MPI_Model.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "m8dWhqf1ZMx4"
      },
      "id": "m8dWhqf1ZMx4",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "9c9c49a3",
      "metadata": {
        "id": "9c9c49a3"
      },
      "source": [
        "# Morgan Stanley Code to Give 2023\n",
        "\n",
        "## Team 18 - TinyMiraclesOnline\n",
        "\n",
        "### Calculating Global MPI for Tiny Miracles' Communities\n",
        "\n",
        "**What is Global Multidimensional Poverty Index?**\n",
        "\n",
        "The Global Multidimensional Poverty Index (Global MPI) is a poverty measure that reflects the multiple deprivations that poor people face in the areas of education, health, and living standards. It was developed in 2010 by the **Oxford Poverty & Human Development Initiative (OPHI)** and the **United Nations Development Programme (UNDP)**.\n",
        "\n",
        "![](https://hdr.undp.org/sites/default/files/styles/original/public/images/2022-03/mpiRoadMap.png?itok=85ASETTg)\n",
        "\n",
        "_Image Source: [https://hdr.undp.org/](https://hdr.undp.org/content/2021-global-multidimensional-poverty-index-mpi)_\n",
        "\n",
        "Global MPI is a contribution towards measuring progress towards target **1.2 of the Sustainable Development Goals (SDGs) which aims at reducing “at least by half the proportion of men, women and children of all ages living in poverty in all its dimensions.\"**\n",
        "\n",
        "Note: The MPI ranges from 0 to 1, and higher values imply higher poverty.\n",
        "\n",
        "_For more details, refer [https://ophi.org.uk/multidimensional-poverty-index/](https://ophi.org.uk/multidimensional-poverty-index/)_"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "Wxp84n7XhiS4"
      },
      "id": "Wxp84n7XhiS4"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### How Global MPI can assist Tiny Miracles?\n",
        "\n",
        "1. Comprehensive Measurement\n",
        "2. Targeted Events & Activities\n",
        "3. Progress Tracking\n",
        "4. Evidence-Based Advocacy\n",
        "5. Collaborative Partnerships\n",
        "\n",
        "By leveraging the Multidimensional Poverty Index, Tiny Miracles can gain deeper insights into poverty dynamics, implement targeted events, track progress effectively, advocate for change, and collaborate with like-minded organizations. This approach enhances the NGO's ability to make a meaningful and sustainable impact on poverty eradication."
      ],
      "metadata": {
        "id": "rCLc88u3iDai"
      },
      "id": "rCLc88u3iDai"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Global MPI Calculaltion Model\n",
        "\n",
        "Here we have implemented a mathematical model to calculate MPI for each family in a community. Then the MPI scores of individual families are used to calculate Global MPI at community level.\n",
        "\n",
        "`Global MPI = H X A`\n",
        "\n",
        "where,\n",
        "\n",
        "* `H` = Head Count Ratio, and\n",
        "* `A` = Instensity of Poverty"
      ],
      "metadata": {
        "id": "ETX5_TQ3P8CR"
      },
      "id": "ETX5_TQ3P8CR"
    },
    {
      "cell_type": "code",
      "source": [
        "# Adding Packages\n",
        "!pip install pandas\n",
        "!pip install pymongo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uCyQZ17G-a_Y",
        "outputId": "063cf9b9-ca3e-4967-8b98-a92b09be0c66"
      },
      "id": "uCyQZ17G-a_Y",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (1.5.3)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2022.7.1)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.22.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pymongo in /usr/local/lib/python3.10/dist-packages (4.3.3)\n",
            "Requirement already satisfied: dnspython<3.0.0,>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from pymongo) (2.3.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "76b433c3",
      "metadata": {
        "id": "76b433c3"
      },
      "outputs": [],
      "source": [
        "from pymongo import MongoClient\n",
        "import pandas as pd\n",
        "from datetime import datetime"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d8f8f87b",
      "metadata": {
        "id": "d8f8f87b"
      },
      "outputs": [],
      "source": [
        "# Here we are connecting our mongodb\n",
        "\n",
        "dbURI =\"mongodb+srv://np68175:np68175@cluster0.uzwqfqr.mongodb.net/?retryWrites=true&w=majority\"\n",
        "client = MongoClient(dbURI)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "39a3063b",
      "metadata": {
        "id": "39a3063b"
      },
      "outputs": [],
      "source": [
        "db = client['test']\n",
        "communities_collection = db['programs']\n",
        "families_collection = db['families']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e9bb6f67",
      "metadata": {
        "id": "e9bb6f67"
      },
      "outputs": [],
      "source": [
        "communities = communities_collection.find()\n",
        "families = families_collection.find()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4bcf27d0",
      "metadata": {
        "id": "4bcf27d0"
      },
      "outputs": [],
      "source": [
        "columns = [\n",
        "    \"community\",\n",
        "    \"familyId\",\n",
        "    \"drinkingWater\",\n",
        "    \"electricity\",\n",
        "    \"cookingFuel\",\n",
        "    \"sanitation\",\n",
        "    \"house\",\n",
        "    \"adharcard\",\n",
        "    \"education\",\n",
        "    \"disease\" ,\n",
        "    \"MPI_Score\",\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2c74ad49",
      "metadata": {
        "id": "2c74ad49"
      },
      "outputs": [],
      "source": [
        "\n",
        "# first we will store only required info in a dictionary format then create a dataframe from it\n",
        "data_list = []\n",
        "\n",
        "\n",
        "for document in families:\n",
        "\n",
        "    row = {\n",
        "        \"community\": document.get(\"community\"),\n",
        "        \"familyId\": document.get(\"familyId\"),\n",
        "        \"drinkingWater\": document.get(\"drinkingWater\"),\n",
        "        \"electricity\": document.get(\"electricity\"),\n",
        "        \"cookingFuel\": document.get(\"cookingFuel\"),\n",
        "        \"sanitation\": document.get(\"sanitation\"),\n",
        "        \"house\": document.get(\"house\"),\n",
        "        \"adharcard\": 1,\n",
        "        \"education\": 1,\n",
        "        \"disease\" : 1,\n",
        "        \"MPI_Score\": document.get(\"MPIscore\", 1),\n",
        "    }\n",
        "\n",
        "    data_list.append(row)\n",
        "\n",
        "# Data Frame for the Families\n",
        "df2 = pd.DataFrame(data_list, columns=columns)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4549439f",
      "metadata": {
        "id": "4549439f",
        "outputId": "469a2f45-2c93-4026-a163-2dbbd40b29cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   community    familyId  drinkingWater  electricity  cookingFuel  sanitation  \\\n",
              "0   Pardeshi  8076794410           True        False         True        True   \n",
              "1      Aarey  8882439247          False         True        False        True   \n",
              "2  RL Colony  8376794410           True        False         True       False   \n",
              "3      Aarey   565887983           True         True         True       False   \n",
              "4   Pardeshi  6588795663           True        False         True       False   \n",
              "\n",
              "   house  adharcard  education  disease  \\\n",
              "0  False          1          1        1   \n",
              "1  False          1          1        1   \n",
              "2  False          1          1        1   \n",
              "3  False          1          1        1   \n",
              "4  False          1          1        1   \n",
              "\n",
              "                                           MPI_Score  \n",
              "0  [{'date': 2023-05-11 21:01:28.167000, 'score':...  \n",
              "1  [{'date': 2023-05-11 21:01:28.200000, 'score':...  \n",
              "2  [{'date': 2023-05-11 21:01:28.233000, 'score':...  \n",
              "3                                                 []  \n",
              "4                                                 []  "
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\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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          "metadata": {},
          "execution_count": 218
        }
      ],
      "source": [
        "df2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f7dfadff",
      "metadata": {
        "id": "f7dfadff"
      },
      "outputs": [],
      "source": [
        "member_collection = db[\"userdetails\"]\n",
        "members = member_collection.find()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7bc40353",
      "metadata": {
        "id": "7bc40353"
      },
      "outputs": [],
      "source": [
        "columns = [\n",
        "    \"community\",\n",
        "    \"familyId\",\n",
        "    \"Member_name\",\n",
        "    \"adharCheck\",\n",
        "    \"age\",\n",
        "    \"education\",\n",
        "    \"gender\",\n",
        "    \"disease\"\n",
        "]\n",
        "\n",
        "data_list = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bafec890",
      "metadata": {
        "id": "bafec890"
      },
      "outputs": [],
      "source": [
        "for document in members:\n",
        "    health_status = len(document.get(\"medicalHistory\", [])) > 0\n",
        "\n",
        "    age = document.get(\"age\")\n",
        "\n",
        "    education = False\n",
        "\n",
        "    if age <= 18:\n",
        "        education = document.get(\"education\") != \"Schooling\"\n",
        "    else:\n",
        "        education = document.get(\"education\") != \"5th pass\"\n",
        "\n",
        "    row = {\n",
        "        \"community\": document.get(\"community\"),\n",
        "        \"familyId\": document.get(\"familyId\"),\n",
        "        \"Member_name\": document.get(\"name\"),\n",
        "        \"adharCheck\": document.get(\"adharCheck\"),\n",
        "        \"age\": document.get(\"age\"),\n",
        "        \"education\": education,\n",
        "        \"gender\": document.get(\"gender\"),\n",
        "        \"disease\": health_status\n",
        "    }\n",
        "\n",
        "    data_list.append(row)\n",
        "\n",
        "# Data Frame for the members\n",
        "df1 = pd.DataFrame(data_list, columns=columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "747a6453",
      "metadata": {
        "id": "747a6453",
        "outputId": "8ce212c9-8ca5-4cbc-e465-dccd55a53690",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        }
      },
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   community    familyId Member_name  adharCheck  age  education  gender  \\\n",
              "0   Pardeshi  8076794410     Maneesh        True   20       True    Male   \n",
              "1   Pardeshi  8076794410        Neha        True    9      False  Female   \n",
              "2      Aarey  8882439247     Kritika        True   27       True  Female   \n",
              "3      Aarey  8882439247      Shorya       False   21       True    Male   \n",
              "4  RL Colony  8376794410       Divya        True   26       True    Male   \n",
              "5  RL Colony  8376794410      Dinesh       False    4       True    Male   \n",
              "6      Aarey   565887983     Deepika       False    6      False  Female   \n",
              "7      Aarey   565887983       Karan        True   21       True    Male   \n",
              "8   Pardeshi  6588795663         Ved        True   21       True  Female   \n",
              "\n",
              "   disease  \n",
              "0     True  \n",
              "1    False  \n",
              "2     True  \n",
              "3     True  \n",
              "4    False  \n",
              "5    False  \n",
              "6     True  \n",
              "7    False  \n",
              "8    False  "
            ],
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>RL Colony</td>\n",
              "      <td>8376794410</td>\n",
              "      <td>Dinesh</td>\n",
              "      <td>False</td>\n",
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              "      <td>True</td>\n",
              "      <td>Male</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Aarey</td>\n",
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              "      <td>Deepika</td>\n",
              "      <td>False</td>\n",
              "      <td>6</td>\n",
              "      <td>False</td>\n",
              "      <td>Female</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Aarey</td>\n",
              "      <td>565887983</td>\n",
              "      <td>Karan</td>\n",
              "      <td>True</td>\n",
              "      <td>21</td>\n",
              "      <td>True</td>\n",
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              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a057e35d-e8f7-4cce-b094-cedc5b235be2');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 222
        }
      ],
      "source": [
        "df1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8361a3cf",
      "metadata": {
        "id": "8361a3cf",
        "outputId": "fd902341-e256-4705-8997-4077f4baaeb5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 1\n",
            "1 0\n",
            "1 0\n",
            "1 1\n",
            "0 0\n"
          ]
        }
      ],
      "source": [
        "for index, family in df2.iterrows():\n",
        "    family_id = family['familyId']\n",
        "\n",
        "#     Indicators\n",
        "    adharCard_indicator = 0 #everyone has adhar\n",
        "    disease_indicator = 0  #no one has disease\n",
        "    education_indicator = 0 # everone is educated upto a mark specified\n",
        "\n",
        "    for member_index, member in df1.iterrows():\n",
        "        if member['familyId'] == family_id:\n",
        "            if member['adharCheck'] == False:\n",
        "                adharCard_indicator = 1\n",
        "\n",
        "            if member['education'] == False:\n",
        "                education_indicator = 1\n",
        "\n",
        "            if member['disease'] == True:\n",
        "                disease_indicator = 1\n",
        "\n",
        "    df2.at[index, 'adharcard'] = adharCard_indicator\n",
        "    df2.at[index, 'education'] = education_indicator\n",
        "    df2.at[index, 'disease'] = disease_indicator\n",
        "    print(adharCard_indicator,education_indicator)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1f53ba8f",
      "metadata": {
        "id": "1f53ba8f",
        "outputId": "00ead9dc-342c-459d-d45f-9a11328300b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   community    familyId  drinkingWater  electricity  cookingFuel  sanitation  \\\n",
              "0   Pardeshi  8076794410           True        False         True        True   \n",
              "1      Aarey  8882439247          False         True        False        True   \n",
              "2  RL Colony  8376794410           True        False         True       False   \n",
              "3      Aarey   565887983           True         True         True       False   \n",
              "4   Pardeshi  6588795663           True        False         True       False   \n",
              "\n",
              "   house  adharcard  education  disease  \\\n",
              "0  False          0          1        1   \n",
              "1  False          1          0        1   \n",
              "2  False          1          0        0   \n",
              "3  False          1          1        1   \n",
              "4  False          0          0        0   \n",
              "\n",
              "                                           MPI_Score  \n",
              "0  [{'date': 2023-05-11 21:01:28.167000, 'score':...  \n",
              "1  [{'date': 2023-05-11 21:01:28.200000, 'score':...  \n",
              "2  [{'date': 2023-05-11 21:01:28.233000, 'score':...  \n",
              "3                                                 []  \n",
              "4                                                 []  "
            ],
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              "      <th>2</th>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          document.querySelector('#df-29540588-7a9d-4e1d-b168-971aac6aec48 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-29540588-7a9d-4e1d-b168-971aac6aec48');\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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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 224
        }
      ],
      "source": [
        "df2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e105cc90",
      "metadata": {
        "id": "e105cc90",
        "outputId": "f8470c27-21b4-4371-bd11-0f6022d42507",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   community    familyId  drinkingWater  electricity  cookingFuel  sanitation  \\\n",
              "0   Pardeshi  8076794410              0            1            0           0   \n",
              "1      Aarey  8882439247              1            0            1           0   \n",
              "2  RL Colony  8376794410              0            1            0           1   \n",
              "3      Aarey   565887983              0            0            0           1   \n",
              "4   Pardeshi  6588795663              0            1            0           1   \n",
              "\n",
              "   house  adharcard  education  disease  \\\n",
              "0      1          0          1        1   \n",
              "1      1          1          0        1   \n",
              "2      1          1          0        0   \n",
              "3      1          1          1        1   \n",
              "4      1          0          0        0   \n",
              "\n",
              "                                           MPI_Score  \n",
              "0  [{'date': 2023-05-11 21:01:28.167000, 'score':...  \n",
              "1  [{'date': 2023-05-11 21:01:28.200000, 'score':...  \n",
              "2  [{'date': 2023-05-11 21:01:28.233000, 'score':...  \n",
              "3                                                 []  \n",
              "4                                                 []  "
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              "          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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 225
        }
      ],
      "source": [
        "df2['drinkingWater'] = df2['drinkingWater'].replace({False: 1, True: 0})\n",
        "df2['electricity'] = df2['electricity'].replace({False: 1, True: 0})\n",
        "df2['cookingFuel'] = df2['cookingFuel'].replace({False: 1, True: 0})\n",
        "df2['sanitation'] = df2['sanitation'].replace({False: 1, True: 0})\n",
        "df2['house'] = df2['house'].replace({False: 1, True: 0})\n",
        "df2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "617db8fe",
      "metadata": {
        "id": "617db8fe"
      },
      "outputs": [],
      "source": [
        "for index, row in df2.iterrows():\n",
        "    mpi = (\n",
        "        row['drinkingWater'] * (1 / 18) +\n",
        "        row['electricity'] * (1 / 18) +\n",
        "        row['cookingFuel'] * (1 / 18) +\n",
        "        row['sanitation'] * (1 / 18) +\n",
        "        row['house'] * (1 / 18) +\n",
        "        row['adharcard'] * (1 / 18) +\n",
        "        row['education'] * (1 / 3) +\n",
        "        row['disease'] * (1 / 3)\n",
        "    )\n",
        "    df2.loc[index, 'MPI_Score'] = mpi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cb551c12",
      "metadata": {
        "id": "cb551c12",
        "outputId": "49f383c9-920b-4464-d368-c89d6c9571eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   community    familyId  drinkingWater  electricity  cookingFuel  sanitation  \\\n",
              "0   Pardeshi  8076794410              0            1            0           0   \n",
              "1      Aarey  8882439247              1            0            1           0   \n",
              "2  RL Colony  8376794410              0            1            0           1   \n",
              "3      Aarey   565887983              0            0            0           1   \n",
              "4   Pardeshi  6588795663              0            1            0           1   \n",
              "\n",
              "   house  adharcard  education  disease MPI_Score  \n",
              "0      1          0          1        1  0.777778  \n",
              "1      1          1          0        1  0.555556  \n",
              "2      1          1          0        0  0.222222  \n",
              "3      1          1          1        1  0.833333  \n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
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        }
      ],
      "source": [
        "df2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d7b08e0b",
      "metadata": {
        "id": "d7b08e0b"
      },
      "outputs": [],
      "source": [
        "# Updating the values in DF2 :\n",
        "for index, row in df2.iterrows():\n",
        "    family_id = row['familyId']\n",
        "    current_date = datetime.now()\n",
        "    mpi = row['MPI_Score']\n",
        "    mpi_score_obj = {\n",
        "        'date': current_date,\n",
        "        'score': mpi\n",
        "    }\n",
        "\n",
        "    # print(mpi_score_obj)\n",
        "    # Will do this once the database is restored to required state\n",
        "    families_collection.update_one(\n",
        "        {'familyId': family_id},\n",
        "        {'$push': {'MPIscore': mpi_score_obj}}\n",
        "    )\n",
        "    # families_collection.update_one({'familyId': family_id}, {'$set': {'MPIscore': mpi}})\n",
        "    # date + Mpi_Score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "00429135",
      "metadata": {
        "id": "00429135"
      },
      "outputs": [],
      "source": [
        "df3 = df2[df2['MPI_Score'] > 0.3][['community', 'MPI_Score']].copy()\n",
        "df3.columns = ['Community', 'MPI_Score']"
      ]
    },
    {
      "cell_type": "code",
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        "df2"
      ],
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        "id": "ZPXrGFzDDW3W",
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      "id": "ZPXrGFzDDW3W",
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   community    familyId  drinkingWater  electricity  cookingFuel  sanitation  \\\n",
              "0   Pardeshi  8076794410              0            1            0           0   \n",
              "1      Aarey  8882439247              1            0            1           0   \n",
              "2  RL Colony  8376794410              0            1            0           1   \n",
              "3      Aarey   565887983              0            0            0           1   \n",
              "4   Pardeshi  6588795663              0            1            0           1   \n",
              "\n",
              "   house  adharcard  education  disease MPI_Score  \n",
              "0      1          0          1        1  0.777778  \n",
              "1      1          1          0        1  0.555556  \n",
              "2      1          1          0        0  0.222222  \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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 230
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Community MPI Score (Global MPI)**"
      ],
      "metadata": {
        "id": "oLPvRsXvlSw4"
      },
      "id": "oLPvRsXvlSw4"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7a75b586",
      "metadata": {
        "id": "7a75b586",
        "outputId": "e1173892-12dd-4e0c-e08a-0502186f9f71",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Aarey', 'Pardeshi', 'RL Colony']"
            ]
          },
          "metadata": {},
          "execution_count": 231
        }
      ],
      "source": [
        "unique_communities =  communities_collection.distinct('name')\n",
        "unique_communities"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "57ce508f",
      "metadata": {
        "id": "57ce508f"
      },
      "outputs": [],
      "source": [
        "df3 = pd.DataFrame({'community': unique_communities, 'MPI_Score': 1})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "181bcb4b",
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          "height": 143
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   community  MPI_Score\n",
              "0      Aarey          1\n",
              "1   Pardeshi          1\n",
              "2  RL Colony          1"
            ],
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              "\n",
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              "      <th>community</th>\n",
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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        }
      ],
      "source": [
        "df3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b289fa24",
      "metadata": {
        "id": "b289fa24",
        "outputId": "e040dea5-a124-4fda-95a2-d31fd335060b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0 0.625\n",
            "0.5 0.5\n"
          ]
        }
      ],
      "source": [
        "poverty_threshold = 0.3\n",
        "for index, row in df3.iterrows():\n",
        "    filtered_df = df2[df2['community'] == row['community']]\n",
        "    deprived_df = filtered_df[filtered_df['MPI_Score'] > poverty_threshold]\n",
        "\n",
        "\n",
        "    if filtered_df.shape[0] > 0 and deprived_df.shape[0]>0:\n",
        "        A = deprived_df.shape[0] / filtered_df.shape[0]\n",
        "        one = 0\n",
        "        zero = 0\n",
        "\n",
        "        indicator_names = ['drinkingWater', 'electricity', 'cookingFuel', 'sanitation', 'house', 'adharcard', 'education', 'disease']\n",
        "\n",
        "        for indicator in indicator_names:\n",
        "\n",
        "            value_counts = deprived_df[indicator].value_counts()\n",
        "            one = one + (value_counts[1] if 1 in value_counts else 0)\n",
        "            zero = zero + (value_counts[0] if 0 in value_counts else 0)\n",
        "\n",
        "        H = one / (one + zero)\n",
        "        print(A,H)\n",
        "        Community_MPI = A*H\n",
        "        df3.loc[index, 'MPI_Score'] = Community_MPI\n",
        "\n",
        "\n",
        "#         H = \"have some doubts\" print(row['community'] , A)\n",
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a86f239d",
      "metadata": {
        "id": "a86f239d",
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        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.25"
            ]
          },
          "metadata": {},
          "execution_count": 236
        }
      ],
      "source": [
        "Community_MPI"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "64e7c4da",
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          "height": 143
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   community  MPI_Score\n",
              "0      Aarey      0.625\n",
              "1   Pardeshi      0.250\n",
              "2  RL Colony      1.000"
            ],
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              "      <th>2</th>\n",
              "      <td>RL Colony</td>\n",
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              "\n",
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          "metadata": {},
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        "df3"
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    {
      "cell_type": "code",
      "execution_count": null,
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      },
      "outputs": [],
      "source": [
        "for index, row in df3.iterrows():\n",
        "    community_id = row['community']\n",
        "    current_date = datetime.now()\n",
        "    mpi = row['MPI_Score']\n",
        "    mpi_score_obj = {'date': current_date,'score': mpi}\n",
        "    communities_collection.update_one(\n",
        "        {'name': community_id},\n",
        "        {'$push': {'MPIscore': mpi_score_obj}}\n",
        "    )"
      ]
    }
  ],
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