{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.14.2\n",
"0.20.3\n"
]
}
],
"source": [
"import pandas as pd\n",
"import os\n",
"import numpy as np\n",
"\n",
"print np.__version__\n",
"print pd.__version__"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th>7</th>\n",
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" <th>10</th>\n",
" <th>11</th>\n",
" <th>12</th>\n",
" <th>13</th>\n",
" <th>14</th>\n",
" <th>15</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>b</td>\n",
" <td>30.83</td>\n",
" <td>0.000</td>\n",
" <td>u</td>\n",
" <td>g</td>\n",
" <td>w</td>\n",
" <td>v</td>\n",
" <td>1.25</td>\n",
" <td>t</td>\n",
" <td>t</td>\n",
" <td>1</td>\n",
" <td>f</td>\n",
" <td>g</td>\n",
" <td>202.0</td>\n",
" <td>0</td>\n",
" <td>+</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>a</td>\n",
" <td>58.67</td>\n",
" <td>4.460</td>\n",
" <td>u</td>\n",
" <td>g</td>\n",
" <td>q</td>\n",
" <td>h</td>\n",
" <td>3.04</td>\n",
" <td>t</td>\n",
" <td>t</td>\n",
" <td>6</td>\n",
" <td>f</td>\n",
" <td>g</td>\n",
" <td>43.0</td>\n",
" <td>560</td>\n",
" <td>+</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>a</td>\n",
" <td>24.50</td>\n",
" <td>0.500</td>\n",
" <td>u</td>\n",
" <td>g</td>\n",
" <td>q</td>\n",
" <td>h</td>\n",
" <td>1.50</td>\n",
" <td>t</td>\n",
" <td>f</td>\n",
" <td>0</td>\n",
" <td>f</td>\n",
" <td>g</td>\n",
" <td>280.0</td>\n",
" <td>824</td>\n",
" <td>+</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>b</td>\n",
" <td>27.83</td>\n",
" <td>1.540</td>\n",
" <td>u</td>\n",
" <td>g</td>\n",
" <td>w</td>\n",
" <td>v</td>\n",
" <td>3.75</td>\n",
" <td>t</td>\n",
" <td>t</td>\n",
" <td>5</td>\n",
" <td>t</td>\n",
" <td>g</td>\n",
" <td>100.0</td>\n",
" <td>3</td>\n",
" <td>+</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>b</td>\n",
" <td>20.17</td>\n",
" <td>5.625</td>\n",
" <td>u</td>\n",
" <td>g</td>\n",
" <td>w</td>\n",
" <td>v</td>\n",
" <td>1.71</td>\n",
" <td>t</td>\n",
" <td>f</td>\n",
" <td>0</td>\n",
" <td>f</td>\n",
" <td>s</td>\n",
" <td>120.0</td>\n",
" <td>0</td>\n",
" <td>+</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15\n",
"0 b 30.83 0.000 u g w v 1.25 t t 1 f g 202.0 0 +\n",
"1 a 58.67 4.460 u g q h 3.04 t t 6 f g 43.0 560 +\n",
"2 a 24.50 0.500 u g q h 1.50 t f 0 f g 280.0 824 +\n",
"3 b 27.83 1.540 u g w v 3.75 t t 5 t g 100.0 3 +\n",
"4 b 20.17 5.625 u g w v 1.71 t f 0 f s 120.0 0 +"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# reading data into pandas dataframe\n",
"# also we know that the missing values in this dataset is denoted by '?', so we are telling pandas beforehand \n",
"# to treat '?' as NaN values\n",
"DATA_DIR = '../data'\n",
"\n",
"df = pd.read_table(\n",
" os.path.abspath(os.path.join(DATA_DIR, 'day11/credit.csv')),\n",
" sep = ',',\n",
" header=None,\n",
" na_values = '?'\n",
" )\n",
"df.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 12\n",
"1 12\n",
"2 0\n",
"3 6\n",
"4 6\n",
"5 9\n",
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"9 0\n",
"10 0\n",
"11 0\n",
"12 0\n",
"13 13\n",
"14 0\n",
"15 0\n",
"dtype: int64"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# list out number of missing value in each of the series of the df\n",
"df.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 object\n",
"1 float64\n",
"2 float64\n",
"3 object\n",
"4 object\n",
"5 object\n",
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"15 object\n",
"dtype: object"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# just have look at the data types to get an idea of what imputation to make for any particular series\n",
"df.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deleting Rows that have missing values"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 0\n",
"1 0\n",
"2 0\n",
"3 0\n",
"4 0\n",
"5 0\n",
"6 0\n",
"7 0\n",
"8 0\n",
"9 0\n",
"10 0\n",
"11 0\n",
"12 0\n",
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"15 0\n",
"dtype: int64"
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"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_ = df.dropna(inplace=False)\n",
"df_.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imputing with Mean value - (For Continuous Data)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 12\n",
"1 0\n",
"2 0\n",
"3 6\n",
"4 6\n",
"5 9\n",
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"11 0\n",
"12 0\n",
"13 13\n",
"14 0\n",
"15 0\n",
"dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# we will just show example of doing it with column number 1; this can be scaled to 'n' number of columns\n",
"df[1].fillna(df[1].mean(), inplace=True)\n",
"df.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imputing with Mode value - (For Categorical Data)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 0\n",
"1 0\n",
"2 0\n",
"3 6\n",
"4 6\n",
"5 9\n",
"6 9\n",
"7 0\n",
"8 0\n",
"9 0\n",
"10 0\n",
"11 0\n",
"12 0\n",
"13 13\n",
"14 0\n",
"15 0\n",
"dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[0].fillna(df[0].mode()[0], inplace=True)\n",
"df.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imputing by adding one more category - (For Categorical Data)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['u', 'y', nan, 'l'], dtype=object)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 3rd column can have these number of possibilities\n",
"df[3].unique()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 0\n",
"1 0\n",
"2 0\n",
"3 0\n",
"4 6\n",
"5 9\n",
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"dtype: int64"
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},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# treating it as new category all together tells model to learn it's dependency with other features for \n",
"# making prediction; adds to one more column; if one hot representation; else another integer value if label encoding\n",
"df[3].fillna('UNK', inplace=True)\n",
"df.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
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" <th>2</th>\n",
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" <th>15</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>206</th>\n",
" <td>a</td>\n",
" <td>71.58</td>\n",
" <td>0.0</td>\n",
" <td>UNK</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>f</td>\n",
" <td>f</td>\n",
" <td>0</td>\n",
" <td>f</td>\n",
" <td>p</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>+</td>\n",
" </tr>\n",
" <tr>\n",
" <th>270</th>\n",
" <td>b</td>\n",
" <td>37.58</td>\n",
" <td>0.0</td>\n",
" <td>UNK</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>0</td>\n",
" <td>f</td>\n",
" <td>p</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>+</td>\n",
" </tr>\n",
" <tr>\n",
" <th>330</th>\n",
" <td>b</td>\n",
" <td>20.42</td>\n",
" <td>0.0</td>\n",
" <td>UNK</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>0</td>\n",
" <td>f</td>\n",
" <td>p</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>-</td>\n",
" </tr>\n",
" <tr>\n",
" <th>456</th>\n",
" <td>b</td>\n",
" <td>34.58</td>\n",
" <td>0.0</td>\n",
" <td>UNK</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>0</td>\n",
" <td>f</td>\n",
" <td>p</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>-</td>\n",
" </tr>\n",
" <tr>\n",
" <th>592</th>\n",
" <td>b</td>\n",
" <td>23.17</td>\n",
" <td>0.0</td>\n",
" <td>UNK</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>0</td>\n",
" <td>f</td>\n",
" <td>p</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>+</td>\n",
" </tr>\n",
" <tr>\n",
" <th>622</th>\n",
" <td>a</td>\n",
" <td>25.58</td>\n",
" <td>0.0</td>\n",
" <td>UNK</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>0</td>\n",
" <td>f</td>\n",
" <td>p</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>+</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15\n",
"206 a 71.58 0.0 UNK NaN NaN NaN 0.0 f f 0 f p NaN 0 +\n",
"270 b 37.58 0.0 UNK NaN NaN NaN 0.0 f f 0 f p NaN 0 +\n",
"330 b 20.42 0.0 UNK NaN NaN NaN 0.0 f f 0 f p NaN 0 -\n",
"456 b 34.58 0.0 UNK NaN NaN NaN 0.0 f f 0 f p NaN 0 -\n",
"592 b 23.17 0.0 UNK NaN NaN NaN 0.0 f f 0 f p NaN 0 +\n",
"622 a 25.58 0.0 UNK NaN NaN NaN 0.0 f f 0 f p NaN 0 +"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df[3] == 'UNK']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imputing by back filling "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 0\n",
"1 0\n",
"2 0\n",
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},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# we can specify a back-fill to propagate the next values backward\n",
"df[13].fillna(method='bfill', inplace=True)\n",
"df.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imputing by forward filling "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 0\n",
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"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# We can specify a forward-fill to propagate the previous value forward\n",
"df[4].fillna(method='ffill', inplace=True)\n",
"df.isnull().sum()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
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