Data Cleaning-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "By predicting status of the functioning of pumps, the Tanzanian Ministry of Water can improve the maintenance operations of the water pumps and make sure that clean, potable water is available to communities across Tanzania.\n",
    "Objective is to predict the status of Tanzanian water pumps. The water pumps are either of these three categories\n",
    "\n",
    "functional,\n",
    "non-functional, or\n",
    "functional but need repair.\n",
    "We have 40 predictor variables.Out of the 40 features in the data, we have 31 categorical variables, 7 numerical variables, and 2 date variable.\n",
    "\n",
    "This is a standard multi-class classification problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the required Libraries and setting up the Seaborn and Pandas Options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Importing required packages\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "# Setting up Pandas\n",
    "pd.set_option('display.width', 500)\n",
    "pd.set_option('display.max_columns', 100)\n",
    "\n",
    "# Setting up Seaborn\n",
    "sns.set_style(\"whitegrid\")\n",
    "#sns.set_context(\"poster\")\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Importing the dataset\n",
    "train=pd.read_csv('Train.csv')\n",
    "test=pd.read_csv('Test.csv')\n",
    "label=pd.read_csv('Train_label.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>amount_tsh</th>\n",
       "      <th>date_recorded</th>\n",
       "      <th>funder</th>\n",
       "      <th>gps_height</th>\n",
       "      <th>installer</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>wpt_name</th>\n",
       "      <th>num_private</th>\n",
       "      <th>basin</th>\n",
       "      <th>subvillage</th>\n",
       "      <th>region</th>\n",
       "      <th>region_code</th>\n",
       "      <th>district_code</th>\n",
       "      <th>lga</th>\n",
       "      <th>ward</th>\n",
       "      <th>population</th>\n",
       "      <th>public_meeting</th>\n",
       "      <th>recorded_by</th>\n",
       "      <th>scheme_management</th>\n",
       "      <th>scheme_name</th>\n",
       "      <th>permit</th>\n",
       "      <th>construction_year</th>\n",
       "      <th>extraction_type</th>\n",
       "      <th>extraction_type_group</th>\n",
       "      <th>extraction_type_class</th>\n",
       "      <th>management</th>\n",
       "      <th>management_group</th>\n",
       "      <th>payment</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>water_quality</th>\n",
       "      <th>quality_group</th>\n",
       "      <th>quantity</th>\n",
       "      <th>quantity_group</th>\n",
       "      <th>source</th>\n",
       "      <th>source_type</th>\n",
       "      <th>source_class</th>\n",
       "      <th>waterpoint_type</th>\n",
       "      <th>waterpoint_type_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>69572</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>2011-03-14</td>\n",
       "      <td>Roman</td>\n",
       "      <td>1390</td>\n",
       "      <td>Roman</td>\n",
       "      <td>34.938093</td>\n",
       "      <td>-9.856322</td>\n",
       "      <td>none</td>\n",
       "      <td>0</td>\n",
       "      <td>Lake Nyasa</td>\n",
       "      <td>Mnyusi B</td>\n",
       "      <td>Iringa</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>Ludewa</td>\n",
       "      <td>Mundindi</td>\n",
       "      <td>109</td>\n",
       "      <td>True</td>\n",
       "      <td>GeoData Consultants Ltd</td>\n",
       "      <td>VWC</td>\n",
       "      <td>Roman</td>\n",
       "      <td>False</td>\n",
       "      <td>1999</td>\n",
       "      <td>gravity</td>\n",
       "      <td>gravity</td>\n",
       "      <td>gravity</td>\n",
       "      <td>vwc</td>\n",
       "      <td>user-group</td>\n",
       "      <td>pay annually</td>\n",
       "      <td>annually</td>\n",
       "      <td>soft</td>\n",
       "      <td>good</td>\n",
       "      <td>enough</td>\n",
       "      <td>enough</td>\n",
       "      <td>spring</td>\n",
       "      <td>spring</td>\n",
       "      <td>groundwater</td>\n",
       "      <td>communal standpipe</td>\n",
       "      <td>communal standpipe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8776</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-03-06</td>\n",
       "      <td>Grumeti</td>\n",
       "      <td>1399</td>\n",
       "      <td>GRUMETI</td>\n",
       "      <td>34.698766</td>\n",
       "      <td>-2.147466</td>\n",
       "      <td>Zahanati</td>\n",
       "      <td>0</td>\n",
       "      <td>Lake Victoria</td>\n",
       "      <td>Nyamara</td>\n",
       "      <td>Mara</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>Serengeti</td>\n",
       "      <td>Natta</td>\n",
       "      <td>280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GeoData Consultants Ltd</td>\n",
       "      <td>Other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>2010</td>\n",
       "      <td>gravity</td>\n",
       "      <td>gravity</td>\n",
       "      <td>gravity</td>\n",
       "      <td>wug</td>\n",
       "      <td>user-group</td>\n",
       "      <td>never pay</td>\n",
       "      <td>never pay</td>\n",
       "      <td>soft</td>\n",
       "      <td>good</td>\n",
       "      <td>insufficient</td>\n",
       "      <td>insufficient</td>\n",
       "      <td>rainwater harvesting</td>\n",
       "      <td>rainwater harvesting</td>\n",
       "      <td>surface</td>\n",
       "      <td>communal standpipe</td>\n",
       "      <td>communal standpipe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34310</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2013-02-25</td>\n",
       "      <td>Lottery Club</td>\n",
       "      <td>686</td>\n",
       "      <td>World vision</td>\n",
       "      <td>37.460664</td>\n",
       "      <td>-3.821329</td>\n",
       "      <td>Kwa Mahundi</td>\n",
       "      <td>0</td>\n",
       "      <td>Pangani</td>\n",
       "      <td>Majengo</td>\n",
       "      <td>Manyara</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>Simanjiro</td>\n",
       "      <td>Ngorika</td>\n",
       "      <td>250</td>\n",
       "      <td>True</td>\n",
       "      <td>GeoData Consultants Ltd</td>\n",
       "      <td>VWC</td>\n",
       "      <td>Nyumba ya mungu pipe scheme</td>\n",
       "      <td>True</td>\n",
       "      <td>2009</td>\n",
       "      <td>gravity</td>\n",
       "      <td>gravity</td>\n",
       "      <td>gravity</td>\n",
       "      <td>vwc</td>\n",
       "      <td>user-group</td>\n",
       "      <td>pay per bucket</td>\n",
       "      <td>per bucket</td>\n",
       "      <td>soft</td>\n",
       "      <td>good</td>\n",
       "      <td>enough</td>\n",
       "      <td>enough</td>\n",
       "      <td>dam</td>\n",
       "      <td>dam</td>\n",
       "      <td>surface</td>\n",
       "      <td>communal standpipe multiple</td>\n",
       "      <td>communal standpipe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>67743</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-01-28</td>\n",
       "      <td>Unicef</td>\n",
       "      <td>263</td>\n",
       "      <td>UNICEF</td>\n",
       "      <td>38.486161</td>\n",
       "      <td>-11.155298</td>\n",
       "      <td>Zahanati Ya Nanyumbu</td>\n",
       "      <td>0</td>\n",
       "      <td>Ruvuma / Southern Coast</td>\n",
       "      <td>Mahakamani</td>\n",
       "      <td>Mtwara</td>\n",
       "      <td>90</td>\n",
       "      <td>63</td>\n",
       "      <td>Nanyumbu</td>\n",
       "      <td>Nanyumbu</td>\n",
       "      <td>58</td>\n",
       "      <td>True</td>\n",
       "      <td>GeoData Consultants Ltd</td>\n",
       "      <td>VWC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>1986</td>\n",
       "      <td>submersible</td>\n",
       "      <td>submersible</td>\n",
       "      <td>submersible</td>\n",
       "      <td>vwc</td>\n",
       "      <td>user-group</td>\n",
       "      <td>never pay</td>\n",
       "      <td>never pay</td>\n",
       "      <td>soft</td>\n",
       "      <td>good</td>\n",
       "      <td>dry</td>\n",
       "      <td>dry</td>\n",
       "      <td>machine dbh</td>\n",
       "      <td>borehole</td>\n",
       "      <td>groundwater</td>\n",
       "      <td>communal standpipe multiple</td>\n",
       "      <td>communal standpipe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19728</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-07-13</td>\n",
       "      <td>Action In A</td>\n",
       "      <td>0</td>\n",
       "      <td>Artisan</td>\n",
       "      <td>31.130847</td>\n",
       "      <td>-1.825359</td>\n",
       "      <td>Shuleni</td>\n",
       "      <td>0</td>\n",
       "      <td>Lake Victoria</td>\n",
       "      <td>Kyanyamisa</td>\n",
       "      <td>Kagera</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>Karagwe</td>\n",
       "      <td>Nyakasimbi</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>GeoData Consultants Ltd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>gravity</td>\n",
       "      <td>gravity</td>\n",
       "      <td>gravity</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>never pay</td>\n",
       "      <td>never pay</td>\n",
       "      <td>soft</td>\n",
       "      <td>good</td>\n",
       "      <td>seasonal</td>\n",
       "      <td>seasonal</td>\n",
       "      <td>rainwater harvesting</td>\n",
       "      <td>rainwater harvesting</td>\n",
       "      <td>surface</td>\n",
       "      <td>communal standpipe</td>\n",
       "      <td>communal standpipe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  amount_tsh date_recorded        funder  gps_height     installer  longitude   latitude              wpt_name  num_private                    basin  subvillage   region  region_code  district_code        lga        ward  population public_meeting              recorded_by scheme_management                  scheme_name permit  construction_year extraction_type extraction_type_group extraction_type_class management management_group         payment payment_type water_quality  \\\n",
       "0  69572      6000.0    2011-03-14         Roman        1390         Roman  34.938093  -9.856322                  none            0               Lake Nyasa    Mnyusi B   Iringa           11              5     Ludewa    Mundindi         109           True  GeoData Consultants Ltd               VWC                        Roman  False               1999         gravity               gravity               gravity        vwc       user-group    pay annually     annually          soft   \n",
       "1   8776         0.0    2013-03-06       Grumeti        1399       GRUMETI  34.698766  -2.147466              Zahanati            0            Lake Victoria     Nyamara     Mara           20              2  Serengeti       Natta         280            NaN  GeoData Consultants Ltd             Other                          NaN   True               2010         gravity               gravity               gravity        wug       user-group       never pay    never pay          soft   \n",
       "2  34310        25.0    2013-02-25  Lottery Club         686  World vision  37.460664  -3.821329           Kwa Mahundi            0                  Pangani     Majengo  Manyara           21              4  Simanjiro     Ngorika         250           True  GeoData Consultants Ltd               VWC  Nyumba ya mungu pipe scheme   True               2009         gravity               gravity               gravity        vwc       user-group  pay per bucket   per bucket          soft   \n",
       "3  67743         0.0    2013-01-28        Unicef         263        UNICEF  38.486161 -11.155298  Zahanati Ya Nanyumbu            0  Ruvuma / Southern Coast  Mahakamani   Mtwara           90             63   Nanyumbu    Nanyumbu          58           True  GeoData Consultants Ltd               VWC                          NaN   True               1986     submersible           submersible           submersible        vwc       user-group       never pay    never pay          soft   \n",
       "4  19728         0.0    2011-07-13   Action In A           0       Artisan  31.130847  -1.825359               Shuleni            0            Lake Victoria  Kyanyamisa   Kagera           18              1    Karagwe  Nyakasimbi           0           True  GeoData Consultants Ltd               NaN                          NaN   True                  0         gravity               gravity               gravity      other            other       never pay    never pay          soft   \n",
       "\n",
       "  quality_group      quantity quantity_group                source           source_type source_class              waterpoint_type waterpoint_type_group  \n",
       "0          good        enough         enough                spring                spring  groundwater           communal standpipe    communal standpipe  \n",
       "1          good  insufficient   insufficient  rainwater harvesting  rainwater harvesting      surface           communal standpipe    communal standpipe  \n",
       "2          good        enough         enough                   dam                   dam      surface  communal standpipe multiple    communal standpipe  \n",
       "3          good           dry            dry           machine dbh              borehole  groundwater  communal standpipe multiple    communal standpipe  \n",
       "4          good      seasonal       seasonal  rainwater harvesting  rainwater harvesting      surface           communal standpipe    communal standpipe  "
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data Exploration\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['id', 'amount_tsh', 'date_recorded', 'funder', 'gps_height',\n",
       "       'installer', 'longitude', 'latitude', 'wpt_name', 'num_private',\n",
       "       'basin', 'subvillage', 'region', 'region_code', 'district_code',\n",
       "       'lga', 'ward', 'population', 'public_meeting', 'recorded_by',\n",
       "       'scheme_management', 'scheme_name', 'permit', 'construction_year',\n",
       "       'extraction_type', 'extraction_type_group', 'extraction_type_class',\n",
       "       'management', 'management_group', 'payment', 'payment_type',\n",
       "       'water_quality', 'quality_group', 'quantity', 'quantity_group',\n",
       "       'source', 'source_type', 'source_class', 'waterpoint_type',\n",
       "       'waterpoint_type_group'], dtype=object)"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Features\n",
    "train.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>status_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>69572</td>\n",
       "      <td>functional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8776</td>\n",
       "      <td>functional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34310</td>\n",
       "      <td>functional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>67743</td>\n",
       "      <td>non functional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19728</td>\n",
       "      <td>functional</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      id    status_group\n",
       "0  69572      functional\n",
       "1   8776      functional\n",
       "2  34310      functional\n",
       "3  67743  non functional\n",
       "4  19728      functional"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Labels\n",
    "label.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "functional                 32259\n",
       "non functional             22824\n",
       "functional needs repair     4317\n",
       "Name: status_group, dtype: int64"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Count of each type of labels\n",
    "label['status_group'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Missing values\n",
    "label['status_group'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b66f61cb70>"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b66e7b7a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Graph 1: Distribution of label\n",
    "sns.countplot(label['status_group'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "functional                 0.543081\n",
       "non functional             0.384242\n",
       "functional needs repair    0.072677\n",
       "Name: status_group, dtype: float64"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label['status_group'].value_counts()/len(label['status_group'])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "By the above result, we can say that there are 54.31% of Functional Pumps, 38.42% of non-functional fumpus and 7.27% of functional but which needs to be repaired.\n",
    "\n",
    "By the above figures, we can roughly estimate that there is 54.31% chance that if we take a random pump in the database to be a functional one.    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For the further analysis we will combine the training_values and training_lables, so that we can get the better insight of the data while exploring and wil also be easy to train."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Combining training data, train, with label data\n",
    "total=pd.merge(train,label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "\n",
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       "        text-align: left;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>amount_tsh</th>\n",
       "      <th>date_recorded</th>\n",
       "      <th>funder</th>\n",
       "      <th>gps_height</th>\n",
       "      <th>installer</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>wpt_name</th>\n",
       "      <th>num_private</th>\n",
       "      <th>basin</th>\n",
       "      <th>subvillage</th>\n",
       "      <th>region</th>\n",
       "      <th>region_code</th>\n",
       "      <th>district_code</th>\n",
       "      <th>lga</th>\n",
       "      <th>ward</th>\n",
       "      <th>population</th>\n",
       "      <th>public_meeting</th>\n",
       "      <th>recorded_by</th>\n",
       "      <th>scheme_management</th>\n",
       "      <th>scheme_name</th>\n",
       "      <th>permit</th>\n",
       "      <th>construction_year</th>\n",
       "      <th>extraction_type</th>\n",
       "      <th>extraction_type_group</th>\n",
       "      <th>extraction_type_class</th>\n",
       "      <th>management</th>\n",
       "      <th>management_group</th>\n",
       "      <th>payment</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>water_quality</th>\n",
       "      <th>quality_group</th>\n",
       "      <th>quantity</th>\n",
       "      <th>quantity_group</th>\n",
       "      <th>source</th>\n",
       "      <th>source_type</th>\n",
       "      <th>source_class</th>\n",
       "      <th>waterpoint_type</th>\n",
       "      <th>waterpoint_type_group</th>\n",
       "      <th>status_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>69572</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>2011-03-14</td>\n",
       "      <td>Roman</td>\n",
       "      <td>1390</td>\n",
       "      <td>Roman</td>\n",
       "      <td>34.938093</td>\n",
       "      <td>-9.856322</td>\n",
       "      <td>none</td>\n",
       "      <td>0</td>\n",
       "      <td>Lake Nyasa</td>\n",
       "      <td>Mnyusi B</td>\n",
       "      <td>Iringa</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>Ludewa</td>\n",
       "      <td>Mundindi</td>\n",
       "      <td>109</td>\n",
       "      <td>True</td>\n",
       "      <td>GeoData Consultants Ltd</td>\n",
       "      <td>VWC</td>\n",
       "      <td>Roman</td>\n",
       "      <td>False</td>\n",
       "      <td>1999</td>\n",
       "      <td>gravity</td>\n",
       "      <td>gravity</td>\n",
       "      <td>gravity</td>\n",
       "      <td>vwc</td>\n",
       "      <td>user-group</td>\n",
       "      <td>pay annually</td>\n",
       "      <td>annually</td>\n",
       "      <td>soft</td>\n",
       "      <td>good</td>\n",
       "      <td>enough</td>\n",
       "      <td>enough</td>\n",
       "      <td>spring</td>\n",
       "      <td>spring</td>\n",
       "      <td>groundwater</td>\n",
       "      <td>communal standpipe</td>\n",
       "      <td>communal standpipe</td>\n",
       "      <td>functional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8776</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-03-06</td>\n",
       "      <td>Grumeti</td>\n",
       "      <td>1399</td>\n",
       "      <td>GRUMETI</td>\n",
       "      <td>34.698766</td>\n",
       "      <td>-2.147466</td>\n",
       "      <td>Zahanati</td>\n",
       "      <td>0</td>\n",
       "      <td>Lake Victoria</td>\n",
       "      <td>Nyamara</td>\n",
       "      <td>Mara</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>Serengeti</td>\n",
       "      <td>Natta</td>\n",
       "      <td>280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GeoData Consultants Ltd</td>\n",
       "      <td>Other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>2010</td>\n",
       "      <td>gravity</td>\n",
       "      <td>gravity</td>\n",
       "      <td>gravity</td>\n",
       "      <td>wug</td>\n",
       "      <td>user-group</td>\n",
       "      <td>never pay</td>\n",
       "      <td>never pay</td>\n",
       "      <td>soft</td>\n",
       "      <td>good</td>\n",
       "      <td>insufficient</td>\n",
       "      <td>insufficient</td>\n",
       "      <td>rainwater harvesting</td>\n",
       "      <td>rainwater harvesting</td>\n",
       "      <td>surface</td>\n",
       "      <td>communal standpipe</td>\n",
       "      <td>communal standpipe</td>\n",
       "      <td>functional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34310</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2013-02-25</td>\n",
       "      <td>Lottery Club</td>\n",
       "      <td>686</td>\n",
       "      <td>World vision</td>\n",
       "      <td>37.460664</td>\n",
       "      <td>-3.821329</td>\n",
       "      <td>Kwa Mahundi</td>\n",
       "      <td>0</td>\n",
       "      <td>Pangani</td>\n",
       "      <td>Majengo</td>\n",
       "      <td>Manyara</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>Simanjiro</td>\n",
       "      <td>Ngorika</td>\n",
       "      <td>250</td>\n",
       "      <td>True</td>\n",
       "      <td>GeoData Consultants Ltd</td>\n",
       "      <td>VWC</td>\n",
       "      <td>Nyumba ya mungu pipe scheme</td>\n",
       "      <td>True</td>\n",
       "      <td>2009</td>\n",
       "      <td>gravity</td>\n",
       "      <td>gravity</td>\n",
       "      <td>gravity</td>\n",
       "      <td>vwc</td>\n",
       "      <td>user-group</td>\n",
       "      <td>pay per bucket</td>\n",
       "      <td>per bucket</td>\n",
       "      <td>soft</td>\n",
       "      <td>good</td>\n",
       "      <td>enough</td>\n",
       "      <td>enough</td>\n",
       "      <td>dam</td>\n",
       "      <td>dam</td>\n",
       "      <td>surface</td>\n",
       "      <td>communal standpipe multiple</td>\n",
       "      <td>communal standpipe</td>\n",
       "      <td>functional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>67743</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-01-28</td>\n",
       "      <td>Unicef</td>\n",
       "      <td>263</td>\n",
       "      <td>UNICEF</td>\n",
       "      <td>38.486161</td>\n",
       "      <td>-11.155298</td>\n",
       "      <td>Zahanati Ya Nanyumbu</td>\n",
       "      <td>0</td>\n",
       "      <td>Ruvuma / Southern Coast</td>\n",
       "      <td>Mahakamani</td>\n",
       "      <td>Mtwara</td>\n",
       "      <td>90</td>\n",
       "      <td>63</td>\n",
       "      <td>Nanyumbu</td>\n",
       "      <td>Nanyumbu</td>\n",
       "      <td>58</td>\n",
       "      <td>True</td>\n",
       "      <td>GeoData Consultants Ltd</td>\n",
       "      <td>VWC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>1986</td>\n",
       "      <td>submersible</td>\n",
       "      <td>submersible</td>\n",
       "      <td>submersible</td>\n",
       "      <td>vwc</td>\n",
       "      <td>user-group</td>\n",
       "      <td>never pay</td>\n",
       "      <td>never pay</td>\n",
       "      <td>soft</td>\n",
       "      <td>good</td>\n",
       "      <td>dry</td>\n",
       "      <td>dry</td>\n",
       "      <td>machine dbh</td>\n",
       "      <td>borehole</td>\n",
       "      <td>groundwater</td>\n",
       "      <td>communal standpipe multiple</td>\n",
       "      <td>communal standpipe</td>\n",
       "      <td>non functional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19728</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-07-13</td>\n",
       "      <td>Action In A</td>\n",
       "      <td>0</td>\n",
       "      <td>Artisan</td>\n",
       "      <td>31.130847</td>\n",
       "      <td>-1.825359</td>\n",
       "      <td>Shuleni</td>\n",
       "      <td>0</td>\n",
       "      <td>Lake Victoria</td>\n",
       "      <td>Kyanyamisa</td>\n",
       "      <td>Kagera</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>Karagwe</td>\n",
       "      <td>Nyakasimbi</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>GeoData Consultants Ltd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>gravity</td>\n",
       "      <td>gravity</td>\n",
       "      <td>gravity</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>never pay</td>\n",
       "      <td>never pay</td>\n",
       "      <td>soft</td>\n",
       "      <td>good</td>\n",
       "      <td>seasonal</td>\n",
       "      <td>seasonal</td>\n",
       "      <td>rainwater harvesting</td>\n",
       "      <td>rainwater harvesting</td>\n",
       "      <td>surface</td>\n",
       "      <td>communal standpipe</td>\n",
       "      <td>communal standpipe</td>\n",
       "      <td>functional</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  amount_tsh date_recorded        funder  gps_height     installer  longitude   latitude              wpt_name  num_private                    basin  subvillage   region  region_code  district_code        lga        ward  population public_meeting              recorded_by scheme_management                  scheme_name permit  construction_year extraction_type extraction_type_group extraction_type_class management management_group         payment payment_type water_quality  \\\n",
       "0  69572      6000.0    2011-03-14         Roman        1390         Roman  34.938093  -9.856322                  none            0               Lake Nyasa    Mnyusi B   Iringa           11              5     Ludewa    Mundindi         109           True  GeoData Consultants Ltd               VWC                        Roman  False               1999         gravity               gravity               gravity        vwc       user-group    pay annually     annually          soft   \n",
       "1   8776         0.0    2013-03-06       Grumeti        1399       GRUMETI  34.698766  -2.147466              Zahanati            0            Lake Victoria     Nyamara     Mara           20              2  Serengeti       Natta         280            NaN  GeoData Consultants Ltd             Other                          NaN   True               2010         gravity               gravity               gravity        wug       user-group       never pay    never pay          soft   \n",
       "2  34310        25.0    2013-02-25  Lottery Club         686  World vision  37.460664  -3.821329           Kwa Mahundi            0                  Pangani     Majengo  Manyara           21              4  Simanjiro     Ngorika         250           True  GeoData Consultants Ltd               VWC  Nyumba ya mungu pipe scheme   True               2009         gravity               gravity               gravity        vwc       user-group  pay per bucket   per bucket          soft   \n",
       "3  67743         0.0    2013-01-28        Unicef         263        UNICEF  38.486161 -11.155298  Zahanati Ya Nanyumbu            0  Ruvuma / Southern Coast  Mahakamani   Mtwara           90             63   Nanyumbu    Nanyumbu          58           True  GeoData Consultants Ltd               VWC                          NaN   True               1986     submersible           submersible           submersible        vwc       user-group       never pay    never pay          soft   \n",
       "4  19728         0.0    2011-07-13   Action In A           0       Artisan  31.130847  -1.825359               Shuleni            0            Lake Victoria  Kyanyamisa   Kagera           18              1    Karagwe  Nyakasimbi           0           True  GeoData Consultants Ltd               NaN                          NaN   True                  0         gravity               gravity               gravity      other            other       never pay    never pay          soft   \n",
       "\n",
       "  quality_group      quantity quantity_group                source           source_type source_class              waterpoint_type waterpoint_type_group    status_group  \n",
       "0          good        enough         enough                spring                spring  groundwater           communal standpipe    communal standpipe      functional  \n",
       "1          good  insufficient   insufficient  rainwater harvesting  rainwater harvesting      surface           communal standpipe    communal standpipe      functional  \n",
       "2          good        enough         enough                   dam                   dam      surface  communal standpipe multiple    communal standpipe      functional  \n",
       "3          good           dry            dry           machine dbh              borehole  groundwater  communal standpipe multiple    communal standpipe  non functional  \n",
       "4          good      seasonal       seasonal  rainwater harvesting  rainwater harvesting      surface           communal standpipe    communal standpipe      functional  "
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 59400 entries, 0 to 59399\n",
      "Data columns (total 41 columns):\n",
      "id                       59400 non-null int64\n",
      "amount_tsh               59400 non-null float64\n",
      "date_recorded            59400 non-null object\n",
      "funder                   55765 non-null object\n",
      "gps_height               59400 non-null int64\n",
      "installer                55745 non-null object\n",
      "longitude                59400 non-null float64\n",
      "latitude                 59400 non-null float64\n",
      "wpt_name                 59400 non-null object\n",
      "num_private              59400 non-null int64\n",
      "basin                    59400 non-null object\n",
      "subvillage               59029 non-null object\n",
      "region                   59400 non-null object\n",
      "region_code              59400 non-null int64\n",
      "district_code            59400 non-null int64\n",
      "lga                      59400 non-null object\n",
      "ward                     59400 non-null object\n",
      "population               59400 non-null int64\n",
      "public_meeting           56066 non-null object\n",
      "recorded_by              59400 non-null object\n",
      "scheme_management        55523 non-null object\n",
      "scheme_name              31234 non-null object\n",
      "permit                   56344 non-null object\n",
      "construction_year        59400 non-null int64\n",
      "extraction_type          59400 non-null object\n",
      "extraction_type_group    59400 non-null object\n",
      "extraction_type_class    59400 non-null object\n",
      "management               59400 non-null object\n",
      "management_group         59400 non-null object\n",
      "payment                  59400 non-null object\n",
      "payment_type             59400 non-null object\n",
      "water_quality            59400 non-null object\n",
      "quality_group            59400 non-null object\n",
      "quantity                 59400 non-null object\n",
      "quantity_group           59400 non-null object\n",
      "source                   59400 non-null object\n",
      "source_type              59400 non-null object\n",
      "source_class             59400 non-null object\n",
      "waterpoint_type          59400 non-null object\n",
      "waterpoint_type_group    59400 non-null object\n",
      "status_group             59400 non-null object\n",
      "dtypes: float64(3), int64(7), object(31)\n",
      "memory usage: 19.0+ MB\n"
     ]
    }
   ],
   "source": [
    "#Checking for the type of the features\n",
    "total.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dropping The Features With Similar Representation of Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The group of features of (extraction_type, extraction_type_group, extraction_type_class), (payment, payment_type),\n",
    "(water_quality, quality_group), (source, source_class), (subvillage, region, region_code, district_code, lga, ward), and (waterpoint_type, waterpoint_type_group) all contain similar representation of data in different grains. Hence, we risk overfitting our data during training by including all the features in our analysis.\n",
    "\n",
    "id can be droped because it is unique for each instance.\n",
    "num_private is ~99% zeros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "total = total.drop(['id','source','wpt_name', 'num_private', \n",
    "          'quantity'],axis = 1)\n",
    "\n",
    "total = total.drop(['quality_group','lga','ward','management', 'payment', \n",
    "           'extraction_type_group','extraction_type_class'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 59400 entries, 0 to 59399\n",
      "Data columns (total 29 columns):\n",
      "amount_tsh               59400 non-null float64\n",
      "date_recorded            59400 non-null object\n",
      "funder                   55765 non-null object\n",
      "gps_height               59400 non-null int64\n",
      "installer                55745 non-null object\n",
      "longitude                59400 non-null float64\n",
      "latitude                 59400 non-null float64\n",
      "basin                    59400 non-null object\n",
      "subvillage               59029 non-null object\n",
      "region                   59400 non-null object\n",
      "region_code              59400 non-null int64\n",
      "district_code            59400 non-null int64\n",
      "population               59400 non-null int64\n",
      "public_meeting           56066 non-null object\n",
      "recorded_by              59400 non-null object\n",
      "scheme_management        55523 non-null object\n",
      "scheme_name              31234 non-null object\n",
      "permit                   56344 non-null object\n",
      "construction_year        59400 non-null int64\n",
      "extraction_type          59400 non-null object\n",
      "management_group         59400 non-null object\n",
      "payment_type             59400 non-null object\n",
      "water_quality            59400 non-null object\n",
      "quantity_group           59400 non-null object\n",
      "source_type              59400 non-null object\n",
      "source_class             59400 non-null object\n",
      "waterpoint_type          59400 non-null object\n",
      "waterpoint_type_group    59400 non-null object\n",
      "status_group             59400 non-null object\n",
      "dtypes: float64(3), int64(5), object(21)\n",
      "memory usage: 13.6+ MB\n"
     ]
    }
   ],
   "source": [
    "total.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "amount_tsh               41639\n",
       "date_recorded                0\n",
       "funder                    3635\n",
       "gps_height               20438\n",
       "installer                 3655\n",
       "longitude                    0\n",
       "latitude                     0\n",
       "basin                        0\n",
       "subvillage                 371\n",
       "region                       0\n",
       "region_code                  0\n",
       "district_code                0\n",
       "population               21381\n",
       "public_meeting            3334\n",
       "recorded_by                  0\n",
       "scheme_management         3877\n",
       "scheme_name              28166\n",
       "permit                    3056\n",
       "construction_year            0\n",
       "extraction_type              0\n",
       "management_group             0\n",
       "payment_type                 0\n",
       "water_quality                0\n",
       "quantity_group               0\n",
       "source_type                  0\n",
       "source_class                 0\n",
       "waterpoint_type              0\n",
       "waterpoint_type_group        0\n",
       "status_group                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.isnull().sum()\n",
    "total.population.min()\n",
    "total['gps_height'].replace(0.0, np.nan, inplace=True)\n",
    "total['population'].replace(0.0, np.nan, inplace=True)\n",
    "total['amount_tsh'].replace(0.0, np.nan, inplace=True)\n",
    "total.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                           0\n",
       "amount_tsh               10410\n",
       "date_recorded                0\n",
       "funder                     869\n",
       "gps_height                5211\n",
       "installer                  877\n",
       "longitude                    0\n",
       "latitude                     0\n",
       "wpt_name                     0\n",
       "num_private                  0\n",
       "basin                        0\n",
       "subvillage                  99\n",
       "region                       0\n",
       "region_code                  0\n",
       "district_code                0\n",
       "lga                          0\n",
       "ward                         0\n",
       "population                5453\n",
       "public_meeting             821\n",
       "recorded_by                  0\n",
       "scheme_management          969\n",
       "scheme_name               7092\n",
       "permit                     737\n",
       "construction_year            0\n",
       "extraction_type              0\n",
       "extraction_type_group        0\n",
       "extraction_type_class        0\n",
       "management                   0\n",
       "management_group             0\n",
       "payment                      0\n",
       "payment_type                 0\n",
       "water_quality                0\n",
       "quality_group                0\n",
       "quantity                     0\n",
       "quantity_group               0\n",
       "source                       0\n",
       "source_type                  0\n",
       "source_class                 0\n",
       "waterpoint_type              0\n",
       "waterpoint_type_group        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()\n",
    "test.population.min()\n",
    "test['gps_height'].replace(0.0, np.nan, inplace=True)\n",
    "test['population'].replace(0.0, np.nan, inplace=True)\n",
    "test['amount_tsh'].replace(0.0, np.nan, inplace=True)\n",
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Handling numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.groupby?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "amount_tsh                   0\n",
       "date_recorded                0\n",
       "funder                    3635\n",
       "gps_height                   0\n",
       "installer                 3655\n",
       "longitude                    0\n",
       "latitude                     0\n",
       "basin                        0\n",
       "subvillage                 371\n",
       "region                       0\n",
       "region_code                  0\n",
       "district_code                0\n",
       "population                   0\n",
       "public_meeting            3334\n",
       "recorded_by                  0\n",
       "scheme_management         3877\n",
       "scheme_name              28166\n",
       "permit                    3056\n",
       "construction_year            0\n",
       "extraction_type              0\n",
       "management_group             0\n",
       "payment_type                 0\n",
       "water_quality                0\n",
       "quantity_group               0\n",
       "source_type                  0\n",
       "source_class                 0\n",
       "waterpoint_type              0\n",
       "waterpoint_type_group        0\n",
       "status_group                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total[\"gps_height\"].fillna(total.groupby(['region', 'district_code'])[\"gps_height\"].transform(\"mean\"), inplace=True)\n",
    "total[\"gps_height\"].fillna(total.groupby(['region'])[\"gps_height\"].transform(\"mean\"), inplace=True)\n",
    "total[\"gps_height\"].fillna(total[\"gps_height\"].mean(), inplace=True)\n",
    "total[\"population\"].fillna(total.groupby(['region', 'district_code'])[\"population\"].transform(\"median\"), inplace=True)\n",
    "total[\"population\"].fillna(total.groupby(['region'])[\"population\"].transform(\"median\"), inplace=True)\n",
    "total[\"population\"].fillna(total[\"population\"].median(), inplace=True)\n",
    "total[\"amount_tsh\"].fillna(total.groupby(['region', 'district_code'])[\"amount_tsh\"].transform(\"median\"), inplace=True)\n",
    "total[\"amount_tsh\"].fillna(total.groupby(['region'])[\"amount_tsh\"].transform(\"median\"), inplace=True)\n",
    "total[\"amount_tsh\"].fillna(total[\"amount_tsh\"].median(), inplace=True)\n",
    "total.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                          0\n",
       "amount_tsh                  0\n",
       "date_recorded               0\n",
       "funder                    869\n",
       "gps_height                  0\n",
       "installer                 877\n",
       "longitude                   0\n",
       "latitude                    0\n",
       "wpt_name                    0\n",
       "num_private                 0\n",
       "basin                       0\n",
       "subvillage                 99\n",
       "region                      0\n",
       "region_code                 0\n",
       "district_code               0\n",
       "lga                         0\n",
       "ward                        0\n",
       "population                  0\n",
       "public_meeting            821\n",
       "recorded_by                 0\n",
       "scheme_management         969\n",
       "scheme_name              7092\n",
       "permit                    737\n",
       "construction_year           0\n",
       "extraction_type             0\n",
       "extraction_type_group       0\n",
       "extraction_type_class       0\n",
       "management                  0\n",
       "management_group            0\n",
       "payment                     0\n",
       "payment_type                0\n",
       "water_quality               0\n",
       "quality_group               0\n",
       "quantity                    0\n",
       "quantity_group              0\n",
       "source                      0\n",
       "source_type                 0\n",
       "source_class                0\n",
       "waterpoint_type             0\n",
       "waterpoint_type_group       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[\"gps_height\"].fillna(total.groupby(['region', 'district_code'])[\"gps_height\"].transform(\"mean\"), inplace=True)\n",
    "test[\"gps_height\"].fillna(total.groupby(['region'])[\"gps_height\"].transform(\"mean\"), inplace=True)\n",
    "test[\"gps_height\"].fillna(total[\"gps_height\"].mean(), inplace=True)\n",
    "test[\"population\"].fillna(total.groupby(['region', 'district_code'])[\"population\"].transform(\"median\"), inplace=True)\n",
    "test[\"population\"].fillna(total.groupby(['region'])[\"population\"].transform(\"median\"), inplace=True)\n",
    "test[\"population\"].fillna(total[\"population\"].median(), inplace=True)\n",
    "test[\"amount_tsh\"].fillna(total.groupby(['region', 'district_code'])[\"amount_tsh\"].transform(\"median\"), inplace=True)\n",
    "test[\"amount_tsh\"].fillna(total.groupby(['region'])[\"amount_tsh\"].transform(\"median\"), inplace=True)\n",
    "test[\"amount_tsh\"].fillna(total[\"amount_tsh\"].median(), inplace=True)\n",
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "amount_tsh                   0\n",
       "date_recorded                0\n",
       "funder                    3635\n",
       "gps_height                   0\n",
       "installer                 3655\n",
       "longitude                    0\n",
       "latitude                     0\n",
       "basin                        0\n",
       "subvillage                 371\n",
       "region                       0\n",
       "region_code                  0\n",
       "district_code                0\n",
       "population                   0\n",
       "public_meeting            3334\n",
       "recorded_by                  0\n",
       "scheme_management         3877\n",
       "scheme_name              28166\n",
       "permit                    3056\n",
       "construction_year            0\n",
       "extraction_type              0\n",
       "management_group             0\n",
       "payment_type                 0\n",
       "water_quality                0\n",
       "quantity_group               0\n",
       "source_type                  0\n",
       "source_class                 0\n",
       "waterpoint_type              0\n",
       "waterpoint_type_group        0\n",
       "status_group                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Handling the missing values\n",
    "total.apply(lambda x:sum(x.isnull()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for 0 values in numerical columns - Population\n",
    "len(total[total['population']==0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20709"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for 0 values in numerical columns - Construction years\n",
    "len(total[total['construction_year']==0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for 0 values in numerical columns - district_code\n",
    "len(total[total['region_code']==0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11    5300\n",
       "17    5011\n",
       "12    4639\n",
       "3     4379\n",
       "5     4040\n",
       "18    3324\n",
       "19    3047\n",
       "2     3024\n",
       "16    2816\n",
       "10    2640\n",
       "4     2513\n",
       "1     2201\n",
       "13    2093\n",
       "14    1979\n",
       "20    1969\n",
       "15    1808\n",
       "6     1609\n",
       "21    1583\n",
       "80    1238\n",
       "60    1025\n",
       "90     917\n",
       "7      805\n",
       "99     423\n",
       "9      390\n",
       "24     326\n",
       "8      300\n",
       "40       1\n",
       "Name: region_code, dtype: int64"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total['region_code'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Funder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Government Of Tanzania',\n",
       " 'Danida',\n",
       " 'Hesawa',\n",
       " 'Rwssp',\n",
       " 'World Bank',\n",
       " 'Kkkt',\n",
       " 'World Vision',\n",
       " 'Unicef',\n",
       " 'Tasaf',\n",
       " 'District Council']"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Funder\n",
    "list(total['funder'].value_counts().head(10).index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Defining a function to substitute any value other that the top 10 with 'Others'\n",
    "def funder_cl(row):\n",
    "    if row['funder']=='Government Of Tanzania':\n",
    "        return 'gov'\n",
    "    elif row['funder']=='Danida':\n",
    "        return 'danida'\n",
    "    elif row['funder']=='Hesawa':\n",
    "        return 'hesawa'\n",
    "    elif row['funder']=='Rwssp':\n",
    "        return 'rwssp'\n",
    "    elif row['funder']=='World Bank':\n",
    "        return 'world_bank'    \n",
    "    elif row['funder']=='Kkkt':\n",
    "        return 'Kkkt'\n",
    "    elif row['funder']=='World Vision':\n",
    "        return 'World Vision'\n",
    "    elif row['funder']=='Unicef':\n",
    "        return 'Unicef'\n",
    "    elif row['funder']=='Tasaf':\n",
    "        return 'Tasaf'\n",
    "    elif row['funder']=='District Council':\n",
    "        return 'District Council'\n",
    "    else:\n",
    "        return 'other'\n",
    "total['funder'] = total.apply(lambda row: funder_cl(row), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "other               36967\n",
       "gov                  9084\n",
       "danida               3114\n",
       "hesawa               2202\n",
       "rwssp                1374\n",
       "world_bank           1349\n",
       "Kkkt                 1287\n",
       "World Vision         1246\n",
       "Unicef               1057\n",
       "Tasaf                 877\n",
       "District Council      843\n",
       "Name: funder, dtype: int64"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total['funder'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# For creating Pivot table\n",
    "replace_values = {'functional':2, 'functional needs repair':1, 'non functional':0}\n",
    "total['status_values']  = total.status_group.replace(replace_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "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>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">status_values</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status_group</th>\n",
       "      <th>functional</th>\n",
       "      <th>functional needs repair</th>\n",
       "      <th>non functional</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>funder</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>District Council</th>\n",
       "      <td>378</td>\n",
       "      <td>45</td>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kkkt</th>\n",
       "      <td>723</td>\n",
       "      <td>66</td>\n",
       "      <td>498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tasaf</th>\n",
       "      <td>493</td>\n",
       "      <td>64</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unicef</th>\n",
       "      <td>600</td>\n",
       "      <td>99</td>\n",
       "      <td>358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>World Vision</th>\n",
       "      <td>743</td>\n",
       "      <td>131</td>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>danida</th>\n",
       "      <td>1713</td>\n",
       "      <td>159</td>\n",
       "      <td>1242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gov</th>\n",
       "      <td>3720</td>\n",
       "      <td>701</td>\n",
       "      <td>4663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hesawa</th>\n",
       "      <td>936</td>\n",
       "      <td>232</td>\n",
       "      <td>1034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>21603</td>\n",
       "      <td>2614</td>\n",
       "      <td>12750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rwssp</th>\n",
       "      <td>805</td>\n",
       "      <td>109</td>\n",
       "      <td>460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>world_bank</th>\n",
       "      <td>545</td>\n",
       "      <td>97</td>\n",
       "      <td>707</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 status_values                                       \n",
       "status_group        functional functional needs repair non functional\n",
       "funder                                                               \n",
       "District Council           378                      45            420\n",
       "Kkkt                       723                      66            498\n",
       "Tasaf                      493                      64            320\n",
       "Unicef                     600                      99            358\n",
       "World Vision               743                     131            372\n",
       "danida                    1713                     159           1242\n",
       "gov                       3720                     701           4663\n",
       "hesawa                     936                     232           1034\n",
       "other                    21603                    2614          12750\n",
       "rwssp                      805                     109            460\n",
       "world_bank                 545                      97            707"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating pivot table for every row\n",
    "piv_table = pd.pivot_table(total,index=['funder', 'status_group'],values='status_values', aggfunc='count')\n",
    "piv_table\n",
    "piv_df= total[['funder','status_group','status_values']]\n",
    "piv_table = piv_df.pivot_table(index='funder',\n",
    "                           columns='status_group', aggfunc='count')\n",
    "piv_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a pivot table that give detailed representation of no.of pumps in each status group are under which extraction_type, let us plot a Heat map and look at Status of the pumps graphically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b66f69a208>"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JJEmSVBOTbUmSJFXKLpIWk21JkiSpJibbkiRJqpRvkGwx2ZYkSZJqYrItSZKkSvWYbPcx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\ndkRERETEgPwX7LSBya+OOcQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b66f8bc9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "a4_dims = (12, 9)\n",
    "fig, ax = pyplot.subplots(figsize=a4_dims)\n",
    "sns.heatmap(piv_table, cmap='GnBu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DWE                   17402\n",
       "Government             1825\n",
       "RWE                    1206\n",
       "Commu                  1060\n",
       "DANIDA                 1050\n",
       "KKKT                    898\n",
       "Hesawa                  840\n",
       "0                       777\n",
       "TCRS                    707\n",
       "Central government      622\n",
       "Name: installer, dtype: int64"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total['installer'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def installer_cl(row):\n",
    "    if row['installer']=='DWE':\n",
    "        return 'dwe'\n",
    "    elif row['installer']=='Government':\n",
    "        return 'gov'\n",
    "    elif row['installer']=='RWE':\n",
    "        return 'rwe'\n",
    "    elif row['installer']=='Commu':\n",
    "        return 'commu'\n",
    "    elif row['installer']=='DANIDA':\n",
    "        return 'danida'\n",
    "    elif row['installer']=='KKKT':\n",
    "        return 'kkkt'\n",
    "    elif row['installer']=='Hesawa':\n",
    "        return 'hesawa'\n",
    "    elif row['installer']=='TCRS':\n",
    "        return 'tcrs'\n",
    "    elif row['installer']=='Central government':\n",
    "        return 'Central government'\n",
    "    else:\n",
    "        return 'other'  \n",
    "total['installer'] = total.apply(lambda row: installer_cl(row), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let us look at the data and check what are the number of pumps in each workin condition, for every Installer. To achive this we need to create the Pivot table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "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>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">status_values</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status_group</th>\n",
       "      <th>functional</th>\n",
       "      <th>functional needs repair</th>\n",
       "      <th>non functional</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>installer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Central government</th>\n",
       "      <td>168</td>\n",
       "      <td>4</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>commu</th>\n",
       "      <td>724</td>\n",
       "      <td>32</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>danida</th>\n",
       "      <td>542</td>\n",
       "      <td>83</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dwe</th>\n",
       "      <td>9433</td>\n",
       "      <td>1622</td>\n",
       "      <td>6347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gov</th>\n",
       "      <td>535</td>\n",
       "      <td>256</td>\n",
       "      <td>1034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hesawa</th>\n",
       "      <td>475</td>\n",
       "      <td>17</td>\n",
       "      <td>348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kkkt</th>\n",
       "      <td>422</td>\n",
       "      <td>62</td>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>19366</td>\n",
       "      <td>2062</td>\n",
       "      <td>12362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rwe</th>\n",
       "      <td>304</td>\n",
       "      <td>137</td>\n",
       "      <td>765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tcrs</th>\n",
       "      <td>290</td>\n",
       "      <td>42</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   status_values                                       \n",
       "status_group          functional functional needs repair non functional\n",
       "installer                                                              \n",
       "Central government           168                       4            450\n",
       "commu                        724                      32            304\n",
       "danida                       542                      83            425\n",
       "dwe                         9433                    1622           6347\n",
       "gov                          535                     256           1034\n",
       "hesawa                       475                      17            348\n",
       "kkkt                         422                      62            414\n",
       "other                      19366                    2062          12362\n",
       "rwe                          304                     137            765\n",
       "tcrs                         290                      42            375"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "piv_df= total[['installer','status_group','status_values']]\n",
    "piv_table = piv_df.pivot_table(index='installer',\n",
    "                           columns='status_group', aggfunc='count')\n",
    "piv_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kqQoG8IUJvCRJklQRE3hJkiRVwRn4wgRekiRJqogJvCRJkqrgfuALE3hJkiSpIibwkiRJ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\nQCMCPAAANCLAAwBAIwI8AAA08gRt5IyjP+WHIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b66f8c12e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = pyplot.subplots(figsize=a4_dims)\n",
    "ax = sns.heatmap(piv_table, cmap='GnBu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subvillages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [],
   "source": [
    "total['subvillage']=total['subvillage'].fillna('Others')\n",
    "test['subvillage']=test['subvillage'].fillna('Others')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Madukani    508\n",
       "Shuleni     506\n",
       "Majengo     502\n",
       "Kati        373\n",
       "Others      371\n",
       "Name: subvillage, dtype: int64"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total['subvillage'].value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Public Meeting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     51011\n",
       "False     5055\n",
       "Name: public_meeting, dtype: int64"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total['public_meeting'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "total['public_meeting'] = total.public_meeting.fillna('Unknown')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scheme_management"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VWC                 36793\n",
       "WUG                  5206\n",
       "Water authority      3153\n",
       "WUA                  2883\n",
       "Water Board          2748\n",
       "Parastatal           1680\n",
       "Private operator     1063\n",
       "Company              1061\n",
       "Other                 766\n",
       "SWC                    97\n",
       "Trust                  72\n",
       "None                    1\n",
       "Name: scheme_management, dtype: int64"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total['scheme_management'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def scheme_cl(row):\n",
    "    if row['scheme_management']=='VWC':\n",
    "        return 'vwc'\n",
    "    elif row['scheme_management']=='WUG':\n",
    "        return 'wug'\n",
    "    elif row['scheme_management']=='Water authority':\n",
    "        return 'wtr_auth'\n",
    "    elif row['scheme_management']=='WUA':\n",
    "        return 'wua'\n",
    "    elif row['scheme_management']=='Water Board':\n",
    "        return 'wtr_brd'\n",
    "    elif row['scheme_management']=='Parastatal':\n",
    "        return 'Parastatal'\n",
    "    elif row['scheme_management']=='Private operator':\n",
    "        return 'pri_optr'\n",
    "    elif row['scheme_management']=='SWC':\n",
    "        return 'swc'\n",
    "    elif row['scheme_management']=='Company':\n",
    "        return 'company'\n",
    "    elif row['scheme_management']=='Trust':\n",
    "        return 'trust'\n",
    "    else:\n",
    "        return 'other'\n",
    "total['scheme_management'] = total.apply(lambda row: scheme_cl(row), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us look at the data and check what are the number of pumps in each working condition, under each scheme management group. To achive this we need to create the Pivot table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "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>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">status_values</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status_group</th>\n",
       "      <th>functional</th>\n",
       "      <th>functional needs repair</th>\n",
       "      <th>non functional</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scheme_management</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Parastatal</th>\n",
       "      <td>966</td>\n",
       "      <td>202</td>\n",
       "      <td>512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company</th>\n",
       "      <td>534</td>\n",
       "      <td>37</td>\n",
       "      <td>490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>2332</td>\n",
       "      <td>245</td>\n",
       "      <td>2067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pri_optr</th>\n",
       "      <td>729</td>\n",
       "      <td>23</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>swc</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trust</th>\n",
       "      <td>46</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vwc</th>\n",
       "      <td>18960</td>\n",
       "      <td>2334</td>\n",
       "      <td>15499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wtr_auth</th>\n",
       "      <td>1618</td>\n",
       "      <td>448</td>\n",
       "      <td>1087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wtr_brd</th>\n",
       "      <td>2053</td>\n",
       "      <td>111</td>\n",
       "      <td>584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wua</th>\n",
       "      <td>1995</td>\n",
       "      <td>239</td>\n",
       "      <td>649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wug</th>\n",
       "      <td>3006</td>\n",
       "      <td>672</td>\n",
       "      <td>1528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  status_values                                       \n",
       "status_group         functional functional needs repair non functional\n",
       "scheme_management                                                     \n",
       "Parastatal                  966                     202            512\n",
       "company                     534                      37            490\n",
       "other                      2332                     245           2067\n",
       "pri_optr                    729                      23            311\n",
       "swc                          20                       1             76\n",
       "trust                        46                       5             21\n",
       "vwc                       18960                    2334          15499\n",
       "wtr_auth                   1618                     448           1087\n",
       "wtr_brd                    2053                     111            584\n",
       "wua                        1995                     239            649\n",
       "wug                        3006                     672           1528"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "piv_df= total[['scheme_management','status_group','status_values']]\n",
    "piv_table = piv_df.pivot_table(index='scheme_management',\n",
    "                           columns='status_group', aggfunc='count')\n",
    "piv_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### we have a pivot table that give detailed representation of no.of pumps in each status group are under which scheme management, let us plot a Heat map and look at Status of the pumps graphically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fpAyTmAj8JiIuocwL11/E21a1gJESEWcDX87MuRGxLXA18BRKJH8/MDMzr4uI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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b670d8fd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = pyplot.subplots(figsize=a4_dims)\n",
    "ax = sns.heatmap(piv_table, cmap='GnBu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scheme_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "K                              682\n",
       "None                           644\n",
       "Borehole                       546\n",
       "Chalinze wate                  405\n",
       "M                              400\n",
       "DANIDA                         379\n",
       "Government                     320\n",
       "Ngana water supplied scheme    270\n",
       "wanging'ombe water supply s    261\n",
       "wanging'ombe supply scheme     234\n",
       "Name: scheme_name, dtype: int64"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total['scheme_name'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "total['scheme_name']=total['scheme_name'].fillna('Others')\n",
    "test['scheme_name']=test['scheme_name'].fillna('Others')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Permit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     38852\n",
       "False    17492\n",
       "Name: permit, dtype: int64"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total['permit'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "total['permit']=total['permit'].fillna('Unknown')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking if there is any null value left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "amount_tsh               0\n",
       "date_recorded            0\n",
       "funder                   0\n",
       "gps_height               0\n",
       "installer                0\n",
       "longitude                0\n",
       "latitude                 0\n",
       "basin                    0\n",
       "subvillage               0\n",
       "region                   0\n",
       "region_code              0\n",
       "district_code            0\n",
       "population               0\n",
       "public_meeting           0\n",
       "recorded_by              0\n",
       "scheme_management        0\n",
       "scheme_name              0\n",
       "permit                   0\n",
       "construction_year        0\n",
       "extraction_type          0\n",
       "management_group         0\n",
       "payment_type             0\n",
       "water_quality            0\n",
       "quantity_group           0\n",
       "source_type              0\n",
       "source_class             0\n",
       "waterpoint_type          0\n",
       "waterpoint_type_group    0\n",
       "status_group             0\n",
       "status_values            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.apply(lambda x:sum(x.isnull()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(total.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(total.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_recorded              356\n",
       "funder                      11\n",
       "installer                   10\n",
       "basin                        9\n",
       "subvillage               19288\n",
       "region                      21\n",
       "public_meeting               3\n",
       "recorded_by                  1\n",
       "scheme_management           11\n",
       "scheme_name               2697\n",
       "permit                       3\n",
       "extraction_type             18\n",
       "management_group             5\n",
       "payment_type                 7\n",
       "water_quality                8\n",
       "quantity_group               5\n",
       "source_type                  7\n",
       "source_class                 3\n",
       "waterpoint_type              7\n",
       "waterpoint_type_group        6\n",
       "status_group                 3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##lets check the data types again.\n",
    "cols = total.select_dtypes(include = ['object'])\n",
    "cols.apply(lambda x: len(x.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1999\n",
       "1    2010\n",
       "2    2009\n",
       "3    1986\n",
       "4       0\n",
       "Name: construction_year, dtype: int64"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total['construction_year'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## changing the construction year to numeric value\n",
    "total['construction_year'] = pd.to_numeric(total.construction_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1999\n",
       "1    2010\n",
       "2    2009\n",
       "3    1986\n",
       "4       0\n",
       "Name: construction_year, dtype: int64"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total['construction_year'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def construction_cl(row):\n",
    "    if row['construction_year'] >= 1960 and row['construction_year'] < 1970:\n",
    "        return '60s'\n",
    "    elif row['construction_year'] >= 1970 and row['construction_year'] < 1980:\n",
    "        return '70s'\n",
    "    elif row['construction_year'] >= 1980 and row['construction_year'] < 1990:\n",
    "        return '80s'\n",
    "    elif row['construction_year'] >= 1990 and row['construction_year'] < 2000:\n",
    "        return '90s'\n",
    "    elif row['construction_year'] >= 2000 and row['construction_year'] < 2010:\n",
    "        return '00s'\n",
    "    elif row['construction_year'] >= 2010:\n",
    "        return '10s'\n",
    "    else:\n",
    "        return 'unknown'\n",
    "    \n",
    "total['construction_year'] = total.apply(lambda row: construction_cl(row), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        90s\n",
       "1        10s\n",
       "2        00s\n",
       "3        80s\n",
       "4    unknown\n",
       "Name: construction_year, dtype: object"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total['construction_year'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "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>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">status_values</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status_group</th>\n",
       "      <th>functional</th>\n",
       "      <th>functional needs repair</th>\n",
       "      <th>non functional</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>construction_year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>00s</th>\n",
       "      <td>9989</td>\n",
       "      <td>977</td>\n",
       "      <td>4364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10s</th>\n",
       "      <td>3794</td>\n",
       "      <td>220</td>\n",
       "      <td>1147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60s</th>\n",
       "      <td>156</td>\n",
       "      <td>42</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70s</th>\n",
       "      <td>1406</td>\n",
       "      <td>348</td>\n",
       "      <td>2652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80s</th>\n",
       "      <td>2220</td>\n",
       "      <td>423</td>\n",
       "      <td>2935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90s</th>\n",
       "      <td>4139</td>\n",
       "      <td>518</td>\n",
       "      <td>3021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>10555</td>\n",
       "      <td>1789</td>\n",
       "      <td>8365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  status_values                                       \n",
       "status_group         functional functional needs repair non functional\n",
       "construction_year                                                     \n",
       "00s                        9989                     977           4364\n",
       "10s                        3794                     220           1147\n",
       "60s                         156                      42            340\n",
       "70s                        1406                     348           2652\n",
       "80s                        2220                     423           2935\n",
       "90s                        4139                     518           3021\n",
       "unknown                   10555                    1789           8365"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "piv_df= total[['construction_year','status_group','status_values']]\n",
    "piv_table = piv_df.pivot_table(index='construction_year',\n",
    "                           columns='status_group', aggfunc='count')\n",
    "piv_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we have a pivot table that give detailed representation of no.of pumps in each status group are constructed in which Decade, let us plot a Heat map and look at Status of the pumps graphically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MW6bSj16L0DsjYhPgamDHzLwgIuZNpcFe3HT3lL4Xqa/8ySaPnOkuSNPi6lXXzHQXpGmx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\nWYRKkiSpuv8fkkWpEpneazkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b66e311978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = pyplot.subplots(figsize=a4_dims)\n",
    "ax = sns.heatmap(piv_table, cmap='GnBu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Datetime recorded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Converting the Month column into date time object.\n",
    "total['month']=pd.to_datetime(total['date_recorded']).dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                   59400\n",
       "unique                    356\n",
       "top       2011-03-15 00:00:00\n",
       "freq                      572\n",
       "first     2002-10-14 00:00:00\n",
       "last      2013-12-03 00:00:00\n",
       "Name: date_recorded, dtype: object"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total['date_recorded'] = pd.to_datetime(total['date_recorded'])\n",
    "total['date_recorded'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### add a new column named data_since_recorded (which is obtained from subratcting date_recorded from latest recorded date) so that we get a new column which contains no. of days since recorded. The most recent data is 2013-12-03. Subtract each date from this point to obtain a 'days_since_recorded' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    59400.000000\n",
       "mean       613.616970\n",
       "std        334.216374\n",
       "min          0.000000\n",
       "25%        297.000000\n",
       "50%        419.000000\n",
       "75%        977.000000\n",
       "max       4068.000000\n",
       "Name: days_since_recorded, dtype: float64"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total['date_recorded'] = pd.datetime(2013, 12, 3) - pd.to_datetime(total['date_recorded'])\n",
    "total.columns = ['days_since_recorded' if x=='date_recorded' else x for x in total.columns]\n",
    "total['days_since_recorded'] = total['days_since_recorded'].astype('timedelta64[D]').astype(int)\n",
    "total['days_since_recorded'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    995\n",
       "1    272\n",
       "2    281\n",
       "3    309\n",
       "4    874\n",
       "Name: days_since_recorded, dtype: int32"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total['days_since_recorded'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lake Victoria              10248\n",
       "Pangani                     8940\n",
       "Rufiji                      7976\n",
       "Internal                    7785\n",
       "Lake Tanganyika             6432\n",
       "Wami / Ruvu                 5987\n",
       "Lake Nyasa                  5085\n",
       "Ruvuma / Southern Coast     4493\n",
       "Lake Rukwa                  2454\n",
       "Name: basin, dtype: int64"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total['basin'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### let us look at the data and check what are the number of pumps in each working condition are under each basin. To achive this we need to create the Pivot table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "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>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">status_values</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status_group</th>\n",
       "      <th>functional</th>\n",
       "      <th>functional needs repair</th>\n",
       "      <th>non functional</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>basin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Internal</th>\n",
       "      <td>4482</td>\n",
       "      <td>557</td>\n",
       "      <td>2746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lake Nyasa</th>\n",
       "      <td>3324</td>\n",
       "      <td>250</td>\n",
       "      <td>1511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lake Rukwa</th>\n",
       "      <td>1000</td>\n",
       "      <td>270</td>\n",
       "      <td>1184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lake Tanganyika</th>\n",
       "      <td>3107</td>\n",
       "      <td>742</td>\n",
       "      <td>2583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lake Victoria</th>\n",
       "      <td>5100</td>\n",
       "      <td>989</td>\n",
       "      <td>4159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pangani</th>\n",
       "      <td>5372</td>\n",
       "      <td>477</td>\n",
       "      <td>3091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rufiji</th>\n",
       "      <td>5068</td>\n",
       "      <td>437</td>\n",
       "      <td>2471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ruvuma / Southern Coast</th>\n",
       "      <td>1670</td>\n",
       "      <td>326</td>\n",
       "      <td>2497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wami / Ruvu</th>\n",
       "      <td>3136</td>\n",
       "      <td>269</td>\n",
       "      <td>2582</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        status_values                                       \n",
       "status_group               functional functional needs repair non functional\n",
       "basin                                                                       \n",
       "Internal                         4482                     557           2746\n",
       "Lake Nyasa                       3324                     250           1511\n",
       "Lake Rukwa                       1000                     270           1184\n",
       "Lake Tanganyika                  3107                     742           2583\n",
       "Lake Victoria                    5100                     989           4159\n",
       "Pangani                          5372                     477           3091\n",
       "Rufiji                           5068                     437           2471\n",
       "Ruvuma / Southern Coast          1670                     326           2497\n",
       "Wami / Ruvu                      3136                     269           2582"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "piv_df= total[['basin','status_group','status_values']]\n",
    "piv_table = piv_df.pivot_table(index='basin',\n",
    "                           columns='status_group', aggfunc='count')\n",
    "piv_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GBiYGkiRJkkwMJEmSpF6cYyBJkiSplkwMJEmSpArvYyBJkiSplkwMJEmSpIoaTjGwMJAk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b671d05c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = pyplot.subplots(figsize=a4_dims)\n",
    "ax = sns.heatmap(piv_table, cmap='GnBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Basin can be a crucial feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GeoData Consultants Ltd    59400\n",
       "Name: recorded_by, dtype: int64"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total['recorded_by'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Every row is same, can be dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "total=total.drop('recorded_by',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extraction type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gravity                      26780\n",
       "nira/tanira                   8154\n",
       "other                         6430\n",
       "submersible                   4764\n",
       "swn 80                        3670\n",
       "mono                          2865\n",
       "india mark ii                 2400\n",
       "afridev                       1770\n",
       "ksb                           1415\n",
       "other - rope pump              451\n",
       "other - swn 81                 229\n",
       "windmill                       117\n",
       "india mark iii                  98\n",
       "cemo                            90\n",
       "other - play pump               85\n",
       "walimi                          48\n",
       "climax                          32\n",
       "other - mkulima/shinyanga        2\n",
       "Name: extraction_type, dtype: int64"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total['extraction_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def extraction_cl(row):\n",
    "    if row['extraction_type']=='gravity':\n",
    "        return 'gravity'\n",
    "    elif row['extraction_type']=='nira/tanira':\n",
    "        return 'nira'\n",
    "    elif row['extraction_type']=='submersible':\n",
    "        return 'submersible'\n",
    "    elif row['extraction_type']=='swn 80':\n",
    "        return 'swn'\n",
    "    elif row['extraction_type']=='mono':\n",
    "        return 'mono'\n",
    "    elif row['extraction_type']=='india mark ii':\n",
    "        return 'indiamark2'\n",
    "    elif row['extraction_type']=='afridev':\n",
    "        return 'afridev'\n",
    "    elif row['extraction_type']=='ksb':\n",
    "        return 'ksb'\n",
    "    elif row['extraction_type']=='windmill':\n",
    "        return 'windmill'\n",
    "    elif row['extraction_type']=='india mark iii':\n",
    "        return 'indiamark3'\n",
    "    else:\n",
    "        return 'other'\n",
    "total['extraction_type'] = total.apply(lambda row: extraction_cl(row), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "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>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">status_values</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status_group</th>\n",
       "      <th>functional</th>\n",
       "      <th>functional needs repair</th>\n",
       "      <th>non functional</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>extraction_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>afridev</th>\n",
       "      <td>1200</td>\n",
       "      <td>42</td>\n",
       "      <td>528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gravity</th>\n",
       "      <td>16048</td>\n",
       "      <td>2701</td>\n",
       "      <td>8031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>indiamark2</th>\n",
       "      <td>1448</td>\n",
       "      <td>79</td>\n",
       "      <td>873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>indiamark3</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ksb</th>\n",
       "      <td>703</td>\n",
       "      <td>26</td>\n",
       "      <td>686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mono</th>\n",
       "      <td>1082</td>\n",
       "      <td>129</td>\n",
       "      <td>1654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nira</th>\n",
       "      <td>5421</td>\n",
       "      <td>641</td>\n",
       "      <td>2092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>1547</td>\n",
       "      <td>252</td>\n",
       "      <td>5568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>submersible</th>\n",
       "      <td>2626</td>\n",
       "      <td>227</td>\n",
       "      <td>1911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>swn</th>\n",
       "      <td>2090</td>\n",
       "      <td>212</td>\n",
       "      <td>1368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windmill</th>\n",
       "      <td>50</td>\n",
       "      <td>7</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                status_values                                       \n",
       "status_group       functional functional needs repair non functional\n",
       "extraction_type                                                     \n",
       "afridev                  1200                      42            528\n",
       "gravity                 16048                    2701           8031\n",
       "indiamark2               1448                      79            873\n",
       "indiamark3                 44                       1             53\n",
       "ksb                       703                      26            686\n",
       "mono                     1082                     129           1654\n",
       "nira                     5421                     641           2092\n",
       "other                    1547                     252           5568\n",
       "submersible              2626                     227           1911\n",
       "swn                      2090                     212           1368\n",
       "windmill                   50                       7             60"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "piv_df= total[['extraction_type','status_group','status_values']]\n",
    "piv_table = piv_df.pivot_table(index='extraction_type',\n",
    "                           columns='status_group', aggfunc='count')\n",
    "piv_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SJEm1DJwLE2dJkiSpARNnSZIk1fLKgcVybZwjYkdg7cw8cYT1NgROyMw5EXEW8M7MXNzG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b670baf128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = pyplot.subplots(figsize=a4_dims)\n",
    "ax = sns.heatmap(piv_table, cmap='GnBu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Management groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user-group    52490\n",
       "commercial     3638\n",
       "parastatal     1768\n",
       "other           943\n",
       "unknown         561\n",
       "Name: management_group, dtype: int64"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total['management_group'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## almost all are managed by user-group we can drop it\n",
    "total = total.drop(['management_group'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "soft                  50818\n",
       "salty                  4856\n",
       "unknown                1876\n",
       "milky                   804\n",
       "coloured                490\n",
       "salty abandoned         339\n",
       "fluoride                200\n",
       "fluoride abandoned       17\n",
       "Name: water_quality, dtype: int64"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total['water_quality'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "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>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">status_values</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status_group</th>\n",
       "      <th>functional</th>\n",
       "      <th>functional needs repair</th>\n",
       "      <th>non functional</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>water_quality</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>coloured</th>\n",
       "      <td>246.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fluoride</th>\n",
       "      <td>151.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fluoride abandoned</th>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>milky</th>\n",
       "      <td>438.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salty</th>\n",
       "      <td>2220.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>2411.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salty abandoned</th>\n",
       "      <td>174.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>soft</th>\n",
       "      <td>28760.0</td>\n",
       "      <td>3904.0</td>\n",
       "      <td>18154.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>264.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1577.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   status_values                                       \n",
       "status_group          functional functional needs repair non functional\n",
       "water_quality                                                          \n",
       "coloured                   246.0                    54.0          190.0\n",
       "fluoride                   151.0                    13.0           36.0\n",
       "fluoride abandoned           6.0                     NaN           11.0\n",
       "milky                      438.0                    14.0          352.0\n",
       "salty                     2220.0                   225.0         2411.0\n",
       "salty abandoned            174.0                    72.0           93.0\n",
       "soft                     28760.0                  3904.0        18154.0\n",
       "unknown                    264.0                    35.0         1577.0"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "piv_df= total[['water_quality','status_group','status_values']]\n",
    "piv_table = piv_df.pivot_table(index='water_quality',\n",
    "                           columns='status_group', aggfunc='count')\n",
    "piv_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## We can drop the location data, at this time I am not going to use location, maybe in future in I will also use this. Didnt remove logitude and  latitude\n",
    "total = total.drop(['region_code', 'district_code'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Dropping the status_values, operation_time, month which are added for the caluculations.\n",
    "total = total.drop(['status_values','month'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performing the same tasks on the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test = test.drop(['region_code', 'district_code',\n",
    "                  'num_private', 'id', 'payment', 'management_group', 'management', \n",
    "                  'extraction_type_class', 'extraction_type_group', 'recorded_by', 'lga',\n",
    "                  'ward', 'wpt_name', 'quantity',\n",
    "                 'quality_group', 'source'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##cleaning the columns as the training data set\n",
    "test.date_recorded = pd.datetime(2013, 12, 3) - pd.to_datetime(test.date_recorded)\n",
    "test.columns = ['days_since_recorded' if x=='date_recorded' else x for x in test.columns]\n",
    "test.days_since_recorded = test.days_since_recorded.astype('timedelta64[D]').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test.permit = test.permit.fillna('Unknown')\n",
    "test.public_meeting = test.public_meeting.fillna('Unknown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test['scheme_management'] = test.apply(lambda row: scheme_cl(row), axis=1)\n",
    "test['construction_year'] = test.apply(lambda row: construction_cl(row), axis=1)\n",
    "test['installer'] = test.apply(lambda row: installer_cl(row), axis=1)\n",
    "test['funder'] = test.apply(lambda row: funder_cl(row), axis=1)\n",
    "test['extraction_type'] = test.apply(lambda row: extraction_cl(row), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\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>amount_tsh</th>\n",
       "      <th>days_since_recorded</th>\n",
       "      <th>funder</th>\n",
       "      <th>gps_height</th>\n",
       "      <th>installer</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>basin</th>\n",
       "      <th>subvillage</th>\n",
       "      <th>region</th>\n",
       "      <th>population</th>\n",
       "      <th>public_meeting</th>\n",
       "      <th>scheme_management</th>\n",
       "      <th>scheme_name</th>\n",
       "      <th>permit</th>\n",
       "      <th>construction_year</th>\n",
       "      <th>extraction_type</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>water_quality</th>\n",
       "      <th>quantity_group</th>\n",
       "      <th>source_type</th>\n",
       "      <th>source_class</th>\n",
       "      <th>waterpoint_type</th>\n",
       "      <th>waterpoint_type_group</th>\n",
       "      <th>status_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6000.0</td>\n",
       "      <td>995</td>\n",
       "      <td>other</td>\n",
       "      <td>1390.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>34.938093</td>\n",
       "      <td>-9.856322</td>\n",
       "      <td>Lake Nyasa</td>\n",
       "      <td>Mnyusi B</td>\n",
       "      <td>Iringa</td>\n",
       "      <td>109.0</td>\n",
       "      <td>True</td>\n",
       "      <td>vwc</td>\n",
       "      <td>Roman</td>\n",
       "      <td>False</td>\n",
       "      <td>90s</td>\n",
       "      <td>gravity</td>\n",
       "      <td>annually</td>\n",
       "      <td>soft</td>\n",
       "      <td>enough</td>\n",
       "      <td>spring</td>\n",
       "      <td>groundwater</td>\n",
       "      <td>communal standpipe</td>\n",
       "      <td>communal standpipe</td>\n",
       "      <td>functional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200.0</td>\n",
       "      <td>272</td>\n",
       "      <td>other</td>\n",
       "      <td>1399.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>34.698766</td>\n",
       "      <td>-2.147466</td>\n",
       "      <td>Lake Victoria</td>\n",
       "      <td>Nyamara</td>\n",
       "      <td>Mara</td>\n",
       "      <td>280.0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>other</td>\n",
       "      <td>Others</td>\n",
       "      <td>True</td>\n",
       "      <td>10s</td>\n",
       "      <td>gravity</td>\n",
       "      <td>never pay</td>\n",
       "      <td>soft</td>\n",
       "      <td>insufficient</td>\n",
       "      <td>rainwater harvesting</td>\n",
       "      <td>surface</td>\n",
       "      <td>communal standpipe</td>\n",
       "      <td>communal standpipe</td>\n",
       "      <td>functional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25.0</td>\n",
       "      <td>281</td>\n",
       "      <td>other</td>\n",
       "      <td>686.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>37.460664</td>\n",
       "      <td>-3.821329</td>\n",
       "      <td>Pangani</td>\n",
       "      <td>Majengo</td>\n",
       "      <td>Manyara</td>\n",
       "      <td>250.0</td>\n",
       "      <td>True</td>\n",
       "      <td>vwc</td>\n",
       "      <td>Nyumba ya mungu pipe scheme</td>\n",
       "      <td>True</td>\n",
       "      <td>00s</td>\n",
       "      <td>gravity</td>\n",
       "      <td>per bucket</td>\n",
       "      <td>soft</td>\n",
       "      <td>enough</td>\n",
       "      <td>dam</td>\n",
       "      <td>surface</td>\n",
       "      <td>communal standpipe multiple</td>\n",
       "      <td>communal standpipe</td>\n",
       "      <td>functional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.0</td>\n",
       "      <td>309</td>\n",
       "      <td>Unicef</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>38.486161</td>\n",
       "      <td>-11.155298</td>\n",
       "      <td>Ruvuma / Southern Coast</td>\n",
       "      <td>Mahakamani</td>\n",
       "      <td>Mtwara</td>\n",
       "      <td>58.0</td>\n",
       "      <td>True</td>\n",
       "      <td>vwc</td>\n",
       "      <td>Others</td>\n",
       "      <td>True</td>\n",
       "      <td>80s</td>\n",
       "      <td>submersible</td>\n",
       "      <td>never pay</td>\n",
       "      <td>soft</td>\n",
       "      <td>dry</td>\n",
       "      <td>borehole</td>\n",
       "      <td>groundwater</td>\n",
       "      <td>communal standpipe multiple</td>\n",
       "      <td>communal standpipe</td>\n",
       "      <td>non functional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>250.0</td>\n",
       "      <td>874</td>\n",
       "      <td>other</td>\n",
       "      <td>1057.545585</td>\n",
       "      <td>other</td>\n",
       "      <td>31.130847</td>\n",
       "      <td>-1.825359</td>\n",
       "      <td>Lake Victoria</td>\n",
       "      <td>Kyanyamisa</td>\n",
       "      <td>Kagera</td>\n",
       "      <td>200.0</td>\n",
       "      <td>True</td>\n",
       "      <td>other</td>\n",
       "      <td>Others</td>\n",
       "      <td>True</td>\n",
       "      <td>unknown</td>\n",
       "      <td>gravity</td>\n",
       "      <td>never pay</td>\n",
       "      <td>soft</td>\n",
       "      <td>seasonal</td>\n",
       "      <td>rainwater harvesting</td>\n",
       "      <td>surface</td>\n",
       "      <td>communal standpipe</td>\n",
       "      <td>communal standpipe</td>\n",
       "      <td>functional</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   amount_tsh  days_since_recorded  funder   gps_height installer  longitude   latitude                    basin  subvillage   region  population public_meeting scheme_management                  scheme_name permit construction_year extraction_type payment_type water_quality quantity_group           source_type source_class              waterpoint_type waterpoint_type_group    status_group\n",
       "0      6000.0                  995   other  1390.000000     other  34.938093  -9.856322               Lake Nyasa    Mnyusi B   Iringa       109.0           True               vwc                        Roman  False               90s         gravity     annually          soft         enough                spring  groundwater           communal standpipe    communal standpipe      functional\n",
       "1       200.0                  272   other  1399.000000     other  34.698766  -2.147466            Lake Victoria     Nyamara     Mara       280.0        Unknown             other                       Others   True               10s         gravity    never pay          soft   insufficient  rainwater harvesting      surface           communal standpipe    communal standpipe      functional\n",
       "2        25.0                  281   other   686.000000     other  37.460664  -3.821329                  Pangani     Majengo  Manyara       250.0           True               vwc  Nyumba ya mungu pipe scheme   True               00s         gravity   per bucket          soft         enough                   dam      surface  communal standpipe multiple    communal standpipe      functional\n",
       "3        50.0                  309  Unicef   263.000000     other  38.486161 -11.155298  Ruvuma / Southern Coast  Mahakamani   Mtwara        58.0           True               vwc                       Others   True               80s     submersible    never pay          soft            dry              borehole  groundwater  communal standpipe multiple    communal standpipe  non functional\n",
       "4       250.0                  874   other  1057.545585     other  31.130847  -1.825359            Lake Victoria  Kyanyamisa   Kagera       200.0           True             other                       Others   True           unknown         gravity    never pay          soft       seasonal  rainwater harvesting      surface           communal standpipe    communal standpipe      functional"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59400, 25)"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>amount_tsh</th>\n",
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       "      <th>funder</th>\n",
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       "      <th>waterpoint_type_group</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>69572</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>2011-03-14</td>\n",
       "      <td>Roman</td>\n",
       "      <td>1390</td>\n",
       "      <td>Roman</td>\n",
       "      <td>34.938093</td>\n",
       "      <td>-9.856322</td>\n",
       "      <td>none</td>\n",
       "      <td>0</td>\n",
       "      <td>Lake Nyasa</td>\n",
       "      <td>Mnyusi B</td>\n",
       "      <td>Iringa</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>Ludewa</td>\n",
       "      <td>Mundindi</td>\n",
       "      <td>109</td>\n",
       "      <td>True</td>\n",
       "      <td>GeoData Consultants Ltd</td>\n",
       "      <td>VWC</td>\n",
       "      <td>Roman</td>\n",
       "      <td>False</td>\n",
       "      <td>1999</td>\n",
       "      <td>gravity</td>\n",
       "      <td>gravity</td>\n",
       "      <td>gravity</td>\n",
       "      <td>vwc</td>\n",
       "      <td>user-group</td>\n",
       "      <td>pay annually</td>\n",
       "      <td>annually</td>\n",
       "      <td>soft</td>\n",
       "      <td>good</td>\n",
       "      <td>enough</td>\n",
       "      <td>enough</td>\n",
       "      <td>spring</td>\n",
       "      <td>spring</td>\n",
       "      <td>groundwater</td>\n",
       "      <td>communal standpipe</td>\n",
       "      <td>communal standpipe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8776</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-03-06</td>\n",
       "      <td>Grumeti</td>\n",
       "      <td>1399</td>\n",
       "      <td>GRUMETI</td>\n",
       "      <td>34.698766</td>\n",
       "      <td>-2.147466</td>\n",
       "      <td>Zahanati</td>\n",
       "      <td>0</td>\n",
       "      <td>Lake Victoria</td>\n",
       "      <td>Nyamara</td>\n",
       "      <td>Mara</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>Serengeti</td>\n",
       "      <td>Natta</td>\n",
       "      <td>280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GeoData Consultants Ltd</td>\n",
       "      <td>Other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>2010</td>\n",
       "      <td>gravity</td>\n",
       "      <td>gravity</td>\n",
       "      <td>gravity</td>\n",
       "      <td>wug</td>\n",
       "      <td>user-group</td>\n",
       "      <td>never pay</td>\n",
       "      <td>never pay</td>\n",
       "      <td>soft</td>\n",
       "      <td>good</td>\n",
       "      <td>insufficient</td>\n",
       "      <td>insufficient</td>\n",
       "      <td>rainwater harvesting</td>\n",
       "      <td>rainwater harvesting</td>\n",
       "      <td>surface</td>\n",
       "      <td>communal standpipe</td>\n",
       "      <td>communal standpipe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34310</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2013-02-25</td>\n",
       "      <td>Lottery Club</td>\n",
       "      <td>686</td>\n",
       "      <td>World vision</td>\n",
       "      <td>37.460664</td>\n",
       "      <td>-3.821329</td>\n",
       "      <td>Kwa Mahundi</td>\n",
       "      <td>0</td>\n",
       "      <td>Pangani</td>\n",
       "      <td>Majengo</td>\n",
       "      <td>Manyara</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>Simanjiro</td>\n",
       "      <td>Ngorika</td>\n",
       "      <td>250</td>\n",
       "      <td>True</td>\n",
       "      <td>GeoData Consultants Ltd</td>\n",
       "      <td>VWC</td>\n",
       "      <td>Nyumba ya mungu pipe scheme</td>\n",
       "      <td>True</td>\n",
       "      <td>2009</td>\n",
       "      <td>gravity</td>\n",
       "      <td>gravity</td>\n",
       "      <td>gravity</td>\n",
       "      <td>vwc</td>\n",
       "      <td>user-group</td>\n",
       "      <td>pay per bucket</td>\n",
       "      <td>per bucket</td>\n",
       "      <td>soft</td>\n",
       "      <td>good</td>\n",
       "      <td>enough</td>\n",
       "      <td>enough</td>\n",
       "      <td>dam</td>\n",
       "      <td>dam</td>\n",
       "      <td>surface</td>\n",
       "      <td>communal standpipe multiple</td>\n",
       "      <td>communal standpipe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>67743</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-01-28</td>\n",
       "      <td>Unicef</td>\n",
       "      <td>263</td>\n",
       "      <td>UNICEF</td>\n",
       "      <td>38.486161</td>\n",
       "      <td>-11.155298</td>\n",
       "      <td>Zahanati Ya Nanyumbu</td>\n",
       "      <td>0</td>\n",
       "      <td>Ruvuma / Southern Coast</td>\n",
       "      <td>Mahakamani</td>\n",
       "      <td>Mtwara</td>\n",
       "      <td>90</td>\n",
       "      <td>63</td>\n",
       "      <td>Nanyumbu</td>\n",
       "      <td>Nanyumbu</td>\n",
       "      <td>58</td>\n",
       "      <td>True</td>\n",
       "      <td>GeoData Consultants Ltd</td>\n",
       "      <td>VWC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>1986</td>\n",
       "      <td>submersible</td>\n",
       "      <td>submersible</td>\n",
       "      <td>submersible</td>\n",
       "      <td>vwc</td>\n",
       "      <td>user-group</td>\n",
       "      <td>never pay</td>\n",
       "      <td>never pay</td>\n",
       "      <td>soft</td>\n",
       "      <td>good</td>\n",
       "      <td>dry</td>\n",
       "      <td>dry</td>\n",
       "      <td>machine dbh</td>\n",
       "      <td>borehole</td>\n",
       "      <td>groundwater</td>\n",
       "      <td>communal standpipe multiple</td>\n",
       "      <td>communal standpipe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19728</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-07-13</td>\n",
       "      <td>Action In A</td>\n",
       "      <td>0</td>\n",
       "      <td>Artisan</td>\n",
       "      <td>31.130847</td>\n",
       "      <td>-1.825359</td>\n",
       "      <td>Shuleni</td>\n",
       "      <td>0</td>\n",
       "      <td>Lake Victoria</td>\n",
       "      <td>Kyanyamisa</td>\n",
       "      <td>Kagera</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>Karagwe</td>\n",
       "      <td>Nyakasimbi</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>GeoData Consultants Ltd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>gravity</td>\n",
       "      <td>gravity</td>\n",
       "      <td>gravity</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>never pay</td>\n",
       "      <td>never pay</td>\n",
       "      <td>soft</td>\n",
       "      <td>good</td>\n",
       "      <td>seasonal</td>\n",
       "      <td>seasonal</td>\n",
       "      <td>rainwater harvesting</td>\n",
       "      <td>rainwater harvesting</td>\n",
       "      <td>surface</td>\n",
       "      <td>communal standpipe</td>\n",
       "      <td>communal standpipe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  amount_tsh date_recorded        funder  gps_height     installer  longitude   latitude              wpt_name  num_private                    basin  subvillage   region  region_code  district_code        lga        ward  population public_meeting              recorded_by scheme_management                  scheme_name permit  construction_year extraction_type extraction_type_group extraction_type_class management management_group         payment payment_type water_quality  \\\n",
       "0  69572      6000.0    2011-03-14         Roman        1390         Roman  34.938093  -9.856322                  none            0               Lake Nyasa    Mnyusi B   Iringa           11              5     Ludewa    Mundindi         109           True  GeoData Consultants Ltd               VWC                        Roman  False               1999         gravity               gravity               gravity        vwc       user-group    pay annually     annually          soft   \n",
       "1   8776         0.0    2013-03-06       Grumeti        1399       GRUMETI  34.698766  -2.147466              Zahanati            0            Lake Victoria     Nyamara     Mara           20              2  Serengeti       Natta         280            NaN  GeoData Consultants Ltd             Other                          NaN   True               2010         gravity               gravity               gravity        wug       user-group       never pay    never pay          soft   \n",
       "2  34310        25.0    2013-02-25  Lottery Club         686  World vision  37.460664  -3.821329           Kwa Mahundi            0                  Pangani     Majengo  Manyara           21              4  Simanjiro     Ngorika         250           True  GeoData Consultants Ltd               VWC  Nyumba ya mungu pipe scheme   True               2009         gravity               gravity               gravity        vwc       user-group  pay per bucket   per bucket          soft   \n",
       "3  67743         0.0    2013-01-28        Unicef         263        UNICEF  38.486161 -11.155298  Zahanati Ya Nanyumbu            0  Ruvuma / Southern Coast  Mahakamani   Mtwara           90             63   Nanyumbu    Nanyumbu          58           True  GeoData Consultants Ltd               VWC                          NaN   True               1986     submersible           submersible           submersible        vwc       user-group       never pay    never pay          soft   \n",
       "4  19728         0.0    2011-07-13   Action In A           0       Artisan  31.130847  -1.825359               Shuleni            0            Lake Victoria  Kyanyamisa   Kagera           18              1    Karagwe  Nyakasimbi           0           True  GeoData Consultants Ltd               NaN                          NaN   True                  0         gravity               gravity               gravity      other            other       never pay    never pay          soft   \n",
       "\n",
       "  quality_group      quantity quantity_group                source           source_type source_class              waterpoint_type waterpoint_type_group  \n",
       "0          good        enough         enough                spring                spring  groundwater           communal standpipe    communal standpipe  \n",
       "1          good  insufficient   insufficient  rainwater harvesting  rainwater harvesting      surface           communal standpipe    communal standpipe  \n",
       "2          good        enough         enough                   dam                   dam      surface  communal standpipe multiple    communal standpipe  \n",
       "3          good           dry            dry           machine dbh              borehole  groundwater  communal standpipe multiple    communal standpipe  \n",
       "4          good      seasonal       seasonal  rainwater harvesting  rainwater harvesting      surface           communal standpipe    communal standpipe  "
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14850, 24)"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59400, 25)"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Exporting the CSV files\n",
    "total.to_csv('training_data_2.csv', index=True)\n",
    "total.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14850, 24)"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.to_csv('test_data_2.csv', index=True)\n",
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "amount_tsh               0\n",
       "days_since_recorded      0\n",
       "funder                   0\n",
       "gps_height               0\n",
       "installer                0\n",
       "longitude                0\n",
       "latitude                 0\n",
       "basin                    0\n",
       "subvillage               0\n",
       "region                   0\n",
       "population               0\n",
       "public_meeting           0\n",
       "scheme_management        0\n",
       "scheme_name              0\n",
       "permit                   0\n",
       "construction_year        0\n",
       "extraction_type          0\n",
       "payment_type             0\n",
       "water_quality            0\n",
       "quantity_group           0\n",
       "source_type              0\n",
       "source_class             0\n",
       "waterpoint_type          0\n",
       "waterpoint_type_group    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.apply(lambda x:sum(x.isnull()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "amount_tsh               0\n",
       "days_since_recorded      0\n",
       "funder                   0\n",
       "gps_height               0\n",
       "installer                0\n",
       "longitude                0\n",
       "latitude                 0\n",
       "basin                    0\n",
       "subvillage               0\n",
       "region                   0\n",
       "population               0\n",
       "public_meeting           0\n",
       "scheme_management        0\n",
       "scheme_name              0\n",
       "permit                   0\n",
       "construction_year        0\n",
       "extraction_type          0\n",
       "payment_type             0\n",
       "water_quality            0\n",
       "quantity_group           0\n",
       "source_type              0\n",
       "source_class             0\n",
       "waterpoint_type          0\n",
       "waterpoint_type_group    0\n",
       "status_group             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.apply(lambda x:sum(x.isnull()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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