boise, idaho | 160 ft (49 m) | 11 | 1995 | washington mutual until 2009 row 14 : 13 | idaho power building idaho power building in downtown boise, idaho | 160 ft (49 m) | 9 | 1990 | none

col : name row 1 : key tower key bank building in downtown boise, idaho

select name where floors = 13 <table_name> : table \begin{tabular}{lllllll} \toprule & rank & name & height & floors & year & notes \\ \midrule 0 & 1 & zions bank building zions bank building in downtown boise, idaho & 323 ft (98 m) & 20 & 2013 & tallest building in state since 2013 \\ 1 & 2 & boise airport air traffic control tower & 295 ft (90 m) & none & 2009 & tallest atc tower in the northwest \\ 2 & 3 & us bank plaza us bank building in downtown boise & 267 ft (81 m) & 19 & 1978 & tallest building in state from 1978 to 2013. "idaho first plaza" until 1989 \\ 3 & 3 & one capital center one capital center in downtown boise, idaho & 206 ft (63 m) & 14 & 1975 & tallest building in boise from 1975 to 1978 \\ 4 & 4 & idaho state capitol idaho state capitol; boise, idaho & 198 ft (60 m) & 4 & 1920 & tallest building in boise from 1920 to 1975 \\ 5 & 5 & the grove hotel grove hotel in downtown boise, idaho & 196 ft (60 m) & 16 & 2000 & none \\ 6 & 6 & the aspen aspen loft building in downtown boise, idaho & 194 ft (59 m) & 17 & 2009 & none \\ 7 & 7 & wells fargo building wells fargo building in downtown boise, idaho & 182 ft (55 m) & 11 & 1988 & first interstate until 1996 \\ 8 & 8 & banner bank building banner bank building in downtown boise, idaho & 181 ft (55 m) & 11 & 2006 & none \\ 9 & 9 & key tower key bank building in downtown boise, idaho & 174 ft (53 m) & 13 & 1963 & bank of idaho until 1981, first interstate until 1988 \\ 10 & 10 & bronco stadium bronco stadium in boise, idaho & 170 ft (52 m) & none & 1970 & none \\ 11 & 11 & hoff building hoff building in downtown boise, idaho & 165 ft (50 m) & 14 & 1930 & none \\ 12 & 12 & chase tower plaza chase building in downtown boise, idaho & 160 ft (49 m) & 11 & 1995 & washington mutual until 2009 \\ 13 & 13 & idaho power building idaho power building in downtown boise, idaho & 160 ft (49 m) & 9 & 1990 & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & name \\ \midrule 0 & key tower key bank building in downtown boise, idaho \\ \bottomrule \end{tabular}

select name where floors = 13 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>rank</th> <th>name</th> <th>height</th> <th>floors</th> <th>year</th> <th>notes</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>zions bank building zions bank building in downtown boise, idaho</td> <td>323 ft (98 m)</td> <td>20</td> <td>2013</td> <td>tallest building in state since 2013</td> </tr> <tr> <th>1</th> <td>2</td> <td>boise airport air traffic control tower</td> <td>295 ft (90 m)</td> <td>none</td> <td>2009</td> <td>tallest atc tower in the northwest</td> </tr> <tr> <th>2</th> <td>3</td> <td>us bank plaza us bank building in downtown boise</td> <td>267 ft (81 m)</td> <td>19</td> <td>1978</td> <td>tallest building in state from 1978 to 2013. "idaho first plaza" until 1989</td> </tr> <tr> <th>3</th> <td>3</td> <td>one capital center one capital center in downtown boise, idaho</td> <td>206 ft (63 m)</td> <td>14</td> <td>1975</td> <td>tallest building in boise from 1975 to 1978</td> </tr> <tr> <th>4</th> <td>4</td> <td>idaho state capitol idaho state capitol; boise, idaho</td> <td>198 ft (60 m)</td> <td>4</td> <td>1920</td> <td>tallest building in boise from 1920 to 1975</td> </tr> <tr> <th>5</th> <td>5</td> <td>the grove hotel grove hotel in downtown boise, idaho</td> <td>196 ft (60 m)</td> <td>16</td> <td>2000</td> <td>none</td> </tr> <tr> <th>6</th> <td>6</td> <td>the aspen aspen loft building in downtown boise, idaho</td> <td>194 ft (59 m)</td> <td>17</td> <td>2009</td> <td>none</td> </tr> <tr> <th>7</th> <td>7</td> <td>wells fargo building wells fargo building in downtown boise, idaho</td> <td>182 ft (55 m)</td> <td>11</td> <td>1988</td> <td>first interstate until 1996</td> </tr> <tr> <th>8</th> <td>8</td> <td>banner bank building banner bank building in downtown boise, idaho</td> <td>181 ft (55 m)</td> <td>11</td> <td>2006</td> <td>none</td> </tr> <tr> <th>9</th> <td>9</td> <td>key tower key bank building in downtown boise, idaho</td> <td>174 ft (53 m)</td> <td>13</td> <td>1963</td> <td>bank of idaho until 1981, first interstate until 1988</td> </tr> <tr> <th>10</th> <td>10</td> <td>bronco stadium bronco stadium in boise, idaho</td> <td>170 ft (52 m)</td> <td>none</td> <td>1970</td> <td>none</td> </tr> <tr> <th>11</th> <td>11</td> <td>hoff building hoff building in downtown boise, idaho</td> <td>165 ft (50 m)</td> <td>14</td> <td>1930</td> <td>none</td> </tr> <tr> <th>12</th> <td>12</td> <td>chase tower plaza chase building in downtown boise, idaho</td> <td>160 ft (49 m)</td> <td>11</td> <td>1995</td> <td>washington mutual until 2009</td> </tr> <tr> <th>13</th> <td>13</td> <td>idaho power building idaho power building in downtown boise, idaho</td> <td>160 ft (49 m)</td> <td>9</td> <td>1990</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>key tower key bank building in downtown boise, idaho</td> </tr> </tbody> </table>

select name where floors = 13 <table_name> : table | | rank | name | height | floors | year | notes | |---:|-------:|:-------------------------------------------------------------------|:--------------|:---------|-------:|:----------------------------------------------------------------------------| | 0 | 1 | zions bank building zions bank building in downtown boise, idaho | 323 ft (98 m) | 20 | 2013 | tallest building in state since 2013 | | 1 | 2 | boise airport air traffic control tower | 295 ft (90 m) | none | 2009 | tallest atc tower in the northwest | | 2 | 3 | us bank plaza us bank building in downtown boise | 267 ft (81 m) | 19 | 1978 | tallest building in state from 1978 to 2013. "idaho first plaza" until 1989 | | 3 | 3 | one capital center one capital center in downtown boise, idaho | 206 ft (63 m) | 14 | 1975 | tallest building in boise from 1975 to 1978 | | 4 | 4 | idaho state capitol idaho state capitol; boise, idaho | 198 ft (60 m) | 4 | 1920 | tallest building in boise from 1920 to 1975 | | 5 | 5 | the grove hotel grove hotel in downtown boise, idaho | 196 ft (60 m) | 16 | 2000 | none | | 6 | 6 | the aspen aspen loft building in downtown boise, idaho | 194 ft (59 m) | 17 | 2009 | none | | 7 | 7 | wells fargo building wells fargo building in downtown boise, idaho | 182 ft (55 m) | 11 | 1988 | first interstate until 1996 | | 8 | 8 | banner bank building banner bank building in downtown boise, idaho | 181 ft (55 m) | 11 | 2006 | none | | 9 | 9 | key tower key bank building in downtown boise, idaho | 174 ft (53 m) | 13 | 1963 | bank of idaho until 1981, first interstate until 1988 | | 10 | 10 | bronco stadium bronco stadium in boise, idaho | 170 ft (52 m) | none | 1970 | none | | 11 | 11 | hoff building hoff building in downtown boise, idaho | 165 ft (50 m) | 14 | 1930 | none | | 12 | 12 | chase tower plaza chase building in downtown boise, idaho | 160 ft (49 m) | 11 | 1995 | washington mutual until 2009 | | 13 | 13 | idaho power building idaho power building in downtown boise, idaho | 160 ft (49 m) | 9 | 1990 | none |

| | name | |---:|:-----------------------------------------------------| | 0 | key tower key bank building in downtown boise, idaho |

select count ( label ) where peak chart positions ger > 769 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 : count ( "label" ) row 1 : 14

select count ( label ) where peak chart positions ger > 769 <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 & count ( "label" ) \\ \midrule 0 & 14 \\ \bottomrule \end{tabular}

select count ( label ) where peak chart positions ger > 769 <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>count ( "label" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>14</td> </tr> </tbody> </table>

select count ( label ) where peak chart positions ger > 769 <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 |

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

select percentage where total voters > 498 col : party | active voters | inactive voters | total voters | percentage row 1 : republican | 13916 | 5342 | 19258 | 25.61% row 2 : democratic | 21493 | 7115 | 28608 | 38.05% row 3 : unaffiliated | 20118 | 7062 | 27180 | 36.15% row 4 : minor parties | 100 | 40 | 140 | 0.19% row 5 : total | 55627 | 19559 | 75186 | 100%

col : percentage row 1 : 100%

select percentage where total voters > 498 <table_name> : table \begin{tabular}{llllll} \toprule & party & active voters & inactive voters & total voters & percentage \\ \midrule 0 & republican & 13916 & 5342 & 19258 & 25.61% \\ 1 & democratic & 21493 & 7115 & 28608 & 38.05% \\ 2 & unaffiliated & 20118 & 7062 & 27180 & 36.15% \\ 3 & minor parties & 100 & 40 & 140 & 0.19% \\ 4 & total & 55627 & 19559 & 75186 & 100% \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & percentage \\ \midrule 0 & 100% \\ \bottomrule \end{tabular}

select percentage where total voters > 498 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>party</th> <th>active voters</th> <th>inactive voters</th> <th>total voters</th> <th>percentage</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>republican</td> <td>13916</td> <td>5342</td> <td>19258</td> <td>25.61%</td> </tr> <tr> <th>1</th> <td>democratic</td> <td>21493</td> <td>7115</td> <td>28608</td> <td>38.05%</td> </tr> <tr> <th>2</th> <td>unaffiliated</td> <td>20118</td> <td>7062</td> <td>27180</td> <td>36.15%</td> </tr> <tr> <th>3</th> <td>minor parties</td> <td>100</td> <td>40</td> <td>140</td> <td>0.19%</td> </tr> <tr> <th>4</th> <td>total</td> <td>55627</td> <td>19559</td> <td>75186</td> <td>100%</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>percentage</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>100%</td> </tr> </tbody> </table>

select percentage where total voters > 498 <table_name> : table | | party | active voters | inactive voters | total voters | percentage | |---:|:--------------|----------------:|------------------:|---------------:|:-------------| | 0 | republican | 13916 | 5342 | 19258 | 25.61% | | 1 | democratic | 21493 | 7115 | 28608 | 38.05% | | 2 | unaffiliated | 20118 | 7062 | 27180 | 36.15% | | 3 | minor parties | 100 | 40 | 140 | 0.19% | | 4 | total | 55627 | 19559 | 75186 | 100% |

| | percentage | |---:|:-------------| | 0 | 100% |

select sum ( w ) where l = 42 col : midwest division v t e | w | l | pct | gb | home | road | div row 1 : y-denver nuggets | 50 | 32 | 0.61 | none | 36-5 | 14-27 | 15-5 row 2 : x-detroit pistons | 44 | 38 | 0.537 | 6 | 30-11 | 14-27 | 12-8 row 3 : x-chicago bulls | 44 | 38 | 0.537 | 6 | 31-10 | 13-28 | 10-10 row 4 : kansas city kings | 40 | 42 | 0.488 | 10 | 28-13 | 12-29 | 7-13 row 5 : indiana pacers | 36 | 46 | 0.439 | 14 | 25-16 | 11-30 | 9-11 row 6 : milwaukee bucks | 30 | 52 | 0.366 | 20 | 24-17 | 6-35 | 7-13

col : sum ( "w" ) row 1 : 40

select sum ( w ) where l = 42 <table_name> : table \begin{tabular}{lllllllll} \toprule & midwest division v t e & w & l & pct & gb & home & road & div \\ \midrule 0 & y-denver nuggets & 50 & 32 & 0.61 & none & 36-5 & 14-27 & 15-5 \\ 1 & x-detroit pistons & 44 & 38 & 0.537 & 6 & 30-11 & 14-27 & 12-8 \\ 2 & x-chicago bulls & 44 & 38 & 0.537 & 6 & 31-10 & 13-28 & 10-10 \\ 3 & kansas city kings & 40 & 42 & 0.488 & 10 & 28-13 & 12-29 & 7-13 \\ 4 & indiana pacers & 36 & 46 & 0.439 & 14 & 25-16 & 11-30 & 9-11 \\ 5 & milwaukee bucks & 30 & 52 & 0.366 & 20 & 24-17 & 6-35 & 7-13 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & sum ( "w" ) \\ \midrule 0 & 40 \\ \bottomrule \end{tabular}

select sum ( w ) where l = 42 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>midwest division v t e</th> <th>w</th> <th>l</th> <th>pct</th> <th>gb</th> <th>home</th> <th>road</th> <th>div</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>y-denver nuggets</td> <td>50</td> <td>32</td> <td>0.61</td> <td>none</td> <td>36-5</td> <td>14-27</td> <td>15-5</td> </tr> <tr> <th>1</th> <td>x-detroit pistons</td> <td>44</td> <td>38</td> <td>0.537</td> <td>6</td> <td>30-11</td> <td>14-27</td> <td>12-8</td> </tr> <tr> <th>2</th> <td>x-chicago bulls</td> <td>44</td> <td>38</td> <td>0.537</td> <td>6</td> <td>31-10</td> <td>13-28</td> <td>10-10</td> </tr> <tr> <th>3</th> <td>kansas city kings</td> <td>40</td> <td>42</td> <td>0.488</td> <td>10</td> <td>28-13</td> <td>12-29</td> <td>7-13</td> </tr> <tr> <th>4</th> <td>indiana pacers</td> <td>36</td> <td>46</td> <td>0.439</td> <td>14</td> <td>25-16</td> <td>11-30</td> <td>9-11</td> </tr> <tr> <th>5</th> <td>milwaukee bucks</td> <td>30</td> <td>52</td> <td>0.366</td> <td>20</td> <td>24-17</td> <td>6-35</td> <td>7-13</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>sum ( "w" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>40</td> </tr> </tbody> </table>

select sum ( w ) where l = 42 <table_name> : table | | midwest division v t e | w | l | pct | gb | home | road | div | |---:|:-------------------------|----:|----:|------:|:-----|:-------|:-------|:------| | 0 | y-denver nuggets | 50 | 32 | 0.61 | none | 36-5 | 14-27 | 15-5 | | 1 | x-detroit pistons | 44 | 38 | 0.537 | 6 | 30-11 | 14-27 | 12-8 | | 2 | x-chicago bulls | 44 | 38 | 0.537 | 6 | 31-10 | 13-28 | 10-10 | | 3 | kansas city kings | 40 | 42 | 0.488 | 10 | 28-13 | 12-29 | 7-13 | | 4 | indiana pacers | 36 | 46 | 0.439 | 14 | 25-16 | 11-30 | 9-11 | | 5 | milwaukee bucks | 30 | 52 | 0.366 | 20 | 24-17 | 6-35 | 7-13 |

| | sum ( "w" ) | |---:|--------------:| | 0 | 40 |

select count ( party leader ) where district (area) = 6 col : political lieutenant | district (area) | took office | left office | party leader row 1 : georges-henri heon | argenteuil (laurentides) | 1949 | 1949 | george a. drew row 2 : leon balcer | trois-rivieres (mauricie) | 1957 | 1965 | john george diefenbaker row 3 : marcel faribault | none | 1967 | 1968 | robert stanfield row 4 : claude wagner | saint-hyacinthe (monteregie) | 1972 | 1978 | robert stanfield joe clark row 5 : lucien bouchard | lac-saint-jean (saguenay-lac-saint-jean) | 1988 | 1990 | brian mulroney row 6 : benoit bouchard | roberval (saguenay-lac-saint-jean) | 1990 | 1993 | brian mulroney row 7 : monique landry | blainville-deux-montagnes (laurentides) | 1993 | 1993 | kim campbell row 8 : andre bachand | richmond-arthabaska (centre-du-quebec & eastern townships) | 1998 | 2004 | joe clark peter mackay

col : count ( "party leader" ) row 1 : 0

select count ( party leader ) where district (area) = 6 <table_name> : table \begin{tabular}{llllll} \toprule & political lieutenant & district (area) & took office & left office & party leader \\ \midrule 0 & georges-henri heon & argenteuil (laurentides) & 1949 & 1949 & george a. drew \\ 1 & leon balcer & trois-rivieres (mauricie) & 1957 & 1965 & john george diefenbaker \\ 2 & marcel faribault & none & 1967 & 1968 & robert stanfield \\ 3 & claude wagner & saint-hyacinthe (monteregie) & 1972 & 1978 & robert stanfield joe clark \\ 4 & lucien bouchard & lac-saint-jean (saguenay-lac-saint-jean) & 1988 & 1990 & brian mulroney \\ 5 & benoit bouchard & roberval (saguenay-lac-saint-jean) & 1990 & 1993 & brian mulroney \\ 6 & monique landry & blainville-deux-montagnes (laurentides) & 1993 & 1993 & kim campbell \\ 7 & andre bachand & richmond-arthabaska (centre-du-quebec & eastern townships) & 1998 & 2004 & joe clark peter mackay \\ \bottomrule \end{tabular}

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

select count ( party leader ) where district (area) = 6 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>political lieutenant</th> <th>district (area)</th> <th>took office</th> <th>left office</th> <th>party leader</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>georges-henri heon</td> <td>argenteuil (laurentides)</td> <td>1949</td> <td>1949</td> <td>george a. drew</td> </tr> <tr> <th>1</th> <td>leon balcer</td> <td>trois-rivieres (mauricie)</td> <td>1957</td> <td>1965</td> <td>john george diefenbaker</td> </tr> <tr> <th>2</th> <td>marcel faribault</td> <td>none</td> <td>1967</td> <td>1968</td> <td>robert stanfield</td> </tr> <tr> <th>3</th> <td>claude wagner</td> <td>saint-hyacinthe (monteregie)</td> <td>1972</td> <td>1978</td> <td>robert stanfield joe clark</td> </tr> <tr> <th>4</th> <td>lucien bouchard</td> <td>lac-saint-jean (saguenay-lac-saint-jean)</td> <td>1988</td> <td>1990</td> <td>brian mulroney</td> </tr> <tr> <th>5</th> <td>benoit bouchard</td> <td>roberval (saguenay-lac-saint-jean)</td> <td>1990</td> <td>1993</td> <td>brian mulroney</td> </tr> <tr> <th>6</th> <td>monique landry</td> <td>blainville-deux-montagnes (laurentides)</td> <td>1993</td> <td>1993</td> <td>kim campbell</td> </tr> <tr> <th>7</th> <td>andre bachand</td> <td>richmond-arthabaska (centre-du-quebec &amp; eastern townships)</td> <td>1998</td> <td>2004</td> <td>joe clark peter mackay</td> </tr> </tbody> </table>

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

select count ( party leader ) where district (area) = 6 <table_name> : table | | political lieutenant | district (area) | took office | left office | party leader | |---:|:-----------------------|:-----------------------------------------------------------|--------------:|--------------:|:---------------------------| | 0 | georges-henri heon | argenteuil (laurentides) | 1949 | 1949 | george a. drew | | 1 | leon balcer | trois-rivieres (mauricie) | 1957 | 1965 | john george diefenbaker | | 2 | marcel faribault | none | 1967 | 1968 | robert stanfield | | 3 | claude wagner | saint-hyacinthe (monteregie) | 1972 | 1978 | robert stanfield joe clark | | 4 | lucien bouchard | lac-saint-jean (saguenay-lac-saint-jean) | 1988 | 1990 | brian mulroney | | 5 | benoit bouchard | roberval (saguenay-lac-saint-jean) | 1990 | 1993 | brian mulroney | | 6 | monique landry | blainville-deux-montagnes (laurentides) | 1993 | 1993 | kim campbell | | 7 | andre bachand | richmond-arthabaska (centre-du-quebec & eastern townships) | 1998 | 2004 | joe clark peter mackay |

| | count ( "party leader" ) | |---:|---------------------------:| | 0 | 0 |

select abs ( winners - runners-up ) where team = 'stuttgart' col : team | winners | runners-up | years won | years lost row 1 : bayern munich | 5 | 4 | 1982, 1987, 1990, 2010, 2012 | 1989, 1994, 2008, 2013 row 2 : borussia dortmund | 5 | 2 | 1989, 1995, 1996, 2008, 2013 | 2011, 2012 row 3 : werder bremen | 4 | 1 | 1988, 1993, 1994, 2009 | 1991 row 4 : kaiserslautern | 1 | 2 | 1991 | 1990, 1996 row 5 : schalke 04 | 1 | 2 | 2011 | 1940, 2010 row 6 : borussia monchengladbach | 1 | 1 | 1977 | 1995 row 7 : stuttgart | 1 | 0 | 1992 | none row 8 : dresdner sc | 1 | 0 | 1940 | none row 9 : hamburg | 0 | 3 | none | 1977, 1982, 1987 row 10 : eintracht frankfurt | 0 | 1 | none | 1988 row 11 : hannover 96 | 0 | 1 | none | 1992 row 12 : bayer leverkusen | 0 | 1 | none | 1993 row 13 : wolfsburg | 0 | 1 | none | 2009

col : "abs ( winners - runners-up )" row 1 : abs ( winners - runners-up )

select abs ( winners - runners-up ) where team = 'stuttgart' <table_name> : table \begin{tabular}{llllll} \toprule & team & winners & runners-up & years won & years lost \\ \midrule 0 & bayern munich & 5 & 4 & 1982, 1987, 1990, 2010, 2012 & 1989, 1994, 2008, 2013 \\ 1 & borussia dortmund & 5 & 2 & 1989, 1995, 1996, 2008, 2013 & 2011, 2012 \\ 2 & werder bremen & 4 & 1 & 1988, 1993, 1994, 2009 & 1991 \\ 3 & kaiserslautern & 1 & 2 & 1991 & 1990, 1996 \\ 4 & schalke 04 & 1 & 2 & 2011 & 1940, 2010 \\ 5 & borussia monchengladbach & 1 & 1 & 1977 & 1995 \\ 6 & stuttgart & 1 & 0 & 1992 & none \\ 7 & dresdner sc & 1 & 0 & 1940 & none \\ 8 & hamburg & 0 & 3 & none & 1977, 1982, 1987 \\ 9 & eintracht frankfurt & 0 & 1 & none & 1988 \\ 10 & hannover 96 & 0 & 1 & none & 1992 \\ 11 & bayer leverkusen & 0 & 1 & none & 1993 \\ 12 & wolfsburg & 0 & 1 & none & 2009 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & "abs ( winners - runners-up )" \\ \midrule 0 & abs ( winners - runners-up ) \\ \bottomrule \end{tabular}

select abs ( winners - runners-up ) where team = 'stuttgart' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>team</th> <th>winners</th> <th>runners-up</th> <th>years won</th> <th>years lost</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>bayern munich</td> <td>5</td> <td>4</td> <td>1982, 1987, 1990, 2010, 2012</td> <td>1989, 1994, 2008, 2013</td> </tr> <tr> <th>1</th> <td>borussia dortmund</td> <td>5</td> <td>2</td> <td>1989, 1995, 1996, 2008, 2013</td> <td>2011, 2012</td> </tr> <tr> <th>2</th> <td>werder bremen</td> <td>4</td> <td>1</td> <td>1988, 1993, 1994, 2009</td> <td>1991</td> </tr> <tr> <th>3</th> <td>kaiserslautern</td> <td>1</td> <td>2</td> <td>1991</td> <td>1990, 1996</td> </tr> <tr> <th>4</th> <td>schalke 04</td> <td>1</td> <td>2</td> <td>2011</td> <td>1940, 2010</td> </tr> <tr> <th>5</th> <td>borussia monchengladbach</td> <td>1</td> <td>1</td> <td>1977</td> <td>1995</td> </tr> <tr> <th>6</th> <td>stuttgart</td> <td>1</td> <td>0</td> <td>1992</td> <td>none</td> </tr> <tr> <th>7</th> <td>dresdner sc</td> <td>1</td> <td>0</td> <td>1940</td> <td>none</td> </tr> <tr> <th>8</th> <td>hamburg</td> <td>0</td> <td>3</td> <td>none</td> <td>1977, 1982, 1987</td> </tr> <tr> <th>9</th> <td>eintracht frankfurt</td> <td>0</td> <td>1</td> <td>none</td> <td>1988</td> </tr> <tr> <th>10</th> <td>hannover 96</td> <td>0</td> <td>1</td> <td>none</td> <td>1992</td> </tr> <tr> <th>11</th> <td>bayer leverkusen</td> <td>0</td> <td>1</td> <td>none</td> <td>1993</td> </tr> <tr> <th>12</th> <td>wolfsburg</td> <td>0</td> <td>1</td> <td>none</td> <td>2009</td> </tr> </tbody> </table>

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

select abs ( winners - runners-up ) where team = 'stuttgart' <table_name> : table | | team | winners | runners-up | years won | years lost | |---:|:-------------------------|----------:|-------------:|:-----------------------------|:-----------------------| | 0 | bayern munich | 5 | 4 | 1982, 1987, 1990, 2010, 2012 | 1989, 1994, 2008, 2013 | | 1 | borussia dortmund | 5 | 2 | 1989, 1995, 1996, 2008, 2013 | 2011, 2012 | | 2 | werder bremen | 4 | 1 | 1988, 1993, 1994, 2009 | 1991 | | 3 | kaiserslautern | 1 | 2 | 1991 | 1990, 1996 | | 4 | schalke 04 | 1 | 2 | 2011 | 1940, 2010 | | 5 | borussia monchengladbach | 1 | 1 | 1977 | 1995 | | 6 | stuttgart | 1 | 0 | 1992 | none | | 7 | dresdner sc | 1 | 0 | 1940 | none | | 8 | hamburg | 0 | 3 | none | 1977, 1982, 1987 | | 9 | eintracht frankfurt | 0 | 1 | none | 1988 | | 10 | hannover 96 | 0 | 1 | none | 1992 | | 11 | bayer leverkusen | 0 | 1 | none | 1993 | | 12 | wolfsburg | 0 | 1 | none | 2009 |

| | "abs ( winners - runners-up )" | |---:|:---------------------------------| | 0 | abs ( winners - runners-up ) |

select count ( capital ) where names of districts = 'jaitpur, kumanu, mahajan, peepasar, udasar' col : s.no. | name of kingdom | name of king | no. of villages | capital | names of districts row 1 : 1 | sihag | chokha singh | 150 | suin | rawatsar, baramsar, purabsar dandusar, gandaisi row 2 : 2 | beniwal | raisal singh | 150 | rasalana | bhukarka, sanduri, manoharpur, kooi, bae row 3 : 3 | johiya | sher singh | 600 | bhurupal | jaitpur, kumanu, mahajan, peepasar, udasar row 4 : 4 | punia | kanha singh | 300 | luddi | bhadra, ajitpura, sidhmukh, rajgarh, dadrewa, sankhoo row 5 : 5 | saharan | pula singh | 300 | bhadang | khejra, phoglo, buchawas, sui, badnu, sirsila row 6 : 6 | godara | pandu singh | 700 | shekhsar | shekhsar, pundrasar, gusainsar (bada), gharsisar, garibdesar, rungaysar, kalu row 7 : 7 | kaswan | kanwarpal singh | 400 | sidhmukh | none

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

select count ( capital ) where names of districts = 'jaitpur, kumanu, mahajan, peepasar, udasar' <table_name> : table \begin{tabular}{lllllll} \toprule & s.no. & name of kingdom & name of king & no. of villages & capital & names of districts \\ \midrule 0 & 1 & sihag & chokha singh & 150 & suin & rawatsar, baramsar, purabsar dandusar, gandaisi \\ 1 & 2 & beniwal & raisal singh & 150 & rasalana & bhukarka, sanduri, manoharpur, kooi, bae \\ 2 & 3 & johiya & sher singh & 600 & bhurupal & jaitpur, kumanu, mahajan, peepasar, udasar \\ 3 & 4 & punia & kanha singh & 300 & luddi & bhadra, ajitpura, sidhmukh, rajgarh, dadrewa, sankhoo \\ 4 & 5 & saharan & pula singh & 300 & bhadang & khejra, phoglo, buchawas, sui, badnu, sirsila \\ 5 & 6 & godara & pandu singh & 700 & shekhsar & shekhsar, pundrasar, gusainsar (bada), gharsisar, garibdesar, rungaysar, kalu \\ 6 & 7 & kaswan & kanwarpal singh & 400 & sidhmukh & none \\ \bottomrule \end{tabular}

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

select count ( capital ) where names of districts = 'jaitpur, kumanu, mahajan, peepasar, udasar' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>s.no.</th> <th>name of kingdom</th> <th>name of king</th> <th>no. of villages</th> <th>capital</th> <th>names of districts</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>sihag</td> <td>chokha singh</td> <td>150</td> <td>suin</td> <td>rawatsar, baramsar, purabsar dandusar, gandaisi</td> </tr> <tr> <th>1</th> <td>2</td> <td>beniwal</td> <td>raisal singh</td> <td>150</td> <td>rasalana</td> <td>bhukarka, sanduri, manoharpur, kooi, bae</td> </tr> <tr> <th>2</th> <td>3</td> <td>johiya</td> <td>sher singh</td> <td>600</td> <td>bhurupal</td> <td>jaitpur, kumanu, mahajan, peepasar, udasar</td> </tr> <tr> <th>3</th> <td>4</td> <td>punia</td> <td>kanha singh</td> <td>300</td> <td>luddi</td> <td>bhadra, ajitpura, sidhmukh, rajgarh, dadrewa, sankhoo</td> </tr> <tr> <th>4</th> <td>5</td> <td>saharan</td> <td>pula singh</td> <td>300</td> <td>bhadang</td> <td>khejra, phoglo, buchawas, sui, badnu, sirsila</td> </tr> <tr> <th>5</th> <td>6</td> <td>godara</td> <td>pandu singh</td> <td>700</td> <td>shekhsar</td> <td>shekhsar, pundrasar, gusainsar (bada), gharsisar, garibdesar, rungaysar, kalu</td> </tr> <tr> <th>6</th> <td>7</td> <td>kaswan</td> <td>kanwarpal singh</td> <td>400</td> <td>sidhmukh</td> <td>none</td> </tr> </tbody> </table>

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

select count ( capital ) where names of districts = 'jaitpur, kumanu, mahajan, peepasar, udasar' <table_name> : table | | s.no. | name of kingdom | name of king | no. of villages | capital | names of districts | |---:|--------:|:------------------|:----------------|------------------:|:----------|:------------------------------------------------------------------------------| | 0 | 1 | sihag | chokha singh | 150 | suin | rawatsar, baramsar, purabsar dandusar, gandaisi | | 1 | 2 | beniwal | raisal singh | 150 | rasalana | bhukarka, sanduri, manoharpur, kooi, bae | | 2 | 3 | johiya | sher singh | 600 | bhurupal | jaitpur, kumanu, mahajan, peepasar, udasar | | 3 | 4 | punia | kanha singh | 300 | luddi | bhadra, ajitpura, sidhmukh, rajgarh, dadrewa, sankhoo | | 4 | 5 | saharan | pula singh | 300 | bhadang | khejra, phoglo, buchawas, sui, badnu, sirsila | | 5 | 6 | godara | pandu singh | 700 | shekhsar | shekhsar, pundrasar, gusainsar (bada), gharsisar, garibdesar, rungaysar, kalu | | 6 | 7 | kaswan | kanwarpal singh | 400 | sidhmukh | none |

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

select population (ab) where city = 'caravaggio' col : pos. | city | population (ab) | surface (km2) | density (ab/km2) | altitude (mslm) row 1 : 1st | bergamo | 121.316 | 39.6 | 3064 | 249 row 2 : 2nd | treviglio | 29.334 | 31.54 | 930 | 125 row 3 : 3rd | seriate | 25.036 | 12.41 | 2017 | 247 row 4 : 4th | dalmine | 23.237 | 11.6 | 2003 | 207 row 5 : 5th | romano di lombardia | 19.472 | 18.03 | 1080 | 120 row 6 : 6th | albino | 18.232 | 31.32 | 582 | 342 row 7 : 7th | caravaggio | 16.258 | 32.81 | 496 | 111 row 8 : 8th | alzano lombardo | 13.821 | 13.43 | 1029 | 304 row 9 : 9th | stezzano | 12.999 | 9.25 | 1405 | 211 row 10 : 10th | osio sotto | 12.083 | 7.45 | 1622 | 182

col : population (ab) row 1 : 16.258

select population (ab) where city = 'caravaggio' <table_name> : table \begin{tabular}{lllllll} \toprule & pos. & city & population (ab) & surface (km2) & density (ab/km2) & altitude (mslm) \\ \midrule 0 & 1st & bergamo & 121.316 & 39.6 & 3064 & 249 \\ 1 & 2nd & treviglio & 29.334 & 31.54 & 930 & 125 \\ 2 & 3rd & seriate & 25.036 & 12.41 & 2017 & 247 \\ 3 & 4th & dalmine & 23.237 & 11.6 & 2003 & 207 \\ 4 & 5th & romano di lombardia & 19.472 & 18.03 & 1080 & 120 \\ 5 & 6th & albino & 18.232 & 31.32 & 582 & 342 \\ 6 & 7th & caravaggio & 16.258 & 32.81 & 496 & 111 \\ 7 & 8th & alzano lombardo & 13.821 & 13.43 & 1029 & 304 \\ 8 & 9th & stezzano & 12.999 & 9.25 & 1405 & 211 \\ 9 & 10th & osio sotto & 12.083 & 7.45 & 1622 & 182 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & population (ab) \\ \midrule 0 & 16.258 \\ \bottomrule \end{tabular}

select population (ab) where city = 'caravaggio' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>pos.</th> <th>city</th> <th>population (ab)</th> <th>surface (km2)</th> <th>density (ab/km2)</th> <th>altitude (mslm)</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1st</td> <td>bergamo</td> <td>121.316</td> <td>39.6</td> <td>3064</td> <td>249</td> </tr> <tr> <th>1</th> <td>2nd</td> <td>treviglio</td> <td>29.334</td> <td>31.54</td> <td>930</td> <td>125</td> </tr> <tr> <th>2</th> <td>3rd</td> <td>seriate</td> <td>25.036</td> <td>12.41</td> <td>2017</td> <td>247</td> </tr> <tr> <th>3</th> <td>4th</td> <td>dalmine</td> <td>23.237</td> <td>11.6</td> <td>2003</td> <td>207</td> </tr> <tr> <th>4</th> <td>5th</td> <td>romano di lombardia</td> <td>19.472</td> <td>18.03</td> <td>1080</td> <td>120</td> </tr> <tr> <th>5</th> <td>6th</td> <td>albino</td> <td>18.232</td> <td>31.32</td> <td>582</td> <td>342</td> </tr> <tr> <th>6</th> <td>7th</td> <td>caravaggio</td> <td>16.258</td> <td>32.81</td> <td>496</td> <td>111</td> </tr> <tr> <th>7</th> <td>8th</td> <td>alzano lombardo</td> <td>13.821</td> <td>13.43</td> <td>1029</td> <td>304</td> </tr> <tr> <th>8</th> <td>9th</td> <td>stezzano</td> <td>12.999</td> <td>9.25</td> <td>1405</td> <td>211</td> </tr> <tr> <th>9</th> <td>10th</td> <td>osio sotto</td> <td>12.083</td> <td>7.45</td> <td>1622</td> <td>182</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>population (ab)</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>16.258</td> </tr> </tbody> </table>

select population (ab) where city = 'caravaggio' <table_name> : table | | pos. | city | population (ab) | surface (km2) | density (ab/km2) | altitude (mslm) | |---:|:-------|:--------------------|------------------:|----------------:|-------------------:|------------------:| | 0 | 1st | bergamo | 121.316 | 39.6 | 3064 | 249 | | 1 | 2nd | treviglio | 29.334 | 31.54 | 930 | 125 | | 2 | 3rd | seriate | 25.036 | 12.41 | 2017 | 247 | | 3 | 4th | dalmine | 23.237 | 11.6 | 2003 | 207 | | 4 | 5th | romano di lombardia | 19.472 | 18.03 | 1080 | 120 | | 5 | 6th | albino | 18.232 | 31.32 | 582 | 342 | | 6 | 7th | caravaggio | 16.258 | 32.81 | 496 | 111 | | 7 | 8th | alzano lombardo | 13.821 | 13.43 | 1029 | 304 | | 8 | 9th | stezzano | 12.999 | 9.25 | 1405 | 211 | | 9 | 10th | osio sotto | 12.083 | 7.45 | 1622 | 182 |

| | population (ab) | |---:|------------------:| | 0 | 16.258 |

select rank where nationality = 'costa rica' col : rank | name | nationality | time | notes row 1 : none | jill sterkel | united states | 1:00.53 | nr, gr row 2 : none | lisa buese | united states | 1:00.59 | none row 3 : none | nancy garapick | canada | 1:02.96 | none row 4 : 4 | shelley cramer | u.s. virgin islands | 1:03.51 | nr row 5 : 5 | susan sloan | canada | 1:03.52 | none row 6 : 6 | maria paris | costa rica | 1:03.58 | none row 7 : 7 | rosanna juncos | argentina | 1:04.75 | none row 8 : 8 | adriana pereira | brazil | 1:06.79 | none

col : rank row 1 : 6

select rank where nationality = 'costa rica' <table_name> : table \begin{tabular}{llllll} \toprule & rank & name & nationality & time & notes \\ \midrule 0 & none & jill sterkel & united states & 1:00.53 & nr, gr \\ 1 & none & lisa buese & united states & 1:00.59 & none \\ 2 & none & nancy garapick & canada & 1:02.96 & none \\ 3 & 4 & shelley cramer & u.s. virgin islands & 1:03.51 & nr \\ 4 & 5 & susan sloan & canada & 1:03.52 & none \\ 5 & 6 & maria paris & costa rica & 1:03.58 & none \\ 6 & 7 & rosanna juncos & argentina & 1:04.75 & none \\ 7 & 8 & adriana pereira & brazil & 1:06.79 & none \\ \bottomrule \end{tabular}

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

select rank where nationality = 'costa rica' <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>jill sterkel</td> <td>united states</td> <td>1:00.53</td> <td>nr, gr</td> </tr> <tr> <th>1</th> <td>none</td> <td>lisa buese</td> <td>united states</td> <td>1:00.59</td> <td>none</td> </tr> <tr> <th>2</th> <td>none</td> <td>nancy garapick</td> <td>canada</td> <td>1:02.96</td> <td>none</td> </tr> <tr> <th>3</th> <td>4</td> <td>shelley cramer</td> <td>u.s. virgin islands</td> <td>1:03.51</td> <td>nr</td> </tr> <tr> <th>4</th> <td>5</td> <td>susan sloan</td> <td>canada</td> <td>1:03.52</td> <td>none</td> </tr> <tr> <th>5</th> <td>6</td> <td>maria paris</td> <td>costa rica</td> <td>1:03.58</td> <td>none</td> </tr> <tr> <th>6</th> <td>7</td> <td>rosanna juncos</td> <td>argentina</td> <td>1:04.75</td> <td>none</td> </tr> <tr> <th>7</th> <td>8</td> <td>adriana pereira</td> <td>brazil</td> <td>1:06.79</td> <td>none</td> </tr> </tbody> </table>

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

select rank where nationality = 'costa rica' <table_name> : table | | rank | name | nationality | time | notes | |---:|:-------|:----------------|:--------------------|:--------|:--------| | 0 | none | jill sterkel | united states | 1:00.53 | nr, gr | | 1 | none | lisa buese | united states | 1:00.59 | none | | 2 | none | nancy garapick | canada | 1:02.96 | none | | 3 | 4 | shelley cramer | u.s. virgin islands | 1:03.51 | nr | | 4 | 5 | susan sloan | canada | 1:03.52 | none | | 5 | 6 | maria paris | costa rica | 1:03.58 | none | | 6 | 7 | rosanna juncos | argentina | 1:04.75 | none | | 7 | 8 | adriana pereira | brazil | 1:06.79 | none |

| | rank | |---:|-------:| | 0 | 6 |

select republican where democratic = 'hank eng' col : district | incumbent | 2008 status | democratic | republican row 1 : 1 | diana degette | re-election | diana degette | george lilly row 2 : 2 | mark udall | open | jared polis | scott starin row 3 : 3 | john salazar | re-election | john salazar | wayne wolf row 4 : 4 | marilyn musgrave | re-election | betsy markey | marilyn musgrave row 5 : 5 | doug lamborn | re-election | hal bidlack | doug lamborn row 6 : 6 | tom tancredo | open | hank eng | mike coffman row 7 : 7 | ed perlmutter | re-election | ed perlmutter | john w. lerew

col : republican row 1 : mike coffman

select republican where democratic = 'hank eng' <table_name> : table \begin{tabular}{llllll} \toprule & district & incumbent & 2008 status & democratic & republican \\ \midrule 0 & 1 & diana degette & re-election & diana degette & george lilly \\ 1 & 2 & mark udall & open & jared polis & scott starin \\ 2 & 3 & john salazar & re-election & john salazar & wayne wolf \\ 3 & 4 & marilyn musgrave & re-election & betsy markey & marilyn musgrave \\ 4 & 5 & doug lamborn & re-election & hal bidlack & doug lamborn \\ 5 & 6 & tom tancredo & open & hank eng & mike coffman \\ 6 & 7 & ed perlmutter & re-election & ed perlmutter & john w. lerew \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & republican \\ \midrule 0 & mike coffman \\ \bottomrule \end{tabular}

select republican where democratic = 'hank eng' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>district</th> <th>incumbent</th> <th>2008 status</th> <th>democratic</th> <th>republican</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>diana degette</td> <td>re-election</td> <td>diana degette</td> <td>george lilly</td> </tr> <tr> <th>1</th> <td>2</td> <td>mark udall</td> <td>open</td> <td>jared polis</td> <td>scott starin</td> </tr> <tr> <th>2</th> <td>3</td> <td>john salazar</td> <td>re-election</td> <td>john salazar</td> <td>wayne wolf</td> </tr> <tr> <th>3</th> <td>4</td> <td>marilyn musgrave</td> <td>re-election</td> <td>betsy markey</td> <td>marilyn musgrave</td> </tr> <tr> <th>4</th> <td>5</td> <td>doug lamborn</td> <td>re-election</td> <td>hal bidlack</td> <td>doug lamborn</td> </tr> <tr> <th>5</th> <td>6</td> <td>tom tancredo</td> <td>open</td> <td>hank eng</td> <td>mike coffman</td> </tr> <tr> <th>6</th> <td>7</td> <td>ed perlmutter</td> <td>re-election</td> <td>ed perlmutter</td> <td>john w. lerew</td> </tr> </tbody> </table>

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

select republican where democratic = 'hank eng' <table_name> : table | | district | incumbent | 2008 status | democratic | republican | |---:|-----------:|:-----------------|:--------------|:--------------|:-----------------| | 0 | 1 | diana degette | re-election | diana degette | george lilly | | 1 | 2 | mark udall | open | jared polis | scott starin | | 2 | 3 | john salazar | re-election | john salazar | wayne wolf | | 3 | 4 | marilyn musgrave | re-election | betsy markey | marilyn musgrave | | 4 | 5 | doug lamborn | re-election | hal bidlack | doug lamborn | | 5 | 6 | tom tancredo | open | hank eng | mike coffman | | 6 | 7 | ed perlmutter | re-election | ed perlmutter | john w. lerew |

| | republican | |---:|:-------------| | 0 | mike coffman |

select count ( * ) where date = '15 july 2012' col : outcome | no. | date | tournament | surface | opponent | score row 1 : runner-up | 1 | 2009-10-25 00:00:00 | kremlin cup, russia | hard (i) | mikhail youzhny | 7-6(7-5), 0-6, 4-6 row 2 : runner-up | 2 | 2010-06-19 00:00:00 | unicef open, netherlands | grass | sergiy stakhovsky | 3-6, 0-6 row 3 : runner-up | 3 | 2011-02-27 00:00:00 | international tennis championships, united states | hard | juan martin del potro | 4-6, 4-6 row 4 : runner-up | 4 | 2011-06-18 00:00:00 | aegon international, united kingdom | grass | andreas seppi | 6-7(5-7), 6-3, 3-5 ret row 5 : winner | 1 | 2011-10-02 00:00:00 | malaysian open, malaysia | hard (i) | marcos baghdatis | 6-4, 7-5 row 6 : winner | 2 | 2011-10-23 00:00:00 | kremlin cup, russia | hard (i) | viktor troicki | 6-4, 6-2 row 7 : runner-up | 5 | 2011-10-30 00:00:00 | st. petersburg open, russia | hard (i) | marin cilic | 3-6, 6-3, 2-6 row 8 : runner-up | 6 | 2012-01-08 00:00:00 | chennai open, india | hard | milos raonic | 7-6(7-4), 6-7(4-7), 6-7(4-7) row 9 : winner | 3 | 2012-07-15 00:00:00 | stuttgart open, germany | clay | juan monaco | 6-4, 5-7, 6-3 row 10 : runner-up | 7 | 2012-07-22 00:00:00 | swiss open, switzerland | clay | thomaz bellucci | 7-6(8-6), 4-6, 2-6 row 11 : winner | 4 | 2013-01-06 00:00:00 | chennai open, india | hard | roberto bautista-agut | 3-6, 6-1, 6-3

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

select count ( * ) where date = '15 july 2012' <table_name> : table \begin{tabular}{llllllll} \toprule & outcome & no. & date & tournament & surface & opponent & score \\ \midrule 0 & runner-up & 1 & 2009-10-25 00:00:00 & kremlin cup, russia & hard (i) & mikhail youzhny & 7-6(7-5), 0-6, 4-6 \\ 1 & runner-up & 2 & 2010-06-19 00:00:00 & unicef open, netherlands & grass & sergiy stakhovsky & 3-6, 0-6 \\ 2 & runner-up & 3 & 2011-02-27 00:00:00 & international tennis championships, united states & hard & juan martin del potro & 4-6, 4-6 \\ 3 & runner-up & 4 & 2011-06-18 00:00:00 & aegon international, united kingdom & grass & andreas seppi & 6-7(5-7), 6-3, 3-5 ret \\ 4 & winner & 1 & 2011-10-02 00:00:00 & malaysian open, malaysia & hard (i) & marcos baghdatis & 6-4, 7-5 \\ 5 & winner & 2 & 2011-10-23 00:00:00 & kremlin cup, russia & hard (i) & viktor troicki & 6-4, 6-2 \\ 6 & runner-up & 5 & 2011-10-30 00:00:00 & st. petersburg open, russia & hard (i) & marin cilic & 3-6, 6-3, 2-6 \\ 7 & runner-up & 6 & 2012-01-08 00:00:00 & chennai open, india & hard & milos raonic & 7-6(7-4), 6-7(4-7), 6-7(4-7) \\ 8 & winner & 3 & 2012-07-15 00:00:00 & stuttgart open, germany & clay & juan monaco & 6-4, 5-7, 6-3 \\ 9 & runner-up & 7 & 2012-07-22 00:00:00 & swiss open, switzerland & clay & thomaz bellucci & 7-6(8-6), 4-6, 2-6 \\ 10 & winner & 4 & 2013-01-06 00:00:00 & chennai open, india & hard & roberto bautista-agut & 3-6, 6-1, 6-3 \\ \bottomrule \end{tabular}

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

select count ( * ) where date = '15 july 2012' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>outcome</th> <th>no.</th> <th>date</th> <th>tournament</th> <th>surface</th> <th>opponent</th> <th>score</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>runner-up</td> <td>1</td> <td>2009-10-25 00:00:00</td> <td>kremlin cup, russia</td> <td>hard (i)</td> <td>mikhail youzhny</td> <td>7-6(7-5), 0-6, 4-6</td> </tr> <tr> <th>1</th> <td>runner-up</td> <td>2</td> <td>2010-06-19 00:00:00</td> <td>unicef open, netherlands</td> <td>grass</td> <td>sergiy stakhovsky</td> <td>3-6, 0-6</td> </tr> <tr> <th>2</th> <td>runner-up</td> <td>3</td> <td>2011-02-27 00:00:00</td> <td>international tennis championships, united states</td> <td>hard</td> <td>juan martin del potro</td> <td>4-6, 4-6</td> </tr> <tr> <th>3</th> <td>runner-up</td> <td>4</td> <td>2011-06-18 00:00:00</td> <td>aegon international, united kingdom</td> <td>grass</td> <td>andreas seppi</td> <td>6-7(5-7), 6-3, 3-5 ret</td> </tr> <tr> <th>4</th> <td>winner</td> <td>1</td> <td>2011-10-02 00:00:00</td> <td>malaysian open, malaysia</td> <td>hard (i)</td> <td>marcos baghdatis</td> <td>6-4, 7-5</td> </tr> <tr> <th>5</th> <td>winner</td> <td>2</td> <td>2011-10-23 00:00:00</td> <td>kremlin cup, russia</td> <td>hard (i)</td> <td>viktor troicki</td> <td>6-4, 6-2</td> </tr> <tr> <th>6</th> <td>runner-up</td> <td>5</td> <td>2011-10-30 00:00:00</td> <td>st. petersburg open, russia</td> <td>hard (i)</td> <td>marin cilic</td> <td>3-6, 6-3, 2-6</td> </tr> <tr> <th>7</th> <td>runner-up</td> <td>6</td> <td>2012-01-08 00:00:00</td> <td>chennai open, india</td> <td>hard</td> <td>milos raonic</td> <td>7-6(7-4), 6-7(4-7), 6-7(4-7)</td> </tr> <tr> <th>8</th> <td>winner</td> <td>3</td> <td>2012-07-15 00:00:00</td> <td>stuttgart open, germany</td> <td>clay</td> <td>juan monaco</td> <td>6-4, 5-7, 6-3</td> </tr> <tr> <th>9</th> <td>runner-up</td> <td>7</td> <td>2012-07-22 00:00:00</td> <td>swiss open, switzerland</td> <td>clay</td> <td>thomaz bellucci</td> <td>7-6(8-6), 4-6, 2-6</td> </tr> <tr> <th>10</th> <td>winner</td> <td>4</td> <td>2013-01-06 00:00:00</td> <td>chennai open, india</td> <td>hard</td> <td>roberto bautista-agut</td> <td>3-6, 6-1, 6-3</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 date = '15 july 2012' <table_name> : table | | outcome | no. | date | tournament | surface | opponent | score | |---:|:----------|------:|:--------------------|:--------------------------------------------------|:----------|:----------------------|:-----------------------------| | 0 | runner-up | 1 | 2009-10-25 00:00:00 | kremlin cup, russia | hard (i) | mikhail youzhny | 7-6(7-5), 0-6, 4-6 | | 1 | runner-up | 2 | 2010-06-19 00:00:00 | unicef open, netherlands | grass | sergiy stakhovsky | 3-6, 0-6 | | 2 | runner-up | 3 | 2011-02-27 00:00:00 | international tennis championships, united states | hard | juan martin del potro | 4-6, 4-6 | | 3 | runner-up | 4 | 2011-06-18 00:00:00 | aegon international, united kingdom | grass | andreas seppi | 6-7(5-7), 6-3, 3-5 ret | | 4 | winner | 1 | 2011-10-02 00:00:00 | malaysian open, malaysia | hard (i) | marcos baghdatis | 6-4, 7-5 | | 5 | winner | 2 | 2011-10-23 00:00:00 | kremlin cup, russia | hard (i) | viktor troicki | 6-4, 6-2 | | 6 | runner-up | 5 | 2011-10-30 00:00:00 | st. petersburg open, russia | hard (i) | marin cilic | 3-6, 6-3, 2-6 | | 7 | runner-up | 6 | 2012-01-08 00:00:00 | chennai open, india | hard | milos raonic | 7-6(7-4), 6-7(4-7), 6-7(4-7) | | 8 | winner | 3 | 2012-07-15 00:00:00 | stuttgart open, germany | clay | juan monaco | 6-4, 5-7, 6-3 | | 9 | runner-up | 7 | 2012-07-22 00:00:00 | swiss open, switzerland | clay | thomaz bellucci | 7-6(8-6), 4-6, 2-6 | | 10 | winner | 4 | 2013-01-06 00:00:00 | chennai open, india | hard | roberto bautista-agut | 3-6, 6-1, 6-3 |

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

select count ( comments ) where type = 'maglev' 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 : count ( "comments" ) row 1 : 7

select count ( comments ) where type = 'maglev' <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 & count ( "comments" ) \\ \midrule 0 & 7 \\ \bottomrule \end{tabular}

select count ( comments ) where type = 'maglev' <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>count ( "comments" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>7</td> </tr> </tbody> </table>

select count ( comments ) where type = 'maglev' <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 |

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

select count ( thread nominal size ) where thread nominal size = 'pg16' col : thread nominal size | outer diameter [mm (in)] | threads per inch (tpi) | pitch [in (mm)] | inner diameter [mm (in)] | cable diameter [mm (in)] row 1 : pg7 | 12.5 (0.492) | 20 | 0.05 (1.270) | 11.28 (0.444) | 3 to 6.5 (0.118 to 0.256) row 2 : pg9 | 15.5 (0.610) | 18 | 0.05556 (1.4112) | 13.86 (0.546) | 4 to 8 (0.157 to 0.315) row 3 : pg11 | 18.6 (0.732) | 18 | 0.05556 (1.4112) | 17.26 (0.680) | 5 to 10 (0.197 to 0.394) row 4 : pg13.5 | 20.4 (0.803) | 18 | 0.05556 (1.4112) | 19.06 (0.750) | 6 to 12 (0.236 to 0.472) row 5 : pg16 | 22.5 (0.886) | 18 | 0.05556 (1.4112) | 21.16 (0.833) | 10 to 14 (0.394 to 0.551) row 6 : pg21 | 28.3 (1.114) | 16 | 0.0625 (1.5875) | 26.78 (1.054) | 13 to 18 (0.512 to 0.709) row 7 : pg29 | 37.0 (1.457) | 16 | 0.0625 (1.5875) | 35.48 (1.397) | 18 to 25 (0.709 to 0.984) row 8 : pg36 | 47.0 (1.850) | 16 | 0.0625 (1.5875) | 45.48 (1.791) | none row 9 : pg42 | 54.0 (2.126) | 16 | 0.0625 (1.5875) | 52.48 (2.066) | none row 10 : pg48 | 59.3 (2.335) | 16 | 0.0625 (1.5875) | 57.78 (2.275) | none

col : count ( "thread nominal size" ) row 1 : 1

select count ( thread nominal size ) where thread nominal size = 'pg16' <table_name> : table \begin{tabular}{lllllll} \toprule & thread nominal size & outer diameter [mm (in)] & threads per inch (tpi) & pitch [in (mm)] & inner diameter [mm (in)] & cable diameter [mm (in)] \\ \midrule 0 & pg7 & 12.5 (0.492) & 20 & 0.05 (1.270) & 11.28 (0.444) & 3 to 6.5 (0.118 to 0.256) \\ 1 & pg9 & 15.5 (0.610) & 18 & 0.05556 (1.4112) & 13.86 (0.546) & 4 to 8 (0.157 to 0.315) \\ 2 & pg11 & 18.6 (0.732) & 18 & 0.05556 (1.4112) & 17.26 (0.680) & 5 to 10 (0.197 to 0.394) \\ 3 & pg13.5 & 20.4 (0.803) & 18 & 0.05556 (1.4112) & 19.06 (0.750) & 6 to 12 (0.236 to 0.472) \\ 4 & pg16 & 22.5 (0.886) & 18 & 0.05556 (1.4112) & 21.16 (0.833) & 10 to 14 (0.394 to 0.551) \\ 5 & pg21 & 28.3 (1.114) & 16 & 0.0625 (1.5875) & 26.78 (1.054) & 13 to 18 (0.512 to 0.709) \\ 6 & pg29 & 37.0 (1.457) & 16 & 0.0625 (1.5875) & 35.48 (1.397) & 18 to 25 (0.709 to 0.984) \\ 7 & pg36 & 47.0 (1.850) & 16 & 0.0625 (1.5875) & 45.48 (1.791) & none \\ 8 & pg42 & 54.0 (2.126) & 16 & 0.0625 (1.5875) & 52.48 (2.066) & none \\ 9 & pg48 & 59.3 (2.335) & 16 & 0.0625 (1.5875) & 57.78 (2.275) & none \\ \bottomrule \end{tabular}

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

select count ( thread nominal size ) where thread nominal size = 'pg16' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>thread nominal size</th> <th>outer diameter [mm (in)]</th> <th>threads per inch (tpi)</th> <th>pitch [in (mm)]</th> <th>inner diameter [mm (in)]</th> <th>cable diameter [mm (in)]</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>pg7</td> <td>12.5 (0.492)</td> <td>20</td> <td>0.05 (1.270)</td> <td>11.28 (0.444)</td> <td>3 to 6.5 (0.118 to 0.256)</td> </tr> <tr> <th>1</th> <td>pg9</td> <td>15.5 (0.610)</td> <td>18</td> <td>0.05556 (1.4112)</td> <td>13.86 (0.546)</td> <td>4 to 8 (0.157 to 0.315)</td> </tr> <tr> <th>2</th> <td>pg11</td> <td>18.6 (0.732)</td> <td>18</td> <td>0.05556 (1.4112)</td> <td>17.26 (0.680)</td> <td>5 to 10 (0.197 to 0.394)</td> </tr> <tr> <th>3</th> <td>pg13.5</td> <td>20.4 (0.803)</td> <td>18</td> <td>0.05556 (1.4112)</td> <td>19.06 (0.750)</td> <td>6 to 12 (0.236 to 0.472)</td> </tr> <tr> <th>4</th> <td>pg16</td> <td>22.5 (0.886)</td> <td>18</td> <td>0.05556 (1.4112)</td> <td>21.16 (0.833)</td> <td>10 to 14 (0.394 to 0.551)</td> </tr> <tr> <th>5</th> <td>pg21</td> <td>28.3 (1.114)</td> <td>16</td> <td>0.0625 (1.5875)</td> <td>26.78 (1.054)</td> <td>13 to 18 (0.512 to 0.709)</td> </tr> <tr> <th>6</th> <td>pg29</td> <td>37.0 (1.457)</td> <td>16</td> <td>0.0625 (1.5875)</td> <td>35.48 (1.397)</td> <td>18 to 25 (0.709 to 0.984)</td> </tr> <tr> <th>7</th> <td>pg36</td> <td>47.0 (1.850)</td> <td>16</td> <td>0.0625 (1.5875)</td> <td>45.48 (1.791)</td> <td>none</td> </tr> <tr> <th>8</th> <td>pg42</td> <td>54.0 (2.126)</td> <td>16</td> <td>0.0625 (1.5875)</td> <td>52.48 (2.066)</td> <td>none</td> </tr> <tr> <th>9</th> <td>pg48</td> <td>59.3 (2.335)</td> <td>16</td> <td>0.0625 (1.5875)</td> <td>57.78 (2.275)</td> <td>none</td> </tr> </tbody> </table>

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

select count ( thread nominal size ) where thread nominal size = 'pg16' <table_name> : table | | thread nominal size | outer diameter [mm (in)] | threads per inch (tpi) | pitch [in (mm)] | inner diameter [mm (in)] | cable diameter [mm (in)] | |---:|:----------------------|:---------------------------|-------------------------:|:------------------|:---------------------------|:---------------------------| | 0 | pg7 | 12.5 (0.492) | 20 | 0.05 (1.270) | 11.28 (0.444) | 3 to 6.5 (0.118 to 0.256) | | 1 | pg9 | 15.5 (0.610) | 18 | 0.05556 (1.4112) | 13.86 (0.546) | 4 to 8 (0.157 to 0.315) | | 2 | pg11 | 18.6 (0.732) | 18 | 0.05556 (1.4112) | 17.26 (0.680) | 5 to 10 (0.197 to 0.394) | | 3 | pg13.5 | 20.4 (0.803) | 18 | 0.05556 (1.4112) | 19.06 (0.750) | 6 to 12 (0.236 to 0.472) | | 4 | pg16 | 22.5 (0.886) | 18 | 0.05556 (1.4112) | 21.16 (0.833) | 10 to 14 (0.394 to 0.551) | | 5 | pg21 | 28.3 (1.114) | 16 | 0.0625 (1.5875) | 26.78 (1.054) | 13 to 18 (0.512 to 0.709) | | 6 | pg29 | 37.0 (1.457) | 16 | 0.0625 (1.5875) | 35.48 (1.397) | 18 to 25 (0.709 to 0.984) | | 7 | pg36 | 47.0 (1.850) | 16 | 0.0625 (1.5875) | 45.48 (1.791) | none | | 8 | pg42 | 54.0 (2.126) | 16 | 0.0625 (1.5875) | 52.48 (2.066) | none | | 9 | pg48 | 59.3 (2.335) | 16 | 0.0625 (1.5875) | 57.78 (2.275) | none |

| | count ( "thread nominal size" ) | |---:|----------------------------------:| | 0 | 1 |

select first elected where delegate = 'galen r. clagett' col : district | counties represented | delegate | party | first elected | committee row 1 : 1a | allegany, garrett | wendell r. beitzel | republican | 2006 | health and government operations row 2 : 1b | allegany | kevin kelly | democratic | 1986 | judiciary row 3 : 1c | allegany, washington | leroy e. myers, jr | republican | 2002 | appropriations row 4 : 2a | washington | andrew a. serafini | republican | 2008 | ways and means row 5 : 2b | washington | christopher b. shank | republican | 1998 | judiciary row 6 : 2c | washington | john p. donoghue | democratic | 1990 | health and government operations row 7 : 3a | frederick | galen r. clagett | democratic | 2002 | appropriations row 8 : 3a | frederick | sue hecht | democratic | 1994 | economic matters row 9 : 3b | frederick, washington | michael hough | republican | 2002 | health and government operations row 10 : 4a | frederick | joseph r. bartlett | republican | 1998 | ways and means row 11 : 4a | frederick | kelly m. schulz | republican | 2011 | environmental matters row 12 : 4b | carroll, frederick | donald b. elliott | republican | 1986 | health and government operations row 13 : 5a | carroll | tanya thornton shewell | republican | 2004 | environmental matters row 14 : 5a | carroll | nancy r. stocksdale | republican | 1994 | appropriations row 15 : 9b | carroll | susan w. krebs | republican | 2002 | ways and means

col : first elected row 1 : 2002

select first elected where delegate = 'galen r. clagett' <table_name> : table \begin{tabular}{lllllll} \toprule & district & counties represented & delegate & party & first elected & committee \\ \midrule 0 & 1a & allegany, garrett & wendell r. beitzel & republican & 2006 & health and government operations \\ 1 & 1b & allegany & kevin kelly & democratic & 1986 & judiciary \\ 2 & 1c & allegany, washington & leroy e. myers, jr & republican & 2002 & appropriations \\ 3 & 2a & washington & andrew a. serafini & republican & 2008 & ways and means \\ 4 & 2b & washington & christopher b. shank & republican & 1998 & judiciary \\ 5 & 2c & washington & john p. donoghue & democratic & 1990 & health and government operations \\ 6 & 3a & frederick & galen r. clagett & democratic & 2002 & appropriations \\ 7 & 3a & frederick & sue hecht & democratic & 1994 & economic matters \\ 8 & 3b & frederick, washington & michael hough & republican & 2002 & health and government operations \\ 9 & 4a & frederick & joseph r. bartlett & republican & 1998 & ways and means \\ 10 & 4a & frederick & kelly m. schulz & republican & 2011 & environmental matters \\ 11 & 4b & carroll, frederick & donald b. elliott & republican & 1986 & health and government operations \\ 12 & 5a & carroll & tanya thornton shewell & republican & 2004 & environmental matters \\ 13 & 5a & carroll & nancy r. stocksdale & republican & 1994 & appropriations \\ 14 & 9b & carroll & susan w. krebs & republican & 2002 & ways and means \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & first elected \\ \midrule 0 & 2002 \\ \bottomrule \end{tabular}

select first elected where delegate = 'galen r. clagett' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>district</th> <th>counties represented</th> <th>delegate</th> <th>party</th> <th>first elected</th> <th>committee</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1a</td> <td>allegany, garrett</td> <td>wendell r. beitzel</td> <td>republican</td> <td>2006</td> <td>health and government operations</td> </tr> <tr> <th>1</th> <td>1b</td> <td>allegany</td> <td>kevin kelly</td> <td>democratic</td> <td>1986</td> <td>judiciary</td> </tr> <tr> <th>2</th> <td>1c</td> <td>allegany, washington</td> <td>leroy e. myers, jr</td> <td>republican</td> <td>2002</td> <td>appropriations</td> </tr> <tr> <th>3</th> <td>2a</td> <td>washington</td> <td>andrew a. serafini</td> <td>republican</td> <td>2008</td> <td>ways and means</td> </tr> <tr> <th>4</th> <td>2b</td> <td>washington</td> <td>christopher b. shank</td> <td>republican</td> <td>1998</td> <td>judiciary</td> </tr> <tr> <th>5</th> <td>2c</td> <td>washington</td> <td>john p. donoghue</td> <td>democratic</td> <td>1990</td> <td>health and government operations</td> </tr> <tr> <th>6</th> <td>3a</td> <td>frederick</td> <td>galen r. clagett</td> <td>democratic</td> <td>2002</td> <td>appropriations</td> </tr> <tr> <th>7</th> <td>3a</td> <td>frederick</td> <td>sue hecht</td> <td>democratic</td> <td>1994</td> <td>economic matters</td> </tr> <tr> <th>8</th> <td>3b</td> <td>frederick, washington</td> <td>michael hough</td> <td>republican</td> <td>2002</td> <td>health and government operations</td> </tr> <tr> <th>9</th> <td>4a</td> <td>frederick</td> <td>joseph r. bartlett</td> <td>republican</td> <td>1998</td> <td>ways and means</td> </tr> <tr> <th>10</th> <td>4a</td> <td>frederick</td> <td>kelly m. schulz</td> <td>republican</td> <td>2011</td> <td>environmental matters</td> </tr> <tr> <th>11</th> <td>4b</td> <td>carroll, frederick</td> <td>donald b. elliott</td> <td>republican</td> <td>1986</td> <td>health and government operations</td> </tr> <tr> <th>12</th> <td>5a</td> <td>carroll</td> <td>tanya thornton shewell</td> <td>republican</td> <td>2004</td> <td>environmental matters</td> </tr> <tr> <th>13</th> <td>5a</td> <td>carroll</td> <td>nancy r. stocksdale</td> <td>republican</td> <td>1994</td> <td>appropriations</td> </tr> <tr> <th>14</th> <td>9b</td> <td>carroll</td> <td>susan w. krebs</td> <td>republican</td> <td>2002</td> <td>ways and means</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>first elected</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>2002</td> </tr> </tbody> </table>

select first elected where delegate = 'galen r. clagett' <table_name> : table | | district | counties represented | delegate | party | first elected | committee | |---:|:-----------|:-----------------------|:-----------------------|:-----------|----------------:|:---------------------------------| | 0 | 1a | allegany, garrett | wendell r. beitzel | republican | 2006 | health and government operations | | 1 | 1b | allegany | kevin kelly | democratic | 1986 | judiciary | | 2 | 1c | allegany, washington | leroy e. myers, jr | republican | 2002 | appropriations | | 3 | 2a | washington | andrew a. serafini | republican | 2008 | ways and means | | 4 | 2b | washington | christopher b. shank | republican | 1998 | judiciary | | 5 | 2c | washington | john p. donoghue | democratic | 1990 | health and government operations | | 6 | 3a | frederick | galen r. clagett | democratic | 2002 | appropriations | | 7 | 3a | frederick | sue hecht | democratic | 1994 | economic matters | | 8 | 3b | frederick, washington | michael hough | republican | 2002 | health and government operations | | 9 | 4a | frederick | joseph r. bartlett | republican | 1998 | ways and means | | 10 | 4a | frederick | kelly m. schulz | republican | 2011 | environmental matters | | 11 | 4b | carroll, frederick | donald b. elliott | republican | 1986 | health and government operations | | 12 | 5a | carroll | tanya thornton shewell | republican | 2004 | environmental matters | | 13 | 5a | carroll | nancy r. stocksdale | republican | 1994 | appropriations | | 14 | 9b | carroll | susan w. krebs | republican | 2002 | ways and means |

| | first elected | |---:|----------------:| | 0 | 2002 |

select location where qualifying rank = 3 col : year | competition | location | event | final-rank | final-score | qualifying rank | qualifying score row 1 : 2008 | 2008 summer olympics | beijing | team | 2 | 186.525 | 2 | 246.8 row 2 : 2008 | 2008 summer olympics | beijing | uneven bars | dnq | none | 23 | 14.8 row 3 : 2008 | olympic trials | philadelphia | all around | 4 | 61.85 | 4 | 61.4 row 4 : 2008 | olympic trials | philadelphia | balance beam | 4 | 15.55 | 4 | 15.8 row 5 : 2008 | olympic trials | philadelphia | floor exercise | 2 | 15.5 | 3 | 15.65 row 6 : 2008 | olympic trials | philadelphia | uneven bars | 6 | 15.2 | 5 | 15.3 row 7 : 2008 | olympic trials | philadelphia | vault | 4 | 15.15 | 3 | 15.1 row 8 : 2008 | u.s. championships | boston | all around | 4 | 61.25 | 4 | 60.75 row 9 : 2008 | u.s. championships | boston | balance beam | 5 | 16 | 5 | 15.4 row 10 : 2008 | u.s. championships | boston | floor exercise | 10 | 14.75 | 4 | 15.2 row 11 : 2008 | u.s. championships | boston | uneven bars | 6 | 15.55 | 6 | 15.15 row 12 : 2008 | american cup | new york city | all around | 3 | 63.075 | 3 | 63.075

col : location row 1 : philadelphia row 2 : philadelphia row 3 : new york city

select location where qualifying rank = 3 <table_name> : table \begin{tabular}{lllllllll} \toprule & year & competition & location & event & final-rank & final-score & qualifying rank & qualifying score \\ \midrule 0 & 2008 & 2008 summer olympics & beijing & team & 2 & 186.525 & 2 & 246.8 \\ 1 & 2008 & 2008 summer olympics & beijing & uneven bars & dnq & none & 23 & 14.8 \\ 2 & 2008 & olympic trials & philadelphia & all around & 4 & 61.85 & 4 & 61.4 \\ 3 & 2008 & olympic trials & philadelphia & balance beam & 4 & 15.55 & 4 & 15.8 \\ 4 & 2008 & olympic trials & philadelphia & floor exercise & 2 & 15.5 & 3 & 15.65 \\ 5 & 2008 & olympic trials & philadelphia & uneven bars & 6 & 15.2 & 5 & 15.3 \\ 6 & 2008 & olympic trials & philadelphia & vault & 4 & 15.15 & 3 & 15.1 \\ 7 & 2008 & u.s. championships & boston & all around & 4 & 61.25 & 4 & 60.75 \\ 8 & 2008 & u.s. championships & boston & balance beam & 5 & 16 & 5 & 15.4 \\ 9 & 2008 & u.s. championships & boston & floor exercise & 10 & 14.75 & 4 & 15.2 \\ 10 & 2008 & u.s. championships & boston & uneven bars & 6 & 15.55 & 6 & 15.15 \\ 11 & 2008 & american cup & new york city & all around & 3 & 63.075 & 3 & 63.075 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & location \\ \midrule 0 & philadelphia \\ 1 & philadelphia \\ 2 & new york city \\ \bottomrule \end{tabular}

select location where qualifying rank = 3 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>year</th> <th>competition</th> <th>location</th> <th>event</th> <th>final-rank</th> <th>final-score</th> <th>qualifying rank</th> <th>qualifying score</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>2008</td> <td>2008 summer olympics</td> <td>beijing</td> <td>team</td> <td>2</td> <td>186.525</td> <td>2</td> <td>246.8</td> </tr> <tr> <th>1</th> <td>2008</td> <td>2008 summer olympics</td> <td>beijing</td> <td>uneven bars</td> <td>dnq</td> <td>none</td> <td>23</td> <td>14.8</td> </tr> <tr> <th>2</th> <td>2008</td> <td>olympic trials</td> <td>philadelphia</td> <td>all around</td> <td>4</td> <td>61.85</td> <td>4</td> <td>61.4</td> </tr> <tr> <th>3</th> <td>2008</td> <td>olympic trials</td> <td>philadelphia</td> <td>balance beam</td> <td>4</td> <td>15.55</td> <td>4</td> <td>15.8</td> </tr> <tr> <th>4</th> <td>2008</td> <td>olympic trials</td> <td>philadelphia</td> <td>floor exercise</td> <td>2</td> <td>15.5</td> <td>3</td> <td>15.65</td> </tr> <tr> <th>5</th> <td>2008</td> <td>olympic trials</td> <td>philadelphia</td> <td>uneven bars</td> <td>6</td> <td>15.2</td> <td>5</td> <td>15.3</td> </tr> <tr> <th>6</th> <td>2008</td> <td>olympic trials</td> <td>philadelphia</td> <td>vault</td> <td>4</td> <td>15.15</td> <td>3</td> <td>15.1</td> </tr> <tr> <th>7</th> <td>2008</td> <td>u.s. championships</td> <td>boston</td> <td>all around</td> <td>4</td> <td>61.25</td> <td>4</td> <td>60.75</td> </tr> <tr> <th>8</th> <td>2008</td> <td>u.s. championships</td> <td>boston</td> <td>balance beam</td> <td>5</td> <td>16</td> <td>5</td> <td>15.4</td> </tr> <tr> <th>9</th> <td>2008</td> <td>u.s. championships</td> <td>boston</td> <td>floor exercise</td> <td>10</td> <td>14.75</td> <td>4</td> <td>15.2</td> </tr> <tr> <th>10</th> <td>2008</td> <td>u.s. championships</td> <td>boston</td> <td>uneven bars</td> <td>6</td> <td>15.55</td> <td>6</td> <td>15.15</td> </tr> <tr> <th>11</th> <td>2008</td> <td>american cup</td> <td>new york city</td> <td>all around</td> <td>3</td> <td>63.075</td> <td>3</td> <td>63.075</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>philadelphia</td> </tr> <tr> <th>1</th> <td>philadelphia</td> </tr> <tr> <th>2</th> <td>new york city</td> </tr> </tbody> </table>

select location where qualifying rank = 3 <table_name> : table | | year | competition | location | event | final-rank | final-score | qualifying rank | qualifying score | |---:|-------:|:---------------------|:--------------|:---------------|:-------------|:--------------|------------------:|-------------------:| | 0 | 2008 | 2008 summer olympics | beijing | team | 2 | 186.525 | 2 | 246.8 | | 1 | 2008 | 2008 summer olympics | beijing | uneven bars | dnq | none | 23 | 14.8 | | 2 | 2008 | olympic trials | philadelphia | all around | 4 | 61.85 | 4 | 61.4 | | 3 | 2008 | olympic trials | philadelphia | balance beam | 4 | 15.55 | 4 | 15.8 | | 4 | 2008 | olympic trials | philadelphia | floor exercise | 2 | 15.5 | 3 | 15.65 | | 5 | 2008 | olympic trials | philadelphia | uneven bars | 6 | 15.2 | 5 | 15.3 | | 6 | 2008 | olympic trials | philadelphia | vault | 4 | 15.15 | 3 | 15.1 | | 7 | 2008 | u.s. championships | boston | all around | 4 | 61.25 | 4 | 60.75 | | 8 | 2008 | u.s. championships | boston | balance beam | 5 | 16 | 5 | 15.4 | | 9 | 2008 | u.s. championships | boston | floor exercise | 10 | 14.75 | 4 | 15.2 | | 10 | 2008 | u.s. championships | boston | uneven bars | 6 | 15.55 | 6 | 15.15 | | 11 | 2008 | american cup | new york city | all around | 3 | 63.075 | 3 | 63.075 |

| | location | |---:|:--------------| | 0 | philadelphia | | 1 | philadelphia | | 2 | new york city |

select jockey where year = 1937 col : year | winner | jockey | trainer | owner | breeder row 1 : 1919 | sir barton | johnny loftus | h. guy bedwell | j. k. l. ross | none row 2 : 1930 | gallant fox | earl sande | jim fitzsimmons | belair stud | belair stud row 3 : 1935 | omaha | willie saunders | jim fitzsimmons | belair stud | belair stud row 4 : 1937 | war admiral | charley kurtsinger | george h. conway | samuel d. riddle | samuel d. riddle row 5 : 1941 | whirlaway | eddie arcaro | ben a. jones | calumet farm | calumet farm row 6 : 1943 | count fleet | johnny longden | don cameron | fannie hertz | fannie hertz row 7 : 1946 | assault | warren mehrtens | max hirsch | king ranch | king ranch row 8 : 1948 | citation | eddie arcaro | horace a. jones | calumet farm | calumet farm row 9 : 1973 | secretariat | ron turcotte | lucien laurin | meadow stable | meadow stud row 10 : 1977 | seattle slew | jean cruguet | william h. turner, jr | karen l. taylor | ben s. castleman row 11 : 1978 | affirmed | steve cauthen | laz barrera | harbor view farm | harbor view farm

col : jockey row 1 : charley kurtsinger

select jockey where year = 1937 <table_name> : table \begin{tabular}{lllllll} \toprule & year & winner & jockey & trainer & owner & breeder \\ \midrule 0 & 1919 & sir barton & johnny loftus & h. guy bedwell & j. k. l. ross & none \\ 1 & 1930 & gallant fox & earl sande & jim fitzsimmons & belair stud & belair stud \\ 2 & 1935 & omaha & willie saunders & jim fitzsimmons & belair stud & belair stud \\ 3 & 1937 & war admiral & charley kurtsinger & george h. conway & samuel d. riddle & samuel d. riddle \\ 4 & 1941 & whirlaway & eddie arcaro & ben a. jones & calumet farm & calumet farm \\ 5 & 1943 & count fleet & johnny longden & don cameron & fannie hertz & fannie hertz \\ 6 & 1946 & assault & warren mehrtens & max hirsch & king ranch & king ranch \\ 7 & 1948 & citation & eddie arcaro & horace a. jones & calumet farm & calumet farm \\ 8 & 1973 & secretariat & ron turcotte & lucien laurin & meadow stable & meadow stud \\ 9 & 1977 & seattle slew & jean cruguet & william h. turner, jr & karen l. taylor & ben s. castleman \\ 10 & 1978 & affirmed & steve cauthen & laz barrera & harbor view farm & harbor view farm \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & jockey \\ \midrule 0 & charley kurtsinger \\ \bottomrule \end{tabular}

select jockey where year = 1937 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>year</th> <th>winner</th> <th>jockey</th> <th>trainer</th> <th>owner</th> <th>breeder</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1919</td> <td>sir barton</td> <td>johnny loftus</td> <td>h. guy bedwell</td> <td>j. k. l. ross</td> <td>none</td> </tr> <tr> <th>1</th> <td>1930</td> <td>gallant fox</td> <td>earl sande</td> <td>jim fitzsimmons</td> <td>belair stud</td> <td>belair stud</td> </tr> <tr> <th>2</th> <td>1935</td> <td>omaha</td> <td>willie saunders</td> <td>jim fitzsimmons</td> <td>belair stud</td> <td>belair stud</td> </tr> <tr> <th>3</th> <td>1937</td> <td>war admiral</td> <td>charley kurtsinger</td> <td>george h. conway</td> <td>samuel d. riddle</td> <td>samuel d. riddle</td> </tr> <tr> <th>4</th> <td>1941</td> <td>whirlaway</td> <td>eddie arcaro</td> <td>ben a. jones</td> <td>calumet farm</td> <td>calumet farm</td> </tr> <tr> <th>5</th> <td>1943</td> <td>count fleet</td> <td>johnny longden</td> <td>don cameron</td> <td>fannie hertz</td> <td>fannie hertz</td> </tr> <tr> <th>6</th> <td>1946</td> <td>assault</td> <td>warren mehrtens</td> <td>max hirsch</td> <td>king ranch</td> <td>king ranch</td> </tr> <tr> <th>7</th> <td>1948</td> <td>citation</td> <td>eddie arcaro</td> <td>horace a. jones</td> <td>calumet farm</td> <td>calumet farm</td> </tr> <tr> <th>8</th> <td>1973</td> <td>secretariat</td> <td>ron turcotte</td> <td>lucien laurin</td> <td>meadow stable</td> <td>meadow stud</td> </tr> <tr> <th>9</th> <td>1977</td> <td>seattle slew</td> <td>jean cruguet</td> <td>william h. turner, jr</td> <td>karen l. taylor</td> <td>ben s. castleman</td> </tr> <tr> <th>10</th> <td>1978</td> <td>affirmed</td> <td>steve cauthen</td> <td>laz barrera</td> <td>harbor view farm</td> <td>harbor view farm</td> </tr> </tbody> </table>

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

select jockey where year = 1937 <table_name> : table | | year | winner | jockey | trainer | owner | breeder | |---:|-------:|:-------------|:-------------------|:----------------------|:-----------------|:-----------------| | 0 | 1919 | sir barton | johnny loftus | h. guy bedwell | j. k. l. ross | none | | 1 | 1930 | gallant fox | earl sande | jim fitzsimmons | belair stud | belair stud | | 2 | 1935 | omaha | willie saunders | jim fitzsimmons | belair stud | belair stud | | 3 | 1937 | war admiral | charley kurtsinger | george h. conway | samuel d. riddle | samuel d. riddle | | 4 | 1941 | whirlaway | eddie arcaro | ben a. jones | calumet farm | calumet farm | | 5 | 1943 | count fleet | johnny longden | don cameron | fannie hertz | fannie hertz | | 6 | 1946 | assault | warren mehrtens | max hirsch | king ranch | king ranch | | 7 | 1948 | citation | eddie arcaro | horace a. jones | calumet farm | calumet farm | | 8 | 1973 | secretariat | ron turcotte | lucien laurin | meadow stable | meadow stud | | 9 | 1977 | seattle slew | jean cruguet | william h. turner, jr | karen l. taylor | ben s. castleman | | 10 | 1978 | affirmed | steve cauthen | laz barrera | harbor view farm | harbor view farm |

| | jockey | |---:|:-------------------| | 0 | charley kurtsinger |

select count ( club ) where club = 'real murcia' col : position | club | played | points | wins | draws | losses | goals for | goals against | goal difference row 1 : 1 | cordoba cf | 30 | 40 | 16 | 8 | 6 | 48 | 22 | 26 row 2 : 2 | cd malaga | 30 | 38 | 14 | 10 | 6 | 52 | 36 | 16 row 3 : 3 | granada cf | 30 | 36 | 15 | 6 | 9 | 48 | 34 | 14 row 4 : 4 | ud las palmas | 30 | 35 | 15 | 5 | 10 | 47 | 39 | 8 row 5 : 5 | recreativo de huelva | 30 | 33 | 13 | 7 | 10 | 43 | 42 | 1 row 6 : 6 | levante ud | 30 | 32 | 14 | 4 | 12 | 49 | 42 | 7 row 7 : 7 | hercules cf | 30 | 32 | 14 | 4 | 12 | 55 | 46 | 9 row 8 : 8 | real murcia | 30 | 31 | 12 | 7 | 11 | 40 | 35 | 5 row 9 : 9 | real jaen | 30 | 31 | 14 | 3 | 13 | 58 | 42 | 16 row 10 : 10 | cadiz cf | 30 | 28 | 12 | 4 | 14 | 43 | 52 | -9 row 11 : 11 | cd cartagena | 30 | 28 | 13 | 2 | 15 | 45 | 56 | -11 row 12 : 12 | cd mestalla | 30 | 27 | 11 | 5 | 14 | 50 | 49 | 1 row 13 : 13 | albacete balompie | 30 | 27 | 10 | 7 | 13 | 27 | 32 | -5 row 14 : 14 | cd san fernando | 30 | 27 | 11 | 5 | 14 | 37 | 47 | -10 row 15 : 15 | atletico ceuta | 30 | 23 | 8 | 7 | 15 | 33 | 48 | -15 row 16 : 16 | cd villarrobledo | 30 | 12 | 4 | 4 | 22 | 26 | 79 | -53

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

select count ( club ) where club = 'real murcia' <table_name> : table \begin{tabular}{lllllllllll} \toprule & position & club & played & points & wins & draws & losses & goals for & goals against & goal difference \\ \midrule 0 & 1 & cordoba cf & 30 & 40 & 16 & 8 & 6 & 48 & 22 & 26 \\ 1 & 2 & cd malaga & 30 & 38 & 14 & 10 & 6 & 52 & 36 & 16 \\ 2 & 3 & granada cf & 30 & 36 & 15 & 6 & 9 & 48 & 34 & 14 \\ 3 & 4 & ud las palmas & 30 & 35 & 15 & 5 & 10 & 47 & 39 & 8 \\ 4 & 5 & recreativo de huelva & 30 & 33 & 13 & 7 & 10 & 43 & 42 & 1 \\ 5 & 6 & levante ud & 30 & 32 & 14 & 4 & 12 & 49 & 42 & 7 \\ 6 & 7 & hercules cf & 30 & 32 & 14 & 4 & 12 & 55 & 46 & 9 \\ 7 & 8 & real murcia & 30 & 31 & 12 & 7 & 11 & 40 & 35 & 5 \\ 8 & 9 & real jaen & 30 & 31 & 14 & 3 & 13 & 58 & 42 & 16 \\ 9 & 10 & cadiz cf & 30 & 28 & 12 & 4 & 14 & 43 & 52 & -9 \\ 10 & 11 & cd cartagena & 30 & 28 & 13 & 2 & 15 & 45 & 56 & -11 \\ 11 & 12 & cd mestalla & 30 & 27 & 11 & 5 & 14 & 50 & 49 & 1 \\ 12 & 13 & albacete balompie & 30 & 27 & 10 & 7 & 13 & 27 & 32 & -5 \\ 13 & 14 & cd san fernando & 30 & 27 & 11 & 5 & 14 & 37 & 47 & -10 \\ 14 & 15 & atletico ceuta & 30 & 23 & 8 & 7 & 15 & 33 & 48 & -15 \\ 15 & 16 & cd villarrobledo & 30 & 12 & 4 & 4 & 22 & 26 & 79 & -53 \\ \bottomrule \end{tabular}

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

select count ( club ) where club = 'real murcia' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>position</th> <th>club</th> <th>played</th> <th>points</th> <th>wins</th> <th>draws</th> <th>losses</th> <th>goals for</th> <th>goals against</th> <th>goal difference</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>cordoba cf</td> <td>30</td> <td>40</td> <td>16</td> <td>8</td> <td>6</td> <td>48</td> <td>22</td> <td>26</td> </tr> <tr> <th>1</th> <td>2</td> <td>cd malaga</td> <td>30</td> <td>38</td> <td>14</td> <td>10</td> <td>6</td> <td>52</td> <td>36</td> <td>16</td> </tr> <tr> <th>2</th> <td>3</td> <td>granada cf</td> <td>30</td> <td>36</td> <td>15</td> <td>6</td> <td>9</td> <td>48</td> <td>34</td> <td>14</td> </tr> <tr> <th>3</th> <td>4</td> <td>ud las palmas</td> <td>30</td> <td>35</td> <td>15</td> <td>5</td> <td>10</td> <td>47</td> <td>39</td> <td>8</td> </tr> <tr> <th>4</th> <td>5</td> <td>recreativo de huelva</td> <td>30</td> <td>33</td> <td>13</td> <td>7</td> <td>10</td> <td>43</td> <td>42</td> <td>1</td> </tr> <tr> <th>5</th> <td>6</td> <td>levante ud</td> <td>30</td> <td>32</td> <td>14</td> <td>4</td> <td>12</td> <td>49</td> <td>42</td> <td>7</td> </tr> <tr> <th>6</th> <td>7</td> <td>hercules cf</td> <td>30</td> <td>32</td> <td>14</td> <td>4</td> <td>12</td> <td>55</td> <td>46</td> <td>9</td> </tr> <tr> <th>7</th> <td>8</td> <td>real murcia</td> <td>30</td> <td>31</td> <td>12</td> <td>7</td> <td>11</td> <td>40</td> <td>35</td> <td>5</td> </tr> <tr> <th>8</th> <td>9</td> <td>real jaen</td> <td>30</td> <td>31</td> <td>14</td> <td>3</td> <td>13</td> <td>58</td> <td>42</td> <td>16</td> </tr> <tr> <th>9</th> <td>10</td> <td>cadiz cf</td> <td>30</td> <td>28</td> <td>12</td> <td>4</td> <td>14</td> <td>43</td> <td>52</td> <td>-9</td> </tr> <tr> <th>10</th> <td>11</td> <td>cd cartagena</td> <td>30</td> <td>28</td> <td>13</td> <td>2</td> <td>15</td> <td>45</td> <td>56</td> <td>-11</td> </tr> <tr> <th>11</th> <td>12</td> <td>cd mestalla</td> <td>30</td> <td>27</td> <td>11</td> <td>5</td> <td>14</td> <td>50</td> <td>49</td> <td>1</td> </tr> <tr> <th>12</th> <td>13</td> <td>albacete balompie</td> <td>30</td> <td>27</td> <td>10</td> <td>7</td> <td>13</td> <td>27</td> <td>32</td> <td>-5</td> </tr> <tr> <th>13</th> <td>14</td> <td>cd san fernando</td> <td>30</td> <td>27</td> <td>11</td> <td>5</td> <td>14</td> <td>37</td> <td>47</td> <td>-10</td> </tr> <tr> <th>14</th> <td>15</td> <td>atletico ceuta</td> <td>30</td> <td>23</td> <td>8</td> <td>7</td> <td>15</td> <td>33</td> <td>48</td> <td>-15</td> </tr> <tr> <th>15</th> <td>16</td> <td>cd villarrobledo</td> <td>30</td> <td>12</td> <td>4</td> <td>4</td> <td>22</td> <td>26</td> <td>79</td> <td>-53</td> </tr> </tbody> </table>

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

select count ( club ) where club = 'real murcia' <table_name> : table | | position | club | played | points | wins | draws | losses | goals for | goals against | goal difference | |---:|-----------:|:---------------------|---------:|---------:|-------:|--------:|---------:|------------:|----------------:|------------------:| | 0 | 1 | cordoba cf | 30 | 40 | 16 | 8 | 6 | 48 | 22 | 26 | | 1 | 2 | cd malaga | 30 | 38 | 14 | 10 | 6 | 52 | 36 | 16 | | 2 | 3 | granada cf | 30 | 36 | 15 | 6 | 9 | 48 | 34 | 14 | | 3 | 4 | ud las palmas | 30 | 35 | 15 | 5 | 10 | 47 | 39 | 8 | | 4 | 5 | recreativo de huelva | 30 | 33 | 13 | 7 | 10 | 43 | 42 | 1 | | 5 | 6 | levante ud | 30 | 32 | 14 | 4 | 12 | 49 | 42 | 7 | | 6 | 7 | hercules cf | 30 | 32 | 14 | 4 | 12 | 55 | 46 | 9 | | 7 | 8 | real murcia | 30 | 31 | 12 | 7 | 11 | 40 | 35 | 5 | | 8 | 9 | real jaen | 30 | 31 | 14 | 3 | 13 | 58 | 42 | 16 | | 9 | 10 | cadiz cf | 30 | 28 | 12 | 4 | 14 | 43 | 52 | -9 | | 10 | 11 | cd cartagena | 30 | 28 | 13 | 2 | 15 | 45 | 56 | -11 | | 11 | 12 | cd mestalla | 30 | 27 | 11 | 5 | 14 | 50 | 49 | 1 | | 12 | 13 | albacete balompie | 30 | 27 | 10 | 7 | 13 | 27 | 32 | -5 | | 13 | 14 | cd san fernando | 30 | 27 | 11 | 5 | 14 | 37 | 47 | -10 | | 14 | 15 | atletico ceuta | 30 | 23 | 8 | 7 | 15 | 33 | 48 | -15 | | 15 | 16 | cd villarrobledo | 30 | 12 | 4 | 4 | 22 | 26 | 79 | -53 |

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

select winner where winner = 'wong kam-po (hkg)' col : year | name | date | stages | distance | winner | time row 1 : 1982 | marlboro tour | 22 april-9 may | 15 | 2,192 km | jacinto sicam (phi) | 61:29:17 row 2 : 1983 | marlboro tour | 16 april-1 may | 14 | 2,313.11 km | romeo bonzo (phi) | 63:54:31 row 3 : 1984 | marlboro tour | 26 may-10 june | 14 | 2,464 km | ruben carino (phi) | 68:08:49 row 4 : 1985 | marlboro tour | 18 april-12 may | 21 | 3,668.97 km | pepito calip (phi) | 97:04:42 row 5 : 1986 | marlboro tour | 26 april-11 may | 10 | 2,900.77 km | rolando pagnanawon (phi) | 77:39:53 row 6 : 1987 | marlboro tour | 21 may-7 june | 17 | 3,282 km | reynaldo dequito (phi) | 88:06:50 row 7 : 1988 | marlboro tour | 28 april-15 may | 17 | 3,544.53 km | armando catalan (phi) | 94:44:03 row 8 : 1989 | marlboro tour | 26 april-14 may | 18 | 3,539.47 km | gerardo igos (phi) | 95:40:23 row 9 : 1990 | marlboro tour | 18 april-6 may | 18 | 3,317.42 km | manuel buenaventura (phi) | 95:58:38 row 10 : 1991 | marlboro tour | 25 april-12 may | 17 | 2,373.61 km | bernardo llentada (phi) | 63:33:17 row 11 : 1992 | marlboro tour | 21 may-7 june | 17 | 2,731.38 km | renato dolosa (phi) | 71:21:49 row 12 : 1993 | marlboro tour | 17 april-9 may | 21 | 3,480 km | carlo guieb (phi) | 91:41:54 row 13 : 1994 | marlboro tour | 17 april-9 may | 20 | 3,563 km | carlo guieb (phi) | 91:24:13 row 14 : 1995 | marlboro tour | 18 march-8 april | 19 | 3,280.33 km | renato dolosa (phi) | 83:43:39 row 15 : 1996 | marlboro tour | 14 april-5 may | 19 | 3,257.29 km | victor espiritu (phi) | 80:50:46 row 16 : 1997 | marlboro tour | 16 april-4 may | 16 | 2,472 km | wong kam-po (hkg) | 62:06:28 row 17 : 1998 | marlboro centennial tour | 15 april-3 may | 16 | 2,494 km | warren davadilla (phi) | 64:58:57 row 18 : 1999-2001 | cancelled | cancelled | cancelled | cancelled | cancelled | cancelled

col : winner row 1 : wong kam-po (hkg)

select winner where winner = 'wong kam-po (hkg)' <table_name> : table \begin{tabular}{llllllll} \toprule & year & name & date & stages & distance & winner & time \\ \midrule 0 & 1982 & marlboro tour & 22 april-9 may & 15 & 2,192 km & jacinto sicam (phi) & 61:29:17 \\ 1 & 1983 & marlboro tour & 16 april-1 may & 14 & 2,313.11 km & romeo bonzo (phi) & 63:54:31 \\ 2 & 1984 & marlboro tour & 26 may-10 june & 14 & 2,464 km & ruben carino (phi) & 68:08:49 \\ 3 & 1985 & marlboro tour & 18 april-12 may & 21 & 3,668.97 km & pepito calip (phi) & 97:04:42 \\ 4 & 1986 & marlboro tour & 26 april-11 may & 10 & 2,900.77 km & rolando pagnanawon (phi) & 77:39:53 \\ 5 & 1987 & marlboro tour & 21 may-7 june & 17 & 3,282 km & reynaldo dequito (phi) & 88:06:50 \\ 6 & 1988 & marlboro tour & 28 april-15 may & 17 & 3,544.53 km & armando catalan (phi) & 94:44:03 \\ 7 & 1989 & marlboro tour & 26 april-14 may & 18 & 3,539.47 km & gerardo igos (phi) & 95:40:23 \\ 8 & 1990 & marlboro tour & 18 april-6 may & 18 & 3,317.42 km & manuel buenaventura (phi) & 95:58:38 \\ 9 & 1991 & marlboro tour & 25 april-12 may & 17 & 2,373.61 km & bernardo llentada (phi) & 63:33:17 \\ 10 & 1992 & marlboro tour & 21 may-7 june & 17 & 2,731.38 km & renato dolosa (phi) & 71:21:49 \\ 11 & 1993 & marlboro tour & 17 april-9 may & 21 & 3,480 km & carlo guieb (phi) & 91:41:54 \\ 12 & 1994 & marlboro tour & 17 april-9 may & 20 & 3,563 km & carlo guieb (phi) & 91:24:13 \\ 13 & 1995 & marlboro tour & 18 march-8 april & 19 & 3,280.33 km & renato dolosa (phi) & 83:43:39 \\ 14 & 1996 & marlboro tour & 14 april-5 may & 19 & 3,257.29 km & victor espiritu (phi) & 80:50:46 \\ 15 & 1997 & marlboro tour & 16 april-4 may & 16 & 2,472 km & wong kam-po (hkg) & 62:06:28 \\ 16 & 1998 & marlboro centennial tour & 15 april-3 may & 16 & 2,494 km & warren davadilla (phi) & 64:58:57 \\ 17 & 1999-2001 & cancelled & cancelled & cancelled & cancelled & cancelled & cancelled \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & winner \\ \midrule 0 & wong kam-po (hkg) \\ \bottomrule \end{tabular}

select winner where winner = 'wong kam-po (hkg)' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>year</th> <th>name</th> <th>date</th> <th>stages</th> <th>distance</th> <th>winner</th> <th>time</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1982</td> <td>marlboro tour</td> <td>22 april-9 may</td> <td>15</td> <td>2,192 km</td> <td>jacinto sicam (phi)</td> <td>61:29:17</td> </tr> <tr> <th>1</th> <td>1983</td> <td>marlboro tour</td> <td>16 april-1 may</td> <td>14</td> <td>2,313.11 km</td> <td>romeo bonzo (phi)</td> <td>63:54:31</td> </tr> <tr> <th>2</th> <td>1984</td> <td>marlboro tour</td> <td>26 may-10 june</td> <td>14</td> <td>2,464 km</td> <td>ruben carino (phi)</td> <td>68:08:49</td> </tr> <tr> <th>3</th> <td>1985</td> <td>marlboro tour</td> <td>18 april-12 may</td> <td>21</td> <td>3,668.97 km</td> <td>pepito calip (phi)</td> <td>97:04:42</td> </tr> <tr> <th>4</th> <td>1986</td> <td>marlboro tour</td> <td>26 april-11 may</td> <td>10</td> <td>2,900.77 km</td> <td>rolando pagnanawon (phi)</td> <td>77:39:53</td> </tr> <tr> <th>5</th> <td>1987</td> <td>marlboro tour</td> <td>21 may-7 june</td> <td>17</td> <td>3,282 km</td> <td>reynaldo dequito (phi)</td> <td>88:06:50</td> </tr> <tr> <th>6</th> <td>1988</td> <td>marlboro tour</td> <td>28 april-15 may</td> <td>17</td> <td>3,544.53 km</td> <td>armando catalan (phi)</td> <td>94:44:03</td> </tr> <tr> <th>7</th> <td>1989</td> <td>marlboro tour</td> <td>26 april-14 may</td> <td>18</td> <td>3,539.47 km</td> <td>gerardo igos (phi)</td> <td>95:40:23</td> </tr> <tr> <th>8</th> <td>1990</td> <td>marlboro tour</td> <td>18 april-6 may</td> <td>18</td> <td>3,317.42 km</td> <td>manuel buenaventura (phi)</td> <td>95:58:38</td> </tr> <tr> <th>9</th> <td>1991</td> <td>marlboro tour</td> <td>25 april-12 may</td> <td>17</td> <td>2,373.61 km</td> <td>bernardo llentada (phi)</td> <td>63:33:17</td> </tr> <tr> <th>10</th> <td>1992</td> <td>marlboro tour</td> <td>21 may-7 june</td> <td>17</td> <td>2,731.38 km</td> <td>renato dolosa (phi)</td> <td>71:21:49</td> </tr> <tr> <th>11</th> <td>1993</td> <td>marlboro tour</td> <td>17 april-9 may</td> <td>21</td> <td>3,480 km</td> <td>carlo guieb (phi)</td> <td>91:41:54</td> </tr> <tr> <th>12</th> <td>1994</td> <td>marlboro tour</td> <td>17 april-9 may</td> <td>20</td> <td>3,563 km</td> <td>carlo guieb (phi)</td> <td>91:24:13</td> </tr> <tr> <th>13</th> <td>1995</td> <td>marlboro tour</td> <td>18 march-8 april</td> <td>19</td> <td>3,280.33 km</td> <td>renato dolosa (phi)</td> <td>83:43:39</td> </tr> <tr> <th>14</th> <td>1996</td> <td>marlboro tour</td> <td>14 april-5 may</td> <td>19</td> <td>3,257.29 km</td> <td>victor espiritu (phi)</td> <td>80:50:46</td> </tr> <tr> <th>15</th> <td>1997</td> <td>marlboro tour</td> <td>16 april-4 may</td> <td>16</td> <td>2,472 km</td> <td>wong kam-po (hkg)</td> <td>62:06:28</td> </tr> <tr> <th>16</th> <td>1998</td> <td>marlboro centennial tour</td> <td>15 april-3 may</td> <td>16</td> <td>2,494 km</td> <td>warren davadilla (phi)</td> <td>64:58:57</td> </tr> <tr> <th>17</th> <td>1999-2001</td> <td>cancelled</td> <td>cancelled</td> <td>cancelled</td> <td>cancelled</td> <td>cancelled</td> <td>cancelled</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>winner</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>wong kam-po (hkg)</td> </tr> </tbody> </table>

select winner where winner = 'wong kam-po (hkg)' <table_name> : table | | year | name | date | stages | distance | winner | time | |---:|:----------|:-------------------------|:-----------------|:----------|:------------|:--------------------------|:----------| | 0 | 1982 | marlboro tour | 22 april-9 may | 15 | 2,192 km | jacinto sicam (phi) | 61:29:17 | | 1 | 1983 | marlboro tour | 16 april-1 may | 14 | 2,313.11 km | romeo bonzo (phi) | 63:54:31 | | 2 | 1984 | marlboro tour | 26 may-10 june | 14 | 2,464 km | ruben carino (phi) | 68:08:49 | | 3 | 1985 | marlboro tour | 18 april-12 may | 21 | 3,668.97 km | pepito calip (phi) | 97:04:42 | | 4 | 1986 | marlboro tour | 26 april-11 may | 10 | 2,900.77 km | rolando pagnanawon (phi) | 77:39:53 | | 5 | 1987 | marlboro tour | 21 may-7 june | 17 | 3,282 km | reynaldo dequito (phi) | 88:06:50 | | 6 | 1988 | marlboro tour | 28 april-15 may | 17 | 3,544.53 km | armando catalan (phi) | 94:44:03 | | 7 | 1989 | marlboro tour | 26 april-14 may | 18 | 3,539.47 km | gerardo igos (phi) | 95:40:23 | | 8 | 1990 | marlboro tour | 18 april-6 may | 18 | 3,317.42 km | manuel buenaventura (phi) | 95:58:38 | | 9 | 1991 | marlboro tour | 25 april-12 may | 17 | 2,373.61 km | bernardo llentada (phi) | 63:33:17 | | 10 | 1992 | marlboro tour | 21 may-7 june | 17 | 2,731.38 km | renato dolosa (phi) | 71:21:49 | | 11 | 1993 | marlboro tour | 17 april-9 may | 21 | 3,480 km | carlo guieb (phi) | 91:41:54 | | 12 | 1994 | marlboro tour | 17 april-9 may | 20 | 3,563 km | carlo guieb (phi) | 91:24:13 | | 13 | 1995 | marlboro tour | 18 march-8 april | 19 | 3,280.33 km | renato dolosa (phi) | 83:43:39 | | 14 | 1996 | marlboro tour | 14 april-5 may | 19 | 3,257.29 km | victor espiritu (phi) | 80:50:46 | | 15 | 1997 | marlboro tour | 16 april-4 may | 16 | 2,472 km | wong kam-po (hkg) | 62:06:28 | | 16 | 1998 | marlboro centennial tour | 15 april-3 may | 16 | 2,494 km | warren davadilla (phi) | 64:58:57 | | 17 | 1999-2001 | cancelled | cancelled | cancelled | cancelled | cancelled | cancelled |

| | winner | |---:|:------------------| | 0 | wong kam-po (hkg) |

select notes where competition = 'central american games' col : year | competition | venue | position | event | notes row 1 : 1997 | central american games | san pedro sula, honduras | 2nd | 10,000 m | none row 2 : 2001 | central american and caribbean championships | guatemala city, guatemala | 6th | 5000 m | 14:57.04 row 3 : 2001 | central american and caribbean championships | guatemala city, guatemala | 3rd | 10,000 m | 30:53.52 row 4 : 2001 | central american games | guatemala city, guatemala | 1st | 5000 m | 14:28.91 a row 5 : 2001 | central american games | guatemala city, guatemala | 2nd | 10,000 m | 30:43.86 a row 6 : 2002 | central american championships | san jose, costa rica | 2nd | 5000 m | 14:30.14 row 7 : 2002 | central american championships | san jose, costa rica | 1st | 10,000 m | 30:05.31 row 8 : 2003 | central american championships | guatemala city, guatemala | 1st | 5000 m | 15:19.35 row 9 : 2003 | central american championships | guatemala city, guatemala | 1st | 10,000 m | 30:16.25 row 10 : 2003 | pan american games | santo domingo, dominican republic | none | 5000 m | dnf row 11 : 2003 | pan american games | santo domingo, dominican republic | 7th | 10,000 m | 30:26.61 row 12 : 2004 | olympic games | athens, greece | 64th | marathon | 2:27:13 row 13 : 2005 | central american championships | san jose, costa rica | 1st | 10,000 m | 30:11.54 row 14 : 2006 | ibero-american championships | ponce, puerto rico | 8th | 5000 m | 14:31.55 row 15 : 2006 | central american and caribbean games | cartagena, colombia | 6th | 5000 m | 14:27.54 row 16 : 2006 | central american and caribbean games | cartagena, colombia | 4th | 10,000 m | 30:09.31 row 17 : 2007 | central american championships | san jose, costa rica | 1st | 5000 m | 14:42.08 row 18 : 2007 | central american championships | san jose, costa rica | 1st | 10,000 m | 29:53.61 row 19 : 2007 | nacac championships | san salvador, el salvador | 1st | 5000 m | 14:33.31 row 20 : 2007 | nacac championships | san salvador, el salvador | 1st | 10,000 m | 29:42.11 row 21 : 2007 | pan american games | rio de janeiro, brazil | 2nd | marathon | 2:14:27 row 22 : 2008 | olympic games | beijing, pr china | 35th | marathon | 2:20:15 row 23 : 2009 | central american championships | guatemala city, guatemala | 1st | 5000 m | 14:55.44 row 24 : 2010 | central american and caribbean games | mayaguez, puerto rico | 1st | marathon | 2:21:35 row 25 : 2010 | central american championships | guatemala city, guatemala | 1st | 10,000 m | 32:06.26 row 26 : 2011 | pan american games | guadalajara, mexico | 5th | marathon | 2:20:27 sb row 27 : 2012 | olympic games | london, united kingdom | 38th | marathon | 2:18:23

col : notes row 1 : none row 2 : 14:28.91 a row 3 : 30:43.86 a

select notes where competition = 'central american games' <table_name> : table \begin{tabular}{lllllll} \toprule & year & competition & venue & position & event & notes \\ \midrule 0 & 1997 & central american games & san pedro sula, honduras & 2nd & 10,000 m & none \\ 1 & 2001 & central american and caribbean championships & guatemala city, guatemala & 6th & 5000 m & 14:57.04 \\ 2 & 2001 & central american and caribbean championships & guatemala city, guatemala & 3rd & 10,000 m & 30:53.52 \\ 3 & 2001 & central american games & guatemala city, guatemala & 1st & 5000 m & 14:28.91 a \\ 4 & 2001 & central american games & guatemala city, guatemala & 2nd & 10,000 m & 30:43.86 a \\ 5 & 2002 & central american championships & san jose, costa rica & 2nd & 5000 m & 14:30.14 \\ 6 & 2002 & central american championships & san jose, costa rica & 1st & 10,000 m & 30:05.31 \\ 7 & 2003 & central american championships & guatemala city, guatemala & 1st & 5000 m & 15:19.35 \\ 8 & 2003 & central american championships & guatemala city, guatemala & 1st & 10,000 m & 30:16.25 \\ 9 & 2003 & pan american games & santo domingo, dominican republic & none & 5000 m & dnf \\ 10 & 2003 & pan american games & santo domingo, dominican republic & 7th & 10,000 m & 30:26.61 \\ 11 & 2004 & olympic games & athens, greece & 64th & marathon & 2:27:13 \\ 12 & 2005 & central american championships & san jose, costa rica & 1st & 10,000 m & 30:11.54 \\ 13 & 2006 & ibero-american championships & ponce, puerto rico & 8th & 5000 m & 14:31.55 \\ 14 & 2006 & central american and caribbean games & cartagena, colombia & 6th & 5000 m & 14:27.54 \\ 15 & 2006 & central american and caribbean games & cartagena, colombia & 4th & 10,000 m & 30:09.31 \\ 16 & 2007 & central american championships & san jose, costa rica & 1st & 5000 m & 14:42.08 \\ 17 & 2007 & central american championships & san jose, costa rica & 1st & 10,000 m & 29:53.61 \\ 18 & 2007 & nacac championships & san salvador, el salvador & 1st & 5000 m & 14:33.31 \\ 19 & 2007 & nacac championships & san salvador, el salvador & 1st & 10,000 m & 29:42.11 \\ 20 & 2007 & pan american games & rio de janeiro, brazil & 2nd & marathon & 2:14:27 \\ 21 & 2008 & olympic games & beijing, pr china & 35th & marathon & 2:20:15 \\ 22 & 2009 & central american championships & guatemala city, guatemala & 1st & 5000 m & 14:55.44 \\ 23 & 2010 & central american and caribbean games & mayaguez, puerto rico & 1st & marathon & 2:21:35 \\ 24 & 2010 & central american championships & guatemala city, guatemala & 1st & 10,000 m & 32:06.26 \\ 25 & 2011 & pan american games & guadalajara, mexico & 5th & marathon & 2:20:27 sb \\ 26 & 2012 & olympic games & london, united kingdom & 38th & marathon & 2:18:23 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & notes \\ \midrule 0 & none \\ 1 & 14:28.91 a \\ 2 & 30:43.86 a \\ \bottomrule \end{tabular}

select notes where competition = 'central american games' <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>1997</td> <td>central american games</td> <td>san pedro sula, honduras</td> <td>2nd</td> <td>10,000 m</td> <td>none</td> </tr> <tr> <th>1</th> <td>2001</td> <td>central american and caribbean championships</td> <td>guatemala city, guatemala</td> <td>6th</td> <td>5000 m</td> <td>14:57.04</td> </tr> <tr> <th>2</th> <td>2001</td> <td>central american and caribbean championships</td> <td>guatemala city, guatemala</td> <td>3rd</td> <td>10,000 m</td> <td>30:53.52</td> </tr> <tr> <th>3</th> <td>2001</td> <td>central american games</td> <td>guatemala city, guatemala</td> <td>1st</td> <td>5000 m</td> <td>14:28.91 a</td> </tr> <tr> <th>4</th> <td>2001</td> <td>central american games</td> <td>guatemala city, guatemala</td> <td>2nd</td> <td>10,000 m</td> <td>30:43.86 a</td> </tr> <tr> <th>5</th> <td>2002</td> <td>central american championships</td> <td>san jose, costa rica</td> <td>2nd</td> <td>5000 m</td> <td>14:30.14</td> </tr> <tr> <th>6</th> <td>2002</td> <td>central american championships</td> <td>san jose, costa rica</td> <td>1st</td> <td>10,000 m</td> <td>30:05.31</td> </tr> <tr> <th>7</th> <td>2003</td> <td>central american championships</td> <td>guatemala city, guatemala</td> <td>1st</td> <td>5000 m</td> <td>15:19.35</td> </tr> <tr> <th>8</th> <td>2003</td> <td>central american championships</td> <td>guatemala city, guatemala</td> <td>1st</td> <td>10,000 m</td> <td>30:16.25</td> </tr> <tr> <th>9</th> <td>2003</td> <td>pan american games</td> <td>santo domingo, dominican republic</td> <td>none</td> <td>5000 m</td> <td>dnf</td> </tr> <tr> <th>10</th> <td>2003</td> <td>pan american games</td> <td>santo domingo, dominican republic</td> <td>7th</td> <td>10,000 m</td> <td>30:26.61</td> </tr> <tr> <th>11</th> <td>2004</td> <td>olympic games</td> <td>athens, greece</td> <td>64th</td> <td>marathon</td> <td>2:27:13</td> </tr> <tr> <th>12</th> <td>2005</td> <td>central american championships</td> <td>san jose, costa rica</td> <td>1st</td> <td>10,000 m</td> <td>30:11.54</td> </tr> <tr> <th>13</th> <td>2006</td> <td>ibero-american championships</td> <td>ponce, puerto rico</td> <td>8th</td> <td>5000 m</td> <td>14:31.55</td> </tr> <tr> <th>14</th> <td>2006</td> <td>central american and caribbean games</td> <td>cartagena, colombia</td> <td>6th</td> <td>5000 m</td> <td>14:27.54</td> </tr> <tr> <th>15</th> <td>2006</td> <td>central american and caribbean games</td> <td>cartagena, colombia</td> <td>4th</td> <td>10,000 m</td> <td>30:09.31</td> </tr> <tr> <th>16</th> <td>2007</td> <td>central american championships</td> <td>san jose, costa rica</td> <td>1st</td> <td>5000 m</td> <td>14:42.08</td> </tr> <tr> <th>17</th> <td>2007</td> <td>central american championships</td> <td>san jose, costa rica</td> <td>1st</td> <td>10,000 m</td> <td>29:53.61</td> </tr> <tr> <th>18</th> <td>2007</td> <td>nacac championships</td> <td>san salvador, el salvador</td> <td>1st</td> <td>5000 m</td> <td>14:33.31</td> </tr> <tr> <th>19</th> <td>2007</td> <td>nacac championships</td> <td>san salvador, el salvador</td> <td>1st</td> <td>10,000 m</td> <td>29:42.11</td> </tr> <tr> <th>20</th> <td>2007</td> <td>pan american games</td> <td>rio de janeiro, brazil</td> <td>2nd</td> <td>marathon</td> <td>2:14:27</td> </tr> <tr> <th>21</th> <td>2008</td> <td>olympic games</td> <td>beijing, pr china</td> <td>35th</td> <td>marathon</td> <td>2:20:15</td> </tr> <tr> <th>22</th> <td>2009</td> <td>central american championships</td> <td>guatemala city, guatemala</td> <td>1st</td> <td>5000 m</td> <td>14:55.44</td> </tr> <tr> <th>23</th> <td>2010</td> <td>central american and caribbean games</td> <td>mayaguez, puerto rico</td> <td>1st</td> <td>marathon</td> <td>2:21:35</td> </tr> <tr> <th>24</th> <td>2010</td> <td>central american championships</td> <td>guatemala city, guatemala</td> <td>1st</td> <td>10,000 m</td> <td>32:06.26</td> </tr> <tr> <th>25</th> <td>2011</td> <td>pan american games</td> <td>guadalajara, mexico</td> <td>5th</td> <td>marathon</td> <td>2:20:27 sb</td> </tr> <tr> <th>26</th> <td>2012</td> <td>olympic games</td> <td>london, united kingdom</td> <td>38th</td> <td>marathon</td> <td>2:18:23</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>notes</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>none</td> </tr> <tr> <th>1</th> <td>14:28.91 a</td> </tr> <tr> <th>2</th> <td>30:43.86 a</td> </tr> </tbody> </table>

select notes where competition = 'central american games' <table_name> : table | | year | competition | venue | position | event | notes | |---:|-------:|:---------------------------------------------|:----------------------------------|:-----------|:---------|:-----------| | 0 | 1997 | central american games | san pedro sula, honduras | 2nd | 10,000 m | none | | 1 | 2001 | central american and caribbean championships | guatemala city, guatemala | 6th | 5000 m | 14:57.04 | | 2 | 2001 | central american and caribbean championships | guatemala city, guatemala | 3rd | 10,000 m | 30:53.52 | | 3 | 2001 | central american games | guatemala city, guatemala | 1st | 5000 m | 14:28.91 a | | 4 | 2001 | central american games | guatemala city, guatemala | 2nd | 10,000 m | 30:43.86 a | | 5 | 2002 | central american championships | san jose, costa rica | 2nd | 5000 m | 14:30.14 | | 6 | 2002 | central american championships | san jose, costa rica | 1st | 10,000 m | 30:05.31 | | 7 | 2003 | central american championships | guatemala city, guatemala | 1st | 5000 m | 15:19.35 | | 8 | 2003 | central american championships | guatemala city, guatemala | 1st | 10,000 m | 30:16.25 | | 9 | 2003 | pan american games | santo domingo, dominican republic | none | 5000 m | dnf | | 10 | 2003 | pan american games | santo domingo, dominican republic | 7th | 10,000 m | 30:26.61 | | 11 | 2004 | olympic games | athens, greece | 64th | marathon | 2:27:13 | | 12 | 2005 | central american championships | san jose, costa rica | 1st | 10,000 m | 30:11.54 | | 13 | 2006 | ibero-american championships | ponce, puerto rico | 8th | 5000 m | 14:31.55 | | 14 | 2006 | central american and caribbean games | cartagena, colombia | 6th | 5000 m | 14:27.54 | | 15 | 2006 | central american and caribbean games | cartagena, colombia | 4th | 10,000 m | 30:09.31 | | 16 | 2007 | central american championships | san jose, costa rica | 1st | 5000 m | 14:42.08 | | 17 | 2007 | central american championships | san jose, costa rica | 1st | 10,000 m | 29:53.61 | | 18 | 2007 | nacac championships | san salvador, el salvador | 1st | 5000 m | 14:33.31 | | 19 | 2007 | nacac championships | san salvador, el salvador | 1st | 10,000 m | 29:42.11 | | 20 | 2007 | pan american games | rio de janeiro, brazil | 2nd | marathon | 2:14:27 | | 21 | 2008 | olympic games | beijing, pr china | 35th | marathon | 2:20:15 | | 22 | 2009 | central american championships | guatemala city, guatemala | 1st | 5000 m | 14:55.44 | | 23 | 2010 | central american and caribbean games | mayaguez, puerto rico | 1st | marathon | 2:21:35 | | 24 | 2010 | central american championships | guatemala city, guatemala | 1st | 10,000 m | 32:06.26 | | 25 | 2011 | pan american games | guadalajara, mexico | 5th | marathon | 2:20:27 sb | | 26 | 2012 | olympic games | london, united kingdom | 38th | marathon | 2:18:23 |

| | notes | |---:|:-----------| | 0 | none | | 1 | 14:28.91 a | | 2 | 30:43.86 a |

select margin of victory where runner(s)-up = 'gene littler, phil rodgers' col : no. | date | tournament | winning score | margin of victory | runner(s)-up row 1 : 1 | 1956-05-27 00:00:00 | dallas centennial open | -12 (64-67-70-67=268) | 1 stroke | dow finsterwald, doug ford row 2 : 2 | 1960-02-21 00:00:00 | tucson open invitational | -9 (67-67-68-69=271) | 3 strokes | bob harris row 3 : 3 | 1961-07-09 00:00:00 | st. paul open invitational | -19 (66-71-68-64=269) | 1 stroke | buster cupit row 4 : 4 | 1963-02-17 00:00:00 | tucson open invitational | -22 (65-67-69-65=266) | 11 strokes | gene littler, phil rodgers row 5 : 5 | 1966-08-28 00:00:00 | philadelphia golf classic | -10 (69-69-68-71=278) | 1 stroke | jack nicklaus row 6 : 6 | 1967-07-24 00:00:00 | pga championship | -7 (71-72-70-68=281) | playoff | don massengale row 7 : 7 | 1968-04-21 00:00:00 | tournament of champions | -8 (70-68-69-69=276) | 1 stroke | julius boros row 8 : 8 | 1970-03-23 00:00:00 | greater jacksonville open | -9 (68-75-70-66=279) | playoff | dale douglass row 9 : 9 | 1975-10-19 00:00:00 | san antonio texas open | -13 (71-67-71-66=275) | playoff | larry hinson row 10 : 10 | 1976-04-18 00:00:00 | mony tournament of champions | -11 (71-68-69-69=277) | 5 strokes | hubert green

col : margin of victory row 1 : 11 strokes

select margin of victory where runner(s)-up = 'gene littler, phil rodgers' <table_name> : table \begin{tabular}{lllllll} \toprule & no. & date & tournament & winning score & margin of victory & runner(s)-up \\ \midrule 0 & 1 & 1956-05-27 00:00:00 & dallas centennial open & -12 (64-67-70-67=268) & 1 stroke & dow finsterwald, doug ford \\ 1 & 2 & 1960-02-21 00:00:00 & tucson open invitational & -9 (67-67-68-69=271) & 3 strokes & bob harris \\ 2 & 3 & 1961-07-09 00:00:00 & st. paul open invitational & -19 (66-71-68-64=269) & 1 stroke & buster cupit \\ 3 & 4 & 1963-02-17 00:00:00 & tucson open invitational & -22 (65-67-69-65=266) & 11 strokes & gene littler, phil rodgers \\ 4 & 5 & 1966-08-28 00:00:00 & philadelphia golf classic & -10 (69-69-68-71=278) & 1 stroke & jack nicklaus \\ 5 & 6 & 1967-07-24 00:00:00 & pga championship & -7 (71-72-70-68=281) & playoff & don massengale \\ 6 & 7 & 1968-04-21 00:00:00 & tournament of champions & -8 (70-68-69-69=276) & 1 stroke & julius boros \\ 7 & 8 & 1970-03-23 00:00:00 & greater jacksonville open & -9 (68-75-70-66=279) & playoff & dale douglass \\ 8 & 9 & 1975-10-19 00:00:00 & san antonio texas open & -13 (71-67-71-66=275) & playoff & larry hinson \\ 9 & 10 & 1976-04-18 00:00:00 & mony tournament of champions & -11 (71-68-69-69=277) & 5 strokes & hubert green \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & margin of victory \\ \midrule 0 & 11 strokes \\ \bottomrule \end{tabular}

select margin of victory where runner(s)-up = 'gene littler, phil rodgers' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>no.</th> <th>date</th> <th>tournament</th> <th>winning score</th> <th>margin of victory</th> <th>runner(s)-up</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>1956-05-27 00:00:00</td> <td>dallas centennial open</td> <td>-12 (64-67-70-67=268)</td> <td>1 stroke</td> <td>dow finsterwald, doug ford</td> </tr> <tr> <th>1</th> <td>2</td> <td>1960-02-21 00:00:00</td> <td>tucson open invitational</td> <td>-9 (67-67-68-69=271)</td> <td>3 strokes</td> <td>bob harris</td> </tr> <tr> <th>2</th> <td>3</td> <td>1961-07-09 00:00:00</td> <td>st. paul open invitational</td> <td>-19 (66-71-68-64=269)</td> <td>1 stroke</td> <td>buster cupit</td> </tr> <tr> <th>3</th> <td>4</td> <td>1963-02-17 00:00:00</td> <td>tucson open invitational</td> <td>-22 (65-67-69-65=266)</td> <td>11 strokes</td> <td>gene littler, phil rodgers</td> </tr> <tr> <th>4</th> <td>5</td> <td>1966-08-28 00:00:00</td> <td>philadelphia golf classic</td> <td>-10 (69-69-68-71=278)</td> <td>1 stroke</td> <td>jack nicklaus</td> </tr> <tr> <th>5</th> <td>6</td> <td>1967-07-24 00:00:00</td> <td>pga championship</td> <td>-7 (71-72-70-68=281)</td> <td>playoff</td> <td>don massengale</td> </tr> <tr> <th>6</th> <td>7</td> <td>1968-04-21 00:00:00</td> <td>tournament of champions</td> <td>-8 (70-68-69-69=276)</td> <td>1 stroke</td> <td>julius boros</td> </tr> <tr> <th>7</th> <td>8</td> <td>1970-03-23 00:00:00</td> <td>greater jacksonville open</td> <td>-9 (68-75-70-66=279)</td> <td>playoff</td> <td>dale douglass</td> </tr> <tr> <th>8</th> <td>9</td> <td>1975-10-19 00:00:00</td> <td>san antonio texas open</td> <td>-13 (71-67-71-66=275)</td> <td>playoff</td> <td>larry hinson</td> </tr> <tr> <th>9</th> <td>10</td> <td>1976-04-18 00:00:00</td> <td>mony tournament of champions</td> <td>-11 (71-68-69-69=277)</td> <td>5 strokes</td> <td>hubert green</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>margin of victory</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>11 strokes</td> </tr> </tbody> </table>

select margin of victory where runner(s)-up = 'gene littler, phil rodgers' <table_name> : table | | no. | date | tournament | winning score | margin of victory | runner(s)-up | |---:|------:|:--------------------|:-----------------------------|:----------------------|:--------------------|:---------------------------| | 0 | 1 | 1956-05-27 00:00:00 | dallas centennial open | -12 (64-67-70-67=268) | 1 stroke | dow finsterwald, doug ford | | 1 | 2 | 1960-02-21 00:00:00 | tucson open invitational | -9 (67-67-68-69=271) | 3 strokes | bob harris | | 2 | 3 | 1961-07-09 00:00:00 | st. paul open invitational | -19 (66-71-68-64=269) | 1 stroke | buster cupit | | 3 | 4 | 1963-02-17 00:00:00 | tucson open invitational | -22 (65-67-69-65=266) | 11 strokes | gene littler, phil rodgers | | 4 | 5 | 1966-08-28 00:00:00 | philadelphia golf classic | -10 (69-69-68-71=278) | 1 stroke | jack nicklaus | | 5 | 6 | 1967-07-24 00:00:00 | pga championship | -7 (71-72-70-68=281) | playoff | don massengale | | 6 | 7 | 1968-04-21 00:00:00 | tournament of champions | -8 (70-68-69-69=276) | 1 stroke | julius boros | | 7 | 8 | 1970-03-23 00:00:00 | greater jacksonville open | -9 (68-75-70-66=279) | playoff | dale douglass | | 8 | 9 | 1975-10-19 00:00:00 | san antonio texas open | -13 (71-67-71-66=275) | playoff | larry hinson | | 9 | 10 | 1976-04-18 00:00:00 | mony tournament of champions | -11 (71-68-69-69=277) | 5 strokes | hubert green |

| | margin of victory | |---:|:--------------------| | 0 | 11 strokes |

select capacity where team = 'widnes vikings (2014 season)' col : team | stadium | capacity | city/area row 1 : bradford bulls (2014 season) | provident stadium | 27000 | bradford, west yorkshire row 2 : castleford tigers (2014 season) | the wish communications stadium | 11750 | castleford, west yorkshire row 3 : catalans dragons (2014 season) | stade gilbert brutus | 14000 | perpignan, pyrenees-orientales, france row 4 : huddersfield giants (2014 season) | john smith's stadium | 24544 | huddersfield, west yorkshire row 5 : hull (2014 season) | kingston communications stadium | 25404 | kingston upon hull, east riding of yorkshire row 6 : hull kingston rovers (2014 season) | ms3 craven park | 9471 | kingston upon hull, east riding of yorkshire row 7 : leeds rhinos (2014 season) | headingley carnegie stadium | 22250 | leeds, west yorkshire row 8 : london broncos (2014 season) | twickenham stoop | 12700 | twickenham, london row 9 : salford city reds (2014 season) | salford city stadium | 12000 | salford, greater manchester row 10 : st helens rlfc (2014 season) | langtree park | 18000 | st helens, merseyside row 11 : wakefield trinity wildcats (2014 season) | rapid solicitors stadium | 11000 | wakefield, west yorkshire row 12 : warrington wolves (2014 season) | halliwell jones stadium | 15500 | warrington, cheshire row 13 : widnes vikings (2014 season) | the select security stadium | 13500 | widnes, cheshire, england row 14 : wigan warriors (2014 season) | dw stadium | 25138 | wigan, greater manchester

col : capacity row 1 : 13500

select capacity where team = 'widnes vikings (2014 season)' <table_name> : table \begin{tabular}{lllll} \toprule & team & stadium & capacity & city/area \\ \midrule 0 & bradford bulls (2014 season) & provident stadium & 27000 & bradford, west yorkshire \\ 1 & castleford tigers (2014 season) & the wish communications stadium & 11750 & castleford, west yorkshire \\ 2 & catalans dragons (2014 season) & stade gilbert brutus & 14000 & perpignan, pyrenees-orientales, france \\ 3 & huddersfield giants (2014 season) & john smith's stadium & 24544 & huddersfield, west yorkshire \\ 4 & hull (2014 season) & kingston communications stadium & 25404 & kingston upon hull, east riding of yorkshire \\ 5 & hull kingston rovers (2014 season) & ms3 craven park & 9471 & kingston upon hull, east riding of yorkshire \\ 6 & leeds rhinos (2014 season) & headingley carnegie stadium & 22250 & leeds, west yorkshire \\ 7 & london broncos (2014 season) & twickenham stoop & 12700 & twickenham, london \\ 8 & salford city reds (2014 season) & salford city stadium & 12000 & salford, greater manchester \\ 9 & st helens rlfc (2014 season) & langtree park & 18000 & st helens, merseyside \\ 10 & wakefield trinity wildcats (2014 season) & rapid solicitors stadium & 11000 & wakefield, west yorkshire \\ 11 & warrington wolves (2014 season) & halliwell jones stadium & 15500 & warrington, cheshire \\ 12 & widnes vikings (2014 season) & the select security stadium & 13500 & widnes, cheshire, england \\ 13 & wigan warriors (2014 season) & dw stadium & 25138 & wigan, greater manchester \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & capacity \\ \midrule 0 & 13500 \\ \bottomrule \end{tabular}

select capacity where team = 'widnes vikings (2014 season)' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>team</th> <th>stadium</th> <th>capacity</th> <th>city/area</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>bradford bulls (2014 season)</td> <td>provident stadium</td> <td>27000</td> <td>bradford, west yorkshire</td> </tr> <tr> <th>1</th> <td>castleford tigers (2014 season)</td> <td>the wish communications stadium</td> <td>11750</td> <td>castleford, west yorkshire</td> </tr> <tr> <th>2</th> <td>catalans dragons (2014 season)</td> <td>stade gilbert brutus</td> <td>14000</td> <td>perpignan, pyrenees-orientales, france</td> </tr> <tr> <th>3</th> <td>huddersfield giants (2014 season)</td> <td>john smith's stadium</td> <td>24544</td> <td>huddersfield, west yorkshire</td> </tr> <tr> <th>4</th> <td>hull (2014 season)</td> <td>kingston communications stadium</td> <td>25404</td> <td>kingston upon hull, east riding of yorkshire</td> </tr> <tr> <th>5</th> <td>hull kingston rovers (2014 season)</td> <td>ms3 craven park</td> <td>9471</td> <td>kingston upon hull, east riding of yorkshire</td> </tr> <tr> <th>6</th> <td>leeds rhinos (2014 season)</td> <td>headingley carnegie stadium</td> <td>22250</td> <td>leeds, west yorkshire</td> </tr> <tr> <th>7</th> <td>london broncos (2014 season)</td> <td>twickenham stoop</td> <td>12700</td> <td>twickenham, london</td> </tr> <tr> <th>8</th> <td>salford city reds (2014 season)</td> <td>salford city stadium</td> <td>12000</td> <td>salford, greater manchester</td> </tr> <tr> <th>9</th> <td>st helens rlfc (2014 season)</td> <td>langtree park</td> <td>18000</td> <td>st helens, merseyside</td> </tr> <tr> <th>10</th> <td>wakefield trinity wildcats (2014 season)</td> <td>rapid solicitors stadium</td> <td>11000</td> <td>wakefield, west yorkshire</td> </tr> <tr> <th>11</th> <td>warrington wolves (2014 season)</td> <td>halliwell jones stadium</td> <td>15500</td> <td>warrington, cheshire</td> </tr> <tr> <th>12</th> <td>widnes vikings (2014 season)</td> <td>the select security stadium</td> <td>13500</td> <td>widnes, cheshire, england</td> </tr> <tr> <th>13</th> <td>wigan warriors (2014 season)</td> <td>dw stadium</td> <td>25138</td> <td>wigan, greater manchester</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>capacity</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>13500</td> </tr> </tbody> </table>

select capacity where team = 'widnes vikings (2014 season)' <table_name> : table | | team | stadium | capacity | city/area | |---:|:-----------------------------------------|:--------------------------------|-----------:|:---------------------------------------------| | 0 | bradford bulls (2014 season) | provident stadium | 27000 | bradford, west yorkshire | | 1 | castleford tigers (2014 season) | the wish communications stadium | 11750 | castleford, west yorkshire | | 2 | catalans dragons (2014 season) | stade gilbert brutus | 14000 | perpignan, pyrenees-orientales, france | | 3 | huddersfield giants (2014 season) | john smith's stadium | 24544 | huddersfield, west yorkshire | | 4 | hull (2014 season) | kingston communications stadium | 25404 | kingston upon hull, east riding of yorkshire | | 5 | hull kingston rovers (2014 season) | ms3 craven park | 9471 | kingston upon hull, east riding of yorkshire | | 6 | leeds rhinos (2014 season) | headingley carnegie stadium | 22250 | leeds, west yorkshire | | 7 | london broncos (2014 season) | twickenham stoop | 12700 | twickenham, london | | 8 | salford city reds (2014 season) | salford city stadium | 12000 | salford, greater manchester | | 9 | st helens rlfc (2014 season) | langtree park | 18000 | st helens, merseyside | | 10 | wakefield trinity wildcats (2014 season) | rapid solicitors stadium | 11000 | wakefield, west yorkshire | | 11 | warrington wolves (2014 season) | halliwell jones stadium | 15500 | warrington, cheshire | | 12 | widnes vikings (2014 season) | the select security stadium | 13500 | widnes, cheshire, england | | 13 | wigan warriors (2014 season) | dw stadium | 25138 | wigan, greater manchester |

| | capacity | |---:|-----------:| | 0 | 13500 |

select notes where competition = 'european championships' col : year | competition | venue | position | notes row 1 : 1999 | world youth championships | bydgoszcz, poland | 13th (q) | 4.60 m row 2 : 2001 | european junior championships | grosseto, italy | 7th | 5.15 m row 3 : 2002 | world junior championships | kingston, jamaica | 8th | 5.30 m row 4 : 2003 | european u23 championships | bydgoszcz, poland | 13th (q) | 5.20 m row 5 : 2005 | european u23 championships | erfurt, germany | 7th | 5.50 m row 6 : 2005 | universiade | izmir, turkey | 5th | 5.50 m row 7 : 2006 | world indoor championships | moscow, russia | 10th (q) | 5.65 m row 8 : 2006 | european championships | gothenburg, sweden | 5th | 5.65 m row 9 : 2007 | european indoor championships | birmingham, united kingdom | 16th (q) | 5.40 m row 10 : 2008 | olympic games | beijing, china | 11th | 5.45 m row 11 : 2010 | european championships | barcelona, spain | 3rd | 5.75 m row 12 : 2012 | european championships | helsinki, finland | none | nm

col : notes row 1 : 5.65 m row 2 : 5.75 m row 3 : nm

select notes where competition = 'european championships' <table_name> : table \begin{tabular}{llllll} \toprule & year & competition & venue & position & notes \\ \midrule 0 & 1999 & world youth championships & bydgoszcz, poland & 13th (q) & 4.60 m \\ 1 & 2001 & european junior championships & grosseto, italy & 7th & 5.15 m \\ 2 & 2002 & world junior championships & kingston, jamaica & 8th & 5.30 m \\ 3 & 2003 & european u23 championships & bydgoszcz, poland & 13th (q) & 5.20 m \\ 4 & 2005 & european u23 championships & erfurt, germany & 7th & 5.50 m \\ 5 & 2005 & universiade & izmir, turkey & 5th & 5.50 m \\ 6 & 2006 & world indoor championships & moscow, russia & 10th (q) & 5.65 m \\ 7 & 2006 & european championships & gothenburg, sweden & 5th & 5.65 m \\ 8 & 2007 & european indoor championships & birmingham, united kingdom & 16th (q) & 5.40 m \\ 9 & 2008 & olympic games & beijing, china & 11th & 5.45 m \\ 10 & 2010 & european championships & barcelona, spain & 3rd & 5.75 m \\ 11 & 2012 & european championships & helsinki, finland & none & nm \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & notes \\ \midrule 0 & 5.65 m \\ 1 & 5.75 m \\ 2 & nm \\ \bottomrule \end{tabular}

select notes where competition = 'european championships' <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>1999</td> <td>world youth championships</td> <td>bydgoszcz, poland</td> <td>13th (q)</td> <td>4.60 m</td> </tr> <tr> <th>1</th> <td>2001</td> <td>european junior championships</td> <td>grosseto, italy</td> <td>7th</td> <td>5.15 m</td> </tr> <tr> <th>2</th> <td>2002</td> <td>world junior championships</td> <td>kingston, jamaica</td> <td>8th</td> <td>5.30 m</td> </tr> <tr> <th>3</th> <td>2003</td> <td>european u23 championships</td> <td>bydgoszcz, poland</td> <td>13th (q)</td> <td>5.20 m</td> </tr> <tr> <th>4</th> <td>2005</td> <td>european u23 championships</td> <td>erfurt, germany</td> <td>7th</td> <td>5.50 m</td> </tr> <tr> <th>5</th> <td>2005</td> <td>universiade</td> <td>izmir, turkey</td> <td>5th</td> <td>5.50 m</td> </tr> <tr> <th>6</th> <td>2006</td> <td>world indoor championships</td> <td>moscow, russia</td> <td>10th (q)</td> <td>5.65 m</td> </tr> <tr> <th>7</th> <td>2006</td> <td>european championships</td> <td>gothenburg, sweden</td> <td>5th</td> <td>5.65 m</td> </tr> <tr> <th>8</th> <td>2007</td> <td>european indoor championships</td> <td>birmingham, united kingdom</td> <td>16th (q)</td> <td>5.40 m</td> </tr> <tr> <th>9</th> <td>2008</td> <td>olympic games</td> <td>beijing, china</td> <td>11th</td> <td>5.45 m</td> </tr> <tr> <th>10</th> <td>2010</td> <td>european championships</td> <td>barcelona, spain</td> <td>3rd</td> <td>5.75 m</td> </tr> <tr> <th>11</th> <td>2012</td> <td>european championships</td> <td>helsinki, finland</td> <td>none</td> <td>nm</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>notes</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>5.65 m</td> </tr> <tr> <th>1</th> <td>5.75 m</td> </tr> <tr> <th>2</th> <td>nm</td> </tr> </tbody> </table>

select notes where competition = 'european championships' <table_name> : table | | year | competition | venue | position | notes | |---:|-------:|:------------------------------|:---------------------------|:-----------|:--------| | 0 | 1999 | world youth championships | bydgoszcz, poland | 13th (q) | 4.60 m | | 1 | 2001 | european junior championships | grosseto, italy | 7th | 5.15 m | | 2 | 2002 | world junior championships | kingston, jamaica | 8th | 5.30 m | | 3 | 2003 | european u23 championships | bydgoszcz, poland | 13th (q) | 5.20 m | | 4 | 2005 | european u23 championships | erfurt, germany | 7th | 5.50 m | | 5 | 2005 | universiade | izmir, turkey | 5th | 5.50 m | | 6 | 2006 | world indoor championships | moscow, russia | 10th (q) | 5.65 m | | 7 | 2006 | european championships | gothenburg, sweden | 5th | 5.65 m | | 8 | 2007 | european indoor championships | birmingham, united kingdom | 16th (q) | 5.40 m | | 9 | 2008 | olympic games | beijing, china | 11th | 5.45 m | | 10 | 2010 | european championships | barcelona, spain | 3rd | 5.75 m | | 11 | 2012 | european championships | helsinki, finland | none | nm |

| | notes | |---:|:--------| | 0 | 5.65 m | | 1 | 5.75 m | | 2 | nm |

select count ( notes ) where rank = 8 col : rank | lane | nation | swimmers | time | time behind | notes row 1 : none | 4 | united states | michael phelps (1:46.49) ryan lochte (1:47.52) peter vanderkaay (1:47.79) klete keller (1:45.53) | 7:07.33 | none | am row 2 : none | 5 | australia | grant hackett (1:47.50) michael klim (1:47.62) nicholas sprenger (1:48.16) ian thorpe (1:44.18) | 7:07.46 | 0.13 | none row 3 : none | 7 | italy | emiliano brembilla (1:48.16) massimiliano rosolino (1:46.24) simone cercato (1:49.85) filippo magnini (1:47.58) | 7:11.83 | 4.5 | none row 4 : 4 | 6 | great britain | simon burnett (1:47.90) gavin meadows (1:48.46) david o'brien (1:49.05) ross davenport (1:47.19) | 7:12.60 | 5.27 | none row 5 : 5 | 2 | canada | brent hayden (1:49.08) brian johns (1:49.15) andrew hurd (1:48.09) rick say (1:47.01) | 7:13.33 | 6 | none row 6 : 6 | 3 | germany | jens schreiber (1:49.08) heiko hell (1:49.15) lars conrad (1:48.23) christian keller (1:50.05) | 7:16.51 | 9.18 | none row 7 : 7 | 8 | france | amaury leveaux (1:48.57) fabien horth (1:48.67) nicolas kintz (1:50.01) nicolas rostoucher (1:50.18) | 7:17.43 | 10.1 | none row 8 : 8 | 1 | greece | apostolos antonopoulos (1:50.34) dimitrios manganas (1:51.33) andreas zisimos (1:50.26) nikolaos xylouris (1:51.09) | 7:23.02 | 15.67 | none

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

select count ( notes ) where rank = 8 <table_name> : table \begin{tabular}{llllllll} \toprule & rank & lane & nation & swimmers & time & time behind & notes \\ \midrule 0 & none & 4 & united states & michael phelps (1:46.49) ryan lochte (1:47.52) peter vanderkaay (1:47.79) klete keller (1:45.53) & 7:07.33 & none & am \\ 1 & none & 5 & australia & grant hackett (1:47.50) michael klim (1:47.62) nicholas sprenger (1:48.16) ian thorpe (1:44.18) & 7:07.46 & 0.13 & none \\ 2 & none & 7 & italy & emiliano brembilla (1:48.16) massimiliano rosolino (1:46.24) simone cercato (1:49.85) filippo magnini (1:47.58) & 7:11.83 & 4.5 & none \\ 3 & 4 & 6 & great britain & simon burnett (1:47.90) gavin meadows (1:48.46) david o'brien (1:49.05) ross davenport (1:47.19) & 7:12.60 & 5.27 & none \\ 4 & 5 & 2 & canada & brent hayden (1:49.08) brian johns (1:49.15) andrew hurd (1:48.09) rick say (1:47.01) & 7:13.33 & 6 & none \\ 5 & 6 & 3 & germany & jens schreiber (1:49.08) heiko hell (1:49.15) lars conrad (1:48.23) christian keller (1:50.05) & 7:16.51 & 9.18 & none \\ 6 & 7 & 8 & france & amaury leveaux (1:48.57) fabien horth (1:48.67) nicolas kintz (1:50.01) nicolas rostoucher (1:50.18) & 7:17.43 & 10.1 & none \\ 7 & 8 & 1 & greece & apostolos antonopoulos (1:50.34) dimitrios manganas (1:51.33) andreas zisimos (1:50.26) nikolaos xylouris (1:51.09) & 7:23.02 & 15.67 & none \\ \bottomrule \end{tabular}

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

select count ( notes ) where rank = 8 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>rank</th> <th>lane</th> <th>nation</th> <th>swimmers</th> <th>time</th> <th>time behind</th> <th>notes</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>none</td> <td>4</td> <td>united states</td> <td>michael phelps (1:46.49) ryan lochte (1:47.52) peter vanderkaay (1:47.79) klete keller (1:45.53)</td> <td>7:07.33</td> <td>none</td> <td>am</td> </tr> <tr> <th>1</th> <td>none</td> <td>5</td> <td>australia</td> <td>grant hackett (1:47.50) michael klim (1:47.62) nicholas sprenger (1:48.16) ian thorpe (1:44.18)</td> <td>7:07.46</td> <td>0.13</td> <td>none</td> </tr> <tr> <th>2</th> <td>none</td> <td>7</td> <td>italy</td> <td>emiliano brembilla (1:48.16) massimiliano rosolino (1:46.24) simone cercato (1:49.85) filippo magnini (1:47.58)</td> <td>7:11.83</td> <td>4.5</td> <td>none</td> </tr> <tr> <th>3</th> <td>4</td> <td>6</td> <td>great britain</td> <td>simon burnett (1:47.90) gavin meadows (1:48.46) david o'brien (1:49.05) ross davenport (1:47.19)</td> <td>7:12.60</td> <td>5.27</td> <td>none</td> </tr> <tr> <th>4</th> <td>5</td> <td>2</td> <td>canada</td> <td>brent hayden (1:49.08) brian johns (1:49.15) andrew hurd (1:48.09) rick say (1:47.01)</td> <td>7:13.33</td> <td>6</td> <td>none</td> </tr> <tr> <th>5</th> <td>6</td> <td>3</td> <td>germany</td> <td>jens schreiber (1:49.08) heiko hell (1:49.15) lars conrad (1:48.23) christian keller (1:50.05)</td> <td>7:16.51</td> <td>9.18</td> <td>none</td> </tr> <tr> <th>6</th> <td>7</td> <td>8</td> <td>france</td> <td>amaury leveaux (1:48.57) fabien horth (1:48.67) nicolas kintz (1:50.01) nicolas rostoucher (1:50.18)</td> <td>7:17.43</td> <td>10.1</td> <td>none</td> </tr> <tr> <th>7</th> <td>8</td> <td>1</td> <td>greece</td> <td>apostolos antonopoulos (1:50.34) dimitrios manganas (1:51.33) andreas zisimos (1:50.26) nikolaos xylouris (1:51.09)</td> <td>7:23.02</td> <td>15.67</td> <td>none</td> </tr> </tbody> </table>

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

select count ( notes ) where rank = 8 <table_name> : table | | rank | lane | nation | swimmers | time | time behind | notes | |---:|:-------|-------:|:--------------|:--------------------------------------------------------------------------------------------------------------------|:--------|:--------------|:--------| | 0 | none | 4 | united states | michael phelps (1:46.49) ryan lochte (1:47.52) peter vanderkaay (1:47.79) klete keller (1:45.53) | 7:07.33 | none | am | | 1 | none | 5 | australia | grant hackett (1:47.50) michael klim (1:47.62) nicholas sprenger (1:48.16) ian thorpe (1:44.18) | 7:07.46 | 0.13 | none | | 2 | none | 7 | italy | emiliano brembilla (1:48.16) massimiliano rosolino (1:46.24) simone cercato (1:49.85) filippo magnini (1:47.58) | 7:11.83 | 4.5 | none | | 3 | 4 | 6 | great britain | simon burnett (1:47.90) gavin meadows (1:48.46) david o'brien (1:49.05) ross davenport (1:47.19) | 7:12.60 | 5.27 | none | | 4 | 5 | 2 | canada | brent hayden (1:49.08) brian johns (1:49.15) andrew hurd (1:48.09) rick say (1:47.01) | 7:13.33 | 6 | none | | 5 | 6 | 3 | germany | jens schreiber (1:49.08) heiko hell (1:49.15) lars conrad (1:48.23) christian keller (1:50.05) | 7:16.51 | 9.18 | none | | 6 | 7 | 8 | france | amaury leveaux (1:48.57) fabien horth (1:48.67) nicolas kintz (1:50.01) nicolas rostoucher (1:50.18) | 7:17.43 | 10.1 | none | | 7 | 8 | 1 | greece | apostolos antonopoulos (1:50.34) dimitrios manganas (1:51.33) andreas zisimos (1:50.26) nikolaos xylouris (1:51.09) | 7:23.02 | 15.67 | none |

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

select count ( notes ) where title = 396 col : year | title | role | notes row 1 : 1987 | frog | kathy | television movie row 2 : 1988 | platinum blonde | none | short film row 3 : 1992 | saved by the bell: hawaiian style | jessie spano | television movie row 4 : 1994 | molly & gina | kimberly sweeny | none row 5 : 1994 | bandit: bandit goes country | beth | television movie row 6 : 1994 | saved by the bell: wedding in las vegas | jessie spano | television movie row 7 : 1995 | white wolves ii: legend of the wild | crystal | none row 8 : 1995 | showgirls | nomi malone | golden raspberry award for worst actress golden raspberry award for worst new star nominated-golden raspberry award for worst actress of the century nominated-golden raspberry award for worst new star of the decade row 9 : 1996 | the first wives club | phoebe lavelle | national board of review award for best cast row 10 : 1997 | a time to revenge | tiffany whittmar | scenes deleted row 11 : 1997 | armitage iii | naomi armitage (voice) | video row 12 : 1997 | the real blonde | tina | none row 13 : 1998 | random encounter | alicia 'allie' brayman | none row 14 : 1999 | taxman | nadia rubakov | none row 15 : 1999 | africa | barbara craig | none row 16 : 1999 | last call | helena | none row 17 : 1999 | tail lights fade | eve | none row 18 : 1999 | any given sunday | mandy murphy | none row 19 : 2000 | becoming dick | maggie | television movie row 20 : 2001 | the curse of the jade scorpion | jill | none row 21 : 2001 | the elevator | celeste | television movie row 22 : 2001 | the shipment | candy colucci | none row 23 : 2002 | cover story | samantha noble | none row 24 : 2002 | roger dodger | andrea | none row 25 : 2003 | detonator | jane dreyer | none row 26 : 2003 | moving malcolm | liz woodward | none row 27 : 2003 | control factor | karen bishop | television movie row 28 : 2003 | student seduction | christie dawson | television movie row 29 : 2008 | meet market | linda | none row 30 : 2008 | the butler's in love | angelique | short film row 31 : 2008 | black widow | olivia whitfield / grace miller | television movie row 32 : 2009 | women in trouble | tracy | none row 33 : 2009 | s. darko | trudy | none row 34 : 2011 | lucky christmas | holly ceroni | television movie

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

select count ( notes ) where title = 396 <table_name> : table \begin{tabular}{lllll} \toprule & year & title & role & notes \\ \midrule 0 & 1987 & frog & kathy & television movie \\ 1 & 1988 & platinum blonde & none & short film \\ 2 & 1992 & saved by the bell: hawaiian style & jessie spano & television movie \\ 3 & 1994 & molly & gina & kimberly sweeny & none \\ 4 & 1994 & bandit: bandit goes country & beth & television movie \\ 5 & 1994 & saved by the bell: wedding in las vegas & jessie spano & television movie \\ 6 & 1995 & white wolves ii: legend of the wild & crystal & none \\ 7 & 1995 & showgirls & nomi malone & golden raspberry award for worst actress golden raspberry award for worst new star nominated-golden raspberry award for worst actress of the century nominated-golden raspberry award for worst new star of the decade \\ 8 & 1996 & the first wives club & phoebe lavelle & national board of review award for best cast \\ 9 & 1997 & a time to revenge & tiffany whittmar & scenes deleted \\ 10 & 1997 & armitage iii & naomi armitage (voice) & video \\ 11 & 1997 & the real blonde & tina & none \\ 12 & 1998 & random encounter & alicia 'allie' brayman & none \\ 13 & 1999 & taxman & nadia rubakov & none \\ 14 & 1999 & africa & barbara craig & none \\ 15 & 1999 & last call & helena & none \\ 16 & 1999 & tail lights fade & eve & none \\ 17 & 1999 & any given sunday & mandy murphy & none \\ 18 & 2000 & becoming dick & maggie & television movie \\ 19 & 2001 & the curse of the jade scorpion & jill & none \\ 20 & 2001 & the elevator & celeste & television movie \\ 21 & 2001 & the shipment & candy colucci & none \\ 22 & 2002 & cover story & samantha noble & none \\ 23 & 2002 & roger dodger & andrea & none \\ 24 & 2003 & detonator & jane dreyer & none \\ 25 & 2003 & moving malcolm & liz woodward & none \\ 26 & 2003 & control factor & karen bishop & television movie \\ 27 & 2003 & student seduction & christie dawson & television movie \\ 28 & 2008 & meet market & linda & none \\ 29 & 2008 & the butler's in love & angelique & short film \\ 30 & 2008 & black widow & olivia whitfield / grace miller & television movie \\ 31 & 2009 & women in trouble & tracy & none \\ 32 & 2009 & s. darko & trudy & none \\ 33 & 2011 & lucky christmas & holly ceroni & television movie \\ \bottomrule \end{tabular}

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

select count ( notes ) where title = 396 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>year</th> <th>title</th> <th>role</th> <th>notes</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1987</td> <td>frog</td> <td>kathy</td> <td>television movie</td> </tr> <tr> <th>1</th> <td>1988</td> <td>platinum blonde</td> <td>none</td> <td>short film</td> </tr> <tr> <th>2</th> <td>1992</td> <td>saved by the bell: hawaiian style</td> <td>jessie spano</td> <td>television movie</td> </tr> <tr> <th>3</th> <td>1994</td> <td>molly &amp; gina</td> <td>kimberly sweeny</td> <td>none</td> </tr> <tr> <th>4</th> <td>1994</td> <td>bandit: bandit goes country</td> <td>beth</td> <td>television movie</td> </tr> <tr> <th>5</th> <td>1994</td> <td>saved by the bell: wedding in las vegas</td> <td>jessie spano</td> <td>television movie</td> </tr> <tr> <th>6</th> <td>1995</td> <td>white wolves ii: legend of the wild</td> <td>crystal</td> <td>none</td> </tr> <tr> <th>7</th> <td>1995</td> <td>showgirls</td> <td>nomi malone</td> <td>golden raspberry award for worst actress golden raspberry award for worst new star nominated-golden raspberry award for worst actress of the century nominated-golden raspberry award for worst new star of the decade</td> </tr> <tr> <th>8</th> <td>1996</td> <td>the first wives club</td> <td>phoebe lavelle</td> <td>national board of review award for best cast</td> </tr> <tr> <th>9</th> <td>1997</td> <td>a time to revenge</td> <td>tiffany whittmar</td> <td>scenes deleted</td> </tr> <tr> <th>10</th> <td>1997</td> <td>armitage iii</td> <td>naomi armitage (voice)</td> <td>video</td> </tr> <tr> <th>11</th> <td>1997</td> <td>the real blonde</td> <td>tina</td> <td>none</td> </tr> <tr> <th>12</th> <td>1998</td> <td>random encounter</td> <td>alicia 'allie' brayman</td> <td>none</td> </tr> <tr> <th>13</th> <td>1999</td> <td>taxman</td> <td>nadia rubakov</td> <td>none</td> </tr> <tr> <th>14</th> <td>1999</td> <td>africa</td> <td>barbara craig</td> <td>none</td> </tr> <tr> <th>15</th> <td>1999</td> <td>last call</td> <td>helena</td> <td>none</td> </tr> <tr> <th>16</th> <td>1999</td> <td>tail lights fade</td> <td>eve</td> <td>none</td> </tr> <tr> <th>17</th> <td>1999</td> <td>any given sunday</td> <td>mandy murphy</td> <td>none</td> </tr> <tr> <th>18</th> <td>2000</td> <td>becoming dick</td> <td>maggie</td> <td>television movie</td> </tr> <tr> <th>19</th> <td>2001</td> <td>the curse of the jade scorpion</td> <td>jill</td> <td>none</td> </tr> <tr> <th>20</th> <td>2001</td> <td>the elevator</td> <td>celeste</td> <td>television movie</td> </tr> <tr> <th>21</th> <td>2001</td> <td>the shipment</td> <td>candy colucci</td> <td>none</td> </tr> <tr> <th>22</th> <td>2002</td> <td>cover story</td> <td>samantha noble</td> <td>none</td> </tr> <tr> <th>23</th> <td>2002</td> <td>roger dodger</td> <td>andrea</td> <td>none</td> </tr> <tr> <th>24</th> <td>2003</td> <td>detonator</td> <td>jane dreyer</td> <td>none</td> </tr> <tr> <th>25</th> <td>2003</td> <td>moving malcolm</td> <td>liz woodward</td> <td>none</td> </tr> <tr> <th>26</th> <td>2003</td> <td>control factor</td> <td>karen bishop</td> <td>television movie</td> </tr> <tr> <th>27</th> <td>2003</td> <td>student seduction</td> <td>christie dawson</td> <td>television movie</td> </tr> <tr> <th>28</th> <td>2008</td> <td>meet market</td> <td>linda</td> <td>none</td> </tr> <tr> <th>29</th> <td>2008</td> <td>the butler's in love</td> <td>angelique</td> <td>short film</td> </tr> <tr> <th>30</th> <td>2008</td> <td>black widow</td> <td>olivia whitfield / grace miller</td> <td>television movie</td> </tr> <tr> <th>31</th> <td>2009</td> <td>women in trouble</td> <td>tracy</td> <td>none</td> </tr> <tr> <th>32</th> <td>2009</td> <td>s. darko</td> <td>trudy</td> <td>none</td> </tr> <tr> <th>33</th> <td>2011</td> <td>lucky christmas</td> <td>holly ceroni</td> <td>television movie</td> </tr> </tbody> </table>

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

select count ( notes ) where title = 396 <table_name> : table | | year | title | role | notes | |---:|-------:|:----------------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 1987 | frog | kathy | television movie | | 1 | 1988 | platinum blonde | none | short film | | 2 | 1992 | saved by the bell: hawaiian style | jessie spano | television movie | | 3 | 1994 | molly & gina | kimberly sweeny | none | | 4 | 1994 | bandit: bandit goes country | beth | television movie | | 5 | 1994 | saved by the bell: wedding in las vegas | jessie spano | television movie | | 6 | 1995 | white wolves ii: legend of the wild | crystal | none | | 7 | 1995 | showgirls | nomi malone | golden raspberry award for worst actress golden raspberry award for worst new star nominated-golden raspberry award for worst actress of the century nominated-golden raspberry award for worst new star of the decade | | 8 | 1996 | the first wives club | phoebe lavelle | national board of review award for best cast | | 9 | 1997 | a time to revenge | tiffany whittmar | scenes deleted | | 10 | 1997 | armitage iii | naomi armitage (voice) | video | | 11 | 1997 | the real blonde | tina | none | | 12 | 1998 | random encounter | alicia 'allie' brayman | none | | 13 | 1999 | taxman | nadia rubakov | none | | 14 | 1999 | africa | barbara craig | none | | 15 | 1999 | last call | helena | none | | 16 | 1999 | tail lights fade | eve | none | | 17 | 1999 | any given sunday | mandy murphy | none | | 18 | 2000 | becoming dick | maggie | television movie | | 19 | 2001 | the curse of the jade scorpion | jill | none | | 20 | 2001 | the elevator | celeste | television movie | | 21 | 2001 | the shipment | candy colucci | none | | 22 | 2002 | cover story | samantha noble | none | | 23 | 2002 | roger dodger | andrea | none | | 24 | 2003 | detonator | jane dreyer | none | | 25 | 2003 | moving malcolm | liz woodward | none | | 26 | 2003 | control factor | karen bishop | television movie | | 27 | 2003 | student seduction | christie dawson | television movie | | 28 | 2008 | meet market | linda | none | | 29 | 2008 | the butler's in love | angelique | short film | | 30 | 2008 | black widow | olivia whitfield / grace miller | television movie | | 31 | 2009 | women in trouble | tracy | none | | 32 | 2009 | s. darko | trudy | none | | 33 | 2011 | lucky christmas | holly ceroni | television movie |

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

select nation where gold > 0 order by gold limit 1 col : rank | nation | gold | silver | bronze | total row 1 : 1 | south korea | 11 | 1 | 2 | 14 row 2 : 2 | chinese taipei | 2 | 2 | 5 | 9 row 3 : 3 | spain | 2 | 0 | 4 | 6 row 4 : 4 | egypt | 1 | 0 | 0 | 1 row 5 : 5 | france | 0 | 2 | 1 | 3 row 6 : 5 | iran | 0 | 2 | 1 | 3 row 7 : 7 | china | 0 | 1 | 1 | 2 row 8 : 7 | cuba | 0 | 1 | 1 | 2 row 9 : 7 | greece | 0 | 1 | 1 | 2 row 10 : 7 | turkey | 0 | 1 | 1 | 2 row 11 : 11 | canada | 0 | 1 | 0 | 1 row 12 : 11 | germany | 0 | 1 | 0 | 1 row 13 : 11 | morocco | 0 | 1 | 0 | 1 row 14 : 11 | philippines | 0 | 1 | 0 | 1 row 15 : 11 | russia | 0 | 1 | 0 | 1 row 16 : 16 | mexico | 0 | 0 | 4 | 4 row 17 : 17 | australia | 0 | 0 | 3 | 3 row 18 : 18 | croatia | 0 | 0 | 2 | 2 row 19 : 18 | united states | 0 | 0 | 2 | 2 row 20 : 20 | ecuador | 0 | 0 | 1 | 1 row 21 : 20 | saudi arabia | 0 | 0 | 1 | 1 row 22 : 20 | thailand | 0 | 0 | 1 | 1 row 23 : 20 | yugoslavia | 0 | 0 | 1 | 1 row 24 : total | total | 16 | 16 | 32 | 64

col : nation row 1 : south korea row 2 : chinese taipei row 3 : spain row 4 : egypt row 5 : total

select nation where gold > 0 order by gold limit 1 <table_name> : table \begin{tabular}{lllllll} \toprule & rank & nation & gold & silver & bronze & total \\ \midrule 0 & 1 & south korea & 11 & 1 & 2 & 14 \\ 1 & 2 & chinese taipei & 2 & 2 & 5 & 9 \\ 2 & 3 & spain & 2 & 0 & 4 & 6 \\ 3 & 4 & egypt & 1 & 0 & 0 & 1 \\ 4 & 5 & france & 0 & 2 & 1 & 3 \\ 5 & 5 & iran & 0 & 2 & 1 & 3 \\ 6 & 7 & china & 0 & 1 & 1 & 2 \\ 7 & 7 & cuba & 0 & 1 & 1 & 2 \\ 8 & 7 & greece & 0 & 1 & 1 & 2 \\ 9 & 7 & turkey & 0 & 1 & 1 & 2 \\ 10 & 11 & canada & 0 & 1 & 0 & 1 \\ 11 & 11 & germany & 0 & 1 & 0 & 1 \\ 12 & 11 & morocco & 0 & 1 & 0 & 1 \\ 13 & 11 & philippines & 0 & 1 & 0 & 1 \\ 14 & 11 & russia & 0 & 1 & 0 & 1 \\ 15 & 16 & mexico & 0 & 0 & 4 & 4 \\ 16 & 17 & australia & 0 & 0 & 3 & 3 \\ 17 & 18 & croatia & 0 & 0 & 2 & 2 \\ 18 & 18 & united states & 0 & 0 & 2 & 2 \\ 19 & 20 & ecuador & 0 & 0 & 1 & 1 \\ 20 & 20 & saudi arabia & 0 & 0 & 1 & 1 \\ 21 & 20 & thailand & 0 & 0 & 1 & 1 \\ 22 & 20 & yugoslavia & 0 & 0 & 1 & 1 \\ 23 & total & total & 16 & 16 & 32 & 64 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & nation \\ \midrule 0 & south korea \\ 1 & chinese taipei \\ 2 & spain \\ 3 & egypt \\ 4 & total \\ \bottomrule \end{tabular}

select nation where gold > 0 order by gold limit 1 <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>south korea</td> <td>11</td> <td>1</td> <td>2</td> <td>14</td> </tr> <tr> <th>1</th> <td>2</td> <td>chinese taipei</td> <td>2</td> <td>2</td> <td>5</td> <td>9</td> </tr> <tr> <th>2</th> <td>3</td> <td>spain</td> <td>2</td> <td>0</td> <td>4</td> <td>6</td> </tr> <tr> <th>3</th> <td>4</td> <td>egypt</td> <td>1</td> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <th>4</th> <td>5</td> <td>france</td> <td>0</td> <td>2</td> <td>1</td> <td>3</td> </tr> <tr> <th>5</th> <td>5</td> <td>iran</td> <td>0</td> <td>2</td> <td>1</td> <td>3</td> </tr> <tr> <th>6</th> <td>7</td> <td>china</td> <td>0</td> <td>1</td> <td>1</td> <td>2</td> </tr> <tr> <th>7</th> <td>7</td> <td>cuba</td> <td>0</td> <td>1</td> <td>1</td> <td>2</td> </tr> <tr> <th>8</th> <td>7</td> <td>greece</td> <td>0</td> <td>1</td> <td>1</td> <td>2</td> </tr> <tr> <th>9</th> <td>7</td> <td>turkey</td> <td>0</td> <td>1</td> <td>1</td> <td>2</td> </tr> <tr> <th>10</th> <td>11</td> <td>canada</td> <td>0</td> <td>1</td> <td>0</td> <td>1</td> </tr> <tr> <th>11</th> <td>11</td> <td>germany</td> <td>0</td> <td>1</td> <td>0</td> <td>1</td> </tr> <tr> <th>12</th> <td>11</td> <td>morocco</td> <td>0</td> <td>1</td> <td>0</td> <td>1</td> </tr> <tr> <th>13</th> <td>11</td> <td>philippines</td> <td>0</td> <td>1</td> <td>0</td> <td>1</td> </tr> <tr> <th>14</th> <td>11</td> <td>russia</td> <td>0</td> <td>1</td> <td>0</td> <td>1</td> </tr> <tr> <th>15</th> <td>16</td> <td>mexico</td> <td>0</td> <td>0</td> <td>4</td> <td>4</td> </tr> <tr> <th>16</th> <td>17</td> <td>australia</td> <td>0</td> <td>0</td> <td>3</td> <td>3</td> </tr> <tr> <th>17</th> <td>18</td> <td>croatia</td> <td>0</td> <td>0</td> <td>2</td> <td>2</td> </tr> <tr> <th>18</th> <td>18</td> <td>united states</td> <td>0</td> <td>0</td> <td>2</td> <td>2</td> </tr> <tr> <th>19</th> <td>20</td> <td>ecuador</td> <td>0</td> <td>0</td> <td>1</td> <td>1</td> </tr> <tr> <th>20</th> <td>20</td> <td>saudi arabia</td> <td>0</td> <td>0</td> <td>1</td> <td>1</td> </tr> <tr> <th>21</th> <td>20</td> <td>thailand</td> <td>0</td> <td>0</td> <td>1</td> <td>1</td> </tr> <tr> <th>22</th> <td>20</td> <td>yugoslavia</td> <td>0</td> <td>0</td> <td>1</td> <td>1</td> </tr> <tr> <th>23</th> <td>total</td> <td>total</td> <td>16</td> <td>16</td> <td>32</td> <td>64</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>south korea</td> </tr> <tr> <th>1</th> <td>chinese taipei</td> </tr> <tr> <th>2</th> <td>spain</td> </tr> <tr> <th>3</th> <td>egypt</td> </tr> <tr> <th>4</th> <td>total</td> </tr> </tbody> </table>

select nation where gold > 0 order by gold limit 1 <table_name> : table | | rank | nation | gold | silver | bronze | total | |---:|:-------|:---------------|-------:|---------:|---------:|--------:| | 0 | 1 | south korea | 11 | 1 | 2 | 14 | | 1 | 2 | chinese taipei | 2 | 2 | 5 | 9 | | 2 | 3 | spain | 2 | 0 | 4 | 6 | | 3 | 4 | egypt | 1 | 0 | 0 | 1 | | 4 | 5 | france | 0 | 2 | 1 | 3 | | 5 | 5 | iran | 0 | 2 | 1 | 3 | | 6 | 7 | china | 0 | 1 | 1 | 2 | | 7 | 7 | cuba | 0 | 1 | 1 | 2 | | 8 | 7 | greece | 0 | 1 | 1 | 2 | | 9 | 7 | turkey | 0 | 1 | 1 | 2 | | 10 | 11 | canada | 0 | 1 | 0 | 1 | | 11 | 11 | germany | 0 | 1 | 0 | 1 | | 12 | 11 | morocco | 0 | 1 | 0 | 1 | | 13 | 11 | philippines | 0 | 1 | 0 | 1 | | 14 | 11 | russia | 0 | 1 | 0 | 1 | | 15 | 16 | mexico | 0 | 0 | 4 | 4 | | 16 | 17 | australia | 0 | 0 | 3 | 3 | | 17 | 18 | croatia | 0 | 0 | 2 | 2 | | 18 | 18 | united states | 0 | 0 | 2 | 2 | | 19 | 20 | ecuador | 0 | 0 | 1 | 1 | | 20 | 20 | saudi arabia | 0 | 0 | 1 | 1 | | 21 | 20 | thailand | 0 | 0 | 1 | 1 | | 22 | 20 | yugoslavia | 0 | 0 | 1 | 1 | | 23 | total | total | 16 | 16 | 32 | 64 |

| | nation | |---:|:---------------| | 0 | south korea | | 1 | chinese taipei | | 2 | spain | | 3 | egypt | | 4 | total |

select names of districts - capital where s.no. = 5 col : s.no. | name of kingdom | name of king | no. of villages | capital | names of districts row 1 : 1 | sihag | chokha singh | 150 | suin | rawatsar, baramsar, purabsar dandusar, gandaisi row 2 : 2 | beniwal | raisal singh | 150 | rasalana | bhukarka, sanduri, manoharpur, kooi, bae row 3 : 3 | johiya | sher singh | 600 | bhurupal | jaitpur, kumanu, mahajan, peepasar, udasar row 4 : 4 | punia | kanha singh | 300 | luddi | bhadra, ajitpura, sidhmukh, rajgarh, dadrewa, sankhoo row 5 : 5 | saharan | pula singh | 300 | bhadang | khejra, phoglo, buchawas, sui, badnu, sirsila row 6 : 6 | godara | pandu singh | 700 | shekhsar | shekhsar, pundrasar, gusainsar (bada), gharsisar, garibdesar, rungaysar, kalu row 7 : 7 | kaswan | kanwarpal singh | 400 | sidhmukh | none

col : "names of districts - capital" row 1 : names of districts - capital

select names of districts - capital where s.no. = 5 <table_name> : table \begin{tabular}{lllllll} \toprule & s.no. & name of kingdom & name of king & no. of villages & capital & names of districts \\ \midrule 0 & 1 & sihag & chokha singh & 150 & suin & rawatsar, baramsar, purabsar dandusar, gandaisi \\ 1 & 2 & beniwal & raisal singh & 150 & rasalana & bhukarka, sanduri, manoharpur, kooi, bae \\ 2 & 3 & johiya & sher singh & 600 & bhurupal & jaitpur, kumanu, mahajan, peepasar, udasar \\ 3 & 4 & punia & kanha singh & 300 & luddi & bhadra, ajitpura, sidhmukh, rajgarh, dadrewa, sankhoo \\ 4 & 5 & saharan & pula singh & 300 & bhadang & khejra, phoglo, buchawas, sui, badnu, sirsila \\ 5 & 6 & godara & pandu singh & 700 & shekhsar & shekhsar, pundrasar, gusainsar (bada), gharsisar, garibdesar, rungaysar, kalu \\ 6 & 7 & kaswan & kanwarpal singh & 400 & sidhmukh & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & "names of districts - capital" \\ \midrule 0 & names of districts - capital \\ \bottomrule \end{tabular}

select names of districts - capital where s.no. = 5 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>s.no.</th> <th>name of kingdom</th> <th>name of king</th> <th>no. of villages</th> <th>capital</th> <th>names of districts</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>sihag</td> <td>chokha singh</td> <td>150</td> <td>suin</td> <td>rawatsar, baramsar, purabsar dandusar, gandaisi</td> </tr> <tr> <th>1</th> <td>2</td> <td>beniwal</td> <td>raisal singh</td> <td>150</td> <td>rasalana</td> <td>bhukarka, sanduri, manoharpur, kooi, bae</td> </tr> <tr> <th>2</th> <td>3</td> <td>johiya</td> <td>sher singh</td> <td>600</td> <td>bhurupal</td> <td>jaitpur, kumanu, mahajan, peepasar, udasar</td> </tr> <tr> <th>3</th> <td>4</td> <td>punia</td> <td>kanha singh</td> <td>300</td> <td>luddi</td> <td>bhadra, ajitpura, sidhmukh, rajgarh, dadrewa, sankhoo</td> </tr> <tr> <th>4</th> <td>5</td> <td>saharan</td> <td>pula singh</td> <td>300</td> <td>bhadang</td> <td>khejra, phoglo, buchawas, sui, badnu, sirsila</td> </tr> <tr> <th>5</th> <td>6</td> <td>godara</td> <td>pandu singh</td> <td>700</td> <td>shekhsar</td> <td>shekhsar, pundrasar, gusainsar (bada), gharsisar, garibdesar, rungaysar, kalu</td> </tr> <tr> <th>6</th> <td>7</td> <td>kaswan</td> <td>kanwarpal singh</td> <td>400</td> <td>sidhmukh</td> <td>none</td> </tr> </tbody> </table>

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

select names of districts - capital where s.no. = 5 <table_name> : table | | s.no. | name of kingdom | name of king | no. of villages | capital | names of districts | |---:|--------:|:------------------|:----------------|------------------:|:----------|:------------------------------------------------------------------------------| | 0 | 1 | sihag | chokha singh | 150 | suin | rawatsar, baramsar, purabsar dandusar, gandaisi | | 1 | 2 | beniwal | raisal singh | 150 | rasalana | bhukarka, sanduri, manoharpur, kooi, bae | | 2 | 3 | johiya | sher singh | 600 | bhurupal | jaitpur, kumanu, mahajan, peepasar, udasar | | 3 | 4 | punia | kanha singh | 300 | luddi | bhadra, ajitpura, sidhmukh, rajgarh, dadrewa, sankhoo | | 4 | 5 | saharan | pula singh | 300 | bhadang | khejra, phoglo, buchawas, sui, badnu, sirsila | | 5 | 6 | godara | pandu singh | 700 | shekhsar | shekhsar, pundrasar, gusainsar (bada), gharsisar, garibdesar, rungaysar, kalu | | 6 | 7 | kaswan | kanwarpal singh | 400 | sidhmukh | none |

| | "names of districts - capital" | |---:|:---------------------------------| | 0 | names of districts - capital |

select state/country where yacht = 'afr midnight rambler' col : position | sail number | yacht | state/country | yacht type | loa (metres) | skipper | elapsed time d:hh:mm:ss row 1 : 1 | us17 | sayonara | usa | farr ilc maxi | 24.13 | larry ellison | 2:19:03:32 row 2 : 2 | c1 | brindabella | nsw | jutson 79 | 24.07 | george snow | 2:21:55:06 row 3 : 3 | yc1000 | ausmaid | sa | farr 47 | 14.24 | kevan pearce | 3:06:02:29 row 4 : 4 | aus70 | ragamuffin | nsw | farr 50 | 15.15 | syd fischer | 3:06:11:29 row 5 : 5 | cok1 | nokia | ci | farr ketch maxi | 25.2 | david witt | 3:09:19:00 row 6 : 6 | sm1 | fudge | vic | elliot 56 | 17.07 | peter hansen | 3:11:00:26 row 7 : 7 | 6606 | quest | nsw | nelson marek 46 | 14.12 | bob steel | 3:14:41:28 row 8 : 8 | 9090 | industrial quest | qld | nelson marek 43 | 13.11 | kevin miller | 3:14:58:46 row 9 : 9 | 4826 | aspect computing | nsw | radford 16.5 sloop | 16.5 | david pescud | 3:15:28:24 row 10 : 10 | 8338 | afr midnight rambler | nsw | hick 35 | 10.66 | ed psaltis bob thomas | 3:16:04:40

col : count ( "state/country" ) row 1 : 1

select state/country where yacht = 'afr midnight rambler' <table_name> : table \begin{tabular}{lllllllll} \toprule & position & sail number & yacht & state/country & yacht type & loa (metres) & skipper & elapsed time d:hh:mm:ss \\ \midrule 0 & 1 & us17 & sayonara & usa & farr ilc maxi & 24.13 & larry ellison & 2:19:03:32 \\ 1 & 2 & c1 & brindabella & nsw & jutson 79 & 24.07 & george snow & 2:21:55:06 \\ 2 & 3 & yc1000 & ausmaid & sa & farr 47 & 14.24 & kevan pearce & 3:06:02:29 \\ 3 & 4 & aus70 & ragamuffin & nsw & farr 50 & 15.15 & syd fischer & 3:06:11:29 \\ 4 & 5 & cok1 & nokia & ci & farr ketch maxi & 25.2 & david witt & 3:09:19:00 \\ 5 & 6 & sm1 & fudge & vic & elliot 56 & 17.07 & peter hansen & 3:11:00:26 \\ 6 & 7 & 6606 & quest & nsw & nelson marek 46 & 14.12 & bob steel & 3:14:41:28 \\ 7 & 8 & 9090 & industrial quest & qld & nelson marek 43 & 13.11 & kevin miller & 3:14:58:46 \\ 8 & 9 & 4826 & aspect computing & nsw & radford 16.5 sloop & 16.5 & david pescud & 3:15:28:24 \\ 9 & 10 & 8338 & afr midnight rambler & nsw & hick 35 & 10.66 & ed psaltis bob thomas & 3:16:04:40 \\ \bottomrule \end{tabular}

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

select state/country where yacht = 'afr midnight rambler' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>position</th> <th>sail number</th> <th>yacht</th> <th>state/country</th> <th>yacht type</th> <th>loa (metres)</th> <th>skipper</th> <th>elapsed time d:hh:mm:ss</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>us17</td> <td>sayonara</td> <td>usa</td> <td>farr ilc maxi</td> <td>24.13</td> <td>larry ellison</td> <td>2:19:03:32</td> </tr> <tr> <th>1</th> <td>2</td> <td>c1</td> <td>brindabella</td> <td>nsw</td> <td>jutson 79</td> <td>24.07</td> <td>george snow</td> <td>2:21:55:06</td> </tr> <tr> <th>2</th> <td>3</td> <td>yc1000</td> <td>ausmaid</td> <td>sa</td> <td>farr 47</td> <td>14.24</td> <td>kevan pearce</td> <td>3:06:02:29</td> </tr> <tr> <th>3</th> <td>4</td> <td>aus70</td> <td>ragamuffin</td> <td>nsw</td> <td>farr 50</td> <td>15.15</td> <td>syd fischer</td> <td>3:06:11:29</td> </tr> <tr> <th>4</th> <td>5</td> <td>cok1</td> <td>nokia</td> <td>ci</td> <td>farr ketch maxi</td> <td>25.2</td> <td>david witt</td> <td>3:09:19:00</td> </tr> <tr> <th>5</th> <td>6</td> <td>sm1</td> <td>fudge</td> <td>vic</td> <td>elliot 56</td> <td>17.07</td> <td>peter hansen</td> <td>3:11:00:26</td> </tr> <tr> <th>6</th> <td>7</td> <td>6606</td> <td>quest</td> <td>nsw</td> <td>nelson marek 46</td> <td>14.12</td> <td>bob steel</td> <td>3:14:41:28</td> </tr> <tr> <th>7</th> <td>8</td> <td>9090</td> <td>industrial quest</td> <td>qld</td> <td>nelson marek 43</td> <td>13.11</td> <td>kevin miller</td> <td>3:14:58:46</td> </tr> <tr> <th>8</th> <td>9</td> <td>4826</td> <td>aspect computing</td> <td>nsw</td> <td>radford 16.5 sloop</td> <td>16.5</td> <td>david pescud</td> <td>3:15:28:24</td> </tr> <tr> <th>9</th> <td>10</td> <td>8338</td> <td>afr midnight rambler</td> <td>nsw</td> <td>hick 35</td> <td>10.66</td> <td>ed psaltis bob thomas</td> <td>3:16:04:40</td> </tr> </tbody> </table>

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

select state/country where yacht = 'afr midnight rambler' <table_name> : table | | position | sail number | yacht | state/country | yacht type | loa (metres) | skipper | elapsed time d:hh:mm:ss | |---:|-----------:|:--------------|:---------------------|:----------------|:-------------------|---------------:|:----------------------|:--------------------------| | 0 | 1 | us17 | sayonara | usa | farr ilc maxi | 24.13 | larry ellison | 2:19:03:32 | | 1 | 2 | c1 | brindabella | nsw | jutson 79 | 24.07 | george snow | 2:21:55:06 | | 2 | 3 | yc1000 | ausmaid | sa | farr 47 | 14.24 | kevan pearce | 3:06:02:29 | | 3 | 4 | aus70 | ragamuffin | nsw | farr 50 | 15.15 | syd fischer | 3:06:11:29 | | 4 | 5 | cok1 | nokia | ci | farr ketch maxi | 25.2 | david witt | 3:09:19:00 | | 5 | 6 | sm1 | fudge | vic | elliot 56 | 17.07 | peter hansen | 3:11:00:26 | | 6 | 7 | 6606 | quest | nsw | nelson marek 46 | 14.12 | bob steel | 3:14:41:28 | | 7 | 8 | 9090 | industrial quest | qld | nelson marek 43 | 13.11 | kevin miller | 3:14:58:46 | | 8 | 9 | 4826 | aspect computing | nsw | radford 16.5 sloop | 16.5 | david pescud | 3:15:28:24 | | 9 | 10 | 8338 | afr midnight rambler | nsw | hick 35 | 10.66 | ed psaltis bob thomas | 3:16:04:40 |

| | count ( "state/country" ) | |---:|----------------------------:| | 0 | 1 |

select count ( * ) where opponent = 'detroit lions' col : week | date | opponent | results final score | results team record | venue | attendance row 1 : 1 | september 18 | washington redskins | l 24-21 | 0-1 | metropolitan stadium | 47,900 row 2 : 2 | september 24 | at detroit lions | w 34-10 | 1-1 | tiger stadium | 54,418 row 3 : 3 | october 1 | miami dolphins | l 16-14 | 1-2 | metropolitan stadium | 47,900 row 4 : 4 | october 8 | st. louis cardinals | l 19-17 | 1-3 | metropolitan stadium | 49,687 row 5 : 5 | october 15 | at denver broncos | w 23-20 | 2-3 | mile high stadium | 51,656 row 6 : 6 | october 23 | at chicago bears | l 13-10 | 2-4 | soldier field | 55,701 row 7 : 7 | october 29 | at green bay packers | w 27-13 | 3-4 | lambeau field | 56,263 row 8 : 8 | november 5 | new orleans saints | w 37-6 | 4-4 | metropolitan stadium | 49,784 row 9 : 9 | november 12 | detroit lions | w 16-14 | 5-4 | metropolitan stadium | 49,784 row 10 : 10 | november 19 | at los angeles rams | w 45-41 | 6-4 | los angeles memorial coliseum | 77,982 row 11 : 11 | november 26 | at pittsburgh steelers | l 23-10 | 6-5 | three rivers stadium | 50,348 row 12 : 12 | december 3 | chicago bears | w 23-10 | 7-5 | metropolitan stadium | 49,784 row 13 : 13 | december 10 | green bay packers | l 23-7 | 7-6 | metropolitan stadium | 49,784 row 14 : 14 | december 16 | at san francisco 49ers | l 20-17 | 7-7 | candlestick park | 61,214

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

select count ( * ) where opponent = 'detroit lions' <table_name> : table \begin{tabular}{llllllll} \toprule & week & date & opponent & results final score & results team record & venue & attendance \\ \midrule 0 & 1 & september 18 & washington redskins & l 24-21 & 0-1 & metropolitan stadium & 47,900 \\ 1 & 2 & september 24 & at detroit lions & w 34-10 & 1-1 & tiger stadium & 54,418 \\ 2 & 3 & october 1 & miami dolphins & l 16-14 & 1-2 & metropolitan stadium & 47,900 \\ 3 & 4 & october 8 & st. louis cardinals & l 19-17 & 1-3 & metropolitan stadium & 49,687 \\ 4 & 5 & october 15 & at denver broncos & w 23-20 & 2-3 & mile high stadium & 51,656 \\ 5 & 6 & october 23 & at chicago bears & l 13-10 & 2-4 & soldier field & 55,701 \\ 6 & 7 & october 29 & at green bay packers & w 27-13 & 3-4 & lambeau field & 56,263 \\ 7 & 8 & november 5 & new orleans saints & w 37-6 & 4-4 & metropolitan stadium & 49,784 \\ 8 & 9 & november 12 & detroit lions & w 16-14 & 5-4 & metropolitan stadium & 49,784 \\ 9 & 10 & november 19 & at los angeles rams & w 45-41 & 6-4 & los angeles memorial coliseum & 77,982 \\ 10 & 11 & november 26 & at pittsburgh steelers & l 23-10 & 6-5 & three rivers stadium & 50,348 \\ 11 & 12 & december 3 & chicago bears & w 23-10 & 7-5 & metropolitan stadium & 49,784 \\ 12 & 13 & december 10 & green bay packers & l 23-7 & 7-6 & metropolitan stadium & 49,784 \\ 13 & 14 & december 16 & at san francisco 49ers & l 20-17 & 7-7 & candlestick park & 61,214 \\ \bottomrule \end{tabular}

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

select count ( * ) where opponent = 'detroit lions' <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>results final score</th> <th>results team record</th> <th>venue</th> <th>attendance</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>september 18</td> <td>washington redskins</td> <td>l 24-21</td> <td>0-1</td> <td>metropolitan stadium</td> <td>47,900</td> </tr> <tr> <th>1</th> <td>2</td> <td>september 24</td> <td>at detroit lions</td> <td>w 34-10</td> <td>1-1</td> <td>tiger stadium</td> <td>54,418</td> </tr> <tr> <th>2</th> <td>3</td> <td>october 1</td> <td>miami dolphins</td> <td>l 16-14</td> <td>1-2</td> <td>metropolitan stadium</td> <td>47,900</td> </tr> <tr> <th>3</th> <td>4</td> <td>october 8</td> <td>st. louis cardinals</td> <td>l 19-17</td> <td>1-3</td> <td>metropolitan stadium</td> <td>49,687</td> </tr> <tr> <th>4</th> <td>5</td> <td>october 15</td> <td>at denver broncos</td> <td>w 23-20</td> <td>2-3</td> <td>mile high stadium</td> <td>51,656</td> </tr> <tr> <th>5</th> <td>6</td> <td>october 23</td> <td>at chicago bears</td> <td>l 13-10</td> <td>2-4</td> <td>soldier field</td> <td>55,701</td> </tr> <tr> <th>6</th> <td>7</td> <td>october 29</td> <td>at green bay packers</td> <td>w 27-13</td> <td>3-4</td> <td>lambeau field</td> <td>56,263</td> </tr> <tr> <th>7</th> <td>8</td> <td>november 5</td> <td>new orleans saints</td> <td>w 37-6</td> <td>4-4</td> <td>metropolitan stadium</td> <td>49,784</td> </tr> <tr> <th>8</th> <td>9</td> <td>november 12</td> <td>detroit lions</td> <td>w 16-14</td> <td>5-4</td> <td>metropolitan stadium</td> <td>49,784</td> </tr> <tr> <th>9</th> <td>10</td> <td>november 19</td> <td>at los angeles rams</td> <td>w 45-41</td> <td>6-4</td> <td>los angeles memorial coliseum</td> <td>77,982</td> </tr> <tr> <th>10</th> <td>11</td> <td>november 26</td> <td>at pittsburgh steelers</td> <td>l 23-10</td> <td>6-5</td> <td>three rivers stadium</td> <td>50,348</td> </tr> <tr> <th>11</th> <td>12</td> <td>december 3</td> <td>chicago bears</td> <td>w 23-10</td> <td>7-5</td> <td>metropolitan stadium</td> <td>49,784</td> </tr> <tr> <th>12</th> <td>13</td> <td>december 10</td> <td>green bay packers</td> <td>l 23-7</td> <td>7-6</td> <td>metropolitan stadium</td> <td>49,784</td> </tr> <tr> <th>13</th> <td>14</td> <td>december 16</td> <td>at san francisco 49ers</td> <td>l 20-17</td> <td>7-7</td> <td>candlestick park</td> <td>61,214</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 = 'detroit lions' <table_name> : table | | week | date | opponent | results final score | results team record | venue | attendance | |---:|-------:|:-------------|:-----------------------|:----------------------|:----------------------|:------------------------------|:-------------| | 0 | 1 | september 18 | washington redskins | l 24-21 | 0-1 | metropolitan stadium | 47,900 | | 1 | 2 | september 24 | at detroit lions | w 34-10 | 1-1 | tiger stadium | 54,418 | | 2 | 3 | october 1 | miami dolphins | l 16-14 | 1-2 | metropolitan stadium | 47,900 | | 3 | 4 | october 8 | st. louis cardinals | l 19-17 | 1-3 | metropolitan stadium | 49,687 | | 4 | 5 | october 15 | at denver broncos | w 23-20 | 2-3 | mile high stadium | 51,656 | | 5 | 6 | october 23 | at chicago bears | l 13-10 | 2-4 | soldier field | 55,701 | | 6 | 7 | october 29 | at green bay packers | w 27-13 | 3-4 | lambeau field | 56,263 | | 7 | 8 | november 5 | new orleans saints | w 37-6 | 4-4 | metropolitan stadium | 49,784 | | 8 | 9 | november 12 | detroit lions | w 16-14 | 5-4 | metropolitan stadium | 49,784 | | 9 | 10 | november 19 | at los angeles rams | w 45-41 | 6-4 | los angeles memorial coliseum | 77,982 | | 10 | 11 | november 26 | at pittsburgh steelers | l 23-10 | 6-5 | three rivers stadium | 50,348 | | 11 | 12 | december 3 | chicago bears | w 23-10 | 7-5 | metropolitan stadium | 49,784 | | 12 | 13 | december 10 | green bay packers | l 23-7 | 7-6 | metropolitan stadium | 49,784 | | 13 | 14 | december 16 | at san francisco 49ers | l 20-17 | 7-7 | candlestick park | 61,214 |

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

select notes where times < 2 order by times desc limit 1 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 : notes row 1 : beat dick togo for the championship row 2 : none row 3 : none row 4 : none row 5 : none row 6 : none row 7 : none row 8 : none row 9 : none row 10 : none row 11 : none row 12 : none row 13 : none row 14 : none row 15 : none row 16 : asian cougar renamed himself kuuga during his reign row 17 : none row 18 : none row 19 : none row 20 : none row 21 : none

select notes where times < 2 order by times desc limit 1 <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 & notes \\ \midrule 0 & beat dick togo for the championship \\ 1 & none \\ 2 & none \\ 3 & none \\ 4 & none \\ 5 & none \\ 6 & none \\ 7 & none \\ 8 & none \\ 9 & none \\ 10 & none \\ 11 & none \\ 12 & none \\ 13 & none \\ 14 & none \\ 15 & asian cougar renamed himself kuuga during his reign \\ 16 & none \\ 17 & none \\ 18 & none \\ 19 & none \\ 20 & none \\ \bottomrule \end{tabular}

select notes where times < 2 order by times desc limit 1 <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>notes</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>beat dick togo for the championship</td> </tr> <tr> <th>1</th> <td>none</td> </tr> <tr> <th>2</th> <td>none</td> </tr> <tr> <th>3</th> <td>none</td> </tr> <tr> <th>4</th> <td>none</td> </tr> <tr> <th>5</th> <td>none</td> </tr> <tr> <th>6</th> <td>none</td> </tr> <tr> <th>7</th> <td>none</td> </tr> <tr> <th>8</th> <td>none</td> </tr> <tr> <th>9</th> <td>none</td> </tr> <tr> <th>10</th> <td>none</td> </tr> <tr> <th>11</th> <td>none</td> </tr> <tr> <th>12</th> <td>none</td> </tr> <tr> <th>13</th> <td>none</td> </tr> <tr> <th>14</th> <td>none</td> </tr> <tr> <th>15</th> <td>asian cougar renamed himself kuuga during his reign</td> </tr> <tr> <th>16</th> <td>none</td> </tr> <tr> <th>17</th> <td>none</td> </tr> <tr> <th>18</th> <td>none</td> </tr> <tr> <th>19</th> <td>none</td> </tr> <tr> <th>20</th> <td>none</td> </tr> </tbody> </table>

select notes where times < 2 order by times desc limit 1 <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 |

| | notes | |---:|:----------------------------------------------------| | 0 | beat dick togo for the championship | | 1 | none | | 2 | none | | 3 | none | | 4 | none | | 5 | none | | 6 | none | | 7 | none | | 8 | none | | 9 | none | | 10 | none | | 11 | none | | 12 | none | | 13 | none | | 14 | none | | 15 | asian cougar renamed himself kuuga during his reign | | 16 | none | | 17 | none | | 18 | none | | 19 | none | | 20 | none |

select count ( nation ) where total = 2 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 : count ( "nation" ) row 1 : 8

select count ( nation ) where total = 2 <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 & count ( "nation" ) \\ \midrule 0 & 8 \\ \bottomrule \end{tabular}

select count ( nation ) where total = 2 <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>count ( "nation" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>8</td> </tr> </tbody> </table>

select count ( nation ) where total = 2 <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 |

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

select count ( * ) where location = 'naga' and location = 652 col : branding | callsign | frequency | power (kw) | location row 1 : mom's radio 97.9 laoag | dwsn-fm | 97.9 mhz | 5 kw | laoag row 2 : mom's radio 95.9 naga | dzrb-fm | 95.9 mhz | 10 kw | naga row 3 : mom's radio 90.3 bacolod | dycp-fm | 90.3 mhz | 5 kw | bacolod row 4 : dyap 88.3 cebu | dyap-fm | 88.3 mhz | 5 kw | cebu row 5 : mom's radio 101.5 tacloban | dyjp-fm | 101.5 mhz | 2.5 kw | tacloban row 6 : mom's radio 101.9 zamboanga | dxjp-fm | 101.9 mhz | 5 kw | zamboanga row 7 : dxss 97.9 davao | dxss | 97.9 mhz | 10 kw | davao

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

select count ( * ) where location = 'naga' and location = 652 <table_name> : table \begin{tabular}{llllll} \toprule & branding & callsign & frequency & power (kw) & location \\ \midrule 0 & mom's radio 97.9 laoag & dwsn-fm & 97.9 mhz & 5 kw & laoag \\ 1 & mom's radio 95.9 naga & dzrb-fm & 95.9 mhz & 10 kw & naga \\ 2 & mom's radio 90.3 bacolod & dycp-fm & 90.3 mhz & 5 kw & bacolod \\ 3 & dyap 88.3 cebu & dyap-fm & 88.3 mhz & 5 kw & cebu \\ 4 & mom's radio 101.5 tacloban & dyjp-fm & 101.5 mhz & 2.5 kw & tacloban \\ 5 & mom's radio 101.9 zamboanga & dxjp-fm & 101.9 mhz & 5 kw & zamboanga \\ 6 & dxss 97.9 davao & dxss & 97.9 mhz & 10 kw & davao \\ \bottomrule \end{tabular}

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

select count ( * ) where location = 'naga' and location = 652 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>branding</th> <th>callsign</th> <th>frequency</th> <th>power (kw)</th> <th>location</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>mom's radio 97.9 laoag</td> <td>dwsn-fm</td> <td>97.9 mhz</td> <td>5 kw</td> <td>laoag</td> </tr> <tr> <th>1</th> <td>mom's radio 95.9 naga</td> <td>dzrb-fm</td> <td>95.9 mhz</td> <td>10 kw</td> <td>naga</td> </tr> <tr> <th>2</th> <td>mom's radio 90.3 bacolod</td> <td>dycp-fm</td> <td>90.3 mhz</td> <td>5 kw</td> <td>bacolod</td> </tr> <tr> <th>3</th> <td>dyap 88.3 cebu</td> <td>dyap-fm</td> <td>88.3 mhz</td> <td>5 kw</td> <td>cebu</td> </tr> <tr> <th>4</th> <td>mom's radio 101.5 tacloban</td> <td>dyjp-fm</td> <td>101.5 mhz</td> <td>2.5 kw</td> <td>tacloban</td> </tr> <tr> <th>5</th> <td>mom's radio 101.9 zamboanga</td> <td>dxjp-fm</td> <td>101.9 mhz</td> <td>5 kw</td> <td>zamboanga</td> </tr> <tr> <th>6</th> <td>dxss 97.9 davao</td> <td>dxss</td> <td>97.9 mhz</td> <td>10 kw</td> <td>davao</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 location = 'naga' and location = 652 <table_name> : table | | branding | callsign | frequency | power (kw) | location | |---:|:----------------------------|:-----------|:------------|:-------------|:-----------| | 0 | mom's radio 97.9 laoag | dwsn-fm | 97.9 mhz | 5 kw | laoag | | 1 | mom's radio 95.9 naga | dzrb-fm | 95.9 mhz | 10 kw | naga | | 2 | mom's radio 90.3 bacolod | dycp-fm | 90.3 mhz | 5 kw | bacolod | | 3 | dyap 88.3 cebu | dyap-fm | 88.3 mhz | 5 kw | cebu | | 4 | mom's radio 101.5 tacloban | dyjp-fm | 101.5 mhz | 2.5 kw | tacloban | | 5 | mom's radio 101.9 zamboanga | dxjp-fm | 101.9 mhz | 5 kw | zamboanga | | 6 | dxss 97.9 davao | dxss | 97.9 mhz | 10 kw | davao |

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

select count ( * ) where network = 'mixed news and classical & roots, rock, and jazz' col : location | frequency | call sign | network | call sign meaning row 1 : beach | 91.9 | k220fi (kdpr) | news and classical | none row 2 : bismarck | 90.5 | kcnd | news and classical | capital of north dakota row 3 : bowman | 91.9 | k220fj (kdpr) | news and classical | none row 4 : crosby | 91.9 | k220ff (kppw) | news and classical | none row 5 : devils lake | 91.7 | kppd | news and classical | prairie public radio devils lake row 6 : dickinson | 89.9 | kdpr | news and classical | dickinson public radio row 7 : fargo | 91.9 | kdsu | mixed news and classical & roots, rock, and jazz | north dakota state university row 8 : grand forks | 89.3 | kund-fm | news and classical | university of north dakota row 9 : grand forks | 90.7 | kfjm | roots, rock, and jazz | folk and jazz music row 10 : jamestown | 91.5 | kprj | news and classical | public radio jamestown row 11 : hettinger | 91.9 | k220fg (kdpr) | news and classical | none row 12 : lisbon | 98.3 | k252ez (kdsu) | mixed news and classical & roots, rock, and jazz | none row 13 : minot | 88.9 | kmpr | news and classical | minot public radio row 14 : plentywood, mt | 91.9 | k220fe (kppw) | news and classical | none row 15 : thief river falls, mn | 88.3 | k202bk (kund-fm) | news and classical | none row 16 : tioga | 91.9 | k220fh (kppw) | news and classical | none row 17 : wahpeton | 94.7 | k234bx (kdsu) | mixed news and classical & roots, rock, and jazz | none row 18 : williston | 89.5 | kppr | roots, rock, and jazz | prairie public radio row 19 : williston | 88.7 | kppw | news and classical | prairie public williston

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

select count ( * ) where network = 'mixed news and classical & roots, rock, and jazz' <table_name> : table \begin{tabular}{llllll} \toprule & location & frequency & call sign & network & call sign meaning \\ \midrule 0 & beach & 91.9 & k220fi (kdpr) & news and classical & none \\ 1 & bismarck & 90.5 & kcnd & news and classical & capital of north dakota \\ 2 & bowman & 91.9 & k220fj (kdpr) & news and classical & none \\ 3 & crosby & 91.9 & k220ff (kppw) & news and classical & none \\ 4 & devils lake & 91.7 & kppd & news and classical & prairie public radio devils lake \\ 5 & dickinson & 89.9 & kdpr & news and classical & dickinson public radio \\ 6 & fargo & 91.9 & kdsu & mixed news and classical & roots, rock, and jazz & north dakota state university \\ 7 & grand forks & 89.3 & kund-fm & news and classical & university of north dakota \\ 8 & grand forks & 90.7 & kfjm & roots, rock, and jazz & folk and jazz music \\ 9 & jamestown & 91.5 & kprj & news and classical & public radio jamestown \\ 10 & hettinger & 91.9 & k220fg (kdpr) & news and classical & none \\ 11 & lisbon & 98.3 & k252ez (kdsu) & mixed news and classical & roots, rock, and jazz & none \\ 12 & minot & 88.9 & kmpr & news and classical & minot public radio \\ 13 & plentywood, mt & 91.9 & k220fe (kppw) & news and classical & none \\ 14 & thief river falls, mn & 88.3 & k202bk (kund-fm) & news and classical & none \\ 15 & tioga & 91.9 & k220fh (kppw) & news and classical & none \\ 16 & wahpeton & 94.7 & k234bx (kdsu) & mixed news and classical & roots, rock, and jazz & none \\ 17 & williston & 89.5 & kppr & roots, rock, and jazz & prairie public radio \\ 18 & williston & 88.7 & kppw & news and classical & prairie public williston \\ \bottomrule \end{tabular}

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

select count ( * ) where network = 'mixed news and classical & roots, rock, and jazz' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>location</th> <th>frequency</th> <th>call sign</th> <th>network</th> <th>call sign meaning</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>beach</td> <td>91.9</td> <td>k220fi (kdpr)</td> <td>news and classical</td> <td>none</td> </tr> <tr> <th>1</th> <td>bismarck</td> <td>90.5</td> <td>kcnd</td> <td>news and classical</td> <td>capital of north dakota</td> </tr> <tr> <th>2</th> <td>bowman</td> <td>91.9</td> <td>k220fj (kdpr)</td> <td>news and classical</td> <td>none</td> </tr> <tr> <th>3</th> <td>crosby</td> <td>91.9</td> <td>k220ff (kppw)</td> <td>news and classical</td> <td>none</td> </tr> <tr> <th>4</th> <td>devils lake</td> <td>91.7</td> <td>kppd</td> <td>news and classical</td> <td>prairie public radio devils lake</td> </tr> <tr> <th>5</th> <td>dickinson</td> <td>89.9</td> <td>kdpr</td> <td>news and classical</td> <td>dickinson public radio</td> </tr> <tr> <th>6</th> <td>fargo</td> <td>91.9</td> <td>kdsu</td> <td>mixed news and classical &amp; roots, rock, and jazz</td> <td>north dakota state university</td> </tr> <tr> <th>7</th> <td>grand forks</td> <td>89.3</td> <td>kund-fm</td> <td>news and classical</td> <td>university of north dakota</td> </tr> <tr> <th>8</th> <td>grand forks</td> <td>90.7</td> <td>kfjm</td> <td>roots, rock, and jazz</td> <td>folk and jazz music</td> </tr> <tr> <th>9</th> <td>jamestown</td> <td>91.5</td> <td>kprj</td> <td>news and classical</td> <td>public radio jamestown</td> </tr> <tr> <th>10</th> <td>hettinger</td> <td>91.9</td> <td>k220fg (kdpr)</td> <td>news and classical</td> <td>none</td> </tr> <tr> <th>11</th> <td>lisbon</td> <td>98.3</td> <td>k252ez (kdsu)</td> <td>mixed news and classical &amp; roots, rock, and jazz</td> <td>none</td> </tr> <tr> <th>12</th> <td>minot</td> <td>88.9</td> <td>kmpr</td> <td>news and classical</td> <td>minot public radio</td> </tr> <tr> <th>13</th> <td>plentywood, mt</td> <td>91.9</td> <td>k220fe (kppw)</td> <td>news and classical</td> <td>none</td> </tr> <tr> <th>14</th> <td>thief river falls, mn</td> <td>88.3</td> <td>k202bk (kund-fm)</td> <td>news and classical</td> <td>none</td> </tr> <tr> <th>15</th> <td>tioga</td> <td>91.9</td> <td>k220fh (kppw)</td> <td>news and classical</td> <td>none</td> </tr> <tr> <th>16</th> <td>wahpeton</td> <td>94.7</td> <td>k234bx (kdsu)</td> <td>mixed news and classical &amp; roots, rock, and jazz</td> <td>none</td> </tr> <tr> <th>17</th> <td>williston</td> <td>89.5</td> <td>kppr</td> <td>roots, rock, and jazz</td> <td>prairie public radio</td> </tr> <tr> <th>18</th> <td>williston</td> <td>88.7</td> <td>kppw</td> <td>news and classical</td> <td>prairie public williston</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 network = 'mixed news and classical & roots, rock, and jazz' <table_name> : table | | location | frequency | call sign | network | call sign meaning | |---:|:----------------------|------------:|:-----------------|:-------------------------------------------------|:---------------------------------| | 0 | beach | 91.9 | k220fi (kdpr) | news and classical | none | | 1 | bismarck | 90.5 | kcnd | news and classical | capital of north dakota | | 2 | bowman | 91.9 | k220fj (kdpr) | news and classical | none | | 3 | crosby | 91.9 | k220ff (kppw) | news and classical | none | | 4 | devils lake | 91.7 | kppd | news and classical | prairie public radio devils lake | | 5 | dickinson | 89.9 | kdpr | news and classical | dickinson public radio | | 6 | fargo | 91.9 | kdsu | mixed news and classical & roots, rock, and jazz | north dakota state university | | 7 | grand forks | 89.3 | kund-fm | news and classical | university of north dakota | | 8 | grand forks | 90.7 | kfjm | roots, rock, and jazz | folk and jazz music | | 9 | jamestown | 91.5 | kprj | news and classical | public radio jamestown | | 10 | hettinger | 91.9 | k220fg (kdpr) | news and classical | none | | 11 | lisbon | 98.3 | k252ez (kdsu) | mixed news and classical & roots, rock, and jazz | none | | 12 | minot | 88.9 | kmpr | news and classical | minot public radio | | 13 | plentywood, mt | 91.9 | k220fe (kppw) | news and classical | none | | 14 | thief river falls, mn | 88.3 | k202bk (kund-fm) | news and classical | none | | 15 | tioga | 91.9 | k220fh (kppw) | news and classical | none | | 16 | wahpeton | 94.7 | k234bx (kdsu) | mixed news and classical & roots, rock, and jazz | none | | 17 | williston | 89.5 | kppr | roots, rock, and jazz | prairie public radio | | 18 | williston | 88.7 | kppw | news and classical | prairie public williston |

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

select count ( * ) where opponent = 'at washington redskins' col : week | date | opponent | result | game site | nfl recap | attendance row 1 : 1 | 1986-09-08 00:00:00 | new york giants | w 31-28 | texas stadium | | 59,804 row 2 : 2 | 1986-09-14 00:00:00 | at detroit lions | w 31-7 | pontiac silverdome | | 73,812 row 3 : 3 | 1986-09-21 00:00:00 | atlanta falcons | l 35-37 | texas stadium | | 62,880 row 4 : 4 | 1986-09-29 00:00:00 | at st. louis cardinals | w 31-7 | busch memorial stadium | | 49,077 row 5 : 5 | 1986-10-05 00:00:00 | at denver broncos | l 14-29 | mile high stadium | | 76,082 row 6 : 6 | 1986-10-12 00:00:00 | washington redskins | w 30-6 | texas stadium | | 63,264 row 7 : 7 | 1986-10-19 00:00:00 | at philadelphia eagles | w 17-14 | veterans stadium | | 68,572 row 8 : 8 | 1986-10-26 00:00:00 | st. louis cardinals | w 37-6 | texas stadium | | 60,756 row 9 : 9 | 1986-11-02 00:00:00 | at new york giants | l 14-17 | giants stadium | | 74,871 row 10 : 10 | 1986-11-09 00:00:00 | los angeles raiders | l 13-17 | texas stadium | | 61,706 row 11 : 11 | 1986-11-16 00:00:00 | at san diego chargers | w 24-21 | jack murphy stadium | | 55,622 row 12 : 12 | 1986-11-23 00:00:00 | at washington redskins | l 14-41 | rfk stadium | | 55,642 row 13 : 13 | 1986-11-27 00:00:00 | seattle seahawks | l 14-31 | texas stadium | | 58,020 row 14 : 14 | 1986-12-07 00:00:00 | at los angeles rams | l 10-29 | anaheim stadium | | 64,949 row 15 : 15 | 1986-12-14 00:00:00 | philadelphia eagles | l 21-23 | texas stadium | | 46,117 row 16 : 16 | 1986-12-21 00:00:00 | chicago bears | l 10-24 | texas stadium | | 57,256

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

select count ( * ) where opponent = 'at washington redskins' <table_name> : table \begin{tabular}{llllllll} \toprule & week & date & opponent & result & game site & nfl recap & attendance \\ \midrule 0 & 1 & 1986-09-08 00:00:00 & new york giants & w 31-28 & texas stadium & & 59,804 \\ 1 & 2 & 1986-09-14 00:00:00 & at detroit lions & w 31-7 & pontiac silverdome & & 73,812 \\ 2 & 3 & 1986-09-21 00:00:00 & atlanta falcons & l 35-37 & texas stadium & & 62,880 \\ 3 & 4 & 1986-09-29 00:00:00 & at st. louis cardinals & w 31-7 & busch memorial stadium & & 49,077 \\ 4 & 5 & 1986-10-05 00:00:00 & at denver broncos & l 14-29 & mile high stadium & & 76,082 \\ 5 & 6 & 1986-10-12 00:00:00 & washington redskins & w 30-6 & texas stadium & & 63,264 \\ 6 & 7 & 1986-10-19 00:00:00 & at philadelphia eagles & w 17-14 & veterans stadium & & 68,572 \\ 7 & 8 & 1986-10-26 00:00:00 & st. louis cardinals & w 37-6 & texas stadium & & 60,756 \\ 8 & 9 & 1986-11-02 00:00:00 & at new york giants & l 14-17 & giants stadium & & 74,871 \\ 9 & 10 & 1986-11-09 00:00:00 & los angeles raiders & l 13-17 & texas stadium & & 61,706 \\ 10 & 11 & 1986-11-16 00:00:00 & at san diego chargers & w 24-21 & jack murphy stadium & & 55,622 \\ 11 & 12 & 1986-11-23 00:00:00 & at washington redskins & l 14-41 & rfk stadium & & 55,642 \\ 12 & 13 & 1986-11-27 00:00:00 & seattle seahawks & l 14-31 & texas stadium & & 58,020 \\ 13 & 14 & 1986-12-07 00:00:00 & at los angeles rams & l 10-29 & anaheim stadium & & 64,949 \\ 14 & 15 & 1986-12-14 00:00:00 & philadelphia eagles & l 21-23 & texas stadium & & 46,117 \\ 15 & 16 & 1986-12-21 00:00:00 & chicago bears & l 10-24 & texas stadium & & 57,256 \\ \bottomrule \end{tabular}

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

select count ( * ) where opponent = 'at washington redskins' <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>result</th> <th>game site</th> <th>nfl recap</th> <th>attendance</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>1986-09-08 00:00:00</td> <td>new york giants</td> <td>w 31-28</td> <td>texas stadium</td> <td></td> <td>59,804</td> </tr> <tr> <th>1</th> <td>2</td> <td>1986-09-14 00:00:00</td> <td>at detroit lions</td> <td>w 31-7</td> <td>pontiac silverdome</td> <td></td> <td>73,812</td> </tr> <tr> <th>2</th> <td>3</td> <td>1986-09-21 00:00:00</td> <td>atlanta falcons</td> <td>l 35-37</td> <td>texas stadium</td> <td></td> <td>62,880</td> </tr> <tr> <th>3</th> <td>4</td> <td>1986-09-29 00:00:00</td> <td>at st. louis cardinals</td> <td>w 31-7</td> <td>busch memorial stadium</td> <td></td> <td>49,077</td> </tr> <tr> <th>4</th> <td>5</td> <td>1986-10-05 00:00:00</td> <td>at denver broncos</td> <td>l 14-29</td> <td>mile high stadium</td> <td></td> <td>76,082</td> </tr> <tr> <th>5</th> <td>6</td> <td>1986-10-12 00:00:00</td> <td>washington redskins</td> <td>w 30-6</td> <td>texas stadium</td> <td></td> <td>63,264</td> </tr> <tr> <th>6</th> <td>7</td> <td>1986-10-19 00:00:00</td> <td>at philadelphia eagles</td> <td>w 17-14</td> <td>veterans stadium</td> <td></td> <td>68,572</td> </tr> <tr> <th>7</th> <td>8</td> <td>1986-10-26 00:00:00</td> <td>st. louis cardinals</td> <td>w 37-6</td> <td>texas stadium</td> <td></td> <td>60,756</td> </tr> <tr> <th>8</th> <td>9</td> <td>1986-11-02 00:00:00</td> <td>at new york giants</td> <td>l 14-17</td> <td>giants stadium</td> <td></td> <td>74,871</td> </tr> <tr> <th>9</th> <td>10</td> <td>1986-11-09 00:00:00</td> <td>los angeles raiders</td> <td>l 13-17</td> <td>texas stadium</td> <td></td> <td>61,706</td> </tr> <tr> <th>10</th> <td>11</td> <td>1986-11-16 00:00:00</td> <td>at san diego chargers</td> <td>w 24-21</td> <td>jack murphy stadium</td> <td></td> <td>55,622</td> </tr> <tr> <th>11</th> <td>12</td> <td>1986-11-23 00:00:00</td> <td>at washington redskins</td> <td>l 14-41</td> <td>rfk stadium</td> <td></td> <td>55,642</td> </tr> <tr> <th>12</th> <td>13</td> <td>1986-11-27 00:00:00</td> <td>seattle seahawks</td> <td>l 14-31</td> <td>texas stadium</td> <td></td> <td>58,020</td> </tr> <tr> <th>13</th> <td>14</td> <td>1986-12-07 00:00:00</td> <td>at los angeles rams</td> <td>l 10-29</td> <td>anaheim stadium</td> <td></td> <td>64,949</td> </tr> <tr> <th>14</th> <td>15</td> <td>1986-12-14 00:00:00</td> <td>philadelphia eagles</td> <td>l 21-23</td> <td>texas stadium</td> <td></td> <td>46,117</td> </tr> <tr> <th>15</th> <td>16</td> <td>1986-12-21 00:00:00</td> <td>chicago bears</td> <td>l 10-24</td> <td>texas stadium</td> <td></td> <td>57,256</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 = 'at washington redskins' <table_name> : table | | week | date | opponent | result | game site | nfl recap | attendance | |---:|-------:|:--------------------|:-----------------------|:---------|:-----------------------|:------------|:-------------| | 0 | 1 | 1986-09-08 00:00:00 | new york giants | w 31-28 | texas stadium | | 59,804 | | 1 | 2 | 1986-09-14 00:00:00 | at detroit lions | w 31-7 | pontiac silverdome | | 73,812 | | 2 | 3 | 1986-09-21 00:00:00 | atlanta falcons | l 35-37 | texas stadium | | 62,880 | | 3 | 4 | 1986-09-29 00:00:00 | at st. louis cardinals | w 31-7 | busch memorial stadium | | 49,077 | | 4 | 5 | 1986-10-05 00:00:00 | at denver broncos | l 14-29 | mile high stadium | | 76,082 | | 5 | 6 | 1986-10-12 00:00:00 | washington redskins | w 30-6 | texas stadium | | 63,264 | | 6 | 7 | 1986-10-19 00:00:00 | at philadelphia eagles | w 17-14 | veterans stadium | | 68,572 | | 7 | 8 | 1986-10-26 00:00:00 | st. louis cardinals | w 37-6 | texas stadium | | 60,756 | | 8 | 9 | 1986-11-02 00:00:00 | at new york giants | l 14-17 | giants stadium | | 74,871 | | 9 | 10 | 1986-11-09 00:00:00 | los angeles raiders | l 13-17 | texas stadium | | 61,706 | | 10 | 11 | 1986-11-16 00:00:00 | at san diego chargers | w 24-21 | jack murphy stadium | | 55,622 | | 11 | 12 | 1986-11-23 00:00:00 | at washington redskins | l 14-41 | rfk stadium | | 55,642 | | 12 | 13 | 1986-11-27 00:00:00 | seattle seahawks | l 14-31 | texas stadium | | 58,020 | | 13 | 14 | 1986-12-07 00:00:00 | at los angeles rams | l 10-29 | anaheim stadium | | 64,949 | | 14 | 15 | 1986-12-14 00:00:00 | philadelphia eagles | l 21-23 | texas stadium | | 46,117 | | 15 | 16 | 1986-12-21 00:00:00 | chicago bears | l 10-24 | texas stadium | | 57,256 |

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

select position where school/club team = 'wayne state' col : round | pick | player | position | school/club team row 1 : 1 | 8 | eric fraser | s | central michigan row 2 : 2 | 16 | tristan black | lb | wayne state row 3 : 3 | 24 | john hashem | ot | regina row 4 : 4 | 28 | spencer armstrong | wr | air force row 5 : 4 | 30 | steve myddelton | de | st. francis xavier row 6 : 4 | 32 | scott mchenry | sb | saskatchewan row 7 : 5 | 40 | osie ukwuoma | dl | queen's row 8 : 6 | 48 | john kanaroski | wr | regina

col : position row 1 : lb

select position where school/club team = 'wayne state' <table_name> : table \begin{tabular}{llllll} \toprule & round & pick & player & position & school/club team \\ \midrule 0 & 1 & 8 & eric fraser & s & central michigan \\ 1 & 2 & 16 & tristan black & lb & wayne state \\ 2 & 3 & 24 & john hashem & ot & regina \\ 3 & 4 & 28 & spencer armstrong & wr & air force \\ 4 & 4 & 30 & steve myddelton & de & st. francis xavier \\ 5 & 4 & 32 & scott mchenry & sb & saskatchewan \\ 6 & 5 & 40 & osie ukwuoma & dl & queen's \\ 7 & 6 & 48 & john kanaroski & wr & regina \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & position \\ \midrule 0 & lb \\ \bottomrule \end{tabular}

select position where school/club team = 'wayne 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>school/club team</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>8</td> <td>eric fraser</td> <td>s</td> <td>central michigan</td> </tr> <tr> <th>1</th> <td>2</td> <td>16</td> <td>tristan black</td> <td>lb</td> <td>wayne state</td> </tr> <tr> <th>2</th> <td>3</td> <td>24</td> <td>john hashem</td> <td>ot</td> <td>regina</td> </tr> <tr> <th>3</th> <td>4</td> <td>28</td> <td>spencer armstrong</td> <td>wr</td> <td>air force</td> </tr> <tr> <th>4</th> <td>4</td> <td>30</td> <td>steve myddelton</td> <td>de</td> <td>st. francis xavier</td> </tr> <tr> <th>5</th> <td>4</td> <td>32</td> <td>scott mchenry</td> <td>sb</td> <td>saskatchewan</td> </tr> <tr> <th>6</th> <td>5</td> <td>40</td> <td>osie ukwuoma</td> <td>dl</td> <td>queen's</td> </tr> <tr> <th>7</th> <td>6</td> <td>48</td> <td>john kanaroski</td> <td>wr</td> <td>regina</td> </tr> </tbody> </table>

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

select position where school/club team = 'wayne state' <table_name> : table | | round | pick | player | position | school/club team | |---:|--------:|-------:|:------------------|:-----------|:-------------------| | 0 | 1 | 8 | eric fraser | s | central michigan | | 1 | 2 | 16 | tristan black | lb | wayne state | | 2 | 3 | 24 | john hashem | ot | regina | | 3 | 4 | 28 | spencer armstrong | wr | air force | | 4 | 4 | 30 | steve myddelton | de | st. francis xavier | | 5 | 4 | 32 | scott mchenry | sb | saskatchewan | | 6 | 5 | 40 | osie ukwuoma | dl | queen's | | 7 | 6 | 48 | john kanaroski | wr | regina |

| | position | |---:|:-----------| | 0 | lb |

select count ( * ) where song = '"viva la musica"' col : draw | artist | song | jury | televote | total | place row 1 : 1 | kasia nova | "the devil" | 0 | 1 | 1 | 11 row 2 : 2 | edi ann | "lovin'u" | 7 | 5 | 12 | 4 row 3 : 3 | izabela kopec | "you've got my love" | 4 | 6 | 10 | 6 row 4 : 4 | starnawski & urban noiz | "it's not a game" | 5 | 0 | 5 | 10 row 5 : 5 | queens | "i say my body" | 0 | 0 | 0 | 12 row 6 : 6 | isis gee | "for life" | 12 | 12 | 24 | 1 row 7 : 7 | man meadow | "viva la musica" | 2 | 10 | 12 | 3 row 8 : 8 | afromental | "thing we've got" | 3 | 4 | 7 | 9 row 9 : 9 | plastic | "do something" | 10 | 2 | 12 | 5 row 10 : 10 | sandra oxenryd | "superhero" | 6 | 3 | 9 | 8 row 11 : 11 | natasza urbanska | "blow over" | 8 | 7 | 15 | 2 row 12 : 12 | margo | "dlatego walcz" | 1 | 8 | 9 | 7

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

select count ( * ) where song = '"viva la musica"' <table_name> : table \begin{tabular}{llllllll} \toprule & draw & artist & song & jury & televote & total & place \\ \midrule 0 & 1 & kasia nova & "the devil" & 0 & 1 & 1 & 11 \\ 1 & 2 & edi ann & "lovin'u" & 7 & 5 & 12 & 4 \\ 2 & 3 & izabela kopec & "you've got my love" & 4 & 6 & 10 & 6 \\ 3 & 4 & starnawski & urban noiz & "it's not a game" & 5 & 0 & 5 & 10 \\ 4 & 5 & queens & "i say my body" & 0 & 0 & 0 & 12 \\ 5 & 6 & isis gee & "for life" & 12 & 12 & 24 & 1 \\ 6 & 7 & man meadow & "viva la musica" & 2 & 10 & 12 & 3 \\ 7 & 8 & afromental & "thing we've got" & 3 & 4 & 7 & 9 \\ 8 & 9 & plastic & "do something" & 10 & 2 & 12 & 5 \\ 9 & 10 & sandra oxenryd & "superhero" & 6 & 3 & 9 & 8 \\ 10 & 11 & natasza urbanska & "blow over" & 8 & 7 & 15 & 2 \\ 11 & 12 & margo & "dlatego walcz" & 1 & 8 & 9 & 7 \\ \bottomrule \end{tabular}

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

select count ( * ) where song = '"viva la musica"' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>draw</th> <th>artist</th> <th>song</th> <th>jury</th> <th>televote</th> <th>total</th> <th>place</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>kasia nova</td> <td>"the devil"</td> <td>0</td> <td>1</td> <td>1</td> <td>11</td> </tr> <tr> <th>1</th> <td>2</td> <td>edi ann</td> <td>"lovin'u"</td> <td>7</td> <td>5</td> <td>12</td> <td>4</td> </tr> <tr> <th>2</th> <td>3</td> <td>izabela kopec</td> <td>"you've got my love"</td> <td>4</td> <td>6</td> <td>10</td> <td>6</td> </tr> <tr> <th>3</th> <td>4</td> <td>starnawski &amp; urban noiz</td> <td>"it's not a game"</td> <td>5</td> <td>0</td> <td>5</td> <td>10</td> </tr> <tr> <th>4</th> <td>5</td> <td>queens</td> <td>"i say my body"</td> <td>0</td> <td>0</td> <td>0</td> <td>12</td> </tr> <tr> <th>5</th> <td>6</td> <td>isis gee</td> <td>"for life"</td> <td>12</td> <td>12</td> <td>24</td> <td>1</td> </tr> <tr> <th>6</th> <td>7</td> <td>man meadow</td> <td>"viva la musica"</td> <td>2</td> <td>10</td> <td>12</td> <td>3</td> </tr> <tr> <th>7</th> <td>8</td> <td>afromental</td> <td>"thing we've got"</td> <td>3</td> <td>4</td> <td>7</td> <td>9</td> </tr> <tr> <th>8</th> <td>9</td> <td>plastic</td> <td>"do something"</td> <td>10</td> <td>2</td> <td>12</td> <td>5</td> </tr> <tr> <th>9</th> <td>10</td> <td>sandra oxenryd</td> <td>"superhero"</td> <td>6</td> <td>3</td> <td>9</td> <td>8</td> </tr> <tr> <th>10</th> <td>11</td> <td>natasza urbanska</td> <td>"blow over"</td> <td>8</td> <td>7</td> <td>15</td> <td>2</td> </tr> <tr> <th>11</th> <td>12</td> <td>margo</td> <td>"dlatego walcz"</td> <td>1</td> <td>8</td> <td>9</td> <td>7</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 song = '"viva la musica"' <table_name> : table | | draw | artist | song | jury | televote | total | place | |---:|-------:|:------------------------|:---------------------|-------:|-----------:|--------:|--------:| | 0 | 1 | kasia nova | "the devil" | 0 | 1 | 1 | 11 | | 1 | 2 | edi ann | "lovin'u" | 7 | 5 | 12 | 4 | | 2 | 3 | izabela kopec | "you've got my love" | 4 | 6 | 10 | 6 | | 3 | 4 | starnawski & urban noiz | "it's not a game" | 5 | 0 | 5 | 10 | | 4 | 5 | queens | "i say my body" | 0 | 0 | 0 | 12 | | 5 | 6 | isis gee | "for life" | 12 | 12 | 24 | 1 | | 6 | 7 | man meadow | "viva la musica" | 2 | 10 | 12 | 3 | | 7 | 8 | afromental | "thing we've got" | 3 | 4 | 7 | 9 | | 8 | 9 | plastic | "do something" | 10 | 2 | 12 | 5 | | 9 | 10 | sandra oxenryd | "superhero" | 6 | 3 | 9 | 8 | | 10 | 11 | natasza urbanska | "blow over" | 8 | 7 | 15 | 2 | | 11 | 12 | margo | "dlatego walcz" | 1 | 8 | 9 | 7 |

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

select count ( to par ) where score = '70-69-68=207' col : place | player | country | score | to par row 1 : 1 | tiger woods | united states | 67-66-67=200 | -16 row 2 : t2 | thomas bjørn | denmark | 69-69-68=206 | -10 row 3 : t2 | david duval | united states | 70-70-66=206 | -10 row 4 : t4 | darren clarke | northern ireland | 70-69-68=207 | -9 row 5 : t4 | loren roberts | united states | 69-68-70=207 | -9 row 6 : t4 | david toms | united states | 69-67-71=207 | -9 row 7 : t7 | ernie els | south africa | 66-72-70=208 | -8 row 8 : t7 | steve flesch | united states | 67-70-71=208 | -8 row 9 : t7 | tom lehman | united states | 68-70-70=208 | -8 row 10 : t7 | dennis paulson | united states | 68-71-70=208 | -8

col : count ( "to par" ) row 1 : 1

select count ( to par ) where score = '70-69-68=207' <table_name> : table \begin{tabular}{llllll} \toprule & place & player & country & score & to par \\ \midrule 0 & 1 & tiger woods & united states & 67-66-67=200 & -16 \\ 1 & t2 & thomas bjørn & denmark & 69-69-68=206 & -10 \\ 2 & t2 & david duval & united states & 70-70-66=206 & -10 \\ 3 & t4 & darren clarke & northern ireland & 70-69-68=207 & -9 \\ 4 & t4 & loren roberts & united states & 69-68-70=207 & -9 \\ 5 & t4 & david toms & united states & 69-67-71=207 & -9 \\ 6 & t7 & ernie els & south africa & 66-72-70=208 & -8 \\ 7 & t7 & steve flesch & united states & 67-70-71=208 & -8 \\ 8 & t7 & tom lehman & united states & 68-70-70=208 & -8 \\ 9 & t7 & dennis paulson & united states & 68-71-70=208 & -8 \\ \bottomrule \end{tabular}

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

select count ( to par ) where score = '70-69-68=207' <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> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>tiger woods</td> <td>united states</td> <td>67-66-67=200</td> <td>-16</td> </tr> <tr> <th>1</th> <td>t2</td> <td>thomas bjørn</td> <td>denmark</td> <td>69-69-68=206</td> <td>-10</td> </tr> <tr> <th>2</th> <td>t2</td> <td>david duval</td> <td>united states</td> <td>70-70-66=206</td> <td>-10</td> </tr> <tr> <th>3</th> <td>t4</td> <td>darren clarke</td> <td>northern ireland</td> <td>70-69-68=207</td> <td>-9</td> </tr> <tr> <th>4</th> <td>t4</td> <td>loren roberts</td> <td>united states</td> <td>69-68-70=207</td> <td>-9</td> </tr> <tr> <th>5</th> <td>t4</td> <td>david toms</td> <td>united states</td> <td>69-67-71=207</td> <td>-9</td> </tr> <tr> <th>6</th> <td>t7</td> <td>ernie els</td> <td>south africa</td> <td>66-72-70=208</td> <td>-8</td> </tr> <tr> <th>7</th> <td>t7</td> <td>steve flesch</td> <td>united states</td> <td>67-70-71=208</td> <td>-8</td> </tr> <tr> <th>8</th> <td>t7</td> <td>tom lehman</td> <td>united states</td> <td>68-70-70=208</td> <td>-8</td> </tr> <tr> <th>9</th> <td>t7</td> <td>dennis paulson</td> <td>united states</td> <td>68-71-70=208</td> <td>-8</td> </tr> </tbody> </table>

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

select count ( to par ) where score = '70-69-68=207' <table_name> : table | | place | player | country | score | to par | |---:|:--------|:---------------|:-----------------|:-------------|---------:| | 0 | 1 | tiger woods | united states | 67-66-67=200 | -16 | | 1 | t2 | thomas bjørn | denmark | 69-69-68=206 | -10 | | 2 | t2 | david duval | united states | 70-70-66=206 | -10 | | 3 | t4 | darren clarke | northern ireland | 70-69-68=207 | -9 | | 4 | t4 | loren roberts | united states | 69-68-70=207 | -9 | | 5 | t4 | david toms | united states | 69-67-71=207 | -9 | | 6 | t7 | ernie els | south africa | 66-72-70=208 | -8 | | 7 | t7 | steve flesch | united states | 67-70-71=208 | -8 | | 8 | t7 | tom lehman | united states | 68-70-70=208 | -8 | | 9 | t7 | dennis paulson | united states | 68-71-70=208 | -8 |

| | count ( "to par" ) | |---:|---------------------:| | 0 | 1 |

select count ( team ) where tyres = 6 col : team | constructor | motorcycle | tyres | no | rider | rounds row 1 : camel yamaha team | yamaha | yamaha yzr-m1 | m | 5 | colin edwards | all row 2 : camel yamaha team | yamaha | yamaha yzr-m1 | m | 46 | valentino rossi | all row 3 : konica minolta honda | honda | honda rc211v | m | 6 | makoto tamada | all row 4 : tech 3 yamaha | yamaha | yamaha yzr-m1 | d | 7 | carlos checa | all row 5 : tech 3 yamaha | yamaha | yamaha yzr-m1 | d | 77 | james ellison | all row 6 : team roberts | team roberts | kr211v | m | 10 | kenny roberts, jr | all row 7 : ducati marlboro team | ducati | ducati desmosedici gp6 | b | 15 | sete gibernau | 1-7, 10-11, 13-16 row 8 : ducati marlboro team | ducati | ducati desmosedici gp6 | b | 65 | loris capirossi | all row 9 : ducati marlboro team | ducati | ducati desmosedici gp6 | b | 66 | alex hofmann | 8-9, 12 row 10 : ducati marlboro team | ducati | ducati desmosedici gp6 | b | 12 | troy bayliss | 17 row 11 : kawasaki racing team | kawasaki | kawasaki ninja zx-rr | b | 17 | randy de puniet | all row 12 : kawasaki racing team | kawasaki | kawasaki ninja zx-rr | b | 56 | shinya nakano | all row 13 : kawasaki racing team | kawasaki | kawasaki ninja zx-rr | b | 8 | naoki matsudo | 15 row 14 : rizla suzuki motogp | suzuki | suzuki gsv-r | b | 21 | john hopkins | all row 15 : rizla suzuki motogp | suzuki | suzuki gsv-r | b | 71 | chris vermeulen | all row 16 : rizla suzuki motogp | suzuki | suzuki gsv-r | b | 64 | kousuke akiyoshi | 15 row 17 : repsol honda team | honda | honda rc211v | m | 26 | dani pedrosa | all row 18 : repsol honda team | honda | honda rc211v | m | 69 | nicky hayden | all row 19 : honda lcr | honda | honda rc211v | m | 27 | casey stoner | all row 20 : pramac d'antin motogp | ducati | ducati desmosedici gp5 | d | 30 | jose luis cardoso | all row 21 : pramac d'antin motogp | ducati | ducati desmosedici gp5 | d | 66 | alex hofmann | 1-7, 10-11, 13-17 row 22 : pramac d'antin motogp | ducati | ducati desmosedici gp5 | d | 22 | ivan silva | 8-9, 12 row 23 : fortuna honda | honda | honda rc211v | m | 24 | toni elias | 1-8, 10-17 row 24 : fortuna honda | honda | honda rc211v | m | 33 | marco melandri | all row 25 : fortuna honda | honda | honda rc211v | m | 84 | michel fabrizio | 9 row 26 : ilmor srt | ilmor | ilmor x3 | m | 8 | garry mccoy | 16-17

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

select count ( team ) where tyres = 6 <table_name> : table \begin{tabular}{llllllll} \toprule & team & constructor & motorcycle & tyres & no & rider & rounds \\ \midrule 0 & camel yamaha team & yamaha & yamaha yzr-m1 & m & 5 & colin edwards & all \\ 1 & camel yamaha team & yamaha & yamaha yzr-m1 & m & 46 & valentino rossi & all \\ 2 & konica minolta honda & honda & honda rc211v & m & 6 & makoto tamada & all \\ 3 & tech 3 yamaha & yamaha & yamaha yzr-m1 & d & 7 & carlos checa & all \\ 4 & tech 3 yamaha & yamaha & yamaha yzr-m1 & d & 77 & james ellison & all \\ 5 & team roberts & team roberts & kr211v & m & 10 & kenny roberts, jr & all \\ 6 & ducati marlboro team & ducati & ducati desmosedici gp6 & b & 15 & sete gibernau & 1-7, 10-11, 13-16 \\ 7 & ducati marlboro team & ducati & ducati desmosedici gp6 & b & 65 & loris capirossi & all \\ 8 & ducati marlboro team & ducati & ducati desmosedici gp6 & b & 66 & alex hofmann & 8-9, 12 \\ 9 & ducati marlboro team & ducati & ducati desmosedici gp6 & b & 12 & troy bayliss & 17 \\ 10 & kawasaki racing team & kawasaki & kawasaki ninja zx-rr & b & 17 & randy de puniet & all \\ 11 & kawasaki racing team & kawasaki & kawasaki ninja zx-rr & b & 56 & shinya nakano & all \\ 12 & kawasaki racing team & kawasaki & kawasaki ninja zx-rr & b & 8 & naoki matsudo & 15 \\ 13 & rizla suzuki motogp & suzuki & suzuki gsv-r & b & 21 & john hopkins & all \\ 14 & rizla suzuki motogp & suzuki & suzuki gsv-r & b & 71 & chris vermeulen & all \\ 15 & rizla suzuki motogp & suzuki & suzuki gsv-r & b & 64 & kousuke akiyoshi & 15 \\ 16 & repsol honda team & honda & honda rc211v & m & 26 & dani pedrosa & all \\ 17 & repsol honda team & honda & honda rc211v & m & 69 & nicky hayden & all \\ 18 & honda lcr & honda & honda rc211v & m & 27 & casey stoner & all \\ 19 & pramac d'antin motogp & ducati & ducati desmosedici gp5 & d & 30 & jose luis cardoso & all \\ 20 & pramac d'antin motogp & ducati & ducati desmosedici gp5 & d & 66 & alex hofmann & 1-7, 10-11, 13-17 \\ 21 & pramac d'antin motogp & ducati & ducati desmosedici gp5 & d & 22 & ivan silva & 8-9, 12 \\ 22 & fortuna honda & honda & honda rc211v & m & 24 & toni elias & 1-8, 10-17 \\ 23 & fortuna honda & honda & honda rc211v & m & 33 & marco melandri & all \\ 24 & fortuna honda & honda & honda rc211v & m & 84 & michel fabrizio & 9 \\ 25 & ilmor srt & ilmor & ilmor x3 & m & 8 & garry mccoy & 16-17 \\ \bottomrule \end{tabular}

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

select count ( team ) where tyres = 6 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>team</th> <th>constructor</th> <th>motorcycle</th> <th>tyres</th> <th>no</th> <th>rider</th> <th>rounds</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>camel yamaha team</td> <td>yamaha</td> <td>yamaha yzr-m1</td> <td>m</td> <td>5</td> <td>colin edwards</td> <td>all</td> </tr> <tr> <th>1</th> <td>camel yamaha team</td> <td>yamaha</td> <td>yamaha yzr-m1</td> <td>m</td> <td>46</td> <td>valentino rossi</td> <td>all</td> </tr> <tr> <th>2</th> <td>konica minolta honda</td> <td>honda</td> <td>honda rc211v</td> <td>m</td> <td>6</td> <td>makoto tamada</td> <td>all</td> </tr> <tr> <th>3</th> <td>tech 3 yamaha</td> <td>yamaha</td> <td>yamaha yzr-m1</td> <td>d</td> <td>7</td> <td>carlos checa</td> <td>all</td> </tr> <tr> <th>4</th> <td>tech 3 yamaha</td> <td>yamaha</td> <td>yamaha yzr-m1</td> <td>d</td> <td>77</td> <td>james ellison</td> <td>all</td> </tr> <tr> <th>5</th> <td>team roberts</td> <td>team roberts</td> <td>kr211v</td> <td>m</td> <td>10</td> <td>kenny roberts, jr</td> <td>all</td> </tr> <tr> <th>6</th> <td>ducati marlboro team</td> <td>ducati</td> <td>ducati desmosedici gp6</td> <td>b</td> <td>15</td> <td>sete gibernau</td> <td>1-7, 10-11, 13-16</td> </tr> <tr> <th>7</th> <td>ducati marlboro team</td> <td>ducati</td> <td>ducati desmosedici gp6</td> <td>b</td> <td>65</td> <td>loris capirossi</td> <td>all</td> </tr> <tr> <th>8</th> <td>ducati marlboro team</td> <td>ducati</td> <td>ducati desmosedici gp6</td> <td>b</td> <td>66</td> <td>alex hofmann</td> <td>8-9, 12</td> </tr> <tr> <th>9</th> <td>ducati marlboro team</td> <td>ducati</td> <td>ducati desmosedici gp6</td> <td>b</td> <td>12</td> <td>troy bayliss</td> <td>17</td> </tr> <tr> <th>10</th> <td>kawasaki racing team</td> <td>kawasaki</td> <td>kawasaki ninja zx-rr</td> <td>b</td> <td>17</td> <td>randy de puniet</td> <td>all</td> </tr> <tr> <th>11</th> <td>kawasaki racing team</td> <td>kawasaki</td> <td>kawasaki ninja zx-rr</td> <td>b</td> <td>56</td> <td>shinya nakano</td> <td>all</td> </tr> <tr> <th>12</th> <td>kawasaki racing team</td> <td>kawasaki</td> <td>kawasaki ninja zx-rr</td> <td>b</td> <td>8</td> <td>naoki matsudo</td> <td>15</td> </tr> <tr> <th>13</th> <td>rizla suzuki motogp</td> <td>suzuki</td> <td>suzuki gsv-r</td> <td>b</td> <td>21</td> <td>john hopkins</td> <td>all</td> </tr> <tr> <th>14</th> <td>rizla suzuki motogp</td> <td>suzuki</td> <td>suzuki gsv-r</td> <td>b</td> <td>71</td> <td>chris vermeulen</td> <td>all</td> </tr> <tr> <th>15</th> <td>rizla suzuki motogp</td> <td>suzuki</td> <td>suzuki gsv-r</td> <td>b</td> <td>64</td> <td>kousuke akiyoshi</td> <td>15</td> </tr> <tr> <th>16</th> <td>repsol honda team</td> <td>honda</td> <td>honda rc211v</td> <td>m</td> <td>26</td> <td>dani pedrosa</td> <td>all</td> </tr> <tr> <th>17</th> <td>repsol honda team</td> <td>honda</td> <td>honda rc211v</td> <td>m</td> <td>69</td> <td>nicky hayden</td> <td>all</td> </tr> <tr> <th>18</th> <td>honda lcr</td> <td>honda</td> <td>honda rc211v</td> <td>m</td> <td>27</td> <td>casey stoner</td> <td>all</td> </tr> <tr> <th>19</th> <td>pramac d'antin motogp</td> <td>ducati</td> <td>ducati desmosedici gp5</td> <td>d</td> <td>30</td> <td>jose luis cardoso</td> <td>all</td> </tr> <tr> <th>20</th> <td>pramac d'antin motogp</td> <td>ducati</td> <td>ducati desmosedici gp5</td> <td>d</td> <td>66</td> <td>alex hofmann</td> <td>1-7, 10-11, 13-17</td> </tr> <tr> <th>21</th> <td>pramac d'antin motogp</td> <td>ducati</td> <td>ducati desmosedici gp5</td> <td>d</td> <td>22</td> <td>ivan silva</td> <td>8-9, 12</td> </tr> <tr> <th>22</th> <td>fortuna honda</td> <td>honda</td> <td>honda rc211v</td> <td>m</td> <td>24</td> <td>toni elias</td> <td>1-8, 10-17</td> </tr> <tr> <th>23</th> <td>fortuna honda</td> <td>honda</td> <td>honda rc211v</td> <td>m</td> <td>33</td> <td>marco melandri</td> <td>all</td> </tr> <tr> <th>24</th> <td>fortuna honda</td> <td>honda</td> <td>honda rc211v</td> <td>m</td> <td>84</td> <td>michel fabrizio</td> <td>9</td> </tr> <tr> <th>25</th> <td>ilmor srt</td> <td>ilmor</td> <td>ilmor x3</td> <td>m</td> <td>8</td> <td>garry mccoy</td> <td>16-17</td> </tr> </tbody> </table>

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

select count ( team ) where tyres = 6 <table_name> : table | | team | constructor | motorcycle | tyres | no | rider | rounds | |---:|:----------------------|:--------------|:-----------------------|:--------|-----:|:------------------|:------------------| | 0 | camel yamaha team | yamaha | yamaha yzr-m1 | m | 5 | colin edwards | all | | 1 | camel yamaha team | yamaha | yamaha yzr-m1 | m | 46 | valentino rossi | all | | 2 | konica minolta honda | honda | honda rc211v | m | 6 | makoto tamada | all | | 3 | tech 3 yamaha | yamaha | yamaha yzr-m1 | d | 7 | carlos checa | all | | 4 | tech 3 yamaha | yamaha | yamaha yzr-m1 | d | 77 | james ellison | all | | 5 | team roberts | team roberts | kr211v | m | 10 | kenny roberts, jr | all | | 6 | ducati marlboro team | ducati | ducati desmosedici gp6 | b | 15 | sete gibernau | 1-7, 10-11, 13-16 | | 7 | ducati marlboro team | ducati | ducati desmosedici gp6 | b | 65 | loris capirossi | all | | 8 | ducati marlboro team | ducati | ducati desmosedici gp6 | b | 66 | alex hofmann | 8-9, 12 | | 9 | ducati marlboro team | ducati | ducati desmosedici gp6 | b | 12 | troy bayliss | 17 | | 10 | kawasaki racing team | kawasaki | kawasaki ninja zx-rr | b | 17 | randy de puniet | all | | 11 | kawasaki racing team | kawasaki | kawasaki ninja zx-rr | b | 56 | shinya nakano | all | | 12 | kawasaki racing team | kawasaki | kawasaki ninja zx-rr | b | 8 | naoki matsudo | 15 | | 13 | rizla suzuki motogp | suzuki | suzuki gsv-r | b | 21 | john hopkins | all | | 14 | rizla suzuki motogp | suzuki | suzuki gsv-r | b | 71 | chris vermeulen | all | | 15 | rizla suzuki motogp | suzuki | suzuki gsv-r | b | 64 | kousuke akiyoshi | 15 | | 16 | repsol honda team | honda | honda rc211v | m | 26 | dani pedrosa | all | | 17 | repsol honda team | honda | honda rc211v | m | 69 | nicky hayden | all | | 18 | honda lcr | honda | honda rc211v | m | 27 | casey stoner | all | | 19 | pramac d'antin motogp | ducati | ducati desmosedici gp5 | d | 30 | jose luis cardoso | all | | 20 | pramac d'antin motogp | ducati | ducati desmosedici gp5 | d | 66 | alex hofmann | 1-7, 10-11, 13-17 | | 21 | pramac d'antin motogp | ducati | ducati desmosedici gp5 | d | 22 | ivan silva | 8-9, 12 | | 22 | fortuna honda | honda | honda rc211v | m | 24 | toni elias | 1-8, 10-17 | | 23 | fortuna honda | honda | honda rc211v | m | 33 | marco melandri | all | | 24 | fortuna honda | honda | honda rc211v | m | 84 | michel fabrizio | 9 | | 25 | ilmor srt | ilmor | ilmor x3 | m | 8 | garry mccoy | 16-17 |

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

select # where opponent = 'san marino' col : # | date | venue | opponent | score | result | competition row 1 : 1 | 1999-03-27 00:00:00 | mestalla, valencia, spain | austria | 3-0 | 9-0 | euro 2000 qualifying row 2 : 2 | 1999-03-27 00:00:00 | mestalla, valencia, spain | austria | 5-0 | 9-0 | euro 2000 qualifying row 3 : 3 | 1999-03-31 00:00:00 | olimpico, serravalle, san marino | san marino | 0-3 | 0-6 | euro 2000 qualifying row 4 : 4 | 1999-09-08 00:00:00 | vivero, badajoz, spain | cyprus | 1-0 | 8-0 | euro 2000 qualifying row 5 : 5 | 1999-09-08 00:00:00 | vivero, badajoz, spain | cyprus | 2-0 | 8-0 | euro 2000 qualifying row 6 : 6 | 1999-09-08 00:00:00 | vivero, badajoz, spain | cyprus | 4-0 | 8-0 | euro 2000 qualifying row 7 : 7 | 2000-01-26 00:00:00 | cartagonova, cartagena, spain | poland | 2-0 | 3-0 | friendly row 8 : 8 | 2000-01-26 00:00:00 | cartagonova, cartagena, spain | poland | 3-0 | 3-0 | friendly

col : # row 1 : 3

select # where opponent = 'san marino' <table_name> : table \begin{tabular}{llllllll} \toprule & # & date & venue & opponent & score & result & competition \\ \midrule 0 & 1 & 1999-03-27 00:00:00 & mestalla, valencia, spain & austria & 3-0 & 9-0 & euro 2000 qualifying \\ 1 & 2 & 1999-03-27 00:00:00 & mestalla, valencia, spain & austria & 5-0 & 9-0 & euro 2000 qualifying \\ 2 & 3 & 1999-03-31 00:00:00 & olimpico, serravalle, san marino & san marino & 0-3 & 0-6 & euro 2000 qualifying \\ 3 & 4 & 1999-09-08 00:00:00 & vivero, badajoz, spain & cyprus & 1-0 & 8-0 & euro 2000 qualifying \\ 4 & 5 & 1999-09-08 00:00:00 & vivero, badajoz, spain & cyprus & 2-0 & 8-0 & euro 2000 qualifying \\ 5 & 6 & 1999-09-08 00:00:00 & vivero, badajoz, spain & cyprus & 4-0 & 8-0 & euro 2000 qualifying \\ 6 & 7 & 2000-01-26 00:00:00 & cartagonova, cartagena, spain & poland & 2-0 & 3-0 & friendly \\ 7 & 8 & 2000-01-26 00:00:00 & cartagonova, cartagena, spain & poland & 3-0 & 3-0 & friendly \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & # \\ \midrule 0 & 3 \\ \bottomrule \end{tabular}

select # where opponent = 'san marino' <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>1999-03-27 00:00:00</td> <td>mestalla, valencia, spain</td> <td>austria</td> <td>3-0</td> <td>9-0</td> <td>euro 2000 qualifying</td> </tr> <tr> <th>1</th> <td>2</td> <td>1999-03-27 00:00:00</td> <td>mestalla, valencia, spain</td> <td>austria</td> <td>5-0</td> <td>9-0</td> <td>euro 2000 qualifying</td> </tr> <tr> <th>2</th> <td>3</td> <td>1999-03-31 00:00:00</td> <td>olimpico, serravalle, san marino</td> <td>san marino</td> <td>0-3</td> <td>0-6</td> <td>euro 2000 qualifying</td> </tr> <tr> <th>3</th> <td>4</td> <td>1999-09-08 00:00:00</td> <td>vivero, badajoz, spain</td> <td>cyprus</td> <td>1-0</td> <td>8-0</td> <td>euro 2000 qualifying</td> </tr> <tr> <th>4</th> <td>5</td> <td>1999-09-08 00:00:00</td> <td>vivero, badajoz, spain</td> <td>cyprus</td> <td>2-0</td> <td>8-0</td> <td>euro 2000 qualifying</td> </tr> <tr> <th>5</th> <td>6</td> <td>1999-09-08 00:00:00</td> <td>vivero, badajoz, spain</td> <td>cyprus</td> <td>4-0</td> <td>8-0</td> <td>euro 2000 qualifying</td> </tr> <tr> <th>6</th> <td>7</td> <td>2000-01-26 00:00:00</td> <td>cartagonova, cartagena, spain</td> <td>poland</td> <td>2-0</td> <td>3-0</td> <td>friendly</td> </tr> <tr> <th>7</th> <td>8</td> <td>2000-01-26 00:00:00</td> <td>cartagonova, cartagena, spain</td> <td>poland</td> <td>3-0</td> <td>3-0</td> <td>friendly</td> </tr> </tbody> </table>

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

select # where opponent = 'san marino' <table_name> : table | | # | date | venue | opponent | score | result | competition | |---:|----:|:--------------------|:---------------------------------|:-----------|:--------|:---------|:---------------------| | 0 | 1 | 1999-03-27 00:00:00 | mestalla, valencia, spain | austria | 3-0 | 9-0 | euro 2000 qualifying | | 1 | 2 | 1999-03-27 00:00:00 | mestalla, valencia, spain | austria | 5-0 | 9-0 | euro 2000 qualifying | | 2 | 3 | 1999-03-31 00:00:00 | olimpico, serravalle, san marino | san marino | 0-3 | 0-6 | euro 2000 qualifying | | 3 | 4 | 1999-09-08 00:00:00 | vivero, badajoz, spain | cyprus | 1-0 | 8-0 | euro 2000 qualifying | | 4 | 5 | 1999-09-08 00:00:00 | vivero, badajoz, spain | cyprus | 2-0 | 8-0 | euro 2000 qualifying | | 5 | 6 | 1999-09-08 00:00:00 | vivero, badajoz, spain | cyprus | 4-0 | 8-0 | euro 2000 qualifying | | 6 | 7 | 2000-01-26 00:00:00 | cartagonova, cartagena, spain | poland | 2-0 | 3-0 | friendly | | 7 | 8 | 2000-01-26 00:00:00 | cartagonova, cartagena, spain | poland | 3-0 | 3-0 | friendly |

| | # | |---:|----:| | 0 | 3 |

select count ( * ) where position > 1854 col : year | competition | venue | position | notes row 1 : 2002 | world junior championships | kingston, jamaica | 1st | 64.51 m (1.75 kg) row 2 : 2002 | asian games | busan, south korea | 1st | 60.76 m row 3 : 2003 | universiade | daegu, south korea | 1st | 62.32 m row 4 : 2003 | asian championships | manila, philippines | 1st | 61.43 m row 5 : 2004 | olympic games | athens, greece | 18th (q) | 60.60 m row 6 : 2005 | world championships | helsinki, finland | 18th (q) | 61.75 m row 7 : 2005 | universiade | izmir, turkey | 7th | 59.67 m row 8 : 2005 | east asian games | macau | 1st | 61.74 m row 9 : 2006 | asian games | doha, qatar | 5th | 58.54 m row 10 : 2009 | asian championships | guangzhou, china | 3rd | 59.27 m

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

select count ( * ) where position > 1854 <table_name> : table \begin{tabular}{llllll} \toprule & year & competition & venue & position & notes \\ \midrule 0 & 2002 & world junior championships & kingston, jamaica & 1st & 64.51 m (1.75 kg) \\ 1 & 2002 & asian games & busan, south korea & 1st & 60.76 m \\ 2 & 2003 & universiade & daegu, south korea & 1st & 62.32 m \\ 3 & 2003 & asian championships & manila, philippines & 1st & 61.43 m \\ 4 & 2004 & olympic games & athens, greece & 18th (q) & 60.60 m \\ 5 & 2005 & world championships & helsinki, finland & 18th (q) & 61.75 m \\ 6 & 2005 & universiade & izmir, turkey & 7th & 59.67 m \\ 7 & 2005 & east asian games & macau & 1st & 61.74 m \\ 8 & 2006 & asian games & doha, qatar & 5th & 58.54 m \\ 9 & 2009 & asian championships & guangzhou, china & 3rd & 59.27 m \\ \bottomrule \end{tabular}

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

select count ( * ) where position > 1854 <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>2002</td> <td>world junior championships</td> <td>kingston, jamaica</td> <td>1st</td> <td>64.51 m (1.75 kg)</td> </tr> <tr> <th>1</th> <td>2002</td> <td>asian games</td> <td>busan, south korea</td> <td>1st</td> <td>60.76 m</td> </tr> <tr> <th>2</th> <td>2003</td> <td>universiade</td> <td>daegu, south korea</td> <td>1st</td> <td>62.32 m</td> </tr> <tr> <th>3</th> <td>2003</td> <td>asian championships</td> <td>manila, philippines</td> <td>1st</td> <td>61.43 m</td> </tr> <tr> <th>4</th> <td>2004</td> <td>olympic games</td> <td>athens, greece</td> <td>18th (q)</td> <td>60.60 m</td> </tr> <tr> <th>5</th> <td>2005</td> <td>world championships</td> <td>helsinki, finland</td> <td>18th (q)</td> <td>61.75 m</td> </tr> <tr> <th>6</th> <td>2005</td> <td>universiade</td> <td>izmir, turkey</td> <td>7th</td> <td>59.67 m</td> </tr> <tr> <th>7</th> <td>2005</td> <td>east asian games</td> <td>macau</td> <td>1st</td> <td>61.74 m</td> </tr> <tr> <th>8</th> <td>2006</td> <td>asian games</td> <td>doha, qatar</td> <td>5th</td> <td>58.54 m</td> </tr> <tr> <th>9</th> <td>2009</td> <td>asian championships</td> <td>guangzhou, china</td> <td>3rd</td> <td>59.27 m</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 position > 1854 <table_name> : table | | year | competition | venue | position | notes | |---:|-------:|:---------------------------|:--------------------|:-----------|:------------------| | 0 | 2002 | world junior championships | kingston, jamaica | 1st | 64.51 m (1.75 kg) | | 1 | 2002 | asian games | busan, south korea | 1st | 60.76 m | | 2 | 2003 | universiade | daegu, south korea | 1st | 62.32 m | | 3 | 2003 | asian championships | manila, philippines | 1st | 61.43 m | | 4 | 2004 | olympic games | athens, greece | 18th (q) | 60.60 m | | 5 | 2005 | world championships | helsinki, finland | 18th (q) | 61.75 m | | 6 | 2005 | universiade | izmir, turkey | 7th | 59.67 m | | 7 | 2005 | east asian games | macau | 1st | 61.74 m | | 8 | 2006 | asian games | doha, qatar | 5th | 58.54 m | | 9 | 2009 | asian championships | guangzhou, china | 3rd | 59.27 m |

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

select count ( nation ) where nation = 'austria' col : rank | nation | gold | silver | bronze | total row 1 : 1 | brazil | 21 | 9 | 12 | 42 row 2 : 2 | united states | 9 | 3 | 6 | 18 row 3 : 3 | china | 1 | 9 | 8 | 18 row 4 : 4 | australia | 1 | 1 | 1 | 3 row 5 : 4 | netherlands | 1 | 1 | 1 | 3 row 6 : 6 | estonia | 1 | 0 | 0 | 1 row 7 : 7 | germany | 0 | 5 | 1 | 6 row 8 : 8 | russia | 0 | 2 | 3 | 5 row 9 : 9 | argentina | 0 | 2 | 0 | 2 row 10 : 10 | switzerland | 0 | 1 | 1 | 2 row 11 : 11 | norway | 0 | 1 | 0 | 1 row 12 : 12 | austria | 0 | 0 | 1 | 1

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

select count ( nation ) where nation = 'austria' <table_name> : table \begin{tabular}{lllllll} \toprule & rank & nation & gold & silver & bronze & total \\ \midrule 0 & 1 & brazil & 21 & 9 & 12 & 42 \\ 1 & 2 & united states & 9 & 3 & 6 & 18 \\ 2 & 3 & china & 1 & 9 & 8 & 18 \\ 3 & 4 & australia & 1 & 1 & 1 & 3 \\ 4 & 4 & netherlands & 1 & 1 & 1 & 3 \\ 5 & 6 & estonia & 1 & 0 & 0 & 1 \\ 6 & 7 & germany & 0 & 5 & 1 & 6 \\ 7 & 8 & russia & 0 & 2 & 3 & 5 \\ 8 & 9 & argentina & 0 & 2 & 0 & 2 \\ 9 & 10 & switzerland & 0 & 1 & 1 & 2 \\ 10 & 11 & norway & 0 & 1 & 0 & 1 \\ 11 & 12 & austria & 0 & 0 & 1 & 1 \\ \bottomrule \end{tabular}

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

select count ( nation ) where nation = 'austria' <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>21</td> <td>9</td> <td>12</td> <td>42</td> </tr> <tr> <th>1</th> <td>2</td> <td>united states</td> <td>9</td> <td>3</td> <td>6</td> <td>18</td> </tr> <tr> <th>2</th> <td>3</td> <td>china</td> <td>1</td> <td>9</td> <td>8</td> <td>18</td> </tr> <tr> <th>3</th> <td>4</td> <td>australia</td> <td>1</td> <td>1</td> <td>1</td> <td>3</td> </tr> <tr> <th>4</th> <td>4</td> <td>netherlands</td> <td>1</td> <td>1</td> <td>1</td> <td>3</td> </tr> <tr> <th>5</th> <td>6</td> <td>estonia</td> <td>1</td> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <th>6</th> <td>7</td> <td>germany</td> <td>0</td> <td>5</td> <td>1</td> <td>6</td> </tr> <tr> <th>7</th> <td>8</td> <td>russia</td> <td>0</td> <td>2</td> <td>3</td> <td>5</td> </tr> <tr> <th>8</th> <td>9</td> <td>argentina</td> <td>0</td> <td>2</td> <td>0</td> <td>2</td> </tr> <tr> <th>9</th> <td>10</td> <td>switzerland</td> <td>0</td> <td>1</td> <td>1</td> <td>2</td> </tr> <tr> <th>10</th> <td>11</td> <td>norway</td> <td>0</td> <td>1</td> <td>0</td> <td>1</td> </tr> <tr> <th>11</th> <td>12</td> <td>austria</td> <td>0</td> <td>0</td> <td>1</td> <td>1</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 = 'austria' <table_name> : table | | rank | nation | gold | silver | bronze | total | |---:|-------:|:--------------|-------:|---------:|---------:|--------:| | 0 | 1 | brazil | 21 | 9 | 12 | 42 | | 1 | 2 | united states | 9 | 3 | 6 | 18 | | 2 | 3 | china | 1 | 9 | 8 | 18 | | 3 | 4 | australia | 1 | 1 | 1 | 3 | | 4 | 4 | netherlands | 1 | 1 | 1 | 3 | | 5 | 6 | estonia | 1 | 0 | 0 | 1 | | 6 | 7 | germany | 0 | 5 | 1 | 6 | | 7 | 8 | russia | 0 | 2 | 3 | 5 | | 8 | 9 | argentina | 0 | 2 | 0 | 2 | | 9 | 10 | switzerland | 0 | 1 | 1 | 2 | | 10 | 11 | norway | 0 | 1 | 0 | 1 | | 11 | 12 | austria | 0 | 0 | 1 | 1 |

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

select votes where votes = votes col : party | votes | % | swing | seats | change row 1 : australian labor party | 166106 | 50.84 | 2.75 | 15 | 3 row 2 : liberal and country league | 119106 | 36.45 | -4.05 | 20 | -3 row 3 : independent | 36271 | 11.1 | 1.03 | 4 | 0 row 4 : other | 5238 | 1.6 | none | 0 | 0 row 5 : total | 326721 | none | none | 39 | none row 6 : liberal and country league | win | 47 | -4.3 | 20 | -3 row 7 : australian labor party | none | 53 | 4.3 | 15 | 3

col : votes row 1 : 166106 row 2 : 119106 row 3 : 36271 row 4 : 5238 row 5 : 326721 row 6 : win row 7 : none

select votes where votes = votes <table_name> : table \begin{tabular}{lllllll} \toprule & party & votes & % & swing & seats & change \\ \midrule 0 & australian labor party & 166106 & 50.84 & 2.75 & 15 & 3 \\ 1 & liberal and country league & 119106 & 36.45 & -4.05 & 20 & -3 \\ 2 & independent & 36271 & 11.1 & 1.03 & 4 & 0 \\ 3 & other & 5238 & 1.6 & none & 0 & 0 \\ 4 & total & 326721 & none & none & 39 & none \\ 5 & liberal and country league & win & 47 & -4.3 & 20 & -3 \\ 6 & australian labor party & none & 53 & 4.3 & 15 & 3 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & votes \\ \midrule 0 & 166106 \\ 1 & 119106 \\ 2 & 36271 \\ 3 & 5238 \\ 4 & 326721 \\ 5 & win \\ 6 & none \\ \bottomrule \end{tabular}

select votes where votes = votes <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>party</th> <th>votes</th> <th>%</th> <th>swing</th> <th>seats</th> <th>change</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>australian labor party</td> <td>166106</td> <td>50.84</td> <td>2.75</td> <td>15</td> <td>3</td> </tr> <tr> <th>1</th> <td>liberal and country league</td> <td>119106</td> <td>36.45</td> <td>-4.05</td> <td>20</td> <td>-3</td> </tr> <tr> <th>2</th> <td>independent</td> <td>36271</td> <td>11.1</td> <td>1.03</td> <td>4</td> <td>0</td> </tr> <tr> <th>3</th> <td>other</td> <td>5238</td> <td>1.6</td> <td>none</td> <td>0</td> <td>0</td> </tr> <tr> <th>4</th> <td>total</td> <td>326721</td> <td>none</td> <td>none</td> <td>39</td> <td>none</td> </tr> <tr> <th>5</th> <td>liberal and country league</td> <td>win</td> <td>47</td> <td>-4.3</td> <td>20</td> <td>-3</td> </tr> <tr> <th>6</th> <td>australian labor party</td> <td>none</td> <td>53</td> <td>4.3</td> <td>15</td> <td>3</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>166106</td> </tr> <tr> <th>1</th> <td>119106</td> </tr> <tr> <th>2</th> <td>36271</td> </tr> <tr> <th>3</th> <td>5238</td> </tr> <tr> <th>4</th> <td>326721</td> </tr> <tr> <th>5</th> <td>win</td> </tr> <tr> <th>6</th> <td>none</td> </tr> </tbody> </table>

select votes where votes = votes <table_name> : table | | party | votes | % | swing | seats | change | |---:|:---------------------------|:--------|:------|:--------|--------:|:---------| | 0 | australian labor party | 166106 | 50.84 | 2.75 | 15 | 3 | | 1 | liberal and country league | 119106 | 36.45 | -4.05 | 20 | -3 | | 2 | independent | 36271 | 11.1 | 1.03 | 4 | 0 | | 3 | other | 5238 | 1.6 | none | 0 | 0 | | 4 | total | 326721 | none | none | 39 | none | | 5 | liberal and country league | win | 47 | -4.3 | 20 | -3 | | 6 | australian labor party | none | 53 | 4.3 | 15 | 3 |

| | votes | |---:|:--------| | 0 | 166106 | | 1 | 119106 | | 2 | 36271 | | 3 | 5238 | | 4 | 326721 | | 5 | win | | 6 | none |

select count ( team ) where team = 'aurora basket jesi' and promotions = 0 col : seasons | team | ch.wins | promotions | relegations row 1 : 10 | basket rimini crabs | 0 | 0 | 0 row 2 : 10 | aurora basket jesi | 0 | 1 | 1 row 3 : 10 | andrea costa imola | 0 | 0 | 3 row 4 : 9 | nuova pallacanestro pavia | 0 | 0 | 0 row 5 : 9 | scafati basket | 1 | 1 | 0 row 6 : 8 | basket club ferrara | 1 | 1 | 0 row 7 : 8 | pallac. reggiana reggio emilia | 2 | 2 | 0 row 8 : 7 | novara | 0 | 0 | 3 row 9 : 7 | dinamo basket sassari | 0 | 1 | 0 row 10 : 6 | a.s. junior pallacanestro casale | 1 | 1 | 1 row 11 : 5 | rb montecatini terme | 0 | 0 | 1 row 12 : 5 | fabriano basket | 0 | 0 | 1 row 13 : 5 | societa veroli basket | 0 | 0 | 0 row 14 : 5 | pistoia basket 2000 | 0 | 0 | 0 row 15 : 4 | juve caserta basket | 0 | 1 | 0 row 16 : 3 | gruppo triboldi basket soresina | 0 | 1 | 0 row 17 : 3 | nuova sebastiani rieti | 1 | 1 | 0 row 18 : 3 | progresso castelmaggiore | 0 | 0 | 0 row 19 : 3 | orlandina basket | 1 | 1 | 1 row 20 : 3 | sutor basket montegranaro | 0 | 1 | 1 row 21 : 3 | virtus ragusa | 0 | 0 | 1 row 22 : 3 | robur basket osimo | 0 | 0 | 1 row 23 : 3 | reyer venezia | 0 | 1 | 0 row 24 : 3 | new basket brindisi | 1 | 2 | 0 row 25 : 2 | pallacanestro messina | 0 | 1 | 0 row 26 : 2 | basket trapani | 0 | 0 | 1 row 27 : 2 | basket draghi castelletto ticino | 0 | 0 | 0 row 28 : 2 | basket livorno | 0 | 0 | 0 row 29 : 2 | pallalcesto amatori udine | 0 | 0 | 0 row 30 : 2 | unione cestistica casalpusterlengo | 0 | 0 | 0 row 31 : 2 | basket barcellona | 0 | 0 | 0 row 32 : 2 | fulgor libertas forli | 0 | 0 | 1 row 33 : 2 | scaligera basket verona | 0 | 0 | 1 row 34 : 1 | societa sportiva basket napoli | 1 | 1 | 0 row 35 : 1 | centro sportivo borgomanero | 0 | 0 | 0 row 36 : 1 | celana basket bergamo | 0 | 0 | 1 row 37 : 1 | teramo basket | 1 | 1 | 0 row 38 : 1 | virtus pallacanestro bologna | 0 | 1 | 0 row 39 : 1 | viola reggio calabria | 0 | 0 | 0 row 40 : 1 | victoria libertas pesaro | 0 | 1 | 0 row 41 : 1 | pallacanestro varese | 1 | 1 | 0 row 42 : 1 | pallacanestro roseto | 0 | 0 | 1 row 43 : 1 | nuova pallacanestro vigevano | 0 | 0 | 0 row 44 : 1 | cestistica san severo | 0 | 0 | 1 row 45 : 1 | basket brescia leonessa | 0 | 0 | 0 row 46 : 1 | unione cestistica piacentina piacenza | 0 | 0 | 0 row 47 : 1 | pallacanestro sant'antimo | 0 | 0 | 0 row 48 : 1 | ostuni basket | 0 | 0 | 0 row 49 : 1 | biancoblu basket bologna | 0 | 0 | 0

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

select count ( team ) where team = 'aurora basket jesi' and promotions = 0 <table_name> : table \begin{tabular}{llllll} \toprule & seasons & team & ch.wins & promotions & relegations \\ \midrule 0 & 10 & basket rimini crabs & 0 & 0 & 0 \\ 1 & 10 & aurora basket jesi & 0 & 1 & 1 \\ 2 & 10 & andrea costa imola & 0 & 0 & 3 \\ 3 & 9 & nuova pallacanestro pavia & 0 & 0 & 0 \\ 4 & 9 & scafati basket & 1 & 1 & 0 \\ 5 & 8 & basket club ferrara & 1 & 1 & 0 \\ 6 & 8 & pallac. reggiana reggio emilia & 2 & 2 & 0 \\ 7 & 7 & novara & 0 & 0 & 3 \\ 8 & 7 & dinamo basket sassari & 0 & 1 & 0 \\ 9 & 6 & a.s. junior pallacanestro casale & 1 & 1 & 1 \\ 10 & 5 & rb montecatini terme & 0 & 0 & 1 \\ 11 & 5 & fabriano basket & 0 & 0 & 1 \\ 12 & 5 & societa veroli basket & 0 & 0 & 0 \\ 13 & 5 & pistoia basket 2000 & 0 & 0 & 0 \\ 14 & 4 & juve caserta basket & 0 & 1 & 0 \\ 15 & 3 & gruppo triboldi basket soresina & 0 & 1 & 0 \\ 16 & 3 & nuova sebastiani rieti & 1 & 1 & 0 \\ 17 & 3 & progresso castelmaggiore & 0 & 0 & 0 \\ 18 & 3 & orlandina basket & 1 & 1 & 1 \\ 19 & 3 & sutor basket montegranaro & 0 & 1 & 1 \\ 20 & 3 & virtus ragusa & 0 & 0 & 1 \\ 21 & 3 & robur basket osimo & 0 & 0 & 1 \\ 22 & 3 & reyer venezia & 0 & 1 & 0 \\ 23 & 3 & new basket brindisi & 1 & 2 & 0 \\ 24 & 2 & pallacanestro messina & 0 & 1 & 0 \\ 25 & 2 & basket trapani & 0 & 0 & 1 \\ 26 & 2 & basket draghi castelletto ticino & 0 & 0 & 0 \\ 27 & 2 & basket livorno & 0 & 0 & 0 \\ 28 & 2 & pallalcesto amatori udine & 0 & 0 & 0 \\ 29 & 2 & unione cestistica casalpusterlengo & 0 & 0 & 0 \\ 30 & 2 & basket barcellona & 0 & 0 & 0 \\ 31 & 2 & fulgor libertas forli & 0 & 0 & 1 \\ 32 & 2 & scaligera basket verona & 0 & 0 & 1 \\ 33 & 1 & societa sportiva basket napoli & 1 & 1 & 0 \\ 34 & 1 & centro sportivo borgomanero & 0 & 0 & 0 \\ 35 & 1 & celana basket bergamo & 0 & 0 & 1 \\ 36 & 1 & teramo basket & 1 & 1 & 0 \\ 37 & 1 & virtus pallacanestro bologna & 0 & 1 & 0 \\ 38 & 1 & viola reggio calabria & 0 & 0 & 0 \\ 39 & 1 & victoria libertas pesaro & 0 & 1 & 0 \\ 40 & 1 & pallacanestro varese & 1 & 1 & 0 \\ 41 & 1 & pallacanestro roseto & 0 & 0 & 1 \\ 42 & 1 & nuova pallacanestro vigevano & 0 & 0 & 0 \\ 43 & 1 & cestistica san severo & 0 & 0 & 1 \\ 44 & 1 & basket brescia leonessa & 0 & 0 & 0 \\ 45 & 1 & unione cestistica piacentina piacenza & 0 & 0 & 0 \\ 46 & 1 & pallacanestro sant'antimo & 0 & 0 & 0 \\ 47 & 1 & ostuni basket & 0 & 0 & 0 \\ 48 & 1 & biancoblu basket bologna & 0 & 0 & 0 \\ \bottomrule \end{tabular}

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

select count ( team ) where team = 'aurora basket jesi' and promotions = 0 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>seasons</th> <th>team</th> <th>ch.wins</th> <th>promotions</th> <th>relegations</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>10</td> <td>basket rimini crabs</td> <td>0</td> <td>0</td> <td>0</td> </tr> <tr> <th>1</th> <td>10</td> <td>aurora basket jesi</td> <td>0</td> <td>1</td> <td>1</td> </tr> <tr> <th>2</th> <td>10</td> <td>andrea costa imola</td> <td>0</td> <td>0</td> <td>3</td> </tr> <tr> <th>3</th> <td>9</td> <td>nuova pallacanestro pavia</td> <td>0</td> <td>0</td> <td>0</td> </tr> <tr> <th>4</th> <td>9</td> <td>scafati basket</td> <td>1</td> <td>1</td> <td>0</td> </tr> <tr> <th>5</th> <td>8</td> <td>basket club ferrara</td> <td>1</td> <td>1</td> <td>0</td> </tr> <tr> <th>6</th> <td>8</td> <td>pallac. reggiana reggio emilia</td> <td>2</td> <td>2</td> <td>0</td> </tr> <tr> <th>7</th> <td>7</td> <td>novara</td> <td>0</td> <td>0</td> <td>3</td> </tr> <tr> <th>8</th> <td>7</td> <td>dinamo basket sassari</td> <td>0</td> <td>1</td> <td>0</td> </tr> <tr> <th>9</th> <td>6</td> <td>a.s. junior pallacanestro casale</td> <td>1</td> <td>1</td> <td>1</td> </tr> <tr> <th>10</th> <td>5</td> <td>rb montecatini terme</td> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <th>11</th> <td>5</td> <td>fabriano basket</td> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <th>12</th> <td>5</td> <td>societa veroli basket</td> <td>0</td> <td>0</td> <td>0</td> </tr> <tr> <th>13</th> <td>5</td> <td>pistoia basket 2000</td> <td>0</td> <td>0</td> <td>0</td> </tr> <tr> <th>14</th> <td>4</td> <td>juve caserta basket</td> <td>0</td> <td>1</td> <td>0</td> </tr> <tr> <th>15</th> <td>3</td> <td>gruppo triboldi basket soresina</td> <td>0</td> <td>1</td> <td>0</td> </tr> <tr> <th>16</th> <td>3</td> <td>nuova sebastiani rieti</td> <td>1</td> <td>1</td> <td>0</td> </tr> <tr> <th>17</th> <td>3</td> <td>progresso castelmaggiore</td> <td>0</td> <td>0</td> <td>0</td> </tr> <tr> <th>18</th> <td>3</td> <td>orlandina basket</td> <td>1</td> <td>1</td> <td>1</td> </tr> <tr> <th>19</th> <td>3</td> <td>sutor basket montegranaro</td> <td>0</td> <td>1</td> <td>1</td> </tr> <tr> <th>20</th> <td>3</td> <td>virtus ragusa</td> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <th>21</th> <td>3</td> <td>robur basket osimo</td> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <th>22</th> <td>3</td> <td>reyer venezia</td> <td>0</td> <td>1</td> <td>0</td> </tr> <tr> <th>23</th> <td>3</td> <td>new basket brindisi</td> <td>1</td> <td>2</td> <td>0</td> </tr> <tr> <th>24</th> <td>2</td> <td>pallacanestro messina</td> <td>0</td> <td>1</td> <td>0</td> </tr> <tr> <th>25</th> <td>2</td> <td>basket trapani</td> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <th>26</th> <td>2</td> <td>basket draghi castelletto ticino</td> <td>0</td> <td>0</td> <td>0</td> </tr> <tr> <th>27</th> <td>2</td> <td>basket livorno</td> <td>0</td> <td>0</td> <td>0</td> </tr> <tr> <th>28</th> <td>2</td> <td>pallalcesto amatori udine</td> <td>0</td> <td>0</td> <td>0</td> </tr> <tr> <th>29</th> <td>2</td> <td>unione cestistica casalpusterlengo</td> <td>0</td> <td>0</td> <td>0</td> </tr> <tr> <th>30</th> <td>2</td> <td>basket barcellona</td> <td>0</td> <td>0</td> <td>0</td> </tr> <tr> <th>31</th> <td>2</td> <td>fulgor libertas forli</td> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <th>32</th> <td>2</td> <td>scaligera basket verona</td> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <th>33</th> <td>1</td> <td>societa sportiva basket napoli</td> <td>1</td> <td>1</td> <td>0</td> </tr> <tr> <th>34</th> <td>1</td> <td>centro sportivo borgomanero</td> <td>0</td> <td>0</td> <td>0</td> </tr> <tr> <th>35</th> <td>1</td> <td>celana basket bergamo</td> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <th>36</th> <td>1</td> <td>teramo basket</td> <td>1</td> <td>1</td> <td>0</td> </tr> <tr> <th>37</th> <td>1</td> <td>virtus pallacanestro bologna</td> <td>0</td> <td>1</td> <td>0</td> </tr> <tr> <th>38</th> <td>1</td> <td>viola reggio calabria</td> <td>0</td> <td>0</td> <td>0</td> </tr> <tr> <th>39</th> <td>1</td> <td>victoria libertas pesaro</td> <td>0</td> <td>1</td> <td>0</td> </tr> <tr> <th>40</th> <td>1</td> <td>pallacanestro varese</td> <td>1</td> <td>1</td> <td>0</td> </tr> <tr> <th>41</th> <td>1</td> <td>pallacanestro roseto</td> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <th>42</th> <td>1</td> <td>nuova pallacanestro vigevano</td> <td>0</td> <td>0</td> <td>0</td> </tr> <tr> <th>43</th> <td>1</td> <td>cestistica san severo</td> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <th>44</th> <td>1</td> <td>basket brescia leonessa</td> <td>0</td> <td>0</td> <td>0</td> </tr> <tr> <th>45</th> <td>1</td> <td>unione cestistica piacentina piacenza</td> <td>0</td> <td>0</td> <td>0</td> </tr> <tr> <th>46</th> <td>1</td> <td>pallacanestro sant'antimo</td> <td>0</td> <td>0</td> <td>0</td> </tr> <tr> <th>47</th> <td>1</td> <td>ostuni basket</td> <td>0</td> <td>0</td> <td>0</td> </tr> <tr> <th>48</th> <td>1</td> <td>biancoblu basket bologna</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>count ( "team" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>0</td> </tr> </tbody> </table>

select count ( team ) where team = 'aurora basket jesi' and promotions = 0 <table_name> : table | | seasons | team | ch.wins | promotions | relegations | |---:|----------:|:--------------------------------------|----------:|-------------:|--------------:| | 0 | 10 | basket rimini crabs | 0 | 0 | 0 | | 1 | 10 | aurora basket jesi | 0 | 1 | 1 | | 2 | 10 | andrea costa imola | 0 | 0 | 3 | | 3 | 9 | nuova pallacanestro pavia | 0 | 0 | 0 | | 4 | 9 | scafati basket | 1 | 1 | 0 | | 5 | 8 | basket club ferrara | 1 | 1 | 0 | | 6 | 8 | pallac. reggiana reggio emilia | 2 | 2 | 0 | | 7 | 7 | novara | 0 | 0 | 3 | | 8 | 7 | dinamo basket sassari | 0 | 1 | 0 | | 9 | 6 | a.s. junior pallacanestro casale | 1 | 1 | 1 | | 10 | 5 | rb montecatini terme | 0 | 0 | 1 | | 11 | 5 | fabriano basket | 0 | 0 | 1 | | 12 | 5 | societa veroli basket | 0 | 0 | 0 | | 13 | 5 | pistoia basket 2000 | 0 | 0 | 0 | | 14 | 4 | juve caserta basket | 0 | 1 | 0 | | 15 | 3 | gruppo triboldi basket soresina | 0 | 1 | 0 | | 16 | 3 | nuova sebastiani rieti | 1 | 1 | 0 | | 17 | 3 | progresso castelmaggiore | 0 | 0 | 0 | | 18 | 3 | orlandina basket | 1 | 1 | 1 | | 19 | 3 | sutor basket montegranaro | 0 | 1 | 1 | | 20 | 3 | virtus ragusa | 0 | 0 | 1 | | 21 | 3 | robur basket osimo | 0 | 0 | 1 | | 22 | 3 | reyer venezia | 0 | 1 | 0 | | 23 | 3 | new basket brindisi | 1 | 2 | 0 | | 24 | 2 | pallacanestro messina | 0 | 1 | 0 | | 25 | 2 | basket trapani | 0 | 0 | 1 | | 26 | 2 | basket draghi castelletto ticino | 0 | 0 | 0 | | 27 | 2 | basket livorno | 0 | 0 | 0 | | 28 | 2 | pallalcesto amatori udine | 0 | 0 | 0 | | 29 | 2 | unione cestistica casalpusterlengo | 0 | 0 | 0 | | 30 | 2 | basket barcellona | 0 | 0 | 0 | | 31 | 2 | fulgor libertas forli | 0 | 0 | 1 | | 32 | 2 | scaligera basket verona | 0 | 0 | 1 | | 33 | 1 | societa sportiva basket napoli | 1 | 1 | 0 | | 34 | 1 | centro sportivo borgomanero | 0 | 0 | 0 | | 35 | 1 | celana basket bergamo | 0 | 0 | 1 | | 36 | 1 | teramo basket | 1 | 1 | 0 | | 37 | 1 | virtus pallacanestro bologna | 0 | 1 | 0 | | 38 | 1 | viola reggio calabria | 0 | 0 | 0 | | 39 | 1 | victoria libertas pesaro | 0 | 1 | 0 | | 40 | 1 | pallacanestro varese | 1 | 1 | 0 | | 41 | 1 | pallacanestro roseto | 0 | 0 | 1 | | 42 | 1 | nuova pallacanestro vigevano | 0 | 0 | 0 | | 43 | 1 | cestistica san severo | 0 | 0 | 1 | | 44 | 1 | basket brescia leonessa | 0 | 0 | 0 | | 45 | 1 | unione cestistica piacentina piacenza | 0 | 0 | 0 | | 46 | 1 | pallacanestro sant'antimo | 0 | 0 | 0 | | 47 | 1 | ostuni basket | 0 | 0 | 0 | | 48 | 1 | biancoblu basket bologna | 0 | 0 | 0 |

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

select type where type = 'half-bridge' col : type | power [w] | relative cost | input range [v] | energy storage | features row 1 : flyback | 0-250 | 1 | 5-600 | mutual inductors | isolated form of the buck-boost converter row 2 : ringing choke converter (rcc) | 0-150 | 1 | 5-600 | transformer | low-cost self-oscillating flyback variant row 3 : half-forward | 0-250 | 1.2 | 5-500 | inductor | none row 4 : forward | 100-200 | none | 60-200 | inductor | isolated form of buck converter row 5 : resonant forward | 0-60 | 1 | 60-400 | inductor and capacitor | single rail input, unregulated output, high efficiency, low emi row 6 : push-pull | 100-1,000 | 1.75 | 50-1,000 | inductor | none row 7 : half-bridge | 0-2,000 | 1.9 | 50-1,000 | inductor | none row 8 : full-bridge | 400-5,000 | >2.0 | 50-1,000 | inductor | very efficient use of transformer, used for highest powers row 9 : resonant, zero voltage switched | >1,000 | >2.0 | none | inductor and capacitor | none row 10 : isolated cuk | none | none | none | two capacitors and two inductors | none

col : type row 1 : half-bridge

select type where type = 'half-bridge' <table_name> : table \begin{tabular}{lllllll} \toprule & type & power [w] & relative cost & input range [v] & energy storage & features \\ \midrule 0 & flyback & 0-250 & 1 & 5-600 & mutual inductors & isolated form of the buck-boost converter \\ 1 & ringing choke converter (rcc) & 0-150 & 1 & 5-600 & transformer & low-cost self-oscillating flyback variant \\ 2 & half-forward & 0-250 & 1.2 & 5-500 & inductor & none \\ 3 & forward & 100-200 & none & 60-200 & inductor & isolated form of buck converter \\ 4 & resonant forward & 0-60 & 1 & 60-400 & inductor and capacitor & single rail input, unregulated output, high efficiency, low emi \\ 5 & push-pull & 100-1,000 & 1.75 & 50-1,000 & inductor & none \\ 6 & half-bridge & 0-2,000 & 1.9 & 50-1,000 & inductor & none \\ 7 & full-bridge & 400-5,000 & >2.0 & 50-1,000 & inductor & very efficient use of transformer, used for highest powers \\ 8 & resonant, zero voltage switched & >1,000 & >2.0 & none & inductor and capacitor & none \\ 9 & isolated cuk & none & none & none & two capacitors and two inductors & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & type \\ \midrule 0 & half-bridge \\ \bottomrule \end{tabular}

select type where type = 'half-bridge' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>type</th> <th>power [w]</th> <th>relative cost</th> <th>input range [v]</th> <th>energy storage</th> <th>features</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>flyback</td> <td>0-250</td> <td>1</td> <td>5-600</td> <td>mutual inductors</td> <td>isolated form of the buck-boost converter</td> </tr> <tr> <th>1</th> <td>ringing choke converter (rcc)</td> <td>0-150</td> <td>1</td> <td>5-600</td> <td>transformer</td> <td>low-cost self-oscillating flyback variant</td> </tr> <tr> <th>2</th> <td>half-forward</td> <td>0-250</td> <td>1.2</td> <td>5-500</td> <td>inductor</td> <td>none</td> </tr> <tr> <th>3</th> <td>forward</td> <td>100-200</td> <td>none</td> <td>60-200</td> <td>inductor</td> <td>isolated form of buck converter</td> </tr> <tr> <th>4</th> <td>resonant forward</td> <td>0-60</td> <td>1</td> <td>60-400</td> <td>inductor and capacitor</td> <td>single rail input, unregulated output, high efficiency, low emi</td> </tr> <tr> <th>5</th> <td>push-pull</td> <td>100-1,000</td> <td>1.75</td> <td>50-1,000</td> <td>inductor</td> <td>none</td> </tr> <tr> <th>6</th> <td>half-bridge</td> <td>0-2,000</td> <td>1.9</td> <td>50-1,000</td> <td>inductor</td> <td>none</td> </tr> <tr> <th>7</th> <td>full-bridge</td> <td>400-5,000</td> <td>&gt;2.0</td> <td>50-1,000</td> <td>inductor</td> <td>very efficient use of transformer, used for highest powers</td> </tr> <tr> <th>8</th> <td>resonant, zero voltage switched</td> <td>&gt;1,000</td> <td>&gt;2.0</td> <td>none</td> <td>inductor and capacitor</td> <td>none</td> </tr> <tr> <th>9</th> <td>isolated cuk</td> <td>none</td> <td>none</td> <td>none</td> <td>two capacitors and two inductors</td> <td>none</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>half-bridge</td> </tr> </tbody> </table>

select type where type = 'half-bridge' <table_name> : table | | type | power [w] | relative cost | input range [v] | energy storage | features | |---:|:--------------------------------|:------------|:----------------|:------------------|:---------------------------------|:----------------------------------------------------------------| | 0 | flyback | 0-250 | 1 | 5-600 | mutual inductors | isolated form of the buck-boost converter | | 1 | ringing choke converter (rcc) | 0-150 | 1 | 5-600 | transformer | low-cost self-oscillating flyback variant | | 2 | half-forward | 0-250 | 1.2 | 5-500 | inductor | none | | 3 | forward | 100-200 | none | 60-200 | inductor | isolated form of buck converter | | 4 | resonant forward | 0-60 | 1 | 60-400 | inductor and capacitor | single rail input, unregulated output, high efficiency, low emi | | 5 | push-pull | 100-1,000 | 1.75 | 50-1,000 | inductor | none | | 6 | half-bridge | 0-2,000 | 1.9 | 50-1,000 | inductor | none | | 7 | full-bridge | 400-5,000 | >2.0 | 50-1,000 | inductor | very efficient use of transformer, used for highest powers | | 8 | resonant, zero voltage switched | >1,000 | >2.0 | none | inductor and capacitor | none | | 9 | isolated cuk | none | none | none | two capacitors and two inductors | none |

| | type | |---:|:------------| | 0 | half-bridge |

select count ( division ) where tier > 751 col : season | tier | division | place row 1 : 1970-01 | 6 | regional | none row 2 : 2001/02 | 6 | 1a reg | none row 3 : 2002/03 | 5 | reg. pref | 11th row 4 : 2003/04 | 5 | reg. pref | 9th row 5 : 2004/05 | 5 | reg. pref | 1st row 6 : 2005/06 | 4 | 3a | 20th row 7 : 2006/07 | 5 | reg. pref | 3rd row 8 : 2007/08 | 5 | reg. pref | 1st row 9 : 2008/09 | 4 | 3a | 16th row 10 : 2009/10 | 4 | 3a | 17th row 11 : 2010/11 | 4 | 3a | 18th row 12 : 2011/12 | 5 | reg. pref | 2nd

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

select count ( division ) where tier > 751 <table_name> : table \begin{tabular}{lllll} \toprule & season & tier & division & place \\ \midrule 0 & 1970-01 & 6 & regional & none \\ 1 & 2001/02 & 6 & 1a reg & none \\ 2 & 2002/03 & 5 & reg. pref & 11th \\ 3 & 2003/04 & 5 & reg. pref & 9th \\ 4 & 2004/05 & 5 & reg. pref & 1st \\ 5 & 2005/06 & 4 & 3a & 20th \\ 6 & 2006/07 & 5 & reg. pref & 3rd \\ 7 & 2007/08 & 5 & reg. pref & 1st \\ 8 & 2008/09 & 4 & 3a & 16th \\ 9 & 2009/10 & 4 & 3a & 17th \\ 10 & 2010/11 & 4 & 3a & 18th \\ 11 & 2011/12 & 5 & reg. pref & 2nd \\ \bottomrule \end{tabular}

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

select count ( division ) where tier > 751 <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>place</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1970-01</td> <td>6</td> <td>regional</td> <td>none</td> </tr> <tr> <th>1</th> <td>2001/02</td> <td>6</td> <td>1a reg</td> <td>none</td> </tr> <tr> <th>2</th> <td>2002/03</td> <td>5</td> <td>reg. pref</td> <td>11th</td> </tr> <tr> <th>3</th> <td>2003/04</td> <td>5</td> <td>reg. pref</td> <td>9th</td> </tr> <tr> <th>4</th> <td>2004/05</td> <td>5</td> <td>reg. pref</td> <td>1st</td> </tr> <tr> <th>5</th> <td>2005/06</td> <td>4</td> <td>3a</td> <td>20th</td> </tr> <tr> <th>6</th> <td>2006/07</td> <td>5</td> <td>reg. pref</td> <td>3rd</td> </tr> <tr> <th>7</th> <td>2007/08</td> <td>5</td> <td>reg. pref</td> <td>1st</td> </tr> <tr> <th>8</th> <td>2008/09</td> <td>4</td> <td>3a</td> <td>16th</td> </tr> <tr> <th>9</th> <td>2009/10</td> <td>4</td> <td>3a</td> <td>17th</td> </tr> <tr> <th>10</th> <td>2010/11</td> <td>4</td> <td>3a</td> <td>18th</td> </tr> <tr> <th>11</th> <td>2011/12</td> <td>5</td> <td>reg. pref</td> <td>2nd</td> </tr> </tbody> </table>

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

select count ( division ) where tier > 751 <table_name> : table | | season | tier | division | place | |---:|:---------|-------:|:-----------|:--------| | 0 | 1970-01 | 6 | regional | none | | 1 | 2001/02 | 6 | 1a reg | none | | 2 | 2002/03 | 5 | reg. pref | 11th | | 3 | 2003/04 | 5 | reg. pref | 9th | | 4 | 2004/05 | 5 | reg. pref | 1st | | 5 | 2005/06 | 4 | 3a | 20th | | 6 | 2006/07 | 5 | reg. pref | 3rd | | 7 | 2007/08 | 5 | reg. pref | 1st | | 8 | 2008/09 | 4 | 3a | 16th | | 9 | 2009/10 | 4 | 3a | 17th | | 10 | 2010/11 | 4 | 3a | 18th | | 11 | 2011/12 | 5 | reg. pref | 2nd |

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

select count ( title ) where date > 1180 col : title | character | broadcaster | episodes | date row 1 : rev | pip | bbc two | 1.0 | 2010 row 2 : the i.t. crowd | nikki | channel 4 | 1.0 | 2010 row 3 : eastenders: e20 | ava | bbc one | 15.0 | 2011 row 4 : fresh meat | caz | channel 4 | 1.0 | 2011 row 5 : phoneshop | daisy | channel 4 | 1.0 | 2011 row 6 : holby city | heidi hall | bbc one | 1.0 | 2012 row 7 : plebs | cynthia | itv2 | 6.0 | 2013 row 8 : blandings | valerie fanshawe | bbc | 2.5 | 2014 row 9 : death in paradise | helen walker | bbc | 3.4 | 2014

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

select count ( title ) where date > 1180 <table_name> : table \begin{tabular}{llllll} \toprule & title & character & broadcaster & episodes & date \\ \midrule 0 & rev & pip & bbc two & 1.0 & 2010 \\ 1 & the i.t. crowd & nikki & channel 4 & 1.0 & 2010 \\ 2 & eastenders: e20 & ava & bbc one & 15.0 & 2011 \\ 3 & fresh meat & caz & channel 4 & 1.0 & 2011 \\ 4 & phoneshop & daisy & channel 4 & 1.0 & 2011 \\ 5 & holby city & heidi hall & bbc one & 1.0 & 2012 \\ 6 & plebs & cynthia & itv2 & 6.0 & 2013 \\ 7 & blandings & valerie fanshawe & bbc & 2.5 & 2014 \\ 8 & death in paradise & helen walker & bbc & 3.4 & 2014 \\ \bottomrule \end{tabular}

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

select count ( title ) where date > 1180 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>title</th> <th>character</th> <th>broadcaster</th> <th>episodes</th> <th>date</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>rev</td> <td>pip</td> <td>bbc two</td> <td>1.0</td> <td>2010</td> </tr> <tr> <th>1</th> <td>the i.t. crowd</td> <td>nikki</td> <td>channel 4</td> <td>1.0</td> <td>2010</td> </tr> <tr> <th>2</th> <td>eastenders: e20</td> <td>ava</td> <td>bbc one</td> <td>15.0</td> <td>2011</td> </tr> <tr> <th>3</th> <td>fresh meat</td> <td>caz</td> <td>channel 4</td> <td>1.0</td> <td>2011</td> </tr> <tr> <th>4</th> <td>phoneshop</td> <td>daisy</td> <td>channel 4</td> <td>1.0</td> <td>2011</td> </tr> <tr> <th>5</th> <td>holby city</td> <td>heidi hall</td> <td>bbc one</td> <td>1.0</td> <td>2012</td> </tr> <tr> <th>6</th> <td>plebs</td> <td>cynthia</td> <td>itv2</td> <td>6.0</td> <td>2013</td> </tr> <tr> <th>7</th> <td>blandings</td> <td>valerie fanshawe</td> <td>bbc</td> <td>2.5</td> <td>2014</td> </tr> <tr> <th>8</th> <td>death in paradise</td> <td>helen walker</td> <td>bbc</td> <td>3.4</td> <td>2014</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>9</td> </tr> </tbody> </table>

select count ( title ) where date > 1180 <table_name> : table | | title | character | broadcaster | episodes | date | |---:|:------------------|:-----------------|:--------------|-----------:|-------:| | 0 | rev | pip | bbc two | 1 | 2010 | | 1 | the i.t. crowd | nikki | channel 4 | 1 | 2010 | | 2 | eastenders: e20 | ava | bbc one | 15 | 2011 | | 3 | fresh meat | caz | channel 4 | 1 | 2011 | | 4 | phoneshop | daisy | channel 4 | 1 | 2011 | | 5 | holby city | heidi hall | bbc one | 1 | 2012 | | 6 | plebs | cynthia | itv2 | 6 | 2013 | | 7 | blandings | valerie fanshawe | bbc | 2.5 | 2014 | | 8 | death in paradise | helen walker | bbc | 3.4 | 2014 |

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

select votes where candidate = 'rice sheppard' col : party | candidate | votes row 1 : labour | james kinney | 5513 row 2 : labour | james east | 4847 row 3 : labour | rice sheppard | 4644 row 4 : citizens' committee | percy abbott | 4233 row 5 : citizens' committee | john bowen | 4093 row 6 : citizens' committee | george h. scott | 3835 row 7 : labour | james findlay | 3775 row 8 : labour | j. j. murray | 3696 row 9 : citizens' committee | valentine richards | 3524 row 10 : citizens' committee | charles grant | 3230 row 11 : independent | charles g. davidson | 2982 row 12 : independent | joseph adair | 2356

col : votes row 1 : 4644

select votes where candidate = 'rice sheppard' <table_name> : table \begin{tabular}{llll} \toprule & party & candidate & votes \\ \midrule 0 & labour & james kinney & 5513 \\ 1 & labour & james east & 4847 \\ 2 & labour & rice sheppard & 4644 \\ 3 & citizens' committee & percy abbott & 4233 \\ 4 & citizens' committee & john bowen & 4093 \\ 5 & citizens' committee & george h. scott & 3835 \\ 6 & labour & james findlay & 3775 \\ 7 & labour & j. j. murray & 3696 \\ 8 & citizens' committee & valentine richards & 3524 \\ 9 & citizens' committee & charles grant & 3230 \\ 10 & independent & charles g. davidson & 2982 \\ 11 & independent & joseph adair & 2356 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & votes \\ \midrule 0 & 4644 \\ \bottomrule \end{tabular}

select votes where candidate = 'rice sheppard' <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> </tr> </thead> <tbody> <tr> <th>0</th> <td>labour</td> <td>james kinney</td> <td>5513</td> </tr> <tr> <th>1</th> <td>labour</td> <td>james east</td> <td>4847</td> </tr> <tr> <th>2</th> <td>labour</td> <td>rice sheppard</td> <td>4644</td> </tr> <tr> <th>3</th> <td>citizens' committee</td> <td>percy abbott</td> <td>4233</td> </tr> <tr> <th>4</th> <td>citizens' committee</td> <td>john bowen</td> <td>4093</td> </tr> <tr> <th>5</th> <td>citizens' committee</td> <td>george h. scott</td> <td>3835</td> </tr> <tr> <th>6</th> <td>labour</td> <td>james findlay</td> <td>3775</td> </tr> <tr> <th>7</th> <td>labour</td> <td>j. j. murray</td> <td>3696</td> </tr> <tr> <th>8</th> <td>citizens' committee</td> <td>valentine richards</td> <td>3524</td> </tr> <tr> <th>9</th> <td>citizens' committee</td> <td>charles grant</td> <td>3230</td> </tr> <tr> <th>10</th> <td>independent</td> <td>charles g. davidson</td> <td>2982</td> </tr> <tr> <th>11</th> <td>independent</td> <td>joseph adair</td> <td>2356</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>4644</td> </tr> </tbody> </table>

select votes where candidate = 'rice sheppard' <table_name> : table | | party | candidate | votes | |---:|:--------------------|:--------------------|--------:| | 0 | labour | james kinney | 5513 | | 1 | labour | james east | 4847 | | 2 | labour | rice sheppard | 4644 | | 3 | citizens' committee | percy abbott | 4233 | | 4 | citizens' committee | john bowen | 4093 | | 5 | citizens' committee | george h. scott | 3835 | | 6 | labour | james findlay | 3775 | | 7 | labour | j. j. murray | 3696 | | 8 | citizens' committee | valentine richards | 3524 | | 9 | citizens' committee | charles grant | 3230 | | 10 | independent | charles g. davidson | 2982 | | 11 | independent | joseph adair | 2356 |

| | votes | |---:|--------:| | 0 | 4644 |

select host(s) where description = 'current events' col : et | days available | program | host(s) | description row 1 : 9am-10am | monday-friday | the morning click | jamie colby (m,t), harris faulkner (w-f) | current events row 2 : 10am-10:30am | monday-friday | none | lauren green (m), uma pemmaraju (t), gregg jarrett (w), arthel neville (f), rick folbaum (f), heather childers | current events row 3 : 11am-11:30am | monday-friday | fbn live | lauren simonetti | business news and information row 4 : 12pm-1pm | monday-friday | on the hunt | jonathan hunt | current events row 5 : 2pm-3pm | monday | entertainment hour | ashley dvorkin | entertainment news, musical performances row 6 : 2pm-3pm | tuesday | none | kimberly guilfoyle | crime and legal news row 7 : 2pm-3pm | wednesday | defcon 3 | kathleen troia mcfarland | military news and information row 8 : 2pm-3pm | thursday | fox car report | gary gastelu | automotive news and information row 9 : 2pm-3pm | friday | spirited debate | lauren green | discussion on religious topics

col : host(s) row 1 : jamie colby (m,t), harris faulkner (w-f) row 2 : lauren green (m), uma pemmaraju (t), gregg jarrett (w), arthel neville (f), rick folbaum (f), heather childers row 3 : jonathan hunt

select host(s) where description = 'current events' <table_name> : table \begin{tabular}{llllll} \toprule & et & days available & program & host(s) & description \\ \midrule 0 & 9am-10am & monday-friday & the morning click & jamie colby (m,t), harris faulkner (w-f) & current events \\ 1 & 10am-10:30am & monday-friday & none & lauren green (m), uma pemmaraju (t), gregg jarrett (w), arthel neville (f), rick folbaum (f), heather childers & current events \\ 2 & 11am-11:30am & monday-friday & fbn live & lauren simonetti & business news and information \\ 3 & 12pm-1pm & monday-friday & on the hunt & jonathan hunt & current events \\ 4 & 2pm-3pm & monday & entertainment hour & ashley dvorkin & entertainment news, musical performances \\ 5 & 2pm-3pm & tuesday & none & kimberly guilfoyle & crime and legal news \\ 6 & 2pm-3pm & wednesday & defcon 3 & kathleen troia mcfarland & military news and information \\ 7 & 2pm-3pm & thursday & fox car report & gary gastelu & automotive news and information \\ 8 & 2pm-3pm & friday & spirited debate & lauren green & discussion on religious topics \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & host(s) \\ \midrule 0 & jamie colby (m,t), harris faulkner (w-f) \\ 1 & lauren green (m), uma pemmaraju (t), gregg jarrett (w), arthel neville (f), rick folbaum (f), heather childers \\ 2 & jonathan hunt \\ \bottomrule \end{tabular}

select host(s) where description = 'current events' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>et</th> <th>days available</th> <th>program</th> <th>host(s)</th> <th>description</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>9am-10am</td> <td>monday-friday</td> <td>the morning click</td> <td>jamie colby (m,t), harris faulkner (w-f)</td> <td>current events</td> </tr> <tr> <th>1</th> <td>10am-10:30am</td> <td>monday-friday</td> <td>none</td> <td>lauren green (m), uma pemmaraju (t), gregg jarrett (w), arthel neville (f), rick folbaum (f), heather childers</td> <td>current events</td> </tr> <tr> <th>2</th> <td>11am-11:30am</td> <td>monday-friday</td> <td>fbn live</td> <td>lauren simonetti</td> <td>business news and information</td> </tr> <tr> <th>3</th> <td>12pm-1pm</td> <td>monday-friday</td> <td>on the hunt</td> <td>jonathan hunt</td> <td>current events</td> </tr> <tr> <th>4</th> <td>2pm-3pm</td> <td>monday</td> <td>entertainment hour</td> <td>ashley dvorkin</td> <td>entertainment news, musical performances</td> </tr> <tr> <th>5</th> <td>2pm-3pm</td> <td>tuesday</td> <td>none</td> <td>kimberly guilfoyle</td> <td>crime and legal news</td> </tr> <tr> <th>6</th> <td>2pm-3pm</td> <td>wednesday</td> <td>defcon 3</td> <td>kathleen troia mcfarland</td> <td>military news and information</td> </tr> <tr> <th>7</th> <td>2pm-3pm</td> <td>thursday</td> <td>fox car report</td> <td>gary gastelu</td> <td>automotive news and information</td> </tr> <tr> <th>8</th> <td>2pm-3pm</td> <td>friday</td> <td>spirited debate</td> <td>lauren green</td> <td>discussion on religious topics</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>host(s)</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>jamie colby (m,t), harris faulkner (w-f)</td> </tr> <tr> <th>1</th> <td>lauren green (m), uma pemmaraju (t), gregg jarrett (w), arthel neville (f), rick folbaum (f), heather childers</td> </tr> <tr> <th>2</th> <td>jonathan hunt</td> </tr> </tbody> </table>

select host(s) where description = 'current events' <table_name> : table | | et | days available | program | host(s) | description | |---:|:-------------|:-----------------|:-------------------|:---------------------------------------------------------------------------------------------------------------|:-----------------------------------------| | 0 | 9am-10am | monday-friday | the morning click | jamie colby (m,t), harris faulkner (w-f) | current events | | 1 | 10am-10:30am | monday-friday | none | lauren green (m), uma pemmaraju (t), gregg jarrett (w), arthel neville (f), rick folbaum (f), heather childers | current events | | 2 | 11am-11:30am | monday-friday | fbn live | lauren simonetti | business news and information | | 3 | 12pm-1pm | monday-friday | on the hunt | jonathan hunt | current events | | 4 | 2pm-3pm | monday | entertainment hour | ashley dvorkin | entertainment news, musical performances | | 5 | 2pm-3pm | tuesday | none | kimberly guilfoyle | crime and legal news | | 6 | 2pm-3pm | wednesday | defcon 3 | kathleen troia mcfarland | military news and information | | 7 | 2pm-3pm | thursday | fox car report | gary gastelu | automotive news and information | | 8 | 2pm-3pm | friday | spirited debate | lauren green | discussion on religious topics |

| | host(s) | |---:|:---------------------------------------------------------------------------------------------------------------| | 0 | jamie colby (m,t), harris faulkner (w-f) | | 1 | lauren green (m), uma pemmaraju (t), gregg jarrett (w), arthel neville (f), rick folbaum (f), heather childers | | 2 | jonathan hunt |

select founded where locality = 'stacksteads' 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 : founded row 1 : 1947

select founded where locality = 'stacksteads' <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 & founded \\ \midrule 0 & 1947 \\ \bottomrule \end{tabular}

select founded where locality = 'stacksteads' <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>founded</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1947</td> </tr> </tbody> </table>

select founded where locality = 'stacksteads' <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 |

| | founded | |---:|----------:| | 0 | 1947 |

select max ( start of term ) where sheriff = 'william mccollom' col : sheriff | start of term row 1 : josiah hobart | 1683 row 2 : john mulford | 1701 row 3 : hugh gray | 1702 row 4 : john brush | 1710 row 5 : daniel youngs | 1718 row 6 : samuel dayton | 1723 row 7 : william sell | 1728 row 8 : joseph smith | 1730 row 9 : david corrie | 1731 row 10 : jacob conklin | 1734 row 11 : thomas higbe | 1740 row 12 : james muirson | 1774 row 13 : thomas wickes | 1785 row 14 : silas halsey | 1787 row 15 : thomas wickes | 1791 row 16 : phinaes carll | 1799 row 17 : josiah reeve | 1803 row 18 : phinaes smith | 1807 row 19 : josiah reeve | 1808 row 20 : benjamin brewster | 1810 row 21 : josiah reeve | 1811 row 22 : benjamin brewster | 1812 row 23 : josiah reeve | 1813 row 24 : nathaniel conklin | 1814 row 25 : josiah reeve | 1815 row 26 : samuel carll | 1819 row 27 : abraham gardiner | 1821 row 28 : samuel smith | 1826 row 29 : abraham gardiner | 1829 row 30 : richard smith | 1832 row 31 : silas horton | 1835 row 32 : samuel miller | 1838 row 33 : david brush | 1841 row 34 : henry penny | 1844 row 35 : david rose | 1847 row 36 : john clark | 1850 row 37 : samuel phillips | 1855 row 38 : george carman | 1856 row 39 : stephen wilson | 1859 row 40 : daniel osborn | 1862 row 41 : george smith | 1868 row 42 : j. henry perkins | 1871 row 43 : egbert lewis | 1874 row 44 : george cooper | 1877 row 45 : robert petty | 1888 row 46 : selah brewster | 1883 row 47 : henry halsey | 1886 row 48 : robert petty | 1888 row 49 : a. m. darling | 1891 row 50 : benjamin wood | 1897 row 51 : j. sheridan wells | 1900 row 52 : henry preston | 1903 row 53 : john wells | 1906 row 54 : charles platt | 1909 row 55 : melville brush | 1912 row 56 : d. henry brown | 1913 row 57 : charles o'dell | 1914 row 58 : amza biggs | 1917 row 59 : john kelly | 1920 row 60 : amza biggs | 1923 row 61 : burton howe | 1926 row 62 : ellis taylor | 1929 row 63 : joseph warta | 1932 row 64 : william mccollom | 1935 row 65 : jacob dreyer | 1938 row 66 : john levy | 1941 row 67 : william mccollom | 1942 row 68 : charles dominy | 1957 row 69 : frank gross | 1962 row 70 : philip corso | 1970 row 71 : donald dilworth | 1976 row 72 : john finnerty | 1977 row 73 : eugene dooley | 1986 row 74 : patrick mahoney | 1990 row 75 : alfred c. tisch | 2002 row 76 : vincent f. demarco | 2006

col : max ( "start of term" ) row 1 : 1942

select max ( start of term ) where sheriff = 'william mccollom' <table_name> : table \begin{tabular}{lll} \toprule & sheriff & start of term \\ \midrule 0 & josiah hobart & 1683 \\ 1 & john mulford & 1701 \\ 2 & hugh gray & 1702 \\ 3 & john brush & 1710 \\ 4 & daniel youngs & 1718 \\ 5 & samuel dayton & 1723 \\ 6 & william sell & 1728 \\ 7 & joseph smith & 1730 \\ 8 & david corrie & 1731 \\ 9 & jacob conklin & 1734 \\ 10 & thomas higbe & 1740 \\ 11 & james muirson & 1774 \\ 12 & thomas wickes & 1785 \\ 13 & silas halsey & 1787 \\ 14 & thomas wickes & 1791 \\ 15 & phinaes carll & 1799 \\ 16 & josiah reeve & 1803 \\ 17 & phinaes smith & 1807 \\ 18 & josiah reeve & 1808 \\ 19 & benjamin brewster & 1810 \\ 20 & josiah reeve & 1811 \\ 21 & benjamin brewster & 1812 \\ 22 & josiah reeve & 1813 \\ 23 & nathaniel conklin & 1814 \\ 24 & josiah reeve & 1815 \\ 25 & samuel carll & 1819 \\ 26 & abraham gardiner & 1821 \\ 27 & samuel smith & 1826 \\ 28 & abraham gardiner & 1829 \\ 29 & richard smith & 1832 \\ 30 & silas horton & 1835 \\ 31 & samuel miller & 1838 \\ 32 & david brush & 1841 \\ 33 & henry penny & 1844 \\ 34 & david rose & 1847 \\ 35 & john clark & 1850 \\ 36 & samuel phillips & 1855 \\ 37 & george carman & 1856 \\ 38 & stephen wilson & 1859 \\ 39 & daniel osborn & 1862 \\ 40 & george smith & 1868 \\ 41 & j. henry perkins & 1871 \\ 42 & egbert lewis & 1874 \\ 43 & george cooper & 1877 \\ 44 & robert petty & 1888 \\ 45 & selah brewster & 1883 \\ 46 & henry halsey & 1886 \\ 47 & robert petty & 1888 \\ 48 & a. m. darling & 1891 \\ 49 & benjamin wood & 1897 \\ 50 & j. sheridan wells & 1900 \\ 51 & henry preston & 1903 \\ 52 & john wells & 1906 \\ 53 & charles platt & 1909 \\ 54 & melville brush & 1912 \\ 55 & d. henry brown & 1913 \\ 56 & charles o'dell & 1914 \\ 57 & amza biggs & 1917 \\ 58 & john kelly & 1920 \\ 59 & amza biggs & 1923 \\ 60 & burton howe & 1926 \\ 61 & ellis taylor & 1929 \\ 62 & joseph warta & 1932 \\ 63 & william mccollom & 1935 \\ 64 & jacob dreyer & 1938 \\ 65 & john levy & 1941 \\ 66 & william mccollom & 1942 \\ 67 & charles dominy & 1957 \\ 68 & frank gross & 1962 \\ 69 & philip corso & 1970 \\ 70 & donald dilworth & 1976 \\ 71 & john finnerty & 1977 \\ 72 & eugene dooley & 1986 \\ 73 & patrick mahoney & 1990 \\ 74 & alfred c. tisch & 2002 \\ 75 & vincent f. demarco & 2006 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & max ( "start of term" ) \\ \midrule 0 & 1942 \\ \bottomrule \end{tabular}

select max ( start of term ) where sheriff = 'william mccollom' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>sheriff</th> <th>start of term</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>josiah hobart</td> <td>1683</td> </tr> <tr> <th>1</th> <td>john mulford</td> <td>1701</td> </tr> <tr> <th>2</th> <td>hugh gray</td> <td>1702</td> </tr> <tr> <th>3</th> <td>john brush</td> <td>1710</td> </tr> <tr> <th>4</th> <td>daniel youngs</td> <td>1718</td> </tr> <tr> <th>5</th> <td>samuel dayton</td> <td>1723</td> </tr> <tr> <th>6</th> <td>william sell</td> <td>1728</td> </tr> <tr> <th>7</th> <td>joseph smith</td> <td>1730</td> </tr> <tr> <th>8</th> <td>david corrie</td> <td>1731</td> </tr> <tr> <th>9</th> <td>jacob conklin</td> <td>1734</td> </tr> <tr> <th>10</th> <td>thomas higbe</td> <td>1740</td> </tr> <tr> <th>11</th> <td>james muirson</td> <td>1774</td> </tr> <tr> <th>12</th> <td>thomas wickes</td> <td>1785</td> </tr> <tr> <th>13</th> <td>silas halsey</td> <td>1787</td> </tr> <tr> <th>14</th> <td>thomas wickes</td> <td>1791</td> </tr> <tr> <th>15</th> <td>phinaes carll</td> <td>1799</td> </tr> <tr> <th>16</th> <td>josiah reeve</td> <td>1803</td> </tr> <tr> <th>17</th> <td>phinaes smith</td> <td>1807</td> </tr> <tr> <th>18</th> <td>josiah reeve</td> <td>1808</td> </tr> <tr> <th>19</th> <td>benjamin brewster</td> <td>1810</td> </tr> <tr> <th>20</th> <td>josiah reeve</td> <td>1811</td> </tr> <tr> <th>21</th> <td>benjamin brewster</td> <td>1812</td> </tr> <tr> <th>22</th> <td>josiah reeve</td> <td>1813</td> </tr> <tr> <th>23</th> <td>nathaniel conklin</td> <td>1814</td> </tr> <tr> <th>24</th> <td>josiah reeve</td> <td>1815</td> </tr> <tr> <th>25</th> <td>samuel carll</td> <td>1819</td> </tr> <tr> <th>26</th> <td>abraham gardiner</td> <td>1821</td> </tr> <tr> <th>27</th> <td>samuel smith</td> <td>1826</td> </tr> <tr> <th>28</th> <td>abraham gardiner</td> <td>1829</td> </tr> <tr> <th>29</th> <td>richard smith</td> <td>1832</td> </tr> <tr> <th>30</th> <td>silas horton</td> <td>1835</td> </tr> <tr> <th>31</th> <td>samuel miller</td> <td>1838</td> </tr> <tr> <th>32</th> <td>david brush</td> <td>1841</td> </tr> <tr> <th>33</th> <td>henry penny</td> <td>1844</td> </tr> <tr> <th>34</th> <td>david rose</td> <td>1847</td> </tr> <tr> <th>35</th> <td>john clark</td> <td>1850</td> </tr> <tr> <th>36</th> <td>samuel phillips</td> <td>1855</td> </tr> <tr> <th>37</th> <td>george carman</td> <td>1856</td> </tr> <tr> <th>38</th> <td>stephen wilson</td> <td>1859</td> </tr> <tr> <th>39</th> <td>daniel osborn</td> <td>1862</td> </tr> <tr> <th>40</th> <td>george smith</td> <td>1868</td> </tr> <tr> <th>41</th> <td>j. henry perkins</td> <td>1871</td> </tr> <tr> <th>42</th> <td>egbert lewis</td> <td>1874</td> </tr> <tr> <th>43</th> <td>george cooper</td> <td>1877</td> </tr> <tr> <th>44</th> <td>robert petty</td> <td>1888</td> </tr> <tr> <th>45</th> <td>selah brewster</td> <td>1883</td> </tr> <tr> <th>46</th> <td>henry halsey</td> <td>1886</td> </tr> <tr> <th>47</th> <td>robert petty</td> <td>1888</td> </tr> <tr> <th>48</th> <td>a. m. darling</td> <td>1891</td> </tr> <tr> <th>49</th> <td>benjamin wood</td> <td>1897</td> </tr> <tr> <th>50</th> <td>j. sheridan wells</td> <td>1900</td> </tr> <tr> <th>51</th> <td>henry preston</td> <td>1903</td> </tr> <tr> <th>52</th> <td>john wells</td> <td>1906</td> </tr> <tr> <th>53</th> <td>charles platt</td> <td>1909</td> </tr> <tr> <th>54</th> <td>melville brush</td> <td>1912</td> </tr> <tr> <th>55</th> <td>d. henry brown</td> <td>1913</td> </tr> <tr> <th>56</th> <td>charles o'dell</td> <td>1914</td> </tr> <tr> <th>57</th> <td>amza biggs</td> <td>1917</td> </tr> <tr> <th>58</th> <td>john kelly</td> <td>1920</td> </tr> <tr> <th>59</th> <td>amza biggs</td> <td>1923</td> </tr> <tr> <th>60</th> <td>burton howe</td> <td>1926</td> </tr> <tr> <th>61</th> <td>ellis taylor</td> <td>1929</td> </tr> <tr> <th>62</th> <td>joseph warta</td> <td>1932</td> </tr> <tr> <th>63</th> <td>william mccollom</td> <td>1935</td> </tr> <tr> <th>64</th> <td>jacob dreyer</td> <td>1938</td> </tr> <tr> <th>65</th> <td>john levy</td> <td>1941</td> </tr> <tr> <th>66</th> <td>william mccollom</td> <td>1942</td> </tr> <tr> <th>67</th> <td>charles dominy</td> <td>1957</td> </tr> <tr> <th>68</th> <td>frank gross</td> <td>1962</td> </tr> <tr> <th>69</th> <td>philip corso</td> <td>1970</td> </tr> <tr> <th>70</th> <td>donald dilworth</td> <td>1976</td> </tr> <tr> <th>71</th> <td>john finnerty</td> <td>1977</td> </tr> <tr> <th>72</th> <td>eugene dooley</td> <td>1986</td> </tr> <tr> <th>73</th> <td>patrick mahoney</td> <td>1990</td> </tr> <tr> <th>74</th> <td>alfred c. tisch</td> <td>2002</td> </tr> <tr> <th>75</th> <td>vincent f. demarco</td> <td>2006</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>max ( "start of term" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1942</td> </tr> </tbody> </table>

select max ( start of term ) where sheriff = 'william mccollom' <table_name> : table | | sheriff | start of term | |---:|:-------------------|----------------:| | 0 | josiah hobart | 1683 | | 1 | john mulford | 1701 | | 2 | hugh gray | 1702 | | 3 | john brush | 1710 | | 4 | daniel youngs | 1718 | | 5 | samuel dayton | 1723 | | 6 | william sell | 1728 | | 7 | joseph smith | 1730 | | 8 | david corrie | 1731 | | 9 | jacob conklin | 1734 | | 10 | thomas higbe | 1740 | | 11 | james muirson | 1774 | | 12 | thomas wickes | 1785 | | 13 | silas halsey | 1787 | | 14 | thomas wickes | 1791 | | 15 | phinaes carll | 1799 | | 16 | josiah reeve | 1803 | | 17 | phinaes smith | 1807 | | 18 | josiah reeve | 1808 | | 19 | benjamin brewster | 1810 | | 20 | josiah reeve | 1811 | | 21 | benjamin brewster | 1812 | | 22 | josiah reeve | 1813 | | 23 | nathaniel conklin | 1814 | | 24 | josiah reeve | 1815 | | 25 | samuel carll | 1819 | | 26 | abraham gardiner | 1821 | | 27 | samuel smith | 1826 | | 28 | abraham gardiner | 1829 | | 29 | richard smith | 1832 | | 30 | silas horton | 1835 | | 31 | samuel miller | 1838 | | 32 | david brush | 1841 | | 33 | henry penny | 1844 | | 34 | david rose | 1847 | | 35 | john clark | 1850 | | 36 | samuel phillips | 1855 | | 37 | george carman | 1856 | | 38 | stephen wilson | 1859 | | 39 | daniel osborn | 1862 | | 40 | george smith | 1868 | | 41 | j. henry perkins | 1871 | | 42 | egbert lewis | 1874 | | 43 | george cooper | 1877 | | 44 | robert petty | 1888 | | 45 | selah brewster | 1883 | | 46 | henry halsey | 1886 | | 47 | robert petty | 1888 | | 48 | a. m. darling | 1891 | | 49 | benjamin wood | 1897 | | 50 | j. sheridan wells | 1900 | | 51 | henry preston | 1903 | | 52 | john wells | 1906 | | 53 | charles platt | 1909 | | 54 | melville brush | 1912 | | 55 | d. henry brown | 1913 | | 56 | charles o'dell | 1914 | | 57 | amza biggs | 1917 | | 58 | john kelly | 1920 | | 59 | amza biggs | 1923 | | 60 | burton howe | 1926 | | 61 | ellis taylor | 1929 | | 62 | joseph warta | 1932 | | 63 | william mccollom | 1935 | | 64 | jacob dreyer | 1938 | | 65 | john levy | 1941 | | 66 | william mccollom | 1942 | | 67 | charles dominy | 1957 | | 68 | frank gross | 1962 | | 69 | philip corso | 1970 | | 70 | donald dilworth | 1976 | | 71 | john finnerty | 1977 | | 72 | eugene dooley | 1986 | | 73 | patrick mahoney | 1990 | | 74 | alfred c. tisch | 2002 | | 75 | vincent f. demarco | 2006 |

| | max ( "start of term" ) | |---:|--------------------------:| | 0 | 1942 |

select count ( * ) where french language group electoral district (as of 2012) = 'brussels capital region' col : dutch language group electoral district (as of 2012) | dutch language group seats | french language group electoral district (as of 2012) | french language group seats row 1 : antwerp | 24 | brussels capital region | 13/14 row 2 : brussels capital region | 1/2 | hainaut | 18 row 3 : east flanders | 20 | liege | 15 row 4 : flemish brabant | 15 | luxembourg | 4 row 5 : limburg | 12 | namur | 6 row 6 : west flanders | 16 | walloon brabant | 5 row 7 : total | 88 | total | 62

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

select count ( * ) where french language group electoral district (as of 2012) = 'brussels capital region' <table_name> : table \begin{tabular}{lllll} \toprule & dutch language group electoral district (as of 2012) & dutch language group seats & french language group electoral district (as of 2012) & french language group seats \\ \midrule 0 & antwerp & 24 & brussels capital region & 13/14 \\ 1 & brussels capital region & 1/2 & hainaut & 18 \\ 2 & east flanders & 20 & liege & 15 \\ 3 & flemish brabant & 15 & luxembourg & 4 \\ 4 & limburg & 12 & namur & 6 \\ 5 & west flanders & 16 & walloon brabant & 5 \\ 6 & total & 88 & total & 62 \\ \bottomrule \end{tabular}

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

select count ( * ) where french language group electoral district (as of 2012) = 'brussels capital region' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>dutch language group electoral district (as of 2012)</th> <th>dutch language group seats</th> <th>french language group electoral district (as of 2012)</th> <th>french language group seats</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>antwerp</td> <td>24</td> <td>brussels capital region</td> <td>13/14</td> </tr> <tr> <th>1</th> <td>brussels capital region</td> <td>1/2</td> <td>hainaut</td> <td>18</td> </tr> <tr> <th>2</th> <td>east flanders</td> <td>20</td> <td>liege</td> <td>15</td> </tr> <tr> <th>3</th> <td>flemish brabant</td> <td>15</td> <td>luxembourg</td> <td>4</td> </tr> <tr> <th>4</th> <td>limburg</td> <td>12</td> <td>namur</td> <td>6</td> </tr> <tr> <th>5</th> <td>west flanders</td> <td>16</td> <td>walloon brabant</td> <td>5</td> </tr> <tr> <th>6</th> <td>total</td> <td>88</td> <td>total</td> <td>62</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 french language group electoral district (as of 2012) = 'brussels capital region' <table_name> : table | | dutch language group electoral district (as of 2012) | dutch language group seats | french language group electoral district (as of 2012) | french language group seats | |---:|:-------------------------------------------------------|:-----------------------------|:--------------------------------------------------------|:------------------------------| | 0 | antwerp | 24 | brussels capital region | 13/14 | | 1 | brussels capital region | 1/2 | hainaut | 18 | | 2 | east flanders | 20 | liege | 15 | | 3 | flemish brabant | 15 | luxembourg | 4 | | 4 | limburg | 12 | namur | 6 | | 5 | west flanders | 16 | walloon brabant | 5 | | 6 | total | 88 | total | 62 |

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

select max ( crowd ) where crowd > 762 col : # | date | opponent | score | win | loss | save | crowd | record row 1 : 1 | apr 3 | @min | w 8-1 | morris | williams | none | 34381 | 1-0 row 2 : 2 | apr 5 | @min | w 7-3 | petry | viola | none | 8373 | 2-0 row 3 : 3 | apr 6 | @chw | w 3-2 | wilcox | dotson | hernandez | 42692 | 3-0 row 4 : 4 | apr 7 | @chw | w 4-0 | morris | bannister | none | 24616 | 4-0 row 5 : 5 | apr 8 | @chw | w 7-3 | lopez | seaver | none | 20478 | 5-0 row 6 : 6 | apr 10 | tex | w 5-1 | petry | stewart | none | 51238 | 6-0 row 7 : 7 | apr 12 | tex | w 9-4 | morris | tanana | none | 19154 | 7-0 row 8 : 8 | apr 13 | @bos | w 13-9 | bair | hurst | none | 35179 | 8-0 row 9 : 9 | apr 18 | kcr | w 4-3 (10) | hernandez | beckwith | none | 12310 | 9-0 row 10 : 10 | apr 19 | kcr | l 2-5 | saberhagen | petry | quisenberry | 12100 | 9-1 row 11 : 11 | apr 20 | chw | w 3-2 | lopez | reed | none | 33554 | 10-1 row 12 : 12 | apr 21 | chw | w 4-1 | rozema | hoyt | bair | 34395 | 11-1 row 13 : 13 | apr 22 | chw | w 9-1 | berenguer | brennan | none | 10603 | 12-1 row 14 : 14 | apr 24 | min | w 6-5 | morris | davis | none | none | 13-1 row 15 : 15 | apr 24 | min | w 4-3 | abbott | viola | lopez | 20315 | 14-1 row 16 : 16 | apr 25 | @tex | w 9-4 | wilcox | stewart | hernandez | 25883 | 15-1 row 17 : 17 | apr 26 | @tex | w 7-5 | bair | tanana | lopez | 13559 | 16-1 row 18 : 18 | apr 27 | cle | l 4-8 (19) | aponte | abbott | none | 34112 | 16-2 row 19 : 19 | apr 28 | cle | w 6-2 | morris | behenna | none | 28253 | 17-2 row 20 : 20 | apr 29 | cle | w 6-1 | petry | spillner | none | 24853 | 18-2

col : max ( "crowd" ) row 1 : none

select max ( crowd ) where crowd > 762 <table_name> : table \begin{tabular}{llllllllll} \toprule & # & date & opponent & score & win & loss & save & crowd & record \\ \midrule 0 & 1 & apr 3 & @min & w 8-1 & morris & williams & none & 34381 & 1-0 \\ 1 & 2 & apr 5 & @min & w 7-3 & petry & viola & none & 8373 & 2-0 \\ 2 & 3 & apr 6 & @chw & w 3-2 & wilcox & dotson & hernandez & 42692 & 3-0 \\ 3 & 4 & apr 7 & @chw & w 4-0 & morris & bannister & none & 24616 & 4-0 \\ 4 & 5 & apr 8 & @chw & w 7-3 & lopez & seaver & none & 20478 & 5-0 \\ 5 & 6 & apr 10 & tex & w 5-1 & petry & stewart & none & 51238 & 6-0 \\ 6 & 7 & apr 12 & tex & w 9-4 & morris & tanana & none & 19154 & 7-0 \\ 7 & 8 & apr 13 & @bos & w 13-9 & bair & hurst & none & 35179 & 8-0 \\ 8 & 9 & apr 18 & kcr & w 4-3 (10) & hernandez & beckwith & none & 12310 & 9-0 \\ 9 & 10 & apr 19 & kcr & l 2-5 & saberhagen & petry & quisenberry & 12100 & 9-1 \\ 10 & 11 & apr 20 & chw & w 3-2 & lopez & reed & none & 33554 & 10-1 \\ 11 & 12 & apr 21 & chw & w 4-1 & rozema & hoyt & bair & 34395 & 11-1 \\ 12 & 13 & apr 22 & chw & w 9-1 & berenguer & brennan & none & 10603 & 12-1 \\ 13 & 14 & apr 24 & min & w 6-5 & morris & davis & none & none & 13-1 \\ 14 & 15 & apr 24 & min & w 4-3 & abbott & viola & lopez & 20315 & 14-1 \\ 15 & 16 & apr 25 & @tex & w 9-4 & wilcox & stewart & hernandez & 25883 & 15-1 \\ 16 & 17 & apr 26 & @tex & w 7-5 & bair & tanana & lopez & 13559 & 16-1 \\ 17 & 18 & apr 27 & cle & l 4-8 (19) & aponte & abbott & none & 34112 & 16-2 \\ 18 & 19 & apr 28 & cle & w 6-2 & morris & behenna & none & 28253 & 17-2 \\ 19 & 20 & apr 29 & cle & w 6-1 & petry & spillner & none & 24853 & 18-2 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & max ( "crowd" ) \\ \midrule 0 & none \\ \bottomrule \end{tabular}

select max ( crowd ) where crowd > 762 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>#</th> <th>date</th> <th>opponent</th> <th>score</th> <th>win</th> <th>loss</th> <th>save</th> <th>crowd</th> <th>record</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>apr 3</td> <td>@min</td> <td>w 8-1</td> <td>morris</td> <td>williams</td> <td>none</td> <td>34381</td> <td>1-0</td> </tr> <tr> <th>1</th> <td>2</td> <td>apr 5</td> <td>@min</td> <td>w 7-3</td> <td>petry</td> <td>viola</td> <td>none</td> <td>8373</td> <td>2-0</td> </tr> <tr> <th>2</th> <td>3</td> <td>apr 6</td> <td>@chw</td> <td>w 3-2</td> <td>wilcox</td> <td>dotson</td> <td>hernandez</td> <td>42692</td> <td>3-0</td> </tr> <tr> <th>3</th> <td>4</td> <td>apr 7</td> <td>@chw</td> <td>w 4-0</td> <td>morris</td> <td>bannister</td> <td>none</td> <td>24616</td> <td>4-0</td> </tr> <tr> <th>4</th> <td>5</td> <td>apr 8</td> <td>@chw</td> <td>w 7-3</td> <td>lopez</td> <td>seaver</td> <td>none</td> <td>20478</td> <td>5-0</td> </tr> <tr> <th>5</th> <td>6</td> <td>apr 10</td> <td>tex</td> <td>w 5-1</td> <td>petry</td> <td>stewart</td> <td>none</td> <td>51238</td> <td>6-0</td> </tr> <tr> <th>6</th> <td>7</td> <td>apr 12</td> <td>tex</td> <td>w 9-4</td> <td>morris</td> <td>tanana</td> <td>none</td> <td>19154</td> <td>7-0</td> </tr> <tr> <th>7</th> <td>8</td> <td>apr 13</td> <td>@bos</td> <td>w 13-9</td> <td>bair</td> <td>hurst</td> <td>none</td> <td>35179</td> <td>8-0</td> </tr> <tr> <th>8</th> <td>9</td> <td>apr 18</td> <td>kcr</td> <td>w 4-3 (10)</td> <td>hernandez</td> <td>beckwith</td> <td>none</td> <td>12310</td> <td>9-0</td> </tr> <tr> <th>9</th> <td>10</td> <td>apr 19</td> <td>kcr</td> <td>l 2-5</td> <td>saberhagen</td> <td>petry</td> <td>quisenberry</td> <td>12100</td> <td>9-1</td> </tr> <tr> <th>10</th> <td>11</td> <td>apr 20</td> <td>chw</td> <td>w 3-2</td> <td>lopez</td> <td>reed</td> <td>none</td> <td>33554</td> <td>10-1</td> </tr> <tr> <th>11</th> <td>12</td> <td>apr 21</td> <td>chw</td> <td>w 4-1</td> <td>rozema</td> <td>hoyt</td> <td>bair</td> <td>34395</td> <td>11-1</td> </tr> <tr> <th>12</th> <td>13</td> <td>apr 22</td> <td>chw</td> <td>w 9-1</td> <td>berenguer</td> <td>brennan</td> <td>none</td> <td>10603</td> <td>12-1</td> </tr> <tr> <th>13</th> <td>14</td> <td>apr 24</td> <td>min</td> <td>w 6-5</td> <td>morris</td> <td>davis</td> <td>none</td> <td>none</td> <td>13-1</td> </tr> <tr> <th>14</th> <td>15</td> <td>apr 24</td> <td>min</td> <td>w 4-3</td> <td>abbott</td> <td>viola</td> <td>lopez</td> <td>20315</td> <td>14-1</td> </tr> <tr> <th>15</th> <td>16</td> <td>apr 25</td> <td>@tex</td> <td>w 9-4</td> <td>wilcox</td> <td>stewart</td> <td>hernandez</td> <td>25883</td> <td>15-1</td> </tr> <tr> <th>16</th> <td>17</td> <td>apr 26</td> <td>@tex</td> <td>w 7-5</td> <td>bair</td> <td>tanana</td> <td>lopez</td> <td>13559</td> <td>16-1</td> </tr> <tr> <th>17</th> <td>18</td> <td>apr 27</td> <td>cle</td> <td>l 4-8 (19)</td> <td>aponte</td> <td>abbott</td> <td>none</td> <td>34112</td> <td>16-2</td> </tr> <tr> <th>18</th> <td>19</td> <td>apr 28</td> <td>cle</td> <td>w 6-2</td> <td>morris</td> <td>behenna</td> <td>none</td> <td>28253</td> <td>17-2</td> </tr> <tr> <th>19</th> <td>20</td> <td>apr 29</td> <td>cle</td> <td>w 6-1</td> <td>petry</td> <td>spillner</td> <td>none</td> <td>24853</td> <td>18-2</td> </tr> </tbody> </table>

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

select max ( crowd ) where crowd > 762 <table_name> : table | | # | date | opponent | score | win | loss | save | crowd | record | |---:|----:|:-------|:-----------|:-----------|:-----------|:----------|:------------|:--------|:---------| | 0 | 1 | apr 3 | @min | w 8-1 | morris | williams | none | 34381 | 1-0 | | 1 | 2 | apr 5 | @min | w 7-3 | petry | viola | none | 8373 | 2-0 | | 2 | 3 | apr 6 | @chw | w 3-2 | wilcox | dotson | hernandez | 42692 | 3-0 | | 3 | 4 | apr 7 | @chw | w 4-0 | morris | bannister | none | 24616 | 4-0 | | 4 | 5 | apr 8 | @chw | w 7-3 | lopez | seaver | none | 20478 | 5-0 | | 5 | 6 | apr 10 | tex | w 5-1 | petry | stewart | none | 51238 | 6-0 | | 6 | 7 | apr 12 | tex | w 9-4 | morris | tanana | none | 19154 | 7-0 | | 7 | 8 | apr 13 | @bos | w 13-9 | bair | hurst | none | 35179 | 8-0 | | 8 | 9 | apr 18 | kcr | w 4-3 (10) | hernandez | beckwith | none | 12310 | 9-0 | | 9 | 10 | apr 19 | kcr | l 2-5 | saberhagen | petry | quisenberry | 12100 | 9-1 | | 10 | 11 | apr 20 | chw | w 3-2 | lopez | reed | none | 33554 | 10-1 | | 11 | 12 | apr 21 | chw | w 4-1 | rozema | hoyt | bair | 34395 | 11-1 | | 12 | 13 | apr 22 | chw | w 9-1 | berenguer | brennan | none | 10603 | 12-1 | | 13 | 14 | apr 24 | min | w 6-5 | morris | davis | none | none | 13-1 | | 14 | 15 | apr 24 | min | w 4-3 | abbott | viola | lopez | 20315 | 14-1 | | 15 | 16 | apr 25 | @tex | w 9-4 | wilcox | stewart | hernandez | 25883 | 15-1 | | 16 | 17 | apr 26 | @tex | w 7-5 | bair | tanana | lopez | 13559 | 16-1 | | 17 | 18 | apr 27 | cle | l 4-8 (19) | aponte | abbott | none | 34112 | 16-2 | | 18 | 19 | apr 28 | cle | w 6-2 | morris | behenna | none | 28253 | 17-2 | | 19 | 20 | apr 29 | cle | w 6-1 | petry | spillner | none | 24853 | 18-2 |

| | max ( "crowd" ) | |---:|:------------------| | 0 | none |

select count ( notes ) where cyclist = 'iryna shpylova' col : rank | cyclist | team | laps down | notes row 1 : 1 | iryna shpylova | ukraine | none | q row 2 : 2 | jessie daams | belgium | none | q row 3 : 3 | eunmi park | south korea | -1 | q row 4 : 4 | evgeniya romanyuta | russia | -1 | q row 5 : 5 | andrea wolfer | switzerland | -1 | q row 6 : 6 | giorgia bronzini | italy | -1 | q row 7 : 7 | leire olaberria dorronsoro | spain | -1 | q row 8 : 8 | joanne kiesanowski | new zealand | -1 | q row 9 : 9 | dulce pliego | mexico | -1 | q row 10 : 10 | jessie maclean | verducci breakaway racing | -1 | q row 11 : 11 | lisa brennauer | germany | -1 | q row 12 : 12 | ashlee ankudinoff | australia | -1 | q row 13 : 13 | wan yiu jamie wong | hong kong | -1 | none row 14 : 14 | lauren franges | united states | -1 | none row 15 : 15 | skye lee armstrong | rodin | -1 | none row 16 : 16 | rosy mccall | gis | -1 | none row 17 : 17 | fatehah mustapa | malaysia | -1 | none row 18 : 18 | yekatsiryna barazna | belarus | -1 | none

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

select count ( notes ) where cyclist = 'iryna shpylova' <table_name> : table \begin{tabular}{llllll} \toprule & rank & cyclist & team & laps down & notes \\ \midrule 0 & 1 & iryna shpylova & ukraine & none & q \\ 1 & 2 & jessie daams & belgium & none & q \\ 2 & 3 & eunmi park & south korea & -1 & q \\ 3 & 4 & evgeniya romanyuta & russia & -1 & q \\ 4 & 5 & andrea wolfer & switzerland & -1 & q \\ 5 & 6 & giorgia bronzini & italy & -1 & q \\ 6 & 7 & leire olaberria dorronsoro & spain & -1 & q \\ 7 & 8 & joanne kiesanowski & new zealand & -1 & q \\ 8 & 9 & dulce pliego & mexico & -1 & q \\ 9 & 10 & jessie maclean & verducci breakaway racing & -1 & q \\ 10 & 11 & lisa brennauer & germany & -1 & q \\ 11 & 12 & ashlee ankudinoff & australia & -1 & q \\ 12 & 13 & wan yiu jamie wong & hong kong & -1 & none \\ 13 & 14 & lauren franges & united states & -1 & none \\ 14 & 15 & skye lee armstrong & rodin & -1 & none \\ 15 & 16 & rosy mccall & gis & -1 & none \\ 16 & 17 & fatehah mustapa & malaysia & -1 & none \\ 17 & 18 & yekatsiryna barazna & belarus & -1 & none \\ \bottomrule \end{tabular}

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

select count ( notes ) where cyclist = 'iryna shpylova' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>rank</th> <th>cyclist</th> <th>team</th> <th>laps down</th> <th>notes</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>iryna shpylova</td> <td>ukraine</td> <td>none</td> <td>q</td> </tr> <tr> <th>1</th> <td>2</td> <td>jessie daams</td> <td>belgium</td> <td>none</td> <td>q</td> </tr> <tr> <th>2</th> <td>3</td> <td>eunmi park</td> <td>south korea</td> <td>-1</td> <td>q</td> </tr> <tr> <th>3</th> <td>4</td> <td>evgeniya romanyuta</td> <td>russia</td> <td>-1</td> <td>q</td> </tr> <tr> <th>4</th> <td>5</td> <td>andrea wolfer</td> <td>switzerland</td> <td>-1</td> <td>q</td> </tr> <tr> <th>5</th> <td>6</td> <td>giorgia bronzini</td> <td>italy</td> <td>-1</td> <td>q</td> </tr> <tr> <th>6</th> <td>7</td> <td>leire olaberria dorronsoro</td> <td>spain</td> <td>-1</td> <td>q</td> </tr> <tr> <th>7</th> <td>8</td> <td>joanne kiesanowski</td> <td>new zealand</td> <td>-1</td> <td>q</td> </tr> <tr> <th>8</th> <td>9</td> <td>dulce pliego</td> <td>mexico</td> <td>-1</td> <td>q</td> </tr> <tr> <th>9</th> <td>10</td> <td>jessie maclean</td> <td>verducci breakaway racing</td> <td>-1</td> <td>q</td> </tr> <tr> <th>10</th> <td>11</td> <td>lisa brennauer</td> <td>germany</td> <td>-1</td> <td>q</td> </tr> <tr> <th>11</th> <td>12</td> <td>ashlee ankudinoff</td> <td>australia</td> <td>-1</td> <td>q</td> </tr> <tr> <th>12</th> <td>13</td> <td>wan yiu jamie wong</td> <td>hong kong</td> <td>-1</td> <td>none</td> </tr> <tr> <th>13</th> <td>14</td> <td>lauren franges</td> <td>united states</td> <td>-1</td> <td>none</td> </tr> <tr> <th>14</th> <td>15</td> <td>skye lee armstrong</td> <td>rodin</td> <td>-1</td> <td>none</td> </tr> <tr> <th>15</th> <td>16</td> <td>rosy mccall</td> <td>gis</td> <td>-1</td> <td>none</td> </tr> <tr> <th>16</th> <td>17</td> <td>fatehah mustapa</td> <td>malaysia</td> <td>-1</td> <td>none</td> </tr> <tr> <th>17</th> <td>18</td> <td>yekatsiryna barazna</td> <td>belarus</td> <td>-1</td> <td>none</td> </tr> </tbody> </table>

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

select count ( notes ) where cyclist = 'iryna shpylova' <table_name> : table | | rank | cyclist | team | laps down | notes | |---:|-------:|:---------------------------|:--------------------------|:------------|:--------| | 0 | 1 | iryna shpylova | ukraine | none | q | | 1 | 2 | jessie daams | belgium | none | q | | 2 | 3 | eunmi park | south korea | -1 | q | | 3 | 4 | evgeniya romanyuta | russia | -1 | q | | 4 | 5 | andrea wolfer | switzerland | -1 | q | | 5 | 6 | giorgia bronzini | italy | -1 | q | | 6 | 7 | leire olaberria dorronsoro | spain | -1 | q | | 7 | 8 | joanne kiesanowski | new zealand | -1 | q | | 8 | 9 | dulce pliego | mexico | -1 | q | | 9 | 10 | jessie maclean | verducci breakaway racing | -1 | q | | 10 | 11 | lisa brennauer | germany | -1 | q | | 11 | 12 | ashlee ankudinoff | australia | -1 | q | | 12 | 13 | wan yiu jamie wong | hong kong | -1 | none | | 13 | 14 | lauren franges | united states | -1 | none | | 14 | 15 | skye lee armstrong | rodin | -1 | none | | 15 | 16 | rosy mccall | gis | -1 | none | | 16 | 17 | fatehah mustapa | malaysia | -1 | none | | 17 | 18 | yekatsiryna barazna | belarus | -1 | none |

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

select count ( * ) where label = 'power house' col : year | name | label | hot black singles | club play singles row 1 : 1982 | "he's gonna take you home" | becket | none | none row 2 : 1982 | "thanks to you" | becket | #44 | #1 row 3 : 1983 | "i need you now" | jive | none | none row 4 : 1984 | "thin line" | power house | none | none row 5 : 1986 | "say it again" | spring | none | none row 6 : 1986 | "say it again" | spring | none | none row 7 : 1987 | "send it c.o.d." | new image | none | none

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

select count ( * ) where label = 'power house' <table_name> : table \begin{tabular}{llllll} \toprule & year & name & label & hot black singles & club play singles \\ \midrule 0 & 1982 & "he's gonna take you home" & becket & none & none \\ 1 & 1982 & "thanks to you" & becket & #44 & #1 \\ 2 & 1983 & "i need you now" & jive & none & none \\ 3 & 1984 & "thin line" & power house & none & none \\ 4 & 1986 & "say it again" & spring & none & none \\ 5 & 1986 & "say it again" & spring & none & none \\ 6 & 1987 & "send it c.o.d." & new image & none & none \\ \bottomrule \end{tabular}

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

select count ( * ) where label = 'power house' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>year</th> <th>name</th> <th>label</th> <th>hot black singles</th> <th>club play singles</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1982</td> <td>"he's gonna take you home"</td> <td>becket</td> <td>none</td> <td>none</td> </tr> <tr> <th>1</th> <td>1982</td> <td>"thanks to you"</td> <td>becket</td> <td>#44</td> <td>#1</td> </tr> <tr> <th>2</th> <td>1983</td> <td>"i need you now"</td> <td>jive</td> <td>none</td> <td>none</td> </tr> <tr> <th>3</th> <td>1984</td> <td>"thin line"</td> <td>power house</td> <td>none</td> <td>none</td> </tr> <tr> <th>4</th> <td>1986</td> <td>"say it again"</td> <td>spring</td> <td>none</td> <td>none</td> </tr> <tr> <th>5</th> <td>1986</td> <td>"say it again"</td> <td>spring</td> <td>none</td> <td>none</td> </tr> <tr> <th>6</th> <td>1987</td> <td>"send it c.o.d."</td> <td>new image</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 label = 'power house' <table_name> : table | | year | name | label | hot black singles | club play singles | |---:|-------:|:---------------------------|:------------|:--------------------|:--------------------| | 0 | 1982 | "he's gonna take you home" | becket | none | none | | 1 | 1982 | "thanks to you" | becket | #44 | #1 | | 2 | 1983 | "i need you now" | jive | none | none | | 3 | 1984 | "thin line" | power house | none | none | | 4 | 1986 | "say it again" | spring | none | none | | 5 | 1986 | "say it again" | spring | none | none | | 6 | 1987 | "send it c.o.d." | new image | none | none |

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

select count ( third place ) where team = 'suriname' col : team | titles | runners-up | third place | fourth place row 1 : trinidad and tobago | 8 (1989, 1992, 1994, 1995, 1996, 1997, 1999, 2001) | 4 (1991, 1998, 2007, 2012) | 2 (1993, 2005) | 0 row 2 : jamaica | 5 (1991, 1998, 2005, 2008, 2010) | 2 (1992, 1993) | 2 (1997, 1999) | 0 row 3 : cuba | 1 (2012) | 3 (1996, 1999, 2005) | 3 (1995, 2007, 2010) | 3 (1992, 2001, 2008) row 4 : martinique | 1 (1993) | 1 (1994) | 3 (1992, 1996, 2001) | 1 (2012) row 5 : haiti | 1 (2007) | 1 (2001) | 3 (1998, 1999, 2012) | 0 row 6 : grenada | 0 | 2 (1989, 2008) | 0 | 2 (1997, 2010) row 7 : guadeloupe | 0 | 1 (2010) | 3 (1989, 1994, 2008) | 1 (2007) row 8 : saint kitts and nevis | 0 | 1 (1997) | 0 | 1 (1993) row 9 : saint vincent and the grenadines | 0 | 1 (1995) | 0 | 0 row 10 : saint lucia | 0 | 0 | 1 (1991) | 0 row 11 : suriname | 0 | 0 | 0 | 2 (1994, 1996) row 12 : netherlands antilles | 0 | 0 | 0 | 1 (1989) row 13 : guyana | 0 | 0 | 0 | 1 (1991) row 14 : cayman islands | 0 | 0 | 0 | 1 (1995) row 15 : antigua and barbuda | 0 | 0 | 0 | 1 (1998) row 16 : barbados | 0 | 0 | 0 | 1 (2005)

col : count ( "third place" ) row 1 : 1

select count ( third place ) where team = 'suriname' <table_name> : table \begin{tabular}{llllll} \toprule & team & titles & runners-up & third place & fourth place \\ \midrule 0 & trinidad and tobago & 8 (1989, 1992, 1994, 1995, 1996, 1997, 1999, 2001) & 4 (1991, 1998, 2007, 2012) & 2 (1993, 2005) & 0 \\ 1 & jamaica & 5 (1991, 1998, 2005, 2008, 2010) & 2 (1992, 1993) & 2 (1997, 1999) & 0 \\ 2 & cuba & 1 (2012) & 3 (1996, 1999, 2005) & 3 (1995, 2007, 2010) & 3 (1992, 2001, 2008) \\ 3 & martinique & 1 (1993) & 1 (1994) & 3 (1992, 1996, 2001) & 1 (2012) \\ 4 & haiti & 1 (2007) & 1 (2001) & 3 (1998, 1999, 2012) & 0 \\ 5 & grenada & 0 & 2 (1989, 2008) & 0 & 2 (1997, 2010) \\ 6 & guadeloupe & 0 & 1 (2010) & 3 (1989, 1994, 2008) & 1 (2007) \\ 7 & saint kitts and nevis & 0 & 1 (1997) & 0 & 1 (1993) \\ 8 & saint vincent and the grenadines & 0 & 1 (1995) & 0 & 0 \\ 9 & saint lucia & 0 & 0 & 1 (1991) & 0 \\ 10 & suriname & 0 & 0 & 0 & 2 (1994, 1996) \\ 11 & netherlands antilles & 0 & 0 & 0 & 1 (1989) \\ 12 & guyana & 0 & 0 & 0 & 1 (1991) \\ 13 & cayman islands & 0 & 0 & 0 & 1 (1995) \\ 14 & antigua and barbuda & 0 & 0 & 0 & 1 (1998) \\ 15 & barbados & 0 & 0 & 0 & 1 (2005) \\ \bottomrule \end{tabular}

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

select count ( third place ) where team = 'suriname' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>team</th> <th>titles</th> <th>runners-up</th> <th>third place</th> <th>fourth place</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>trinidad and tobago</td> <td>8 (1989, 1992, 1994, 1995, 1996, 1997, 1999, 2001)</td> <td>4 (1991, 1998, 2007, 2012)</td> <td>2 (1993, 2005)</td> <td>0</td> </tr> <tr> <th>1</th> <td>jamaica</td> <td>5 (1991, 1998, 2005, 2008, 2010)</td> <td>2 (1992, 1993)</td> <td>2 (1997, 1999)</td> <td>0</td> </tr> <tr> <th>2</th> <td>cuba</td> <td>1 (2012)</td> <td>3 (1996, 1999, 2005)</td> <td>3 (1995, 2007, 2010)</td> <td>3 (1992, 2001, 2008)</td> </tr> <tr> <th>3</th> <td>martinique</td> <td>1 (1993)</td> <td>1 (1994)</td> <td>3 (1992, 1996, 2001)</td> <td>1 (2012)</td> </tr> <tr> <th>4</th> <td>haiti</td> <td>1 (2007)</td> <td>1 (2001)</td> <td>3 (1998, 1999, 2012)</td> <td>0</td> </tr> <tr> <th>5</th> <td>grenada</td> <td>0</td> <td>2 (1989, 2008)</td> <td>0</td> <td>2 (1997, 2010)</td> </tr> <tr> <th>6</th> <td>guadeloupe</td> <td>0</td> <td>1 (2010)</td> <td>3 (1989, 1994, 2008)</td> <td>1 (2007)</td> </tr> <tr> <th>7</th> <td>saint kitts and nevis</td> <td>0</td> <td>1 (1997)</td> <td>0</td> <td>1 (1993)</td> </tr> <tr> <th>8</th> <td>saint vincent and the grenadines</td> <td>0</td> <td>1 (1995)</td> <td>0</td> <td>0</td> </tr> <tr> <th>9</th> <td>saint lucia</td> <td>0</td> <td>0</td> <td>1 (1991)</td> <td>0</td> </tr> <tr> <th>10</th> <td>suriname</td> <td>0</td> <td>0</td> <td>0</td> <td>2 (1994, 1996)</td> </tr> <tr> <th>11</th> <td>netherlands antilles</td> <td>0</td> <td>0</td> <td>0</td> <td>1 (1989)</td> </tr> <tr> <th>12</th> <td>guyana</td> <td>0</td> <td>0</td> <td>0</td> <td>1 (1991)</td> </tr> <tr> <th>13</th> <td>cayman islands</td> <td>0</td> <td>0</td> <td>0</td> <td>1 (1995)</td> </tr> <tr> <th>14</th> <td>antigua and barbuda</td> <td>0</td> <td>0</td> <td>0</td> <td>1 (1998)</td> </tr> <tr> <th>15</th> <td>barbados</td> <td>0</td> <td>0</td> <td>0</td> <td>1 (2005)</td> </tr> </tbody> </table>

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

select count ( third place ) where team = 'suriname' <table_name> : table | | team | titles | runners-up | third place | fourth place | |---:|:---------------------------------|:---------------------------------------------------|:---------------------------|:---------------------|:---------------------| | 0 | trinidad and tobago | 8 (1989, 1992, 1994, 1995, 1996, 1997, 1999, 2001) | 4 (1991, 1998, 2007, 2012) | 2 (1993, 2005) | 0 | | 1 | jamaica | 5 (1991, 1998, 2005, 2008, 2010) | 2 (1992, 1993) | 2 (1997, 1999) | 0 | | 2 | cuba | 1 (2012) | 3 (1996, 1999, 2005) | 3 (1995, 2007, 2010) | 3 (1992, 2001, 2008) | | 3 | martinique | 1 (1993) | 1 (1994) | 3 (1992, 1996, 2001) | 1 (2012) | | 4 | haiti | 1 (2007) | 1 (2001) | 3 (1998, 1999, 2012) | 0 | | 5 | grenada | 0 | 2 (1989, 2008) | 0 | 2 (1997, 2010) | | 6 | guadeloupe | 0 | 1 (2010) | 3 (1989, 1994, 2008) | 1 (2007) | | 7 | saint kitts and nevis | 0 | 1 (1997) | 0 | 1 (1993) | | 8 | saint vincent and the grenadines | 0 | 1 (1995) | 0 | 0 | | 9 | saint lucia | 0 | 0 | 1 (1991) | 0 | | 10 | suriname | 0 | 0 | 0 | 2 (1994, 1996) | | 11 | netherlands antilles | 0 | 0 | 0 | 1 (1989) | | 12 | guyana | 0 | 0 | 0 | 1 (1991) | | 13 | cayman islands | 0 | 0 | 0 | 1 (1995) | | 14 | antigua and barbuda | 0 | 0 | 0 | 1 (1998) | | 15 | barbados | 0 | 0 | 0 | 1 (2005) |

| | count ( "third place" ) | |---:|--------------------------:| | 0 | 1 |

select count ( nationality ) where school/club team < 225 col : player | no. | nationality | position | years for jazz | school/club team row 1 : jim farmer | 30 | united states | guard | 1988-89 | alabama row 2 : derrick favors | 15 | united states | forward | 2011-present | georgia tech row 3 : kyrylo fesenko | 44 | ukraine | center | 2007-11 | cherkasy monkeys (ukraine) row 4 : derek fisher | 2 | united states | guard | 2006-2007 | arkansas-little rock row 5 : greg foster | 44 | united states | center/forward | 1995-99 | utep row 6 : bernie fryer | 25 | united states | guard | 1975-76 | byu row 7 : todd fuller | 52 | united states | center | 1998-99 | north carolina state row 8 : terry furlow | 25 | united states | guard/forward | 1979-80 | michigan state

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

select count ( nationality ) where school/club team < 225 <table_name> : table \begin{tabular}{lllllll} \toprule & player & no. & nationality & position & years for jazz & school/club team \\ \midrule 0 & jim farmer & 30 & united states & guard & 1988-89 & alabama \\ 1 & derrick favors & 15 & united states & forward & 2011-present & georgia tech \\ 2 & kyrylo fesenko & 44 & ukraine & center & 2007-11 & cherkasy monkeys (ukraine) \\ 3 & derek fisher & 2 & united states & guard & 2006-2007 & arkansas-little rock \\ 4 & greg foster & 44 & united states & center/forward & 1995-99 & utep \\ 5 & bernie fryer & 25 & united states & guard & 1975-76 & byu \\ 6 & todd fuller & 52 & united states & center & 1998-99 & north carolina state \\ 7 & terry furlow & 25 & united states & guard/forward & 1979-80 & michigan state \\ \bottomrule \end{tabular}

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

select count ( nationality ) where school/club team < 225 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>player</th> <th>no.</th> <th>nationality</th> <th>position</th> <th>years for jazz</th> <th>school/club team</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>jim farmer</td> <td>30</td> <td>united states</td> <td>guard</td> <td>1988-89</td> <td>alabama</td> </tr> <tr> <th>1</th> <td>derrick favors</td> <td>15</td> <td>united states</td> <td>forward</td> <td>2011-present</td> <td>georgia tech</td> </tr> <tr> <th>2</th> <td>kyrylo fesenko</td> <td>44</td> <td>ukraine</td> <td>center</td> <td>2007-11</td> <td>cherkasy monkeys (ukraine)</td> </tr> <tr> <th>3</th> <td>derek fisher</td> <td>2</td> <td>united states</td> <td>guard</td> <td>2006-2007</td> <td>arkansas-little rock</td> </tr> <tr> <th>4</th> <td>greg foster</td> <td>44</td> <td>united states</td> <td>center/forward</td> <td>1995-99</td> <td>utep</td> </tr> <tr> <th>5</th> <td>bernie fryer</td> <td>25</td> <td>united states</td> <td>guard</td> <td>1975-76</td> <td>byu</td> </tr> <tr> <th>6</th> <td>todd fuller</td> <td>52</td> <td>united states</td> <td>center</td> <td>1998-99</td> <td>north carolina state</td> </tr> <tr> <th>7</th> <td>terry furlow</td> <td>25</td> <td>united states</td> <td>guard/forward</td> <td>1979-80</td> <td>michigan state</td> </tr> </tbody> </table>

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

select count ( nationality ) where school/club team < 225 <table_name> : table | | player | no. | nationality | position | years for jazz | school/club team | |---:|:---------------|------:|:--------------|:---------------|:-----------------|:---------------------------| | 0 | jim farmer | 30 | united states | guard | 1988-89 | alabama | | 1 | derrick favors | 15 | united states | forward | 2011-present | georgia tech | | 2 | kyrylo fesenko | 44 | ukraine | center | 2007-11 | cherkasy monkeys (ukraine) | | 3 | derek fisher | 2 | united states | guard | 2006-2007 | arkansas-little rock | | 4 | greg foster | 44 | united states | center/forward | 1995-99 | utep | | 5 | bernie fryer | 25 | united states | guard | 1975-76 | byu | | 6 | todd fuller | 52 | united states | center | 1998-99 | north carolina state | | 7 | terry furlow | 25 | united states | guard/forward | 1979-80 | michigan state |

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

select count ( date ) where date = 'november 30, 1969' col : week | date | opponent | result | record | attendance row 1 : 1 | 1969-09-14 00:00:00 | at cincinnati bengals | l 27-21 | 0-1-0 | 24487 row 2 : 2 | 1969-09-20 00:00:00 | at oakland raiders | l 20-17 | 0-2-0 | 48477 row 3 : 3 | 1969-09-28 00:00:00 | at houston oilers | l 20-10 | 0-3-0 | 40387 row 4 : 4 | 1969-10-04 00:00:00 | oakland raiders | t 20-20 | 0-3-1 | 32668 row 5 : 5 | 1969-10-11 00:00:00 | san diego chargers | l 21-14 | 0-4-1 | 33073 row 6 : 6 | 1969-10-19 00:00:00 | at kansas city chiefs | l 17-10 | 0-5-1 | 47038 row 7 : 7 | 1969-10-26 00:00:00 | buffalo bills | w 24-6 | 1-5-1 | 39194 row 8 : 8 | 1969-11-02 00:00:00 | at new york jets | l 34-31 | 1-6-1 | 60793 row 9 : 9 | 1969-11-09 00:00:00 | at boston patriots | w 17-16 | 2-6-1 | 10665 row 10 : 10 | 1969-11-16 00:00:00 | at buffalo bills | l 28-3 | 2-7-1 | 32334 row 11 : 11 | 1969-11-23 00:00:00 | houston oilers | l 32-7 | 2-8-1 | 27114 row 12 : 12 | 1969-11-30 00:00:00 | boston patriots | l 38-23 | 2-9-1 | 27179 row 13 : 13 | 1969-12-07 00:00:00 | denver broncos | w 27-24 | 3-9-1 | 24972 row 14 : 14 | 1969-12-14 00:00:00 | new york jets | l 27-9 | 3-10-1 | 42148

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

select count ( date ) where date = 'november 30, 1969' <table_name> : table \begin{tabular}{lllllll} \toprule & week & date & opponent & result & record & attendance \\ \midrule 0 & 1 & 1969-09-14 00:00:00 & at cincinnati bengals & l 27-21 & 0-1-0 & 24487 \\ 1 & 2 & 1969-09-20 00:00:00 & at oakland raiders & l 20-17 & 0-2-0 & 48477 \\ 2 & 3 & 1969-09-28 00:00:00 & at houston oilers & l 20-10 & 0-3-0 & 40387 \\ 3 & 4 & 1969-10-04 00:00:00 & oakland raiders & t 20-20 & 0-3-1 & 32668 \\ 4 & 5 & 1969-10-11 00:00:00 & san diego chargers & l 21-14 & 0-4-1 & 33073 \\ 5 & 6 & 1969-10-19 00:00:00 & at kansas city chiefs & l 17-10 & 0-5-1 & 47038 \\ 6 & 7 & 1969-10-26 00:00:00 & buffalo bills & w 24-6 & 1-5-1 & 39194 \\ 7 & 8 & 1969-11-02 00:00:00 & at new york jets & l 34-31 & 1-6-1 & 60793 \\ 8 & 9 & 1969-11-09 00:00:00 & at boston patriots & w 17-16 & 2-6-1 & 10665 \\ 9 & 10 & 1969-11-16 00:00:00 & at buffalo bills & l 28-3 & 2-7-1 & 32334 \\ 10 & 11 & 1969-11-23 00:00:00 & houston oilers & l 32-7 & 2-8-1 & 27114 \\ 11 & 12 & 1969-11-30 00:00:00 & boston patriots & l 38-23 & 2-9-1 & 27179 \\ 12 & 13 & 1969-12-07 00:00:00 & denver broncos & w 27-24 & 3-9-1 & 24972 \\ 13 & 14 & 1969-12-14 00:00:00 & new york jets & l 27-9 & 3-10-1 & 42148 \\ \bottomrule \end{tabular}

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

select count ( date ) where date = 'november 30, 1969' <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>result</th> <th>record</th> <th>attendance</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>1969-09-14 00:00:00</td> <td>at cincinnati bengals</td> <td>l 27-21</td> <td>0-1-0</td> <td>24487</td> </tr> <tr> <th>1</th> <td>2</td> <td>1969-09-20 00:00:00</td> <td>at oakland raiders</td> <td>l 20-17</td> <td>0-2-0</td> <td>48477</td> </tr> <tr> <th>2</th> <td>3</td> <td>1969-09-28 00:00:00</td> <td>at houston oilers</td> <td>l 20-10</td> <td>0-3-0</td> <td>40387</td> </tr> <tr> <th>3</th> <td>4</td> <td>1969-10-04 00:00:00</td> <td>oakland raiders</td> <td>t 20-20</td> <td>0-3-1</td> <td>32668</td> </tr> <tr> <th>4</th> <td>5</td> <td>1969-10-11 00:00:00</td> <td>san diego chargers</td> <td>l 21-14</td> <td>0-4-1</td> <td>33073</td> </tr> <tr> <th>5</th> <td>6</td> <td>1969-10-19 00:00:00</td> <td>at kansas city chiefs</td> <td>l 17-10</td> <td>0-5-1</td> <td>47038</td> </tr> <tr> <th>6</th> <td>7</td> <td>1969-10-26 00:00:00</td> <td>buffalo bills</td> <td>w 24-6</td> <td>1-5-1</td> <td>39194</td> </tr> <tr> <th>7</th> <td>8</td> <td>1969-11-02 00:00:00</td> <td>at new york jets</td> <td>l 34-31</td> <td>1-6-1</td> <td>60793</td> </tr> <tr> <th>8</th> <td>9</td> <td>1969-11-09 00:00:00</td> <td>at boston patriots</td> <td>w 17-16</td> <td>2-6-1</td> <td>10665</td> </tr> <tr> <th>9</th> <td>10</td> <td>1969-11-16 00:00:00</td> <td>at buffalo bills</td> <td>l 28-3</td> <td>2-7-1</td> <td>32334</td> </tr> <tr> <th>10</th> <td>11</td> <td>1969-11-23 00:00:00</td> <td>houston oilers</td> <td>l 32-7</td> <td>2-8-1</td> <td>27114</td> </tr> <tr> <th>11</th> <td>12</td> <td>1969-11-30 00:00:00</td> <td>boston patriots</td> <td>l 38-23</td> <td>2-9-1</td> <td>27179</td> </tr> <tr> <th>12</th> <td>13</td> <td>1969-12-07 00:00:00</td> <td>denver broncos</td> <td>w 27-24</td> <td>3-9-1</td> <td>24972</td> </tr> <tr> <th>13</th> <td>14</td> <td>1969-12-14 00:00:00</td> <td>new york jets</td> <td>l 27-9</td> <td>3-10-1</td> <td>42148</td> </tr> </tbody> </table>

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

select count ( date ) where date = 'november 30, 1969' <table_name> : table | | week | date | opponent | result | record | attendance | |---:|-------:|:--------------------|:----------------------|:---------|:---------|-------------:| | 0 | 1 | 1969-09-14 00:00:00 | at cincinnati bengals | l 27-21 | 0-1-0 | 24487 | | 1 | 2 | 1969-09-20 00:00:00 | at oakland raiders | l 20-17 | 0-2-0 | 48477 | | 2 | 3 | 1969-09-28 00:00:00 | at houston oilers | l 20-10 | 0-3-0 | 40387 | | 3 | 4 | 1969-10-04 00:00:00 | oakland raiders | t 20-20 | 0-3-1 | 32668 | | 4 | 5 | 1969-10-11 00:00:00 | san diego chargers | l 21-14 | 0-4-1 | 33073 | | 5 | 6 | 1969-10-19 00:00:00 | at kansas city chiefs | l 17-10 | 0-5-1 | 47038 | | 6 | 7 | 1969-10-26 00:00:00 | buffalo bills | w 24-6 | 1-5-1 | 39194 | | 7 | 8 | 1969-11-02 00:00:00 | at new york jets | l 34-31 | 1-6-1 | 60793 | | 8 | 9 | 1969-11-09 00:00:00 | at boston patriots | w 17-16 | 2-6-1 | 10665 | | 9 | 10 | 1969-11-16 00:00:00 | at buffalo bills | l 28-3 | 2-7-1 | 32334 | | 10 | 11 | 1969-11-23 00:00:00 | houston oilers | l 32-7 | 2-8-1 | 27114 | | 11 | 12 | 1969-11-30 00:00:00 | boston patriots | l 38-23 | 2-9-1 | 27179 | | 12 | 13 | 1969-12-07 00:00:00 | denver broncos | w 27-24 | 3-9-1 | 24972 | | 13 | 14 | 1969-12-14 00:00:00 | new york jets | l 27-9 | 3-10-1 | 42148 |

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

select position where division = 'regionalliga sud' order by position asc limit 1 col : season | division | tier | position | cup row 1 : 1994-95 | regionalliga sud | iii | 7 | quarter-final row 2 : 1995-96 | regionalliga sud | iii | 13 | round 1 row 3 : 1997-97 | regionalliga sud | iii | 8 | dnq row 4 : 1997-98 | regionalliga sud | iii | 6 | dnq row 5 : 1998-99 | regionalliga sud | iii | 8 | dnq row 6 : 1999-00 | regionalliga sud | iii | 5 | dnq row 7 : 2000-01 | regionalliga sud | iii | 9 | dnq row 8 : 2001-02 | regionalliga sud | iii | 10 | dnq row 9 : 2002-03 | regionalliga sud | iii | 4 | round 1 row 10 : 2003-04 | regionalliga sud | iii | 1 | dnq row 11 : 2004-05 | regionalliga sud | iii | 6 | quarter-final row 12 : 2005-06 | regionalliga sud | iii | 11 | dnq row 13 : 2006-07 | regionalliga sud | iii | 8 | dnq row 14 : 2007-08 | regionalliga sud | iii | 8 | dnq row 15 : 2008-09 | 3. liga | iii | 5 | none row 16 : 2009-10 | 3. liga | iii | 8 | none row 17 : 2010-11 | 3. liga | iii | 20 | none row 18 : 2011-12 | regionalliga sud | iv | 14 | none row 19 : 2012-13 | regionalliga bayern | iv | 2 | none

col : position row 1 : 1

select position where division = 'regionalliga sud' order by position asc limit 1 <table_name> : table \begin{tabular}{llllll} \toprule & season & division & tier & position & cup \\ \midrule 0 & 1994-95 & regionalliga sud & iii & 7 & quarter-final \\ 1 & 1995-96 & regionalliga sud & iii & 13 & round 1 \\ 2 & 1997-97 & regionalliga sud & iii & 8 & dnq \\ 3 & 1997-98 & regionalliga sud & iii & 6 & dnq \\ 4 & 1998-99 & regionalliga sud & iii & 8 & dnq \\ 5 & 1999-00 & regionalliga sud & iii & 5 & dnq \\ 6 & 2000-01 & regionalliga sud & iii & 9 & dnq \\ 7 & 2001-02 & regionalliga sud & iii & 10 & dnq \\ 8 & 2002-03 & regionalliga sud & iii & 4 & round 1 \\ 9 & 2003-04 & regionalliga sud & iii & 1 & dnq \\ 10 & 2004-05 & regionalliga sud & iii & 6 & quarter-final \\ 11 & 2005-06 & regionalliga sud & iii & 11 & dnq \\ 12 & 2006-07 & regionalliga sud & iii & 8 & dnq \\ 13 & 2007-08 & regionalliga sud & iii & 8 & dnq \\ 14 & 2008-09 & 3. liga & iii & 5 & none \\ 15 & 2009-10 & 3. liga & iii & 8 & none \\ 16 & 2010-11 & 3. liga & iii & 20 & none \\ 17 & 2011-12 & regionalliga sud & iv & 14 & none \\ 18 & 2012-13 & regionalliga bayern & iv & 2 & none \\ \bottomrule \end{tabular}

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

select position where division = 'regionalliga sud' order by position asc limit 1 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>season</th> <th>division</th> <th>tier</th> <th>position</th> <th>cup</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1994-95</td> <td>regionalliga sud</td> <td>iii</td> <td>7</td> <td>quarter-final</td> </tr> <tr> <th>1</th> <td>1995-96</td> <td>regionalliga sud</td> <td>iii</td> <td>13</td> <td>round 1</td> </tr> <tr> <th>2</th> <td>1997-97</td> <td>regionalliga sud</td> <td>iii</td> <td>8</td> <td>dnq</td> </tr> <tr> <th>3</th> <td>1997-98</td> <td>regionalliga sud</td> <td>iii</td> <td>6</td> <td>dnq</td> </tr> <tr> <th>4</th> <td>1998-99</td> <td>regionalliga sud</td> <td>iii</td> <td>8</td> <td>dnq</td> </tr> <tr> <th>5</th> <td>1999-00</td> <td>regionalliga sud</td> <td>iii</td> <td>5</td> <td>dnq</td> </tr> <tr> <th>6</th> <td>2000-01</td> <td>regionalliga sud</td> <td>iii</td> <td>9</td> <td>dnq</td> </tr> <tr> <th>7</th> <td>2001-02</td> <td>regionalliga sud</td> <td>iii</td> <td>10</td> <td>dnq</td> </tr> <tr> <th>8</th> <td>2002-03</td> <td>regionalliga sud</td> <td>iii</td> <td>4</td> <td>round 1</td> </tr> <tr> <th>9</th> <td>2003-04</td> <td>regionalliga sud</td> <td>iii</td> <td>1</td> <td>dnq</td> </tr> <tr> <th>10</th> <td>2004-05</td> <td>regionalliga sud</td> <td>iii</td> <td>6</td> <td>quarter-final</td> </tr> <tr> <th>11</th> <td>2005-06</td> <td>regionalliga sud</td> <td>iii</td> <td>11</td> <td>dnq</td> </tr> <tr> <th>12</th> <td>2006-07</td> <td>regionalliga sud</td> <td>iii</td> <td>8</td> <td>dnq</td> </tr> <tr> <th>13</th> <td>2007-08</td> <td>regionalliga sud</td> <td>iii</td> <td>8</td> <td>dnq</td> </tr> <tr> <th>14</th> <td>2008-09</td> <td>3. liga</td> <td>iii</td> <td>5</td> <td>none</td> </tr> <tr> <th>15</th> <td>2009-10</td> <td>3. liga</td> <td>iii</td> <td>8</td> <td>none</td> </tr> <tr> <th>16</th> <td>2010-11</td> <td>3. liga</td> <td>iii</td> <td>20</td> <td>none</td> </tr> <tr> <th>17</th> <td>2011-12</td> <td>regionalliga sud</td> <td>iv</td> <td>14</td> <td>none</td> </tr> <tr> <th>18</th> <td>2012-13</td> <td>regionalliga bayern</td> <td>iv</td> <td>2</td> <td>none</td> </tr> </tbody> </table>

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

select position where division = 'regionalliga sud' order by position asc limit 1 <table_name> : table | | season | division | tier | position | cup | |---:|:---------|:--------------------|:-------|-----------:|:--------------| | 0 | 1994-95 | regionalliga sud | iii | 7 | quarter-final | | 1 | 1995-96 | regionalliga sud | iii | 13 | round 1 | | 2 | 1997-97 | regionalliga sud | iii | 8 | dnq | | 3 | 1997-98 | regionalliga sud | iii | 6 | dnq | | 4 | 1998-99 | regionalliga sud | iii | 8 | dnq | | 5 | 1999-00 | regionalliga sud | iii | 5 | dnq | | 6 | 2000-01 | regionalliga sud | iii | 9 | dnq | | 7 | 2001-02 | regionalliga sud | iii | 10 | dnq | | 8 | 2002-03 | regionalliga sud | iii | 4 | round 1 | | 9 | 2003-04 | regionalliga sud | iii | 1 | dnq | | 10 | 2004-05 | regionalliga sud | iii | 6 | quarter-final | | 11 | 2005-06 | regionalliga sud | iii | 11 | dnq | | 12 | 2006-07 | regionalliga sud | iii | 8 | dnq | | 13 | 2007-08 | regionalliga sud | iii | 8 | dnq | | 14 | 2008-09 | 3. liga | iii | 5 | none | | 15 | 2009-10 | 3. liga | iii | 8 | none | | 16 | 2010-11 | 3. liga | iii | 20 | none | | 17 | 2011-12 | regionalliga sud | iv | 14 | none | | 18 | 2012-13 | regionalliga bayern | iv | 2 | none |

| | position | |---:|-----------:| | 0 | 1 |

select count ( season ) where season = '2006-07 serie a' col : season | average | high | low | season tickets row 1 : 2001-02 serie a | 17956 | 27498 | 14704 | 12156 row 2 : 2002-03 serie a | 16306 | 25603 | 12980 | 11544 row 3 : 2003-04 serie a | 15904 | 23663 | 11463 | 10439 row 4 : 2004-05 serie a | 14044 | 23010 | 10711 | 9923 row 5 : 2005-06 serie a | 14372 | 23116 | 11575 | 10639 row 6 : 2006-07 serie a | 14644 | 20488 | 11946 | 11120 row 7 : 2007-08 serie a | 15427 | 25149 | 11890 | 11386 row 8 : 2008-09 serie b | 10031 | 12265 | 9261 | none row 9 : 2009-10 serie a | 17061 | 21323 | 11870 | 13691 row 10 : 2010-11 serie a | 14524 | 19615 | 11975 | 10578 row 11 : 2011-12 serie a | 13646 | 19481 | 10800 | 9620 row 12 : 2012-13 serie a | 12740 | 19073 | 9627 | 10546

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

select count ( season ) where season = '2006-07 serie a' <table_name> : table \begin{tabular}{llllll} \toprule & season & average & high & low & season tickets \\ \midrule 0 & 2001-02 serie a & 17956 & 27498 & 14704 & 12156 \\ 1 & 2002-03 serie a & 16306 & 25603 & 12980 & 11544 \\ 2 & 2003-04 serie a & 15904 & 23663 & 11463 & 10439 \\ 3 & 2004-05 serie a & 14044 & 23010 & 10711 & 9923 \\ 4 & 2005-06 serie a & 14372 & 23116 & 11575 & 10639 \\ 5 & 2006-07 serie a & 14644 & 20488 & 11946 & 11120 \\ 6 & 2007-08 serie a & 15427 & 25149 & 11890 & 11386 \\ 7 & 2008-09 serie b & 10031 & 12265 & 9261 & none \\ 8 & 2009-10 serie a & 17061 & 21323 & 11870 & 13691 \\ 9 & 2010-11 serie a & 14524 & 19615 & 11975 & 10578 \\ 10 & 2011-12 serie a & 13646 & 19481 & 10800 & 9620 \\ 11 & 2012-13 serie a & 12740 & 19073 & 9627 & 10546 \\ \bottomrule \end{tabular}

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

select count ( season ) where season = '2006-07 serie a' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>season</th> <th>average</th> <th>high</th> <th>low</th> <th>season tickets</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>2001-02 serie a</td> <td>17956</td> <td>27498</td> <td>14704</td> <td>12156</td> </tr> <tr> <th>1</th> <td>2002-03 serie a</td> <td>16306</td> <td>25603</td> <td>12980</td> <td>11544</td> </tr> <tr> <th>2</th> <td>2003-04 serie a</td> <td>15904</td> <td>23663</td> <td>11463</td> <td>10439</td> </tr> <tr> <th>3</th> <td>2004-05 serie a</td> <td>14044</td> <td>23010</td> <td>10711</td> <td>9923</td> </tr> <tr> <th>4</th> <td>2005-06 serie a</td> <td>14372</td> <td>23116</td> <td>11575</td> <td>10639</td> </tr> <tr> <th>5</th> <td>2006-07 serie a</td> <td>14644</td> <td>20488</td> <td>11946</td> <td>11120</td> </tr> <tr> <th>6</th> <td>2007-08 serie a</td> <td>15427</td> <td>25149</td> <td>11890</td> <td>11386</td> </tr> <tr> <th>7</th> <td>2008-09 serie b</td> <td>10031</td> <td>12265</td> <td>9261</td> <td>none</td> </tr> <tr> <th>8</th> <td>2009-10 serie a</td> <td>17061</td> <td>21323</td> <td>11870</td> <td>13691</td> </tr> <tr> <th>9</th> <td>2010-11 serie a</td> <td>14524</td> <td>19615</td> <td>11975</td> <td>10578</td> </tr> <tr> <th>10</th> <td>2011-12 serie a</td> <td>13646</td> <td>19481</td> <td>10800</td> <td>9620</td> </tr> <tr> <th>11</th> <td>2012-13 serie a</td> <td>12740</td> <td>19073</td> <td>9627</td> <td>10546</td> </tr> </tbody> </table>

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

select count ( season ) where season = '2006-07 serie a' <table_name> : table | | season | average | high | low | season tickets | |---:|:----------------|----------:|-------:|------:|:-----------------| | 0 | 2001-02 serie a | 17956 | 27498 | 14704 | 12156 | | 1 | 2002-03 serie a | 16306 | 25603 | 12980 | 11544 | | 2 | 2003-04 serie a | 15904 | 23663 | 11463 | 10439 | | 3 | 2004-05 serie a | 14044 | 23010 | 10711 | 9923 | | 4 | 2005-06 serie a | 14372 | 23116 | 11575 | 10639 | | 5 | 2006-07 serie a | 14644 | 20488 | 11946 | 11120 | | 6 | 2007-08 serie a | 15427 | 25149 | 11890 | 11386 | | 7 | 2008-09 serie b | 10031 | 12265 | 9261 | none | | 8 | 2009-10 serie a | 17061 | 21323 | 11870 | 13691 | | 9 | 2010-11 serie a | 14524 | 19615 | 11975 | 10578 | | 10 | 2011-12 serie a | 13646 | 19481 | 10800 | 9620 | | 11 | 2012-13 serie a | 12740 | 19073 | 9627 | 10546 |

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

select count ( motorcycle ) where no = 0 col : team | constructor | motorcycle | tyres | no | rider | rounds row 1 : camel yamaha team | yamaha | yamaha yzr-m1 | m | 5 | colin edwards | all row 2 : camel yamaha team | yamaha | yamaha yzr-m1 | m | 46 | valentino rossi | all row 3 : konica minolta honda | honda | honda rc211v | m | 6 | makoto tamada | all row 4 : tech 3 yamaha | yamaha | yamaha yzr-m1 | d | 7 | carlos checa | all row 5 : tech 3 yamaha | yamaha | yamaha yzr-m1 | d | 77 | james ellison | all row 6 : team roberts | team roberts | kr211v | m | 10 | kenny roberts, jr | all row 7 : ducati marlboro team | ducati | ducati desmosedici gp6 | b | 15 | sete gibernau | 1-7, 10-11, 13-16 row 8 : ducati marlboro team | ducati | ducati desmosedici gp6 | b | 65 | loris capirossi | all row 9 : ducati marlboro team | ducati | ducati desmosedici gp6 | b | 66 | alex hofmann | 8-9, 12 row 10 : ducati marlboro team | ducati | ducati desmosedici gp6 | b | 12 | troy bayliss | 17 row 11 : kawasaki racing team | kawasaki | kawasaki ninja zx-rr | b | 17 | randy de puniet | all row 12 : kawasaki racing team | kawasaki | kawasaki ninja zx-rr | b | 56 | shinya nakano | all row 13 : kawasaki racing team | kawasaki | kawasaki ninja zx-rr | b | 8 | naoki matsudo | 15 row 14 : rizla suzuki motogp | suzuki | suzuki gsv-r | b | 21 | john hopkins | all row 15 : rizla suzuki motogp | suzuki | suzuki gsv-r | b | 71 | chris vermeulen | all row 16 : rizla suzuki motogp | suzuki | suzuki gsv-r | b | 64 | kousuke akiyoshi | 15 row 17 : repsol honda team | honda | honda rc211v | m | 26 | dani pedrosa | all row 18 : repsol honda team | honda | honda rc211v | m | 69 | nicky hayden | all row 19 : honda lcr | honda | honda rc211v | m | 27 | casey stoner | all row 20 : pramac d'antin motogp | ducati | ducati desmosedici gp5 | d | 30 | jose luis cardoso | all row 21 : pramac d'antin motogp | ducati | ducati desmosedici gp5 | d | 66 | alex hofmann | 1-7, 10-11, 13-17 row 22 : pramac d'antin motogp | ducati | ducati desmosedici gp5 | d | 22 | ivan silva | 8-9, 12 row 23 : fortuna honda | honda | honda rc211v | m | 24 | toni elias | 1-8, 10-17 row 24 : fortuna honda | honda | honda rc211v | m | 33 | marco melandri | all row 25 : fortuna honda | honda | honda rc211v | m | 84 | michel fabrizio | 9 row 26 : ilmor srt | ilmor | ilmor x3 | m | 8 | garry mccoy | 16-17

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

select count ( motorcycle ) where no = 0 <table_name> : table \begin{tabular}{llllllll} \toprule & team & constructor & motorcycle & tyres & no & rider & rounds \\ \midrule 0 & camel yamaha team & yamaha & yamaha yzr-m1 & m & 5 & colin edwards & all \\ 1 & camel yamaha team & yamaha & yamaha yzr-m1 & m & 46 & valentino rossi & all \\ 2 & konica minolta honda & honda & honda rc211v & m & 6 & makoto tamada & all \\ 3 & tech 3 yamaha & yamaha & yamaha yzr-m1 & d & 7 & carlos checa & all \\ 4 & tech 3 yamaha & yamaha & yamaha yzr-m1 & d & 77 & james ellison & all \\ 5 & team roberts & team roberts & kr211v & m & 10 & kenny roberts, jr & all \\ 6 & ducati marlboro team & ducati & ducati desmosedici gp6 & b & 15 & sete gibernau & 1-7, 10-11, 13-16 \\ 7 & ducati marlboro team & ducati & ducati desmosedici gp6 & b & 65 & loris capirossi & all \\ 8 & ducati marlboro team & ducati & ducati desmosedici gp6 & b & 66 & alex hofmann & 8-9, 12 \\ 9 & ducati marlboro team & ducati & ducati desmosedici gp6 & b & 12 & troy bayliss & 17 \\ 10 & kawasaki racing team & kawasaki & kawasaki ninja zx-rr & b & 17 & randy de puniet & all \\ 11 & kawasaki racing team & kawasaki & kawasaki ninja zx-rr & b & 56 & shinya nakano & all \\ 12 & kawasaki racing team & kawasaki & kawasaki ninja zx-rr & b & 8 & naoki matsudo & 15 \\ 13 & rizla suzuki motogp & suzuki & suzuki gsv-r & b & 21 & john hopkins & all \\ 14 & rizla suzuki motogp & suzuki & suzuki gsv-r & b & 71 & chris vermeulen & all \\ 15 & rizla suzuki motogp & suzuki & suzuki gsv-r & b & 64 & kousuke akiyoshi & 15 \\ 16 & repsol honda team & honda & honda rc211v & m & 26 & dani pedrosa & all \\ 17 & repsol honda team & honda & honda rc211v & m & 69 & nicky hayden & all \\ 18 & honda lcr & honda & honda rc211v & m & 27 & casey stoner & all \\ 19 & pramac d'antin motogp & ducati & ducati desmosedici gp5 & d & 30 & jose luis cardoso & all \\ 20 & pramac d'antin motogp & ducati & ducati desmosedici gp5 & d & 66 & alex hofmann & 1-7, 10-11, 13-17 \\ 21 & pramac d'antin motogp & ducati & ducati desmosedici gp5 & d & 22 & ivan silva & 8-9, 12 \\ 22 & fortuna honda & honda & honda rc211v & m & 24 & toni elias & 1-8, 10-17 \\ 23 & fortuna honda & honda & honda rc211v & m & 33 & marco melandri & all \\ 24 & fortuna honda & honda & honda rc211v & m & 84 & michel fabrizio & 9 \\ 25 & ilmor srt & ilmor & ilmor x3 & m & 8 & garry mccoy & 16-17 \\ \bottomrule \end{tabular}

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

select count ( motorcycle ) where no = 0 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>team</th> <th>constructor</th> <th>motorcycle</th> <th>tyres</th> <th>no</th> <th>rider</th> <th>rounds</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>camel yamaha team</td> <td>yamaha</td> <td>yamaha yzr-m1</td> <td>m</td> <td>5</td> <td>colin edwards</td> <td>all</td> </tr> <tr> <th>1</th> <td>camel yamaha team</td> <td>yamaha</td> <td>yamaha yzr-m1</td> <td>m</td> <td>46</td> <td>valentino rossi</td> <td>all</td> </tr> <tr> <th>2</th> <td>konica minolta honda</td> <td>honda</td> <td>honda rc211v</td> <td>m</td> <td>6</td> <td>makoto tamada</td> <td>all</td> </tr> <tr> <th>3</th> <td>tech 3 yamaha</td> <td>yamaha</td> <td>yamaha yzr-m1</td> <td>d</td> <td>7</td> <td>carlos checa</td> <td>all</td> </tr> <tr> <th>4</th> <td>tech 3 yamaha</td> <td>yamaha</td> <td>yamaha yzr-m1</td> <td>d</td> <td>77</td> <td>james ellison</td> <td>all</td> </tr> <tr> <th>5</th> <td>team roberts</td> <td>team roberts</td> <td>kr211v</td> <td>m</td> <td>10</td> <td>kenny roberts, jr</td> <td>all</td> </tr> <tr> <th>6</th> <td>ducati marlboro team</td> <td>ducati</td> <td>ducati desmosedici gp6</td> <td>b</td> <td>15</td> <td>sete gibernau</td> <td>1-7, 10-11, 13-16</td> </tr> <tr> <th>7</th> <td>ducati marlboro team</td> <td>ducati</td> <td>ducati desmosedici gp6</td> <td>b</td> <td>65</td> <td>loris capirossi</td> <td>all</td> </tr> <tr> <th>8</th> <td>ducati marlboro team</td> <td>ducati</td> <td>ducati desmosedici gp6</td> <td>b</td> <td>66</td> <td>alex hofmann</td> <td>8-9, 12</td> </tr> <tr> <th>9</th> <td>ducati marlboro team</td> <td>ducati</td> <td>ducati desmosedici gp6</td> <td>b</td> <td>12</td> <td>troy bayliss</td> <td>17</td> </tr> <tr> <th>10</th> <td>kawasaki racing team</td> <td>kawasaki</td> <td>kawasaki ninja zx-rr</td> <td>b</td> <td>17</td> <td>randy de puniet</td> <td>all</td> </tr> <tr> <th>11</th> <td>kawasaki racing team</td> <td>kawasaki</td> <td>kawasaki ninja zx-rr</td> <td>b</td> <td>56</td> <td>shinya nakano</td> <td>all</td> </tr> <tr> <th>12</th> <td>kawasaki racing team</td> <td>kawasaki</td> <td>kawasaki ninja zx-rr</td> <td>b</td> <td>8</td> <td>naoki matsudo</td> <td>15</td> </tr> <tr> <th>13</th> <td>rizla suzuki motogp</td> <td>suzuki</td> <td>suzuki gsv-r</td> <td>b</td> <td>21</td> <td>john hopkins</td> <td>all</td> </tr> <tr> <th>14</th> <td>rizla suzuki motogp</td> <td>suzuki</td> <td>suzuki gsv-r</td> <td>b</td> <td>71</td> <td>chris vermeulen</td> <td>all</td> </tr> <tr> <th>15</th> <td>rizla suzuki motogp</td> <td>suzuki</td> <td>suzuki gsv-r</td> <td>b</td> <td>64</td> <td>kousuke akiyoshi</td> <td>15</td> </tr> <tr> <th>16</th> <td>repsol honda team</td> <td>honda</td> <td>honda rc211v</td> <td>m</td> <td>26</td> <td>dani pedrosa</td> <td>all</td> </tr> <tr> <th>17</th> <td>repsol honda team</td> <td>honda</td> <td>honda rc211v</td> <td>m</td> <td>69</td> <td>nicky hayden</td> <td>all</td> </tr> <tr> <th>18</th> <td>honda lcr</td> <td>honda</td> <td>honda rc211v</td> <td>m</td> <td>27</td> <td>casey stoner</td> <td>all</td> </tr> <tr> <th>19</th> <td>pramac d'antin motogp</td> <td>ducati</td> <td>ducati desmosedici gp5</td> <td>d</td> <td>30</td> <td>jose luis cardoso</td> <td>all</td> </tr> <tr> <th>20</th> <td>pramac d'antin motogp</td> <td>ducati</td> <td>ducati desmosedici gp5</td> <td>d</td> <td>66</td> <td>alex hofmann</td> <td>1-7, 10-11, 13-17</td> </tr> <tr> <th>21</th> <td>pramac d'antin motogp</td> <td>ducati</td> <td>ducati desmosedici gp5</td> <td>d</td> <td>22</td> <td>ivan silva</td> <td>8-9, 12</td> </tr> <tr> <th>22</th> <td>fortuna honda</td> <td>honda</td> <td>honda rc211v</td> <td>m</td> <td>24</td> <td>toni elias</td> <td>1-8, 10-17</td> </tr> <tr> <th>23</th> <td>fortuna honda</td> <td>honda</td> <td>honda rc211v</td> <td>m</td> <td>33</td> <td>marco melandri</td> <td>all</td> </tr> <tr> <th>24</th> <td>fortuna honda</td> <td>honda</td> <td>honda rc211v</td> <td>m</td> <td>84</td> <td>michel fabrizio</td> <td>9</td> </tr> <tr> <th>25</th> <td>ilmor srt</td> <td>ilmor</td> <td>ilmor x3</td> <td>m</td> <td>8</td> <td>garry mccoy</td> <td>16-17</td> </tr> </tbody> </table>

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

select count ( motorcycle ) where no = 0 <table_name> : table | | team | constructor | motorcycle | tyres | no | rider | rounds | |---:|:----------------------|:--------------|:-----------------------|:--------|-----:|:------------------|:------------------| | 0 | camel yamaha team | yamaha | yamaha yzr-m1 | m | 5 | colin edwards | all | | 1 | camel yamaha team | yamaha | yamaha yzr-m1 | m | 46 | valentino rossi | all | | 2 | konica minolta honda | honda | honda rc211v | m | 6 | makoto tamada | all | | 3 | tech 3 yamaha | yamaha | yamaha yzr-m1 | d | 7 | carlos checa | all | | 4 | tech 3 yamaha | yamaha | yamaha yzr-m1 | d | 77 | james ellison | all | | 5 | team roberts | team roberts | kr211v | m | 10 | kenny roberts, jr | all | | 6 | ducati marlboro team | ducati | ducati desmosedici gp6 | b | 15 | sete gibernau | 1-7, 10-11, 13-16 | | 7 | ducati marlboro team | ducati | ducati desmosedici gp6 | b | 65 | loris capirossi | all | | 8 | ducati marlboro team | ducati | ducati desmosedici gp6 | b | 66 | alex hofmann | 8-9, 12 | | 9 | ducati marlboro team | ducati | ducati desmosedici gp6 | b | 12 | troy bayliss | 17 | | 10 | kawasaki racing team | kawasaki | kawasaki ninja zx-rr | b | 17 | randy de puniet | all | | 11 | kawasaki racing team | kawasaki | kawasaki ninja zx-rr | b | 56 | shinya nakano | all | | 12 | kawasaki racing team | kawasaki | kawasaki ninja zx-rr | b | 8 | naoki matsudo | 15 | | 13 | rizla suzuki motogp | suzuki | suzuki gsv-r | b | 21 | john hopkins | all | | 14 | rizla suzuki motogp | suzuki | suzuki gsv-r | b | 71 | chris vermeulen | all | | 15 | rizla suzuki motogp | suzuki | suzuki gsv-r | b | 64 | kousuke akiyoshi | 15 | | 16 | repsol honda team | honda | honda rc211v | m | 26 | dani pedrosa | all | | 17 | repsol honda team | honda | honda rc211v | m | 69 | nicky hayden | all | | 18 | honda lcr | honda | honda rc211v | m | 27 | casey stoner | all | | 19 | pramac d'antin motogp | ducati | ducati desmosedici gp5 | d | 30 | jose luis cardoso | all | | 20 | pramac d'antin motogp | ducati | ducati desmosedici gp5 | d | 66 | alex hofmann | 1-7, 10-11, 13-17 | | 21 | pramac d'antin motogp | ducati | ducati desmosedici gp5 | d | 22 | ivan silva | 8-9, 12 | | 22 | fortuna honda | honda | honda rc211v | m | 24 | toni elias | 1-8, 10-17 | | 23 | fortuna honda | honda | honda rc211v | m | 33 | marco melandri | all | | 24 | fortuna honda | honda | honda rc211v | m | 84 | michel fabrizio | 9 | | 25 | ilmor srt | ilmor | ilmor x3 | m | 8 | garry mccoy | 16-17 |

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

select runners-up where years lost = '1990, 1996' col : team | winners | runners-up | years won | years lost row 1 : bayern munich | 5 | 4 | 1982, 1987, 1990, 2010, 2012 | 1989, 1994, 2008, 2013 row 2 : borussia dortmund | 5 | 2 | 1989, 1995, 1996, 2008, 2013 | 2011, 2012 row 3 : werder bremen | 4 | 1 | 1988, 1993, 1994, 2009 | 1991 row 4 : kaiserslautern | 1 | 2 | 1991 | 1990, 1996 row 5 : schalke 04 | 1 | 2 | 2011 | 1940, 2010 row 6 : borussia monchengladbach | 1 | 1 | 1977 | 1995 row 7 : stuttgart | 1 | 0 | 1992 | none row 8 : dresdner sc | 1 | 0 | 1940 | none row 9 : hamburg | 0 | 3 | none | 1977, 1982, 1987 row 10 : eintracht frankfurt | 0 | 1 | none | 1988 row 11 : hannover 96 | 0 | 1 | none | 1992 row 12 : bayer leverkusen | 0 | 1 | none | 1993 row 13 : wolfsburg | 0 | 1 | none | 2009

col : runners-up row 1 : 2

select runners-up where years lost = '1990, 1996' <table_name> : table \begin{tabular}{llllll} \toprule & team & winners & runners-up & years won & years lost \\ \midrule 0 & bayern munich & 5 & 4 & 1982, 1987, 1990, 2010, 2012 & 1989, 1994, 2008, 2013 \\ 1 & borussia dortmund & 5 & 2 & 1989, 1995, 1996, 2008, 2013 & 2011, 2012 \\ 2 & werder bremen & 4 & 1 & 1988, 1993, 1994, 2009 & 1991 \\ 3 & kaiserslautern & 1 & 2 & 1991 & 1990, 1996 \\ 4 & schalke 04 & 1 & 2 & 2011 & 1940, 2010 \\ 5 & borussia monchengladbach & 1 & 1 & 1977 & 1995 \\ 6 & stuttgart & 1 & 0 & 1992 & none \\ 7 & dresdner sc & 1 & 0 & 1940 & none \\ 8 & hamburg & 0 & 3 & none & 1977, 1982, 1987 \\ 9 & eintracht frankfurt & 0 & 1 & none & 1988 \\ 10 & hannover 96 & 0 & 1 & none & 1992 \\ 11 & bayer leverkusen & 0 & 1 & none & 1993 \\ 12 & wolfsburg & 0 & 1 & none & 2009 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & runners-up \\ \midrule 0 & 2 \\ \bottomrule \end{tabular}

select runners-up where years lost = '1990, 1996' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>team</th> <th>winners</th> <th>runners-up</th> <th>years won</th> <th>years lost</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>bayern munich</td> <td>5</td> <td>4</td> <td>1982, 1987, 1990, 2010, 2012</td> <td>1989, 1994, 2008, 2013</td> </tr> <tr> <th>1</th> <td>borussia dortmund</td> <td>5</td> <td>2</td> <td>1989, 1995, 1996, 2008, 2013</td> <td>2011, 2012</td> </tr> <tr> <th>2</th> <td>werder bremen</td> <td>4</td> <td>1</td> <td>1988, 1993, 1994, 2009</td> <td>1991</td> </tr> <tr> <th>3</th> <td>kaiserslautern</td> <td>1</td> <td>2</td> <td>1991</td> <td>1990, 1996</td> </tr> <tr> <th>4</th> <td>schalke 04</td> <td>1</td> <td>2</td> <td>2011</td> <td>1940, 2010</td> </tr> <tr> <th>5</th> <td>borussia monchengladbach</td> <td>1</td> <td>1</td> <td>1977</td> <td>1995</td> </tr> <tr> <th>6</th> <td>stuttgart</td> <td>1</td> <td>0</td> <td>1992</td> <td>none</td> </tr> <tr> <th>7</th> <td>dresdner sc</td> <td>1</td> <td>0</td> <td>1940</td> <td>none</td> </tr> <tr> <th>8</th> <td>hamburg</td> <td>0</td> <td>3</td> <td>none</td> <td>1977, 1982, 1987</td> </tr> <tr> <th>9</th> <td>eintracht frankfurt</td> <td>0</td> <td>1</td> <td>none</td> <td>1988</td> </tr> <tr> <th>10</th> <td>hannover 96</td> <td>0</td> <td>1</td> <td>none</td> <td>1992</td> </tr> <tr> <th>11</th> <td>bayer leverkusen</td> <td>0</td> <td>1</td> <td>none</td> <td>1993</td> </tr> <tr> <th>12</th> <td>wolfsburg</td> <td>0</td> <td>1</td> <td>none</td> <td>2009</td> </tr> </tbody> </table>

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

select runners-up where years lost = '1990, 1996' <table_name> : table | | team | winners | runners-up | years won | years lost | |---:|:-------------------------|----------:|-------------:|:-----------------------------|:-----------------------| | 0 | bayern munich | 5 | 4 | 1982, 1987, 1990, 2010, 2012 | 1989, 1994, 2008, 2013 | | 1 | borussia dortmund | 5 | 2 | 1989, 1995, 1996, 2008, 2013 | 2011, 2012 | | 2 | werder bremen | 4 | 1 | 1988, 1993, 1994, 2009 | 1991 | | 3 | kaiserslautern | 1 | 2 | 1991 | 1990, 1996 | | 4 | schalke 04 | 1 | 2 | 2011 | 1940, 2010 | | 5 | borussia monchengladbach | 1 | 1 | 1977 | 1995 | | 6 | stuttgart | 1 | 0 | 1992 | none | | 7 | dresdner sc | 1 | 0 | 1940 | none | | 8 | hamburg | 0 | 3 | none | 1977, 1982, 1987 | | 9 | eintracht frankfurt | 0 | 1 | none | 1988 | | 10 | hannover 96 | 0 | 1 | none | 1992 | | 11 | bayer leverkusen | 0 | 1 | none | 1993 | | 12 | wolfsburg | 0 | 1 | none | 2009 |

| | runners-up | |---:|-------------:| | 0 | 2 |

select enrollment where institution = 'clemson university' col : institution | location | enrollment | nickname | varsity sports | rugby since | head coach row 1 : clemson university | clemson, south carolina | 20576 | tigers | 19 | 1967 | justin hickey row 2 : maryland | college park, maryland | 37641 | terrapins | 20 | 1968 | jeff soeken row 3 : navy | annapolis, maryland | 4576 | midshipmen | 30 | 1963 | mike flanagan row 4 : north carolina | chapel hill, north carolina | 29340 | tar heels | 28 | 1966 | pete earsman row 5 : north carolina state | raleigh, north carolina | 34767 | wolfpack | 25 | 1965 | jim latham row 6 : university of virginia | charlottesville, virginia | 20895 | cavaliers | 25 | 1961 | jon roberts row 7 : virginia tech | blacksburg, virginia | 30379 | hokies | 21 | 1891 / 1968 | andy richards row 8 : wake forest | winston-salem, north carolina | 7079 | demon deacons | 18 | 1973 | patrick kane

col : enrollment row 1 : 20576

select enrollment where institution = 'clemson university' <table_name> : table \begin{tabular}{llllllll} \toprule & institution & location & enrollment & nickname & varsity sports & rugby since & head coach \\ \midrule 0 & clemson university & clemson, south carolina & 20576 & tigers & 19 & 1967 & justin hickey \\ 1 & maryland & college park, maryland & 37641 & terrapins & 20 & 1968 & jeff soeken \\ 2 & navy & annapolis, maryland & 4576 & midshipmen & 30 & 1963 & mike flanagan \\ 3 & north carolina & chapel hill, north carolina & 29340 & tar heels & 28 & 1966 & pete earsman \\ 4 & north carolina state & raleigh, north carolina & 34767 & wolfpack & 25 & 1965 & jim latham \\ 5 & university of virginia & charlottesville, virginia & 20895 & cavaliers & 25 & 1961 & jon roberts \\ 6 & virginia tech & blacksburg, virginia & 30379 & hokies & 21 & 1891 / 1968 & andy richards \\ 7 & wake forest & winston-salem, north carolina & 7079 & demon deacons & 18 & 1973 & patrick kane \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & enrollment \\ \midrule 0 & 20576 \\ \bottomrule \end{tabular}

select enrollment where institution = 'clemson university' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>institution</th> <th>location</th> <th>enrollment</th> <th>nickname</th> <th>varsity sports</th> <th>rugby since</th> <th>head coach</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>clemson university</td> <td>clemson, south carolina</td> <td>20576</td> <td>tigers</td> <td>19</td> <td>1967</td> <td>justin hickey</td> </tr> <tr> <th>1</th> <td>maryland</td> <td>college park, maryland</td> <td>37641</td> <td>terrapins</td> <td>20</td> <td>1968</td> <td>jeff soeken</td> </tr> <tr> <th>2</th> <td>navy</td> <td>annapolis, maryland</td> <td>4576</td> <td>midshipmen</td> <td>30</td> <td>1963</td> <td>mike flanagan</td> </tr> <tr> <th>3</th> <td>north carolina</td> <td>chapel hill, north carolina</td> <td>29340</td> <td>tar heels</td> <td>28</td> <td>1966</td> <td>pete earsman</td> </tr> <tr> <th>4</th> <td>north carolina state</td> <td>raleigh, north carolina</td> <td>34767</td> <td>wolfpack</td> <td>25</td> <td>1965</td> <td>jim latham</td> </tr> <tr> <th>5</th> <td>university of virginia</td> <td>charlottesville, virginia</td> <td>20895</td> <td>cavaliers</td> <td>25</td> <td>1961</td> <td>jon roberts</td> </tr> <tr> <th>6</th> <td>virginia tech</td> <td>blacksburg, virginia</td> <td>30379</td> <td>hokies</td> <td>21</td> <td>1891 / 1968</td> <td>andy richards</td> </tr> <tr> <th>7</th> <td>wake forest</td> <td>winston-salem, north carolina</td> <td>7079</td> <td>demon deacons</td> <td>18</td> <td>1973</td> <td>patrick kane</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>enrollment</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>20576</td> </tr> </tbody> </table>

select enrollment where institution = 'clemson university' <table_name> : table | | institution | location | enrollment | nickname | varsity sports | rugby since | head coach | |---:|:-----------------------|:------------------------------|-------------:|:--------------|-----------------:|:--------------|:--------------| | 0 | clemson university | clemson, south carolina | 20576 | tigers | 19 | 1967 | justin hickey | | 1 | maryland | college park, maryland | 37641 | terrapins | 20 | 1968 | jeff soeken | | 2 | navy | annapolis, maryland | 4576 | midshipmen | 30 | 1963 | mike flanagan | | 3 | north carolina | chapel hill, north carolina | 29340 | tar heels | 28 | 1966 | pete earsman | | 4 | north carolina state | raleigh, north carolina | 34767 | wolfpack | 25 | 1965 | jim latham | | 5 | university of virginia | charlottesville, virginia | 20895 | cavaliers | 25 | 1961 | jon roberts | | 6 | virginia tech | blacksburg, virginia | 30379 | hokies | 21 | 1891 / 1968 | andy richards | | 7 | wake forest | winston-salem, north carolina | 7079 | demon deacons | 18 | 1973 | patrick kane |

| | enrollment | |---:|-------------:| | 0 | 20576 |

select count ( commissioned ) where pennant = 75 col : name | pennant | namesake | builder | ordered | laid down | launched | commissioned | fate row 1 : leander | 75 | leander of abydos | hm dockyard, devonport | 18 february 1928 | 1 august 1928 | 13 july 1929 | 23 july 1931 | transferred to royal new zealand navy as hmnzs leander 1941-1945 broken up at blyth 1950 row 2 : orion | 85 | orion the hunter | hm dockyard, devonport | 24 march 1931 | 26 september 1931 | 24 november 1932 | 18 january 1934 | broken up at dalmuir, 1949 row 3 : neptune | 20 | neptune, god of the sea | hm dockyard, portsmouth | 2 march 1931 | 24 september 1931 | 31 january 1933 | 23 february 1934 | sunk in minefield off tripoli, 19 december 1941 row 4 : ajax | 22 | ajax the great | vickers armstrong | 1 october 1932 | 7 february 1933 | 1 march 1934 | 12 april 1935 | broken up at newport, 1949 row 5 : achilles | 70 | achilles | cammell laird | 16 february 1931 | 11 june 1931 | 1 september 1932 | 24 march 1936 | transferred to royal new zealand navy as hmnzs achilles 1941-1946 sold to indian navy as hims delhi 1948 row 6 : amphion | 29 | amphion of thebes | hm dockyard, portsmouth | 1 december 1932 | 22 june 1933 | 27 july 1934 | 15 june 1936 | sold to royal australian navy as hmas perth, 1939 sunk in torpedo attack, 1 march 1942 row 7 : apollo | 63 | apollo, god of light | hm dockyard, devonport | 1 march 1933 | 15 august 1933 | 9 october 1934 | 13 january 1936 | sold to royal australian navy as hmas hobart, 1938 broken up at osaka, 1962 row 8 : sydney (ex-phaeton) | 48 | city of sydney | swan hunter | 10 february 1933 | 8 july 1933 | 22 september 1934 | 24 september 1935 | sunk in surface action, 19 november 1941

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

select count ( commissioned ) where pennant = 75 <table_name> : table \begin{tabular}{llllllllll} \toprule & name & pennant & namesake & builder & ordered & laid down & launched & commissioned & fate \\ \midrule 0 & leander & 75 & leander of abydos & hm dockyard, devonport & 18 february 1928 & 1 august 1928 & 13 july 1929 & 23 july 1931 & transferred to royal new zealand navy as hmnzs leander 1941-1945 broken up at blyth 1950 \\ 1 & orion & 85 & orion the hunter & hm dockyard, devonport & 24 march 1931 & 26 september 1931 & 24 november 1932 & 18 january 1934 & broken up at dalmuir, 1949 \\ 2 & neptune & 20 & neptune, god of the sea & hm dockyard, portsmouth & 2 march 1931 & 24 september 1931 & 31 january 1933 & 23 february 1934 & sunk in minefield off tripoli, 19 december 1941 \\ 3 & ajax & 22 & ajax the great & vickers armstrong & 1 october 1932 & 7 february 1933 & 1 march 1934 & 12 april 1935 & broken up at newport, 1949 \\ 4 & achilles & 70 & achilles & cammell laird & 16 february 1931 & 11 june 1931 & 1 september 1932 & 24 march 1936 & transferred to royal new zealand navy as hmnzs achilles 1941-1946 sold to indian navy as hims delhi 1948 \\ 5 & amphion & 29 & amphion of thebes & hm dockyard, portsmouth & 1 december 1932 & 22 june 1933 & 27 july 1934 & 15 june 1936 & sold to royal australian navy as hmas perth, 1939 sunk in torpedo attack, 1 march 1942 \\ 6 & apollo & 63 & apollo, god of light & hm dockyard, devonport & 1 march 1933 & 15 august 1933 & 9 october 1934 & 13 january 1936 & sold to royal australian navy as hmas hobart, 1938 broken up at osaka, 1962 \\ 7 & sydney (ex-phaeton) & 48 & city of sydney & swan hunter & 10 february 1933 & 8 july 1933 & 22 september 1934 & 24 september 1935 & sunk in surface action, 19 november 1941 \\ \bottomrule \end{tabular}

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

select count ( commissioned ) where pennant = 75 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>name</th> <th>pennant</th> <th>namesake</th> <th>builder</th> <th>ordered</th> <th>laid down</th> <th>launched</th> <th>commissioned</th> <th>fate</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>leander</td> <td>75</td> <td>leander of abydos</td> <td>hm dockyard, devonport</td> <td>18 february 1928</td> <td>1 august 1928</td> <td>13 july 1929</td> <td>23 july 1931</td> <td>transferred to royal new zealand navy as hmnzs leander 1941-1945 broken up at blyth 1950</td> </tr> <tr> <th>1</th> <td>orion</td> <td>85</td> <td>orion the hunter</td> <td>hm dockyard, devonport</td> <td>24 march 1931</td> <td>26 september 1931</td> <td>24 november 1932</td> <td>18 january 1934</td> <td>broken up at dalmuir, 1949</td> </tr> <tr> <th>2</th> <td>neptune</td> <td>20</td> <td>neptune, god of the sea</td> <td>hm dockyard, portsmouth</td> <td>2 march 1931</td> <td>24 september 1931</td> <td>31 january 1933</td> <td>23 february 1934</td> <td>sunk in minefield off tripoli, 19 december 1941</td> </tr> <tr> <th>3</th> <td>ajax</td> <td>22</td> <td>ajax the great</td> <td>vickers armstrong</td> <td>1 october 1932</td> <td>7 february 1933</td> <td>1 march 1934</td> <td>12 april 1935</td> <td>broken up at newport, 1949</td> </tr> <tr> <th>4</th> <td>achilles</td> <td>70</td> <td>achilles</td> <td>cammell laird</td> <td>16 february 1931</td> <td>11 june 1931</td> <td>1 september 1932</td> <td>24 march 1936</td> <td>transferred to royal new zealand navy as hmnzs achilles 1941-1946 sold to indian navy as hims delhi 1948</td> </tr> <tr> <th>5</th> <td>amphion</td> <td>29</td> <td>amphion of thebes</td> <td>hm dockyard, portsmouth</td> <td>1 december 1932</td> <td>22 june 1933</td> <td>27 july 1934</td> <td>15 june 1936</td> <td>sold to royal australian navy as hmas perth, 1939 sunk in torpedo attack, 1 march 1942</td> </tr> <tr> <th>6</th> <td>apollo</td> <td>63</td> <td>apollo, god of light</td> <td>hm dockyard, devonport</td> <td>1 march 1933</td> <td>15 august 1933</td> <td>9 october 1934</td> <td>13 january 1936</td> <td>sold to royal australian navy as hmas hobart, 1938 broken up at osaka, 1962</td> </tr> <tr> <th>7</th> <td>sydney (ex-phaeton)</td> <td>48</td> <td>city of sydney</td> <td>swan hunter</td> <td>10 february 1933</td> <td>8 july 1933</td> <td>22 september 1934</td> <td>24 september 1935</td> <td>sunk in surface action, 19 november 1941</td> </tr> </tbody> </table>

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

select count ( commissioned ) where pennant = 75 <table_name> : table | | name | pennant | namesake | builder | ordered | laid down | launched | commissioned | fate | |---:|:--------------------|----------:|:------------------------|:------------------------|:-----------------|:------------------|:------------------|:------------------|:---------------------------------------------------------------------------------------------------------| | 0 | leander | 75 | leander of abydos | hm dockyard, devonport | 18 february 1928 | 1 august 1928 | 13 july 1929 | 23 july 1931 | transferred to royal new zealand navy as hmnzs leander 1941-1945 broken up at blyth 1950 | | 1 | orion | 85 | orion the hunter | hm dockyard, devonport | 24 march 1931 | 26 september 1931 | 24 november 1932 | 18 january 1934 | broken up at dalmuir, 1949 | | 2 | neptune | 20 | neptune, god of the sea | hm dockyard, portsmouth | 2 march 1931 | 24 september 1931 | 31 january 1933 | 23 february 1934 | sunk in minefield off tripoli, 19 december 1941 | | 3 | ajax | 22 | ajax the great | vickers armstrong | 1 october 1932 | 7 february 1933 | 1 march 1934 | 12 april 1935 | broken up at newport, 1949 | | 4 | achilles | 70 | achilles | cammell laird | 16 february 1931 | 11 june 1931 | 1 september 1932 | 24 march 1936 | transferred to royal new zealand navy as hmnzs achilles 1941-1946 sold to indian navy as hims delhi 1948 | | 5 | amphion | 29 | amphion of thebes | hm dockyard, portsmouth | 1 december 1932 | 22 june 1933 | 27 july 1934 | 15 june 1936 | sold to royal australian navy as hmas perth, 1939 sunk in torpedo attack, 1 march 1942 | | 6 | apollo | 63 | apollo, god of light | hm dockyard, devonport | 1 march 1933 | 15 august 1933 | 9 october 1934 | 13 january 1936 | sold to royal australian navy as hmas hobart, 1938 broken up at osaka, 1962 | | 7 | sydney (ex-phaeton) | 48 | city of sydney | swan hunter | 10 february 1933 | 8 july 1933 | 22 september 1934 | 24 september 1935 | sunk in surface action, 19 november 1941 |

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

select 2010 where school = 'francisco bravo medical magnet high school' col : school | 2007 | 2008 | 2009 | 2010 | 2011 row 1 : francisco bravo medical magnet high school | 807 | 818.0 | 815.0 | 820.0 | 832 row 2 : marc and eva stern math and science school | 718 | 792.0 | 788.0 | 788.0 | 809 row 3 : oscar de la hoya animo charter high school | 662 | 726.0 | 709.0 | 710.0 | 744 row 4 : james a. garfield high school | 553 | 597.0 | 593.0 | 632.0 | 705 row 5 : abraham lincoln high school | 594 | 609.0 | 588.0 | 616.0 | 643 row 6 : wood | | | | | row 7 : wilson high school | 582 | 585.0 | 600.0 | 615.0 | 636 row 8 : theodore roosevelt high school | 557 | 551.0 | 576.0 | 608.0 | none row 9 : thomas jefferson high school | 457 | 516.0 | 514.0 | 546.0 | 546 row 10 : santee education complex | none | 502.0 | 521.0 | 552.0 | 565

col : 2010 row 1 : 820

select 2010 where school = 'francisco bravo medical magnet high school' <table_name> : table \begin{tabular}{lllllll} \toprule & school & 2007 & 2008 & 2009 & 2010 & 2011 \\ \midrule 0 & francisco bravo medical magnet high school & 807 & 818.0 & 815.0 & 820.0 & 832 \\ 1 & marc and eva stern math and science school & 718 & 792.0 & 788.0 & 788.0 & 809 \\ 2 & oscar de la hoya animo charter high school & 662 & 726.0 & 709.0 & 710.0 & 744 \\ 3 & james a. garfield high school & 553 & 597.0 & 593.0 & 632.0 & 705 \\ 4 & abraham lincoln high school & 594 & 609.0 & 588.0 & 616.0 & 643 \\ 5 & wood & & & & & \\ 6 & wilson high school & 582 & 585.0 & 600.0 & 615.0 & 636 \\ 7 & theodore roosevelt high school & 557 & 551.0 & 576.0 & 608.0 & none \\ 8 & thomas jefferson high school & 457 & 516.0 & 514.0 & 546.0 & 546 \\ 9 & santee education complex & none & 502.0 & 521.0 & 552.0 & 565 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & 2010 \\ \midrule 0 & 820 \\ \bottomrule \end{tabular}

select 2010 where school = 'francisco bravo medical magnet high school' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>school</th> <th>2007</th> <th>2008</th> <th>2009</th> <th>2010</th> <th>2011</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>francisco bravo medical magnet high school</td> <td>807</td> <td>818.0</td> <td>815.0</td> <td>820.0</td> <td>832</td> </tr> <tr> <th>1</th> <td>marc and eva stern math and science school</td> <td>718</td> <td>792.0</td> <td>788.0</td> <td>788.0</td> <td>809</td> </tr> <tr> <th>2</th> <td>oscar de la hoya animo charter high school</td> <td>662</td> <td>726.0</td> <td>709.0</td> <td>710.0</td> <td>744</td> </tr> <tr> <th>3</th> <td>james a. garfield high school</td> <td>553</td> <td>597.0</td> <td>593.0</td> <td>632.0</td> <td>705</td> </tr> <tr> <th>4</th> <td>abraham lincoln high school</td> <td>594</td> <td>609.0</td> <td>588.0</td> <td>616.0</td> <td>643</td> </tr> <tr> <th>5</th> <td>wood</td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <th>6</th> <td>wilson high school</td> <td>582</td> <td>585.0</td> <td>600.0</td> <td>615.0</td> <td>636</td> </tr> <tr> <th>7</th> <td>theodore roosevelt high school</td> <td>557</td> <td>551.0</td> <td>576.0</td> <td>608.0</td> <td>none</td> </tr> <tr> <th>8</th> <td>thomas jefferson high school</td> <td>457</td> <td>516.0</td> <td>514.0</td> <td>546.0</td> <td>546</td> </tr> <tr> <th>9</th> <td>santee education complex</td> <td>none</td> <td>502.0</td> <td>521.0</td> <td>552.0</td> <td>565</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>2010</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>820</td> </tr> </tbody> </table>

select 2010 where school = 'francisco bravo medical magnet high school' <table_name> : table | | school | 2007 | 2008 | 2009 | 2010 | 2011 | |---:|:-------------------------------------------|:-------|:-------|:-------|:-------|:-------| | 0 | francisco bravo medical magnet high school | 807 | 818.0 | 815.0 | 820.0 | 832 | | 1 | marc and eva stern math and science school | 718 | 792.0 | 788.0 | 788.0 | 809 | | 2 | oscar de la hoya animo charter high school | 662 | 726.0 | 709.0 | 710.0 | 744 | | 3 | james a. garfield high school | 553 | 597.0 | 593.0 | 632.0 | 705 | | 4 | abraham lincoln high school | 594 | 609.0 | 588.0 | 616.0 | 643 | | 5 | wood | | | | | | | 6 | wilson high school | 582 | 585.0 | 600.0 | 615.0 | 636 | | 7 | theodore roosevelt high school | 557 | 551.0 | 576.0 | 608.0 | none | | 8 | thomas jefferson high school | 457 | 516.0 | 514.0 | 546.0 | 546 | | 9 | santee education complex | none | 502.0 | 521.0 | 552.0 | 565 |

| | 2010 | |---:|-------:| | 0 | 820 |

select type where works number > 1836 order by works number desc limit 1 col : number | builder | type | date | works number | notes row 1 : 51 | baldwin locomotive works | ds4-4-750 | 1949 | 74408 | acquired new 1949, retired 1970/wrecked-scrapped row 2 : 52 | baldwin locomotive works | ds4-4-750 | 1949 | 74409 | acquired new 1949, retired in 1970 and scrapped in 1990 row 3 : 53 | baldwin locomotive works | ds4-4-1000 | 1949 | 74193 | ex-pan american engineering w8380; nee army corps of engineers w8380, acquired 1956, retired in 1985, to john bradley, 1985; to roots of motive power collection row 4 : 54 | baldwin locomotive works | s-12 | 1952/1953 | 75823 | ex-nw (3307); nee wab 307, wrecked 1968, retired 1970, scrapped row 5 : 55 | baldwin locomotive works | rs-12 | 1955 | 76024 | decorated for the united states bicentennial nee mr 32, acquired 1968, scrapped 1995 row 6 : 56 | baldwin locomotive works | rs-12 | 1955 | 76105 | nee mr 33, acquired 1970, retired 1985 to john bradley, 1985; to travel town museum (los angeles, ca)56 row 7 : 57 | baldwin locomotive works | s-12 | 1953 | 75914 | nee sp 1539, acquired 1970, retired unknown, stored at willits, california row 8 : 61 | american locomotive works | rs-11 | 1979 | none | former sp. status unknown row 9 : 62 | american locomotive works | rs-11 | 1979 | none | former sp. status unknown row 10 : 63 | american locomotive works | rs-11 | 1979 | none | former sp. sold to nvrr 1987 row 11 : 64 | electro-motive diesel | gp9m | 1987 | none | former sp # 3411. operational and in regular service row 12 : 65 | electro-motive diesel | gp9m | 1987 | none | former sp # 3412. operational row 13 : 66 | electro-motive diesel | gp9m | 1956 | none | former c&o # 6145. acquired 1998. operational and in regular service row 14 : 67 | electro-motive diesel | gp9m | 6/1954 | 19554 | built as bangor and aroostook railroad # 77; acquired 1998; never delivered row 15 : m-80 | mack | railbus | 1923 | none | purchased 1925. wrecked twice: in 1957 with a delivery truck, and in 1964 with m-100. scrapped 1964 row 16 : m-100 | edwards rail car company | motor car | 1925 | none | acquired from moorhead & north forks railroad; operable. recently repainted back to historic yellow scheme row 17 : m-200 | ss&iw | motor car | 1927 | none | ex-trc #22; nee-lpn 20; to niles canyon railway, 1975; operable on niles canyon railway row 18 : m-300 | american car and foundry company | motor car | 1935 | none | m-300 at fort bragg ex-slgw; nee ar; operable

col : type row 1 : ds4-4-750 row 2 : ds4-4-750 row 3 : ds4-4-1000 row 4 : s-12 row 5 : rs-12 row 6 : rs-12 row 7 : s-12 row 8 : rs-11 row 9 : rs-11 row 10 : rs-11 row 11 : gp9m row 12 : gp9m row 13 : gp9m row 14 : gp9m row 15 : railbus row 16 : motor car row 17 : motor car row 18 : motor car

select type where works number > 1836 order by works number desc limit 1 <table_name> : table \begin{tabular}{lllllll} \toprule & number & builder & type & date & works number & notes \\ \midrule 0 & 51 & baldwin locomotive works & ds4-4-750 & 1949 & 74408 & acquired new 1949, retired 1970/wrecked-scrapped \\ 1 & 52 & baldwin locomotive works & ds4-4-750 & 1949 & 74409 & acquired new 1949, retired in 1970 and scrapped in 1990 \\ 2 & 53 & baldwin locomotive works & ds4-4-1000 & 1949 & 74193 & ex-pan american engineering w8380; nee army corps of engineers w8380, acquired 1956, retired in 1985, to john bradley, 1985; to roots of motive power collection \\ 3 & 54 & baldwin locomotive works & s-12 & 1952/1953 & 75823 & ex-nw (3307); nee wab 307, wrecked 1968, retired 1970, scrapped \\ 4 & 55 & baldwin locomotive works & rs-12 & 1955 & 76024 & decorated for the united states bicentennial nee mr 32, acquired 1968, scrapped 1995 \\ 5 & 56 & baldwin locomotive works & rs-12 & 1955 & 76105 & nee mr 33, acquired 1970, retired 1985 to john bradley, 1985; to travel town museum (los angeles, ca)56 \\ 6 & 57 & baldwin locomotive works & s-12 & 1953 & 75914 & nee sp 1539, acquired 1970, retired unknown, stored at willits, california \\ 7 & 61 & american locomotive works & rs-11 & 1979 & none & former sp. status unknown \\ 8 & 62 & american locomotive works & rs-11 & 1979 & none & former sp. status unknown \\ 9 & 63 & american locomotive works & rs-11 & 1979 & none & former sp. sold to nvrr 1987 \\ 10 & 64 & electro-motive diesel & gp9m & 1987 & none & former sp # 3411. operational and in regular service \\ 11 & 65 & electro-motive diesel & gp9m & 1987 & none & former sp # 3412. operational \\ 12 & 66 & electro-motive diesel & gp9m & 1956 & none & former c&o # 6145. acquired 1998. operational and in regular service \\ 13 & 67 & electro-motive diesel & gp9m & 6/1954 & 19554 & built as bangor and aroostook railroad # 77; acquired 1998; never delivered \\ 14 & m-80 & mack & railbus & 1923 & none & purchased 1925. wrecked twice: in 1957 with a delivery truck, and in 1964 with m-100. scrapped 1964 \\ 15 & m-100 & edwards rail car company & motor car & 1925 & none & acquired from moorhead & north forks railroad; operable. recently repainted back to historic yellow scheme \\ 16 & m-200 & ss&iw & motor car & 1927 & none & ex-trc #22; nee-lpn 20; to niles canyon railway, 1975; operable on niles canyon railway \\ 17 & m-300 & american car and foundry company & motor car & 1935 & none & m-300 at fort bragg ex-slgw; nee ar; operable \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & type \\ \midrule 0 & ds4-4-750 \\ 1 & ds4-4-750 \\ 2 & ds4-4-1000 \\ 3 & s-12 \\ 4 & rs-12 \\ 5 & rs-12 \\ 6 & s-12 \\ 7 & rs-11 \\ 8 & rs-11 \\ 9 & rs-11 \\ 10 & gp9m \\ 11 & gp9m \\ 12 & gp9m \\ 13 & gp9m \\ 14 & railbus \\ 15 & motor car \\ 16 & motor car \\ 17 & motor car \\ \bottomrule \end{tabular}

select type where works number > 1836 order by works number desc limit 1 <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>works number</th> <th>notes</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>51</td> <td>baldwin locomotive works</td> <td>ds4-4-750</td> <td>1949</td> <td>74408</td> <td>acquired new 1949, retired 1970/wrecked-scrapped</td> </tr> <tr> <th>1</th> <td>52</td> <td>baldwin locomotive works</td> <td>ds4-4-750</td> <td>1949</td> <td>74409</td> <td>acquired new 1949, retired in 1970 and scrapped in 1990</td> </tr> <tr> <th>2</th> <td>53</td> <td>baldwin locomotive works</td> <td>ds4-4-1000</td> <td>1949</td> <td>74193</td> <td>ex-pan american engineering w8380; nee army corps of engineers w8380, acquired 1956, retired in 1985, to john bradley, 1985; to roots of motive power collection</td> </tr> <tr> <th>3</th> <td>54</td> <td>baldwin locomotive works</td> <td>s-12</td> <td>1952/1953</td> <td>75823</td> <td>ex-nw (3307); nee wab 307, wrecked 1968, retired 1970, scrapped</td> </tr> <tr> <th>4</th> <td>55</td> <td>baldwin locomotive works</td> <td>rs-12</td> <td>1955</td> <td>76024</td> <td>decorated for the united states bicentennial nee mr 32, acquired 1968, scrapped 1995</td> </tr> <tr> <th>5</th> <td>56</td> <td>baldwin locomotive works</td> <td>rs-12</td> <td>1955</td> <td>76105</td> <td>nee mr 33, acquired 1970, retired 1985 to john bradley, 1985; to travel town museum (los angeles, ca)56</td> </tr> <tr> <th>6</th> <td>57</td> <td>baldwin locomotive works</td> <td>s-12</td> <td>1953</td> <td>75914</td> <td>nee sp 1539, acquired 1970, retired unknown, stored at willits, california</td> </tr> <tr> <th>7</th> <td>61</td> <td>american locomotive works</td> <td>rs-11</td> <td>1979</td> <td>none</td> <td>former sp. status unknown</td> </tr> <tr> <th>8</th> <td>62</td> <td>american locomotive works</td> <td>rs-11</td> <td>1979</td> <td>none</td> <td>former sp. status unknown</td> </tr> <tr> <th>9</th> <td>63</td> <td>american locomotive works</td> <td>rs-11</td> <td>1979</td> <td>none</td> <td>former sp. sold to nvrr 1987</td> </tr> <tr> <th>10</th> <td>64</td> <td>electro-motive diesel</td> <td>gp9m</td> <td>1987</td> <td>none</td> <td>former sp # 3411. operational and in regular service</td> </tr> <tr> <th>11</th> <td>65</td> <td>electro-motive diesel</td> <td>gp9m</td> <td>1987</td> <td>none</td> <td>former sp # 3412. operational</td> </tr> <tr> <th>12</th> <td>66</td> <td>electro-motive diesel</td> <td>gp9m</td> <td>1956</td> <td>none</td> <td>former c&amp;o # 6145. acquired 1998. operational and in regular service</td> </tr> <tr> <th>13</th> <td>67</td> <td>electro-motive diesel</td> <td>gp9m</td> <td>6/1954</td> <td>19554</td> <td>built as bangor and aroostook railroad # 77; acquired 1998; never delivered</td> </tr> <tr> <th>14</th> <td>m-80</td> <td>mack</td> <td>railbus</td> <td>1923</td> <td>none</td> <td>purchased 1925. wrecked twice: in 1957 with a delivery truck, and in 1964 with m-100. scrapped 1964</td> </tr> <tr> <th>15</th> <td>m-100</td> <td>edwards rail car company</td> <td>motor car</td> <td>1925</td> <td>none</td> <td>acquired from moorhead &amp; north forks railroad; operable. recently repainted back to historic yellow scheme</td> </tr> <tr> <th>16</th> <td>m-200</td> <td>ss&amp;iw</td> <td>motor car</td> <td>1927</td> <td>none</td> <td>ex-trc #22; nee-lpn 20; to niles canyon railway, 1975; operable on niles canyon railway</td> </tr> <tr> <th>17</th> <td>m-300</td> <td>american car and foundry company</td> <td>motor car</td> <td>1935</td> <td>none</td> <td>m-300 at fort bragg ex-slgw; nee ar; operable</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>ds4-4-750</td> </tr> <tr> <th>1</th> <td>ds4-4-750</td> </tr> <tr> <th>2</th> <td>ds4-4-1000</td> </tr> <tr> <th>3</th> <td>s-12</td> </tr> <tr> <th>4</th> <td>rs-12</td> </tr> <tr> <th>5</th> <td>rs-12</td> </tr> <tr> <th>6</th> <td>s-12</td> </tr> <tr> <th>7</th> <td>rs-11</td> </tr> <tr> <th>8</th> <td>rs-11</td> </tr> <tr> <th>9</th> <td>rs-11</td> </tr> <tr> <th>10</th> <td>gp9m</td> </tr> <tr> <th>11</th> <td>gp9m</td> </tr> <tr> <th>12</th> <td>gp9m</td> </tr> <tr> <th>13</th> <td>gp9m</td> </tr> <tr> <th>14</th> <td>railbus</td> </tr> <tr> <th>15</th> <td>motor car</td> </tr> <tr> <th>16</th> <td>motor car</td> </tr> <tr> <th>17</th> <td>motor car</td> </tr> </tbody> </table>

select type where works number > 1836 order by works number desc limit 1 <table_name> : table | | number | builder | type | date | works number | notes | |---:|:---------|:---------------------------------|:-----------|:----------|:---------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 51 | baldwin locomotive works | ds4-4-750 | 1949 | 74408 | acquired new 1949, retired 1970/wrecked-scrapped | | 1 | 52 | baldwin locomotive works | ds4-4-750 | 1949 | 74409 | acquired new 1949, retired in 1970 and scrapped in 1990 | | 2 | 53 | baldwin locomotive works | ds4-4-1000 | 1949 | 74193 | ex-pan american engineering w8380; nee army corps of engineers w8380, acquired 1956, retired in 1985, to john bradley, 1985; to roots of motive power collection | | 3 | 54 | baldwin locomotive works | s-12 | 1952/1953 | 75823 | ex-nw (3307); nee wab 307, wrecked 1968, retired 1970, scrapped | | 4 | 55 | baldwin locomotive works | rs-12 | 1955 | 76024 | decorated for the united states bicentennial nee mr 32, acquired 1968, scrapped 1995 | | 5 | 56 | baldwin locomotive works | rs-12 | 1955 | 76105 | nee mr 33, acquired 1970, retired 1985 to john bradley, 1985; to travel town museum (los angeles, ca)56 | | 6 | 57 | baldwin locomotive works | s-12 | 1953 | 75914 | nee sp 1539, acquired 1970, retired unknown, stored at willits, california | | 7 | 61 | american locomotive works | rs-11 | 1979 | none | former sp. status unknown | | 8 | 62 | american locomotive works | rs-11 | 1979 | none | former sp. status unknown | | 9 | 63 | american locomotive works | rs-11 | 1979 | none | former sp. sold to nvrr 1987 | | 10 | 64 | electro-motive diesel | gp9m | 1987 | none | former sp # 3411. operational and in regular service | | 11 | 65 | electro-motive diesel | gp9m | 1987 | none | former sp # 3412. operational | | 12 | 66 | electro-motive diesel | gp9m | 1956 | none | former c&o # 6145. acquired 1998. operational and in regular service | | 13 | 67 | electro-motive diesel | gp9m | 6/1954 | 19554 | built as bangor and aroostook railroad # 77; acquired 1998; never delivered | | 14 | m-80 | mack | railbus | 1923 | none | purchased 1925. wrecked twice: in 1957 with a delivery truck, and in 1964 with m-100. scrapped 1964 | | 15 | m-100 | edwards rail car company | motor car | 1925 | none | acquired from moorhead & north forks railroad; operable. recently repainted back to historic yellow scheme | | 16 | m-200 | ss&iw | motor car | 1927 | none | ex-trc #22; nee-lpn 20; to niles canyon railway, 1975; operable on niles canyon railway | | 17 | m-300 | american car and foundry company | motor car | 1935 | none | m-300 at fort bragg ex-slgw; nee ar; operable |

| | type | |---:|:-----------| | 0 | ds4-4-750 | | 1 | ds4-4-750 | | 2 | ds4-4-1000 | | 3 | s-12 | | 4 | rs-12 | | 5 | rs-12 | | 6 | s-12 | | 7 | rs-11 | | 8 | rs-11 | | 9 | rs-11 | | 10 | gp9m | | 11 | gp9m | | 12 | gp9m | | 13 | gp9m | | 14 | railbus | | 15 | motor car | | 16 | motor car | | 17 | motor car |

select count ( town ) where enrollment (2010-11 & 2011-12) = 743 col : school | team name | town | county | enrollment (2010-11 & 2011-12) | primary mshsaa class* | football class row 1 : benton high school | (lady) cardinals | st. joseph | buchanan | 846 | 4 | 4 row 2 : bishop leblond high school | (lady) golden eagles | st. joseph | buchanan | 314 | 3 | 2 row 3 : cameron high school | (lady) dragons | cameron | clinton | 503 | 3 | 3 row 4 : chillicothe high school (missouri) | (lady) hornets | chillicothe | livingston | 638 | 4 | 3 row 5 : lafayette high school | fighting irish | st. joseph | buchanan | 801 | 4 | 4 row 6 : maryville high school | (lady) spoofhounds | maryville | nodaway | 473 | 3 | 2 row 7 : savannah high school | (lady) savages | savannah | andrew | 743 | 4 | 4 row 8 : smithville high school | (lady) warriors | smithville | clay | 727 | 4 | 4

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

select count ( town ) where enrollment (2010-11 & 2011-12) = 743 <table_name> : table \begin{tabular}{llllllll} \toprule & school & team name & town & county & enrollment (2010-11 & 2011-12) & primary mshsaa class* & football class \\ \midrule 0 & benton high school & (lady) cardinals & st. joseph & buchanan & 846 & 4 & 4 \\ 1 & bishop leblond high school & (lady) golden eagles & st. joseph & buchanan & 314 & 3 & 2 \\ 2 & cameron high school & (lady) dragons & cameron & clinton & 503 & 3 & 3 \\ 3 & chillicothe high school (missouri) & (lady) hornets & chillicothe & livingston & 638 & 4 & 3 \\ 4 & lafayette high school & fighting irish & st. joseph & buchanan & 801 & 4 & 4 \\ 5 & maryville high school & (lady) spoofhounds & maryville & nodaway & 473 & 3 & 2 \\ 6 & savannah high school & (lady) savages & savannah & andrew & 743 & 4 & 4 \\ 7 & smithville high school & (lady) warriors & smithville & clay & 727 & 4 & 4 \\ \bottomrule \end{tabular}

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

select count ( town ) where enrollment (2010-11 & 2011-12) = 743 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>school</th> <th>team name</th> <th>town</th> <th>county</th> <th>enrollment (2010-11 &amp; 2011-12)</th> <th>primary mshsaa class*</th> <th>football class</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>benton high school</td> <td>(lady) cardinals</td> <td>st. joseph</td> <td>buchanan</td> <td>846</td> <td>4</td> <td>4</td> </tr> <tr> <th>1</th> <td>bishop leblond high school</td> <td>(lady) golden eagles</td> <td>st. joseph</td> <td>buchanan</td> <td>314</td> <td>3</td> <td>2</td> </tr> <tr> <th>2</th> <td>cameron high school</td> <td>(lady) dragons</td> <td>cameron</td> <td>clinton</td> <td>503</td> <td>3</td> <td>3</td> </tr> <tr> <th>3</th> <td>chillicothe high school (missouri)</td> <td>(lady) hornets</td> <td>chillicothe</td> <td>livingston</td> <td>638</td> <td>4</td> <td>3</td> </tr> <tr> <th>4</th> <td>lafayette high school</td> <td>fighting irish</td> <td>st. joseph</td> <td>buchanan</td> <td>801</td> <td>4</td> <td>4</td> </tr> <tr> <th>5</th> <td>maryville high school</td> <td>(lady) spoofhounds</td> <td>maryville</td> <td>nodaway</td> <td>473</td> <td>3</td> <td>2</td> </tr> <tr> <th>6</th> <td>savannah high school</td> <td>(lady) savages</td> <td>savannah</td> <td>andrew</td> <td>743</td> <td>4</td> <td>4</td> </tr> <tr> <th>7</th> <td>smithville high school</td> <td>(lady) warriors</td> <td>smithville</td> <td>clay</td> <td>727</td> <td>4</td> <td>4</td> </tr> </tbody> </table>

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

select count ( town ) where enrollment (2010-11 & 2011-12) = 743 <table_name> : table | | school | team name | town | county | enrollment (2010-11 & 2011-12) | primary mshsaa class* | football class | |---:|:-----------------------------------|:---------------------|:------------|:-----------|---------------------------------:|------------------------:|-----------------:| | 0 | benton high school | (lady) cardinals | st. joseph | buchanan | 846 | 4 | 4 | | 1 | bishop leblond high school | (lady) golden eagles | st. joseph | buchanan | 314 | 3 | 2 | | 2 | cameron high school | (lady) dragons | cameron | clinton | 503 | 3 | 3 | | 3 | chillicothe high school (missouri) | (lady) hornets | chillicothe | livingston | 638 | 4 | 3 | | 4 | lafayette high school | fighting irish | st. joseph | buchanan | 801 | 4 | 4 | | 5 | maryville high school | (lady) spoofhounds | maryville | nodaway | 473 | 3 | 2 | | 6 | savannah high school | (lady) savages | savannah | andrew | 743 | 4 | 4 | | 7 | smithville high school | (lady) warriors | smithville | clay | 727 | 4 | 4 |

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

select count ( * ) where establishment = 1927 col : school name | city | grades | establishment | charter row 1 : aliamanu intermediate school | honolulu | 7-8 | 1958 | none row 2 : anuenue school | honolulu | k-12 | 1958 | charter row 3 : central middle school | honolulu | 6-8 | 1926 | none row 4 : dole middle school | honolulu | 6-8 | 1955 | none row 5 : halau ku mana ncpcs | honolulu | 6-12 | 2000 | charter row 6 : jarrett middle school | honolulu | 6-8 | 1955 | none row 7 : kaimuki middle school | honolulu | 6-8 | 1939 | none row 8 : kalakaua middle school | honolulu | 6-8 | 1928 | none row 9 : kawananakoa middle school | honolulu | 6-8 | 1927 | none row 10 : moanalua middle school | honolulu | 7-8 | 1967 | none row 11 : niu valley middle school | honolulu | 6-8 | 1955 | none row 12 : stevenson middle school | honolulu | 6-8 | 1937 | none row 13 : washington middle school | honolulu | 6-8 | 1854 | none

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

select count ( * ) where establishment = 1927 <table_name> : table \begin{tabular}{llllll} \toprule & school name & city & grades & establishment & charter \\ \midrule 0 & aliamanu intermediate school & honolulu & 7-8 & 1958 & none \\ 1 & anuenue school & honolulu & k-12 & 1958 & charter \\ 2 & central middle school & honolulu & 6-8 & 1926 & none \\ 3 & dole middle school & honolulu & 6-8 & 1955 & none \\ 4 & halau ku mana ncpcs & honolulu & 6-12 & 2000 & charter \\ 5 & jarrett middle school & honolulu & 6-8 & 1955 & none \\ 6 & kaimuki middle school & honolulu & 6-8 & 1939 & none \\ 7 & kalakaua middle school & honolulu & 6-8 & 1928 & none \\ 8 & kawananakoa middle school & honolulu & 6-8 & 1927 & none \\ 9 & moanalua middle school & honolulu & 7-8 & 1967 & none \\ 10 & niu valley middle school & honolulu & 6-8 & 1955 & none \\ 11 & stevenson middle school & honolulu & 6-8 & 1937 & none \\ 12 & washington middle school & honolulu & 6-8 & 1854 & none \\ \bottomrule \end{tabular}

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

select count ( * ) where establishment = 1927 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>school name</th> <th>city</th> <th>grades</th> <th>establishment</th> <th>charter</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>aliamanu intermediate school</td> <td>honolulu</td> <td>7-8</td> <td>1958</td> <td>none</td> </tr> <tr> <th>1</th> <td>anuenue school</td> <td>honolulu</td> <td>k-12</td> <td>1958</td> <td>charter</td> </tr> <tr> <th>2</th> <td>central middle school</td> <td>honolulu</td> <td>6-8</td> <td>1926</td> <td>none</td> </tr> <tr> <th>3</th> <td>dole middle school</td> <td>honolulu</td> <td>6-8</td> <td>1955</td> <td>none</td> </tr> <tr> <th>4</th> <td>halau ku mana ncpcs</td> <td>honolulu</td> <td>6-12</td> <td>2000</td> <td>charter</td> </tr> <tr> <th>5</th> <td>jarrett middle school</td> <td>honolulu</td> <td>6-8</td> <td>1955</td> <td>none</td> </tr> <tr> <th>6</th> <td>kaimuki middle school</td> <td>honolulu</td> <td>6-8</td> <td>1939</td> <td>none</td> </tr> <tr> <th>7</th> <td>kalakaua middle school</td> <td>honolulu</td> <td>6-8</td> <td>1928</td> <td>none</td> </tr> <tr> <th>8</th> <td>kawananakoa middle school</td> <td>honolulu</td> <td>6-8</td> <td>1927</td> <td>none</td> </tr> <tr> <th>9</th> <td>moanalua middle school</td> <td>honolulu</td> <td>7-8</td> <td>1967</td> <td>none</td> </tr> <tr> <th>10</th> <td>niu valley middle school</td> <td>honolulu</td> <td>6-8</td> <td>1955</td> <td>none</td> </tr> <tr> <th>11</th> <td>stevenson middle school</td> <td>honolulu</td> <td>6-8</td> <td>1937</td> <td>none</td> </tr> <tr> <th>12</th> <td>washington middle school</td> <td>honolulu</td> <td>6-8</td> <td>1854</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 establishment = 1927 <table_name> : table | | school name | city | grades | establishment | charter | |---:|:-----------------------------|:---------|:---------|----------------:|:----------| | 0 | aliamanu intermediate school | honolulu | 7-8 | 1958 | none | | 1 | anuenue school | honolulu | k-12 | 1958 | charter | | 2 | central middle school | honolulu | 6-8 | 1926 | none | | 3 | dole middle school | honolulu | 6-8 | 1955 | none | | 4 | halau ku mana ncpcs | honolulu | 6-12 | 2000 | charter | | 5 | jarrett middle school | honolulu | 6-8 | 1955 | none | | 6 | kaimuki middle school | honolulu | 6-8 | 1939 | none | | 7 | kalakaua middle school | honolulu | 6-8 | 1928 | none | | 8 | kawananakoa middle school | honolulu | 6-8 | 1927 | none | | 9 | moanalua middle school | honolulu | 7-8 | 1967 | none | | 10 | niu valley middle school | honolulu | 6-8 | 1955 | none | | 11 | stevenson middle school | honolulu | 6-8 | 1937 | none | | 12 | washington middle school | honolulu | 6-8 | 1854 | none |

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

select count ( * ) where gold = 'tokyo' col : year | location | gold | silver | bronze row 1 : 1958 | tokyo | sachika kamo | desideria ampon | liu shang-ku row 2 : 1962 | jakarta | akiko fukui | reiko miyagi | desideria ampon patricia yngayo row 3 : 1966 | bangkok | lany kaligis | kazuko kuromatsu | lita liem sugiarto phanow sudsawasdi row 4 : 1974 | tehran | lita liem sugiarto | paulina pesahov | lany kaligis row 5 : 1978 | bangkok | lee duk-hee | chen chuan | kiyoko nomura sonoe yonezawa row 6 : 1982 | new delhi | etsuko inoue | kim soo-ok | yu liqiao row 7 : 1986 | seoul | li xinyi | lee jeong-soon | kim soo-ok row 8 : 1990 | beijing | akiko kijimuta | chen li | park mal-sim kim il-soon row 9 : 1994 | hiroshima | kimiko date | naoko sawamatsu | yayuk basuki chen li row 10 : 1998 | bangkok | yayuk basuki | tamarine tanasugarn | li fang yi jingqian row 11 : 2002 | busan | iroda tulyaganova | tamarine tanasugarn | shinobu asagoe cho yoon-jeong row 12 : 2006 | doha | zheng jie | sania mirza | li na aiko nakamura row 13 : 2010 | guangzhou | peng shuai | akgul amanmuradova | kimiko date-krumm sania mirza

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

select count ( * ) where gold = 'tokyo' <table_name> : table \begin{tabular}{llllll} \toprule & year & location & gold & silver & bronze \\ \midrule 0 & 1958 & tokyo & sachika kamo & desideria ampon & liu shang-ku \\ 1 & 1962 & jakarta & akiko fukui & reiko miyagi & desideria ampon patricia yngayo \\ 2 & 1966 & bangkok & lany kaligis & kazuko kuromatsu & lita liem sugiarto phanow sudsawasdi \\ 3 & 1974 & tehran & lita liem sugiarto & paulina pesahov & lany kaligis \\ 4 & 1978 & bangkok & lee duk-hee & chen chuan & kiyoko nomura sonoe yonezawa \\ 5 & 1982 & new delhi & etsuko inoue & kim soo-ok & yu liqiao \\ 6 & 1986 & seoul & li xinyi & lee jeong-soon & kim soo-ok \\ 7 & 1990 & beijing & akiko kijimuta & chen li & park mal-sim kim il-soon \\ 8 & 1994 & hiroshima & kimiko date & naoko sawamatsu & yayuk basuki chen li \\ 9 & 1998 & bangkok & yayuk basuki & tamarine tanasugarn & li fang yi jingqian \\ 10 & 2002 & busan & iroda tulyaganova & tamarine tanasugarn & shinobu asagoe cho yoon-jeong \\ 11 & 2006 & doha & zheng jie & sania mirza & li na aiko nakamura \\ 12 & 2010 & guangzhou & peng shuai & akgul amanmuradova & kimiko date-krumm sania mirza \\ \bottomrule \end{tabular}

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

select count ( * ) where gold = 'tokyo' <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>1958</td> <td>tokyo</td> <td>sachika kamo</td> <td>desideria ampon</td> <td>liu shang-ku</td> </tr> <tr> <th>1</th> <td>1962</td> <td>jakarta</td> <td>akiko fukui</td> <td>reiko miyagi</td> <td>desideria ampon patricia yngayo</td> </tr> <tr> <th>2</th> <td>1966</td> <td>bangkok</td> <td>lany kaligis</td> <td>kazuko kuromatsu</td> <td>lita liem sugiarto phanow sudsawasdi</td> </tr> <tr> <th>3</th> <td>1974</td> <td>tehran</td> <td>lita liem sugiarto</td> <td>paulina pesahov</td> <td>lany kaligis</td> </tr> <tr> <th>4</th> <td>1978</td> <td>bangkok</td> <td>lee duk-hee</td> <td>chen chuan</td> <td>kiyoko nomura sonoe yonezawa</td> </tr> <tr> <th>5</th> <td>1982</td> <td>new delhi</td> <td>etsuko inoue</td> <td>kim soo-ok</td> <td>yu liqiao</td> </tr> <tr> <th>6</th> <td>1986</td> <td>seoul</td> <td>li xinyi</td> <td>lee jeong-soon</td> <td>kim soo-ok</td> </tr> <tr> <th>7</th> <td>1990</td> <td>beijing</td> <td>akiko kijimuta</td> <td>chen li</td> <td>park mal-sim kim il-soon</td> </tr> <tr> <th>8</th> <td>1994</td> <td>hiroshima</td> <td>kimiko date</td> <td>naoko sawamatsu</td> <td>yayuk basuki chen li</td> </tr> <tr> <th>9</th> <td>1998</td> <td>bangkok</td> <td>yayuk basuki</td> <td>tamarine tanasugarn</td> <td>li fang yi jingqian</td> </tr> <tr> <th>10</th> <td>2002</td> <td>busan</td> <td>iroda tulyaganova</td> <td>tamarine tanasugarn</td> <td>shinobu asagoe cho yoon-jeong</td> </tr> <tr> <th>11</th> <td>2006</td> <td>doha</td> <td>zheng jie</td> <td>sania mirza</td> <td>li na aiko nakamura</td> </tr> <tr> <th>12</th> <td>2010</td> <td>guangzhou</td> <td>peng shuai</td> <td>akgul amanmuradova</td> <td>kimiko date-krumm sania mirza</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 gold = 'tokyo' <table_name> : table | | year | location | gold | silver | bronze | |---:|-------:|:-----------|:-------------------|:--------------------|:-------------------------------------| | 0 | 1958 | tokyo | sachika kamo | desideria ampon | liu shang-ku | | 1 | 1962 | jakarta | akiko fukui | reiko miyagi | desideria ampon patricia yngayo | | 2 | 1966 | bangkok | lany kaligis | kazuko kuromatsu | lita liem sugiarto phanow sudsawasdi | | 3 | 1974 | tehran | lita liem sugiarto | paulina pesahov | lany kaligis | | 4 | 1978 | bangkok | lee duk-hee | chen chuan | kiyoko nomura sonoe yonezawa | | 5 | 1982 | new delhi | etsuko inoue | kim soo-ok | yu liqiao | | 6 | 1986 | seoul | li xinyi | lee jeong-soon | kim soo-ok | | 7 | 1990 | beijing | akiko kijimuta | chen li | park mal-sim kim il-soon | | 8 | 1994 | hiroshima | kimiko date | naoko sawamatsu | yayuk basuki chen li | | 9 | 1998 | bangkok | yayuk basuki | tamarine tanasugarn | li fang yi jingqian | | 10 | 2002 | busan | iroda tulyaganova | tamarine tanasugarn | shinobu asagoe cho yoon-jeong | | 11 | 2006 | doha | zheng jie | sania mirza | li na aiko nakamura | | 12 | 2010 | guangzhou | peng shuai | akgul amanmuradova | kimiko date-krumm sania mirza |

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

select grid where constructor = 'minardi-cosworth' col : pos | no | driver | constructor | laps | time/retired | grid | points row 1 : 1 | 9 | kimi raikkonen | mclaren-mercedes | 44 | 1:30:01.295 | 2 | 10 row 2 : 2 | 5 | fernando alonso | renault | 44 | 28.394 | 4 | 8 row 3 : 3 | 3 | jenson button | bar-honda | 44 | 32.077 | 8 | 6 row 4 : 4 | 7 | mark webber | williams-bmw | 44 | +1:09.167 | 9 | 5 row 5 : 5 | 2 | rubens barrichello | ferrari | 44 | +1:18.136 | 12 | 4 row 6 : 6 | 11 | jacques villeneuve | sauber-petronas | 44 | +1:27.435 | 14 | 3 row 7 : 7 | 17 | ralf schumacher | toyota | 44 | +1:27.574 | 5 | 2 row 8 : 8 | 18 | tiago monteiro | jordan-toyota | 43 | +1 lap | 19 | 1 row 9 : 9 | 15 | christian klien | red bull-cosworth | 43 | +1 lap | 16 | none row 10 : 10 | 12 | felipe massa | sauber-petronas | 43 | +1 lap | 7 | none row 11 : 11 | 19 | narain karthikeyan | jordan-toyota | 43 | +1 lap | 20 | none row 12 : 12 | 21 | christijan albers | minardi-cosworth | 42 | +2 laps | 18 | none row 13 : 13 | 20 | robert doornbos | minardi-cosworth | 41 | +3 laps | 17 | none row 14 : 14 | 10 | juan pablo montoya | mclaren-mercedes | 40 | collision | 1 | none row 15 : 15 | 8 | antonio pizzonia | williams-bmw | 39 | collision | 15 | none row 16 : ret | 16 | jarno trulli | toyota | 34 | accident | 3 | none row 17 : ret | 14 | david coulthard | red bull-cosworth | 18 | engine | 11 | none row 18 : ret | 1 | michael schumacher | ferrari | 13 | collision | 6 | none row 19 : ret | 4 | takuma sato | bar-honda | 13 | collision | 10 | none row 20 : ret | 6 | giancarlo fisichella | renault | 10 | accident | 13 | none

col : grid row 1 : 18 row 2 : 17

select grid where constructor = 'minardi-cosworth' <table_name> : table \begin{tabular}{lllllllll} \toprule & pos & no & driver & constructor & laps & time/retired & grid & points \\ \midrule 0 & 1 & 9 & kimi raikkonen & mclaren-mercedes & 44 & 1:30:01.295 & 2 & 10 \\ 1 & 2 & 5 & fernando alonso & renault & 44 & 28.394 & 4 & 8 \\ 2 & 3 & 3 & jenson button & bar-honda & 44 & 32.077 & 8 & 6 \\ 3 & 4 & 7 & mark webber & williams-bmw & 44 & +1:09.167 & 9 & 5 \\ 4 & 5 & 2 & rubens barrichello & ferrari & 44 & +1:18.136 & 12 & 4 \\ 5 & 6 & 11 & jacques villeneuve & sauber-petronas & 44 & +1:27.435 & 14 & 3 \\ 6 & 7 & 17 & ralf schumacher & toyota & 44 & +1:27.574 & 5 & 2 \\ 7 & 8 & 18 & tiago monteiro & jordan-toyota & 43 & +1 lap & 19 & 1 \\ 8 & 9 & 15 & christian klien & red bull-cosworth & 43 & +1 lap & 16 & none \\ 9 & 10 & 12 & felipe massa & sauber-petronas & 43 & +1 lap & 7 & none \\ 10 & 11 & 19 & narain karthikeyan & jordan-toyota & 43 & +1 lap & 20 & none \\ 11 & 12 & 21 & christijan albers & minardi-cosworth & 42 & +2 laps & 18 & none \\ 12 & 13 & 20 & robert doornbos & minardi-cosworth & 41 & +3 laps & 17 & none \\ 13 & 14 & 10 & juan pablo montoya & mclaren-mercedes & 40 & collision & 1 & none \\ 14 & 15 & 8 & antonio pizzonia & williams-bmw & 39 & collision & 15 & none \\ 15 & ret & 16 & jarno trulli & toyota & 34 & accident & 3 & none \\ 16 & ret & 14 & david coulthard & red bull-cosworth & 18 & engine & 11 & none \\ 17 & ret & 1 & michael schumacher & ferrari & 13 & collision & 6 & none \\ 18 & ret & 4 & takuma sato & bar-honda & 13 & collision & 10 & none \\ 19 & ret & 6 & giancarlo fisichella & renault & 10 & accident & 13 & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & grid \\ \midrule 0 & 18 \\ 1 & 17 \\ \bottomrule \end{tabular}

select grid where constructor = 'minardi-cosworth' <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>9</td> <td>kimi raikkonen</td> <td>mclaren-mercedes</td> <td>44</td> <td>1:30:01.295</td> <td>2</td> <td>10</td> </tr> <tr> <th>1</th> <td>2</td> <td>5</td> <td>fernando alonso</td> <td>renault</td> <td>44</td> <td>28.394</td> <td>4</td> <td>8</td> </tr> <tr> <th>2</th> <td>3</td> <td>3</td> <td>jenson button</td> <td>bar-honda</td> <td>44</td> <td>32.077</td> <td>8</td> <td>6</td> </tr> <tr> <th>3</th> <td>4</td> <td>7</td> <td>mark webber</td> <td>williams-bmw</td> <td>44</td> <td>+1:09.167</td> <td>9</td> <td>5</td> </tr> <tr> <th>4</th> <td>5</td> <td>2</td> <td>rubens barrichello</td> <td>ferrari</td> <td>44</td> <td>+1:18.136</td> <td>12</td> <td>4</td> </tr> <tr> <th>5</th> <td>6</td> <td>11</td> <td>jacques villeneuve</td> <td>sauber-petronas</td> <td>44</td> <td>+1:27.435</td> <td>14</td> <td>3</td> </tr> <tr> <th>6</th> <td>7</td> <td>17</td> <td>ralf schumacher</td> <td>toyota</td> <td>44</td> <td>+1:27.574</td> <td>5</td> <td>2</td> </tr> <tr> <th>7</th> <td>8</td> <td>18</td> <td>tiago monteiro</td> <td>jordan-toyota</td> <td>43</td> <td>+1 lap</td> <td>19</td> <td>1</td> </tr> <tr> <th>8</th> <td>9</td> <td>15</td> <td>christian klien</td> <td>red bull-cosworth</td> <td>43</td> <td>+1 lap</td> <td>16</td> <td>none</td> </tr> <tr> <th>9</th> <td>10</td> <td>12</td> <td>felipe massa</td> <td>sauber-petronas</td> <td>43</td> <td>+1 lap</td> <td>7</td> <td>none</td> </tr> <tr> <th>10</th> <td>11</td> <td>19</td> <td>narain karthikeyan</td> <td>jordan-toyota</td> <td>43</td> <td>+1 lap</td> <td>20</td> <td>none</td> </tr> <tr> <th>11</th> <td>12</td> <td>21</td> <td>christijan albers</td> <td>minardi-cosworth</td> <td>42</td> <td>+2 laps</td> <td>18</td> <td>none</td> </tr> <tr> <th>12</th> <td>13</td> <td>20</td> <td>robert doornbos</td> <td>minardi-cosworth</td> <td>41</td> <td>+3 laps</td> <td>17</td> <td>none</td> </tr> <tr> <th>13</th> <td>14</td> <td>10</td> <td>juan pablo montoya</td> <td>mclaren-mercedes</td> <td>40</td> <td>collision</td> <td>1</td> <td>none</td> </tr> <tr> <th>14</th> <td>15</td> <td>8</td> <td>antonio pizzonia</td> <td>williams-bmw</td> <td>39</td> <td>collision</td> <td>15</td> <td>none</td> </tr> <tr> <th>15</th> <td>ret</td> <td>16</td> <td>jarno trulli</td> <td>toyota</td> <td>34</td> <td>accident</td> <td>3</td> <td>none</td> </tr> <tr> <th>16</th> <td>ret</td> <td>14</td> <td>david coulthard</td> <td>red bull-cosworth</td> <td>18</td> <td>engine</td> <td>11</td> <td>none</td> </tr> <tr> <th>17</th> <td>ret</td> <td>1</td> <td>michael schumacher</td> <td>ferrari</td> <td>13</td> <td>collision</td> <td>6</td> <td>none</td> </tr> <tr> <th>18</th> <td>ret</td> <td>4</td> <td>takuma sato</td> <td>bar-honda</td> <td>13</td> <td>collision</td> <td>10</td> <td>none</td> </tr> <tr> <th>19</th> <td>ret</td> <td>6</td> <td>giancarlo fisichella</td> <td>renault</td> <td>10</td> <td>accident</td> <td>13</td> <td>none</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>grid</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>18</td> </tr> <tr> <th>1</th> <td>17</td> </tr> </tbody> </table>

select grid where constructor = 'minardi-cosworth' <table_name> : table | | pos | no | driver | constructor | laps | time/retired | grid | points | |---:|:------|-----:|:---------------------|:------------------|-------:|:---------------|-------:|:---------| | 0 | 1 | 9 | kimi raikkonen | mclaren-mercedes | 44 | 1:30:01.295 | 2 | 10 | | 1 | 2 | 5 | fernando alonso | renault | 44 | 28.394 | 4 | 8 | | 2 | 3 | 3 | jenson button | bar-honda | 44 | 32.077 | 8 | 6 | | 3 | 4 | 7 | mark webber | williams-bmw | 44 | +1:09.167 | 9 | 5 | | 4 | 5 | 2 | rubens barrichello | ferrari | 44 | +1:18.136 | 12 | 4 | | 5 | 6 | 11 | jacques villeneuve | sauber-petronas | 44 | +1:27.435 | 14 | 3 | | 6 | 7 | 17 | ralf schumacher | toyota | 44 | +1:27.574 | 5 | 2 | | 7 | 8 | 18 | tiago monteiro | jordan-toyota | 43 | +1 lap | 19 | 1 | | 8 | 9 | 15 | christian klien | red bull-cosworth | 43 | +1 lap | 16 | none | | 9 | 10 | 12 | felipe massa | sauber-petronas | 43 | +1 lap | 7 | none | | 10 | 11 | 19 | narain karthikeyan | jordan-toyota | 43 | +1 lap | 20 | none | | 11 | 12 | 21 | christijan albers | minardi-cosworth | 42 | +2 laps | 18 | none | | 12 | 13 | 20 | robert doornbos | minardi-cosworth | 41 | +3 laps | 17 | none | | 13 | 14 | 10 | juan pablo montoya | mclaren-mercedes | 40 | collision | 1 | none | | 14 | 15 | 8 | antonio pizzonia | williams-bmw | 39 | collision | 15 | none | | 15 | ret | 16 | jarno trulli | toyota | 34 | accident | 3 | none | | 16 | ret | 14 | david coulthard | red bull-cosworth | 18 | engine | 11 | none | | 17 | ret | 1 | michael schumacher | ferrari | 13 | collision | 6 | none | | 18 | ret | 4 | takuma sato | bar-honda | 13 | collision | 10 | none | | 19 | ret | 6 | giancarlo fisichella | renault | 10 | accident | 13 | none |

| | grid | |---:|-------:| | 0 | 18 | | 1 | 17 |

select call sign where frequency > '91.9' order by frequency asc limit 1354 col : location | frequency | call sign | network | call sign meaning row 1 : beach | 91.9 | k220fi (kdpr) | news and classical | none row 2 : bismarck | 90.5 | kcnd | news and classical | capital of north dakota row 3 : bowman | 91.9 | k220fj (kdpr) | news and classical | none row 4 : crosby | 91.9 | k220ff (kppw) | news and classical | none row 5 : devils lake | 91.7 | kppd | news and classical | prairie public radio devils lake row 6 : dickinson | 89.9 | kdpr | news and classical | dickinson public radio row 7 : fargo | 91.9 | kdsu | mixed news and classical & roots, rock, and jazz | north dakota state university row 8 : grand forks | 89.3 | kund-fm | news and classical | university of north dakota row 9 : grand forks | 90.7 | kfjm | roots, rock, and jazz | folk and jazz music row 10 : jamestown | 91.5 | kprj | news and classical | public radio jamestown row 11 : hettinger | 91.9 | k220fg (kdpr) | news and classical | none row 12 : lisbon | 98.3 | k252ez (kdsu) | mixed news and classical & roots, rock, and jazz | none row 13 : minot | 88.9 | kmpr | news and classical | minot public radio row 14 : plentywood, mt | 91.9 | k220fe (kppw) | news and classical | none row 15 : thief river falls, mn | 88.3 | k202bk (kund-fm) | news and classical | none row 16 : tioga | 91.9 | k220fh (kppw) | news and classical | none row 17 : wahpeton | 94.7 | k234bx (kdsu) | mixed news and classical & roots, rock, and jazz | none row 18 : williston | 89.5 | kppr | roots, rock, and jazz | prairie public radio row 19 : williston | 88.7 | kppw | news and classical | prairie public williston

col : call sign row 1 : k234bx (kdsu) row 2 : k252ez (kdsu)

select call sign where frequency > '91.9' order by frequency asc limit 1354 <table_name> : table \begin{tabular}{llllll} \toprule & location & frequency & call sign & network & call sign meaning \\ \midrule 0 & beach & 91.9 & k220fi (kdpr) & news and classical & none \\ 1 & bismarck & 90.5 & kcnd & news and classical & capital of north dakota \\ 2 & bowman & 91.9 & k220fj (kdpr) & news and classical & none \\ 3 & crosby & 91.9 & k220ff (kppw) & news and classical & none \\ 4 & devils lake & 91.7 & kppd & news and classical & prairie public radio devils lake \\ 5 & dickinson & 89.9 & kdpr & news and classical & dickinson public radio \\ 6 & fargo & 91.9 & kdsu & mixed news and classical & roots, rock, and jazz & north dakota state university \\ 7 & grand forks & 89.3 & kund-fm & news and classical & university of north dakota \\ 8 & grand forks & 90.7 & kfjm & roots, rock, and jazz & folk and jazz music \\ 9 & jamestown & 91.5 & kprj & news and classical & public radio jamestown \\ 10 & hettinger & 91.9 & k220fg (kdpr) & news and classical & none \\ 11 & lisbon & 98.3 & k252ez (kdsu) & mixed news and classical & roots, rock, and jazz & none \\ 12 & minot & 88.9 & kmpr & news and classical & minot public radio \\ 13 & plentywood, mt & 91.9 & k220fe (kppw) & news and classical & none \\ 14 & thief river falls, mn & 88.3 & k202bk (kund-fm) & news and classical & none \\ 15 & tioga & 91.9 & k220fh (kppw) & news and classical & none \\ 16 & wahpeton & 94.7 & k234bx (kdsu) & mixed news and classical & roots, rock, and jazz & none \\ 17 & williston & 89.5 & kppr & roots, rock, and jazz & prairie public radio \\ 18 & williston & 88.7 & kppw & news and classical & prairie public williston \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & call sign \\ \midrule 0 & k234bx (kdsu) \\ 1 & k252ez (kdsu) \\ \bottomrule \end{tabular}

select call sign where frequency > '91.9' order by frequency asc limit 1354 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>location</th> <th>frequency</th> <th>call sign</th> <th>network</th> <th>call sign meaning</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>beach</td> <td>91.9</td> <td>k220fi (kdpr)</td> <td>news and classical</td> <td>none</td> </tr> <tr> <th>1</th> <td>bismarck</td> <td>90.5</td> <td>kcnd</td> <td>news and classical</td> <td>capital of north dakota</td> </tr> <tr> <th>2</th> <td>bowman</td> <td>91.9</td> <td>k220fj (kdpr)</td> <td>news and classical</td> <td>none</td> </tr> <tr> <th>3</th> <td>crosby</td> <td>91.9</td> <td>k220ff (kppw)</td> <td>news and classical</td> <td>none</td> </tr> <tr> <th>4</th> <td>devils lake</td> <td>91.7</td> <td>kppd</td> <td>news and classical</td> <td>prairie public radio devils lake</td> </tr> <tr> <th>5</th> <td>dickinson</td> <td>89.9</td> <td>kdpr</td> <td>news and classical</td> <td>dickinson public radio</td> </tr> <tr> <th>6</th> <td>fargo</td> <td>91.9</td> <td>kdsu</td> <td>mixed news and classical &amp; roots, rock, and jazz</td> <td>north dakota state university</td> </tr> <tr> <th>7</th> <td>grand forks</td> <td>89.3</td> <td>kund-fm</td> <td>news and classical</td> <td>university of north dakota</td> </tr> <tr> <th>8</th> <td>grand forks</td> <td>90.7</td> <td>kfjm</td> <td>roots, rock, and jazz</td> <td>folk and jazz music</td> </tr> <tr> <th>9</th> <td>jamestown</td> <td>91.5</td> <td>kprj</td> <td>news and classical</td> <td>public radio jamestown</td> </tr> <tr> <th>10</th> <td>hettinger</td> <td>91.9</td> <td>k220fg (kdpr)</td> <td>news and classical</td> <td>none</td> </tr> <tr> <th>11</th> <td>lisbon</td> <td>98.3</td> <td>k252ez (kdsu)</td> <td>mixed news and classical &amp; roots, rock, and jazz</td> <td>none</td> </tr> <tr> <th>12</th> <td>minot</td> <td>88.9</td> <td>kmpr</td> <td>news and classical</td> <td>minot public radio</td> </tr> <tr> <th>13</th> <td>plentywood, mt</td> <td>91.9</td> <td>k220fe (kppw)</td> <td>news and classical</td> <td>none</td> </tr> <tr> <th>14</th> <td>thief river falls, mn</td> <td>88.3</td> <td>k202bk (kund-fm)</td> <td>news and classical</td> <td>none</td> </tr> <tr> <th>15</th> <td>tioga</td> <td>91.9</td> <td>k220fh (kppw)</td> <td>news and classical</td> <td>none</td> </tr> <tr> <th>16</th> <td>wahpeton</td> <td>94.7</td> <td>k234bx (kdsu)</td> <td>mixed news and classical &amp; roots, rock, and jazz</td> <td>none</td> </tr> <tr> <th>17</th> <td>williston</td> <td>89.5</td> <td>kppr</td> <td>roots, rock, and jazz</td> <td>prairie public radio</td> </tr> <tr> <th>18</th> <td>williston</td> <td>88.7</td> <td>kppw</td> <td>news and classical</td> <td>prairie public williston</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>call sign</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>k234bx (kdsu)</td> </tr> <tr> <th>1</th> <td>k252ez (kdsu)</td> </tr> </tbody> </table>

select call sign where frequency > '91.9' order by frequency asc limit 1354 <table_name> : table | | location | frequency | call sign | network | call sign meaning | |---:|:----------------------|------------:|:-----------------|:-------------------------------------------------|:---------------------------------| | 0 | beach | 91.9 | k220fi (kdpr) | news and classical | none | | 1 | bismarck | 90.5 | kcnd | news and classical | capital of north dakota | | 2 | bowman | 91.9 | k220fj (kdpr) | news and classical | none | | 3 | crosby | 91.9 | k220ff (kppw) | news and classical | none | | 4 | devils lake | 91.7 | kppd | news and classical | prairie public radio devils lake | | 5 | dickinson | 89.9 | kdpr | news and classical | dickinson public radio | | 6 | fargo | 91.9 | kdsu | mixed news and classical & roots, rock, and jazz | north dakota state university | | 7 | grand forks | 89.3 | kund-fm | news and classical | university of north dakota | | 8 | grand forks | 90.7 | kfjm | roots, rock, and jazz | folk and jazz music | | 9 | jamestown | 91.5 | kprj | news and classical | public radio jamestown | | 10 | hettinger | 91.9 | k220fg (kdpr) | news and classical | none | | 11 | lisbon | 98.3 | k252ez (kdsu) | mixed news and classical & roots, rock, and jazz | none | | 12 | minot | 88.9 | kmpr | news and classical | minot public radio | | 13 | plentywood, mt | 91.9 | k220fe (kppw) | news and classical | none | | 14 | thief river falls, mn | 88.3 | k202bk (kund-fm) | news and classical | none | | 15 | tioga | 91.9 | k220fh (kppw) | news and classical | none | | 16 | wahpeton | 94.7 | k234bx (kdsu) | mixed news and classical & roots, rock, and jazz | none | | 17 | williston | 89.5 | kppr | roots, rock, and jazz | prairie public radio | | 18 | williston | 88.7 | kppw | news and classical | prairie public williston |

| | call sign | |---:|:--------------| | 0 | k234bx (kdsu) | | 1 | k252ez (kdsu) |

select count ( * ) where year < 499 col : year | league | record | finish | manager | playoffs | notes row 1 : 1947 | sunset league | 69-69 | 4th | thomas lloyd | none | none row 2 : 1948 | sunset league | 77-63 | 3rd | thomas lloyd | league champs | none row 3 : 1949 | sunset league | 49-75 | 7th | lilio marcucci | none | none row 4 : 1950 | far west league | 75-63 | 3rd | joe borich | lost in 1st round | none row 5 : 1951 | far west league | 52-65 | 3rd | cotton pippen | none | none row 6 : 1955 | california league | 40-106 | 8th | leonard noren | none | channel cities moved to reno july 1 row 7 : 1956 | california league | 73-67 | 4th | ray perry | lost in 1st round | none row 8 : 1957 | california league | 79-59 | 2nd | ray perry | lost league finals | none row 9 : 1958 | california league | 68-69 | 6th | ray perry | none | none row 10 : 1959 | california league | 81-58 | 2nd | ray perry | none | none row 11 : 1960 | california league | 89-51 | 1st | tom saffell | league champs | none row 12 : 1961 | california league | 97-43 | 1st | roy smalley | league champs | none row 13 : 1962 | california league | 70-68 | 4th | roy smalley | lost league finals | none row 14 : 1963 | california league | 71-69 | 5th | tom saffell | none | none row 15 : 1964 | california league | 66-71 | 6th | tom saffell / harvey koepf | none | none row 16 : 1966 | california league | 69-71 | 4th (t) | phil cavarretta | none | none row 17 : 1967 | california league | 56-82 | 8th | phil cavarretta | none | none row 18 : 1968 | california league | 67-72 | 5th | clay bryant | none | none row 19 : 1969 | california league | 72-68 | 3rd (t) | ken aspromonte | none | none row 20 : 1970 | california league | 79-61 | 2nd | pinky may | none | none row 21 : 1971 | california league | 64-75 | 7th | pinky may | none | none row 22 : 1972 | california league | 50-88 | 8th | lou klimchock | none | none row 23 : 1973 | california league | 70-70 | 4th (t) | lou klimchock | none | none row 24 : 1974 | california league | 61-79 | 6th (t) | joe azcue / interim (june 29-july 3) / del youngblood | none | none row 25 : 1975 | california league | 86-54 | 1st | harry warner | league champs | none row 26 : 1976 | california league | 75-62 | 3rd | johnny goryl | league champs | none row 27 : 1977 | california league | 59-81 | 5th | glenn ezell | none | none row 28 : 1978 | california league | 62-78 | 5th | eddie watt | none | none row 29 : 1979 | california league | 74-67 | 3rd | eddie watt | none | none row 30 : 1980 | california league | 75-66 | 2nd (t) | jack maloof | none | none row 31 : 1981 | california league | 81-58 | 2nd | jack maloof | lost in 1st round | none row 32 : 1988 | california league | 39-103 | 10th | nate oliver | none | none row 33 : 1989 | california league | 68-74 | 6th | eli grba | none | none row 34 : 1990 | california league | 71-68 | 6th | mike brown | none | none row 35 : 1991 | california league | 59-77 | 7th | mal fichman | none | none row 36 : 1992 | california league | 65-71 | 8th | gary jones | none | none

col : count ( "*" ) row 1 : 36

select count ( * ) where year < 499 <table_name> : table \begin{tabular}{llllllll} \toprule & year & league & record & finish & manager & playoffs & notes \\ \midrule 0 & 1947 & sunset league & 69-69 & 4th & thomas lloyd & none & none \\ 1 & 1948 & sunset league & 77-63 & 3rd & thomas lloyd & league champs & none \\ 2 & 1949 & sunset league & 49-75 & 7th & lilio marcucci & none & none \\ 3 & 1950 & far west league & 75-63 & 3rd & joe borich & lost in 1st round & none \\ 4 & 1951 & far west league & 52-65 & 3rd & cotton pippen & none & none \\ 5 & 1955 & california league & 40-106 & 8th & leonard noren & none & channel cities moved to reno july 1 \\ 6 & 1956 & california league & 73-67 & 4th & ray perry & lost in 1st round & none \\ 7 & 1957 & california league & 79-59 & 2nd & ray perry & lost league finals & none \\ 8 & 1958 & california league & 68-69 & 6th & ray perry & none & none \\ 9 & 1959 & california league & 81-58 & 2nd & ray perry & none & none \\ 10 & 1960 & california league & 89-51 & 1st & tom saffell & league champs & none \\ 11 & 1961 & california league & 97-43 & 1st & roy smalley & league champs & none \\ 12 & 1962 & california league & 70-68 & 4th & roy smalley & lost league finals & none \\ 13 & 1963 & california league & 71-69 & 5th & tom saffell & none & none \\ 14 & 1964 & california league & 66-71 & 6th & tom saffell / harvey koepf & none & none \\ 15 & 1966 & california league & 69-71 & 4th (t) & phil cavarretta & none & none \\ 16 & 1967 & california league & 56-82 & 8th & phil cavarretta & none & none \\ 17 & 1968 & california league & 67-72 & 5th & clay bryant & none & none \\ 18 & 1969 & california league & 72-68 & 3rd (t) & ken aspromonte & none & none \\ 19 & 1970 & california league & 79-61 & 2nd & pinky may & none & none \\ 20 & 1971 & california league & 64-75 & 7th & pinky may & none & none \\ 21 & 1972 & california league & 50-88 & 8th & lou klimchock & none & none \\ 22 & 1973 & california league & 70-70 & 4th (t) & lou klimchock & none & none \\ 23 & 1974 & california league & 61-79 & 6th (t) & joe azcue / interim (june 29-july 3) / del youngblood & none & none \\ 24 & 1975 & california league & 86-54 & 1st & harry warner & league champs & none \\ 25 & 1976 & california league & 75-62 & 3rd & johnny goryl & league champs & none \\ 26 & 1977 & california league & 59-81 & 5th & glenn ezell & none & none \\ 27 & 1978 & california league & 62-78 & 5th & eddie watt & none & none \\ 28 & 1979 & california league & 74-67 & 3rd & eddie watt & none & none \\ 29 & 1980 & california league & 75-66 & 2nd (t) & jack maloof & none & none \\ 30 & 1981 & california league & 81-58 & 2nd & jack maloof & lost in 1st round & none \\ 31 & 1988 & california league & 39-103 & 10th & nate oliver & none & none \\ 32 & 1989 & california league & 68-74 & 6th & eli grba & none & none \\ 33 & 1990 & california league & 71-68 & 6th & mike brown & none & none \\ 34 & 1991 & california league & 59-77 & 7th & mal fichman & none & none \\ 35 & 1992 & california league & 65-71 & 8th & gary jones & none & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 36 \\ \bottomrule \end{tabular}

select count ( * ) where year < 499 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>year</th> <th>league</th> <th>record</th> <th>finish</th> <th>manager</th> <th>playoffs</th> <th>notes</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1947</td> <td>sunset league</td> <td>69-69</td> <td>4th</td> <td>thomas lloyd</td> <td>none</td> <td>none</td> </tr> <tr> <th>1</th> <td>1948</td> <td>sunset league</td> <td>77-63</td> <td>3rd</td> <td>thomas lloyd</td> <td>league champs</td> <td>none</td> </tr> <tr> <th>2</th> <td>1949</td> <td>sunset league</td> <td>49-75</td> <td>7th</td> <td>lilio marcucci</td> <td>none</td> <td>none</td> </tr> <tr> <th>3</th> <td>1950</td> <td>far west league</td> <td>75-63</td> <td>3rd</td> <td>joe borich</td> <td>lost in 1st round</td> <td>none</td> </tr> <tr> <th>4</th> <td>1951</td> <td>far west league</td> <td>52-65</td> <td>3rd</td> <td>cotton pippen</td> <td>none</td> <td>none</td> </tr> <tr> <th>5</th> <td>1955</td> <td>california league</td> <td>40-106</td> <td>8th</td> <td>leonard noren</td> <td>none</td> <td>channel cities moved to reno july 1</td> </tr> <tr> <th>6</th> <td>1956</td> <td>california league</td> <td>73-67</td> <td>4th</td> <td>ray perry</td> <td>lost in 1st round</td> <td>none</td> </tr> <tr> <th>7</th> <td>1957</td> <td>california league</td> <td>79-59</td> <td>2nd</td> <td>ray perry</td> <td>lost league finals</td> <td>none</td> </tr> <tr> <th>8</th> <td>1958</td> <td>california league</td> <td>68-69</td> <td>6th</td> <td>ray perry</td> <td>none</td> <td>none</td> </tr> <tr> <th>9</th> <td>1959</td> <td>california league</td> <td>81-58</td> <td>2nd</td> <td>ray perry</td> <td>none</td> <td>none</td> </tr> <tr> <th>10</th> <td>1960</td> <td>california league</td> <td>89-51</td> <td>1st</td> <td>tom saffell</td> <td>league champs</td> <td>none</td> </tr> <tr> <th>11</th> <td>1961</td> <td>california league</td> <td>97-43</td> <td>1st</td> <td>roy smalley</td> <td>league champs</td> <td>none</td> </tr> <tr> <th>12</th> <td>1962</td> <td>california league</td> <td>70-68</td> <td>4th</td> <td>roy smalley</td> <td>lost league finals</td> <td>none</td> </tr> <tr> <th>13</th> <td>1963</td> <td>california league</td> <td>71-69</td> <td>5th</td> <td>tom saffell</td> <td>none</td> <td>none</td> </tr> <tr> <th>14</th> <td>1964</td> <td>california league</td> <td>66-71</td> <td>6th</td> <td>tom saffell / harvey koepf</td> <td>none</td> <td>none</td> </tr> <tr> <th>15</th> <td>1966</td> <td>california league</td> <td>69-71</td> <td>4th (t)</td> <td>phil cavarretta</td> <td>none</td> <td>none</td> </tr> <tr> <th>16</th> <td>1967</td> <td>california league</td> <td>56-82</td> <td>8th</td> <td>phil cavarretta</td> <td>none</td> <td>none</td> </tr> <tr> <th>17</th> <td>1968</td> <td>california league</td> <td>67-72</td> <td>5th</td> <td>clay bryant</td> <td>none</td> <td>none</td> </tr> <tr> <th>18</th> <td>1969</td> <td>california league</td> <td>72-68</td> <td>3rd (t)</td> <td>ken aspromonte</td> <td>none</td> <td>none</td> </tr> <tr> <th>19</th> <td>1970</td> <td>california league</td> <td>79-61</td> <td>2nd</td> <td>pinky may</td> <td>none</td> <td>none</td> </tr> <tr> <th>20</th> <td>1971</td> <td>california league</td> <td>64-75</td> <td>7th</td> <td>pinky may</td> <td>none</td> <td>none</td> </tr> <tr> <th>21</th> <td>1972</td> <td>california league</td> <td>50-88</td> <td>8th</td> <td>lou klimchock</td> <td>none</td> <td>none</td> </tr> <tr> <th>22</th> <td>1973</td> <td>california league</td> <td>70-70</td> <td>4th (t)</td> <td>lou klimchock</td> <td>none</td> <td>none</td> </tr> <tr> <th>23</th> <td>1974</td> <td>california league</td> <td>61-79</td> <td>6th (t)</td> <td>joe azcue / interim (june 29-july 3) / del youngblood</td> <td>none</td> <td>none</td> </tr> <tr> <th>24</th> <td>1975</td> <td>california league</td> <td>86-54</td> <td>1st</td> <td>harry warner</td> <td>league champs</td> <td>none</td> </tr> <tr> <th>25</th> <td>1976</td> <td>california league</td> <td>75-62</td> <td>3rd</td> <td>johnny goryl</td> <td>league champs</td> <td>none</td> </tr> <tr> <th>26</th> <td>1977</td> <td>california league</td> <td>59-81</td> <td>5th</td> <td>glenn ezell</td> <td>none</td> <td>none</td> </tr> <tr> <th>27</th> <td>1978</td> <td>california league</td> <td>62-78</td> <td>5th</td> <td>eddie watt</td> <td>none</td> <td>none</td> </tr> <tr> <th>28</th> <td>1979</td> <td>california league</td> <td>74-67</td> <td>3rd</td> <td>eddie watt</td> <td>none</td> <td>none</td> </tr> <tr> <th>29</th> <td>1980</td> <td>california league</td> <td>75-66</td> <td>2nd (t)</td> <td>jack maloof</td> <td>none</td> <td>none</td> </tr> <tr> <th>30</th> <td>1981</td> <td>california league</td> <td>81-58</td> <td>2nd</td> <td>jack maloof</td> <td>lost in 1st round</td> <td>none</td> </tr> <tr> <th>31</th> <td>1988</td> <td>california league</td> <td>39-103</td> <td>10th</td> <td>nate oliver</td> <td>none</td> <td>none</td> </tr> <tr> <th>32</th> <td>1989</td> <td>california league</td> <td>68-74</td> <td>6th</td> <td>eli grba</td> <td>none</td> <td>none</td> </tr> <tr> <th>33</th> <td>1990</td> <td>california league</td> <td>71-68</td> <td>6th</td> <td>mike brown</td> <td>none</td> <td>none</td> </tr> <tr> <th>34</th> <td>1991</td> <td>california league</td> <td>59-77</td> <td>7th</td> <td>mal fichman</td> <td>none</td> <td>none</td> </tr> <tr> <th>35</th> <td>1992</td> <td>california league</td> <td>65-71</td> <td>8th</td> <td>gary jones</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>36</td> </tr> </tbody> </table>

select count ( * ) where year < 499 <table_name> : table | | year | league | record | finish | manager | playoffs | notes | |---:|-------:|:------------------|:---------|:---------|:------------------------------------------------------|:-------------------|:------------------------------------| | 0 | 1947 | sunset league | 69-69 | 4th | thomas lloyd | none | none | | 1 | 1948 | sunset league | 77-63 | 3rd | thomas lloyd | league champs | none | | 2 | 1949 | sunset league | 49-75 | 7th | lilio marcucci | none | none | | 3 | 1950 | far west league | 75-63 | 3rd | joe borich | lost in 1st round | none | | 4 | 1951 | far west league | 52-65 | 3rd | cotton pippen | none | none | | 5 | 1955 | california league | 40-106 | 8th | leonard noren | none | channel cities moved to reno july 1 | | 6 | 1956 | california league | 73-67 | 4th | ray perry | lost in 1st round | none | | 7 | 1957 | california league | 79-59 | 2nd | ray perry | lost league finals | none | | 8 | 1958 | california league | 68-69 | 6th | ray perry | none | none | | 9 | 1959 | california league | 81-58 | 2nd | ray perry | none | none | | 10 | 1960 | california league | 89-51 | 1st | tom saffell | league champs | none | | 11 | 1961 | california league | 97-43 | 1st | roy smalley | league champs | none | | 12 | 1962 | california league | 70-68 | 4th | roy smalley | lost league finals | none | | 13 | 1963 | california league | 71-69 | 5th | tom saffell | none | none | | 14 | 1964 | california league | 66-71 | 6th | tom saffell / harvey koepf | none | none | | 15 | 1966 | california league | 69-71 | 4th (t) | phil cavarretta | none | none | | 16 | 1967 | california league | 56-82 | 8th | phil cavarretta | none | none | | 17 | 1968 | california league | 67-72 | 5th | clay bryant | none | none | | 18 | 1969 | california league | 72-68 | 3rd (t) | ken aspromonte | none | none | | 19 | 1970 | california league | 79-61 | 2nd | pinky may | none | none | | 20 | 1971 | california league | 64-75 | 7th | pinky may | none | none | | 21 | 1972 | california league | 50-88 | 8th | lou klimchock | none | none | | 22 | 1973 | california league | 70-70 | 4th (t) | lou klimchock | none | none | | 23 | 1974 | california league | 61-79 | 6th (t) | joe azcue / interim (june 29-july 3) / del youngblood | none | none | | 24 | 1975 | california league | 86-54 | 1st | harry warner | league champs | none | | 25 | 1976 | california league | 75-62 | 3rd | johnny goryl | league champs | none | | 26 | 1977 | california league | 59-81 | 5th | glenn ezell | none | none | | 27 | 1978 | california league | 62-78 | 5th | eddie watt | none | none | | 28 | 1979 | california league | 74-67 | 3rd | eddie watt | none | none | | 29 | 1980 | california league | 75-66 | 2nd (t) | jack maloof | none | none | | 30 | 1981 | california league | 81-58 | 2nd | jack maloof | lost in 1st round | none | | 31 | 1988 | california league | 39-103 | 10th | nate oliver | none | none | | 32 | 1989 | california league | 68-74 | 6th | eli grba | none | none | | 33 | 1990 | california league | 71-68 | 6th | mike brown | none | none | | 34 | 1991 | california league | 59-77 | 7th | mal fichman | none | none | | 35 | 1992 | california league | 65-71 | 8th | gary jones | none | none |

| | count ( "*" ) | |---:|----------------:| | 0 | 36 |

select position where event = '400 m' col : year | competition | venue | position | event | notes row 1 : 2001 | world youth championships | debrecen, hungary | 4th | 400 m | none row 2 : 2002 | asian games | busan, south korea | 2nd | 4x400 m relay | none row 3 : 2005 | asian championships | incheon, south korea | 2nd | 4x400 m relay | none row 4 : 2005 | universiade | izmir, turkey | 6th | 4x400 m relay | none row 5 : 2006 | asian games | doha, qatar | 2nd | 4x400 m relay | none row 6 : 2006 | asian games | doha, qatar | 1st | 400 m | none row 7 : 2006 | world cup | athens, greece | 7th | 400 m | none row 8 : 2007 | universiade | bangkok, thailand | 1st | 400 m | none row 9 : 2011 | universiade | shenzhen, china | none | 400 m | dq

col : position row 1 : 4th row 2 : 1st row 3 : 7th row 4 : 1st row 5 : none

select position where event = '400 m' <table_name> : table \begin{tabular}{lllllll} \toprule & year & competition & venue & position & event & notes \\ \midrule 0 & 2001 & world youth championships & debrecen, hungary & 4th & 400 m & none \\ 1 & 2002 & asian games & busan, south korea & 2nd & 4x400 m relay & none \\ 2 & 2005 & asian championships & incheon, south korea & 2nd & 4x400 m relay & none \\ 3 & 2005 & universiade & izmir, turkey & 6th & 4x400 m relay & none \\ 4 & 2006 & asian games & doha, qatar & 2nd & 4x400 m relay & none \\ 5 & 2006 & asian games & doha, qatar & 1st & 400 m & none \\ 6 & 2006 & world cup & athens, greece & 7th & 400 m & none \\ 7 & 2007 & universiade & bangkok, thailand & 1st & 400 m & none \\ 8 & 2011 & universiade & shenzhen, china & none & 400 m & dq \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & position \\ \midrule 0 & 4th \\ 1 & 1st \\ 2 & 7th \\ 3 & 1st \\ 4 & none \\ \bottomrule \end{tabular}

select position where event = '400 m' <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>2001</td> <td>world youth championships</td> <td>debrecen, hungary</td> <td>4th</td> <td>400 m</td> <td>none</td> </tr> <tr> <th>1</th> <td>2002</td> <td>asian games</td> <td>busan, south korea</td> <td>2nd</td> <td>4x400 m relay</td> <td>none</td> </tr> <tr> <th>2</th> <td>2005</td> <td>asian championships</td> <td>incheon, south korea</td> <td>2nd</td> <td>4x400 m relay</td> <td>none</td> </tr> <tr> <th>3</th> <td>2005</td> <td>universiade</td> <td>izmir, turkey</td> <td>6th</td> <td>4x400 m relay</td> <td>none</td> </tr> <tr> <th>4</th> <td>2006</td> <td>asian games</td> <td>doha, qatar</td> <td>2nd</td> <td>4x400 m relay</td> <td>none</td> </tr> <tr> <th>5</th> <td>2006</td> <td>asian games</td> <td>doha, qatar</td> <td>1st</td> <td>400 m</td> <td>none</td> </tr> <tr> <th>6</th> <td>2006</td> <td>world cup</td> <td>athens, greece</td> <td>7th</td> <td>400 m</td> <td>none</td> </tr> <tr> <th>7</th> <td>2007</td> <td>universiade</td> <td>bangkok, thailand</td> <td>1st</td> <td>400 m</td> <td>none</td> </tr> <tr> <th>8</th> <td>2011</td> <td>universiade</td> <td>shenzhen, china</td> <td>none</td> <td>400 m</td> <td>dq</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>position</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>4th</td> </tr> <tr> <th>1</th> <td>1st</td> </tr> <tr> <th>2</th> <td>7th</td> </tr> <tr> <th>3</th> <td>1st</td> </tr> <tr> <th>4</th> <td>none</td> </tr> </tbody> </table>

select position where event = '400 m' <table_name> : table | | year | competition | venue | position | event | notes | |---:|-------:|:--------------------------|:---------------------|:-----------|:--------------|:--------| | 0 | 2001 | world youth championships | debrecen, hungary | 4th | 400 m | none | | 1 | 2002 | asian games | busan, south korea | 2nd | 4x400 m relay | none | | 2 | 2005 | asian championships | incheon, south korea | 2nd | 4x400 m relay | none | | 3 | 2005 | universiade | izmir, turkey | 6th | 4x400 m relay | none | | 4 | 2006 | asian games | doha, qatar | 2nd | 4x400 m relay | none | | 5 | 2006 | asian games | doha, qatar | 1st | 400 m | none | | 6 | 2006 | world cup | athens, greece | 7th | 400 m | none | | 7 | 2007 | universiade | bangkok, thailand | 1st | 400 m | none | | 8 | 2011 | universiade | shenzhen, china | none | 400 m | dq |

| | position | |---:|:-----------| | 0 | 4th | | 1 | 1st | | 2 | 7th | | 3 | 1st | | 4 | none |

select capital where capital = 'cairo' col : nr. | name | area (km2) | population (2006) | capital | club(s) row 1 : 1 | alexandria | 2900 | 4110015 | alexandria | al itthad al sakandary - el-olympi - haras el hodood row 2 : 2 | asyut | 25926 | 3441597 | asyut | petrol asyout row 3 : 3 | cairo | 3435 | 7786640 | cairo | al-ahly - al mokawloon - enppi - el-jaish - el-shorta - itesalat row 4 : 4 | gharbia | 25400 | 3790670 | tanta | ghazl el-mehalla row 5 : 5 | giza | 85153 | 6272571 | giza | zamalek- tersana row 6 : 6 | ismailia | 1442 | 942832 | ismailia | ismaily row 7 : 7 | port said | 72 | 570768 | port said | al masry row 8 : 6 | suez | 17840 | 510935 | suez | petrojet

col : capital row 1 : cairo

select capital where capital = 'cairo' <table_name> : table \begin{tabular}{lllllll} \toprule & nr. & name & area (km2) & population (2006) & capital & club(s) \\ \midrule 0 & 1 & alexandria & 2900 & 4110015 & alexandria & al itthad al sakandary - el-olympi - haras el hodood \\ 1 & 2 & asyut & 25926 & 3441597 & asyut & petrol asyout \\ 2 & 3 & cairo & 3435 & 7786640 & cairo & al-ahly - al mokawloon - enppi - el-jaish - el-shorta - itesalat \\ 3 & 4 & gharbia & 25400 & 3790670 & tanta & ghazl el-mehalla \\ 4 & 5 & giza & 85153 & 6272571 & giza & zamalek- tersana \\ 5 & 6 & ismailia & 1442 & 942832 & ismailia & ismaily \\ 6 & 7 & port said & 72 & 570768 & port said & al masry \\ 7 & 6 & suez & 17840 & 510935 & suez & petrojet \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & capital \\ \midrule 0 & cairo \\ \bottomrule \end{tabular}

select capital where capital = 'cairo' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>nr.</th> <th>name</th> <th>area (km2)</th> <th>population (2006)</th> <th>capital</th> <th>club(s)</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>alexandria</td> <td>2900</td> <td>4110015</td> <td>alexandria</td> <td>al itthad al sakandary - el-olympi - haras el hodood</td> </tr> <tr> <th>1</th> <td>2</td> <td>asyut</td> <td>25926</td> <td>3441597</td> <td>asyut</td> <td>petrol asyout</td> </tr> <tr> <th>2</th> <td>3</td> <td>cairo</td> <td>3435</td> <td>7786640</td> <td>cairo</td> <td>al-ahly - al mokawloon - enppi - el-jaish - el-shorta - itesalat</td> </tr> <tr> <th>3</th> <td>4</td> <td>gharbia</td> <td>25400</td> <td>3790670</td> <td>tanta</td> <td>ghazl el-mehalla</td> </tr> <tr> <th>4</th> <td>5</td> <td>giza</td> <td>85153</td> <td>6272571</td> <td>giza</td> <td>zamalek- tersana</td> </tr> <tr> <th>5</th> <td>6</td> <td>ismailia</td> <td>1442</td> <td>942832</td> <td>ismailia</td> <td>ismaily</td> </tr> <tr> <th>6</th> <td>7</td> <td>port said</td> <td>72</td> <td>570768</td> <td>port said</td> <td>al masry</td> </tr> <tr> <th>7</th> <td>6</td> <td>suez</td> <td>17840</td> <td>510935</td> <td>suez</td> <td>petrojet</td> </tr> </tbody> </table>

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

select capital where capital = 'cairo' <table_name> : table | | nr. | name | area (km2) | population (2006) | capital | club(s) | |---:|------:|:-----------|-------------:|--------------------:|:-----------|:-----------------------------------------------------------------| | 0 | 1 | alexandria | 2900 | 4110015 | alexandria | al itthad al sakandary - el-olympi - haras el hodood | | 1 | 2 | asyut | 25926 | 3441597 | asyut | petrol asyout | | 2 | 3 | cairo | 3435 | 7786640 | cairo | al-ahly - al mokawloon - enppi - el-jaish - el-shorta - itesalat | | 3 | 4 | gharbia | 25400 | 3790670 | tanta | ghazl el-mehalla | | 4 | 5 | giza | 85153 | 6272571 | giza | zamalek- tersana | | 5 | 6 | ismailia | 1442 | 942832 | ismailia | ismaily | | 6 | 7 | port said | 72 | 570768 | port said | al masry | | 7 | 6 | suez | 17840 | 510935 | suez | petrojet |

| | capital | |---:|:----------| | 0 | cairo |

select count ( * ) where country = 10 and country = '5' col : rank | country | box office | year | box office from national films row 1 : 1 | canada/united states | $10.8 billion | 2012 | none row 2 : 2 | china | $3.6 billion | 2013 | 59% (2013) row 3 : 3 | japan | $1.88 billion | 2013 | 61% (2013) row 4 : 4 | united kingdom | $1.7 billion | 2012 | 36.1% (2011) row 5 : 5 | france | $1.7 billion | 2012 | 33.3% (2013) row 6 : 6 | south korea | $1.47 billion | 2013 | 59.7% (2013) row 7 : 7 | india | $1.4 billion | 2012 | none row 8 : 8 | germany | $1.3 billion | 2012 | none row 9 : 9 | russia | $1.2 billion | 2012 | none row 10 : 10 | australia | $1.2 billion | 2012 | 4.1% (2011) row 11 : 11 | italy | $0.84 billion | 2013 | 30% (2013) row 12 : 12 | brazil | $0.72 billion | 2013 | 17% (2013) row 13 : none | world | $34.7 billion | 2012 | none

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

select count ( * ) where country = 10 and country = '5' <table_name> : table \begin{tabular}{llllll} \toprule & rank & country & box office & year & box office from national films \\ \midrule 0 & 1 & canada/united states & $10.8 billion & 2012 & none \\ 1 & 2 & china & $3.6 billion & 2013 & 59% (2013) \\ 2 & 3 & japan & $1.88 billion & 2013 & 61% (2013) \\ 3 & 4 & united kingdom & $1.7 billion & 2012 & 36.1% (2011) \\ 4 & 5 & france & $1.7 billion & 2012 & 33.3% (2013) \\ 5 & 6 & south korea & $1.47 billion & 2013 & 59.7% (2013) \\ 6 & 7 & india & $1.4 billion & 2012 & none \\ 7 & 8 & germany & $1.3 billion & 2012 & none \\ 8 & 9 & russia & $1.2 billion & 2012 & none \\ 9 & 10 & australia & $1.2 billion & 2012 & 4.1% (2011) \\ 10 & 11 & italy & $0.84 billion & 2013 & 30% (2013) \\ 11 & 12 & brazil & $0.72 billion & 2013 & 17% (2013) \\ 12 & none & world & $34.7 billion & 2012 & none \\ \bottomrule \end{tabular}

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

select count ( * ) where country = 10 and country = '5' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>rank</th> <th>country</th> <th>box office</th> <th>year</th> <th>box office from national films</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>canada/united states</td> <td>$10.8 billion</td> <td>2012</td> <td>none</td> </tr> <tr> <th>1</th> <td>2</td> <td>china</td> <td>$3.6 billion</td> <td>2013</td> <td>59% (2013)</td> </tr> <tr> <th>2</th> <td>3</td> <td>japan</td> <td>$1.88 billion</td> <td>2013</td> <td>61% (2013)</td> </tr> <tr> <th>3</th> <td>4</td> <td>united kingdom</td> <td>$1.7 billion</td> <td>2012</td> <td>36.1% (2011)</td> </tr> <tr> <th>4</th> <td>5</td> <td>france</td> <td>$1.7 billion</td> <td>2012</td> <td>33.3% (2013)</td> </tr> <tr> <th>5</th> <td>6</td> <td>south korea</td> <td>$1.47 billion</td> <td>2013</td> <td>59.7% (2013)</td> </tr> <tr> <th>6</th> <td>7</td> <td>india</td> <td>$1.4 billion</td> <td>2012</td> <td>none</td> </tr> <tr> <th>7</th> <td>8</td> <td>germany</td> <td>$1.3 billion</td> <td>2012</td> <td>none</td> </tr> <tr> <th>8</th> <td>9</td> <td>russia</td> <td>$1.2 billion</td> <td>2012</td> <td>none</td> </tr> <tr> <th>9</th> <td>10</td> <td>australia</td> <td>$1.2 billion</td> <td>2012</td> <td>4.1% (2011)</td> </tr> <tr> <th>10</th> <td>11</td> <td>italy</td> <td>$0.84 billion</td> <td>2013</td> <td>30% (2013)</td> </tr> <tr> <th>11</th> <td>12</td> <td>brazil</td> <td>$0.72 billion</td> <td>2013</td> <td>17% (2013)</td> </tr> <tr> <th>12</th> <td>none</td> <td>world</td> <td>$34.7 billion</td> <td>2012</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 country = 10 and country = '5' <table_name> : table | | rank | country | box office | year | box office from national films | |---:|:-------|:---------------------|:--------------|-------:|:---------------------------------| | 0 | 1 | canada/united states | $10.8 billion | 2012 | none | | 1 | 2 | china | $3.6 billion | 2013 | 59% (2013) | | 2 | 3 | japan | $1.88 billion | 2013 | 61% (2013) | | 3 | 4 | united kingdom | $1.7 billion | 2012 | 36.1% (2011) | | 4 | 5 | france | $1.7 billion | 2012 | 33.3% (2013) | | 5 | 6 | south korea | $1.47 billion | 2013 | 59.7% (2013) | | 6 | 7 | india | $1.4 billion | 2012 | none | | 7 | 8 | germany | $1.3 billion | 2012 | none | | 8 | 9 | russia | $1.2 billion | 2012 | none | | 9 | 10 | australia | $1.2 billion | 2012 | 4.1% (2011) | | 10 | 11 | italy | $0.84 billion | 2013 | 30% (2013) | | 11 | 12 | brazil | $0.72 billion | 2013 | 17% (2013) | | 12 | none | world | $34.7 billion | 2012 | none |

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

select count ( * ) where result = '0-3' col : date | location | opponent | result | competition row 1 : 2013-01-19 00:00:00 | concepcion, chile | chile | 0-3 | f row 2 : 2013-02-06 00:00:00 | santa cruz de la sierra, bolivia | bolivia | 1-2 | f row 3 : 2013-03-20 00:00:00 | muscat, oman | oman | 0-3 | f row 4 : 2013-03-24 00:00:00 | santo domingo, dominican republic | dominican republic | 1-3 | f row 5 : 2013-06-08 00:00:00 | miami, united states | spain | 1-2 | f row 6 : 2013-06-11 00:00:00 | rio de janeiro, brazil | italy | 2-2 | f row 7 : 2013-07-08 00:00:00 | harrison, united states | honduras | 0-2 | gc row 8 : 2013-07-12 00:00:00 | miami gardens, united states | trinidad and tobago | 2-0 | gc row 9 : 2013-07-15 00:00:00 | houston, united states | el salvador | 0-1 | gc row 10 : 2013-09-06 00:00:00 | incheon, south korea | south korea | 1-4 | f row 11 : 2014-03-05 00:00:00 | mitrovica, kosovo | kosovo | 0-0 | f

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

select count ( * ) where result = '0-3' <table_name> : table \begin{tabular}{llllll} \toprule & date & location & opponent & result & competition \\ \midrule 0 & 2013-01-19 00:00:00 & concepcion, chile & chile & 0-3 & f \\ 1 & 2013-02-06 00:00:00 & santa cruz de la sierra, bolivia & bolivia & 1-2 & f \\ 2 & 2013-03-20 00:00:00 & muscat, oman & oman & 0-3 & f \\ 3 & 2013-03-24 00:00:00 & santo domingo, dominican republic & dominican republic & 1-3 & f \\ 4 & 2013-06-08 00:00:00 & miami, united states & spain & 1-2 & f \\ 5 & 2013-06-11 00:00:00 & rio de janeiro, brazil & italy & 2-2 & f \\ 6 & 2013-07-08 00:00:00 & harrison, united states & honduras & 0-2 & gc \\ 7 & 2013-07-12 00:00:00 & miami gardens, united states & trinidad and tobago & 2-0 & gc \\ 8 & 2013-07-15 00:00:00 & houston, united states & el salvador & 0-1 & gc \\ 9 & 2013-09-06 00:00:00 & incheon, south korea & south korea & 1-4 & f \\ 10 & 2014-03-05 00:00:00 & mitrovica, kosovo & kosovo & 0-0 & f \\ \bottomrule \end{tabular}

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

select count ( * ) where result = '0-3' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>date</th> <th>location</th> <th>opponent</th> <th>result</th> <th>competition</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>2013-01-19 00:00:00</td> <td>concepcion, chile</td> <td>chile</td> <td>0-3</td> <td>f</td> </tr> <tr> <th>1</th> <td>2013-02-06 00:00:00</td> <td>santa cruz de la sierra, bolivia</td> <td>bolivia</td> <td>1-2</td> <td>f</td> </tr> <tr> <th>2</th> <td>2013-03-20 00:00:00</td> <td>muscat, oman</td> <td>oman</td> <td>0-3</td> <td>f</td> </tr> <tr> <th>3</th> <td>2013-03-24 00:00:00</td> <td>santo domingo, dominican republic</td> <td>dominican republic</td> <td>1-3</td> <td>f</td> </tr> <tr> <th>4</th> <td>2013-06-08 00:00:00</td> <td>miami, united states</td> <td>spain</td> <td>1-2</td> <td>f</td> </tr> <tr> <th>5</th> <td>2013-06-11 00:00:00</td> <td>rio de janeiro, brazil</td> <td>italy</td> <td>2-2</td> <td>f</td> </tr> <tr> <th>6</th> <td>2013-07-08 00:00:00</td> <td>harrison, united states</td> <td>honduras</td> <td>0-2</td> <td>gc</td> </tr> <tr> <th>7</th> <td>2013-07-12 00:00:00</td> <td>miami gardens, united states</td> <td>trinidad and tobago</td> <td>2-0</td> <td>gc</td> </tr> <tr> <th>8</th> <td>2013-07-15 00:00:00</td> <td>houston, united states</td> <td>el salvador</td> <td>0-1</td> <td>gc</td> </tr> <tr> <th>9</th> <td>2013-09-06 00:00:00</td> <td>incheon, south korea</td> <td>south korea</td> <td>1-4</td> <td>f</td> </tr> <tr> <th>10</th> <td>2014-03-05 00:00:00</td> <td>mitrovica, kosovo</td> <td>kosovo</td> <td>0-0</td> <td>f</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 result = '0-3' <table_name> : table | | date | location | opponent | result | competition | |---:|:--------------------|:----------------------------------|:--------------------|:---------|:--------------| | 0 | 2013-01-19 00:00:00 | concepcion, chile | chile | 0-3 | f | | 1 | 2013-02-06 00:00:00 | santa cruz de la sierra, bolivia | bolivia | 1-2 | f | | 2 | 2013-03-20 00:00:00 | muscat, oman | oman | 0-3 | f | | 3 | 2013-03-24 00:00:00 | santo domingo, dominican republic | dominican republic | 1-3 | f | | 4 | 2013-06-08 00:00:00 | miami, united states | spain | 1-2 | f | | 5 | 2013-06-11 00:00:00 | rio de janeiro, brazil | italy | 2-2 | f | | 6 | 2013-07-08 00:00:00 | harrison, united states | honduras | 0-2 | gc | | 7 | 2013-07-12 00:00:00 | miami gardens, united states | trinidad and tobago | 2-0 | gc | | 8 | 2013-07-15 00:00:00 | houston, united states | el salvador | 0-1 | gc | | 9 | 2013-09-06 00:00:00 | incheon, south korea | south korea | 1-4 | f | | 10 | 2014-03-05 00:00:00 | mitrovica, kosovo | kosovo | 0-0 | f |

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

select count ( name ) where fa cup apps > 9 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 ( "name" ) row 1 : 0

select count ( name ) where fa cup apps > 9 <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 ( "name" ) \\ \midrule 0 & 0 \\ \bottomrule \end{tabular}

select count ( name ) where fa cup apps > 9 <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 ( "name" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>0</td> </tr> </tbody> </table>

select count ( name ) where fa cup apps > 9 <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 ( "name" ) | |---:|-------------------:| | 0 | 0 |

select count ( * ) where october 13-14 124 = 154 col : may 20-21 118 | march 9 120 | december 25-26 122 | october 13-14 124 | august 1-2 126 row 1 : may 21, 1993 | march 9, 1997 | december 25, 2000 | october 14, 2004 | august 1, 2008 row 2 : 128 | 130 | 132 | 134 | 136 row 3 : may 20, 2012 | march 9, 2016 | december 26, 2019 | october 14, 2023 | august 2, 2027 row 4 : 138 | 140 | 142 | 144 | 146 row 5 : may 21, 2031 | march 9, 2035 | december 26, 2038 | october 14, 2042 | august 2, 2046 row 6 : 148 | 150 | 152 | 154 | 156 row 7 : may 20, 2050 | march 9, 2054 | december 26, 2057 | october 13, 2061 | august 2, 2065

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

select count ( * ) where october 13-14 124 = 154 <table_name> : table \begin{tabular}{llllll} \toprule & may 20-21 118 & march 9 120 & december 25-26 122 & october 13-14 124 & august 1-2 126 \\ \midrule 0 & may 21, 1993 & march 9, 1997 & december 25, 2000 & october 14, 2004 & august 1, 2008 \\ 1 & 128 & 130 & 132 & 134 & 136 \\ 2 & may 20, 2012 & march 9, 2016 & december 26, 2019 & october 14, 2023 & august 2, 2027 \\ 3 & 138 & 140 & 142 & 144 & 146 \\ 4 & may 21, 2031 & march 9, 2035 & december 26, 2038 & october 14, 2042 & august 2, 2046 \\ 5 & 148 & 150 & 152 & 154 & 156 \\ 6 & may 20, 2050 & march 9, 2054 & december 26, 2057 & october 13, 2061 & august 2, 2065 \\ \bottomrule \end{tabular}

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

select count ( * ) where october 13-14 124 = 154 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>may 20-21 118</th> <th>march 9 120</th> <th>december 25-26 122</th> <th>october 13-14 124</th> <th>august 1-2 126</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>may 21, 1993</td> <td>march 9, 1997</td> <td>december 25, 2000</td> <td>october 14, 2004</td> <td>august 1, 2008</td> </tr> <tr> <th>1</th> <td>128</td> <td>130</td> <td>132</td> <td>134</td> <td>136</td> </tr> <tr> <th>2</th> <td>may 20, 2012</td> <td>march 9, 2016</td> <td>december 26, 2019</td> <td>october 14, 2023</td> <td>august 2, 2027</td> </tr> <tr> <th>3</th> <td>138</td> <td>140</td> <td>142</td> <td>144</td> <td>146</td> </tr> <tr> <th>4</th> <td>may 21, 2031</td> <td>march 9, 2035</td> <td>december 26, 2038</td> <td>october 14, 2042</td> <td>august 2, 2046</td> </tr> <tr> <th>5</th> <td>148</td> <td>150</td> <td>152</td> <td>154</td> <td>156</td> </tr> <tr> <th>6</th> <td>may 20, 2050</td> <td>march 9, 2054</td> <td>december 26, 2057</td> <td>october 13, 2061</td> <td>august 2, 2065</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 october 13-14 124 = 154 <table_name> : table | | may 20-21 118 | march 9 120 | december 25-26 122 | october 13-14 124 | august 1-2 126 | |---:|:----------------|:--------------|:---------------------|:--------------------|:-----------------| | 0 | may 21, 1993 | march 9, 1997 | december 25, 2000 | october 14, 2004 | august 1, 2008 | | 1 | 128 | 130 | 132 | 134 | 136 | | 2 | may 20, 2012 | march 9, 2016 | december 26, 2019 | october 14, 2023 | august 2, 2027 | | 3 | 138 | 140 | 142 | 144 | 146 | | 4 | may 21, 2031 | march 9, 2035 | december 26, 2038 | october 14, 2042 | august 2, 2046 | | 5 | 148 | 150 | 152 | 154 | 156 | | 6 | may 20, 2050 | march 9, 2054 | december 26, 2057 | october 13, 2061 | august 2, 2065 |

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

select count ( * ) where nation = 'spain' col : rank | nation | gold | silver | bronze | total row 1 : 1 | china | 63 | 46 | 32 | 141 row 2 : 2 | great britain | 35 | 30 | 29 | 94 row 3 : 3 | canada | 28 | 19 | 25 | 72 row 4 : 4 | united states | 27 | 22 | 39 | 88 row 5 : 5 | australia | 26 | 38 | 36 | 100 row 6 : 6 | ukraine | 24 | 12 | 19 | 55 row 7 : 7 | spain | 20 | 27 | 24 | 71 row 8 : 8 | germany | 19 | 28 | 31 | 78 row 9 : 9 | france | 18 | 26 | 30 | 74 row 10 : 10 | japan | 17 | 16 | 20 | 53

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

select count ( * ) where nation = 'spain' <table_name> : table \begin{tabular}{lllllll} \toprule & rank & nation & gold & silver & bronze & total \\ \midrule 0 & 1 & china & 63 & 46 & 32 & 141 \\ 1 & 2 & great britain & 35 & 30 & 29 & 94 \\ 2 & 3 & canada & 28 & 19 & 25 & 72 \\ 3 & 4 & united states & 27 & 22 & 39 & 88 \\ 4 & 5 & australia & 26 & 38 & 36 & 100 \\ 5 & 6 & ukraine & 24 & 12 & 19 & 55 \\ 6 & 7 & spain & 20 & 27 & 24 & 71 \\ 7 & 8 & germany & 19 & 28 & 31 & 78 \\ 8 & 9 & france & 18 & 26 & 30 & 74 \\ 9 & 10 & japan & 17 & 16 & 20 & 53 \\ \bottomrule \end{tabular}

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

select count ( * ) where nation = 'spain' <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>china</td> <td>63</td> <td>46</td> <td>32</td> <td>141</td> </tr> <tr> <th>1</th> <td>2</td> <td>great britain</td> <td>35</td> <td>30</td> <td>29</td> <td>94</td> </tr> <tr> <th>2</th> <td>3</td> <td>canada</td> <td>28</td> <td>19</td> <td>25</td> <td>72</td> </tr> <tr> <th>3</th> <td>4</td> <td>united states</td> <td>27</td> <td>22</td> <td>39</td> <td>88</td> </tr> <tr> <th>4</th> <td>5</td> <td>australia</td> <td>26</td> <td>38</td> <td>36</td> <td>100</td> </tr> <tr> <th>5</th> <td>6</td> <td>ukraine</td> <td>24</td> <td>12</td> <td>19</td> <td>55</td> </tr> <tr> <th>6</th> <td>7</td> <td>spain</td> <td>20</td> <td>27</td> <td>24</td> <td>71</td> </tr> <tr> <th>7</th> <td>8</td> <td>germany</td> <td>19</td> <td>28</td> <td>31</td> <td>78</td> </tr> <tr> <th>8</th> <td>9</td> <td>france</td> <td>18</td> <td>26</td> <td>30</td> <td>74</td> </tr> <tr> <th>9</th> <td>10</td> <td>japan</td> <td>17</td> <td>16</td> <td>20</td> <td>53</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 nation = 'spain' <table_name> : table | | rank | nation | gold | silver | bronze | total | |---:|-------:|:--------------|-------:|---------:|---------:|--------:| | 0 | 1 | china | 63 | 46 | 32 | 141 | | 1 | 2 | great britain | 35 | 30 | 29 | 94 | | 2 | 3 | canada | 28 | 19 | 25 | 72 | | 3 | 4 | united states | 27 | 22 | 39 | 88 | | 4 | 5 | australia | 26 | 38 | 36 | 100 | | 5 | 6 | ukraine | 24 | 12 | 19 | 55 | | 6 | 7 | spain | 20 | 27 | 24 | 71 | | 7 | 8 | germany | 19 | 28 | 31 | 78 | | 8 | 9 | france | 18 | 26 | 30 | 74 | | 9 | 10 | japan | 17 | 16 | 20 | 53 |

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

select count ( * ) where goals > 0 col : name | pos. | caps | goals | club row 1 : olle ahlund | mf | 6 | 0 | degerfors if row 2 : henry andersson | gk | 2 | 0 | ifk goteborg row 3 : bertil backvall | fw | 1 | 0 | hammarby if stockholm row 4 : henry "garvis" carlsson | fw | 5 | 4 | aik solna row 5 : arvid "emma" emanuelsson | mf | 5 | 0 | if elfsborg boras row 6 : rune "killing" emanuelsson | mf/df | 3 | 0 | ifk goteborg row 7 : karl-erik grahn | mf | 4 | 1 | if elfsborg boras row 8 : gunnar "il professore" gren | fw | 6 | 9 | ifk goteborg row 9 : erik "mulle" holmqvist | fw | 2 | 1 | ifk norrkoping row 10 : oskar "masse" holmqvist | df | 2 | 0 | ifk norrkoping row 11 : ove karlsson-widricks | df | 1 | 0 | aik solna row 12 : borje leander | df/mf | 4 | 0 | aik solna row 13 : gosta malm | df | 2 | 0 | ifk norrkoping row 14 : malte "svarta blixten" martensson | fw | 1 | 0 | halsingborgs if row 15 : harry nilsson | df | 6 | 0 | aik solna row 16 : stellan nilsson | fw | 2 | 1 | malmo ff row 17 : bertil nordahl | df/mf | 2 | 0 | degerfors if row 18 : gunnar nordahl | fw | 5 | 6 | ifk norrkoping row 19 : knut nordahl | fw | 2 | 0 | ifk norrkoping row 20 : arne nyberg | fw | 6 | 4 | ifk goteborg row 21 : stig nystrom | fw | 1 | 1 | djurgardens if stockholm row 22 : vincent persson | fw | 1 | 1 | degerfors if row 23 : birger "bian" rosengren | mf | 1 | 0 | ifk norrkoping row 24 : carl-erik "pigge" sandberg | fw | 1 | 0 | malmo ff row 25 : carl "timpa" simonsson | fw | 1 | 0 | jonkopings sodra if row 26 : gustav "gurra" sjoberg | gk | 6 | 0 | aik solna row 27 : borje tapper | fw | 1 | 5 | malmo ff row 28 : lennart "skinnet" wigren | mf | 1 | 0 | ifk norrkoping

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

select count ( * ) where goals > 0 <table_name> : table \begin{tabular}{llllll} \toprule & name & pos. & caps & goals & club \\ \midrule 0 & olle ahlund & mf & 6 & 0 & degerfors if \\ 1 & henry andersson & gk & 2 & 0 & ifk goteborg \\ 2 & bertil backvall & fw & 1 & 0 & hammarby if stockholm \\ 3 & henry "garvis" carlsson & fw & 5 & 4 & aik solna \\ 4 & arvid "emma" emanuelsson & mf & 5 & 0 & if elfsborg boras \\ 5 & rune "killing" emanuelsson & mf/df & 3 & 0 & ifk goteborg \\ 6 & karl-erik grahn & mf & 4 & 1 & if elfsborg boras \\ 7 & gunnar "il professore" gren & fw & 6 & 9 & ifk goteborg \\ 8 & erik "mulle" holmqvist & fw & 2 & 1 & ifk norrkoping \\ 9 & oskar "masse" holmqvist & df & 2 & 0 & ifk norrkoping \\ 10 & ove karlsson-widricks & df & 1 & 0 & aik solna \\ 11 & borje leander & df/mf & 4 & 0 & aik solna \\ 12 & gosta malm & df & 2 & 0 & ifk norrkoping \\ 13 & malte "svarta blixten" martensson & fw & 1 & 0 & halsingborgs if \\ 14 & harry nilsson & df & 6 & 0 & aik solna \\ 15 & stellan nilsson & fw & 2 & 1 & malmo ff \\ 16 & bertil nordahl & df/mf & 2 & 0 & degerfors if \\ 17 & gunnar nordahl & fw & 5 & 6 & ifk norrkoping \\ 18 & knut nordahl & fw & 2 & 0 & ifk norrkoping \\ 19 & arne nyberg & fw & 6 & 4 & ifk goteborg \\ 20 & stig nystrom & fw & 1 & 1 & djurgardens if stockholm \\ 21 & vincent persson & fw & 1 & 1 & degerfors if \\ 22 & birger "bian" rosengren & mf & 1 & 0 & ifk norrkoping \\ 23 & carl-erik "pigge" sandberg & fw & 1 & 0 & malmo ff \\ 24 & carl "timpa" simonsson & fw & 1 & 0 & jonkopings sodra if \\ 25 & gustav "gurra" sjoberg & gk & 6 & 0 & aik solna \\ 26 & borje tapper & fw & 1 & 5 & malmo ff \\ 27 & lennart "skinnet" wigren & mf & 1 & 0 & ifk norrkoping \\ \bottomrule \end{tabular}

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

select count ( * ) where goals > 0 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>name</th> <th>pos.</th> <th>caps</th> <th>goals</th> <th>club</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>olle ahlund</td> <td>mf</td> <td>6</td> <td>0</td> <td>degerfors if</td> </tr> <tr> <th>1</th> <td>henry andersson</td> <td>gk</td> <td>2</td> <td>0</td> <td>ifk goteborg</td> </tr> <tr> <th>2</th> <td>bertil backvall</td> <td>fw</td> <td>1</td> <td>0</td> <td>hammarby if stockholm</td> </tr> <tr> <th>3</th> <td>henry "garvis" carlsson</td> <td>fw</td> <td>5</td> <td>4</td> <td>aik solna</td> </tr> <tr> <th>4</th> <td>arvid "emma" emanuelsson</td> <td>mf</td> <td>5</td> <td>0</td> <td>if elfsborg boras</td> </tr> <tr> <th>5</th> <td>rune "killing" emanuelsson</td> <td>mf/df</td> <td>3</td> <td>0</td> <td>ifk goteborg</td> </tr> <tr> <th>6</th> <td>karl-erik grahn</td> <td>mf</td> <td>4</td> <td>1</td> <td>if elfsborg boras</td> </tr> <tr> <th>7</th> <td>gunnar "il professore" gren</td> <td>fw</td> <td>6</td> <td>9</td> <td>ifk goteborg</td> </tr> <tr> <th>8</th> <td>erik "mulle" holmqvist</td> <td>fw</td> <td>2</td> <td>1</td> <td>ifk norrkoping</td> </tr> <tr> <th>9</th> <td>oskar "masse" holmqvist</td> <td>df</td> <td>2</td> <td>0</td> <td>ifk norrkoping</td> </tr> <tr> <th>10</th> <td>ove karlsson-widricks</td> <td>df</td> <td>1</td> <td>0</td> <td>aik solna</td> </tr> <tr> <th>11</th> <td>borje leander</td> <td>df/mf</td> <td>4</td> <td>0</td> <td>aik solna</td> </tr> <tr> <th>12</th> <td>gosta malm</td> <td>df</td> <td>2</td> <td>0</td> <td>ifk norrkoping</td> </tr> <tr> <th>13</th> <td>malte "svarta blixten" martensson</td> <td>fw</td> <td>1</td> <td>0</td> <td>halsingborgs if</td> </tr> <tr> <th>14</th> <td>harry nilsson</td> <td>df</td> <td>6</td> <td>0</td> <td>aik solna</td> </tr> <tr> <th>15</th> <td>stellan nilsson</td> <td>fw</td> <td>2</td> <td>1</td> <td>malmo ff</td> </tr> <tr> <th>16</th> <td>bertil nordahl</td> <td>df/mf</td> <td>2</td> <td>0</td> <td>degerfors if</td> </tr> <tr> <th>17</th> <td>gunnar nordahl</td> <td>fw</td> <td>5</td> <td>6</td> <td>ifk norrkoping</td> </tr> <tr> <th>18</th> <td>knut nordahl</td> <td>fw</td> <td>2</td> <td>0</td> <td>ifk norrkoping</td> </tr> <tr> <th>19</th> <td>arne nyberg</td> <td>fw</td> <td>6</td> <td>4</td> <td>ifk goteborg</td> </tr> <tr> <th>20</th> <td>stig nystrom</td> <td>fw</td> <td>1</td> <td>1</td> <td>djurgardens if stockholm</td> </tr> <tr> <th>21</th> <td>vincent persson</td> <td>fw</td> <td>1</td> <td>1</td> <td>degerfors if</td> </tr> <tr> <th>22</th> <td>birger "bian" rosengren</td> <td>mf</td> <td>1</td> <td>0</td> <td>ifk norrkoping</td> </tr> <tr> <th>23</th> <td>carl-erik "pigge" sandberg</td> <td>fw</td> <td>1</td> <td>0</td> <td>malmo ff</td> </tr> <tr> <th>24</th> <td>carl "timpa" simonsson</td> <td>fw</td> <td>1</td> <td>0</td> <td>jonkopings sodra if</td> </tr> <tr> <th>25</th> <td>gustav "gurra" sjoberg</td> <td>gk</td> <td>6</td> <td>0</td> <td>aik solna</td> </tr> <tr> <th>26</th> <td>borje tapper</td> <td>fw</td> <td>1</td> <td>5</td> <td>malmo ff</td> </tr> <tr> <th>27</th> <td>lennart "skinnet" wigren</td> <td>mf</td> <td>1</td> <td>0</td> <td>ifk norrkoping</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 goals > 0 <table_name> : table | | name | pos. | caps | goals | club | |---:|:----------------------------------|:-------|-------:|--------:|:-------------------------| | 0 | olle ahlund | mf | 6 | 0 | degerfors if | | 1 | henry andersson | gk | 2 | 0 | ifk goteborg | | 2 | bertil backvall | fw | 1 | 0 | hammarby if stockholm | | 3 | henry "garvis" carlsson | fw | 5 | 4 | aik solna | | 4 | arvid "emma" emanuelsson | mf | 5 | 0 | if elfsborg boras | | 5 | rune "killing" emanuelsson | mf/df | 3 | 0 | ifk goteborg | | 6 | karl-erik grahn | mf | 4 | 1 | if elfsborg boras | | 7 | gunnar "il professore" gren | fw | 6 | 9 | ifk goteborg | | 8 | erik "mulle" holmqvist | fw | 2 | 1 | ifk norrkoping | | 9 | oskar "masse" holmqvist | df | 2 | 0 | ifk norrkoping | | 10 | ove karlsson-widricks | df | 1 | 0 | aik solna | | 11 | borje leander | df/mf | 4 | 0 | aik solna | | 12 | gosta malm | df | 2 | 0 | ifk norrkoping | | 13 | malte "svarta blixten" martensson | fw | 1 | 0 | halsingborgs if | | 14 | harry nilsson | df | 6 | 0 | aik solna | | 15 | stellan nilsson | fw | 2 | 1 | malmo ff | | 16 | bertil nordahl | df/mf | 2 | 0 | degerfors if | | 17 | gunnar nordahl | fw | 5 | 6 | ifk norrkoping | | 18 | knut nordahl | fw | 2 | 0 | ifk norrkoping | | 19 | arne nyberg | fw | 6 | 4 | ifk goteborg | | 20 | stig nystrom | fw | 1 | 1 | djurgardens if stockholm | | 21 | vincent persson | fw | 1 | 1 | degerfors if | | 22 | birger "bian" rosengren | mf | 1 | 0 | ifk norrkoping | | 23 | carl-erik "pigge" sandberg | fw | 1 | 0 | malmo ff | | 24 | carl "timpa" simonsson | fw | 1 | 0 | jonkopings sodra if | | 25 | gustav "gurra" sjoberg | gk | 6 | 0 | aik solna | | 26 | borje tapper | fw | 1 | 5 | malmo ff | | 27 | lennart "skinnet" wigren | mf | 1 | 0 | ifk norrkoping |

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

select min ( represented since ) where state = 'norway' col : state | membership | parliament | membership status | represented since | members row 1 : denmark | full | the folketing | sovereign state | 1952 | 16 row 2 : iceland | full | alþingi | sovereign state | 1952 | 7 row 3 : norway | full | the storting | sovereign state | 1952 | 20 row 4 : sweden | full | the riksdag | sovereign state | 1952 | 20 row 5 : finland | full | eduskunta | sovereign state | 1955 | 18 row 6 : greenland | associate | landsting | self-governing region of the danish realm | 1984 | 2 row 7 : faroe islands | associate | løgting | self-governing region of the danish realm | 1970 | 2 row 8 : aland islands | associate | lagting | self-governing region of finland | 1970 | 2 row 9 : estonia | observers | none | none | none | none row 10 : latvia | observers | none | none | none | none row 11 : lithuania | observers | none | none | none | none

col : min ( "represented since" ) row 1 : 1952

select min ( represented since ) where state = 'norway' <table_name> : table \begin{tabular}{lllllll} \toprule & state & membership & parliament & membership status & represented since & members \\ \midrule 0 & denmark & full & the folketing & sovereign state & 1952 & 16 \\ 1 & iceland & full & alþingi & sovereign state & 1952 & 7 \\ 2 & norway & full & the storting & sovereign state & 1952 & 20 \\ 3 & sweden & full & the riksdag & sovereign state & 1952 & 20 \\ 4 & finland & full & eduskunta & sovereign state & 1955 & 18 \\ 5 & greenland & associate & landsting & self-governing region of the danish realm & 1984 & 2 \\ 6 & faroe islands & associate & løgting & self-governing region of the danish realm & 1970 & 2 \\ 7 & aland islands & associate & lagting & self-governing region of finland & 1970 & 2 \\ 8 & estonia & observers & none & none & none & none \\ 9 & latvia & observers & none & none & none & none \\ 10 & lithuania & observers & none & none & none & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & min ( "represented since" ) \\ \midrule 0 & 1952 \\ \bottomrule \end{tabular}

select min ( represented since ) where state = 'norway' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>state</th> <th>membership</th> <th>parliament</th> <th>membership status</th> <th>represented since</th> <th>members</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>denmark</td> <td>full</td> <td>the folketing</td> <td>sovereign state</td> <td>1952</td> <td>16</td> </tr> <tr> <th>1</th> <td>iceland</td> <td>full</td> <td>alþingi</td> <td>sovereign state</td> <td>1952</td> <td>7</td> </tr> <tr> <th>2</th> <td>norway</td> <td>full</td> <td>the storting</td> <td>sovereign state</td> <td>1952</td> <td>20</td> </tr> <tr> <th>3</th> <td>sweden</td> <td>full</td> <td>the riksdag</td> <td>sovereign state</td> <td>1952</td> <td>20</td> </tr> <tr> <th>4</th> <td>finland</td> <td>full</td> <td>eduskunta</td> <td>sovereign state</td> <td>1955</td> <td>18</td> </tr> <tr> <th>5</th> <td>greenland</td> <td>associate</td> <td>landsting</td> <td>self-governing region of the danish realm</td> <td>1984</td> <td>2</td> </tr> <tr> <th>6</th> <td>faroe islands</td> <td>associate</td> <td>løgting</td> <td>self-governing region of the danish realm</td> <td>1970</td> <td>2</td> </tr> <tr> <th>7</th> <td>aland islands</td> <td>associate</td> <td>lagting</td> <td>self-governing region of finland</td> <td>1970</td> <td>2</td> </tr> <tr> <th>8</th> <td>estonia</td> <td>observers</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>9</th> <td>latvia</td> <td>observers</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>10</th> <td>lithuania</td> <td>observers</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>min ( "represented since" )</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1952</td> </tr> </tbody> </table>

select min ( represented since ) where state = 'norway' <table_name> : table | | state | membership | parliament | membership status | represented since | members | |---:|:--------------|:-------------|:--------------|:------------------------------------------|:--------------------|:----------| | 0 | denmark | full | the folketing | sovereign state | 1952 | 16 | | 1 | iceland | full | alþingi | sovereign state | 1952 | 7 | | 2 | norway | full | the storting | sovereign state | 1952 | 20 | | 3 | sweden | full | the riksdag | sovereign state | 1952 | 20 | | 4 | finland | full | eduskunta | sovereign state | 1955 | 18 | | 5 | greenland | associate | landsting | self-governing region of the danish realm | 1984 | 2 | | 6 | faroe islands | associate | løgting | self-governing region of the danish realm | 1970 | 2 | | 7 | aland islands | associate | lagting | self-governing region of finland | 1970 | 2 | | 8 | estonia | observers | none | none | none | none | | 9 | latvia | observers | none | none | none | none | | 10 | lithuania | observers | none | none | none | none |

| | min ( "represented since" ) | |---:|------------------------------:| | 0 | 1952 |

select count ( name ) where nationality = 'or' col : rank | name | nationality | time (hand) | notes row 1 : 1 | tommy green | great britain | 4:50:10 | or row 2 : 2 | janis dalins | latvia | 4:57:20 | none row 3 : 3 | ugo frigerio | italy | 4:59:06 | none row 4 : 4 | karl hahnel | germany | 5:06:06 | none row 5 : 5 | ettore rivolta | italy | 5:07:39 | none row 6 : 6 | paul sievert | germany | 5:16:41 | none row 7 : 7 | henri quintric | france | 5:27:25 | none row 8 : 8 | ernie crosbie | united states | 5:28:02 | none row 9 : 9 | bill chisholm | united states | 5:51:00 | none row 10 : 10 | alfred maasik | estonia | 6:19:00 | none row 11 : none | henry cieman | canada | none | dnf row 12 : none | john moralis | greece | none | dnf row 13 : none | francesco pretti | italy | none | dnf row 14 : none | arthur tell schwab | switzerland | none | dnf row 15 : none | harry hinkel | united states | none | dnf

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

select count ( name ) where nationality = 'or' <table_name> : table \begin{tabular}{llllll} \toprule & rank & name & nationality & time (hand) & notes \\ \midrule 0 & 1 & tommy green & great britain & 4:50:10 & or \\ 1 & 2 & janis dalins & latvia & 4:57:20 & none \\ 2 & 3 & ugo frigerio & italy & 4:59:06 & none \\ 3 & 4 & karl hahnel & germany & 5:06:06 & none \\ 4 & 5 & ettore rivolta & italy & 5:07:39 & none \\ 5 & 6 & paul sievert & germany & 5:16:41 & none \\ 6 & 7 & henri quintric & france & 5:27:25 & none \\ 7 & 8 & ernie crosbie & united states & 5:28:02 & none \\ 8 & 9 & bill chisholm & united states & 5:51:00 & none \\ 9 & 10 & alfred maasik & estonia & 6:19:00 & none \\ 10 & none & henry cieman & canada & none & dnf \\ 11 & none & john moralis & greece & none & dnf \\ 12 & none & francesco pretti & italy & none & dnf \\ 13 & none & arthur tell schwab & switzerland & none & dnf \\ 14 & none & harry hinkel & united states & none & dnf \\ \bottomrule \end{tabular}

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

select count ( name ) where nationality = 'or' <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 (hand)</th> <th>notes</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>tommy green</td> <td>great britain</td> <td>4:50:10</td> <td>or</td> </tr> <tr> <th>1</th> <td>2</td> <td>janis dalins</td> <td>latvia</td> <td>4:57:20</td> <td>none</td> </tr> <tr> <th>2</th> <td>3</td> <td>ugo frigerio</td> <td>italy</td> <td>4:59:06</td> <td>none</td> </tr> <tr> <th>3</th> <td>4</td> <td>karl hahnel</td> <td>germany</td> <td>5:06:06</td> <td>none</td> </tr> <tr> <th>4</th> <td>5</td> <td>ettore rivolta</td> <td>italy</td> <td>5:07:39</td> <td>none</td> </tr> <tr> <th>5</th> <td>6</td> <td>paul sievert</td> <td>germany</td> <td>5:16:41</td> <td>none</td> </tr> <tr> <th>6</th> <td>7</td> <td>henri quintric</td> <td>france</td> <td>5:27:25</td> <td>none</td> </tr> <tr> <th>7</th> <td>8</td> <td>ernie crosbie</td> <td>united states</td> <td>5:28:02</td> <td>none</td> </tr> <tr> <th>8</th> <td>9</td> <td>bill chisholm</td> <td>united states</td> <td>5:51:00</td> <td>none</td> </tr> <tr> <th>9</th> <td>10</td> <td>alfred maasik</td> <td>estonia</td> <td>6:19:00</td> <td>none</td> </tr> <tr> <th>10</th> <td>none</td> <td>henry cieman</td> <td>canada</td> <td>none</td> <td>dnf</td> </tr> <tr> <th>11</th> <td>none</td> <td>john moralis</td> <td>greece</td> <td>none</td> <td>dnf</td> </tr> <tr> <th>12</th> <td>none</td> <td>francesco pretti</td> <td>italy</td> <td>none</td> <td>dnf</td> </tr> <tr> <th>13</th> <td>none</td> <td>arthur tell schwab</td> <td>switzerland</td> <td>none</td> <td>dnf</td> </tr> <tr> <th>14</th> <td>none</td> <td>harry hinkel</td> <td>united states</td> <td>none</td> <td>dnf</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>0</td> </tr> </tbody> </table>

select count ( name ) where nationality = 'or' <table_name> : table | | rank | name | nationality | time (hand) | notes | |---:|:-------|:-------------------|:--------------|:--------------|:--------| | 0 | 1 | tommy green | great britain | 4:50:10 | or | | 1 | 2 | janis dalins | latvia | 4:57:20 | none | | 2 | 3 | ugo frigerio | italy | 4:59:06 | none | | 3 | 4 | karl hahnel | germany | 5:06:06 | none | | 4 | 5 | ettore rivolta | italy | 5:07:39 | none | | 5 | 6 | paul sievert | germany | 5:16:41 | none | | 6 | 7 | henri quintric | france | 5:27:25 | none | | 7 | 8 | ernie crosbie | united states | 5:28:02 | none | | 8 | 9 | bill chisholm | united states | 5:51:00 | none | | 9 | 10 | alfred maasik | estonia | 6:19:00 | none | | 10 | none | henry cieman | canada | none | dnf | | 11 | none | john moralis | greece | none | dnf | | 12 | none | francesco pretti | italy | none | dnf | | 13 | none | arthur tell schwab | switzerland | none | dnf | | 14 | none | harry hinkel | united states | none | dnf |

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

select count ( * ) where township = 'caneyville' col : township | fips | population center | population | population density /km2 (/sq mi) | land area km2 (sq mi) | water area km2 (sq mi) | water % | geographic coordinates row 1 : belleville | 5575 | none | 675 | 4 (11) | 155 (60) | 0 (0) | 0.14% | 37°3′50′′n 96°7′48′′w / 37.06389°n 96.13000°w row 2 : caneyville | 10425 | none | 88 | 1 (2) | 144 (55) | 1 (0) | 0.38% | 37°15′23′′n 96°27′27′′w / 37.25639°n 96.45750°w row 3 : center | 11575 | none | 75 | 1 (1) | 144 (56) | 1 (0) | 0.80% | 37°15′28′′n 96°18′31′′w / 37.25778°n 96.30861°w row 4 : harrison | 30275 | none | 114 | 1 (2) | 142 (55) | 0 (0) | 0.14% | 37°4′3′′n 96°26′32′′w / 37.06750°n 96.44222°w row 5 : hendricks | 31300 | none | 179 | 1 (3) | 142 (55) | 0 (0) | 0.05% | 37°1′12′′n 96°18′42′′w / 37.02000°n 96.31167°w row 6 : jefferson | 35125 | cedar vale | 834 | 6 (15) | 144 (56) | 1 (0) | 0.45% | 37°6′45′′n 96°29′12′′w / 37.11250°n 96.48667°w row 7 : lafayette | 37650 | none | 65 | 0 (1) | 154 (59) | 2 (1) | 1.40% | 37°15′36′′n 96°10′20′′w / 37.26000°n 96.17222°w row 8 : little caney | 41525 | none | 353 | 3 (8) | 117 (45) | 0 (0) | 0.31% | 37°3′51′′n 96°1′5′′w / 37.06417°n 96.01806°w row 9 : salt creek | 62775 | none | 123 | 1 (3) | 127 (49) | 0 (0) | 0.34% | 37°15′13′′n 96°1′34′′w / 37.25361°n 96.02611°w row 10 : sedan | 63775 | sedan | 1660 | 13 (34) | 128 (49) | 1 (0) | 0.65% | 37°7′50′′n 96°11′6′′w / 37.13056°n 96.18500°w row 11 : summit | 69025 | none | 106 | 1 (2) | 144 (56) | 1 (0) | 0.40% | 37°8′17′′n 96°19′53′′w / 37.13806°n 96.33139°w row 12 : washington | 75550 | none | 87 | 1 (2) | 121 (47) | 1 (0) | 0.71% | 37°7′41′′n 96°0′52′′w / 37.12806°n 96.01444°w

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

select count ( * ) where township = 'caneyville' <table_name> : table \begin{tabular}{llllllllll} \toprule & township & fips & population center & population & population density /km2 (/sq mi) & land area km2 (sq mi) & water area km2 (sq mi) & water % & geographic coordinates \\ \midrule 0 & belleville & 5575 & none & 675 & 4 (11) & 155 (60) & 0 (0) & 0.14% & 37°3′50′′n 96°7′48′′w / 37.06389°n 96.13000°w \\ 1 & caneyville & 10425 & none & 88 & 1 (2) & 144 (55) & 1 (0) & 0.38% & 37°15′23′′n 96°27′27′′w / 37.25639°n 96.45750°w \\ 2 & center & 11575 & none & 75 & 1 (1) & 144 (56) & 1 (0) & 0.80% & 37°15′28′′n 96°18′31′′w / 37.25778°n 96.30861°w \\ 3 & harrison & 30275 & none & 114 & 1 (2) & 142 (55) & 0 (0) & 0.14% & 37°4′3′′n 96°26′32′′w / 37.06750°n 96.44222°w \\ 4 & hendricks & 31300 & none & 179 & 1 (3) & 142 (55) & 0 (0) & 0.05% & 37°1′12′′n 96°18′42′′w / 37.02000°n 96.31167°w \\ 5 & jefferson & 35125 & cedar vale & 834 & 6 (15) & 144 (56) & 1 (0) & 0.45% & 37°6′45′′n 96°29′12′′w / 37.11250°n 96.48667°w \\ 6 & lafayette & 37650 & none & 65 & 0 (1) & 154 (59) & 2 (1) & 1.40% & 37°15′36′′n 96°10′20′′w / 37.26000°n 96.17222°w \\ 7 & little caney & 41525 & none & 353 & 3 (8) & 117 (45) & 0 (0) & 0.31% & 37°3′51′′n 96°1′5′′w / 37.06417°n 96.01806°w \\ 8 & salt creek & 62775 & none & 123 & 1 (3) & 127 (49) & 0 (0) & 0.34% & 37°15′13′′n 96°1′34′′w / 37.25361°n 96.02611°w \\ 9 & sedan & 63775 & sedan & 1660 & 13 (34) & 128 (49) & 1 (0) & 0.65% & 37°7′50′′n 96°11′6′′w / 37.13056°n 96.18500°w \\ 10 & summit & 69025 & none & 106 & 1 (2) & 144 (56) & 1 (0) & 0.40% & 37°8′17′′n 96°19′53′′w / 37.13806°n 96.33139°w \\ 11 & washington & 75550 & none & 87 & 1 (2) & 121 (47) & 1 (0) & 0.71% & 37°7′41′′n 96°0′52′′w / 37.12806°n 96.01444°w \\ \bottomrule \end{tabular}

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

select count ( * ) where township = 'caneyville' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>township</th> <th>fips</th> <th>population center</th> <th>population</th> <th>population density /km2 (/sq mi)</th> <th>land area km2 (sq mi)</th> <th>water area km2 (sq mi)</th> <th>water %</th> <th>geographic coordinates</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>belleville</td> <td>5575</td> <td>none</td> <td>675</td> <td>4 (11)</td> <td>155 (60)</td> <td>0 (0)</td> <td>0.14%</td> <td>37°3′50′′n 96°7′48′′w / 37.06389°n 96.13000°w</td> </tr> <tr> <th>1</th> <td>caneyville</td> <td>10425</td> <td>none</td> <td>88</td> <td>1 (2)</td> <td>144 (55)</td> <td>1 (0)</td> <td>0.38%</td> <td>37°15′23′′n 96°27′27′′w / 37.25639°n 96.45750°w</td> </tr> <tr> <th>2</th> <td>center</td> <td>11575</td> <td>none</td> <td>75</td> <td>1 (1)</td> <td>144 (56)</td> <td>1 (0)</td> <td>0.80%</td> <td>37°15′28′′n 96°18′31′′w / 37.25778°n 96.30861°w</td> </tr> <tr> <th>3</th> <td>harrison</td> <td>30275</td> <td>none</td> <td>114</td> <td>1 (2)</td> <td>142 (55)</td> <td>0 (0)</td> <td>0.14%</td> <td>37°4′3′′n 96°26′32′′w / 37.06750°n 96.44222°w</td> </tr> <tr> <th>4</th> <td>hendricks</td> <td>31300</td> <td>none</td> <td>179</td> <td>1 (3)</td> <td>142 (55)</td> <td>0 (0)</td> <td>0.05%</td> <td>37°1′12′′n 96°18′42′′w / 37.02000°n 96.31167°w</td> </tr> <tr> <th>5</th> <td>jefferson</td> <td>35125</td> <td>cedar vale</td> <td>834</td> <td>6 (15)</td> <td>144 (56)</td> <td>1 (0)</td> <td>0.45%</td> <td>37°6′45′′n 96°29′12′′w / 37.11250°n 96.48667°w</td> </tr> <tr> <th>6</th> <td>lafayette</td> <td>37650</td> <td>none</td> <td>65</td> <td>0 (1)</td> <td>154 (59)</td> <td>2 (1)</td> <td>1.40%</td> <td>37°15′36′′n 96°10′20′′w / 37.26000°n 96.17222°w</td> </tr> <tr> <th>7</th> <td>little caney</td> <td>41525</td> <td>none</td> <td>353</td> <td>3 (8)</td> <td>117 (45)</td> <td>0 (0)</td> <td>0.31%</td> <td>37°3′51′′n 96°1′5′′w / 37.06417°n 96.01806°w</td> </tr> <tr> <th>8</th> <td>salt creek</td> <td>62775</td> <td>none</td> <td>123</td> <td>1 (3)</td> <td>127 (49)</td> <td>0 (0)</td> <td>0.34%</td> <td>37°15′13′′n 96°1′34′′w / 37.25361°n 96.02611°w</td> </tr> <tr> <th>9</th> <td>sedan</td> <td>63775</td> <td>sedan</td> <td>1660</td> <td>13 (34)</td> <td>128 (49)</td> <td>1 (0)</td> <td>0.65%</td> <td>37°7′50′′n 96°11′6′′w / 37.13056°n 96.18500°w</td> </tr> <tr> <th>10</th> <td>summit</td> <td>69025</td> <td>none</td> <td>106</td> <td>1 (2)</td> <td>144 (56)</td> <td>1 (0)</td> <td>0.40%</td> <td>37°8′17′′n 96°19′53′′w / 37.13806°n 96.33139°w</td> </tr> <tr> <th>11</th> <td>washington</td> <td>75550</td> <td>none</td> <td>87</td> <td>1 (2)</td> <td>121 (47)</td> <td>1 (0)</td> <td>0.71%</td> <td>37°7′41′′n 96°0′52′′w / 37.12806°n 96.01444°w</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 township = 'caneyville' <table_name> : table | | township | fips | population center | population | population density /km2 (/sq mi) | land area km2 (sq mi) | water area km2 (sq mi) | water % | geographic coordinates | |---:|:-------------|-------:|:--------------------|-------------:|:-----------------------------------|:------------------------|:-------------------------|:----------|:------------------------------------------------| | 0 | belleville | 5575 | none | 675 | 4 (11) | 155 (60) | 0 (0) | 0.14% | 37°3′50′′n 96°7′48′′w / 37.06389°n 96.13000°w | | 1 | caneyville | 10425 | none | 88 | 1 (2) | 144 (55) | 1 (0) | 0.38% | 37°15′23′′n 96°27′27′′w / 37.25639°n 96.45750°w | | 2 | center | 11575 | none | 75 | 1 (1) | 144 (56) | 1 (0) | 0.80% | 37°15′28′′n 96°18′31′′w / 37.25778°n 96.30861°w | | 3 | harrison | 30275 | none | 114 | 1 (2) | 142 (55) | 0 (0) | 0.14% | 37°4′3′′n 96°26′32′′w / 37.06750°n 96.44222°w | | 4 | hendricks | 31300 | none | 179 | 1 (3) | 142 (55) | 0 (0) | 0.05% | 37°1′12′′n 96°18′42′′w / 37.02000°n 96.31167°w | | 5 | jefferson | 35125 | cedar vale | 834 | 6 (15) | 144 (56) | 1 (0) | 0.45% | 37°6′45′′n 96°29′12′′w / 37.11250°n 96.48667°w | | 6 | lafayette | 37650 | none | 65 | 0 (1) | 154 (59) | 2 (1) | 1.40% | 37°15′36′′n 96°10′20′′w / 37.26000°n 96.17222°w | | 7 | little caney | 41525 | none | 353 | 3 (8) | 117 (45) | 0 (0) | 0.31% | 37°3′51′′n 96°1′5′′w / 37.06417°n 96.01806°w | | 8 | salt creek | 62775 | none | 123 | 1 (3) | 127 (49) | 0 (0) | 0.34% | 37°15′13′′n 96°1′34′′w / 37.25361°n 96.02611°w | | 9 | sedan | 63775 | sedan | 1660 | 13 (34) | 128 (49) | 1 (0) | 0.65% | 37°7′50′′n 96°11′6′′w / 37.13056°n 96.18500°w | | 10 | summit | 69025 | none | 106 | 1 (2) | 144 (56) | 1 (0) | 0.40% | 37°8′17′′n 96°19′53′′w / 37.13806°n 96.33139°w | | 11 | washington | 75550 | none | 87 | 1 (2) | 121 (47) | 1 (0) | 0.71% | 37°7′41′′n 96°0′52′′w / 37.12806°n 96.01444°w |

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

select count ( position ) where team = '1' col : season | series | team | races | wins | poles | f/laps | podiums | points | position row 1 : 2001 | formula bmw junior cup iberia | none | 3 | 0 | 0 | 0 | 0 | 38 | 18th row 2 : 2002 | formula bmw adac | viva racing | 20 | 9 | 5 | 1 | 13 | 264 | 1st row 3 : 2003 | formula 3 euro series | team rosberg | 20 | 1 | 1 | 2 | 5 | 45 | 8th row 4 : 2003 | masters of formula 3 | team rosberg | 1 | 0 | 0 | 0 | 0 | none | nc row 5 : 2003 | macau grand prix | carlin motorsport | 1 | 0 | 0 | 0 | 0 | none | nc row 6 : 2003 | korea super prix | carlin motorsport | 1 | 0 | 0 | 0 | 0 | none | 11th row 7 : 2004 | formula 3 euro series | team rosberg | 19 | 3 | 2 | 2 | 5 | 70 | 4th row 8 : 2004 | macau grand prix | team rosberg | 1 | 0 | 0 | 0 | 0 | none | nc row 9 : 2004 | masters of formula 3 | team rosberg | 1 | 0 | 0 | 0 | 0 | none | 6th row 10 : 2004 | bahrain superprix | team rosberg | 1 | 0 | 0 | 0 | 1 | none | 2nd row 11 : 2005 | gp2 series | art grand prix | 23 | 5 | 4 | 5 | 12 | 120 | 1st row 12 : 2005 | formula one | bmw williams f1 team | none | none | none | none | none | none | test driver row 13 : 2006 | formula one | williamsf1 team | 18 | 0 | 0 | 1 | 0 | 4 | 17th row 14 : 2007 | formula one | at&t williams | 17 | 0 | 0 | 0 | 0 | 20 | 9th row 15 : 2008 | formula one | at&t williams | 18 | 0 | 0 | 0 | 2 | 17 | 13th row 16 : 2009 | formula one | at&t williams | 17 | 0 | 0 | 1 | 0 | 34.5 | 7th row 17 : 2010 | formula one | mercedes gp petronas f1 team | 19 | 0 | 0 | 0 | 3 | 142 | 7th row 18 : 2011 | formula one | mercedes gp petronas f1 team | 19 | 0 | 0 | 0 | 0 | 89 | 7th row 19 : 2012 | formula one | mercedes amg petronas f1 team | 20 | 1 | 1 | 2 | 2 | 93 | 9th row 20 : 2013 | formula one | mercedes amg petronas f1 team | 19 | 2 | 3 | 0 | 4 | 171 | 6th row 21 : 2014 | formula one | mercedes amg petronas f1 team | 2 | 1 | 0 | 1 | 2 | 43 | 1st

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

select count ( position ) where team = '1' <table_name> : table \begin{tabular}{lllllllllll} \toprule & season & series & team & races & wins & poles & f/laps & podiums & points & position \\ \midrule 0 & 2001 & formula bmw junior cup iberia & none & 3 & 0 & 0 & 0 & 0 & 38 & 18th \\ 1 & 2002 & formula bmw adac & viva racing & 20 & 9 & 5 & 1 & 13 & 264 & 1st \\ 2 & 2003 & formula 3 euro series & team rosberg & 20 & 1 & 1 & 2 & 5 & 45 & 8th \\ 3 & 2003 & masters of formula 3 & team rosberg & 1 & 0 & 0 & 0 & 0 & none & nc \\ 4 & 2003 & macau grand prix & carlin motorsport & 1 & 0 & 0 & 0 & 0 & none & nc \\ 5 & 2003 & korea super prix & carlin motorsport & 1 & 0 & 0 & 0 & 0 & none & 11th \\ 6 & 2004 & formula 3 euro series & team rosberg & 19 & 3 & 2 & 2 & 5 & 70 & 4th \\ 7 & 2004 & macau grand prix & team rosberg & 1 & 0 & 0 & 0 & 0 & none & nc \\ 8 & 2004 & masters of formula 3 & team rosberg & 1 & 0 & 0 & 0 & 0 & none & 6th \\ 9 & 2004 & bahrain superprix & team rosberg & 1 & 0 & 0 & 0 & 1 & none & 2nd \\ 10 & 2005 & gp2 series & art grand prix & 23 & 5 & 4 & 5 & 12 & 120 & 1st \\ 11 & 2005 & formula one & bmw williams f1 team & none & none & none & none & none & none & test driver \\ 12 & 2006 & formula one & williamsf1 team & 18 & 0 & 0 & 1 & 0 & 4 & 17th \\ 13 & 2007 & formula one & at&t williams & 17 & 0 & 0 & 0 & 0 & 20 & 9th \\ 14 & 2008 & formula one & at&t williams & 18 & 0 & 0 & 0 & 2 & 17 & 13th \\ 15 & 2009 & formula one & at&t williams & 17 & 0 & 0 & 1 & 0 & 34.5 & 7th \\ 16 & 2010 & formula one & mercedes gp petronas f1 team & 19 & 0 & 0 & 0 & 3 & 142 & 7th \\ 17 & 2011 & formula one & mercedes gp petronas f1 team & 19 & 0 & 0 & 0 & 0 & 89 & 7th \\ 18 & 2012 & formula one & mercedes amg petronas f1 team & 20 & 1 & 1 & 2 & 2 & 93 & 9th \\ 19 & 2013 & formula one & mercedes amg petronas f1 team & 19 & 2 & 3 & 0 & 4 & 171 & 6th \\ 20 & 2014 & formula one & mercedes amg petronas f1 team & 2 & 1 & 0 & 1 & 2 & 43 & 1st \\ \bottomrule \end{tabular}

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

select count ( position ) where team = '1' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>season</th> <th>series</th> <th>team</th> <th>races</th> <th>wins</th> <th>poles</th> <th>f/laps</th> <th>podiums</th> <th>points</th> <th>position</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>2001</td> <td>formula bmw junior cup iberia</td> <td>none</td> <td>3</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>38</td> <td>18th</td> </tr> <tr> <th>1</th> <td>2002</td> <td>formula bmw adac</td> <td>viva racing</td> <td>20</td> <td>9</td> <td>5</td> <td>1</td> <td>13</td> <td>264</td> <td>1st</td> </tr> <tr> <th>2</th> <td>2003</td> <td>formula 3 euro series</td> <td>team rosberg</td> <td>20</td> <td>1</td> <td>1</td> <td>2</td> <td>5</td> <td>45</td> <td>8th</td> </tr> <tr> <th>3</th> <td>2003</td> <td>masters of formula 3</td> <td>team rosberg</td> <td>1</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>none</td> <td>nc</td> </tr> <tr> <th>4</th> <td>2003</td> <td>macau grand prix</td> <td>carlin motorsport</td> <td>1</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>none</td> <td>nc</td> </tr> <tr> <th>5</th> <td>2003</td> <td>korea super prix</td> <td>carlin motorsport</td> <td>1</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>none</td> <td>11th</td> </tr> <tr> <th>6</th> <td>2004</td> <td>formula 3 euro series</td> <td>team rosberg</td> <td>19</td> <td>3</td> <td>2</td> <td>2</td> <td>5</td> <td>70</td> <td>4th</td> </tr> <tr> <th>7</th> <td>2004</td> <td>macau grand prix</td> <td>team rosberg</td> <td>1</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>none</td> <td>nc</td> </tr> <tr> <th>8</th> <td>2004</td> <td>masters of formula 3</td> <td>team rosberg</td> <td>1</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>none</td> <td>6th</td> </tr> <tr> <th>9</th> <td>2004</td> <td>bahrain superprix</td> <td>team rosberg</td> <td>1</td> <td>0</td> <td>0</td> <td>0</td> <td>1</td> <td>none</td> <td>2nd</td> </tr> <tr> <th>10</th> <td>2005</td> <td>gp2 series</td> <td>art grand prix</td> <td>23</td> <td>5</td> <td>4</td> <td>5</td> <td>12</td> <td>120</td> <td>1st</td> </tr> <tr> <th>11</th> <td>2005</td> <td>formula one</td> <td>bmw williams f1 team</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> <td>test driver</td> </tr> <tr> <th>12</th> <td>2006</td> <td>formula one</td> <td>williamsf1 team</td> <td>18</td> <td>0</td> <td>0</td> <td>1</td> <td>0</td> <td>4</td> <td>17th</td> </tr> <tr> <th>13</th> <td>2007</td> <td>formula one</td> <td>at&amp;t williams</td> <td>17</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>20</td> <td>9th</td> </tr> <tr> <th>14</th> <td>2008</td> <td>formula one</td> <td>at&amp;t williams</td> <td>18</td> <td>0</td> <td>0</td> <td>0</td> <td>2</td> <td>17</td> <td>13th</td> </tr> <tr> <th>15</th> <td>2009</td> <td>formula one</td> <td>at&amp;t williams</td> <td>17</td> <td>0</td> <td>0</td> <td>1</td> <td>0</td> <td>34.5</td> <td>7th</td> </tr> <tr> <th>16</th> <td>2010</td> <td>formula one</td> <td>mercedes gp petronas f1 team</td> <td>19</td> <td>0</td> <td>0</td> <td>0</td> <td>3</td> <td>142</td> <td>7th</td> </tr> <tr> <th>17</th> <td>2011</td> <td>formula one</td> <td>mercedes gp petronas f1 team</td> <td>19</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>89</td> <td>7th</td> </tr> <tr> <th>18</th> <td>2012</td> <td>formula one</td> <td>mercedes amg petronas f1 team</td> <td>20</td> <td>1</td> <td>1</td> <td>2</td> <td>2</td> <td>93</td> <td>9th</td> </tr> <tr> <th>19</th> <td>2013</td> <td>formula one</td> <td>mercedes amg petronas f1 team</td> <td>19</td> <td>2</td> <td>3</td> <td>0</td> <td>4</td> <td>171</td> <td>6th</td> </tr> <tr> <th>20</th> <td>2014</td> <td>formula one</td> <td>mercedes amg petronas f1 team</td> <td>2</td> <td>1</td> <td>0</td> <td>1</td> <td>2</td> <td>43</td> <td>1st</td> </tr> </tbody> </table>

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

select count ( position ) where team = '1' <table_name> : table | | season | series | team | races | wins | poles | f/laps | podiums | points | position | |---:|---------:|:------------------------------|:------------------------------|:--------|:-------|:--------|:---------|:----------|:---------|:------------| | 0 | 2001 | formula bmw junior cup iberia | none | 3 | 0 | 0 | 0 | 0 | 38 | 18th | | 1 | 2002 | formula bmw adac | viva racing | 20 | 9 | 5 | 1 | 13 | 264 | 1st | | 2 | 2003 | formula 3 euro series | team rosberg | 20 | 1 | 1 | 2 | 5 | 45 | 8th | | 3 | 2003 | masters of formula 3 | team rosberg | 1 | 0 | 0 | 0 | 0 | none | nc | | 4 | 2003 | macau grand prix | carlin motorsport | 1 | 0 | 0 | 0 | 0 | none | nc | | 5 | 2003 | korea super prix | carlin motorsport | 1 | 0 | 0 | 0 | 0 | none | 11th | | 6 | 2004 | formula 3 euro series | team rosberg | 19 | 3 | 2 | 2 | 5 | 70 | 4th | | 7 | 2004 | macau grand prix | team rosberg | 1 | 0 | 0 | 0 | 0 | none | nc | | 8 | 2004 | masters of formula 3 | team rosberg | 1 | 0 | 0 | 0 | 0 | none | 6th | | 9 | 2004 | bahrain superprix | team rosberg | 1 | 0 | 0 | 0 | 1 | none | 2nd | | 10 | 2005 | gp2 series | art grand prix | 23 | 5 | 4 | 5 | 12 | 120 | 1st | | 11 | 2005 | formula one | bmw williams f1 team | none | none | none | none | none | none | test driver | | 12 | 2006 | formula one | williamsf1 team | 18 | 0 | 0 | 1 | 0 | 4 | 17th | | 13 | 2007 | formula one | at&t williams | 17 | 0 | 0 | 0 | 0 | 20 | 9th | | 14 | 2008 | formula one | at&t williams | 18 | 0 | 0 | 0 | 2 | 17 | 13th | | 15 | 2009 | formula one | at&t williams | 17 | 0 | 0 | 1 | 0 | 34.5 | 7th | | 16 | 2010 | formula one | mercedes gp petronas f1 team | 19 | 0 | 0 | 0 | 3 | 142 | 7th | | 17 | 2011 | formula one | mercedes gp petronas f1 team | 19 | 0 | 0 | 0 | 0 | 89 | 7th | | 18 | 2012 | formula one | mercedes amg petronas f1 team | 20 | 1 | 1 | 2 | 2 | 93 | 9th | | 19 | 2013 | formula one | mercedes amg petronas f1 team | 19 | 2 | 3 | 0 | 4 | 171 | 6th | | 20 | 2014 | formula one | mercedes amg petronas f1 team | 2 | 1 | 0 | 1 | 2 | 43 | 1st |

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

select count ( * ) where points > 731 col : pos | no | driver | constructor | laps | time/retired | grid | points row 1 : 1 | 1 | alain prost | mclaren-tag | 82 | 1:54:20.388 | 4 | 9 row 2 : 2 | 6 | nelson piquet | williams-honda | 82 | + 4.205 | 2 | 6 row 3 : 3 | 28 | stefan johansson | ferrari | 81 | + 1 lap | 12 | 4 row 4 : 4 | 3 | martin brundle | tyrrell-renault | 81 | + 1 lap | 16 | 3 row 5 : 5 | 4 | philippe streiff | tyrrell-renault | 80 | out of fuel | 10 | 2 row 6 : 6 | 11 | johnny dumfries | lotus-renault | 80 | + 2 laps | 14 | 1 row 7 : 7 | 25 | rene arnoux | ligier-renault | 79 | + 3 laps | 5 | none row 8 : 8 | 26 | philippe alliot | ligier-renault | 79 | + 3 laps | 8 | none row 9 : 9 | 14 | jonathan palmer | zakspeed | 77 | + 5 laps | 21 | none row 10 : 10 | 19 | teo fabi | benetton-bmw | 77 | + 5 laps | 13 | none row 11 : nc | 16 | patrick tambay | lola-ford | 70 | not classified | 17 | none row 12 : ret | 5 | nigel mansell | williams-honda | 63 | tyre | 1 | none row 13 : ret | 7 | riccardo patrese | brabham-bmw | 63 | electrical | 19 | none row 14 : ret | 2 | keke rosberg | mclaren-tag | 62 | tyre | 7 | none row 15 : nc | 22 | allen berg | osella-alfa romeo | 61 | not classified | 26 | none row 16 : ret | 8 | derek warwick | brabham-bmw | 57 | brakes | 20 | none row 17 : ret | 17 | christian danner | arrows-bmw | 52 | engine | 24 | none row 18 : ret | 18 | thierry boutsen | arrows-bmw | 50 | engine | 22 | none row 19 : ret | 12 | ayrton senna | lotus-renault | 43 | engine | 3 | none row 20 : ret | 23 | andrea de cesaris | minardi-motori moderni | 40 | mechanical | 11 | none row 21 : ret | 20 | gerhard berger | benetton-bmw | 40 | engine | 6 | none row 22 : ret | 29 | huub rothengatter | zakspeed | 29 | suspension | 23 | none row 23 : ret | 15 | alan jones | lola-ford | 16 | engine | 15 | none row 24 : ret | 24 | alessandro nannini | minardi-motori moderni | 10 | accident | 18 | none row 25 : ret | 21 | piercarlo ghinzani | osella-alfa romeo | 2 | transmission | 25 | none row 26 : ret | 27 | michele alboreto | ferrari | 0 | collision | 9 | none

col : count ( "*" ) row 1 : 21

select count ( * ) where points > 731 <table_name> : table \begin{tabular}{lllllllll} \toprule & pos & no & driver & constructor & laps & time/retired & grid & points \\ \midrule 0 & 1 & 1 & alain prost & mclaren-tag & 82 & 1:54:20.388 & 4 & 9 \\ 1 & 2 & 6 & nelson piquet & williams-honda & 82 & + 4.205 & 2 & 6 \\ 2 & 3 & 28 & stefan johansson & ferrari & 81 & + 1 lap & 12 & 4 \\ 3 & 4 & 3 & martin brundle & tyrrell-renault & 81 & + 1 lap & 16 & 3 \\ 4 & 5 & 4 & philippe streiff & tyrrell-renault & 80 & out of fuel & 10 & 2 \\ 5 & 6 & 11 & johnny dumfries & lotus-renault & 80 & + 2 laps & 14 & 1 \\ 6 & 7 & 25 & rene arnoux & ligier-renault & 79 & + 3 laps & 5 & none \\ 7 & 8 & 26 & philippe alliot & ligier-renault & 79 & + 3 laps & 8 & none \\ 8 & 9 & 14 & jonathan palmer & zakspeed & 77 & + 5 laps & 21 & none \\ 9 & 10 & 19 & teo fabi & benetton-bmw & 77 & + 5 laps & 13 & none \\ 10 & nc & 16 & patrick tambay & lola-ford & 70 & not classified & 17 & none \\ 11 & ret & 5 & nigel mansell & williams-honda & 63 & tyre & 1 & none \\ 12 & ret & 7 & riccardo patrese & brabham-bmw & 63 & electrical & 19 & none \\ 13 & ret & 2 & keke rosberg & mclaren-tag & 62 & tyre & 7 & none \\ 14 & nc & 22 & allen berg & osella-alfa romeo & 61 & not classified & 26 & none \\ 15 & ret & 8 & derek warwick & brabham-bmw & 57 & brakes & 20 & none \\ 16 & ret & 17 & christian danner & arrows-bmw & 52 & engine & 24 & none \\ 17 & ret & 18 & thierry boutsen & arrows-bmw & 50 & engine & 22 & none \\ 18 & ret & 12 & ayrton senna & lotus-renault & 43 & engine & 3 & none \\ 19 & ret & 23 & andrea de cesaris & minardi-motori moderni & 40 & mechanical & 11 & none \\ 20 & ret & 20 & gerhard berger & benetton-bmw & 40 & engine & 6 & none \\ 21 & ret & 29 & huub rothengatter & zakspeed & 29 & suspension & 23 & none \\ 22 & ret & 15 & alan jones & lola-ford & 16 & engine & 15 & none \\ 23 & ret & 24 & alessandro nannini & minardi-motori moderni & 10 & accident & 18 & none \\ 24 & ret & 21 & piercarlo ghinzani & osella-alfa romeo & 2 & transmission & 25 & none \\ 25 & ret & 27 & michele alboreto & ferrari & 0 & collision & 9 & none \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "*" ) \\ \midrule 0 & 21 \\ \bottomrule \end{tabular}

select count ( * ) where points > 731 <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>1</td> <td>alain prost</td> <td>mclaren-tag</td> <td>82</td> <td>1:54:20.388</td> <td>4</td> <td>9</td> </tr> <tr> <th>1</th> <td>2</td> <td>6</td> <td>nelson piquet</td> <td>williams-honda</td> <td>82</td> <td>+ 4.205</td> <td>2</td> <td>6</td> </tr> <tr> <th>2</th> <td>3</td> <td>28</td> <td>stefan johansson</td> <td>ferrari</td> <td>81</td> <td>+ 1 lap</td> <td>12</td> <td>4</td> </tr> <tr> <th>3</th> <td>4</td> <td>3</td> <td>martin brundle</td> <td>tyrrell-renault</td> <td>81</td> <td>+ 1 lap</td> <td>16</td> <td>3</td> </tr> <tr> <th>4</th> <td>5</td> <td>4</td> <td>philippe streiff</td> <td>tyrrell-renault</td> <td>80</td> <td>out of fuel</td> <td>10</td> <td>2</td> </tr> <tr> <th>5</th> <td>6</td> <td>11</td> <td>johnny dumfries</td> <td>lotus-renault</td> <td>80</td> <td>+ 2 laps</td> <td>14</td> <td>1</td> </tr> <tr> <th>6</th> <td>7</td> <td>25</td> <td>rene arnoux</td> <td>ligier-renault</td> <td>79</td> <td>+ 3 laps</td> <td>5</td> <td>none</td> </tr> <tr> <th>7</th> <td>8</td> <td>26</td> <td>philippe alliot</td> <td>ligier-renault</td> <td>79</td> <td>+ 3 laps</td> <td>8</td> <td>none</td> </tr> <tr> <th>8</th> <td>9</td> <td>14</td> <td>jonathan palmer</td> <td>zakspeed</td> <td>77</td> <td>+ 5 laps</td> <td>21</td> <td>none</td> </tr> <tr> <th>9</th> <td>10</td> <td>19</td> <td>teo fabi</td> <td>benetton-bmw</td> <td>77</td> <td>+ 5 laps</td> <td>13</td> <td>none</td> </tr> <tr> <th>10</th> <td>nc</td> <td>16</td> <td>patrick tambay</td> <td>lola-ford</td> <td>70</td> <td>not classified</td> <td>17</td> <td>none</td> </tr> <tr> <th>11</th> <td>ret</td> <td>5</td> <td>nigel mansell</td> <td>williams-honda</td> <td>63</td> <td>tyre</td> <td>1</td> <td>none</td> </tr> <tr> <th>12</th> <td>ret</td> <td>7</td> <td>riccardo patrese</td> <td>brabham-bmw</td> <td>63</td> <td>electrical</td> <td>19</td> <td>none</td> </tr> <tr> <th>13</th> <td>ret</td> <td>2</td> <td>keke rosberg</td> <td>mclaren-tag</td> <td>62</td> <td>tyre</td> <td>7</td> <td>none</td> </tr> <tr> <th>14</th> <td>nc</td> <td>22</td> <td>allen berg</td> <td>osella-alfa romeo</td> <td>61</td> <td>not classified</td> <td>26</td> <td>none</td> </tr> <tr> <th>15</th> <td>ret</td> <td>8</td> <td>derek warwick</td> <td>brabham-bmw</td> <td>57</td> <td>brakes</td> <td>20</td> <td>none</td> </tr> <tr> <th>16</th> <td>ret</td> <td>17</td> <td>christian danner</td> <td>arrows-bmw</td> <td>52</td> <td>engine</td> <td>24</td> <td>none</td> </tr> <tr> <th>17</th> <td>ret</td> <td>18</td> <td>thierry boutsen</td> <td>arrows-bmw</td> <td>50</td> <td>engine</td> <td>22</td> <td>none</td> </tr> <tr> <th>18</th> <td>ret</td> <td>12</td> <td>ayrton senna</td> <td>lotus-renault</td> <td>43</td> <td>engine</td> <td>3</td> <td>none</td> </tr> <tr> <th>19</th> <td>ret</td> <td>23</td> <td>andrea de cesaris</td> <td>minardi-motori moderni</td> <td>40</td> <td>mechanical</td> <td>11</td> <td>none</td> </tr> <tr> <th>20</th> <td>ret</td> <td>20</td> <td>gerhard berger</td> <td>benetton-bmw</td> <td>40</td> <td>engine</td> <td>6</td> <td>none</td> </tr> <tr> <th>21</th> <td>ret</td> <td>29</td> <td>huub rothengatter</td> <td>zakspeed</td> <td>29</td> <td>suspension</td> <td>23</td> <td>none</td> </tr> <tr> <th>22</th> <td>ret</td> <td>15</td> <td>alan jones</td> <td>lola-ford</td> <td>16</td> <td>engine</td> <td>15</td> <td>none</td> </tr> <tr> <th>23</th> <td>ret</td> <td>24</td> <td>alessandro nannini</td> <td>minardi-motori moderni</td> <td>10</td> <td>accident</td> <td>18</td> <td>none</td> </tr> <tr> <th>24</th> <td>ret</td> <td>21</td> <td>piercarlo ghinzani</td> <td>osella-alfa romeo</td> <td>2</td> <td>transmission</td> <td>25</td> <td>none</td> </tr> <tr> <th>25</th> <td>ret</td> <td>27</td> <td>michele alboreto</td> <td>ferrari</td> <td>0</td> <td>collision</td> <td>9</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>21</td> </tr> </tbody> </table>

select count ( * ) where points > 731 <table_name> : table | | pos | no | driver | constructor | laps | time/retired | grid | points | |---:|:------|-----:|:-------------------|:-----------------------|-------:|:---------------|-------:|:---------| | 0 | 1 | 1 | alain prost | mclaren-tag | 82 | 1:54:20.388 | 4 | 9 | | 1 | 2 | 6 | nelson piquet | williams-honda | 82 | + 4.205 | 2 | 6 | | 2 | 3 | 28 | stefan johansson | ferrari | 81 | + 1 lap | 12 | 4 | | 3 | 4 | 3 | martin brundle | tyrrell-renault | 81 | + 1 lap | 16 | 3 | | 4 | 5 | 4 | philippe streiff | tyrrell-renault | 80 | out of fuel | 10 | 2 | | 5 | 6 | 11 | johnny dumfries | lotus-renault | 80 | + 2 laps | 14 | 1 | | 6 | 7 | 25 | rene arnoux | ligier-renault | 79 | + 3 laps | 5 | none | | 7 | 8 | 26 | philippe alliot | ligier-renault | 79 | + 3 laps | 8 | none | | 8 | 9 | 14 | jonathan palmer | zakspeed | 77 | + 5 laps | 21 | none | | 9 | 10 | 19 | teo fabi | benetton-bmw | 77 | + 5 laps | 13 | none | | 10 | nc | 16 | patrick tambay | lola-ford | 70 | not classified | 17 | none | | 11 | ret | 5 | nigel mansell | williams-honda | 63 | tyre | 1 | none | | 12 | ret | 7 | riccardo patrese | brabham-bmw | 63 | electrical | 19 | none | | 13 | ret | 2 | keke rosberg | mclaren-tag | 62 | tyre | 7 | none | | 14 | nc | 22 | allen berg | osella-alfa romeo | 61 | not classified | 26 | none | | 15 | ret | 8 | derek warwick | brabham-bmw | 57 | brakes | 20 | none | | 16 | ret | 17 | christian danner | arrows-bmw | 52 | engine | 24 | none | | 17 | ret | 18 | thierry boutsen | arrows-bmw | 50 | engine | 22 | none | | 18 | ret | 12 | ayrton senna | lotus-renault | 43 | engine | 3 | none | | 19 | ret | 23 | andrea de cesaris | minardi-motori moderni | 40 | mechanical | 11 | none | | 20 | ret | 20 | gerhard berger | benetton-bmw | 40 | engine | 6 | none | | 21 | ret | 29 | huub rothengatter | zakspeed | 29 | suspension | 23 | none | | 22 | ret | 15 | alan jones | lola-ford | 16 | engine | 15 | none | | 23 | ret | 24 | alessandro nannini | minardi-motori moderni | 10 | accident | 18 | none | | 24 | ret | 21 | piercarlo ghinzani | osella-alfa romeo | 2 | transmission | 25 | none | | 25 | ret | 27 | michele alboreto | ferrari | 0 | collision | 9 | none |

| | count ( "*" ) | |---:|----------------:| | 0 | 21 |

select count ( month ) where sep < '20.7 (69.3)' col : month | jan | feb | mar | apr | may | jun | jul | aug | sep | oct | nov | dec | year row 1 : average high °c (°f) | 17.3 (63.1) | 19.5 (67.1) | 22.6 (72.7) | 25.9 (78.6) | 27.2 (81) | 29.3 (84.7) | 31.8 (89.2) | 31.4 (88.5) | 28.9 (84) | 25.5 (77.9) | 21.7 (71.1) | 19.2 (66.6) | 24.76 (76.57) row 2 : average low °c (°f) | 7.9 (46.2) | 9.4 (48.9) | 12.5 (54.5) | 17.6 (63.7) | 19.2 (66.6) | 21.6 (70.9) | 23.8 (74.8) | 22.5 (72.5) | 20.7 (69.3) | 16.5 (61.7) | 14.1 (57.4) | 8.5 (47.3) | 15.94 (60.69) row 3 : precipitation mm (inches) | 235.9 (9.287) | 129.2 (5.087) | 82.8 (3.26) | 33.6 (1.323) | 4.7 (0.185) | 0.2 (0.008) | 0.0 (0) | 0.2 (0.008) | 3.2 (0.126) | 58.0 (2.283) | 107.4 (4.228) | 214.5 (8.445) | 857.3 (33.752) row 4 : avg. precipitation days | 13.9 | 11.4 | 8.6 | 3.6 | 2.4 | 0.1 | 0.0 | 0.1 | 1.8 | 4.9 | 8.0 | 11.8 | 63.7

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

select count ( month ) where sep < '20.7 (69.3)' <table_name> : table \begin{tabular}{lllllllllllllll} \toprule & month & jan & feb & mar & apr & may & jun & jul & aug & sep & oct & nov & dec & year \\ \midrule 0 & average high °c (°f) & 17.3 (63.1) & 19.5 (67.1) & 22.6 (72.7) & 25.9 (78.6) & 27.2 (81) & 29.3 (84.7) & 31.8 (89.2) & 31.4 (88.5) & 28.9 (84) & 25.5 (77.9) & 21.7 (71.1) & 19.2 (66.6) & 24.76 (76.57) \\ 1 & average low °c (°f) & 7.9 (46.2) & 9.4 (48.9) & 12.5 (54.5) & 17.6 (63.7) & 19.2 (66.6) & 21.6 (70.9) & 23.8 (74.8) & 22.5 (72.5) & 20.7 (69.3) & 16.5 (61.7) & 14.1 (57.4) & 8.5 (47.3) & 15.94 (60.69) \\ 2 & precipitation mm (inches) & 235.9 (9.287) & 129.2 (5.087) & 82.8 (3.26) & 33.6 (1.323) & 4.7 (0.185) & 0.2 (0.008) & 0.0 (0) & 0.2 (0.008) & 3.2 (0.126) & 58.0 (2.283) & 107.4 (4.228) & 214.5 (8.445) & 857.3 (33.752) \\ 3 & avg. precipitation days & 13.9 & 11.4 & 8.6 & 3.6 & 2.4 & 0.1 & 0.0 & 0.1 & 1.8 & 4.9 & 8.0 & 11.8 & 63.7 \\ \bottomrule \end{tabular}

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

select count ( month ) where sep < '20.7 (69.3)' <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>average high °c (°f)</td> <td>17.3 (63.1)</td> <td>19.5 (67.1)</td> <td>22.6 (72.7)</td> <td>25.9 (78.6)</td> <td>27.2 (81)</td> <td>29.3 (84.7)</td> <td>31.8 (89.2)</td> <td>31.4 (88.5)</td> <td>28.9 (84)</td> <td>25.5 (77.9)</td> <td>21.7 (71.1)</td> <td>19.2 (66.6)</td> <td>24.76 (76.57)</td> </tr> <tr> <th>1</th> <td>average low °c (°f)</td> <td>7.9 (46.2)</td> <td>9.4 (48.9)</td> <td>12.5 (54.5)</td> <td>17.6 (63.7)</td> <td>19.2 (66.6)</td> <td>21.6 (70.9)</td> <td>23.8 (74.8)</td> <td>22.5 (72.5)</td> <td>20.7 (69.3)</td> <td>16.5 (61.7)</td> <td>14.1 (57.4)</td> <td>8.5 (47.3)</td> <td>15.94 (60.69)</td> </tr> <tr> <th>2</th> <td>precipitation mm (inches)</td> <td>235.9 (9.287)</td> <td>129.2 (5.087)</td> <td>82.8 (3.26)</td> <td>33.6 (1.323)</td> <td>4.7 (0.185)</td> <td>0.2 (0.008)</td> <td>0.0 (0)</td> <td>0.2 (0.008)</td> <td>3.2 (0.126)</td> <td>58.0 (2.283)</td> <td>107.4 (4.228)</td> <td>214.5 (8.445)</td> <td>857.3 (33.752)</td> </tr> <tr> <th>3</th> <td>avg. precipitation days</td> <td>13.9</td> <td>11.4</td> <td>8.6</td> <td>3.6</td> <td>2.4</td> <td>0.1</td> <td>0.0</td> <td>0.1</td> <td>1.8</td> <td>4.9</td> <td>8.0</td> <td>11.8</td> <td>63.7</td> </tr> </tbody> </table>

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

select count ( month ) where sep < '20.7 (69.3)' <table_name> : table | | month | jan | feb | mar | apr | may | jun | jul | aug | sep | oct | nov | dec | year | |---:|:--------------------------|:--------------|:--------------|:------------|:-------------|:------------|:------------|:------------|:------------|:------------|:-------------|:--------------|:--------------|:---------------| | 0 | average high °c (°f) | 17.3 (63.1) | 19.5 (67.1) | 22.6 (72.7) | 25.9 (78.6) | 27.2 (81) | 29.3 (84.7) | 31.8 (89.2) | 31.4 (88.5) | 28.9 (84) | 25.5 (77.9) | 21.7 (71.1) | 19.2 (66.6) | 24.76 (76.57) | | 1 | average low °c (°f) | 7.9 (46.2) | 9.4 (48.9) | 12.5 (54.5) | 17.6 (63.7) | 19.2 (66.6) | 21.6 (70.9) | 23.8 (74.8) | 22.5 (72.5) | 20.7 (69.3) | 16.5 (61.7) | 14.1 (57.4) | 8.5 (47.3) | 15.94 (60.69) | | 2 | precipitation mm (inches) | 235.9 (9.287) | 129.2 (5.087) | 82.8 (3.26) | 33.6 (1.323) | 4.7 (0.185) | 0.2 (0.008) | 0.0 (0) | 0.2 (0.008) | 3.2 (0.126) | 58.0 (2.283) | 107.4 (4.228) | 214.5 (8.445) | 857.3 (33.752) | | 3 | avg. precipitation days | 13.9 | 11.4 | 8.6 | 3.6 | 2.4 | 0.1 | 0.0 | 0.1 | 1.8 | 4.9 | 8.0 | 11.8 | 63.7 |

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

select year where competition = 'world junior championships' col : year | competition | venue | position | notes row 1 : 2002 | world junior championships | kingston, jamaica | 1st | 64.51 m (1.75 kg) row 2 : 2002 | asian games | busan, south korea | 1st | 60.76 m row 3 : 2003 | universiade | daegu, south korea | 1st | 62.32 m row 4 : 2003 | asian championships | manila, philippines | 1st | 61.43 m row 5 : 2004 | olympic games | athens, greece | 18th (q) | 60.60 m row 6 : 2005 | world championships | helsinki, finland | 18th (q) | 61.75 m row 7 : 2005 | universiade | izmir, turkey | 7th | 59.67 m row 8 : 2005 | east asian games | macau | 1st | 61.74 m row 9 : 2006 | asian games | doha, qatar | 5th | 58.54 m row 10 : 2009 | asian championships | guangzhou, china | 3rd | 59.27 m

col : year row 1 : 2002

select year where competition = 'world junior championships' <table_name> : table \begin{tabular}{llllll} \toprule & year & competition & venue & position & notes \\ \midrule 0 & 2002 & world junior championships & kingston, jamaica & 1st & 64.51 m (1.75 kg) \\ 1 & 2002 & asian games & busan, south korea & 1st & 60.76 m \\ 2 & 2003 & universiade & daegu, south korea & 1st & 62.32 m \\ 3 & 2003 & asian championships & manila, philippines & 1st & 61.43 m \\ 4 & 2004 & olympic games & athens, greece & 18th (q) & 60.60 m \\ 5 & 2005 & world championships & helsinki, finland & 18th (q) & 61.75 m \\ 6 & 2005 & universiade & izmir, turkey & 7th & 59.67 m \\ 7 & 2005 & east asian games & macau & 1st & 61.74 m \\ 8 & 2006 & asian games & doha, qatar & 5th & 58.54 m \\ 9 & 2009 & asian championships & guangzhou, china & 3rd & 59.27 m \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & year \\ \midrule 0 & 2002 \\ \bottomrule \end{tabular}

select year where competition = 'world junior championships' <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>2002</td> <td>world junior championships</td> <td>kingston, jamaica</td> <td>1st</td> <td>64.51 m (1.75 kg)</td> </tr> <tr> <th>1</th> <td>2002</td> <td>asian games</td> <td>busan, south korea</td> <td>1st</td> <td>60.76 m</td> </tr> <tr> <th>2</th> <td>2003</td> <td>universiade</td> <td>daegu, south korea</td> <td>1st</td> <td>62.32 m</td> </tr> <tr> <th>3</th> <td>2003</td> <td>asian championships</td> <td>manila, philippines</td> <td>1st</td> <td>61.43 m</td> </tr> <tr> <th>4</th> <td>2004</td> <td>olympic games</td> <td>athens, greece</td> <td>18th (q)</td> <td>60.60 m</td> </tr> <tr> <th>5</th> <td>2005</td> <td>world championships</td> <td>helsinki, finland</td> <td>18th (q)</td> <td>61.75 m</td> </tr> <tr> <th>6</th> <td>2005</td> <td>universiade</td> <td>izmir, turkey</td> <td>7th</td> <td>59.67 m</td> </tr> <tr> <th>7</th> <td>2005</td> <td>east asian games</td> <td>macau</td> <td>1st</td> <td>61.74 m</td> </tr> <tr> <th>8</th> <td>2006</td> <td>asian games</td> <td>doha, qatar</td> <td>5th</td> <td>58.54 m</td> </tr> <tr> <th>9</th> <td>2009</td> <td>asian championships</td> <td>guangzhou, china</td> <td>3rd</td> <td>59.27 m</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>2002</td> </tr> </tbody> </table>

select year where competition = 'world junior championships' <table_name> : table | | year | competition | venue | position | notes | |---:|-------:|:---------------------------|:--------------------|:-----------|:------------------| | 0 | 2002 | world junior championships | kingston, jamaica | 1st | 64.51 m (1.75 kg) | | 1 | 2002 | asian games | busan, south korea | 1st | 60.76 m | | 2 | 2003 | universiade | daegu, south korea | 1st | 62.32 m | | 3 | 2003 | asian championships | manila, philippines | 1st | 61.43 m | | 4 | 2004 | olympic games | athens, greece | 18th (q) | 60.60 m | | 5 | 2005 | world championships | helsinki, finland | 18th (q) | 61.75 m | | 6 | 2005 | universiade | izmir, turkey | 7th | 59.67 m | | 7 | 2005 | east asian games | macau | 1st | 61.74 m | | 8 | 2006 | asian games | doha, qatar | 5th | 58.54 m | | 9 | 2009 | asian championships | guangzhou, china | 3rd | 59.27 m |

| | year | |---:|-------:| | 0 | 2002 |

select date where attendance = 4000 col : match day | date | opponent | h/a | score | aberdeen scorer(s) | attendance row 1 : 1 | 15 august | dundee | a | 3-1 | soye, walker, cail | 10000 row 2 : 2 | 22 august | rangers | h | 0-2 | none | 15000 row 3 : 3 | 29 august | morton | a | 1-1 | cail | 4500 row 4 : 4 | 5 september | clyde | h | 2-0 | maclachlan, archibald | 6000 row 5 : 5 | 12 september | ayr united | a | 0-1 | none | 2000 row 6 : 6 | 19 september | motherwell | h | 3-1 | j. wyllie, maclachlan, walker | 7000 row 7 : 7 | 26 september | heart of midlothian | a | 0-2 | none | 14000 row 8 : 8 | 28 september | queen's park | h | 1-1 | main | 5000 row 9 : 9 | 3 october | st. mirren | h | 0-0 | none | 6000 row 10 : 10 | 10 october | airdrieonians | a | 0-3 | none | 7000 row 11 : 11 | 17 october | third lanark | h | 1-2 | archibald | 6000 row 12 : 12 | 24 october | falkirk | a | 1-1 | j. wyllie | 5500 row 13 : 13 | 31 october | hibernian | a | 2-1 | chatwin, main | 4000 row 14 : 14 | 7 november | raith rovers | h | 1-3 | main | 6000 row 15 : 15 | 14 november | hamilton academical | a | 0-3 | none | 4000 row 16 : 16 | 21 november | dumbarton | h | 0-0 | none | 5000 row 17 : 17 | 28 november | kilmarnock | a | 2-5 | maclachlan, mcleod | 2500 row 18 : 18 | 5 december | celtic | h | 0-1 | none | 7000 row 19 : 19 | 12 december | partick thistle | a | 0-3 | none | 6000 row 20 : 20 | 19 december | kilmarnock | h | 3-0 | maclachlan, cail, main | 4000 row 21 : 21 | 26 december | motherwell | a | 1-1 | walker | 3000 row 22 : 22 | 1 january | dundee | h | 2-1 | walker, j. wyllie | 7000 row 23 : 23 | 2 january | raith rovers | a | 1-5 | cail | 6000 row 24 : 24 | 9 january | ayr united | h | 1-1 | cail | 4500 row 25 : 25 | 16 january | clyde | a | 0-3 | none | 3000 row 26 : 26 | 23 january | falkirk | h | 1-2 | walker | 4000 row 27 : 27 | 30 january | dumbarton | a | 2-3 | cail, walker | 3000 row 28 : 28 | 6 february | morton | h | 2-0 | brewster, archibald | 2000 row 29 : 29 | 13 february | st. mirren | a | 2-0 | cail, walker | 3000 row 30 : 30 | 20 february | hibernian | h | 0-0 | none | 8500 row 31 : 31 | 27 february | third lanark | a | 1-0 | walker | 5000 row 32 : 32 | 6 march | partick thistle | h | 0-0 | none | 6000 row 33 : 33 | 13 march | queen's park | a | 1-3 | cail | 6000 row 34 : 34 | 20 march | airdrieonians | h | 3-0 | brewster, cail, main | 5500 row 35 : 35 | 27 march | rangers | a | 1-1 | w. wylie | 10000 row 36 : 36 | 3 april | heart of midlothian | h | 0-0 | none | 6000 row 37 : 37 | 10 april | celtic | a | 0-1 | none | 10000 row 38 : 38 | 17 april | hamilton academical | h | 1-0 | j. wyllie | 4000

col : date row 1 : 31 october row 2 : 14 november row 3 : 19 december row 4 : 23 january row 5 : 17 april

select date where attendance = 4000 <table_name> : table \begin{tabular}{llllllll} \toprule & match day & date & opponent & h/a & score & aberdeen scorer(s) & attendance \\ \midrule 0 & 1 & 15 august & dundee & a & 3-1 & soye, walker, cail & 10000 \\ 1 & 2 & 22 august & rangers & h & 0-2 & none & 15000 \\ 2 & 3 & 29 august & morton & a & 1-1 & cail & 4500 \\ 3 & 4 & 5 september & clyde & h & 2-0 & maclachlan, archibald & 6000 \\ 4 & 5 & 12 september & ayr united & a & 0-1 & none & 2000 \\ 5 & 6 & 19 september & motherwell & h & 3-1 & j. wyllie, maclachlan, walker & 7000 \\ 6 & 7 & 26 september & heart of midlothian & a & 0-2 & none & 14000 \\ 7 & 8 & 28 september & queen's park & h & 1-1 & main & 5000 \\ 8 & 9 & 3 october & st. mirren & h & 0-0 & none & 6000 \\ 9 & 10 & 10 october & airdrieonians & a & 0-3 & none & 7000 \\ 10 & 11 & 17 october & third lanark & h & 1-2 & archibald & 6000 \\ 11 & 12 & 24 october & falkirk & a & 1-1 & j. wyllie & 5500 \\ 12 & 13 & 31 october & hibernian & a & 2-1 & chatwin, main & 4000 \\ 13 & 14 & 7 november & raith rovers & h & 1-3 & main & 6000 \\ 14 & 15 & 14 november & hamilton academical & a & 0-3 & none & 4000 \\ 15 & 16 & 21 november & dumbarton & h & 0-0 & none & 5000 \\ 16 & 17 & 28 november & kilmarnock & a & 2-5 & maclachlan, mcleod & 2500 \\ 17 & 18 & 5 december & celtic & h & 0-1 & none & 7000 \\ 18 & 19 & 12 december & partick thistle & a & 0-3 & none & 6000 \\ 19 & 20 & 19 december & kilmarnock & h & 3-0 & maclachlan, cail, main & 4000 \\ 20 & 21 & 26 december & motherwell & a & 1-1 & walker & 3000 \\ 21 & 22 & 1 january & dundee & h & 2-1 & walker, j. wyllie & 7000 \\ 22 & 23 & 2 january & raith rovers & a & 1-5 & cail & 6000 \\ 23 & 24 & 9 january & ayr united & h & 1-1 & cail & 4500 \\ 24 & 25 & 16 january & clyde & a & 0-3 & none & 3000 \\ 25 & 26 & 23 january & falkirk & h & 1-2 & walker & 4000 \\ 26 & 27 & 30 january & dumbarton & a & 2-3 & cail, walker & 3000 \\ 27 & 28 & 6 february & morton & h & 2-0 & brewster, archibald & 2000 \\ 28 & 29 & 13 february & st. mirren & a & 2-0 & cail, walker & 3000 \\ 29 & 30 & 20 february & hibernian & h & 0-0 & none & 8500 \\ 30 & 31 & 27 february & third lanark & a & 1-0 & walker & 5000 \\ 31 & 32 & 6 march & partick thistle & h & 0-0 & none & 6000 \\ 32 & 33 & 13 march & queen's park & a & 1-3 & cail & 6000 \\ 33 & 34 & 20 march & airdrieonians & h & 3-0 & brewster, cail, main & 5500 \\ 34 & 35 & 27 march & rangers & a & 1-1 & w. wylie & 10000 \\ 35 & 36 & 3 april & heart of midlothian & h & 0-0 & none & 6000 \\ 36 & 37 & 10 april & celtic & a & 0-1 & none & 10000 \\ 37 & 38 & 17 april & hamilton academical & h & 1-0 & j. wyllie & 4000 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & date \\ \midrule 0 & 31 october \\ 1 & 14 november \\ 2 & 19 december \\ 3 & 23 january \\ 4 & 17 april \\ \bottomrule \end{tabular}

select date where attendance = 4000 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>match day</th> <th>date</th> <th>opponent</th> <th>h/a</th> <th>score</th> <th>aberdeen scorer(s)</th> <th>attendance</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>15 august</td> <td>dundee</td> <td>a</td> <td>3-1</td> <td>soye, walker, cail</td> <td>10000</td> </tr> <tr> <th>1</th> <td>2</td> <td>22 august</td> <td>rangers</td> <td>h</td> <td>0-2</td> <td>none</td> <td>15000</td> </tr> <tr> <th>2</th> <td>3</td> <td>29 august</td> <td>morton</td> <td>a</td> <td>1-1</td> <td>cail</td> <td>4500</td> </tr> <tr> <th>3</th> <td>4</td> <td>5 september</td> <td>clyde</td> <td>h</td> <td>2-0</td> <td>maclachlan, archibald</td> <td>6000</td> </tr> <tr> <th>4</th> <td>5</td> <td>12 september</td> <td>ayr united</td> <td>a</td> <td>0-1</td> <td>none</td> <td>2000</td> </tr> <tr> <th>5</th> <td>6</td> <td>19 september</td> <td>motherwell</td> <td>h</td> <td>3-1</td> <td>j. wyllie, maclachlan, walker</td> <td>7000</td> </tr> <tr> <th>6</th> <td>7</td> <td>26 september</td> <td>heart of midlothian</td> <td>a</td> <td>0-2</td> <td>none</td> <td>14000</td> </tr> <tr> <th>7</th> <td>8</td> <td>28 september</td> <td>queen's park</td> <td>h</td> <td>1-1</td> <td>main</td> <td>5000</td> </tr> <tr> <th>8</th> <td>9</td> <td>3 october</td> <td>st. mirren</td> <td>h</td> <td>0-0</td> <td>none</td> <td>6000</td> </tr> <tr> <th>9</th> <td>10</td> <td>10 october</td> <td>airdrieonians</td> <td>a</td> <td>0-3</td> <td>none</td> <td>7000</td> </tr> <tr> <th>10</th> <td>11</td> <td>17 october</td> <td>third lanark</td> <td>h</td> <td>1-2</td> <td>archibald</td> <td>6000</td> </tr> <tr> <th>11</th> <td>12</td> <td>24 october</td> <td>falkirk</td> <td>a</td> <td>1-1</td> <td>j. wyllie</td> <td>5500</td> </tr> <tr> <th>12</th> <td>13</td> <td>31 october</td> <td>hibernian</td> <td>a</td> <td>2-1</td> <td>chatwin, main</td> <td>4000</td> </tr> <tr> <th>13</th> <td>14</td> <td>7 november</td> <td>raith rovers</td> <td>h</td> <td>1-3</td> <td>main</td> <td>6000</td> </tr> <tr> <th>14</th> <td>15</td> <td>14 november</td> <td>hamilton academical</td> <td>a</td> <td>0-3</td> <td>none</td> <td>4000</td> </tr> <tr> <th>15</th> <td>16</td> <td>21 november</td> <td>dumbarton</td> <td>h</td> <td>0-0</td> <td>none</td> <td>5000</td> </tr> <tr> <th>16</th> <td>17</td> <td>28 november</td> <td>kilmarnock</td> <td>a</td> <td>2-5</td> <td>maclachlan, mcleod</td> <td>2500</td> </tr> <tr> <th>17</th> <td>18</td> <td>5 december</td> <td>celtic</td> <td>h</td> <td>0-1</td> <td>none</td> <td>7000</td> </tr> <tr> <th>18</th> <td>19</td> <td>12 december</td> <td>partick thistle</td> <td>a</td> <td>0-3</td> <td>none</td> <td>6000</td> </tr> <tr> <th>19</th> <td>20</td> <td>19 december</td> <td>kilmarnock</td> <td>h</td> <td>3-0</td> <td>maclachlan, cail, main</td> <td>4000</td> </tr> <tr> <th>20</th> <td>21</td> <td>26 december</td> <td>motherwell</td> <td>a</td> <td>1-1</td> <td>walker</td> <td>3000</td> </tr> <tr> <th>21</th> <td>22</td> <td>1 january</td> <td>dundee</td> <td>h</td> <td>2-1</td> <td>walker, j. wyllie</td> <td>7000</td> </tr> <tr> <th>22</th> <td>23</td> <td>2 january</td> <td>raith rovers</td> <td>a</td> <td>1-5</td> <td>cail</td> <td>6000</td> </tr> <tr> <th>23</th> <td>24</td> <td>9 january</td> <td>ayr united</td> <td>h</td> <td>1-1</td> <td>cail</td> <td>4500</td> </tr> <tr> <th>24</th> <td>25</td> <td>16 january</td> <td>clyde</td> <td>a</td> <td>0-3</td> <td>none</td> <td>3000</td> </tr> <tr> <th>25</th> <td>26</td> <td>23 january</td> <td>falkirk</td> <td>h</td> <td>1-2</td> <td>walker</td> <td>4000</td> </tr> <tr> <th>26</th> <td>27</td> <td>30 january</td> <td>dumbarton</td> <td>a</td> <td>2-3</td> <td>cail, walker</td> <td>3000</td> </tr> <tr> <th>27</th> <td>28</td> <td>6 february</td> <td>morton</td> <td>h</td> <td>2-0</td> <td>brewster, archibald</td> <td>2000</td> </tr> <tr> <th>28</th> <td>29</td> <td>13 february</td> <td>st. mirren</td> <td>a</td> <td>2-0</td> <td>cail, walker</td> <td>3000</td> </tr> <tr> <th>29</th> <td>30</td> <td>20 february</td> <td>hibernian</td> <td>h</td> <td>0-0</td> <td>none</td> <td>8500</td> </tr> <tr> <th>30</th> <td>31</td> <td>27 february</td> <td>third lanark</td> <td>a</td> <td>1-0</td> <td>walker</td> <td>5000</td> </tr> <tr> <th>31</th> <td>32</td> <td>6 march</td> <td>partick thistle</td> <td>h</td> <td>0-0</td> <td>none</td> <td>6000</td> </tr> <tr> <th>32</th> <td>33</td> <td>13 march</td> <td>queen's park</td> <td>a</td> <td>1-3</td> <td>cail</td> <td>6000</td> </tr> <tr> <th>33</th> <td>34</td> <td>20 march</td> <td>airdrieonians</td> <td>h</td> <td>3-0</td> <td>brewster, cail, main</td> <td>5500</td> </tr> <tr> <th>34</th> <td>35</td> <td>27 march</td> <td>rangers</td> <td>a</td> <td>1-1</td> <td>w. wylie</td> <td>10000</td> </tr> <tr> <th>35</th> <td>36</td> <td>3 april</td> <td>heart of midlothian</td> <td>h</td> <td>0-0</td> <td>none</td> <td>6000</td> </tr> <tr> <th>36</th> <td>37</td> <td>10 april</td> <td>celtic</td> <td>a</td> <td>0-1</td> <td>none</td> <td>10000</td> </tr> <tr> <th>37</th> <td>38</td> <td>17 april</td> <td>hamilton academical</td> <td>h</td> <td>1-0</td> <td>j. wyllie</td> <td>4000</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>31 october</td> </tr> <tr> <th>1</th> <td>14 november</td> </tr> <tr> <th>2</th> <td>19 december</td> </tr> <tr> <th>3</th> <td>23 january</td> </tr> <tr> <th>4</th> <td>17 april</td> </tr> </tbody> </table>

select date where attendance = 4000 <table_name> : table | | match day | date | opponent | h/a | score | aberdeen scorer(s) | attendance | |---:|------------:|:-------------|:--------------------|:------|:--------|:------------------------------|-------------:| | 0 | 1 | 15 august | dundee | a | 3-1 | soye, walker, cail | 10000 | | 1 | 2 | 22 august | rangers | h | 0-2 | none | 15000 | | 2 | 3 | 29 august | morton | a | 1-1 | cail | 4500 | | 3 | 4 | 5 september | clyde | h | 2-0 | maclachlan, archibald | 6000 | | 4 | 5 | 12 september | ayr united | a | 0-1 | none | 2000 | | 5 | 6 | 19 september | motherwell | h | 3-1 | j. wyllie, maclachlan, walker | 7000 | | 6 | 7 | 26 september | heart of midlothian | a | 0-2 | none | 14000 | | 7 | 8 | 28 september | queen's park | h | 1-1 | main | 5000 | | 8 | 9 | 3 october | st. mirren | h | 0-0 | none | 6000 | | 9 | 10 | 10 october | airdrieonians | a | 0-3 | none | 7000 | | 10 | 11 | 17 october | third lanark | h | 1-2 | archibald | 6000 | | 11 | 12 | 24 october | falkirk | a | 1-1 | j. wyllie | 5500 | | 12 | 13 | 31 october | hibernian | a | 2-1 | chatwin, main | 4000 | | 13 | 14 | 7 november | raith rovers | h | 1-3 | main | 6000 | | 14 | 15 | 14 november | hamilton academical | a | 0-3 | none | 4000 | | 15 | 16 | 21 november | dumbarton | h | 0-0 | none | 5000 | | 16 | 17 | 28 november | kilmarnock | a | 2-5 | maclachlan, mcleod | 2500 | | 17 | 18 | 5 december | celtic | h | 0-1 | none | 7000 | | 18 | 19 | 12 december | partick thistle | a | 0-3 | none | 6000 | | 19 | 20 | 19 december | kilmarnock | h | 3-0 | maclachlan, cail, main | 4000 | | 20 | 21 | 26 december | motherwell | a | 1-1 | walker | 3000 | | 21 | 22 | 1 january | dundee | h | 2-1 | walker, j. wyllie | 7000 | | 22 | 23 | 2 january | raith rovers | a | 1-5 | cail | 6000 | | 23 | 24 | 9 january | ayr united | h | 1-1 | cail | 4500 | | 24 | 25 | 16 january | clyde | a | 0-3 | none | 3000 | | 25 | 26 | 23 january | falkirk | h | 1-2 | walker | 4000 | | 26 | 27 | 30 january | dumbarton | a | 2-3 | cail, walker | 3000 | | 27 | 28 | 6 february | morton | h | 2-0 | brewster, archibald | 2000 | | 28 | 29 | 13 february | st. mirren | a | 2-0 | cail, walker | 3000 | | 29 | 30 | 20 february | hibernian | h | 0-0 | none | 8500 | | 30 | 31 | 27 february | third lanark | a | 1-0 | walker | 5000 | | 31 | 32 | 6 march | partick thistle | h | 0-0 | none | 6000 | | 32 | 33 | 13 march | queen's park | a | 1-3 | cail | 6000 | | 33 | 34 | 20 march | airdrieonians | h | 3-0 | brewster, cail, main | 5500 | | 34 | 35 | 27 march | rangers | a | 1-1 | w. wylie | 10000 | | 35 | 36 | 3 april | heart of midlothian | h | 0-0 | none | 6000 | | 36 | 37 | 10 april | celtic | a | 0-1 | none | 10000 | | 37 | 38 | 17 april | hamilton academical | h | 1-0 | j. wyllie | 4000 |

| | date | |---:|:------------| | 0 | 31 october | | 1 | 14 november | | 2 | 19 december | | 3 | 23 january | | 4 | 17 april |

select count ( distinct venue ) where venue = 871 col : year | competition | venue | position | notes row 1 : 2005 | world youth championships | marrakech, morocco | 6th | 5.05 m row 2 : 2006 | world junior championships | beijing, china | 5th | 5.30 m row 3 : 2008 | olympic games | beijing, china | 10th | 5.45 m row 4 : 2009 | european u23 championships | kaunas, lithuania | 8th | 5.15 m row 5 : 2009 | world championships | berlin, germany | 22nd (q) | 5.40 m row 6 : 2010 | european championships | barcelona, spain | 10th | 5.60 m row 7 : 2011 | world championships | daegu, south korea | 9th | 5.65 m row 8 : 2012 | european championships | helsinki, finland | 6th | 5.60 m row 9 : 2012 | olympic games | london, united kingdom | 8th | 5.65 m row 10 : 2013 | european indoor championships | gothenburg, sweden | 5th | 5.71 m row 11 : 2014 | world indoor championships | sopot, poland | 3rd | 5.80 m

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

select count ( distinct venue ) where venue = 871 <table_name> : table \begin{tabular}{llllll} \toprule & year & competition & venue & position & notes \\ \midrule 0 & 2005 & world youth championships & marrakech, morocco & 6th & 5.05 m \\ 1 & 2006 & world junior championships & beijing, china & 5th & 5.30 m \\ 2 & 2008 & olympic games & beijing, china & 10th & 5.45 m \\ 3 & 2009 & european u23 championships & kaunas, lithuania & 8th & 5.15 m \\ 4 & 2009 & world championships & berlin, germany & 22nd (q) & 5.40 m \\ 5 & 2010 & european championships & barcelona, spain & 10th & 5.60 m \\ 6 & 2011 & world championships & daegu, south korea & 9th & 5.65 m \\ 7 & 2012 & european championships & helsinki, finland & 6th & 5.60 m \\ 8 & 2012 & olympic games & london, united kingdom & 8th & 5.65 m \\ 9 & 2013 & european indoor championships & gothenburg, sweden & 5th & 5.71 m \\ 10 & 2014 & world indoor championships & sopot, poland & 3rd & 5.80 m \\ \bottomrule \end{tabular}

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

select count ( distinct venue ) where venue = 871 <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>2005</td> <td>world youth championships</td> <td>marrakech, morocco</td> <td>6th</td> <td>5.05 m</td> </tr> <tr> <th>1</th> <td>2006</td> <td>world junior championships</td> <td>beijing, china</td> <td>5th</td> <td>5.30 m</td> </tr> <tr> <th>2</th> <td>2008</td> <td>olympic games</td> <td>beijing, china</td> <td>10th</td> <td>5.45 m</td> </tr> <tr> <th>3</th> <td>2009</td> <td>european u23 championships</td> <td>kaunas, lithuania</td> <td>8th</td> <td>5.15 m</td> </tr> <tr> <th>4</th> <td>2009</td> <td>world championships</td> <td>berlin, germany</td> <td>22nd (q)</td> <td>5.40 m</td> </tr> <tr> <th>5</th> <td>2010</td> <td>european championships</td> <td>barcelona, spain</td> <td>10th</td> <td>5.60 m</td> </tr> <tr> <th>6</th> <td>2011</td> <td>world championships</td> <td>daegu, south korea</td> <td>9th</td> <td>5.65 m</td> </tr> <tr> <th>7</th> <td>2012</td> <td>european championships</td> <td>helsinki, finland</td> <td>6th</td> <td>5.60 m</td> </tr> <tr> <th>8</th> <td>2012</td> <td>olympic games</td> <td>london, united kingdom</td> <td>8th</td> <td>5.65 m</td> </tr> <tr> <th>9</th> <td>2013</td> <td>european indoor championships</td> <td>gothenburg, sweden</td> <td>5th</td> <td>5.71 m</td> </tr> <tr> <th>10</th> <td>2014</td> <td>world indoor championships</td> <td>sopot, poland</td> <td>3rd</td> <td>5.80 m</td> </tr> </tbody> </table>

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

select count ( distinct venue ) where venue = 871 <table_name> : table | | year | competition | venue | position | notes | |---:|-------:|:------------------------------|:-----------------------|:-----------|:--------| | 0 | 2005 | world youth championships | marrakech, morocco | 6th | 5.05 m | | 1 | 2006 | world junior championships | beijing, china | 5th | 5.30 m | | 2 | 2008 | olympic games | beijing, china | 10th | 5.45 m | | 3 | 2009 | european u23 championships | kaunas, lithuania | 8th | 5.15 m | | 4 | 2009 | world championships | berlin, germany | 22nd (q) | 5.40 m | | 5 | 2010 | european championships | barcelona, spain | 10th | 5.60 m | | 6 | 2011 | world championships | daegu, south korea | 9th | 5.65 m | | 7 | 2012 | european championships | helsinki, finland | 6th | 5.60 m | | 8 | 2012 | olympic games | london, united kingdom | 8th | 5.65 m | | 9 | 2013 | european indoor championships | gothenburg, sweden | 5th | 5.71 m | | 10 | 2014 | world indoor championships | sopot, poland | 3rd | 5.80 m |

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

select count ( score ) where to par > 1599 col : place | player | country | score | to par | money ($) row 1 : 1 | larry nelson | united states | 70-66-66-71=273 | -7 | 60000 row 2 : 2 | fuzzy zoeller | united states | 70-68-68-71=277 | -3 | 40000 row 3 : 3 | dan pohl | united states | 69-67-73-69=278 | -2 | 25000 row 4 : t4 | isao aoki | japan | 75-68-66-70=279 | -1 | 13146 row 5 : t4 | keith fergus | united states | 71-71-69-68=279 | -1 | 13146 row 6 : t4 | bob gilder | united states | 74-69-70-66=279 | -1 | 13146 row 7 : t4 | tom kite | united states | 71-67-69-72=279 | -1 | 13146 row 8 : t4 | bruce lietzke | united states | 70-70-71-68=279 | -1 | 13146 row 9 : t4 | jack nicklaus | united states | 71-68-71-69=279 | -1 | 13146 row 10 : t4 | greg norman | australia | 73-67-68-71=279 | -1 | 13146

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

select count ( score ) where to par > 1599 <table_name> : table \begin{tabular}{lllllll} \toprule & place & player & country & score & to par & money ($) \\ \midrule 0 & 1 & larry nelson & united states & 70-66-66-71=273 & -7 & 60000 \\ 1 & 2 & fuzzy zoeller & united states & 70-68-68-71=277 & -3 & 40000 \\ 2 & 3 & dan pohl & united states & 69-67-73-69=278 & -2 & 25000 \\ 3 & t4 & isao aoki & japan & 75-68-66-70=279 & -1 & 13146 \\ 4 & t4 & keith fergus & united states & 71-71-69-68=279 & -1 & 13146 \\ 5 & t4 & bob gilder & united states & 74-69-70-66=279 & -1 & 13146 \\ 6 & t4 & tom kite & united states & 71-67-69-72=279 & -1 & 13146 \\ 7 & t4 & bruce lietzke & united states & 70-70-71-68=279 & -1 & 13146 \\ 8 & t4 & jack nicklaus & united states & 71-68-71-69=279 & -1 & 13146 \\ 9 & t4 & greg norman & australia & 73-67-68-71=279 & -1 & 13146 \\ \bottomrule \end{tabular}

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

select count ( score ) where to par > 1599 <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>larry nelson</td> <td>united states</td> <td>70-66-66-71=273</td> <td>-7</td> <td>60000</td> </tr> <tr> <th>1</th> <td>2</td> <td>fuzzy zoeller</td> <td>united states</td> <td>70-68-68-71=277</td> <td>-3</td> <td>40000</td> </tr> <tr> <th>2</th> <td>3</td> <td>dan pohl</td> <td>united states</td> <td>69-67-73-69=278</td> <td>-2</td> <td>25000</td> </tr> <tr> <th>3</th> <td>t4</td> <td>isao aoki</td> <td>japan</td> <td>75-68-66-70=279</td> <td>-1</td> <td>13146</td> </tr> <tr> <th>4</th> <td>t4</td> <td>keith fergus</td> <td>united states</td> <td>71-71-69-68=279</td> <td>-1</td> <td>13146</td> </tr> <tr> <th>5</th> <td>t4</td> <td>bob gilder</td> <td>united states</td> <td>74-69-70-66=279</td> <td>-1</td> <td>13146</td> </tr> <tr> <th>6</th> <td>t4</td> <td>tom kite</td> <td>united states</td> <td>71-67-69-72=279</td> <td>-1</td> <td>13146</td> </tr> <tr> <th>7</th> <td>t4</td> <td>bruce lietzke</td> <td>united states</td> <td>70-70-71-68=279</td> <td>-1</td> <td>13146</td> </tr> <tr> <th>8</th> <td>t4</td> <td>jack nicklaus</td> <td>united states</td> <td>71-68-71-69=279</td> <td>-1</td> <td>13146</td> </tr> <tr> <th>9</th> <td>t4</td> <td>greg norman</td> <td>australia</td> <td>73-67-68-71=279</td> <td>-1</td> <td>13146</td> </tr> </tbody> </table>

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

select count ( score ) where to par > 1599 <table_name> : table | | place | player | country | score | to par | money ($) | |---:|:--------|:--------------|:--------------|:----------------|---------:|------------:| | 0 | 1 | larry nelson | united states | 70-66-66-71=273 | -7 | 60000 | | 1 | 2 | fuzzy zoeller | united states | 70-68-68-71=277 | -3 | 40000 | | 2 | 3 | dan pohl | united states | 69-67-73-69=278 | -2 | 25000 | | 3 | t4 | isao aoki | japan | 75-68-66-70=279 | -1 | 13146 | | 4 | t4 | keith fergus | united states | 71-71-69-68=279 | -1 | 13146 | | 5 | t4 | bob gilder | united states | 74-69-70-66=279 | -1 | 13146 | | 6 | t4 | tom kite | united states | 71-67-69-72=279 | -1 | 13146 | | 7 | t4 | bruce lietzke | united states | 70-70-71-68=279 | -1 | 13146 | | 8 | t4 | jack nicklaus | united states | 71-68-71-69=279 | -1 | 13146 | | 9 | t4 | greg norman | australia | 73-67-68-71=279 | -1 | 13146 |

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

select count ( spectators ) where time (edt) = 0 col : date | time (edt) | team #1 | res. | team #2 | round | spectators row 1 : 1994-06-18 00:00:00 | 16.0 | italy | 0-1 | republic of ireland | group e | 75338 row 2 : 1994-06-23 00:00:00 | 16.0 | italy | 1-0 | norway | group e | 74624 row 3 : 1994-06-25 00:00:00 | 12.3 | saudi arabia | 2-1 | morocco | group f | 76322 row 4 : 1994-06-28 00:00:00 | 12.3 | republic of ireland | 0-0 | norway | group e | 72404 row 5 : 1994-07-05 00:00:00 | 16.3 | mexico | 1-1 (1-3 on pen.) | bulgaria | round of 16 | 71030 row 6 : 1994-07-10 00:00:00 | 12.0 | bulgaria | 2-1 | germany | quarterfinals | 72000 row 7 : 1994-07-13 00:00:00 | 16.0 | bulgaria | 1-2 | italy | semifinals | 74110

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

select count ( spectators ) where time (edt) = 0 <table_name> : table \begin{tabular}{llllllll} \toprule & date & time (edt) & team #1 & res. & team #2 & round & spectators \\ \midrule 0 & 1994-06-18 00:00:00 & 16.0 & italy & 0-1 & republic of ireland & group e & 75338 \\ 1 & 1994-06-23 00:00:00 & 16.0 & italy & 1-0 & norway & group e & 74624 \\ 2 & 1994-06-25 00:00:00 & 12.3 & saudi arabia & 2-1 & morocco & group f & 76322 \\ 3 & 1994-06-28 00:00:00 & 12.3 & republic of ireland & 0-0 & norway & group e & 72404 \\ 4 & 1994-07-05 00:00:00 & 16.3 & mexico & 1-1 (1-3 on pen.) & bulgaria & round of 16 & 71030 \\ 5 & 1994-07-10 00:00:00 & 12.0 & bulgaria & 2-1 & germany & quarterfinals & 72000 \\ 6 & 1994-07-13 00:00:00 & 16.0 & bulgaria & 1-2 & italy & semifinals & 74110 \\ \bottomrule \end{tabular}

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

select count ( spectators ) where time (edt) = 0 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>date</th> <th>time (edt)</th> <th>team #1</th> <th>res.</th> <th>team #2</th> <th>round</th> <th>spectators</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1994-06-18 00:00:00</td> <td>16.0</td> <td>italy</td> <td>0-1</td> <td>republic of ireland</td> <td>group e</td> <td>75338</td> </tr> <tr> <th>1</th> <td>1994-06-23 00:00:00</td> <td>16.0</td> <td>italy</td> <td>1-0</td> <td>norway</td> <td>group e</td> <td>74624</td> </tr> <tr> <th>2</th> <td>1994-06-25 00:00:00</td> <td>12.3</td> <td>saudi arabia</td> <td>2-1</td> <td>morocco</td> <td>group f</td> <td>76322</td> </tr> <tr> <th>3</th> <td>1994-06-28 00:00:00</td> <td>12.3</td> <td>republic of ireland</td> <td>0-0</td> <td>norway</td> <td>group e</td> <td>72404</td> </tr> <tr> <th>4</th> <td>1994-07-05 00:00:00</td> <td>16.3</td> <td>mexico</td> <td>1-1 (1-3 on pen.)</td> <td>bulgaria</td> <td>round of 16</td> <td>71030</td> </tr> <tr> <th>5</th> <td>1994-07-10 00:00:00</td> <td>12.0</td> <td>bulgaria</td> <td>2-1</td> <td>germany</td> <td>quarterfinals</td> <td>72000</td> </tr> <tr> <th>6</th> <td>1994-07-13 00:00:00</td> <td>16.0</td> <td>bulgaria</td> <td>1-2</td> <td>italy</td> <td>semifinals</td> <td>74110</td> </tr> </tbody> </table>

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

select count ( spectators ) where time (edt) = 0 <table_name> : table | | date | time (edt) | team #1 | res. | team #2 | round | spectators | |---:|:--------------------|-------------:|:--------------------|:------------------|:--------------------|:--------------|-------------:| | 0 | 1994-06-18 00:00:00 | 16 | italy | 0-1 | republic of ireland | group e | 75338 | | 1 | 1994-06-23 00:00:00 | 16 | italy | 1-0 | norway | group e | 74624 | | 2 | 1994-06-25 00:00:00 | 12.3 | saudi arabia | 2-1 | morocco | group f | 76322 | | 3 | 1994-06-28 00:00:00 | 12.3 | republic of ireland | 0-0 | norway | group e | 72404 | | 4 | 1994-07-05 00:00:00 | 16.3 | mexico | 1-1 (1-3 on pen.) | bulgaria | round of 16 | 71030 | | 5 | 1994-07-10 00:00:00 | 12 | bulgaria | 2-1 | germany | quarterfinals | 72000 | | 6 | 1994-07-13 00:00:00 | 16 | bulgaria | 1-2 | italy | semifinals | 74110 |

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

select count ( episode ) where share = 2 col : # | episode | air date | timeslot | rating | share | 18-49 | viewers | weekly rank row 1 : 1 | "goodbye city...hello country" | april 30, 2008 | 9:00 p.m | 1.7 | 3 | 1.0/3 (#5) | 2.37 (#5) | 89/96 row 2 : 2 | "pigs & cows & sheeps...oh my" | may 7, 2008 | 9:00 p.m | 1.5 | 2 | 1.0/3 (#5) | 2.11 (#5) | 90/97 row 3 : 3 | "wet 'n wild" | may 14, 2008 | 9:00 p.m | 1.3 | 2 | 0.9/2 (#5) | 1.96 (#5) | 90/96 row 4 : 4 | "do-si-do" | may 28, 2008 | 9:00 p.m | 1.8 | 3 | 0.9/2 (#5) | 2.44 (#5) | 75/87 row 5 : 5 | "as country as apple pie" | june 4, 2008 | 9:00 p.m | 1.3 | 2 | 0.8/2 (#5) | 2.01 (#5) | 79/91 row 6 : 6 | "it's show time" | june 11, 2008 | 9:00 p.m | 1.3 | 2 | 0.8/2 (#5) | 2.04 (#5) | 76/88 row 7 : 7 | "a family affair" | june 18, 2008 | 9:00 p.m | 1.5 | 3 | 0.8/2 (#5) | 2.00 (#5) | none row 8 : 8 | "farmer picks a wife" | june 25, 2008 | 9:00 p.m | 1.6 | 3 | 1.0/3 (#5) | 2.45 (#5) | none

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

select count ( episode ) where share = 2 <table_name> : table \begin{tabular}{llllllllll} \toprule & # & episode & air date & timeslot & rating & share & 18-49 & viewers & weekly rank \\ \midrule 0 & 1 & "goodbye city...hello country" & april 30, 2008 & 9:00 p.m & 1.7 & 3 & 1.0/3 (#5) & 2.37 (#5) & 89/96 \\ 1 & 2 & "pigs & cows & sheeps...oh my" & may 7, 2008 & 9:00 p.m & 1.5 & 2 & 1.0/3 (#5) & 2.11 (#5) & 90/97 \\ 2 & 3 & "wet 'n wild" & may 14, 2008 & 9:00 p.m & 1.3 & 2 & 0.9/2 (#5) & 1.96 (#5) & 90/96 \\ 3 & 4 & "do-si-do" & may 28, 2008 & 9:00 p.m & 1.8 & 3 & 0.9/2 (#5) & 2.44 (#5) & 75/87 \\ 4 & 5 & "as country as apple pie" & june 4, 2008 & 9:00 p.m & 1.3 & 2 & 0.8/2 (#5) & 2.01 (#5) & 79/91 \\ 5 & 6 & "it's show time" & june 11, 2008 & 9:00 p.m & 1.3 & 2 & 0.8/2 (#5) & 2.04 (#5) & 76/88 \\ 6 & 7 & "a family affair" & june 18, 2008 & 9:00 p.m & 1.5 & 3 & 0.8/2 (#5) & 2.00 (#5) & none \\ 7 & 8 & "farmer picks a wife" & june 25, 2008 & 9:00 p.m & 1.6 & 3 & 1.0/3 (#5) & 2.45 (#5) & none \\ \bottomrule \end{tabular}

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

select count ( episode ) where share = 2 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>#</th> <th>episode</th> <th>air date</th> <th>timeslot</th> <th>rating</th> <th>share</th> <th>18-49</th> <th>viewers</th> <th>weekly rank</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>"goodbye city...hello country"</td> <td>april 30, 2008</td> <td>9:00 p.m</td> <td>1.7</td> <td>3</td> <td>1.0/3 (#5)</td> <td>2.37 (#5)</td> <td>89/96</td> </tr> <tr> <th>1</th> <td>2</td> <td>"pigs &amp; cows &amp; sheeps...oh my"</td> <td>may 7, 2008</td> <td>9:00 p.m</td> <td>1.5</td> <td>2</td> <td>1.0/3 (#5)</td> <td>2.11 (#5)</td> <td>90/97</td> </tr> <tr> <th>2</th> <td>3</td> <td>"wet 'n wild"</td> <td>may 14, 2008</td> <td>9:00 p.m</td> <td>1.3</td> <td>2</td> <td>0.9/2 (#5)</td> <td>1.96 (#5)</td> <td>90/96</td> </tr> <tr> <th>3</th> <td>4</td> <td>"do-si-do"</td> <td>may 28, 2008</td> <td>9:00 p.m</td> <td>1.8</td> <td>3</td> <td>0.9/2 (#5)</td> <td>2.44 (#5)</td> <td>75/87</td> </tr> <tr> <th>4</th> <td>5</td> <td>"as country as apple pie"</td> <td>june 4, 2008</td> <td>9:00 p.m</td> <td>1.3</td> <td>2</td> <td>0.8/2 (#5)</td> <td>2.01 (#5)</td> <td>79/91</td> </tr> <tr> <th>5</th> <td>6</td> <td>"it's show time"</td> <td>june 11, 2008</td> <td>9:00 p.m</td> <td>1.3</td> <td>2</td> <td>0.8/2 (#5)</td> <td>2.04 (#5)</td> <td>76/88</td> </tr> <tr> <th>6</th> <td>7</td> <td>"a family affair"</td> <td>june 18, 2008</td> <td>9:00 p.m</td> <td>1.5</td> <td>3</td> <td>0.8/2 (#5)</td> <td>2.00 (#5)</td> <td>none</td> </tr> <tr> <th>7</th> <td>8</td> <td>"farmer picks a wife"</td> <td>june 25, 2008</td> <td>9:00 p.m</td> <td>1.6</td> <td>3</td> <td>1.0/3 (#5)</td> <td>2.45 (#5)</td> <td>none</td> </tr> </tbody> </table>

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

select count ( episode ) where share = 2 <table_name> : table | | # | episode | air date | timeslot | rating | share | 18-49 | viewers | weekly rank | |---:|----:|:-------------------------------|:---------------|:-----------|---------:|--------:|:-----------|:----------|:--------------| | 0 | 1 | "goodbye city...hello country" | april 30, 2008 | 9:00 p.m | 1.7 | 3 | 1.0/3 (#5) | 2.37 (#5) | 89/96 | | 1 | 2 | "pigs & cows & sheeps...oh my" | may 7, 2008 | 9:00 p.m | 1.5 | 2 | 1.0/3 (#5) | 2.11 (#5) | 90/97 | | 2 | 3 | "wet 'n wild" | may 14, 2008 | 9:00 p.m | 1.3 | 2 | 0.9/2 (#5) | 1.96 (#5) | 90/96 | | 3 | 4 | "do-si-do" | may 28, 2008 | 9:00 p.m | 1.8 | 3 | 0.9/2 (#5) | 2.44 (#5) | 75/87 | | 4 | 5 | "as country as apple pie" | june 4, 2008 | 9:00 p.m | 1.3 | 2 | 0.8/2 (#5) | 2.01 (#5) | 79/91 | | 5 | 6 | "it's show time" | june 11, 2008 | 9:00 p.m | 1.3 | 2 | 0.8/2 (#5) | 2.04 (#5) | 76/88 | | 6 | 7 | "a family affair" | june 18, 2008 | 9:00 p.m | 1.5 | 3 | 0.8/2 (#5) | 2.00 (#5) | none | | 7 | 8 | "farmer picks a wife" | june 25, 2008 | 9:00 p.m | 1.6 | 3 | 1.0/3 (#5) | 2.45 (#5) | none |

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

select count ( selected latin american countries ) where average receipt per visitor 2009 (usd/turista) < ( select max ( average receipt per visitor 2009 (usd/turista) ) ) col : selected latin american countries | internl. tourism arrivals 2010 (x 1000) | internl. tourism receipts. 2010 (usd (x1000) | average receipt per visitor 2009 (usd/turista) | tourist arrivals per 1000 inhab (estimated) 2007 | receipts per capita 2005 usd | revenues as % exports of goods and services 2003 | tourism income % gdp 2003 | % direct and indirect employment in tourism 2005 | world ranking tourism competitiv. ttci 2011 | 2011 ttci index row 1 : argentina | 5288 | 4930 | 932 | 115 | 57 | 7.4 | 1.8 | 9.1 | 60 | 4.2 row 2 : brazil | 5161 | 5919 | 1147 | 26 | 18 | 3.2 | 0.5 | 7 | 52 | 4.36 row 3 : chile | 2766 | 1636 | 591 | 151 | 73 | 5.3 | 1.9 | 6.8 | 57 | 4.27 row 4 : costa rica | 2100 | 2111 | 1005 | 442 | 343 | 17.5 | 8.1 | 13.3 | 44 | 4.43 row 5 : cuba | 2507 | none | none | 188 | 169 | none | none | none | none | none row 6 : dominican republic | 4125 | 4240 | 1028 | 408 | 353 | 36.2 | 18.8 | 19.8 | 72 | 3.99 row 7 : mexico | 22395 | 11872 | 530 | 201 | 103 | 5.7 | 1.6 | 14.2 | 43 | 4.43 row 8 : panama | 1317 | 1676 | 1273 | 330 | 211 | 10.6 | 6.3 | 12.9 | 56 | 4.3 row 9 : peru | 2299 | 2274 | 989 | 65 | 41 | 9 | 1.6 | 7.6 | 69 | 4.04 row 10 : uruguay | 2352 | 1496 | 636 | 525 | 145 | 14.2 | 3.6 | 10.7 | 58 | 4.24

col : count ( "selected latin american countries" ) row 1 : 0

select count ( selected latin american countries ) where average receipt per visitor 2009 (usd/turista) < ( select max ( average receipt per visitor 2009 (usd/turista) ) ) <table_name> : table \begin{tabular}{llllllllllll} \toprule & selected latin american countries & internl. tourism arrivals 2010 (x 1000) & internl. tourism receipts. 2010 (usd (x1000) & average receipt per visitor 2009 (usd/turista) & tourist arrivals per 1000 inhab (estimated) 2007 & receipts per capita 2005 usd & revenues as % exports of goods and services 2003 & tourism income % gdp 2003 & % direct and indirect employment in tourism 2005 & world ranking tourism competitiv. ttci 2011 & 2011 ttci index \\ \midrule 0 & argentina & 5288 & 4930 & 932 & 115 & 57 & 7.4 & 1.8 & 9.1 & 60 & 4.2 \\ 1 & brazil & 5161 & 5919 & 1147 & 26 & 18 & 3.2 & 0.5 & 7 & 52 & 4.36 \\ 2 & chile & 2766 & 1636 & 591 & 151 & 73 & 5.3 & 1.9 & 6.8 & 57 & 4.27 \\ 3 & costa rica & 2100 & 2111 & 1005 & 442 & 343 & 17.5 & 8.1 & 13.3 & 44 & 4.43 \\ 4 & cuba & 2507 & none & none & 188 & 169 & none & none & none & none & none \\ 5 & dominican republic & 4125 & 4240 & 1028 & 408 & 353 & 36.2 & 18.8 & 19.8 & 72 & 3.99 \\ 6 & mexico & 22395 & 11872 & 530 & 201 & 103 & 5.7 & 1.6 & 14.2 & 43 & 4.43 \\ 7 & panama & 1317 & 1676 & 1273 & 330 & 211 & 10.6 & 6.3 & 12.9 & 56 & 4.3 \\ 8 & peru & 2299 & 2274 & 989 & 65 & 41 & 9 & 1.6 & 7.6 & 69 & 4.04 \\ 9 & uruguay & 2352 & 1496 & 636 & 525 & 145 & 14.2 & 3.6 & 10.7 & 58 & 4.24 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & count ( "selected latin american countries" ) \\ \midrule 0 & 0 \\ \bottomrule \end{tabular}

select count ( selected latin american countries ) where average receipt per visitor 2009 (usd/turista) < ( select max ( average receipt per visitor 2009 (usd/turista) ) ) <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>selected latin american countries</th> <th>internl. tourism arrivals 2010 (x 1000)</th> <th>internl. tourism receipts. 2010 (usd (x1000)</th> <th>average receipt per visitor 2009 (usd/turista)</th> <th>tourist arrivals per 1000 inhab (estimated) 2007</th> <th>receipts per capita 2005 usd</th> <th>revenues as % exports of goods and services 2003</th> <th>tourism income % gdp 2003</th> <th>% direct and indirect employment in tourism 2005</th> <th>world ranking tourism competitiv. ttci 2011</th> <th>2011 ttci index</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>argentina</td> <td>5288</td> <td>4930</td> <td>932</td> <td>115</td> <td>57</td> <td>7.4</td> <td>1.8</td> <td>9.1</td> <td>60</td> <td>4.2</td> </tr> <tr> <th>1</th> <td>brazil</td> <td>5161</td> <td>5919</td> <td>1147</td> <td>26</td> <td>18</td> <td>3.2</td> <td>0.5</td> <td>7</td> <td>52</td> <td>4.36</td> </tr> <tr> <th>2</th> <td>chile</td> <td>2766</td> <td>1636</td> <td>591</td> <td>151</td> <td>73</td> <td>5.3</td> <td>1.9</td> <td>6.8</td> <td>57</td> <td>4.27</td> </tr> <tr> <th>3</th> <td>costa rica</td> <td>2100</td> <td>2111</td> <td>1005</td> <td>442</td> <td>343</td> <td>17.5</td> <td>8.1</td> <td>13.3</td> <td>44</td> <td>4.43</td> </tr> <tr> <th>4</th> <td>cuba</td> <td>2507</td> <td>none</td> <td>none</td> <td>188</td> <td>169</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> <td>none</td> </tr> <tr> <th>5</th> <td>dominican republic</td> <td>4125</td> <td>4240</td> <td>1028</td> <td>408</td> <td>353</td> <td>36.2</td> <td>18.8</td> <td>19.8</td> <td>72</td> <td>3.99</td> </tr> <tr> <th>6</th> <td>mexico</td> <td>22395</td> <td>11872</td> <td>530</td> <td>201</td> <td>103</td> <td>5.7</td> <td>1.6</td> <td>14.2</td> <td>43</td> <td>4.43</td> </tr> <tr> <th>7</th> <td>panama</td> <td>1317</td> <td>1676</td> <td>1273</td> <td>330</td> <td>211</td> <td>10.6</td> <td>6.3</td> <td>12.9</td> <td>56</td> <td>4.3</td> </tr> <tr> <th>8</th> <td>peru</td> <td>2299</td> <td>2274</td> <td>989</td> <td>65</td> <td>41</td> <td>9</td> <td>1.6</td> <td>7.6</td> <td>69</td> <td>4.04</td> </tr> <tr> <th>9</th> <td>uruguay</td> <td>2352</td> <td>1496</td> <td>636</td> <td>525</td> <td>145</td> <td>14.2</td> <td>3.6</td> <td>10.7</td> <td>58</td> <td>4.24</td> </tr> </tbody> </table>

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

select count ( selected latin american countries ) where average receipt per visitor 2009 (usd/turista) < ( select max ( average receipt per visitor 2009 (usd/turista) ) ) <table_name> : table | | selected latin american countries | internl. tourism arrivals 2010 (x 1000) | internl. tourism receipts. 2010 (usd (x1000) | average receipt per visitor 2009 (usd/turista) | tourist arrivals per 1000 inhab (estimated) 2007 | receipts per capita 2005 usd | revenues as % exports of goods and services 2003 | tourism income % gdp 2003 | % direct and indirect employment in tourism 2005 | world ranking tourism competitiv. ttci 2011 | 2011 ttci index | |---:|:------------------------------------|------------------------------------------:|:-----------------------------------------------|:-------------------------------------------------|---------------------------------------------------:|-------------------------------:|:---------------------------------------------------|:----------------------------|:---------------------------------------------------|:----------------------------------------------|:------------------| | 0 | argentina | 5288 | 4930 | 932 | 115 | 57 | 7.4 | 1.8 | 9.1 | 60 | 4.2 | | 1 | brazil | 5161 | 5919 | 1147 | 26 | 18 | 3.2 | 0.5 | 7 | 52 | 4.36 | | 2 | chile | 2766 | 1636 | 591 | 151 | 73 | 5.3 | 1.9 | 6.8 | 57 | 4.27 | | 3 | costa rica | 2100 | 2111 | 1005 | 442 | 343 | 17.5 | 8.1 | 13.3 | 44 | 4.43 | | 4 | cuba | 2507 | none | none | 188 | 169 | none | none | none | none | none | | 5 | dominican republic | 4125 | 4240 | 1028 | 408 | 353 | 36.2 | 18.8 | 19.8 | 72 | 3.99 | | 6 | mexico | 22395 | 11872 | 530 | 201 | 103 | 5.7 | 1.6 | 14.2 | 43 | 4.43 | | 7 | panama | 1317 | 1676 | 1273 | 330 | 211 | 10.6 | 6.3 | 12.9 | 56 | 4.3 | | 8 | peru | 2299 | 2274 | 989 | 65 | 41 | 9 | 1.6 | 7.6 | 69 | 4.04 | | 9 | uruguay | 2352 | 1496 | 636 | 525 | 145 | 14.2 | 3.6 | 10.7 | 58 | 4.24 |

| | count ( "selected latin american countries" ) | |---:|------------------------------------------------:| | 0 | 0 |

select count ( scorers ) where scorers = 'huistra' col : date | round | opponent | venue | result | attendance | scorers row 1 : 1992-09-16 00:00:00 | r1 | lyngby | h | 2-0 | 40036 | hateley, huistra row 2 : 1992-09-30 00:00:00 | r1 | lyngby | a | 1-0 | 4273 | durrant row 3 : 1992-10-21 00:00:00 | r2 | leeds united | h | 2-1 | 43251 | mccoist, lukic (o.g.) row 4 : 1992-11-04 00:00:00 | r2 | leeds united | a | 2-1 | 25118 | hateley, mccoist row 5 : 1992-11-25 00:00:00 | gs | marseille | h | 2-2 | 41624 | mcswegan, hateley row 6 : 1992-12-09 00:00:00 | gs | cska moscow | a | 1-0 | 9000 | ferguson row 7 : 1993-03-03 00:00:00 | gs | club brugge | a | 1-1 | 19000 | huistra row 8 : 1993-03-17 00:00:00 | gs | club brugge | h | 2-1 | 42731 | durrant, nisbet row 9 : 1993-04-07 00:00:00 | gs | marseille | a | 1-1 | 46000 | durrant row 10 : 1993-04-21 00:00:00 | gs | cska moscow | h | 0-0 | 43142 | none

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

select count ( scorers ) where scorers = 'huistra' <table_name> : table \begin{tabular}{llllllll} \toprule & date & round & opponent & venue & result & attendance & scorers \\ \midrule 0 & 1992-09-16 00:00:00 & r1 & lyngby & h & 2-0 & 40036 & hateley, huistra \\ 1 & 1992-09-30 00:00:00 & r1 & lyngby & a & 1-0 & 4273 & durrant \\ 2 & 1992-10-21 00:00:00 & r2 & leeds united & h & 2-1 & 43251 & mccoist, lukic (o.g.) \\ 3 & 1992-11-04 00:00:00 & r2 & leeds united & a & 2-1 & 25118 & hateley, mccoist \\ 4 & 1992-11-25 00:00:00 & gs & marseille & h & 2-2 & 41624 & mcswegan, hateley \\ 5 & 1992-12-09 00:00:00 & gs & cska moscow & a & 1-0 & 9000 & ferguson \\ 6 & 1993-03-03 00:00:00 & gs & club brugge & a & 1-1 & 19000 & huistra \\ 7 & 1993-03-17 00:00:00 & gs & club brugge & h & 2-1 & 42731 & durrant, nisbet \\ 8 & 1993-04-07 00:00:00 & gs & marseille & a & 1-1 & 46000 & durrant \\ 9 & 1993-04-21 00:00:00 & gs & cska moscow & h & 0-0 & 43142 & none \\ \bottomrule \end{tabular}

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

select count ( scorers ) where scorers = 'huistra' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>date</th> <th>round</th> <th>opponent</th> <th>venue</th> <th>result</th> <th>attendance</th> <th>scorers</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1992-09-16 00:00:00</td> <td>r1</td> <td>lyngby</td> <td>h</td> <td>2-0</td> <td>40036</td> <td>hateley, huistra</td> </tr> <tr> <th>1</th> <td>1992-09-30 00:00:00</td> <td>r1</td> <td>lyngby</td> <td>a</td> <td>1-0</td> <td>4273</td> <td>durrant</td> </tr> <tr> <th>2</th> <td>1992-10-21 00:00:00</td> <td>r2</td> <td>leeds united</td> <td>h</td> <td>2-1</td> <td>43251</td> <td>mccoist, lukic (o.g.)</td> </tr> <tr> <th>3</th> <td>1992-11-04 00:00:00</td> <td>r2</td> <td>leeds united</td> <td>a</td> <td>2-1</td> <td>25118</td> <td>hateley, mccoist</td> </tr> <tr> <th>4</th> <td>1992-11-25 00:00:00</td> <td>gs</td> <td>marseille</td> <td>h</td> <td>2-2</td> <td>41624</td> <td>mcswegan, hateley</td> </tr> <tr> <th>5</th> <td>1992-12-09 00:00:00</td> <td>gs</td> <td>cska moscow</td> <td>a</td> <td>1-0</td> <td>9000</td> <td>ferguson</td> </tr> <tr> <th>6</th> <td>1993-03-03 00:00:00</td> <td>gs</td> <td>club brugge</td> <td>a</td> <td>1-1</td> <td>19000</td> <td>huistra</td> </tr> <tr> <th>7</th> <td>1993-03-17 00:00:00</td> <td>gs</td> <td>club brugge</td> <td>h</td> <td>2-1</td> <td>42731</td> <td>durrant, nisbet</td> </tr> <tr> <th>8</th> <td>1993-04-07 00:00:00</td> <td>gs</td> <td>marseille</td> <td>a</td> <td>1-1</td> <td>46000</td> <td>durrant</td> </tr> <tr> <th>9</th> <td>1993-04-21 00:00:00</td> <td>gs</td> <td>cska moscow</td> <td>h</td> <td>0-0</td> <td>43142</td> <td>none</td> </tr> </tbody> </table>

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

select count ( scorers ) where scorers = 'huistra' <table_name> : table | | date | round | opponent | venue | result | attendance | scorers | |---:|:--------------------|:--------|:-------------|:--------|:---------|-------------:|:----------------------| | 0 | 1992-09-16 00:00:00 | r1 | lyngby | h | 2-0 | 40036 | hateley, huistra | | 1 | 1992-09-30 00:00:00 | r1 | lyngby | a | 1-0 | 4273 | durrant | | 2 | 1992-10-21 00:00:00 | r2 | leeds united | h | 2-1 | 43251 | mccoist, lukic (o.g.) | | 3 | 1992-11-04 00:00:00 | r2 | leeds united | a | 2-1 | 25118 | hateley, mccoist | | 4 | 1992-11-25 00:00:00 | gs | marseille | h | 2-2 | 41624 | mcswegan, hateley | | 5 | 1992-12-09 00:00:00 | gs | cska moscow | a | 1-0 | 9000 | ferguson | | 6 | 1993-03-03 00:00:00 | gs | club brugge | a | 1-1 | 19000 | huistra | | 7 | 1993-03-17 00:00:00 | gs | club brugge | h | 2-1 | 42731 | durrant, nisbet | | 8 | 1993-04-07 00:00:00 | gs | marseille | a | 1-1 | 46000 | durrant | | 9 | 1993-04-21 00:00:00 | gs | cska moscow | h | 0-0 | 43142 | none |

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

select rank where county = 'tipperary' col : rank | player | county | tally | total | opposition row 1 : 1 | nicky english | tipperary | 2-12 | 18 | antrim row 2 : 2 | mark corrigan | offaly | 3-7 | 16 | kilkenny row 3 : 3 | joe hennessy | kerry | 3-5 | 14 | limerick row 4 : 3 | finbarr delaney | cork | 1-11 | 14 | waterford row 5 : 5 | nicky english | tipperary | 0-13 | 13 | waterford row 6 : 5 | adrian ronan | kilkenny | 1-10 | 13 | westmeath row 7 : 7 | nicky english | tipperary | 2-5 | 11 | limerick row 8 : 7 | danny mcnaughton | antrim | 2-5 | 11 | down row 9 : 9 | m. j. ryan | dublin | 1-7 | 10 | laois row 10 : 10 | gary kirby | limerick | 1-6 | 9 | kerry row 11 : 10 | pat murphy | waterford | 3-0 | 9 | cork

col : rank row 1 : 1 row 2 : 5 row 3 : 7

select rank where county = 'tipperary' <table_name> : table \begin{tabular}{lllllll} \toprule & rank & player & county & tally & total & opposition \\ \midrule 0 & 1 & nicky english & tipperary & 2-12 & 18 & antrim \\ 1 & 2 & mark corrigan & offaly & 3-7 & 16 & kilkenny \\ 2 & 3 & joe hennessy & kerry & 3-5 & 14 & limerick \\ 3 & 3 & finbarr delaney & cork & 1-11 & 14 & waterford \\ 4 & 5 & nicky english & tipperary & 0-13 & 13 & waterford \\ 5 & 5 & adrian ronan & kilkenny & 1-10 & 13 & westmeath \\ 6 & 7 & nicky english & tipperary & 2-5 & 11 & limerick \\ 7 & 7 & danny mcnaughton & antrim & 2-5 & 11 & down \\ 8 & 9 & m. j. ryan & dublin & 1-7 & 10 & laois \\ 9 & 10 & gary kirby & limerick & 1-6 & 9 & kerry \\ 10 & 10 & pat murphy & waterford & 3-0 & 9 & cork \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & rank \\ \midrule 0 & 1 \\ 1 & 5 \\ 2 & 7 \\ \bottomrule \end{tabular}

select rank where county = 'tipperary' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>rank</th> <th>player</th> <th>county</th> <th>tally</th> <th>total</th> <th>opposition</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>nicky english</td> <td>tipperary</td> <td>2-12</td> <td>18</td> <td>antrim</td> </tr> <tr> <th>1</th> <td>2</td> <td>mark corrigan</td> <td>offaly</td> <td>3-7</td> <td>16</td> <td>kilkenny</td> </tr> <tr> <th>2</th> <td>3</td> <td>joe hennessy</td> <td>kerry</td> <td>3-5</td> <td>14</td> <td>limerick</td> </tr> <tr> <th>3</th> <td>3</td> <td>finbarr delaney</td> <td>cork</td> <td>1-11</td> <td>14</td> <td>waterford</td> </tr> <tr> <th>4</th> <td>5</td> <td>nicky english</td> <td>tipperary</td> <td>0-13</td> <td>13</td> <td>waterford</td> </tr> <tr> <th>5</th> <td>5</td> <td>adrian ronan</td> <td>kilkenny</td> <td>1-10</td> <td>13</td> <td>westmeath</td> </tr> <tr> <th>6</th> <td>7</td> <td>nicky english</td> <td>tipperary</td> <td>2-5</td> <td>11</td> <td>limerick</td> </tr> <tr> <th>7</th> <td>7</td> <td>danny mcnaughton</td> <td>antrim</td> <td>2-5</td> <td>11</td> <td>down</td> </tr> <tr> <th>8</th> <td>9</td> <td>m. j. ryan</td> <td>dublin</td> <td>1-7</td> <td>10</td> <td>laois</td> </tr> <tr> <th>9</th> <td>10</td> <td>gary kirby</td> <td>limerick</td> <td>1-6</td> <td>9</td> <td>kerry</td> </tr> <tr> <th>10</th> <td>10</td> <td>pat murphy</td> <td>waterford</td> <td>3-0</td> <td>9</td> <td>cork</td> </tr> </tbody> </table>

<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>rank</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> </tr> <tr> <th>1</th> <td>5</td> </tr> <tr> <th>2</th> <td>7</td> </tr> </tbody> </table>

select rank where county = 'tipperary' <table_name> : table | | rank | player | county | tally | total | opposition | |---:|-------:|:-----------------|:----------|:--------|--------:|:-------------| | 0 | 1 | nicky english | tipperary | 2-12 | 18 | antrim | | 1 | 2 | mark corrigan | offaly | 3-7 | 16 | kilkenny | | 2 | 3 | joe hennessy | kerry | 3-5 | 14 | limerick | | 3 | 3 | finbarr delaney | cork | 1-11 | 14 | waterford | | 4 | 5 | nicky english | tipperary | 0-13 | 13 | waterford | | 5 | 5 | adrian ronan | kilkenny | 1-10 | 13 | westmeath | | 6 | 7 | nicky english | tipperary | 2-5 | 11 | limerick | | 7 | 7 | danny mcnaughton | antrim | 2-5 | 11 | down | | 8 | 9 | m. j. ryan | dublin | 1-7 | 10 | laois | | 9 | 10 | gary kirby | limerick | 1-6 | 9 | kerry | | 10 | 10 | pat murphy | waterford | 3-0 | 9 | cork |

| | rank | |---:|-------:| | 0 | 1 | | 1 | 5 | | 2 | 7 |

select count ( * ) where position = '8th' col : year | competition | venue | position | notes row 1 : 1999 | european junior championships | riga, latvia | 6th | 49.65 m row 2 : 2001 | european u23 championships | amsterdam, netherlands | 10th | 53.30 m row 3 : 2005 | universiade | izmir, turkey | 9th | 58.29 m row 4 : 2006 | european championships | gothenburg, sweden | 15th (q) | 59.54 m row 5 : 2007 | universiade | bangkok, thailand | 1st | 61.55 m row 6 : 2008 | olympic games | beijing, china | 18th (q) | 61.32 m row 7 : 2009 | world championships | berlin, germany | 8th | 63.17 m row 8 : 2010 | european championships | barcelona, spain | 15th (q) | 60.76 m row 9 : 2011 | world championships | daegu, south korea | 21st (q) | 61.47 m row 10 : 2012 | european championships | helsinki, finland | 7th | 62.85 m row 11 : 2012 | olympic games | london, united kingdom | 24th (q) | 60.81 m row 12 : 2013 | world championships | moscow, russia | 18th (q) | 59.85 m

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

select count ( * ) where position = '8th' <table_name> : table \begin{tabular}{llllll} \toprule & year & competition & venue & position & notes \\ \midrule 0 & 1999 & european junior championships & riga, latvia & 6th & 49.65 m \\ 1 & 2001 & european u23 championships & amsterdam, netherlands & 10th & 53.30 m \\ 2 & 2005 & universiade & izmir, turkey & 9th & 58.29 m \\ 3 & 2006 & european championships & gothenburg, sweden & 15th (q) & 59.54 m \\ 4 & 2007 & universiade & bangkok, thailand & 1st & 61.55 m \\ 5 & 2008 & olympic games & beijing, china & 18th (q) & 61.32 m \\ 6 & 2009 & world championships & berlin, germany & 8th & 63.17 m \\ 7 & 2010 & european championships & barcelona, spain & 15th (q) & 60.76 m \\ 8 & 2011 & world championships & daegu, south korea & 21st (q) & 61.47 m \\ 9 & 2012 & european championships & helsinki, finland & 7th & 62.85 m \\ 10 & 2012 & olympic games & london, united kingdom & 24th (q) & 60.81 m \\ 11 & 2013 & world championships & moscow, russia & 18th (q) & 59.85 m \\ \bottomrule \end{tabular}

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

select count ( * ) where position = '8th' <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>1999</td> <td>european junior championships</td> <td>riga, latvia</td> <td>6th</td> <td>49.65 m</td> </tr> <tr> <th>1</th> <td>2001</td> <td>european u23 championships</td> <td>amsterdam, netherlands</td> <td>10th</td> <td>53.30 m</td> </tr> <tr> <th>2</th> <td>2005</td> <td>universiade</td> <td>izmir, turkey</td> <td>9th</td> <td>58.29 m</td> </tr> <tr> <th>3</th> <td>2006</td> <td>european championships</td> <td>gothenburg, sweden</td> <td>15th (q)</td> <td>59.54 m</td> </tr> <tr> <th>4</th> <td>2007</td> <td>universiade</td> <td>bangkok, thailand</td> <td>1st</td> <td>61.55 m</td> </tr> <tr> <th>5</th> <td>2008</td> <td>olympic games</td> <td>beijing, china</td> <td>18th (q)</td> <td>61.32 m</td> </tr> <tr> <th>6</th> <td>2009</td> <td>world championships</td> <td>berlin, germany</td> <td>8th</td> <td>63.17 m</td> </tr> <tr> <th>7</th> <td>2010</td> <td>european championships</td> <td>barcelona, spain</td> <td>15th (q)</td> <td>60.76 m</td> </tr> <tr> <th>8</th> <td>2011</td> <td>world championships</td> <td>daegu, south korea</td> <td>21st (q)</td> <td>61.47 m</td> </tr> <tr> <th>9</th> <td>2012</td> <td>european championships</td> <td>helsinki, finland</td> <td>7th</td> <td>62.85 m</td> </tr> <tr> <th>10</th> <td>2012</td> <td>olympic games</td> <td>london, united kingdom</td> <td>24th (q)</td> <td>60.81 m</td> </tr> <tr> <th>11</th> <td>2013</td> <td>world championships</td> <td>moscow, russia</td> <td>18th (q)</td> <td>59.85 m</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 position = '8th' <table_name> : table | | year | competition | venue | position | notes | |---:|-------:|:------------------------------|:-----------------------|:-----------|:--------| | 0 | 1999 | european junior championships | riga, latvia | 6th | 49.65 m | | 1 | 2001 | european u23 championships | amsterdam, netherlands | 10th | 53.30 m | | 2 | 2005 | universiade | izmir, turkey | 9th | 58.29 m | | 3 | 2006 | european championships | gothenburg, sweden | 15th (q) | 59.54 m | | 4 | 2007 | universiade | bangkok, thailand | 1st | 61.55 m | | 5 | 2008 | olympic games | beijing, china | 18th (q) | 61.32 m | | 6 | 2009 | world championships | berlin, germany | 8th | 63.17 m | | 7 | 2010 | european championships | barcelona, spain | 15th (q) | 60.76 m | | 8 | 2011 | world championships | daegu, south korea | 21st (q) | 61.47 m | | 9 | 2012 | european championships | helsinki, finland | 7th | 62.85 m | | 10 | 2012 | olympic games | london, united kingdom | 24th (q) | 60.81 m | | 11 | 2013 | world championships | moscow, russia | 18th (q) | 59.85 m |

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

select win-loss record where playoffs appearances = 0 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-loss record row 1 : 0-0 row 2 : 0-0 row 3 : 11-51 row 4 : 20-40 row 5 : 18-42 row 6 : 15-45 row 7 : 46-122 row 8 : 22-38 row 9 : 19-43

select win-loss record where playoffs appearances = 0 <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-loss record \\ \midrule 0 & 0-0 \\ 1 & 0-0 \\ 2 & 11-51 \\ 3 & 20-40 \\ 4 & 18-42 \\ 5 & 15-45 \\ 6 & 46-122 \\ 7 & 22-38 \\ 8 & 19-43 \\ \bottomrule \end{tabular}

select win-loss record where playoffs appearances = 0 <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-loss record</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>0-0</td> </tr> <tr> <th>1</th> <td>0-0</td> </tr> <tr> <th>2</th> <td>11-51</td> </tr> <tr> <th>3</th> <td>20-40</td> </tr> <tr> <th>4</th> <td>18-42</td> </tr> <tr> <th>5</th> <td>15-45</td> </tr> <tr> <th>6</th> <td>46-122</td> </tr> <tr> <th>7</th> <td>22-38</td> </tr> <tr> <th>8</th> <td>19-43</td> </tr> </tbody> </table>

select win-loss record where playoffs appearances = 0 <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-loss record | |---:|:------------------| | 0 | 0-0 | | 1 | 0-0 | | 2 | 11-51 | | 3 | 20-40 | | 4 | 18-42 | | 5 | 15-45 | | 6 | 46-122 | | 7 | 22-38 | | 8 | 19-43 |

select count ( * ) where notes = '3 episodes' col : year | title | role | notes row 1 : 1979 | california fever | lisa bannister | episode: "the girl from somewhere" row 2 : 1984 | faerie tale theatre | snow white | episode: "snow white and the seven dwarfs" row 3 : 1993 | performance | beatrice-joanna | episode: "the changeling" row 4 : 1995 | if not for you | jessie kent | 8 episodes row 5 : 1996 | tracey takes on | judge loring | episode: "vanity" row 6 : 1996 | tales from the crypt | laura kendall | episode: "horror in the night" row 7 : 1999 | the scarlet pimpernel | lady margaret blakeney | 3 episodes row 8 : 2001 | table 12 | mel | episode: "preserves" row 9 : 2003 | the brotherhood of poland, new hampshire | helen shaw | 7 episodes row 10 : 2006 | three moons over milford | laura davis | 8 episodes row 11 : 2007 | law and order: special victims unit | dr. faith sutton | episode: "harm" row 12 : 2007-2008 | freezing | elizabeth | 3 episodes row 13 : 2008 | agatha christie: poirot | dame celia westholme | episode: "appointment with death" row 14 : 2009 | 10 minute tales | the ex-wife | episode: "the running of the deer" row 15 : 2010-present | downton abbey | cora, countess of grantham | 29 episodes screen actors guild award for outstanding performance by an ensemble in a drama series nominated-golden globe award for best actress - miniseries or television film nominated-primetime emmy award for outstanding lead actress in a miniseries or a movie nominated-satellite award for best actress - miniseries or television film

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

select count ( * ) where notes = '3 episodes' <table_name> : table \begin{tabular}{lllll} \toprule & year & title & role & notes \\ \midrule 0 & 1979 & california fever & lisa bannister & episode: "the girl from somewhere" \\ 1 & 1984 & faerie tale theatre & snow white & episode: "snow white and the seven dwarfs" \\ 2 & 1993 & performance & beatrice-joanna & episode: "the changeling" \\ 3 & 1995 & if not for you & jessie kent & 8 episodes \\ 4 & 1996 & tracey takes on & judge loring & episode: "vanity" \\ 5 & 1996 & tales from the crypt & laura kendall & episode: "horror in the night" \\ 6 & 1999 & the scarlet pimpernel & lady margaret blakeney & 3 episodes \\ 7 & 2001 & table 12 & mel & episode: "preserves" \\ 8 & 2003 & the brotherhood of poland, new hampshire & helen shaw & 7 episodes \\ 9 & 2006 & three moons over milford & laura davis & 8 episodes \\ 10 & 2007 & law and order: special victims unit & dr. faith sutton & episode: "harm" \\ 11 & 2007-2008 & freezing & elizabeth & 3 episodes \\ 12 & 2008 & agatha christie: poirot & dame celia westholme & episode: "appointment with death" \\ 13 & 2009 & 10 minute tales & the ex-wife & episode: "the running of the deer" \\ 14 & 2010-present & downton abbey & cora, countess of grantham & 29 episodes screen actors guild award for outstanding performance by an ensemble in a drama series nominated-golden globe award for best actress - miniseries or television film nominated-primetime emmy award for outstanding lead actress in a miniseries or a movie nominated-satellite award for best actress - miniseries or television film \\ \bottomrule \end{tabular}

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

select count ( * ) where notes = '3 episodes' <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>year</th> <th>title</th> <th>role</th> <th>notes</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1979</td> <td>california fever</td> <td>lisa bannister</td> <td>episode: "the girl from somewhere"</td> </tr> <tr> <th>1</th> <td>1984</td> <td>faerie tale theatre</td> <td>snow white</td> <td>episode: "snow white and the seven dwarfs"</td> </tr> <tr> <th>2</th> <td>1993</td> <td>performance</td> <td>beatrice-joanna</td> <td>episode: "the changeling"</td> </tr> <tr> <th>3</th> <td>1995</td> <td>if not for you</td> <td>jessie kent</td> <td>8 episodes</td> </tr> <tr> <th>4</th> <td>1996</td> <td>tracey takes on</td> <td>judge loring</td> <td>episode: "vanity"</td> </tr> <tr> <th>5</th> <td>1996</td> <td>tales from the crypt</td> <td>laura kendall</td> <td>episode: "horror in the night"</td> </tr> <tr> <th>6</th> <td>1999</td> <td>the scarlet pimpernel</td> <td>lady margaret blakeney</td> <td>3 episodes</td> </tr> <tr> <th>7</th> <td>2001</td> <td>table 12</td> <td>mel</td> <td>episode: "preserves"</td> </tr> <tr> <th>8</th> <td>2003</td> <td>the brotherhood of poland, new hampshire</td> <td>helen shaw</td> <td>7 episodes</td> </tr> <tr> <th>9</th> <td>2006</td> <td>three moons over milford</td> <td>laura davis</td> <td>8 episodes</td> </tr> <tr> <th>10</th> <td>2007</td> <td>law and order: special victims unit</td> <td>dr. faith sutton</td> <td>episode: "harm"</td> </tr> <tr> <th>11</th> <td>2007-2008</td> <td>freezing</td> <td>elizabeth</td> <td>3 episodes</td> </tr> <tr> <th>12</th> <td>2008</td> <td>agatha christie: poirot</td> <td>dame celia westholme</td> <td>episode: "appointment with death"</td> </tr> <tr> <th>13</th> <td>2009</td> <td>10 minute tales</td> <td>the ex-wife</td> <td>episode: "the running of the deer"</td> </tr> <tr> <th>14</th> <td>2010-present</td> <td>downton abbey</td> <td>cora, countess of grantham</td> <td>29 episodes screen actors guild award for outstanding performance by an ensemble in a drama series nominated-golden globe award for best actress - miniseries or television film nominated-primetime emmy award for outstanding lead actress in a miniseries or a movie nominated-satellite award for best actress - miniseries or television film</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 notes = '3 episodes' <table_name> : table | | year | title | role | notes | |---:|:-------------|:-----------------------------------------|:---------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 1979 | california fever | lisa bannister | episode: "the girl from somewhere" | | 1 | 1984 | faerie tale theatre | snow white | episode: "snow white and the seven dwarfs" | | 2 | 1993 | performance | beatrice-joanna | episode: "the changeling" | | 3 | 1995 | if not for you | jessie kent | 8 episodes | | 4 | 1996 | tracey takes on | judge loring | episode: "vanity" | | 5 | 1996 | tales from the crypt | laura kendall | episode: "horror in the night" | | 6 | 1999 | the scarlet pimpernel | lady margaret blakeney | 3 episodes | | 7 | 2001 | table 12 | mel | episode: "preserves" | | 8 | 2003 | the brotherhood of poland, new hampshire | helen shaw | 7 episodes | | 9 | 2006 | three moons over milford | laura davis | 8 episodes | | 10 | 2007 | law and order: special victims unit | dr. faith sutton | episode: "harm" | | 11 | 2007-2008 | freezing | elizabeth | 3 episodes | | 12 | 2008 | agatha christie: poirot | dame celia westholme | episode: "appointment with death" | | 13 | 2009 | 10 minute tales | the ex-wife | episode: "the running of the deer" | | 14 | 2010-present | downton abbey | cora, countess of grantham | 29 episodes screen actors guild award for outstanding performance by an ensemble in a drama series nominated-golden globe award for best actress - miniseries or television film nominated-primetime emmy award for outstanding lead actress in a miniseries or a movie nominated-satellite award for best actress - miniseries or television film |

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

select sum ( pennant ) where launched = 1929 col : name | pennant | namesake | builder | ordered | laid down | launched | commissioned | fate row 1 : leander | 75 | leander of abydos | hm dockyard, devonport | 18 february 1928 | 1 august 1928 | 13 july 1929 | 23 july 1931 | transferred to royal new zealand navy as hmnzs leander 1941-1945 broken up at blyth 1950 row 2 : orion | 85 | orion the hunter | hm dockyard, devonport | 24 march 1931 | 26 september 1931 | 24 november 1932 | 18 january 1934 | broken up at dalmuir, 1949 row 3 : neptune | 20 | neptune, god of the sea | hm dockyard, portsmouth | 2 march 1931 | 24 september 1931 | 31 january 1933 | 23 february 1934 | sunk in minefield off tripoli, 19 december 1941 row 4 : ajax | 22 | ajax the great | vickers armstrong | 1 october 1932 | 7 february 1933 | 1 march 1934 | 12 april 1935 | broken up at newport, 1949 row 5 : achilles | 70 | achilles | cammell laird | 16 february 1931 | 11 june 1931 | 1 september 1932 | 24 march 1936 | transferred to royal new zealand navy as hmnzs achilles 1941-1946 sold to indian navy as hims delhi 1948 row 6 : amphion | 29 | amphion of thebes | hm dockyard, portsmouth | 1 december 1932 | 22 june 1933 | 27 july 1934 | 15 june 1936 | sold to royal australian navy as hmas perth, 1939 sunk in torpedo attack, 1 march 1942 row 7 : apollo | 63 | apollo, god of light | hm dockyard, devonport | 1 march 1933 | 15 august 1933 | 9 october 1934 | 13 january 1936 | sold to royal australian navy as hmas hobart, 1938 broken up at osaka, 1962 row 8 : sydney (ex-phaeton) | 48 | city of sydney | swan hunter | 10 february 1933 | 8 july 1933 | 22 september 1934 | 24 september 1935 | sunk in surface action, 19 november 1941

col : sum ( "pennant" ) row 1 :

select sum ( pennant ) where launched = 1929 <table_name> : table \begin{tabular}{llllllllll} \toprule & name & pennant & namesake & builder & ordered & laid down & launched & commissioned & fate \\ \midrule 0 & leander & 75 & leander of abydos & hm dockyard, devonport & 18 february 1928 & 1 august 1928 & 13 july 1929 & 23 july 1931 & transferred to royal new zealand navy as hmnzs leander 1941-1945 broken up at blyth 1950 \\ 1 & orion & 85 & orion the hunter & hm dockyard, devonport & 24 march 1931 & 26 september 1931 & 24 november 1932 & 18 january 1934 & broken up at dalmuir, 1949 \\ 2 & neptune & 20 & neptune, god of the sea & hm dockyard, portsmouth & 2 march 1931 & 24 september 1931 & 31 january 1933 & 23 february 1934 & sunk in minefield off tripoli, 19 december 1941 \\ 3 & ajax & 22 & ajax the great & vickers armstrong & 1 october 1932 & 7 february 1933 & 1 march 1934 & 12 april 1935 & broken up at newport, 1949 \\ 4 & achilles & 70 & achilles & cammell laird & 16 february 1931 & 11 june 1931 & 1 september 1932 & 24 march 1936 & transferred to royal new zealand navy as hmnzs achilles 1941-1946 sold to indian navy as hims delhi 1948 \\ 5 & amphion & 29 & amphion of thebes & hm dockyard, portsmouth & 1 december 1932 & 22 june 1933 & 27 july 1934 & 15 june 1936 & sold to royal australian navy as hmas perth, 1939 sunk in torpedo attack, 1 march 1942 \\ 6 & apollo & 63 & apollo, god of light & hm dockyard, devonport & 1 march 1933 & 15 august 1933 & 9 october 1934 & 13 january 1936 & sold to royal australian navy as hmas hobart, 1938 broken up at osaka, 1962 \\ 7 & sydney (ex-phaeton) & 48 & city of sydney & swan hunter & 10 february 1933 & 8 july 1933 & 22 september 1934 & 24 september 1935 & sunk in surface action, 19 november 1941 \\ \bottomrule \end{tabular}

\begin{tabular}{ll} \toprule & sum ( "pennant" ) \\ \midrule 0 & \\ \bottomrule \end{tabular}

select sum ( pennant ) where launched = 1929 <table_name> : table <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>name</th> <th>pennant</th> <th>namesake</th> <th>builder</th> <th>ordered</th> <th>laid down</th> <th>launched</th> <th>commissioned</th> <th>fate</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>leander</td> <td>75</td> <td>leander of abydos</td> <td>hm dockyard, devonport</td> <td>18 february 1928</td> <td>1 august 1928</td> <td>13 july 1929</td> <td>23 july 1931</td> <td>transferred to royal new zealand navy as hmnzs leander 1941-1945 broken up at blyth 1950</td> </tr> <tr> <th>1</th> <td>orion</td> <td>85</td> <td>orion the hunter</td> <td>hm dockyard, devonport</td> <td>24 march 1931</td> <td>26 september 1931</td> <td>24 november 1932</td> <td>18 january 1934</td> <td>broken up at dalmuir, 1949</td> </tr> <tr> <th>2</th> <td>neptune</td> <td>20</td> <td>neptune, god of the sea</td> <td>hm dockyard, portsmouth</td> <td>2 march 1931</td> <td>24 september 1931</td> <td>31 january 1933</td> <td>23 february 1934</td> <td>sunk in minefield off tripoli, 19 december 1941</td> </tr> <tr> <th>3</th> <td>ajax</td> <td>22</td> <td>ajax the great</td> <td>vickers armstrong</td> <td>1 october 1932</td> <td>7 february 1933</td> <td>1 march 1934</td> <td>12 april 1935</td> <td>broken up at newport, 1949</td> </tr> <tr> <th>4</th> <td>achilles</td> <td>70</td> <td>achilles</td> <td>cammell laird</td> <td>16 february 1931</td> <td>11 june 1931</td> <td>1 september 1932</td> <td>24 march 1936</td> <td>transferred to royal new zealand navy as hmnzs achilles 1941-1946 sold to indian navy as hims delhi 1948</td> </tr> <tr> <th>5</th> <td>amphion</td> <td>29</td> <td>amphion of thebes</td> <td>hm dockyard, portsmouth</td> <td>1 december 1932</td> <td>22 june 1933</td> <td>27 july 1934</td> <td>15 june 1936</td> <td>sold to royal australian navy as hmas perth, 1939 sunk in torpedo attack, 1 march 1942</td> </tr> <tr> <th>6</th> <td>apollo</td> <td>63</td> <td>apollo, god of light</td> <td>hm dockyard, devonport</td> <td>1 march 1933</td> <td>15 august 1933</td> <td>9 october 1934</td> <td>13 january 1936</td> <td>sold to royal australian navy as hmas hobart, 1938 broken up at osaka, 1962</td> </tr> <tr> <th>7</th> <td>sydney (ex-phaeton)</td> <td>48</td> <td>city of sydney</td> <td>swan hunter</td> <td>10 february 1933</td> <td>8 july 1933</td> <td>22 september 1934</td> <td>24 september 1935</td> <td>sunk in surface action, 19 november 1941</td> </tr> </tbody> </table>

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

select sum ( pennant ) where launched = 1929 <table_name> : table | | name | pennant | namesake | builder | ordered | laid down | launched | commissioned | fate | |---:|:--------------------|----------:|:------------------------|:------------------------|:-----------------|:------------------|:------------------|:------------------|:---------------------------------------------------------------------------------------------------------| | 0 | leander | 75 | leander of abydos | hm dockyard, devonport | 18 february 1928 | 1 august 1928 | 13 july 1929 | 23 july 1931 | transferred to royal new zealand navy as hmnzs leander 1941-1945 broken up at blyth 1950 | | 1 | orion | 85 | orion the hunter | hm dockyard, devonport | 24 march 1931 | 26 september 1931 | 24 november 1932 | 18 january 1934 | broken up at dalmuir, 1949 | | 2 | neptune | 20 | neptune, god of the sea | hm dockyard, portsmouth | 2 march 1931 | 24 september 1931 | 31 january 1933 | 23 february 1934 | sunk in minefield off tripoli, 19 december 1941 | | 3 | ajax | 22 | ajax the great | vickers armstrong | 1 october 1932 | 7 february 1933 | 1 march 1934 | 12 april 1935 | broken up at newport, 1949 | | 4 | achilles | 70 | achilles | cammell laird | 16 february 1931 | 11 june 1931 | 1 september 1932 | 24 march 1936 | transferred to royal new zealand navy as hmnzs achilles 1941-1946 sold to indian navy as hims delhi 1948 | | 5 | amphion | 29 | amphion of thebes | hm dockyard, portsmouth | 1 december 1932 | 22 june 1933 | 27 july 1934 | 15 june 1936 | sold to royal australian navy as hmas perth, 1939 sunk in torpedo attack, 1 march 1942 | | 6 | apollo | 63 | apollo, god of light | hm dockyard, devonport | 1 march 1933 | 15 august 1933 | 9 october 1934 | 13 january 1936 | sold to royal australian navy as hmas hobart, 1938 broken up at osaka, 1962 | | 7 | sydney (ex-phaeton) | 48 | city of sydney | swan hunter | 10 february 1933 | 8 july 1933 | 22 september 1934 | 24 september 1935 | sunk in surface action, 19 november 1941 |

| | sum ( "pennant" ) | |---:|:--------------------| | 0 | |