2. Data cleaning & Feature engineering.ipynb

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      "name": "waterpumps - cleaning/engineering.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/BrendaLoznik/waterpumps/blob/main/2.%20Data%20cleaning%20%26%20Feature%20engineering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1 Housekeeping"
      ],
      "metadata": {
        "id": "RfJ8u7C6MbZg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.1 Load libraries"
      ],
      "metadata": {
        "id": "_mdrBZhhMlGQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install dirty_cat"
      ],
      "metadata": {
        "id": "ueARO0FzVE4g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#basic libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from dirty_cat import SimilarityEncoder\n",
        "from sklearn.preprocessing import normalize"
      ],
      "metadata": {
        "id": "D7TFVh5SMa1b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.set_option(\"display.max_columns\", 100)\n",
        "pd.set_option(\"display.max_rows\", 200)"
      ],
      "metadata": {
        "id": "XGVa-6yyMfN8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.2 Load data"
      ],
      "metadata": {
        "id": "XOfSIU_cMjSA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive \n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kHm2SLbcMfut",
        "outputId": "7a33fba3-032b-43c8-d1fe-2b26cfc24d86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#import data\n",
        "train_labels = pd.read_csv('/content/gdrive/MyDrive/Kaggle/Waterpump-github/train_labels.csv')\n",
        "train_values = pd.read_csv('/content/gdrive/MyDrive/Kaggle/Waterpump-github/train_values.csv', parse_dates = ['date_recorded' ],  na_values = [0, '0'])\n",
        "test_values = pd.read_csv('/content/gdrive/MyDrive/Kaggle/Waterpump-github/test_values.csv', parse_dates = ['date_recorded' ] , na_values = [0, '0'])\n",
        "\n",
        "#merge train values and train labels to single dataset\n",
        "train= pd.merge(train_labels , train_values, on='id')\n"
      ],
      "metadata": {
        "id": "W4IVFLovMpJk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#concatenate train and test for data visualization\n",
        "raw = pd.concat([train, test_values])\n",
        "raw.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
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        "id": "5jz2Y9_7MsUT",
        "outputId": "0e37229c-de3e-4802-ceb3-9619a14bd7db"
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      "execution_count": null,
      "outputs": [
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              "      <th>quantity</th>\n",
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              "      <th>0</th>\n",
              "      <td>69572</td>\n",
              "      <td>functional</td>\n",
              "      <td>6000.0</td>\n",
              "      <td>2011-03-14</td>\n",
              "      <td>Roman</td>\n",
              "      <td>1390.0</td>\n",
              "      <td>Roman</td>\n",
              "      <td>34.938093</td>\n",
              "      <td>-9.856322</td>\n",
              "      <td>none</td>\n",
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              "      <td>Lake Nyasa</td>\n",
              "      <td>Mnyusi B</td>\n",
              "      <td>Iringa</td>\n",
              "      <td>11</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Ludewa</td>\n",
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              "      <td>109.0</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>1999.0</td>\n",
              "      <td>gravity</td>\n",
              "      <td>gravity</td>\n",
              "      <td>gravity</td>\n",
              "      <td>vwc</td>\n",
              "      <td>user-group</td>\n",
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              "      <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",
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              "      <th>1</th>\n",
              "      <td>8776</td>\n",
              "      <td>functional</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2013-03-06</td>\n",
              "      <td>Grumeti</td>\n",
              "      <td>1399.0</td>\n",
              "      <td>GRUMETI</td>\n",
              "      <td>34.698766</td>\n",
              "      <td>-2.147466</td>\n",
              "      <td>Zahanati</td>\n",
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              "      <td>Lake Victoria</td>\n",
              "      <td>Nyamara</td>\n",
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              "      <td>2.0</td>\n",
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              "      <td>2010.0</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",
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              "      <td>rainwater harvesting</td>\n",
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              "      <td>communal standpipe</td>\n",
              "      <td>communal standpipe</td>\n",
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              "      <th>2</th>\n",
              "      <td>34310</td>\n",
              "      <td>functional</td>\n",
              "      <td>25.0</td>\n",
              "      <td>2013-02-25</td>\n",
              "      <td>Lottery Club</td>\n",
              "      <td>686.0</td>\n",
              "      <td>World vision</td>\n",
              "      <td>37.460664</td>\n",
              "      <td>-3.821329</td>\n",
              "      <td>Kwa Mahundi</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pangani</td>\n",
              "      <td>Majengo</td>\n",
              "      <td>Manyara</td>\n",
              "      <td>21</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Simanjiro</td>\n",
              "      <td>Ngorika</td>\n",
              "      <td>250.0</td>\n",
              "      <td>True</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
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              "      <td>Nyumba ya mungu pipe scheme</td>\n",
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              "      <td>2009.0</td>\n",
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              "      <td>communal standpipe multiple</td>\n",
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              "      <th>3</th>\n",
              "      <td>67743</td>\n",
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              "      <td>-11.155298</td>\n",
              "      <td>Zahanati Ya Nanyumbu</td>\n",
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              "      <td>Ruvuma / Southern Coast</td>\n",
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              "      <td>90</td>\n",
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              "      <th>4</th>\n",
              "      <td>19728</td>\n",
              "      <td>functional</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2011-07-13</td>\n",
              "      <td>Action In A</td>\n",
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              "      <td>31.130847</td>\n",
              "      <td>-1.825359</td>\n",
              "      <td>Shuleni</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>Kyanyamisa</td>\n",
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              "      <td>18</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>rainwater harvesting</td>\n",
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              "      <td>communal standpipe</td>\n",
              "      <td>communal standpipe</td>\n",
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              "      id    status_group  amount_tsh date_recorded        funder  gps_height  \\\n",
              "0  69572      functional      6000.0    2011-03-14         Roman      1390.0   \n",
              "1   8776      functional         NaN    2013-03-06       Grumeti      1399.0   \n",
              "2  34310      functional        25.0    2013-02-25  Lottery Club       686.0   \n",
              "3  67743  non functional         NaN    2013-01-28        Unicef       263.0   \n",
              "4  19728      functional         NaN    2011-07-13   Action In A         NaN   \n",
              "\n",
              "      installer  longitude   latitude              wpt_name  num_private  \\\n",
              "0         Roman  34.938093  -9.856322                  none          NaN   \n",
              "1       GRUMETI  34.698766  -2.147466              Zahanati          NaN   \n",
              "2  World vision  37.460664  -3.821329           Kwa Mahundi          NaN   \n",
              "3        UNICEF  38.486161 -11.155298  Zahanati Ya Nanyumbu          NaN   \n",
              "4       Artisan  31.130847  -1.825359               Shuleni          NaN   \n",
              "\n",
              "                     basin  subvillage   region  region_code  district_code  \\\n",
              "0               Lake Nyasa    Mnyusi B   Iringa           11            5.0   \n",
              "1            Lake Victoria     Nyamara     Mara           20            2.0   \n",
              "2                  Pangani     Majengo  Manyara           21            4.0   \n",
              "3  Ruvuma / Southern Coast  Mahakamani   Mtwara           90           63.0   \n",
              "4            Lake Victoria  Kyanyamisa   Kagera           18            1.0   \n",
              "\n",
              "         lga        ward  population public_meeting              recorded_by  \\\n",
              "0     Ludewa    Mundindi       109.0           True  GeoData Consultants Ltd   \n",
              "1  Serengeti       Natta       280.0            NaN  GeoData Consultants Ltd   \n",
              "2  Simanjiro     Ngorika       250.0           True  GeoData Consultants Ltd   \n",
              "3   Nanyumbu    Nanyumbu        58.0           True  GeoData Consultants Ltd   \n",
              "4    Karagwe  Nyakasimbi         NaN           True  GeoData Consultants Ltd   \n",
              "\n",
              "  scheme_management                  scheme_name permit  construction_year  \\\n",
              "0               VWC                        Roman  False             1999.0   \n",
              "1             Other                          NaN   True             2010.0   \n",
              "2               VWC  Nyumba ya mungu pipe scheme   True             2009.0   \n",
              "3               VWC                          NaN   True             1986.0   \n",
              "4               NaN                          NaN   True                NaN   \n",
              "\n",
              "  extraction_type extraction_type_group extraction_type_class management  \\\n",
              "0         gravity               gravity               gravity        vwc   \n",
              "1         gravity               gravity               gravity        wug   \n",
              "2         gravity               gravity               gravity        vwc   \n",
              "3     submersible           submersible           submersible        vwc   \n",
              "4         gravity               gravity               gravity      other   \n",
              "\n",
              "  management_group         payment payment_type water_quality quality_group  \\\n",
              "0       user-group    pay annually     annually          soft          good   \n",
              "1       user-group       never pay    never pay          soft          good   \n",
              "2       user-group  pay per bucket   per bucket          soft          good   \n",
              "3       user-group       never pay    never pay          soft          good   \n",
              "4            other       never pay    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  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.3 Custom functions"
      ],
      "metadata": {
        "id": "XH3Sy0s9U9mK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "def plot_similarity(labels, features):\n",
        "  \n",
        "    normalized_features = normalize(features)\n",
        "    \n",
        "    # Create correction matrix\n",
        "    corr = np.inner(normalized_features, normalized_features)\n",
        "    \n",
        "    # Plot\n",
        "    mask = np.triu(np.ones_like(corr, dtype=bool))\n",
        "    fig, ax = plt.subplots(figsize=(30,30)) \n",
        "    ax = sns.heatmap(corr, mask=mask, cmap= \"RdYlGn\", vmin=0, linewidths=1, annot=True, fmt=\".2f\", xticklabels=labels, annot_kws={\"size\": 9})\n",
        "    ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n",
        "    ax.set_yticklabels(ax.get_xticklabels(), rotation=0)\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "def encode_and_plot(labels):\n",
        "  \n",
        "    enc = SimilarityEncoder(similarity=\"ngram\") # Encode\n",
        "    X_enc = enc.fit_transform(labels.reshape(-1, 1))\n",
        "    \n",
        "    plot_similarity(labels, X_enc) # Plot"
      ],
      "metadata": {
        "id": "N83QGzXNU8Dh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def distribution_checker (data, xlabel):\n",
        "  grouped = data.groupby([xlabel, 'status_group'])['id'].count().reset_index()\n",
        "  pivot = grouped.pivot_table(index = xlabel, columns = 'status_group', fill_value = 0)\n",
        "  mi=pivot.columns\n",
        "  mi.tolist()\n",
        "  ind = pd.Index([ str(e[1])  for e in mi.tolist()])\n",
        "  pivot.columns = ind\n",
        "  pivot['nr_pumps'] = pivot['functional'] +pivot['functional needs repair'] +pivot['non functional']\n",
        "  pivot['all_pumps'] = pivot['nr_pumps'].sum()\n",
        "  pivot['perc_total_pumps'] =((pivot['nr_pumps']/pivot['all_pumps'])*100).round(1)\n",
        "  pivot['perc_functional'] = ((pivot['functional']/pivot['nr_pumps'])*100).round(1)\n",
        "  pivot['perc_non_functional'] = ((pivot['non functional']/pivot['nr_pumps'])*100).round(1)\n",
        "  pivot['perc_functional_needs_repair'] = ((pivot['functional needs repair']/pivot['nr_pumps'])*100).round(1)\n",
        "  pivot = pivot.drop(['functional', 'functional needs repair', 'non functional', 'all_pumps'], axis=1)\n",
        "  return(pivot)"
      ],
      "metadata": {
        "id": "rNVgQ0s8dvk3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2 Data cleaning"
      ],
      "metadata": {
        "id": "pAfPqz5SNQ-o"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.1 Missing data"
      ],
      "metadata": {
        "id": "Xf5vWv_DNr54"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "missing = round((raw.isna().sum())/len(raw)*100,1)\n",
        "missing = missing.sort_values(ascending = False)\n",
        "missing.head(15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Y-0llaGNn-R",
        "outputId": "7772769f-c637-4963-9741-5119a3d9d0f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "num_private          98.7\n",
              "amount_tsh           70.1\n",
              "scheme_name          47.5\n",
              "population           36.1\n",
              "construction_year    35.0\n",
              "gps_height           34.5\n",
              "status_group         20.0\n",
              "installer             7.4\n",
              "funder                7.4\n",
              "scheme_management     6.5\n",
              "public_meeting        5.6\n",
              "permit                5.1\n",
              "longitude             3.1\n",
              "subvillage            0.6\n",
              "district_code         0.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have 13 features (including the 'status_group') with missing data. I will not impute features that have >60% missing data or features that I will not use (e.g. scheme_name)."
      ],
      "metadata": {
        "id": "xjxgwGbpN8xc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "#### 2.1.1 longitude"
      ],
      "metadata": {
        "id": "aWpqlb46ORWW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#correct arrangement of region location\n",
        "region = raw.groupby(['region', 'lga', 'ward', 'subvillage'])['id'].sum().reset_index()\n",
        "region.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "5kyp4PNYN8PD",
        "outputId": "d30b364d-8f5c-4543-fa2b-2c26f56bc5b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>region</th>\n",
              "      <th>lga</th>\n",
              "      <th>ward</th>\n",
              "      <th>subvillage</th>\n",
              "      <th>id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Arusha</td>\n",
              "      <td>Arusha Rural</td>\n",
              "      <td>Bangata</td>\n",
              "      <td>Engikaleti</td>\n",
              "      <td>843711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Arusha</td>\n",
              "      <td>Arusha Rural</td>\n",
              "      <td>Bangata</td>\n",
              "      <td>Gwasei</td>\n",
              "      <td>69607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Arusha</td>\n",
              "      <td>Arusha Rural</td>\n",
              "      <td>Bangata</td>\n",
              "      <td>Kwaniche</td>\n",
              "      <td>142313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Arusha</td>\n",
              "      <td>Arusha Rural</td>\n",
              "      <td>Bangata</td>\n",
              "      <td>Lobolosi</td>\n",
              "      <td>51080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Arusha</td>\n",
              "      <td>Arusha Rural</td>\n",
              "      <td>Bangata</td>\n",
              "      <td>Melenyi</td>\n",
              "      <td>63946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Arusha</td>\n",
              "      <td>Arusha Rural</td>\n",
              "      <td>Bangata</td>\n",
              "      <td>Ndukenyi</td>\n",
              "      <td>73715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Arusha</td>\n",
              "      <td>Arusha Rural</td>\n",
              "      <td>Bangata</td>\n",
              "      <td>Ngwasei</td>\n",
              "      <td>109834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Arusha</td>\n",
              "      <td>Arusha Rural</td>\n",
              "      <td>Bangata</td>\n",
              "      <td>Sanganani</td>\n",
              "      <td>201984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Arusha</td>\n",
              "      <td>Arusha Rural</td>\n",
              "      <td>Bangata</td>\n",
              "      <td>Sangananu</td>\n",
              "      <td>20484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Arusha</td>\n",
              "      <td>Arusha Rural</td>\n",
              "      <td>Bwawani</td>\n",
              "      <td>Buluga</td>\n",
              "      <td>34309</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   region           lga     ward  subvillage      id\n",
              "0  Arusha  Arusha Rural  Bangata  Engikaleti  843711\n",
              "1  Arusha  Arusha Rural  Bangata      Gwasei   69607\n",
              "2  Arusha  Arusha Rural  Bangata    Kwaniche  142313\n",
              "3  Arusha  Arusha Rural  Bangata    Lobolosi   51080\n",
              "4  Arusha  Arusha Rural  Bangata     Melenyi   63946\n",
              "5  Arusha  Arusha Rural  Bangata    Ndukenyi   73715\n",
              "6  Arusha  Arusha Rural  Bangata     Ngwasei  109834\n",
              "7  Arusha  Arusha Rural  Bangata   Sanganani  201984\n",
              "8  Arusha  Arusha Rural  Bangata   Sangananu   20484\n",
              "9  Arusha  Arusha Rural  Bwawani      Buluga   34309"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "* It would be a 'waste' of accuracy so simply impute by the mean longitude of the region, because for some pumps we can surely get some more detail\n",
        "* I will impute by the mean longitude of the subvillage if it is known, otherwise we move up to ward, lga and region"
      ],
      "metadata": {
        "id": "2j8O-2frOcOG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#create mean longitude on the lowest granularity level (subvillage)\n",
        "means_longitude_subvillage = raw.groupby(['region', 'lga', 'ward', 'subvillage'])['longitude'].mean().reset_index()\n",
        "means_longitude_subvillage = means_longitude_subvillage.rename(columns={\"longitude\": \"longitude_imputed_subvillage\"})\n",
        "\n",
        "#ward level\n",
        "means_longitude_ward = raw.groupby(['region', 'lga', 'ward',])['longitude'].mean().reset_index()\n",
        "means_longitude_ward = means_longitude_ward.rename(columns={\"longitude\": \"longitude_imputed_ward\"})\n",
        "\n",
        "#lga level\n",
        "means_longitude_lga = raw.groupby(['region', 'lga'])['longitude'].mean().reset_index()\n",
        "means_longitude_lga = means_longitude_lga .rename(columns={\"longitude\": \"longitude_imputed_lga\"})\n",
        "\n",
        "#region level\n",
        "means_longitude_region = raw.groupby(['region'])['longitude'].mean().reset_index()\n",
        "means_longitude_region = means_longitude_region.rename(columns={\"longitude\": \"longitude_imputed_region\"})\n",
        "means_longitude_region.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "BU1MN1HVNqaR",
        "outputId": "102c91a1-5a02-4a45-edb0-910db080d237"
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "      <div>\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>region</th>\n",
              "      <th>longitude_imputed_region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Arusha</td>\n",
              "      <td>36.554067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Dar es Salaam</td>\n",
              "      <td>39.212935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Dodoma</td>\n",
              "      <td>36.041964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Iringa</td>\n",
              "      <td>34.895921</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kagera</td>\n",
              "      <td>31.233092</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              style=\"display:none;\">\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          region  longitude_imputed_region\n",
              "0         Arusha                 36.554067\n",
              "1  Dar es Salaam                 39.212935\n",
              "2         Dodoma                 36.041964\n",
              "3         Iringa                 34.895921\n",
              "4         Kagera                 31.233092"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#merge the aggregated dataframes as new columns to the original df\n",
        "raw= raw.merge(means_longitude_subvillage, how = 'left', on = ['region', 'lga', 'ward', 'subvillage'])\n",
        "raw= raw.merge(means_longitude_ward, how = 'left', on = ['region', 'lga', 'ward'])\n",
        "raw = raw.merge(means_longitude_lga, how = 'left', on = ['region', 'lga'])\n",
        "raw = raw.merge(means_longitude_region, how = 'left', on = ['region'])\n",
        "\n",
        "#select the right longitude level based on the availability of information\n",
        "raw['imputed_longitude'] = np.where(raw['longitude'].isna(), raw['longitude_imputed_subvillage'], raw['longitude']) #if longitude is missing, impute it by the mean of the subvillage\n",
        "raw['imputed_longitude'] = np.where(raw['imputed_longitude'].isna(), raw['longitude_imputed_ward'], raw['imputed_longitude']) #if subvillage mean is missing, impute it by the ward\n",
        "raw['imputed_longitude'] = np.where(raw['imputed_longitude'].isna(), raw['longitude_imputed_lga'], raw['imputed_longitude'])\n",
        "raw['imputed_longitude'] = np.where(raw['imputed_longitude'].isna(), raw['longitude_imputed_region'], raw['imputed_longitude'])\n",
        "\n",
        "#drop redundant columns\n",
        "raw= raw.drop(['longitude_imputed_subvillage','longitude_imputed_ward' , 'longitude_imputed_lga' , 'longitude_imputed_region', 'longitude'], axis=1)\n",
        "\n"
      ],
      "metadata": {
        "id": "pbqT_xj3Ow_J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "#### 2.1.2 Permit"
      ],
      "metadata": {
        "id": "S1mDTYEKPW8l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Based on EDA I expect that the permit feature will not be an important predictor of waterpump functionality\n",
        "# I can  impute by the mode (69% of the pumps have a permit), but I will use the mode of this features based on the 'public_meeting' and 'management_group' columns\n",
        "public_meeting = raw.groupby(['public_meeting','management_group' ,'permit'])['id'].count().reset_index()\n",
        "pivot = public_meeting.pivot(index = ['public_meeting', 'management_group', ], columns = 'permit')\n",
        "\n",
        "mi=pivot .columns\n",
        "mi.tolist()\n",
        "ind = pd.Index([ str(e[1])  for e in mi.tolist()])\n",
        "pivot .columns = ind\n",
        "\n",
        "pivot['total_permit'] = pivot['False'] + pivot['True']\n",
        "pivot['has permit'] = round((pivot['True'] /pivot['total_permit'])*100,1)\n",
        "pivot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "XZvqupylPRgn",
        "outputId": "8cc4839a-2ef8-4f11-fd52-d9b9bdeebcad"
      },
      "execution_count": null,
      "outputs": [
        {
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              "      <th>False</th>\n",
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              "      <td>308</td>\n",
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              "      <td>65.6</td>\n",
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              "    <tr>\n",
              "      <th>user-group</th>\n",
              "      <td>2308</td>\n",
              "      <td>1615</td>\n",
              "      <td>3923</td>\n",
              "      <td>41.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">True</th>\n",
              "      <th>commercial</th>\n",
              "      <td>642</td>\n",
              "      <td>2389</td>\n",
              "      <td>3031</td>\n",
              "      <td>78.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>other</th>\n",
              "      <td>216</td>\n",
              "      <td>306</td>\n",
              "      <td>522</td>\n",
              "      <td>58.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>parastatal</th>\n",
              "      <td>348</td>\n",
              "      <td>881</td>\n",
              "      <td>1229</td>\n",
              "      <td>71.7</td>\n",
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              "      <th>unknown</th>\n",
              "      <td>58</td>\n",
              "      <td>190</td>\n",
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              "      <td>76.6</td>\n",
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              "    <tr>\n",
              "      <th>user-group</th>\n",
              "      <td>16038</td>\n",
              "      <td>39699</td>\n",
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              "            + ' to learn more about interactive tables.';\n",
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            "text/plain": [
              "                                 False   True  total_permit  has permit\n",
              "public_meeting management_group                                        \n",
              "False          commercial          312    678           990        68.5\n",
              "               other                18    389           407        95.6\n",
              "               parastatal          308    164           472        34.7\n",
              "               unknown              32     61            93        65.6\n",
              "               user-group         2308   1615          3923        41.2\n",
              "True           commercial          642   2389          3031        78.8\n",
              "               other               216    306           522        58.6\n",
              "               parastatal          348    881          1229        71.7\n",
              "               unknown              58    190           248        76.6\n",
              "               user-group        16038  39699         55737        71.2"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#impute by mode\n",
        "permit_mg_mode= raw.groupby(['public_meeting', 'management_group'])['permit'].agg(pd.Series.mode).reset_index()\n",
        "permit_mg_mode  = permit_mg_mode .rename(columns={\"permit\": \"imputed_permit_mg\"})\n",
        "raw = raw.merge(permit_mg_mode, how = 'left', on = ['public_meeting', 'management_group'])\n",
        "\n",
        "raw['imputed_permit'] = np.where(raw['permit'].isna(), raw['imputed_permit_mg'], raw['permit'])  #if permit is missing, replace it by the mode of public meeting - management group\n",
        "raw['imputed_permit']  = np.where(raw['imputed_permit'] .isna(), raw['permit'].mode(), raw['imputed_permit'])  #if eitther public meeting or management group is missing, then use the mode of permit (True)\n",
        "\n",
        "#drop original permit column\n",
        "raw = raw.drop(['permit', 'imputed_permit_mg'], axis=1)\n"
      ],
      "metadata": {
        "id": "z8atYFWJPr-n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "IbaeyKevbMaU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "#### 2.1.3 Public Meeting"
      ],
      "metadata": {
        "id": "YDOccWiwRm9Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Over 90% of the pumps have a public meeting. I will therefore impute by the mode.\n",
        "raw['public_meeting']=raw['public_meeting'].fillna(raw['public_meeting'].mode()[0])"
      ],
      "metadata": {
        "id": "F6TbuRmeRiDd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "#### 2.1.4 Scheme management"
      ],
      "metadata": {
        "id": "QWRw14k7T6o-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# it seems like a logical choice to impute missing scheme management values by the mode of the management - scheme-management as there is a lot of overlap here\n",
        "check = raw.groupby([  'management_group', 'scheme_management' , 'management'])['id'].count().reset_index()\n",
        "check = check.sort_values('management')\n",
        "check.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "xfkgY9nzT8ai",
        "outputId": "0fb9f679-6246-4ea2-95d8-585e92045688"
      },
      "execution_count": null,
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        {
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              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>other</td>\n",
              "      <td>VWC</td>\n",
              "      <td>other</td>\n",
              "      <td>57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>other</td>\n",
              "      <td>WUG</td>\n",
              "      <td>other</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f59b9f92-9346-4198-87cf-e0708e0fda5c')\"\n",
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              "  </svg>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f59b9f92-9346-4198-87cf-e0708e0fda5c');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   management_group scheme_management management   id\n",
              "0        commercial           Company    company  848\n",
              "21       commercial               WUG    company    4\n",
              "19       commercial               WUA    company    1\n",
              "15       commercial               VWC    company    2\n",
              "7        commercial  Private operator    company    2\n",
              "11       commercial             Trust    company    1\n",
              "36            other               WUA      other    2\n",
              "32            other  Private operator      other    4\n",
              "34            other               VWC      other   57\n",
              "37            other               WUG      other   18"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scheme_mode = raw.groupby(['management'])['scheme_management'].agg(pd.Series.mode).reset_index()\n",
        "scheme_mode = scheme_mode.rename(columns={\"scheme_management\": \"imputed_scheme_management\"})\n",
        "scheme_mode \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "F_uOrJHxUEhK",
        "outputId": "7a81e5be-3b34-43ee-97e2-c6b78e5deafa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-bfad02ad-9a27-4362-8384-4d2f5b23eef4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>management</th>\n",
              "      <th>imputed_scheme_management</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>company</td>\n",
              "      <td>Company</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>other</td>\n",
              "      <td>Other</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>other - school</td>\n",
              "      <td>SWC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>parastatal</td>\n",
              "      <td>Parastatal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>private operator</td>\n",
              "      <td>Private operator</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>trust</td>\n",
              "      <td>Trust</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>unknown</td>\n",
              "      <td>VWC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>vwc</td>\n",
              "      <td>VWC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>water authority</td>\n",
              "      <td>Water authority</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>water board</td>\n",
              "      <td>Water Board</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>wua</td>\n",
              "      <td>WUA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>wug</td>\n",
              "      <td>WUG</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bfad02ad-9a27-4362-8384-4d2f5b23eef4')\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "  </style>\n",
              "\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          management imputed_scheme_management\n",
              "0            company                   Company\n",
              "1              other                     Other\n",
              "2     other - school                       SWC\n",
              "3         parastatal                Parastatal\n",
              "4   private operator          Private operator\n",
              "5              trust                     Trust\n",
              "6            unknown                       VWC\n",
              "7                vwc                       VWC\n",
              "8    water authority           Water authority\n",
              "9        water board               Water Board\n",
              "10               wua                       WUA\n",
              "11               wug                       WUG"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#merge scheme_mode to original df and use it to replace missing values\n",
        "raw = raw.merge(scheme_mode, how = 'left', on = [ 'management'])\n",
        "raw['imputed_scheme__management'] = np.where(raw['scheme_management'].isna(), raw['imputed_scheme_management'], raw['scheme_management'])\n",
        "\n",
        "#drop redundant columns\n",
        "raw= raw.drop(['scheme_management', 'imputed_scheme_management'],axis=1)\n",
        "\n"
      ],
      "metadata": {
        "id": "84M_waVRUUcx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "#### 2.1.5 Installer"
      ],
      "metadata": {
        "id": "ToSAAbn6Usrr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#I noticed a lot of variation in captialization, so I will first convert al entries to lowercase\n",
        "raw['installer'] = raw['installer'].str.lower()\n",
        "\n",
        "#plot top 10 installers\n",
        "installer = raw.groupby('installer')['id'].count().reset_index()\n",
        "installer = installer.sort_values('id', ascending = False)\n",
        "installer.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "rRjSZ4N7Ur_J",
        "outputId": "1007e31a-bea7-4550-d636-0e3acf1eff6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-5f7b34d7-ab3d-4dfd-acb9-d618a9d7d400\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>installer</th>\n",
              "      <th>id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>433</th>\n",
              "      <td>dwe</td>\n",
              "      <td>21756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>570</th>\n",
              "      <td>government</td>\n",
              "      <td>2367</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>650</th>\n",
              "      <td>hesawa</td>\n",
              "      <td>1768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1620</th>\n",
              "      <td>rwe</td>\n",
              "      <td>1498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>295</th>\n",
              "      <td>commu</td>\n",
              "      <td>1354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>353</th>\n",
              "      <td>danida</td>\n",
              "      <td>1306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>406</th>\n",
              "      <td>district council</td>\n",
              "      <td>1181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>879</th>\n",
              "      <td>kkkt</td>\n",
              "      <td>1135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>230</th>\n",
              "      <td>central government</td>\n",
              "      <td>933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1847</th>\n",
              "      <td>tcrs</td>\n",
              "      <td>887</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "          const element = document.querySelector('#df-5f7b34d7-ab3d-4dfd-acb9-d618a9d7d400');\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "               installer     id\n",
              "433                  dwe  21756\n",
              "570           government   2367\n",
              "650               hesawa   1768\n",
              "1620                 rwe   1498\n",
              "295                commu   1354\n",
              "353               danida   1306\n",
              "406     district council   1181\n",
              "879                 kkkt   1135\n",
              "230   central government    933\n",
              "1847                tcrs    887"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#look at correlatoins in the names of top 60 installers\n",
        "top_installers = installer.nlargest(60, 'id')['installer'].unique()\n",
        "encode_and_plot(top_installers)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "8pNfhrb0VRAR",
        "outputId": "62259102-2177-4ce7-bbcc-c3c81a9178ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2160x2160 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#there are a few categories in the top 60 most common categories whose naims look a lot alike and are probably typo's. We will merge them together\n",
        "raw['installer'] = np.where( raw['installer']=='gove', 'gover', raw['installer'] )\n",
        "raw['installer'] = np.where( raw['installer']=='community', 'commu', raw['installer'] )\n",
        "raw['installer'] = np.where( raw['installer']=='danid', 'danida', raw['installer'] )\n",
        "\n",
        "installer = raw.groupby('installer')['id'].count().reset_index()\n",
        "installer = installer.sort_values('id', ascending = False)\n",
        "installer.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "DJIBIG5OVYJu",
        "outputId": "0d25a6a0-5b6d-4274-e507-4a1568b2437b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d81ef0dd-212e-49ca-882a-4941ef087251\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>installer</th>\n",
              "      <th>id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>431</th>\n",
              "      <td>dwe</td>\n",
              "      <td>21756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>government</td>\n",
              "      <td>2367</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>351</th>\n",
              "      <td>danida</td>\n",
              "      <td>2092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>295</th>\n",
              "      <td>commu</td>\n",
              "      <td>2044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>647</th>\n",
              "      <td>hesawa</td>\n",
              "      <td>1768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1617</th>\n",
              "      <td>rwe</td>\n",
              "      <td>1498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>404</th>\n",
              "      <td>district council</td>\n",
              "      <td>1181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>876</th>\n",
              "      <td>kkkt</td>\n",
              "      <td>1135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>230</th>\n",
              "      <td>central government</td>\n",
              "      <td>933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>562</th>\n",
              "      <td>gover</td>\n",
              "      <td>929</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d81ef0dd-212e-49ca-882a-4941ef087251')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d81ef0dd-212e-49ca-882a-4941ef087251 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d81ef0dd-212e-49ca-882a-4941ef087251');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "               installer     id\n",
              "431                  dwe  21756\n",
              "567           government   2367\n",
              "351               danida   2092\n",
              "295                commu   2044\n",
              "647               hesawa   1768\n",
              "1617                 rwe   1498\n",
              "404     district council   1181\n",
              "876                 kkkt   1135\n",
              "230   central government    933\n",
              "562                gover    929"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#I want to keep the most frequent installers and combine the rarer classes together. I have played around with the optimum number of installers to keep, and I decided on the top 150.\n",
        "\n",
        "#create list of top 150 installers\n",
        "top_installers = installer.nlargest(150, 'id')['installer'].unique()\n",
        "\n",
        "#replace funders that are not in top 10 with 'other'\n",
        "raw['installer'] = np.where(raw['installer'].isin(top_installers), raw['installer'], 'other')\n"
      ],
      "metadata": {
        "id": "b5wlvPgbVrBc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2.1.6 Funder"
      ],
      "metadata": {
        "id": "1Mv9QnhWWRDb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#set al entries to lowercase\n",
        "raw['funder'] = raw['funder'].str.lower()\n",
        "\n",
        "\n",
        "funder = raw.groupby('funder')['id'].count().reset_index()\n",
        "funder = funder.sort_values('id', ascending = False)\n",
        "funder.head(10)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "t5PaN7MvWOjI",
        "outputId": "a219411e-1e62-4372-ba7a-0949cfdb600a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>funder</th>\n",
              "      <th>id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>506</th>\n",
              "      <td>government of tanzania</td>\n",
              "      <td>11299</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>292</th>\n",
              "      <td>danida</td>\n",
              "      <td>3907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>572</th>\n",
              "      <td>hesawa</td>\n",
              "      <td>2782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1598</th>\n",
              "      <td>rwssp</td>\n",
              "      <td>1703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2105</th>\n",
              "      <td>world bank</td>\n",
              "      <td>1701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>807</th>\n",
              "      <td>kkkt</td>\n",
              "      <td>1623</td>\n",
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              "    <tr>\n",
              "      <th>2107</th>\n",
              "      <td>world vision</td>\n",
              "      <td>1562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960</th>\n",
              "      <td>unicef</td>\n",
              "      <td>1324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1840</th>\n",
              "      <td>tasaf</td>\n",
              "      <td>1135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>326</th>\n",
              "      <td>dhv</td>\n",
              "      <td>1065</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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            "text/plain": [
              "                      funder     id\n",
              "506   government of tanzania  11299\n",
              "292                   danida   3907\n",
              "572                   hesawa   2782\n",
              "1598                   rwssp   1703\n",
              "2105              world bank   1701\n",
              "807                     kkkt   1623\n",
              "2107            world vision   1562\n",
              "1960                  unicef   1324\n",
              "1840                   tasaf   1135\n",
              "326                      dhv   1065"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create list of top 150 funders\n",
        "top_funders = funder.nlargest(150, 'id')['funder'].unique()\n",
        "\n",
        "#replace funders that are not in top 150 with 'other'\n",
        "raw['funder'] = np.where(raw['funder'].isin(top_funders), raw['funder'], 'other')\n"
      ],
      "metadata": {
        "id": "tOvxzzL7WYqg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2.1.7 Construction Year"
      ],
      "metadata": {
        "id": "Jwxb-hQaW6hh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#because of the wide spread of construction years, I don't want to impude by the overall mean\n",
        "raw['construction_year'].min(),  raw['construction_year'].max() , raw['construction_year'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uQtfJSQHW5R7",
        "outputId": "4575ff02-1170-4b82-8c57-6cebe96b72e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1960.0, 2013.0, 1996.8709430210642)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#We can see that the mean construction year by extraction type group gives much more detailed information\n",
        "mean_construction = raw.groupby('extraction_type_group')['construction_year'].mean().reset_index()\n",
        "mean_construction "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "ZHD6yvnKXIji",
        "outputId": "cfebc611-7b28-4f16-edca-07f36f581417"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>extraction_type_group</th>\n",
              "      <th>construction_year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>afridev</td>\n",
              "      <td>2002.215589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>gravity</td>\n",
              "      <td>1996.014441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>india mark ii</td>\n",
              "      <td>2001.187973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>india mark iii</td>\n",
              "      <td>2003.975904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>mono</td>\n",
              "      <td>1992.824017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>nira/tanira</td>\n",
              "      <td>1999.523877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>other</td>\n",
              "      <td>1993.272477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>other handpump</td>\n",
              "      <td>2000.594203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>other motorpump</td>\n",
              "      <td>2011.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>rope pump</td>\n",
              "      <td>2005.634888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>submersible</td>\n",
              "      <td>1999.372897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>swn 80</td>\n",
              "      <td>1996.607039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>wind-powered</td>\n",
              "      <td>1996.012346</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ],
            "text/plain": [
              "   extraction_type_group  construction_year\n",
              "0                afridev        2002.215589\n",
              "1                gravity        1996.014441\n",
              "2          india mark ii        2001.187973\n",
              "3         india mark iii        2003.975904\n",
              "4                   mono        1992.824017\n",
              "5            nira/tanira        1999.523877\n",
              "6                  other        1993.272477\n",
              "7         other handpump        2000.594203\n",
              "8        other motorpump        2011.400000\n",
              "9              rope pump        2005.634888\n",
              "10           submersible        1999.372897\n",
              "11                swn 80        1996.607039\n",
              "12          wind-powered        1996.012346"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create df with the mean extraction year by extraction type group\n",
        "mean_construction = raw.groupby('extraction_type_group')['construction_year'].mean().reset_index()\n",
        "mean_construction  = mean_construction .rename(columns={\"construction_year\": \"imputed_construction_year\"})\n",
        "\n",
        "#merge this df to the main df and replace missing values\n",
        "raw = raw.merge(mean_construction, how =  'left', on =  'extraction_type_group')\n",
        "raw['construction_year_imputed'] = np.where(raw['construction_year'].isna(), raw['imputed_construction_year'], raw['construction_year'] )\n",
        "\n",
        "#drop redundant columns\n",
        "raw=raw.drop(['imputed_construction_year', 'construction_year'], axis=1)"
      ],
      "metadata": {
        "id": "lNWbeb3CXXxB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2.1.8 GPS height"
      ],
      "metadata": {
        "id": "y4K_8zsAYR2e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tanzania is not located below sealevel, so any negative value for gps height cannot be correct. The normal error is gps altitude is is +- 15 meter and this applies 95% of the time. The other 5% of the time you can have any value. HOwever, here we don't know if the gps height indicates the height of the water level, or the locaton of the well....  If I had known this for sure, I would clamp any negative values to zero. Around 2.5% of the records display a negative altitude. I will impute missing values based on the mean of the regional features "
      ],
      "metadata": {
        "id": "fUq1-BSAYZWu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#create mean on the lowest granularity level (subvillage)\n",
        "means_altitude_subvillage = raw.groupby(['region', 'lga', 'ward', 'subvillage'])['gps_height'].mean().reset_index()#\n",
        "means_altitude_subvillage = means_altitude_subvillage.rename(columns={\"gps_height\": \"gps_height_imputed_subvillage\"})\n",
        "\n",
        "#ward level\n",
        "means_altitude_ward = raw.groupby(['region', 'lga', 'ward',])['gps_height'].mean().reset_index()\n",
        "means_altitude_ward = means_altitude_ward.rename(columns={\"gps_height\": \"gps_height_imputed_ward\"})\n",
        "\n",
        "#lga level\n",
        "means_altitude_lga = raw.groupby(['region', 'lga'])['gps_height'].mean().reset_index()\n",
        "means_altitude_lga = means_altitude_lga .rename(columns={\"gps_height\": \"gps_height_imputed_lga\"})\n",
        "\n",
        "#region level\n",
        "means_altitude_region = raw.groupby(['region'])['gps_height'].mean().reset_index()\n",
        "means_altitude_region = means_altitude_region.rename(columns={\"gps_height\": \"gps_height_imputed_region\"})\n",
        "\n",
        "#region basin\n",
        "means_altitude_basin = raw.groupby(['basin'])['gps_height'].mean().reset_index()\n",
        "means_altitude_basin = means_altitude_basin.rename(columns={\"gps_height\": \"gps_height_imputed_basin\"})\n",
        "\n",
        "#merge the aggregated dataframes as new columns to the original df\n",
        "raw= raw.merge(means_altitude_subvillage, how = 'left', on = ['region', 'lga', 'ward', 'subvillage'])\n",
        "raw = raw.merge(means_altitude_ward, how = 'left', on = ['region', 'lga', 'ward'])\n",
        "raw = raw.merge(means_altitude_lga, how = 'left', on = ['region', 'lga'])\n",
        "raw = raw.merge(means_altitude_region, how = 'left', on = ['region'])\n",
        "raw = raw.merge(means_altitude_basin, how = 'left', on = ['basin'])\n",
        "\n",
        "#create final imputed longitude column\n",
        "raw['imputed_gps_height'] = np.where(raw['gps_height'].isna(), raw['gps_height_imputed_subvillage'], raw['gps_height']) #if longitude is missing, impute it by the mean of the subvillage\n",
        "raw['imputed_gps_height'] = np.where(raw['imputed_gps_height'].isna(), raw['gps_height_imputed_ward'], raw['imputed_gps_height']) #if subvillage mean is missing, impute it by the ward\n",
        "raw['imputed_gps_height'] = np.where(raw['imputed_gps_height'].isna(), raw['gps_height_imputed_lga'], raw['imputed_gps_height'])\n",
        "raw['imputed_gps_height'] = np.where(raw['imputed_gps_height'].isna(), raw['gps_height_imputed_region'], raw['imputed_gps_height'])\n",
        "raw['imputed_gps_height'] = np.where(raw['imputed_gps_height'].isna(), raw['gps_height_imputed_basin'], raw['imputed_gps_height'])\n",
        "\n",
        "#drop redundant columns\n",
        "raw= raw.drop(['gps_height_imputed_subvillage','gps_height_imputed_ward' , 'gps_height_imputed_lga' , 'gps_height_imputed_region', 'gps_height', 'gps_height_imputed_basin'], axis=1)\n"
      ],
      "metadata": {
        "id": "GgEuWOEeYRKO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2.1.9 Population"
      ],
      "metadata": {
        "id": "Pa2qLYtlY5m6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#create mean on the lowest granularity level (subvillage)\n",
        "means_population_subvillage = raw.groupby(['region', 'lga', 'ward', 'subvillage'])['population'].mean().reset_index()#\n",
        "means_population_subvillage = means_population_subvillage.rename(columns={\"population\": \"population_imputed_subvillage\"})\n",
        "\n",
        "#ward level\n",
        "means_population_ward = raw.groupby(['region', 'lga', 'ward',])['population'].mean().reset_index()\n",
        "means_population_ward = means_population_ward.rename(columns={\"population\": \"population_imputed_ward\"})\n",
        "\n",
        "#lga level\n",
        "means_population_lga = raw.groupby(['region', 'lga'])['population'].mean().reset_index()\n",
        "means_population_lga = means_population_lga .rename(columns={\"population\": \"population_imputed_lga\"})\n",
        "\n",
        "#region level\n",
        "means_population_region = raw.groupby(['region'])['population'].mean().reset_index()\n",
        "means_population_region = means_population_region.rename(columns={\"population\": \"population_imputed_region\"})\n",
        "\n",
        "#region basin\n",
        "means_population_basin = raw.groupby(['basin'])['population'].mean().reset_index()\n",
        "means_population_basin = means_population_basin.rename(columns={\"population\": \"population_imputed_basin\"})\n",
        "\n",
        "#merge the aggregated dataframes as new columns to the original df\n",
        "raw= raw.merge(means_population_subvillage, how = 'left', on = ['region', 'lga', 'ward', 'subvillage'])\n",
        "raw = raw.merge(means_population_ward, how = 'left', on = ['region', 'lga', 'ward'])\n",
        "raw = raw.merge(means_population_lga, how = 'left', on = ['region', 'lga'])\n",
        "raw = raw.merge(means_population_region, how = 'left', on = ['region'])\n",
        "raw = raw.merge(means_population_basin, how = 'left', on = ['basin'])\n",
        "\n",
        "#create final imputed longitude column\n",
        "raw['imputed_population'] = np.where(raw['population'].isna(), raw['population_imputed_subvillage'], raw['population']) #if longitude is missing, impute it by the mean of the subvillage\n",
        "raw['imputed_population'] = np.where(raw['imputed_population'].isna(), raw['population_imputed_ward'], raw['imputed_population']) #if subvillage mean is missing, impute it by the ward\n",
        "raw['imputed_population'] = np.where(raw['imputed_population'].isna(), raw['population_imputed_lga'], raw['imputed_population'])\n",
        "raw['imputed_population'] = np.where(raw['imputed_population'].isna(), raw['population_imputed_region'], raw['imputed_population'])\n",
        "raw['imputed_population'] = np.where(raw['imputed_population'].isna(), raw['population_imputed_basin'], raw['imputed_population'])\n",
        "\n",
        "#drop redundant columns\n",
        "raw= raw.drop(['population_imputed_subvillage','population_imputed_ward' , 'population_imputed_lga' , 'population_imputed_region', 'population', 'population_imputed_basin'], axis=1)"
      ],
      "metadata": {
        "id": "wYek3Y2JY4ZV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "RdpL_6sOaZxm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.2 Data types "
      ],
      "metadata": {
        "id": "yWXepmeNZIYM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "raw.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uPbQ3QdTZYMq",
        "outputId": "f31fda66-2526-47a5-b3ab-95539ac00ea7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 74249 entries, 0 to 74248\n",
            "Data columns (total 41 columns):\n",
            " #   Column                      Non-Null Count  Dtype         \n",
            "---  ------                      --------------  -----         \n",
            " 0   id                          74249 non-null  int64         \n",
            " 1   status_group                59399 non-null  object        \n",
            " 2   amount_tsh                  22201 non-null  float64       \n",
            " 3   date_recorded               74249 non-null  datetime64[ns]\n",
            " 4   funder                      74249 non-null  object        \n",
            " 5   installer                   74249 non-null  object        \n",
            " 6   latitude                    74249 non-null  float64       \n",
            " 7   wpt_name                    74249 non-null  object        \n",
            " 8   num_private                 951 non-null    float64       \n",
            " 9   basin                       74249 non-null  object        \n",
            " 10  subvillage                  73779 non-null  object        \n",
            " 11  region                      74249 non-null  object        \n",
            " 12  region_code                 74249 non-null  int64         \n",
            " 13  district_code               74222 non-null  float64       \n",
            " 14  lga                         74249 non-null  object        \n",
            " 15  ward                        74249 non-null  object        \n",
            " 16  public_meeting              74249 non-null  bool          \n",
            " 17  recorded_by                 74249 non-null  object        \n",
            " 18  scheme_name                 38992 non-null  object        \n",
            " 19  extraction_type             74249 non-null  object        \n",
            " 20  extraction_type_group       74249 non-null  object        \n",
            " 21  extraction_type_class       74249 non-null  object        \n",
            " 22  management                  74249 non-null  object        \n",
            " 23  management_group            74249 non-null  object        \n",
            " 24  payment                     74249 non-null  object        \n",
            " 25  payment_type                74249 non-null  object        \n",
            " 26  water_quality               74249 non-null  object        \n",
            " 27  quality_group               74249 non-null  object        \n",
            " 28  quantity                    74249 non-null  object        \n",
            " 29  quantity_group              74249 non-null  object        \n",
            " 30  source                      74249 non-null  object        \n",
            " 31  source_type                 74249 non-null  object        \n",
            " 32  source_class                74249 non-null  object        \n",
            " 33  waterpoint_type             74249 non-null  object        \n",
            " 34  waterpoint_type_group       74249 non-null  object        \n",
            " 35  imputed_longitude           74249 non-null  float64       \n",
            " 36  imputed_permit              74249 non-null  object        \n",
            " 37  imputed_scheme__management  74249 non-null  object        \n",
            " 38  construction_year_imputed   74249 non-null  float64       \n",
            " 39  imputed_gps_height          74249 non-null  float64       \n",
            " 40  imputed_population          74249 non-null  float64       \n",
            "dtypes: bool(1), datetime64[ns](1), float64(8), int64(2), object(29)\n",
            "memory usage: 23.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#replace string to integer\n",
        "raw['public_meeting'] = raw['public_meeting'].replace({True: 1, False: 0})\n",
        "raw['imputed_permit'] = raw['imputed_permit'].replace({True: 1, False: 0})\n",
        "\n",
        "\n",
        "#change to integer\n",
        "raw[['imputed_gps_height', 'construction_year_imputed', 'imputed_population']] = raw[['imputed_gps_height', 'construction_year_imputed', 'imputed_population']].astype('int')\n",
        "\n",
        "#change type to categorical\n",
        "raw[[ 'num_private', 'region_code', 'district_code', 'num_private']] = raw[[ 'num_private', 'region_code', 'district_code', 'num_private']].astype('str')\n",
        "\n",
        "#remove decimal\n",
        "raw['district_code'] = raw['district_code'].str.split(\".\").str[0]\n"
      ],
      "metadata": {
        "id": "BtneS5QmZB8L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#save cleaned df\n",
        "raw= raw.rename(columns={\"imputed_permit\": \"permit\", \"imputed_scheme__management\": \"scheme_management\",\n",
        "                    \"imputed_gps_height\": \"gps_height\", 'construction_year_imputed': 'construction_year', \n",
        "                   'imputed_population': 'population', 'imputed_longitude': 'longitude'}, errors=\"raise\")\n",
        "\n",
        "#raw.to_csv(\"cleaned_df.csv\")"
      ],
      "metadata": {
        "id": "0WmZRBspafbw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raw.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "evOaRfm0a8dV",
        "outputId": "9c7375aa-13c0-4b33-9648-052299682a9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-26eaad9a-4066-4be8-a307-a2f82236035f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\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",
              "      <th>amount_tsh</th>\n",
              "      <th>date_recorded</th>\n",
              "      <th>funder</th>\n",
              "      <th>installer</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>public_meeting</th>\n",
              "      <th>recorded_by</th>\n",
              "      <th>scheme_name</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>longitude</th>\n",
              "      <th>permit</th>\n",
              "      <th>scheme_management</th>\n",
              "      <th>construction_year</th>\n",
              "      <th>gps_height</th>\n",
              "      <th>population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>69572</td>\n",
              "      <td>functional</td>\n",
              "      <td>6000.0</td>\n",
              "      <td>2011-03-14</td>\n",
              "      <td>roman</td>\n",
              "      <td>roman</td>\n",
              "      <td>-9.856322</td>\n",
              "      <td>none</td>\n",
              "      <td>nan</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>1</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>Roman</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>34.938093</td>\n",
              "      <td>0</td>\n",
              "      <td>VWC</td>\n",
              "      <td>1999</td>\n",
              "      <td>1390</td>\n",
              "      <td>109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8776</td>\n",
              "      <td>functional</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2013-03-06</td>\n",
              "      <td>grumeti</td>\n",
              "      <td>grumeti</td>\n",
              "      <td>-2.147466</td>\n",
              "      <td>Zahanati</td>\n",
              "      <td>nan</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>1</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>NaN</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>34.698766</td>\n",
              "      <td>1</td>\n",
              "      <td>Other</td>\n",
              "      <td>2010</td>\n",
              "      <td>1399</td>\n",
              "      <td>280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>34310</td>\n",
              "      <td>functional</td>\n",
              "      <td>25.0</td>\n",
              "      <td>2013-02-25</td>\n",
              "      <td>other</td>\n",
              "      <td>world vision</td>\n",
              "      <td>-3.821329</td>\n",
              "      <td>Kwa Mahundi</td>\n",
              "      <td>nan</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>1</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>Nyumba ya mungu pipe scheme</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>37.460664</td>\n",
              "      <td>1</td>\n",
              "      <td>VWC</td>\n",
              "      <td>2009</td>\n",
              "      <td>686</td>\n",
              "      <td>250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>67743</td>\n",
              "      <td>non functional</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2013-01-28</td>\n",
              "      <td>unicef</td>\n",
              "      <td>unicef</td>\n",
              "      <td>-11.155298</td>\n",
              "      <td>Zahanati Ya Nanyumbu</td>\n",
              "      <td>nan</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>1</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>NaN</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>38.486161</td>\n",
              "      <td>1</td>\n",
              "      <td>VWC</td>\n",
              "      <td>1986</td>\n",
              "      <td>263</td>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19728</td>\n",
              "      <td>functional</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2011-07-13</td>\n",
              "      <td>other</td>\n",
              "      <td>artisan</td>\n",
              "      <td>-1.825359</td>\n",
              "      <td>Shuleni</td>\n",
              "      <td>nan</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>1</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>NaN</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>31.130847</td>\n",
              "      <td>1</td>\n",
              "      <td>Other</td>\n",
              "      <td>1996</td>\n",
              "      <td>1328</td>\n",
              "      <td>532</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-26eaad9a-4066-4be8-a307-a2f82236035f')\"\n",
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              "      \n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      id    status_group  amount_tsh date_recorded   funder     installer  \\\n",
              "0  69572      functional      6000.0    2011-03-14    roman         roman   \n",
              "1   8776      functional         NaN    2013-03-06  grumeti       grumeti   \n",
              "2  34310      functional        25.0    2013-02-25    other  world vision   \n",
              "3  67743  non functional         NaN    2013-01-28   unicef        unicef   \n",
              "4  19728      functional         NaN    2011-07-13    other       artisan   \n",
              "\n",
              "    latitude              wpt_name num_private                    basin  \\\n",
              "0  -9.856322                  none         nan               Lake Nyasa   \n",
              "1  -2.147466              Zahanati         nan            Lake Victoria   \n",
              "2  -3.821329           Kwa Mahundi         nan                  Pangani   \n",
              "3 -11.155298  Zahanati Ya Nanyumbu         nan  Ruvuma / Southern Coast   \n",
              "4  -1.825359               Shuleni         nan            Lake Victoria   \n",
              "\n",
              "   subvillage   region region_code district_code        lga        ward  \\\n",
              "0    Mnyusi B   Iringa          11             5     Ludewa    Mundindi   \n",
              "1     Nyamara     Mara          20             2  Serengeti       Natta   \n",
              "2     Majengo  Manyara          21             4  Simanjiro     Ngorika   \n",
              "3  Mahakamani   Mtwara          90            63   Nanyumbu    Nanyumbu   \n",
              "4  Kyanyamisa   Kagera          18             1    Karagwe  Nyakasimbi   \n",
              "\n",
              "   public_meeting              recorded_by                  scheme_name  \\\n",
              "0               1  GeoData Consultants Ltd                        Roman   \n",
              "1               1  GeoData Consultants Ltd                          NaN   \n",
              "2               1  GeoData Consultants Ltd  Nyumba ya mungu pipe scheme   \n",
              "3               1  GeoData Consultants Ltd                          NaN   \n",
              "4               1  GeoData Consultants Ltd                          NaN   \n",
              "\n",
              "  extraction_type extraction_type_group extraction_type_class management  \\\n",
              "0         gravity               gravity               gravity        vwc   \n",
              "1         gravity               gravity               gravity        wug   \n",
              "2         gravity               gravity               gravity        vwc   \n",
              "3     submersible           submersible           submersible        vwc   \n",
              "4         gravity               gravity               gravity      other   \n",
              "\n",
              "  management_group         payment payment_type water_quality quality_group  \\\n",
              "0       user-group    pay annually     annually          soft          good   \n",
              "1       user-group       never pay    never pay          soft          good   \n",
              "2       user-group  pay per bucket   per bucket          soft          good   \n",
              "3       user-group       never pay    never pay          soft          good   \n",
              "4            other       never pay    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  longitude  \\\n",
              "0  groundwater           communal standpipe    communal standpipe  34.938093   \n",
              "1      surface           communal standpipe    communal standpipe  34.698766   \n",
              "2      surface  communal standpipe multiple    communal standpipe  37.460664   \n",
              "3  groundwater  communal standpipe multiple    communal standpipe  38.486161   \n",
              "4      surface           communal standpipe    communal standpipe  31.130847   \n",
              "\n",
              "   permit scheme_management  construction_year  gps_height  population  \n",
              "0       0               VWC               1999        1390         109  \n",
              "1       1             Other               2010        1399         280  \n",
              "2       1               VWC               2009         686         250  \n",
              "3       1               VWC               1986         263          58  \n",
              "4       1             Other               1996        1328         532  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3 Feature engineering"
      ],
      "metadata": {
        "id": "4MPC1HNYdMQU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "final_df = raw.copy()"
      ],
      "metadata": {
        "id": "jCpAjUW7dVdy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3.1 Age"
      ],
      "metadata": {
        "id": "_ZFcNzcLdRo0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#create age feature\n",
        "final_df['recorded_year'] = pd.DatetimeIndex(final_df ['date_recorded']).year\n",
        "final_df[ 'age'] = final_df['recorded_year'] - final_df['construction_year']\n",
        "final_df = final_df.drop('recorded_year',axis=1)"
      ],
      "metadata": {
        "id": "F2GD9aJlas7H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3.2 Season"
      ],
      "metadata": {
        "id": "oV2qyyvMdgWt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "final_df['month'] = pd.DatetimeIndex(final_df['date_recorded']).month\n",
        "\n",
        "# season encoder\n",
        "season_mapper = {1: 'short dry',2:'short dry', 3: 'long rain', 4: 'long rain', 5: 'long rain',6: 'long dry', \n",
        "                 7: 'long dry', 8: 'long dry', 9: 'long dry', 10: 'long dry', 11:'short rain', 12:'short rain'}\n",
        "#.p feature values to scale\n",
        "final_df['season']=final_df['month'].replace(season_mapper)\n",
        "final_df=final_df.drop('month', axis=1)\n"
      ],
      "metadata": {
        "id": "qm4UUBDAdfrB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "distribution_checker(final_df, 'season')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "v6052Cb7dq0P",
        "outputId": "16ec8342-69e2-4e99-d10a-b0eb1654fc0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-2fb60b9f-8ab2-4152-ac59-11d590416a93\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>nr_pumps</th>\n",
              "      <th>perc_total_pumps</th>\n",
              "      <th>perc_functional</th>\n",
              "      <th>perc_non_functional</th>\n",
              "      <th>perc_functional_needs_repair</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>season</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>long dry</th>\n",
              "      <td>16432</td>\n",
              "      <td>27.7</td>\n",
              "      <td>51.5</td>\n",
              "      <td>41.6</td>\n",
              "      <td>6.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>long rain</th>\n",
              "      <td>22242</td>\n",
              "      <td>37.4</td>\n",
              "      <td>59.8</td>\n",
              "      <td>34.1</td>\n",
              "      <td>6.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>short dry</th>\n",
              "      <td>18756</td>\n",
              "      <td>31.6</td>\n",
              "      <td>50.4</td>\n",
              "      <td>40.3</td>\n",
              "      <td>9.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>short rain</th>\n",
              "      <td>1969</td>\n",
              "      <td>3.3</td>\n",
              "      <td>52.4</td>\n",
              "      <td>43.0</td>\n",
              "      <td>4.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2fb60b9f-8ab2-4152-ac59-11d590416a93');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "            nr_pumps  perc_total_pumps  perc_functional  perc_non_functional  \\\n",
              "season                                                                         \n",
              "long dry       16432              27.7             51.5                 41.6   \n",
              "long rain      22242              37.4             59.8                 34.1   \n",
              "short dry      18756              31.6             50.4                 40.3   \n",
              "short rain      1969               3.3             52.4                 43.0   \n",
              "\n",
              "            perc_functional_needs_repair  \n",
              "season                                    \n",
              "long dry                             6.9  \n",
              "long rain                            6.1  \n",
              "short dry                            9.3  \n",
              "short rain                           4.6  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3.3 Amount tsh missing"
      ],
      "metadata": {
        "id": "cAVec-Jrd627"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#where amount tsh isn't missing, the percentage of functional pumps is a lot higher\n",
        "final_df['amount_tsh_missing'] = np.where( final_df['amount_tsh'].isna(), 1, 0)\n",
        "distribution_checker(final_df, 'amount_tsh_missing')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "QMqw6C57d0e_",
        "outputId": "ff29628d-cdfe-4b9c-b963-62edb7924d22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>nr_pumps</th>\n",
              "      <th>perc_total_pumps</th>\n",
              "      <th>perc_functional</th>\n",
              "      <th>perc_non_functional</th>\n",
              "      <th>perc_functional_needs_repair</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>amount_tsh_missing</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17761</td>\n",
              "      <td>29.9</td>\n",
              "      <td>70.7</td>\n",
              "      <td>22.2</td>\n",
              "      <td>7.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>41638</td>\n",
              "      <td>70.1</td>\n",
              "      <td>47.3</td>\n",
              "      <td>45.4</td>\n",
              "      <td>7.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f38722e2-70e9-43cc-b2ae-fbcf6fe26d84')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f38722e2-70e9-43cc-b2ae-fbcf6fe26d84 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f38722e2-70e9-43cc-b2ae-fbcf6fe26d84');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                    nr_pumps  perc_total_pumps  perc_functional  \\\n",
              "amount_tsh_missing                                                \n",
              "0                      17761              29.9             70.7   \n",
              "1                      41638              70.1             47.3   \n",
              "\n",
              "                    perc_non_functional  perc_functional_needs_repair  \n",
              "amount_tsh_missing                                                     \n",
              "0                                  22.2                           7.1  \n",
              "1                                  45.4                           7.3  "
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3.4 Region district"
      ],
      "metadata": {
        "id": "0PgQ6jP4eLJI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "final_df['region_district'] = final_df['region']+ \"-\" + final_df['district_code']\n",
        "region_district = distribution_checker(final_df,  'region_district')\n",
        "region_district.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "3tLB3debeDGf",
        "outputId": "f10627df-f916-4eb7-81e4-8da00bfe00e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d6e432a4-110b-4cd6-92ff-5b808b63a8db\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>nr_pumps</th>\n",
              "      <th>perc_total_pumps</th>\n",
              "      <th>perc_functional</th>\n",
              "      <th>perc_non_functional</th>\n",
              "      <th>perc_functional_needs_repair</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>region_district</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Arusha-1</th>\n",
              "      <td>189</td>\n",
              "      <td>0.3</td>\n",
              "      <td>50.3</td>\n",
              "      <td>33.9</td>\n",
              "      <td>15.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arusha-2</th>\n",
              "      <td>1206</td>\n",
              "      <td>2.0</td>\n",
              "      <td>69.6</td>\n",
              "      <td>26.6</td>\n",
              "      <td>3.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arusha-3</th>\n",
              "      <td>109</td>\n",
              "      <td>0.2</td>\n",
              "      <td>71.6</td>\n",
              "      <td>20.2</td>\n",
              "      <td>8.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arusha-30</th>\n",
              "      <td>326</td>\n",
              "      <td>0.5</td>\n",
              "      <td>96.9</td>\n",
              "      <td>2.8</td>\n",
              "      <td>0.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arusha-5</th>\n",
              "      <td>201</td>\n",
              "      <td>0.3</td>\n",
              "      <td>54.2</td>\n",
              "      <td>31.8</td>\n",
              "      <td>13.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d6e432a4-110b-4cd6-92ff-5b808b63a8db')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d6e432a4-110b-4cd6-92ff-5b808b63a8db button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d6e432a4-110b-4cd6-92ff-5b808b63a8db');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                 nr_pumps  perc_total_pumps  perc_functional  \\\n",
              "region_district                                                \n",
              "Arusha-1              189               0.3             50.3   \n",
              "Arusha-2             1206               2.0             69.6   \n",
              "Arusha-3              109               0.2             71.6   \n",
              "Arusha-30             326               0.5             96.9   \n",
              "Arusha-5              201               0.3             54.2   \n",
              "\n",
              "                 perc_non_functional  perc_functional_needs_repair  \n",
              "region_district                                                     \n",
              "Arusha-1                        33.9                          15.9  \n",
              "Arusha-2                        26.6                           3.8  \n",
              "Arusha-3                        20.2                           8.3  \n",
              "Arusha-30                        2.8                           0.3  \n",
              "Arusha-5                        31.8                          13.9  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "#### 3.5 Coordinates"
      ],
      "metadata": {
        "id": "-BMLZqSQefPP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#two decimal places is 1.1 km accurate. This will provide enough information on the location. Using the full coordinate doesn't provide a lot of general information, but does result in high cardinality\n",
        "final_df['longitude'] = round(final_df['longitude'], 2)\n",
        "final_df['latitude'] = round(final_df['latitude'],2)"
      ],
      "metadata": {
        "id": "6DLslV04eVRP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3.6 Extraction"
      ],
      "metadata": {
        "id": "qU-kOuB9e42n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "final_df.groupby([  'extraction_type_class', 'extraction_type_group', 'extraction_type'])['id'].count().reset_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "id": "p_JUS-MQe1zR",
        "outputId": "b23cce21-17fd-49f9-c309-7cfd5c3ebd4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b672fe14-73fc-46ac-89ac-5124952fcf3b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>extraction_type_class</th>\n",
              "      <th>extraction_type_group</th>\n",
              "      <th>extraction_type</th>\n",
              "      <th>id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>gravity</td>\n",
              "      <td>gravity</td>\n",
              "      <td>gravity</td>\n",
              "      <td>33263</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>handpump</td>\n",
              "      <td>afridev</td>\n",
              "      <td>afridev</td>\n",
              "      <td>2207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>handpump</td>\n",
              "      <td>india mark ii</td>\n",
              "      <td>india mark ii</td>\n",
              "      <td>3029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>handpump</td>\n",
              "      <td>india mark iii</td>\n",
              "      <td>india mark iii</td>\n",
              "      <td>135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>handpump</td>\n",
              "      <td>nira/tanira</td>\n",
              "      <td>nira/tanira</td>\n",
              "      <td>10205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>handpump</td>\n",
              "      <td>other handpump</td>\n",
              "      <td>other - mkulima/shinyanga</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>handpump</td>\n",
              "      <td>other handpump</td>\n",
              "      <td>other - play pump</td>\n",
              "      <td>101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>handpump</td>\n",
              "      <td>other handpump</td>\n",
              "      <td>other - swn 81</td>\n",
              "      <td>284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>handpump</td>\n",
              "      <td>other handpump</td>\n",
              "      <td>walimi</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>handpump</td>\n",
              "      <td>swn 80</td>\n",
              "      <td>swn 80</td>\n",
              "      <td>4588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>motorpump</td>\n",
              "      <td>mono</td>\n",
              "      <td>mono</td>\n",
              "      <td>3628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>motorpump</td>\n",
              "      <td>other motorpump</td>\n",
              "      <td>cemo</td>\n",
              "      <td>108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>motorpump</td>\n",
              "      <td>other motorpump</td>\n",
              "      <td>climax</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>other</td>\n",
              "      <td>other</td>\n",
              "      <td>other</td>\n",
              "      <td>8102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>rope pump</td>\n",
              "      <td>rope pump</td>\n",
              "      <td>other - rope pump</td>\n",
              "      <td>572</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>submersible</td>\n",
              "      <td>submersible</td>\n",
              "      <td>ksb</td>\n",
              "      <td>1790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>submersible</td>\n",
              "      <td>submersible</td>\n",
              "      <td>submersible</td>\n",
              "      <td>5982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>wind-powered</td>\n",
              "      <td>wind-powered</td>\n",
              "      <td>windmill</td>\n",
              "      <td>152</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b672fe14-73fc-46ac-89ac-5124952fcf3b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b672fe14-73fc-46ac-89ac-5124952fcf3b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b672fe14-73fc-46ac-89ac-5124952fcf3b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   extraction_type_class extraction_type_group            extraction_type  \\\n",
              "0                gravity               gravity                    gravity   \n",
              "1               handpump               afridev                    afridev   \n",
              "2               handpump         india mark ii              india mark ii   \n",
              "3               handpump        india mark iii             india mark iii   \n",
              "4               handpump           nira/tanira                nira/tanira   \n",
              "5               handpump        other handpump  other - mkulima/shinyanga   \n",
              "6               handpump        other handpump          other - play pump   \n",
              "7               handpump        other handpump             other - swn 81   \n",
              "8               handpump        other handpump                     walimi   \n",
              "9               handpump                swn 80                     swn 80   \n",
              "10             motorpump                  mono                       mono   \n",
              "11             motorpump       other motorpump                       cemo   \n",
              "12             motorpump       other motorpump                     climax   \n",
              "13                 other                 other                      other   \n",
              "14             rope pump             rope pump          other - rope pump   \n",
              "15           submersible           submersible                        ksb   \n",
              "16           submersible           submersible                submersible   \n",
              "17          wind-powered          wind-powered                   windmill   \n",
              "\n",
              "       id  \n",
              "0   33263  \n",
              "1    2207  \n",
              "2    3029  \n",
              "3     135  \n",
              "4   10205  \n",
              "5       2  \n",
              "6     101  \n",
              "7     284  \n",
              "8      60  \n",
              "9    4588  \n",
              "10   3628  \n",
              "11    108  \n",
              "12     41  \n",
              "13   8102  \n",
              "14    572  \n",
              "15   1790  \n",
              "16   5982  \n",
              "17    152  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#i want to keep extraction type class and I will group the extraction type group en type together\n",
        "\n",
        "#swn 80 and swn 81 become swn\n",
        "#cemo + climax become other motorpump\n",
        "# other -mkulima, other -play and walimi become other handpump\n",
        "\n",
        "swn = ['other - swn 81', 'swn80']\n",
        "final_df['extraction_type'] =np.where(final_df['extraction_type'].isin(swn), 'swn',  final_df['extraction_type'])\n",
        "\n",
        "other_handpump = ['other - mkulima/shinyanga', 'other - play pump', 'other - walimi']\n",
        "final_df['extraction_type']=np.where(final_df['extraction_type'].isin(other_handpump), 'other handpump',  final_df['extraction_type'])\n",
        "\n",
        "other_motorpump = ['cemo', 'climax']\n",
        "final_df['extraction_type'] =np.where(final_df['extraction_type'].isin(other_motorpump), 'other motorpump',  final_df['extraction_type'])\n"
      ],
      "metadata": {
        "id": "xVKxxsCWe8P2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "#### 3.7 autonomy"
      ],
      "metadata": {
        "id": "WECSfzEogAQt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#autonomous = government, VWC, town council ..... also water authority?, parastatal (=state company)? SWC?\n",
        "#autonomous = WUA, WUG, board, trust, school\n",
        "#private = private, company\n",
        "\n",
        "non = ['VWC', 'Water authority', 'Parastatal', 'SWC']\n",
        "autonomous = ['WUG', 'WUA', 'Water Board', 'Trust']\n",
        "private = ['Company', 'Private operator']\n",
        "other = ['None', 'Other']\n",
        "\n",
        "final_df['authority_scheme'] = final_df['scheme_management']\n",
        "final_df.loc[final_df['authority_scheme'].isin(non),'authority_scheme']='non-autonomous'\n",
        "final_df.loc[final_df['authority_scheme'].isin(autonomous ),'authority_scheme']='autonomous'\n",
        "final_df.loc[final_df['authority_scheme'].isin(private),'authority_scheme']='private'\n",
        "final_df.loc[final_df['authority_scheme'].isin(other ),'authority_scheme']='other'"
      ],
      "metadata": {
        "id": "NcpVio1bf4mF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "autonomy = distribution_checker(final_df, 'authority_scheme')\n",
        "autonomy= autonomy.sort_values('nr_pumps', ascending= False)\n",
        "autonomy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Lu_ivOz0gfps",
        "outputId": "1e6f2c5f-9fd3-49e9-b49b-427903c89e9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-70bf9021-6618-41a7-9e19-c2eb2f966fb4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>nr_pumps</th>\n",
              "      <th>perc_total_pumps</th>\n",
              "      <th>perc_functional</th>\n",
              "      <th>perc_non_functional</th>\n",
              "      <th>perc_functional_needs_repair</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>authority_scheme</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>non-autonomous</th>\n",
              "      <td>44653</td>\n",
              "      <td>75.2</td>\n",
              "      <td>51.2</td>\n",
              "      <td>41.7</td>\n",
              "      <td>7.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>autonomous</th>\n",
              "      <td>11549</td>\n",
              "      <td>19.4</td>\n",
              "      <td>64.6</td>\n",
              "      <td>26.5</td>\n",
              "      <td>8.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>private</th>\n",
              "      <td>2244</td>\n",
              "      <td>3.8</td>\n",
              "      <td>60.1</td>\n",
              "      <td>37.1</td>\n",
              "      <td>2.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>other</th>\n",
              "      <td>953</td>\n",
              "      <td>1.6</td>\n",
              "      <td>61.5</td>\n",
              "      <td>34.6</td>\n",
              "      <td>3.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-70bf9021-6618-41a7-9e19-c2eb2f966fb4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-70bf9021-6618-41a7-9e19-c2eb2f966fb4 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-70bf9021-6618-41a7-9e19-c2eb2f966fb4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                  nr_pumps  perc_total_pumps  perc_functional  \\\n",
              "authority_scheme                                                \n",
              "non-autonomous       44653              75.2             51.2   \n",
              "autonomous           11549              19.4             64.6   \n",
              "private               2244               3.8             60.1   \n",
              "other                  953               1.6             61.5   \n",
              "\n",
              "                  perc_non_functional  perc_functional_needs_repair  \n",
              "authority_scheme                                                     \n",
              "non-autonomous                   41.7                           7.1  \n",
              "autonomous                       26.5                           8.9  \n",
              "private                          37.1                           2.8  \n",
              "other                            34.6                           3.9  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3.8 source type"
      ],
      "metadata": {
        "id": "SGxaWTQNgqLI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#source type by source class\n",
        "final_df.groupby([  'source_class', 'source_type','source' ])['id'].count().reset_index()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "0m2HV5MRgni7",
        "outputId": "764febfd-07bc-4311-9b0a-418ce95d07f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-dd91f3a3-9841-4d7b-b24d-83ee6ad60be5\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>source_class</th>\n",
              "      <th>source_type</th>\n",
              "      <th>source</th>\n",
              "      <th>id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>groundwater</td>\n",
              "      <td>borehole</td>\n",
              "      <td>hand dtw</td>\n",
              "      <td>1108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>groundwater</td>\n",
              "      <td>borehole</td>\n",
              "      <td>machine dbh</td>\n",
              "      <td>13822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>groundwater</td>\n",
              "      <td>shallow well</td>\n",
              "      <td>shallow well</td>\n",
              "      <td>21139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>groundwater</td>\n",
              "      <td>spring</td>\n",
              "      <td>spring</td>\n",
              "      <td>21216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>surface</td>\n",
              "      <td>dam</td>\n",
              "      <td>dam</td>\n",
              "      <td>840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>surface</td>\n",
              "      <td>rainwater harvesting</td>\n",
              "      <td>rainwater harvesting</td>\n",
              "      <td>2863</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>surface</td>\n",
              "      <td>river/lake</td>\n",
              "      <td>lake</td>\n",
              "      <td>950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>surface</td>\n",
              "      <td>river/lake</td>\n",
              "      <td>river</td>\n",
              "      <td>11964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>unknown</td>\n",
              "      <td>other</td>\n",
              "      <td>other</td>\n",
              "      <td>261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>unknown</td>\n",
              "      <td>other</td>\n",
              "      <td>unknown</td>\n",
              "      <td>86</td>\n",
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              "</table>\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dd91f3a3-9841-4d7b-b24d-83ee6ad60be5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dd91f3a3-9841-4d7b-b24d-83ee6ad60be5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  source_class           source_type                source     id\n",
              "0  groundwater              borehole              hand dtw   1108\n",
              "1  groundwater              borehole           machine dbh  13822\n",
              "2  groundwater          shallow well          shallow well  21139\n",
              "3  groundwater                spring                spring  21216\n",
              "4      surface                   dam                   dam    840\n",
              "5      surface  rainwater harvesting  rainwater harvesting   2863\n",
              "6      surface            river/lake                  lake    950\n",
              "7      surface            river/lake                 river  11964\n",
              "8      unknown                 other                 other    261\n",
              "9      unknown                 other               unknown     86"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#keep source, but the rare classes will be put together\n",
        "other = ['other',  'unknown']\n",
        "final_df['source'] = np.where(final_df['source']=='unknown', 'other', final_df['source'])"
      ],
      "metadata": {
        "id": "PDdyY5-tg09D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3.9 Drop redundant features"
      ],
      "metadata": {
        "id": "zDYtzrKUhBpB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#during EDA I already decided what features to keep and which ones to drop\n",
        "final_df = final_df.drop(['amount_tsh', 'date_recorded', 'wpt_name', 'num_private', 'subvillage', 'region',  'district_code', 'lga', 'ward', 'recorded_by', 'scheme_name', 'extraction_type_group', 'management', 'management_group', 'payment', 'quality_group', 'quantity_group', 'source_class', 'source_type', 'waterpoint_type_group', 'construction_year'], axis=1)\n",
        "final_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "V1FTN8qChAxb",
        "outputId": "8be39afa-c3eb-4fab-c79c-e8731060e1c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-45be3c7a-b27f-4a87-85d5-93d5feef4bbd\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\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",
              "      <th>funder</th>\n",
              "      <th>installer</th>\n",
              "      <th>latitude</th>\n",
              "      <th>basin</th>\n",
              "      <th>region_code</th>\n",
              "      <th>public_meeting</th>\n",
              "      <th>extraction_type</th>\n",
              "      <th>extraction_type_class</th>\n",
              "      <th>payment_type</th>\n",
              "      <th>water_quality</th>\n",
              "      <th>quantity</th>\n",
              "      <th>source</th>\n",
              "      <th>waterpoint_type</th>\n",
              "      <th>longitude</th>\n",
              "      <th>permit</th>\n",
              "      <th>scheme_management</th>\n",
              "      <th>gps_height</th>\n",
              "      <th>population</th>\n",
              "      <th>age</th>\n",
              "      <th>season</th>\n",
              "      <th>amount_tsh_missing</th>\n",
              "      <th>region_district</th>\n",
              "      <th>authority_scheme</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>69572</td>\n",
              "      <td>functional</td>\n",
              "      <td>roman</td>\n",
              "      <td>roman</td>\n",
              "      <td>-9.86</td>\n",
              "      <td>Lake Nyasa</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>gravity</td>\n",
              "      <td>gravity</td>\n",
              "      <td>annually</td>\n",
              "      <td>soft</td>\n",
              "      <td>enough</td>\n",
              "      <td>spring</td>\n",
              "      <td>communal standpipe</td>\n",
              "      <td>34.94</td>\n",
              "      <td>0</td>\n",
              "      <td>VWC</td>\n",
              "      <td>1390</td>\n",
              "      <td>109</td>\n",
              "      <td>12</td>\n",
              "      <td>long rain</td>\n",
              "      <td>0</td>\n",
              "      <td>Iringa-5</td>\n",
              "      <td>non-autonomous</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8776</td>\n",
              "      <td>functional</td>\n",
              "      <td>grumeti</td>\n",
              "      <td>grumeti</td>\n",
              "      <td>-2.15</td>\n",
              "      <td>Lake Victoria</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>gravity</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>communal standpipe</td>\n",
              "      <td>34.70</td>\n",
              "      <td>1</td>\n",
              "      <td>Other</td>\n",
              "      <td>1399</td>\n",
              "      <td>280</td>\n",
              "      <td>3</td>\n",
              "      <td>long rain</td>\n",
              "      <td>1</td>\n",
              "      <td>Mara-2</td>\n",
              "      <td>other</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>34310</td>\n",
              "      <td>functional</td>\n",
              "      <td>other</td>\n",
              "      <td>world vision</td>\n",
              "      <td>-3.82</td>\n",
              "      <td>Pangani</td>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>gravity</td>\n",
              "      <td>gravity</td>\n",
              "      <td>per bucket</td>\n",
              "      <td>soft</td>\n",
              "      <td>enough</td>\n",
              "      <td>dam</td>\n",
              "      <td>communal standpipe multiple</td>\n",
              "      <td>37.46</td>\n",
              "      <td>1</td>\n",
              "      <td>VWC</td>\n",
              "      <td>686</td>\n",
              "      <td>250</td>\n",
              "      <td>4</td>\n",
              "      <td>short dry</td>\n",
              "      <td>0</td>\n",
              "      <td>Manyara-4</td>\n",
              "      <td>non-autonomous</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>67743</td>\n",
              "      <td>non functional</td>\n",
              "      <td>unicef</td>\n",
              "      <td>unicef</td>\n",
              "      <td>-11.16</td>\n",
              "      <td>Ruvuma / Southern Coast</td>\n",
              "      <td>90</td>\n",
              "      <td>1</td>\n",
              "      <td>submersible</td>\n",
              "      <td>submersible</td>\n",
              "      <td>never pay</td>\n",
              "      <td>soft</td>\n",
              "      <td>dry</td>\n",
              "      <td>machine dbh</td>\n",
              "      <td>communal standpipe multiple</td>\n",
              "      <td>38.49</td>\n",
              "      <td>1</td>\n",
              "      <td>VWC</td>\n",
              "      <td>263</td>\n",
              "      <td>58</td>\n",
              "      <td>27</td>\n",
              "      <td>short dry</td>\n",
              "      <td>1</td>\n",
              "      <td>Mtwara-63</td>\n",
              "      <td>non-autonomous</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19728</td>\n",
              "      <td>functional</td>\n",
              "      <td>other</td>\n",
              "      <td>artisan</td>\n",
              "      <td>-1.83</td>\n",
              "      <td>Lake Victoria</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>gravity</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>communal standpipe</td>\n",
              "      <td>31.13</td>\n",
              "      <td>1</td>\n",
              "      <td>Other</td>\n",
              "      <td>1328</td>\n",
              "      <td>532</td>\n",
              "      <td>15</td>\n",
              "      <td>long dry</td>\n",
              "      <td>1</td>\n",
              "      <td>Kagera-1</td>\n",
              "      <td>other</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-45be3c7a-b27f-4a87-85d5-93d5feef4bbd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-45be3c7a-b27f-4a87-85d5-93d5feef4bbd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "        }\n",
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              "  "
            ],
            "text/plain": [
              "      id    status_group   funder     installer  latitude  \\\n",
              "0  69572      functional    roman         roman     -9.86   \n",
              "1   8776      functional  grumeti       grumeti     -2.15   \n",
              "2  34310      functional    other  world vision     -3.82   \n",
              "3  67743  non functional   unicef        unicef    -11.16   \n",
              "4  19728      functional    other       artisan     -1.83   \n",
              "\n",
              "                     basin region_code  public_meeting extraction_type  \\\n",
              "0               Lake Nyasa          11               1         gravity   \n",
              "1            Lake Victoria          20               1         gravity   \n",
              "2                  Pangani          21               1         gravity   \n",
              "3  Ruvuma / Southern Coast          90               1     submersible   \n",
              "4            Lake Victoria          18               1         gravity   \n",
              "\n",
              "  extraction_type_class payment_type water_quality      quantity  \\\n",
              "0               gravity     annually          soft        enough   \n",
              "1               gravity    never pay          soft  insufficient   \n",
              "2               gravity   per bucket          soft        enough   \n",
              "3           submersible    never pay          soft           dry   \n",
              "4               gravity    never pay          soft      seasonal   \n",
              "\n",
              "                 source              waterpoint_type  longitude  permit  \\\n",
              "0                spring           communal standpipe      34.94       0   \n",
              "1  rainwater harvesting           communal standpipe      34.70       1   \n",
              "2                   dam  communal standpipe multiple      37.46       1   \n",
              "3           machine dbh  communal standpipe multiple      38.49       1   \n",
              "4  rainwater harvesting           communal standpipe      31.13       1   \n",
              "\n",
              "  scheme_management  gps_height  population  age     season  \\\n",
              "0               VWC        1390         109   12  long rain   \n",
              "1             Other        1399         280    3  long rain   \n",
              "2               VWC         686         250    4  short dry   \n",
              "3               VWC         263          58   27  short dry   \n",
              "4             Other        1328         532   15   long dry   \n",
              "\n",
              "   amount_tsh_missing region_district authority_scheme  \n",
              "0                   0        Iringa-5   non-autonomous  \n",
              "1                   1          Mara-2            other  \n",
              "2                   0       Manyara-4   non-autonomous  \n",
              "3                   1       Mtwara-63   non-autonomous  \n",
              "4                   1        Kagera-1            other  "
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3.10 Encoding"
      ],
      "metadata": {
        "id": "-joBY16fhV9V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#define train and test \n",
        "train_set = final_df [final_df [\"id\"].isin(train_values[\"id\"])]\n",
        "test_set =  final_df  [final_df  [\"id\"].isin(test_values[\"id\"])]"
      ],
      "metadata": {
        "id": "FOysiN37hM4j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = train_set.drop(['id', 'status_group'], axis=1)\n",
        "y = train_set['status_group']"
      ],
      "metadata": {
        "id": "os5cLcwghctJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_cols = [cname for cname in X .columns if\n",
        "                   X [cname].dtype == \"object\"]\n",
        "\n",
        "categorical_cols"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vsR_40UtiImx",
        "outputId": "865fa4e4-2904-427c-c25d-421edb0d75ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['funder',\n",
              " 'installer',\n",
              " 'basin',\n",
              " 'region_code',\n",
              " 'extraction_type',\n",
              " 'extraction_type_class',\n",
              " 'payment_type',\n",
              " 'water_quality',\n",
              " 'quantity',\n",
              " 'source',\n",
              " 'waterpoint_type',\n",
              " 'scheme_management',\n",
              " 'season',\n",
              " 'region_district',\n",
              " 'authority_scheme']"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Encode Categorical Columns \n",
        "for col in categorical_cols:\n",
        "  le = LabelEncoder()\n",
        "  train_set[col] = le.fit_transform(train_set[col])\n",
        "  test_set[col] = le.transform(test_set[col])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RSKKW1azijKZ",
        "outputId": "97c531b8-b1ba-43d3-9eaf-7e4d3fc8eb8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#merge the encoded train and test sets together\n",
        "final_df = train_set.append(test_set)\n",
        "final_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "WQMFD5OAi13A",
        "outputId": "31a5431a-8be7-471c-8558-12c4c9406c71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>2</th>\n",
              "      <td>34310</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>67743</td>\n",
              "      <td>non functional</td>\n",
              "      <td>136</td>\n",
              "      <td>129</td>\n",
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              "      <td>1</td>\n",
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              "      <th>4</th>\n",
              "      <td>19728</td>\n",
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              "  "
            ],
            "text/plain": [
              "      id    status_group  funder  installer  latitude  basin  region_code  \\\n",
              "0  69572      functional     107        108     -9.86      1            2   \n",
              "1   8776      functional      45         47     -2.15      4           12   \n",
              "2  34310      functional      95        148     -3.82      5           13   \n",
              "3  67743  non functional     136        129    -11.16      7           25   \n",
              "4  19728      functional      95          7     -1.83      4            9   \n",
              "\n",
              "   public_meeting  extraction_type  extraction_type_class  payment_type  \\\n",
              "0               1                1                      0             0   \n",
              "1               1                1                      0             2   \n",
              "2               1                1                      0             5   \n",
              "3               1               11                      5             2   \n",
              "4               1                1                      0             2   \n",
              "\n",
              "   water_quality  quantity  source  waterpoint_type  longitude  permit  \\\n",
              "0              6         1       8                1      34.94       0   \n",
              "1              6         2       5                1      34.70       1   \n",
              "2              6         1       0                2      37.46       1   \n",
              "3              6         0       3                2      38.49       1   \n",
              "4              6         3       5                1      31.13       1   \n",
              "\n",
              "   scheme_management  gps_height  population  age  season  amount_tsh_missing  \\\n",
              "0                  7        1390         109   12       1                   0   \n",
              "1                  2        1399         280    3       1                   1   \n",
              "2                  7         686         250    4       2                   0   \n",
              "3                  7         263          58   27       2                   1   \n",
              "4                  2        1328         532   15       0                   1   \n",
              "\n",
              "   region_district  authority_scheme  \n",
              "0               20                 1  \n",
              "1               55                 2  \n",
              "2               52                 1  \n",
              "3               76                 1  \n",
              "4               22                 2  "
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#final_df.to_csv(\"final_df.csv\")"
      ],
      "metadata": {
        "id": "7CvicJyvjx_X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Future work**\n",
        "* Calculate the mean/median/mode only in the train set and use this to impute missing data in both the train and the test set in order to avoid-overfitting"
      ],
      "metadata": {
        "id": "HWNKpkY9MaYu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "aFg9r8GVj8VP"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}