data_explore.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#explore the data\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#load the training data\n",
    "train_values = pd.read_csv('training_set_values.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#load the training set labels\n",
    "train_labels = pd.read_csv('training_set_labels.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#load test set values\n",
    "test_values = pd.read_csv('test_set_values.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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>...</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</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>...</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</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>...</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</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>...</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</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>...</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</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>...</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",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  amount_tsh date_recorded        funder  gps_height     installer  \\\n",
       "0  69572        6000    2011-03-14         Roman        1390         Roman   \n",
       "1   8776           0    2013-03-06       Grumeti        1399       GRUMETI   \n",
       "2  34310          25    2013-02-25  Lottery Club         686  World vision   \n",
       "3  67743           0    2013-01-28        Unicef         263        UNICEF   \n",
       "4  19728           0    2011-07-13   Action In A           0       Artisan   \n",
       "\n",
       "   longitude   latitude              wpt_name  num_private  \\\n",
       "0  34.938093  -9.856322                  none            0   \n",
       "1  34.698766  -2.147466              Zahanati            0   \n",
       "2  37.460664  -3.821329           Kwa Mahundi            0   \n",
       "3  38.486161 -11.155298  Zahanati Ya Nanyumbu            0   \n",
       "4  31.130847  -1.825359               Shuleni            0   \n",
       "\n",
       "           ...          payment_type water_quality quality_group  \\\n",
       "0          ...              annually          soft          good   \n",
       "1          ...             never pay          soft          good   \n",
       "2          ...            per bucket          soft          good   \n",
       "3          ...             never pay          soft          good   \n",
       "4          ...             never pay          soft          good   \n",
       "\n",
       "       quantity  quantity_group                source           source_type  \\\n",
       "0        enough          enough                spring                spring   \n",
       "1  insufficient    insufficient  rainwater harvesting  rainwater harvesting   \n",
       "2        enough          enough                   dam                   dam   \n",
       "3           dry             dry           machine dbh              borehole   \n",
       "4      seasonal        seasonal  rainwater harvesting  rainwater harvesting   \n",
       "\n",
       "   source_class              waterpoint_type waterpoint_type_group  \n",
       "0   groundwater           communal standpipe    communal standpipe  \n",
       "1       surface           communal standpipe    communal standpipe  \n",
       "2       surface  communal standpipe multiple    communal standpipe  \n",
       "3   groundwater  communal standpipe multiple    communal standpipe  \n",
       "4       surface           communal standpipe    communal standpipe  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_values.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59400"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_values)\n",
    "#59400 observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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",
       "</div>"
      ],
      "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59400"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_labels)\n",
    "#59400 observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14850"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_values)\n",
    "#test values have only 14850 rows, much less than training values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['id',\n",
       " 'amount_tsh',\n",
       " 'date_recorded',\n",
       " 'funder',\n",
       " 'gps_height',\n",
       " 'installer',\n",
       " 'longitude',\n",
       " 'latitude',\n",
       " 'wpt_name',\n",
       " 'num_private',\n",
       " 'basin',\n",
       " 'subvillage',\n",
       " 'region',\n",
       " 'region_code',\n",
       " 'district_code',\n",
       " 'lga',\n",
       " 'ward',\n",
       " 'population',\n",
       " 'public_meeting',\n",
       " 'recorded_by',\n",
       " 'scheme_management',\n",
       " 'scheme_name',\n",
       " 'permit',\n",
       " 'construction_year',\n",
       " 'extraction_type',\n",
       " 'extraction_type_group',\n",
       " 'extraction_type_class',\n",
       " 'management',\n",
       " 'management_group',\n",
       " 'payment',\n",
       " 'payment_type',\n",
       " 'water_quality',\n",
       " 'quality_group',\n",
       " 'quantity',\n",
       " 'quantity_group',\n",
       " 'source',\n",
       " 'source_type',\n",
       " 'source_class',\n",
       " 'waterpoint_type',\n",
       " 'waterpoint_type_group']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(train_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(train_values))\n",
    "#there are 40 column headings in train df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#combine the training df with the training labels df (this adds the outcome column called 'status_group')\n",
    "combined = train_values.merge(train_labels, on='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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>...</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>...</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>...</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>...</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>...</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>...</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",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  amount_tsh date_recorded        funder  gps_height     installer  \\\n",
       "0  69572      6000.0    2011-03-14         Roman        1390         Roman   \n",
       "1   8776         0.0    2013-03-06       Grumeti        1399       GRUMETI   \n",
       "2  34310        25.0    2013-02-25  Lottery Club         686  World vision   \n",
       "3  67743         0.0    2013-01-28        Unicef         263        UNICEF   \n",
       "4  19728         0.0    2011-07-13   Action In A           0       Artisan   \n",
       "\n",
       "   longitude   latitude              wpt_name  num_private       ...        \\\n",
       "0  34.938093  -9.856322                  none            0       ...         \n",
       "1  34.698766  -2.147466              Zahanati            0       ...         \n",
       "2  37.460664  -3.821329           Kwa Mahundi            0       ...         \n",
       "3  38.486161 -11.155298  Zahanati Ya Nanyumbu            0       ...         \n",
       "4  31.130847  -1.825359               Shuleni            0       ...         \n",
       "\n",
       "  water_quality quality_group      quantity  quantity_group  \\\n",
       "0          soft          good        enough          enough   \n",
       "1          soft          good  insufficient    insufficient   \n",
       "2          soft          good        enough          enough   \n",
       "3          soft          good           dry             dry   \n",
       "4          soft          good      seasonal        seasonal   \n",
       "\n",
       "                 source           source_type source_class  \\\n",
       "0                spring                spring  groundwater   \n",
       "1  rainwater harvesting  rainwater harvesting      surface   \n",
       "2                   dam                   dam      surface   \n",
       "3           machine dbh              borehole  groundwater   \n",
       "4  rainwater harvesting  rainwater harvesting      surface   \n",
       "\n",
       "               waterpoint_type waterpoint_type_group    status_group  \n",
       "0           communal standpipe    communal standpipe      functional  \n",
       "1           communal standpipe    communal standpipe      functional  \n",
       "2  communal standpipe multiple    communal standpipe      functional  \n",
       "3  communal standpipe multiple    communal standpipe  non functional  \n",
       "4           communal standpipe    communal standpipe      functional  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59400"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#make sure the length is the same\n",
    "len(combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf4e3b00>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4CvgKJRBdkZnXP4V2SJIkSRolI/1K2X1abvcBfwFeN5IZM/N24CXN7RspF2APLHMucO6A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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc692ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#look at types of pumps\n",
    "plt.figure(figsize=(13,6))\n",
    "sns.countplot(combined.extraction_type_class, palette = 'Set3')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xbadc2b0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mmOL6WorZc/el+GAdHRGTKK4Y+IXMvJ3iQkGdH/AbeelD+48UAUREzAR2Km9/\nH9g/MxcCcymCZEseBXaIiM5rT5xI/VlP76eYcvx15f05FMdfOvvRufxxXZY7oezTWyiuL/FLpK3k\nAXMNe+VlRG+guBDPRuBiigv5fBL4CPB+igPnz1NcjKjzgPVbKK8aWC5/E8UV3x6iCJL3U3xIX0ER\nOBuAszOz3gV5OvvydopjI5sorgmxQ2YeW05ZfkFm3lfWexfFAfMxFJe1PTkzn4uIY8rlVwJ3AAd1\nOeA+jSKQTiuveihtFcNDGiTKCxd9mSIk1pZXl9slMz/TB23fA5zfGT7StvJsC6mflaOIHWqKRlCM\nBr5NMWJ4MCLWA7+jOBOsL7iVqD7lyEOSVJkHzCVJlRkekqTKDA9JUmWGhySpMsNDklSZ4SFJquz/\nA9LMwG+1VfK7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xba86780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#look at countplot of outcomes we will eventually predict\n",
    "sns.countplot(combined.status_group, palette = 'Set3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#most are functional, then non-functional, many less need repair - needs repair will be harder to predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf3b3dd8>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FdoUhXw73kqRdj6FCkkaRzs7l3HvTZRw0dWKrm7JN7lv5KLz8PQ73kqRdjKFC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\njyWpPaFahQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf40c588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#look at management types\n",
    "plt.figure(figsize=(13,6))\n",
    "sns.countplot(combined.management, palette = 'Set3')\n",
    "#wwc - what is that? will find out later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "vwc                 40507\n",
       "wug                  6515\n",
       "water board          2933\n",
       "wua                  2535\n",
       "private operator     1971\n",
       "parastatal           1768\n",
       "water authority       904\n",
       "other                 844\n",
       "company               685\n",
       "unknown               561\n",
       "other - school         99\n",
       "trust                  78\n",
       "Name: management, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined['management'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(combined['management'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#there are 12 management types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined['management_group'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(combined['management_group'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#there are 5 subgroups under management"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1175af60>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hXFyTK6wTw58kSRq01csfGa629gOIiFaKbV7nUgSvJuBGNm0nu7GsNwn4OMVI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\nA2xBcdOXstQBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11600710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#view management group by management type\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.countplot(data=combined, x='management_group',hue='management')\n",
    "# Put the legend out of the figure\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b39b38>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gnWEYboCTaZoRmeXfAe0LdA9ERERKFmWziIjcUQp00mua5udAGoBhGHbA28Bw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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a336a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#status_group vs extraction_type_class\n",
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 4))\n",
    "sns.countplot(data=combined, x='status_group',hue='extraction_type_class', ax=ax1)\n",
    "sns.countplot(data=combined, x='extraction_type_class',hue='status_group', ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#look at water pump recorded over years\n",
    "combined['date_recorded'] = pd.to_datetime(combined['date_recorded'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check it worked\n",
    "type(combined['date_recorded'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc5179e8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iTqEueSWTKd+P3fk/tntDsfDjYvE4w6MT2eHq8xxT0+ENU+/PuV9zw3RZVxsj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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc552cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined['date_recorded'].value_counts().sort_index().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xcea8ba8>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xPr193rdmR9i9otb6Q2n6kFfVWn+svW+6P3nUAo3dX5nkHbXW65Pc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\nSZIkSZKkzhl0kiRJkiRJUuf+B/4aTdYd5kehAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc6cda58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#looking at regions\n",
    "plt.figure(figsize=(20, 7))\n",
    "sns.countplot(combined.region, palette = 'Set3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iringa           5294\n",
       "Shinyanga        4982\n",
       "Mbeya            4639\n",
       "Kilimanjaro      4379\n",
       "Morogoro         4006\n",
       "Arusha           3350\n",
       "Kagera           3316\n",
       "Mwanza           3102\n",
       "Kigoma           2816\n",
       "Ruvuma           2640\n",
       "Pwani            2635\n",
       "Tanga            2547\n",
       "Dodoma           2201\n",
       "Singida          2093\n",
       "Mara             1969\n",
       "Tabora           1959\n",
       "Rukwa            1808\n",
       "Mtwara           1730\n",
       "Manyara          1583\n",
       "Lindi            1546\n",
       "Dar es Salaam     805\n",
       "Name: region, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined['region'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#interesting that the capital region has the fewest - maybe bc there is more water access there and less smaller pumps are needed??"
   ]
  }
 ],
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