{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Wine"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Introduction:\n",
"\n",
"This exercise is a adaptation from the UCI Wine dataset.\n",
"The only pupose is to practice deleting data with pandas.\n",
"\n",
"### Step 1. Import the necessary libraries"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 2. Import the dataset from this [address](https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data). "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 3. Assign it to a variable called wine"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
" 1 14.23 1.71 2.43 15.6 127 2.8 3.06 .28 2.29 5.64 1.04 3.92 \\\n",
"0 1 13.20 1.78 2.14 11.2 100 2.65 2.76 0.26 1.28 4.38 1.05 3.40 \n",
"1 1 13.16 2.36 2.67 18.6 101 2.80 3.24 0.30 2.81 5.68 1.03 3.17 \n",
"2 1 14.37 1.95 2.50 16.8 113 3.85 3.49 0.24 2.18 7.80 0.86 3.45 \n",
"3 1 13.24 2.59 2.87 21.0 118 2.80 2.69 0.39 1.82 4.32 1.04 2.93 \n",
"4 1 14.20 1.76 2.45 15.2 112 3.27 3.39 0.34 1.97 6.75 1.05 2.85 \n",
"\n",
" 1065 \n",
"0 1050 \n",
"1 1185 \n",
"2 1480 \n",
"3 735 \n",
"4 1450 "
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 4. Delete the first, fourth, seventh, nineth, eleventh, thirteenth and fourteenth columns"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
" 14.23 1.71 15.6 127 3.06 2.29 5.64\n",
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"2 14.37 1.95 16.8 113 3.49 2.18 7.80\n",
"3 13.24 2.59 21.0 118 2.69 1.82 4.32\n",
"4 14.20 1.76 15.2 112 3.39 1.97 6.75"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 5. Assign the columns as below:\n",
"\n",
"The attributes are (dontated by Riccardo Leardi, riclea '@' anchem.unige.it): \n",
"1) alcohol \n",
"2) malic_acid \n",
"3) alcalinity_of_ash \n",
"4) magnesium \n",
"5) flavanoids \n",
"6) proanthocyanins \n",
"7) hue "
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
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" \n",
" | \n",
" alcohol | \n",
" malic_acid | \n",
" alcalinity_of_ash | \n",
" magnesium | \n",
" flavanoids | \n",
" proanthocyanins | \n",
" hue | \n",
"
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"
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" 1.97 | \n",
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"
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"
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],
"text/plain": [
" alcohol malic_acid alcalinity_of_ash magnesium flavanoids \\\n",
"0 13.20 1.78 11.2 100 2.76 \n",
"1 13.16 2.36 18.6 101 3.24 \n",
"2 14.37 1.95 16.8 113 3.49 \n",
"3 13.24 2.59 21.0 118 2.69 \n",
"4 14.20 1.76 15.2 112 3.39 \n",
"\n",
" proanthocyanins hue \n",
"0 1.28 4.38 \n",
"1 2.81 5.68 \n",
"2 2.18 7.80 \n",
"3 1.82 4.32 \n",
"4 1.97 6.75 "
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 6. Set the values of the first 3 rows from alcohol as NaN"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
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" | \n",
" alcohol | \n",
" malic_acid | \n",
" alcalinity_of_ash | \n",
" magnesium | \n",
" flavanoids | \n",
" proanthocyanins | \n",
" hue | \n",
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" 5.68 | \n",
"
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" 2 | \n",
" NaN | \n",
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" 7.80 | \n",
"
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" 3 | \n",
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" 2.59 | \n",
" 21.0 | \n",
" 118 | \n",
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" 1.82 | \n",
" 4.32 | \n",
"
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" \n",
" 4 | \n",
" 14.20 | \n",
" 1.76 | \n",
" 15.2 | \n",
" 112 | \n",
" 3.39 | \n",
" 1.97 | \n",
" 6.75 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" alcohol malic_acid alcalinity_of_ash magnesium flavanoids \\\n",
"0 NaN 1.78 11.2 100 2.76 \n",
"1 NaN 2.36 18.6 101 3.24 \n",
"2 NaN 1.95 16.8 113 3.49 \n",
"3 13.24 2.59 21.0 118 2.69 \n",
"4 14.20 1.76 15.2 112 3.39 \n",
"\n",
" proanthocyanins hue \n",
"0 1.28 4.38 \n",
"1 2.81 5.68 \n",
"2 2.18 7.80 \n",
"3 1.82 4.32 \n",
"4 1.97 6.75 "
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 7. Now set the value of the rows 3 and 4 of magnesium as NaN"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
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" malic_acid | \n",
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" magnesium | \n",
" flavanoids | \n",
" proanthocyanins | \n",
" hue | \n",
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"
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" 13.24 | \n",
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" 21.0 | \n",
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" 1.82 | \n",
" 4.32 | \n",
"
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" 4 | \n",
" 14.20 | \n",
" 1.76 | \n",
" 15.2 | \n",
" 112.0 | \n",
" 3.39 | \n",
" 1.97 | \n",
" 6.75 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" alcohol malic_acid alcalinity_of_ash magnesium flavanoids \\\n",
"0 NaN 1.78 11.2 100.0 2.76 \n",
"1 NaN 2.36 18.6 101.0 3.24 \n",
"2 NaN 1.95 16.8 NaN 3.49 \n",
"3 13.24 2.59 21.0 NaN 2.69 \n",
"4 14.20 1.76 15.2 112.0 3.39 \n",
"\n",
" proanthocyanins hue \n",
"0 1.28 4.38 \n",
"1 2.81 5.68 \n",
"2 2.18 7.80 \n",
"3 1.82 4.32 \n",
"4 1.97 6.75 "
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 8. Fill the value of NaN with the number 10 in alcohol and 100 in magnesium"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
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"
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"
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],
"text/plain": [
" alcohol malic_acid alcalinity_of_ash magnesium flavanoids \\\n",
"0 10.00 1.78 11.2 100.0 2.76 \n",
"1 10.00 2.36 18.6 101.0 3.24 \n",
"2 10.00 1.95 16.8 100.0 3.49 \n",
"3 13.24 2.59 21.0 100.0 2.69 \n",
"4 14.20 1.76 15.2 112.0 3.39 \n",
"\n",
" proanthocyanins hue \n",
"0 1.28 4.38 \n",
"1 2.81 5.68 \n",
"2 2.18 7.80 \n",
"3 1.82 4.32 \n",
"4 1.97 6.75 "
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 9. Count the number of missing values"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"alcohol 0\n",
"malic_acid 0\n",
"alcalinity_of_ash 0\n",
"magnesium 0\n",
"flavanoids 0\n",
"proanthocyanins 0\n",
"hue 0\n",
"dtype: int64"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 10. Create an array of 10 random numbers up until 10"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([6, 6, 7, 4, 9, 4, 0, 1, 0, 8])"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# the number will be randoms, so yours will be different"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 11. Set the rows of the random numbers in the column"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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" NaN | \n",
" 2.16 | \n",
" 18.0 | \n",
" 105.0 | \n",
" 3.32 | \n",
" 2.38 | \n",
" 5.75 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" alcohol malic_acid alcalinity_of_ash magnesium flavanoids \\\n",
"0 NaN 1.78 11.2 100.0 2.76 \n",
"1 NaN 2.36 18.6 101.0 3.24 \n",
"2 10.00 1.95 16.8 100.0 3.49 \n",
"3 13.24 2.59 21.0 100.0 2.69 \n",
"4 NaN 1.76 15.2 112.0 3.39 \n",
"5 14.39 1.87 14.6 96.0 2.52 \n",
"6 NaN 2.15 17.6 121.0 2.51 \n",
"7 NaN 1.64 14.0 97.0 2.98 \n",
"8 NaN 1.35 16.0 98.0 3.15 \n",
"9 NaN 2.16 18.0 105.0 3.32 \n",
"\n",
" proanthocyanins hue \n",
"0 1.28 4.38 \n",
"1 2.81 5.68 \n",
"2 2.18 7.80 \n",
"3 1.82 4.32 \n",
"4 1.97 6.75 \n",
"5 1.98 5.25 \n",
"6 1.25 5.05 \n",
"7 1.98 5.20 \n",
"8 1.85 7.22 \n",
"9 2.38 5.75 "
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# the number will be randoms, so yours will be different"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 12. How many missing values do we have?"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"alcohol 7\n",
"malic_acid 0\n",
"alcalinity_of_ash 0\n",
"magnesium 0\n",
"flavanoids 0\n",
"proanthocyanins 0\n",
"hue 0\n",
"dtype: int64"
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# the number will be randoms, so yours will be different"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 14. Print only the non-null values in alcohol"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2 10.00\n",
"3 13.24\n",
"5 14.39\n",
"10 14.12\n",
"11 13.75\n",
"12 14.75\n",
"13 14.38\n",
"14 13.63\n",
"15 14.30\n",
"16 13.83\n",
"17 14.19\n",
"18 13.64\n",
"19 14.06\n",
"20 12.93\n",
"21 13.71\n",
"22 12.85\n",
"23 13.50\n",
"24 13.05\n",
"25 13.39\n",
"26 13.30\n",
"27 13.87\n",
"28 14.02\n",
"29 13.73\n",
"30 13.58\n",
"31 13.68\n",
"32 13.76\n",
"33 13.51\n",
"34 13.48\n",
"35 13.28\n",
"36 13.05\n",
" ... \n",
"147 13.32\n",
"148 13.08\n",
"149 13.50\n",
"150 12.79\n",
"151 13.11\n",
"152 13.23\n",
"153 12.58\n",
"154 13.17\n",
"155 13.84\n",
"156 12.45\n",
"157 14.34\n",
"158 13.48\n",
"159 12.36\n",
"160 13.69\n",
"161 12.85\n",
"162 12.96\n",
"163 13.78\n",
"164 13.73\n",
"165 13.45\n",
"166 12.82\n",
"167 13.58\n",
"168 13.40\n",
"169 12.20\n",
"170 12.77\n",
"171 14.16\n",
"172 13.71\n",
"173 13.40\n",
"174 13.27\n",
"175 13.17\n",
"176 14.13\n",
"Name: alcohol, dtype: float64"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# the number will be randoms, so yours will be different"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 13. Delete the rows that contain missing values"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" alcohol | \n",
" malic_acid | \n",
" alcalinity_of_ash | \n",
" magnesium | \n",
" flavanoids | \n",
" proanthocyanins | \n",
" hue | \n",
"
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" \n",
" \n",
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" 2 | \n",
" 10.00 | \n",
" 1.95 | \n",
" 16.8 | \n",
" 100.0 | \n",
" 3.49 | \n",
" 2.18 | \n",
" 7.80 | \n",
"
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" \n",
" 3 | \n",
" 13.24 | \n",
" 2.59 | \n",
" 21.0 | \n",
" 100.0 | \n",
" 2.69 | \n",
" 1.82 | \n",
" 4.32 | \n",
"
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" \n",
" 5 | \n",
" 14.39 | \n",
" 1.87 | \n",
" 14.6 | \n",
" 96.0 | \n",
" 2.52 | \n",
" 1.98 | \n",
" 5.25 | \n",
"
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" \n",
" 10 | \n",
" 14.12 | \n",
" 1.48 | \n",
" 16.8 | \n",
" 95.0 | \n",
" 2.43 | \n",
" 1.57 | \n",
" 5.00 | \n",
"
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" \n",
" 11 | \n",
" 13.75 | \n",
" 1.73 | \n",
" 16.0 | \n",
" 89.0 | \n",
" 2.76 | \n",
" 1.81 | \n",
" 5.60 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" alcohol malic_acid alcalinity_of_ash magnesium flavanoids \\\n",
"2 10.00 1.95 16.8 100.0 3.49 \n",
"3 13.24 2.59 21.0 100.0 2.69 \n",
"5 14.39 1.87 14.6 96.0 2.52 \n",
"10 14.12 1.48 16.8 95.0 2.43 \n",
"11 13.75 1.73 16.0 89.0 2.76 \n",
"\n",
" proanthocyanins hue \n",
"2 2.18 7.80 \n",
"3 1.82 4.32 \n",
"5 1.98 5.25 \n",
"10 1.57 5.00 \n",
"11 1.81 5.60 "
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# the number will be randoms, so yours will be different"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 15. Reset the index, so it starts with 0 again"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" alcohol | \n",
" malic_acid | \n",
" alcalinity_of_ash | \n",
" magnesium | \n",
" flavanoids | \n",
" proanthocyanins | \n",
" hue | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 10.00 | \n",
" 1.95 | \n",
" 16.8 | \n",
" 100.0 | \n",
" 3.49 | \n",
" 2.18 | \n",
" 7.80 | \n",
"
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" \n",
" 1 | \n",
" 13.24 | \n",
" 2.59 | \n",
" 21.0 | \n",
" 100.0 | \n",
" 2.69 | \n",
" 1.82 | \n",
" 4.32 | \n",
"
\n",
" \n",
" 2 | \n",
" 14.39 | \n",
" 1.87 | \n",
" 14.6 | \n",
" 96.0 | \n",
" 2.52 | \n",
" 1.98 | \n",
" 5.25 | \n",
"
\n",
" \n",
" 3 | \n",
" 14.12 | \n",
" 1.48 | \n",
" 16.8 | \n",
" 95.0 | \n",
" 2.43 | \n",
" 1.57 | \n",
" 5.00 | \n",
"
\n",
" \n",
" 4 | \n",
" 13.75 | \n",
" 1.73 | \n",
" 16.0 | \n",
" 89.0 | \n",
" 2.76 | \n",
" 1.81 | \n",
" 5.60 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" alcohol malic_acid alcalinity_of_ash magnesium flavanoids \\\n",
"0 10.00 1.95 16.8 100.0 3.49 \n",
"1 13.24 2.59 21.0 100.0 2.69 \n",
"2 14.39 1.87 14.6 96.0 2.52 \n",
"3 14.12 1.48 16.8 95.0 2.43 \n",
"4 13.75 1.73 16.0 89.0 2.76 \n",
"\n",
" proanthocyanins hue \n",
"0 2.18 7.80 \n",
"1 1.82 4.32 \n",
"2 1.98 5.25 \n",
"3 1.57 5.00 \n",
"4 1.81 5.60 "
]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### BONUS: Create your own question and answer it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
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"nbformat_minor": 0
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