{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___\n",
"\n",
" \n",
"___"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Missing Data\n",
"\n",
"Let's show a few convenient methods to deal with Missing Data in pandas:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = pd.DataFrame({'A':[1,2,np.nan],\n",
" 'B':[5,np.nan,np.nan],\n",
" 'C':[1,2,3]})"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"
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"
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" \n",
" \n",
" | \n",
" A | \n",
" B | \n",
" C | \n",
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"text/plain": [
" A B C\n",
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"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
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"source": [
"df"
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{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
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" \n",
" \n",
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" A | \n",
" B | \n",
" C | \n",
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" 5.0 | \n",
" 1 | \n",
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"
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"text/plain": [
" A B C\n",
"0 1.0 5.0 1"
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"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"df.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
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" \n",
" \n",
" | \n",
" C | \n",
"
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" 0 | \n",
" 1 | \n",
"
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" 2 | \n",
" 3 | \n",
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"text/plain": [
" C\n",
"0 1\n",
"1 2\n",
"2 3"
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},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dropna(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" A | \n",
" B | \n",
" C | \n",
"
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" \n",
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" 0 | \n",
" 1.0 | \n",
" 5.0 | \n",
" 1 | \n",
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" 2.0 | \n",
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" 2 | \n",
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"text/plain": [
" A B C\n",
"0 1.0 5.0 1\n",
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},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dropna(thresh=2)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" A | \n",
" B | \n",
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" 0 | \n",
" 1 | \n",
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"
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" \n",
" 1 | \n",
" 2 | \n",
" FILL VALUE | \n",
" 2 | \n",
"
\n",
" \n",
" 2 | \n",
" FILL VALUE | \n",
" FILL VALUE | \n",
" 3 | \n",
"
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" \n",
"
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"
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],
"text/plain": [
" A B C\n",
"0 1 5 1\n",
"1 2 FILL VALUE 2\n",
"2 FILL VALUE FILL VALUE 3"
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},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.fillna(value='FILL VALUE')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 1.0\n",
"1 2.0\n",
"2 1.5\n",
"Name: A, dtype: float64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['A'].fillna(value=df['A'].mean())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Great Job!"
]
}
],
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