EDA_Visualisation.ipynb.txt

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6F9Bz_yZB8TT"
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      "source": [
        "import requests\n",
        "import pprint\n",
        "\n",
        "BASE_URL = \"https://ubaformapi-qyaec74aq-fastapis-build.vercel.app\"\n",
        "\n",
        "\n",
        "def get_access_token(data):\n",
        "    url = BASE_URL + '/login'\n",
        "    headers = {\n",
        "        \"accept\": \"application/json\",\n",
        "    }\n",
        "    response = requests.post(url, params=data, headers=headers)\n",
        "    access_token = response.json()['access_token']\n",
        "    return access_token\n",
        "\n",
        "\n",
        "def test_get_fromdb_owner(village_name):\n",
        "    url = BASE_URL + \"/api/get_data\"\n",
        "    signincred = {\n",
        "        \"AADHAR_NO\": \"***\",\n",
        "        \"password\": \"***\",\n",
        "        \"village_name\": \"None\",\n",
        "        \"role\": \"GOVTOff\"\n",
        "    }\n",
        "    params = {\"village_name\": f\"{village_name}\"}\n",
        "    headers = {\n",
        "        \"accept\": \"application/json\",\n",
        "        \"Authorization\": f\"Bearer {get_access_token(signincred)}\",\n",
        "        \"Content-Type\": \"application/json\",\n",
        "    }\n",
        "    response = requests.get(url=url, params=params, headers=headers)\n",
        "    return response.json()\n",
        "\n",
        "\n",
        "data = test_get_fromdb_owner(\"Sehore\") # [\"Aastha\", \"Sehore\", \"string\"]\n",
        "#pprint.pprint(data)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data[\"data\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8DzBhAuvCTXu",
        "outputId": "c867bc4a-2675-4f5d-927f-6a3656c5f046"
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'gen_ho_info': [{'_id': '6353e6c5e10045213e4835a8', 'ho_id': 'A1', 'hoh_name': 'Dada', 'hoh_gender': 'Male', 'category': 'OBC', 'pov_status': 'BPL', 'own_house': True, 'house_type': 'pucca', 'toilet': 'Private', 'drainage_status': 'open', 'waste_collection_sys': 'doorstep', 'compost_pit': 'Individual', 'biogas_plant': 'Group', 'annual_income': 120000.0, '__id': '6353e6c3e10045213e4835a6'}, {'_id': '6353f4ba5e34323ab1d52691', 'ho_id': 'A1', 'hoh_name': 'Dada', 'hoh_gender': 'Male', 'category': 'OBC', 'pov_status': 'BPL', 'own_house': True, 'house_type': 'pucca', 'toilet': 'Private', 'drainage_status': 'open', 'waste_collection_sys': 'doorstep', 'compost_pit': 'Individual', 'biogas_plant': 'Group', 'annual_income': 120000.0, '__id': '6353f4ba5e34323ab1d5268f'}, {'_id': '6353fe814d4a09529d7c4601', 'ho_id': 'A1', 'hoh_name': 'Dada', 'hoh_gender': 'Male', 'category': 'OBC', 'pov_status': 'BPL', 'own_house': True, 'house_type': 'pucca', 'toilet': 'Private', 'drainage_status': 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'AADHAR_No': '1234', 'has_bank_acc': True, 'is_computer_literate': True, 'has_SSP': True, 'health_prob': 'None', 'has_MNREGA': True, 'SHG': True, 'occupations': 'Student', '__id': '63583f3535eaecc64110cf99'}, {'_id': '63583f3635eaecc64110cf9d', 'name': 'Anmol', 'age': 20, 'sex': 'Male', 'martial_status': 'U', 'education': 'College', 'schooling_status': '3 year', 'AADHAR_No': '2345', 'has_bank_acc': True, 'is_computer_literate': True, 'has_SSP': True, 'health_prob': 'None', 'has_MNREGA': True, 'SHG': True, 'occupations': 'Student', '__id': '63583f3535eaecc64110cf99'}, {'_id': '63583f3635eaecc64110cf9e', 'name': 'Hemanth', 'age': 20, 'sex': 'Male', 'martial_status': 'U', 'education': 'College', 'schooling_status': '3 year', 'AADHAR_No': '3456', 'has_bank_acc': True, 'is_computer_literate': True, 'has_SSP': True, 'health_prob': 'None', 'has_MNREGA': True, 'SHG': True, 'occupations': 'Student', '__id': '63583f3535eaecc64110cf99'}, {'_id': '63583f3635eaecc64110cf9f', 'name': 'Gargi', 'age': 20, 'sex': 'Female', 'martial_status': 'U', 'education': 'College', 'schooling_status': '3 year', 'AADHAR_No': '4567', 'has_bank_acc': True, 'is_computer_literate': True, 'has_SSP': True, 'health_prob': 'None', 'has_MNREGA': True, 'SHG': True, 'occupations': 'Student', '__id': '63583f3535eaecc64110cf99'}, {'_id': '63583f7c35eaecc64110cfae', 'name': 'Mayuresh', 'age': 20, 'sex': 'Male', 'martial_status': 'U', 'education': 'College', 'schooling_status': '3 year', 'AADHAR_No': '1234', 'has_bank_acc': True, 'is_computer_literate': True, 'has_SSP': True, 'health_prob': 'None', 'has_MNREGA': True, 'SHG': True, 'occupations': 'Student', '__id': '63583f7b35eaecc64110cfab'}, {'_id': '63583f7c35eaecc64110cfaf', 'name': 'Anmol', 'age': 20, 'sex': 'Male', 'martial_status': 'U', 'education': 'College', 'schooling_status': '3 year', 'AADHAR_No': '2345', 'has_bank_acc': True, 'is_computer_literate': True, 'has_SSP': True, 'health_prob': 'None', 'has_MNREGA': True, 'SHG': True, 'occupations': 'Student', '__id': '63583f7b35eaecc64110cfab'}, {'_id': '63583f7c35eaecc64110cfb0', 'name': 'Hemanth', 'age': 20, 'sex': 'Male', 'martial_status': 'U', 'education': 'College', 'schooling_status': '3 year', 'AADHAR_No': '3456', 'has_bank_acc': True, 'is_computer_literate': True, 'has_SSP': True, 'health_prob': 'None', 'has_MNREGA': True, 'SHG': True, 'occupations': 'Student', '__id': '63583f7b35eaecc64110cfab'}, {'_id': '63583f7c35eaecc64110cfb1', 'name': 'Gargi', 'age': 20, 'sex': 'Female', 'martial_status': 'U', 'education': 'College', 'schooling_status': '3 year', 'AADHAR_No': '4567', 'has_bank_acc': True, 'is_computer_literate': True, 'has_SSP': True, 'health_prob': 'None', 'has_MNREGA': True, 'SHG': True, 'occupations': 'Student', '__id': '63583f7b35eaecc64110cfab'}]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"data\"].keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5EyjAD6hCiIN",
        "outputId": "edea3dca-4e4c-4811-94f3-40f80d73bea9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['gen_ho_info', 'agri_inputs', 'source_of_energy', 'land_holding_info', 'water_source', 'agri_products', 'livestock_nos', 'major_problems', 'meta', 'govt_schemes', 'respondent_prof', 'mig_status', 'fam_info'])"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "Qd2eiQXADMfe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#data[\"data\"][\"fam_info\"]"
      ],
      "metadata": {
        "id": "qHfOdC7OEwvd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#data[\"data\"]['agri_inputs']"
      ],
      "metadata": {
        "id": "hoGcWz_wC5Rv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import json"
      ],
      "metadata": {
        "id": "K8Kr4gGoDZyd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_json = json.dumps(data['data'])"
      ],
      "metadata": {
        "id": "jfk70lGzDhkK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#print(data_json)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kf8Ur50mDssp",
        "outputId": "dddc7947-4952-46c9-90f1-a599f50f5424"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\"gen_ho_info\": [{\"_id\": \"6353e6c5e10045213e4835a8\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": \"Private\", \"drainage_status\": \"open\", \"waste_collection_sys\": \"doorstep\", \"compost_pit\": \"Individual\", \"biogas_plant\": \"Group\", \"annual_income\": 120000.0, \"__id\": \"6353e6c3e10045213e4835a6\"}, {\"_id\": \"6353f4ba5e34323ab1d52691\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": \"Private\", \"drainage_status\": \"open\", \"waste_collection_sys\": \"doorstep\", \"compost_pit\": \"Individual\", \"biogas_plant\": \"Group\", \"annual_income\": 120000.0, \"__id\": \"6353f4ba5e34323ab1d5268f\"}, {\"_id\": \"6353fe814d4a09529d7c4601\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": \"Private\", \"drainage_status\": \"open\", \"waste_collection_sys\": \"doorstep\", \"compost_pit\": \"Individual\", \"biogas_plant\": \"Group\", \"annual_income\": 120000.0, \"__id\": \"6353fe804d4a09529d7c45ff\"}, {\"_id\": \"63564101789e6aaf7f0ff8ce\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": \"Private\", \"drainage_status\": \"open\", \"waste_collection_sys\": \"doorstep\", \"compost_pit\": \"Individual\", \"biogas_plant\": \"Group\", \"annual_income\": 120000.0, \"__id\": \"63564100789e6aaf7f0ff8cc\"}, {\"_id\": \"6356841c6239429e9ec61229\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": \"Private\", \"drainage_status\": \"open\", \"waste_collection_sys\": \"doorstep\", \"compost_pit\": \"Individual\", \"biogas_plant\": \"Group\", \"annual_income\": 120000.0, \"__id\": \"6356841b6239429e9ec61227\"}, {\"_id\": \"635771f798da8b98bcddcc8c\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": \"Private\", \"drainage_status\": \"open\", \"waste_collection_sys\": \"doorstep\", \"compost_pit\": \"Individual\", \"biogas_plant\": \"Group\", \"annual_income\": 120000.0, \"__id\": \"635771f798da8b98bcddcc8a\"}, {\"_id\": \"63578185541ca16808edefbd\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": \"Private\", \"drainage_status\": \"open\", \"waste_collection_sys\": \"doorstep\", \"compost_pit\": \"Individual\", \"biogas_plant\": \"Group\", \"annual_income\": 120000.0, \"__id\": \"63578184541ca16808edefbb\"}, {\"_id\": \"6357b81c267dfc8b5f2b8dba\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": \"Private\", \"drainage_status\": \"open\", \"waste_collection_sys\": \"doorstep\", \"compost_pit\": \"Individual\", \"biogas_plant\": \"Group\", \"annual_income\": 120000.0, \"__id\": \"6357b81c267dfc8b5f2b8db8\"}, {\"_id\": \"6357c46b4c76f4f25afe2d36\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": \"Private\", \"drainage_status\": \"open\", \"waste_collection_sys\": \"doorstep\", \"compost_pit\": \"Individual\", \"biogas_plant\": \"Group\", \"annual_income\": 120000.0, \"__id\": \"6357c46a4c76f4f25afe2d34\"}, {\"_id\": \"6357c4b96a86421101b0aab8\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": \"Private\", \"drainage_status\": \"open\", \"waste_collection_sys\": \"doorstep\", \"compost_pit\": \"Individual\", \"biogas_plant\": \"Group\", \"annual_income\": 120000.0, \"__id\": \"6357c4b96a86421101b0aab6\"}, {\"_id\": \"6357c735492b489bfa3e124b\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": \"Private\", \"drainage_status\": \"open\", \"waste_collection_sys\": \"doorstep\", \"compost_pit\": \"Individual\", \"biogas_plant\": \"Group\", \"annual_income\": 120000.0, \"__id\": \"6357c734492b489bfa3e1249\"}, {\"_id\": \"6357ccd9c36ac55f992f3ef2\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": \"Private\", \"drainage_status\": \"open\", \"waste_collection_sys\": \"doorstep\", \"compost_pit\": \"Individual\", \"biogas_plant\": \"Group\", \"annual_income\": 120000.0, \"__id\": \"6357ccd9c36ac55f992f3ef0\"}, {\"_id\": \"6357cf6b5d2fedc5a584cf90\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": \"Private\", \"drainage_status\": \"open\", \"waste_collection_sys\": \"doorstep\", \"compost_pit\": \"Individual\", \"biogas_plant\": \"Group\", \"annual_income\": 120000.0, \"__id\": \"6357cf6a5d2fedc5a584cf8e\"}, {\"_id\": \"6357cff95d2fedc5a584cfa0\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": \"Private\", \"drainage_status\": \"open\", \"waste_collection_sys\": \"doorstep\", \"compost_pit\": \"Individual\", \"biogas_plant\": \"Group\", \"annual_income\": 120000.0, \"__id\": \"6357cff95d2fedc5a584cf9e\"}, {\"_id\": \"6357d03bcd671b45d68ea656\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": \"Private\", \"drainage_status\": \"open\", \"waste_collection_sys\": \"doorstep\", \"compost_pit\": \"Individual\", \"biogas_plant\": \"Group\", \"annual_income\": 120000.0, \"__id\": \"6357d03bcd671b45d68ea654\"}, {\"_id\": \"6357d095cdc1e4f4ae16ca42\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": \"Private\", \"drainage_status\": \"open\", \"waste_collection_sys\": \"doorstep\", \"compost_pit\": \"Individual\", \"biogas_plant\": \"Group\", \"annual_income\": 120000.0, \"__id\": \"6357d095cdc1e4f4ae16ca40\"}, {\"_id\": \"6357d0bccdc1e4f4ae16ca53\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": \"Private\", \"drainage_status\": \"open\", \"waste_collection_sys\": \"doorstep\", \"compost_pit\": \"Individual\", \"biogas_plant\": \"Group\", \"annual_income\": 120000.0, \"__id\": \"6357d0bccdc1e4f4ae16ca51\"}, {\"_id\": \"6357d0e8cdc1e4f4ae16ca64\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": \"Private\", \"drainage_status\": \"open\", \"waste_collection_sys\": \"doorstep\", \"compost_pit\": \"Individual\", \"biogas_plant\": \"Group\", \"annual_income\": 120000.0, \"__id\": \"6357d0e8cdc1e4f4ae16ca62\"}, {\"_id\": \"6357d109e3cc3d0261f94673\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": \"Private\", \"drainage_status\": \"open\", \"waste_collection_sys\": \"doorstep\", \"compost_pit\": \"Individual\", \"biogas_plant\": \"Group\", \"annual_income\": 120000.0, \"__id\": \"6357d109e3cc3d0261f94671\"}, {\"_id\": \"6357e60e5c9b36349ae33110\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": \"Private\", \"drainage_status\": \"open\", \"waste_collection_sys\": \"doorstep\", \"compost_pit\": \"Individual\", \"biogas_plant\": \"Group\", \"annual_income\": 120000.0, \"__id\": \"6357e60e5c9b36349ae3310e\"}, {\"_id\": \"6357edccd886876adaa3327e\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": \"Private\", \"drainage_status\": \"open\", \"waste_collection_sys\": \"doorstep\", \"compost_pit\": \"Individual\", \"biogas_plant\": \"Group\", \"annual_income\": 120000.0, \"__id\": \"6357edccd886876adaa3327c\"}, {\"_id\": \"6357eef137f538cf6cf777f9\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": \"Private\", \"drainage_status\": \"open\", \"waste_collection_sys\": \"doorstep\", \"compost_pit\": \"Individual\", \"biogas_plant\": \"Group\", \"annual_income\": 120000.0, \"__id\": \"6357eef037f538cf6cf777f7\"}, {\"_id\": \"6357f109844d595746fd4e73\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": \"Private\", \"drainage_status\": \"open\", \"waste_collection_sys\": \"doorstep\", \"compost_pit\": \"Individual\", \"biogas_plant\": \"Group\", \"annual_income\": 120000.0, \"__id\": \"6357f109844d595746fd4e71\"}, {\"_id\": \"6357f15d844d595746fd4e85\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": \"Private\", \"drainage_status\": \"open\", \"waste_collection_sys\": \"doorstep\", \"compost_pit\": \"Individual\", \"biogas_plant\": \"Group\", \"annual_income\": 120000.0, \"__id\": \"6357f15c844d595746fd4e83\"}, {\"_id\": \"6357f16b844d595746fd4e96\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": \"Private\", \"drainage_status\": \"open\", \"waste_collection_sys\": \"doorstep\", \"compost_pit\": \"Individual\", \"biogas_plant\": \"Group\", \"annual_income\": 120000.0, \"__id\": \"6357f16b844d595746fd4e94\"}, {\"_id\": \"6357f60c16f2e59297778588\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": \"Private\", \"drainage_status\": \"open\", \"waste_collection_sys\": \"doorstep\", \"compost_pit\": \"Individual\", \"biogas_plant\": \"Group\", \"annual_income\": 120000.0, \"__id\": \"6357f60b16f2e59297778586\"}, {\"_id\": \"6357facd7fc70c0cb0ba4b59\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": \"Private\", \"drainage_status\": \"open\", \"waste_collection_sys\": \"doorstep\", \"compost_pit\": \"Individual\", \"biogas_plant\": \"Group\", \"annual_income\": 120000.0, \"__id\": \"6357facc7fc70c0cb0ba4b57\"}, {\"_id\": \"6358100b0367d83c9e3ce3f3\", \"ho_id\": \"A1\", \"hoh_name\": \"Dada\", \"hoh_gender\": \"Male\", \"category\": \"OBC\", \"pov_status\": \"BPL\", \"own_house\": true, \"house_type\": \"pucca\", \"toilet\": 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\"sex\": \"Female\", \"martial_status\": \"U\", \"education\": \"College\", \"schooling_status\": \"3 year\", \"AADHAR_No\": \"4567\", \"has_bank_acc\": true, \"is_computer_literate\": true, \"has_SSP\": true, \"health_prob\": \"None\", \"has_MNREGA\": true, \"SHG\": true, \"occupations\": \"Student\", \"__id\": \"63582b532dcb5e7b99ff7f62\"}, {\"_id\": \"63582c08ea95ff61fbc81437\", \"name\": \"Mayuresh\", \"age\": 20, \"sex\": \"Male\", \"martial_status\": \"U\", \"education\": \"College\", \"schooling_status\": \"3 year\", \"AADHAR_No\": \"1234\", \"has_bank_acc\": true, \"is_computer_literate\": true, \"has_SSP\": true, \"health_prob\": \"None\", \"has_MNREGA\": true, \"SHG\": true, \"occupations\": \"Student\", \"__id\": \"63582c07ea95ff61fbc81434\"}, {\"_id\": \"63582c08ea95ff61fbc81438\", \"name\": \"Anmol\", \"age\": 20, \"sex\": \"Male\", \"martial_status\": \"U\", \"education\": \"College\", \"schooling_status\": \"3 year\", \"AADHAR_No\": \"2345\", \"has_bank_acc\": true, \"is_computer_literate\": true, \"has_SSP\": true, \"health_prob\": \"None\", \"has_MNREGA\": true, \"SHG\": true, \"occupations\": \"Student\", \"__id\": \"63582c07ea95ff61fbc81434\"}, {\"_id\": \"63582c08ea95ff61fbc81439\", \"name\": \"Hemanth\", \"age\": 20, \"sex\": \"Male\", \"martial_status\": \"U\", \"education\": \"College\", \"schooling_status\": \"3 year\", \"AADHAR_No\": \"3456\", \"has_bank_acc\": true, \"is_computer_literate\": true, \"has_SSP\": true, \"health_prob\": \"None\", \"has_MNREGA\": true, \"SHG\": true, \"occupations\": \"Student\", \"__id\": \"63582c07ea95ff61fbc81434\"}, {\"_id\": \"63582c08ea95ff61fbc8143a\", \"name\": \"Gargi\", \"age\": 20, \"sex\": \"Female\", \"martial_status\": \"U\", \"education\": \"College\", \"schooling_status\": \"3 year\", \"AADHAR_No\": \"4567\", \"has_bank_acc\": true, \"is_computer_literate\": true, \"has_SSP\": true, \"health_prob\": \"None\", \"has_MNREGA\": true, \"SHG\": true, \"occupations\": \"Student\", \"__id\": \"63582c07ea95ff61fbc81434\"}, {\"_id\": \"63583eb535eaecc64110cf78\", \"name\": \"Mayuresh\", \"age\": 20, \"sex\": \"Male\", \"martial_status\": \"U\", \"education\": \"College\", \"schooling_status\": \"3 year\", \"AADHAR_No\": \"1234\", \"has_bank_acc\": true, \"is_computer_literate\": true, \"has_SSP\": true, \"health_prob\": \"None\", \"has_MNREGA\": true, \"SHG\": true, \"occupations\": \"Student\", \"__id\": \"63583eb535eaecc64110cf75\"}, {\"_id\": \"63583eb535eaecc64110cf79\", \"name\": \"Anmol\", \"age\": 20, \"sex\": \"Male\", \"martial_status\": \"U\", \"education\": \"College\", \"schooling_status\": \"3 year\", \"AADHAR_No\": \"2345\", \"has_bank_acc\": true, \"is_computer_literate\": true, \"has_SSP\": true, \"health_prob\": \"None\", \"has_MNREGA\": true, \"SHG\": true, \"occupations\": \"Student\", \"__id\": \"63583eb535eaecc64110cf75\"}, {\"_id\": \"63583eb535eaecc64110cf7a\", \"name\": \"Hemanth\", \"age\": 20, \"sex\": \"Male\", \"martial_status\": \"U\", \"education\": \"College\", \"schooling_status\": \"3 year\", \"AADHAR_No\": \"3456\", \"has_bank_acc\": true, \"is_computer_literate\": true, \"has_SSP\": true, \"health_prob\": \"None\", \"has_MNREGA\": true, \"SHG\": true, \"occupations\": \"Student\", \"__id\": \"63583eb535eaecc64110cf75\"}, {\"_id\": \"63583eb535eaecc64110cf7b\", \"name\": \"Gargi\", \"age\": 20, \"sex\": \"Female\", \"martial_status\": \"U\", \"education\": \"College\", \"schooling_status\": \"3 year\", \"AADHAR_No\": \"4567\", \"has_bank_acc\": true, \"is_computer_literate\": true, \"has_SSP\": true, \"health_prob\": \"None\", \"has_MNREGA\": true, \"SHG\": true, \"occupations\": \"Student\", \"__id\": \"63583eb535eaecc64110cf75\"}, {\"_id\": \"63583ef035eaecc64110cf8a\", \"name\": \"Mayuresh\", \"age\": 20, \"sex\": \"Male\", \"martial_status\": \"U\", \"education\": \"College\", \"schooling_status\": \"3 year\", \"AADHAR_No\": \"1234\", \"has_bank_acc\": true, \"is_computer_literate\": true, \"has_SSP\": true, \"health_prob\": \"None\", \"has_MNREGA\": true, \"SHG\": true, \"occupations\": \"Student\", \"__id\": \"63583eef35eaecc64110cf87\"}, {\"_id\": \"63583ef035eaecc64110cf8b\", \"name\": \"Anmol\", \"age\": 20, \"sex\": \"Male\", \"martial_status\": \"U\", \"education\": \"College\", \"schooling_status\": \"3 year\", \"AADHAR_No\": \"2345\", \"has_bank_acc\": true, \"is_computer_literate\": true, \"has_SSP\": true, \"health_prob\": \"None\", \"has_MNREGA\": true, \"SHG\": true, \"occupations\": \"Student\", \"__id\": \"63583eef35eaecc64110cf87\"}, {\"_id\": \"63583ef035eaecc64110cf8c\", \"name\": \"Hemanth\", \"age\": 20, \"sex\": \"Male\", \"martial_status\": \"U\", \"education\": \"College\", \"schooling_status\": \"3 year\", \"AADHAR_No\": \"3456\", \"has_bank_acc\": true, \"is_computer_literate\": true, \"has_SSP\": true, \"health_prob\": \"None\", \"has_MNREGA\": true, \"SHG\": true, \"occupations\": \"Student\", \"__id\": \"63583eef35eaecc64110cf87\"}, {\"_id\": 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\"3 year\", \"AADHAR_No\": \"2345\", \"has_bank_acc\": true, \"is_computer_literate\": true, \"has_SSP\": true, \"health_prob\": \"None\", \"has_MNREGA\": true, \"SHG\": true, \"occupations\": \"Student\", \"__id\": \"63583f3535eaecc64110cf99\"}, {\"_id\": \"63583f3635eaecc64110cf9e\", \"name\": \"Hemanth\", \"age\": 20, \"sex\": \"Male\", \"martial_status\": \"U\", \"education\": \"College\", \"schooling_status\": \"3 year\", \"AADHAR_No\": \"3456\", \"has_bank_acc\": true, \"is_computer_literate\": true, \"has_SSP\": true, \"health_prob\": \"None\", \"has_MNREGA\": true, \"SHG\": true, \"occupations\": \"Student\", \"__id\": \"63583f3535eaecc64110cf99\"}, {\"_id\": \"63583f3635eaecc64110cf9f\", \"name\": \"Gargi\", \"age\": 20, \"sex\": \"Female\", \"martial_status\": \"U\", \"education\": \"College\", \"schooling_status\": \"3 year\", \"AADHAR_No\": \"4567\", \"has_bank_acc\": true, \"is_computer_literate\": true, \"has_SSP\": true, \"health_prob\": \"None\", \"has_MNREGA\": true, 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\"sex\": \"Male\", \"martial_status\": \"U\", \"education\": \"College\", \"schooling_status\": \"3 year\", \"AADHAR_No\": \"3456\", \"has_bank_acc\": true, \"is_computer_literate\": true, \"has_SSP\": true, \"health_prob\": \"None\", \"has_MNREGA\": true, \"SHG\": true, \"occupations\": \"Student\", \"__id\": \"63583f7b35eaecc64110cfab\"}, {\"_id\": \"63583f7c35eaecc64110cfb1\", \"name\": \"Gargi\", \"age\": 20, \"sex\": \"Female\", \"martial_status\": \"U\", \"education\": \"College\", \"schooling_status\": \"3 year\", \"AADHAR_No\": \"4567\", \"has_bank_acc\": true, \"is_computer_literate\": true, \"has_SSP\": true, \"health_prob\": \"None\", \"has_MNREGA\": true, \"SHG\": true, \"occupations\": \"Student\", \"__id\": \"63583f7b35eaecc64110cfab\"}]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#new_dict = {'agri_inputs':data['data']['agri_inputs']}"
      ],
      "metadata": {
        "id": "6Fi-gd4_Dxxu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#x = json.dumps(new_dict)"
      ],
      "metadata": {
        "id": "piRUde5PENLL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#pd.read_json(x)"
      ],
      "metadata": {
        "id": "LYz1sIiGESQG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "KEY - FAM_INFO"
      ],
      "metadata": {
        "id": "cYS65YmjKVsS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(data[\"data\"][\"fam_info\"])\n",
        "#df = df.set_index('__id')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "NPc-rxsgEWUD",
        "outputId": "ef4e87d4-d671-4858-b932-ac37aa7063c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        _id      name  age     sex martial_status education  \\\n",
              "0  6353e6c6e10045213e4835a9  Mayuresh   20    Male              U   College   \n",
              "1  6353e6c6e10045213e4835aa     Anmol   20    Male              U   College   \n",
              "2  6353e6c6e10045213e4835ab   Hemanth   20    Male              U   College   \n",
              "3  6353e6c6e10045213e4835ac     Gargi   20  Female              U   College   \n",
              "4  6353f4ba5e34323ab1d52692  Mayuresh   20    Male              U   College   \n",
              "\n",
              "  schooling_status AADHAR_No  has_bank_acc  is_computer_literate  has_SSP  \\\n",
              "0           3 year      1234          True                  True     True   \n",
              "1           3 year      2345          True                  True     True   \n",
              "2           3 year      3456          True                  True     True   \n",
              "3           3 year      4567          True                  True     True   \n",
              "4           3 year      1234          True                  True     True   \n",
              "\n",
              "  health_prob  has_MNREGA   SHG occupations                      __id  \n",
              "0        None        True  True     Student  6353e6c3e10045213e4835a6  \n",
              "1        None        True  True     Student  6353e6c3e10045213e4835a6  \n",
              "2        None        True  True     Student  6353e6c3e10045213e4835a6  \n",
              "3        None        True  True     Student  6353e6c3e10045213e4835a6  \n",
              "4        None        True  True     Student  6353f4ba5e34323ab1d5268f  "
            ],
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              "      <td>Gargi</td>\n",
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              "      <td>Mayuresh</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a5d6ae42-8b8f-4de1-a58a-89ac7639b1c5');\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",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt = px.data.tips()\n",
        "dt.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "lRqRu3PEYmd2",
        "outputId": "33c9dc7f-7da9-4d0e-a3df-91578e8c6da2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   total_bill   tip     sex smoker  day    time  size\n",
              "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
              "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
              "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
              "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
              "4       24.59  3.61  Female     No  Sun  Dinner     4"
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              "      <th>1</th>\n",
              "      <td>10.34</td>\n",
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              "      <td>3.50</td>\n",
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              "      <th>3</th>\n",
              "      <td>23.68</td>\n",
              "      <td>3.31</td>\n",
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              "      <th>4</th>\n",
              "      <td>24.59</td>\n",
              "      <td>3.61</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-dd5192f9-9031-4f8a-aabf-d0e7fd49cb17');\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",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.pie(dt, values='tip', names='day', color_discrete_sequence=px.colors.sequential.RdBu)\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "ewJ-bU-0Zvv-",
        "outputId": "2b50c709-fb73-4d88-f636-a50b847cc638"
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      "execution_count": null,
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    {
      "cell_type": "code",
      "source": [
        "fig = px.pie(df, values='age', names='occupations', color_discrete_sequence=px.colors.sequential.RdBu)\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
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        "id": "v9piqRkda1-9",
        "outputId": "898663e6-74d7-4b3f-ba29-5fe53671e1ff"
      },
      "execution_count": null,
      "outputs": [
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      "cell_type": "code",
      "source": [
        "fig = px.pie(df, values='age', names='education', color_discrete_sequence=px.colors.sequential.RdBu)\n",
        "fig.show()"
      ],
      "metadata": {
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        "id": "Ld6359Jtc0so",
        "outputId": "dbf395f4-831f-4803-a04a-d6df2cbbcb6e"
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      "execution_count": null,
      "outputs": [
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    {
      "cell_type": "code",
      "source": [
        "#df.loc()['6353e6c3e10045213e4835a6']"
      ],
      "metadata": {
        "id": "3Kijb7emVbP7"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zv3ymggFGjXR",
        "outputId": "4f372ea0-c885-431e-c19f-36e0b4136558"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 252 entries, 0 to 251\n",
            "Data columns (total 16 columns):\n",
            " #   Column                Non-Null Count  Dtype \n",
            "---  ------                --------------  ----- \n",
            " 0   _id                   252 non-null    object\n",
            " 1   name                  252 non-null    object\n",
            " 2   age                   252 non-null    int64 \n",
            " 3   sex                   252 non-null    object\n",
            " 4   martial_status        252 non-null    object\n",
            " 5   education             252 non-null    object\n",
            " 6   schooling_status      252 non-null    object\n",
            " 7   AADHAR_No             252 non-null    object\n",
            " 8   has_bank_acc          252 non-null    bool  \n",
            " 9   is_computer_literate  252 non-null    bool  \n",
            " 10  has_SSP               252 non-null    bool  \n",
            " 11  health_prob           252 non-null    object\n",
            " 12  has_MNREGA            252 non-null    bool  \n",
            " 13  SHG                   252 non-null    bool  \n",
            " 14  occupations           252 non-null    object\n",
            " 15  __id                  252 non-null    object\n",
            "dtypes: bool(5), int64(1), object(10)\n",
            "memory usage: 23.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"__id\"].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z7tCS0LpG6yi",
        "outputId": "78fe5348-27f6-49ee-8dfc-cd984e9f5744"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "58"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['sex'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YYGuZZVbJSVi",
        "outputId": "fd3d8cc8-54d9-4f4a-b0e4-184377a50629"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Male      189\n",
              "Female     63\n",
              "Name: sex, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "r86rAzm8KNDK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "px.histogram(df, x=\"occupations\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "s0_2Y5BQW-T-",
        "outputId": "02c2d973-1c46-4d5e-c584-80c50bb2089e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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      "source": [
        "px.box(df, x=\"sex\", y=\"total_bill\", color=\"smoker\")"
      ],
      "metadata": {
        "id": "px2QA70VX1AV"
      },
      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "px.histogram(df, x=\"sex\")"
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      "execution_count": null,
      "outputs": [
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    {
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      "source": [
        "px.histogram(df, x=\"martial_status\")"
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          "metadata": {}
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      ]
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    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FC_YIecFQp0p",
        "outputId": "13d941af-2d02-44c2-996f-52df047d493a"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['_id', 'name', 'age', 'sex', 'martial_status', 'education',\n",
              "       'schooling_status', 'AADHAR_No', 'has_bank_acc', 'is_computer_literate',\n",
              "       'has_SSP', 'health_prob', 'has_MNREGA', 'SHG', 'occupations', '__id'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x ='has_MNREGA', data = df)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "lM5j8UMlLkZE",
        "outputId": "bab53c99-b18d-4ae8-d8c0-ca56c2f70fb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tips = df\n",
        "cols = [\"martial_status\", \"has_bank_acc\", \"is_computer_literate\", \"has_MNREGA\",\"sex\"]\n",
        "f, axs = plt.subplots(1, len(cols), figsize=(20, 4))\n",
        "for ax, col in zip(axs, cols):\n",
        "    sns.countplot(data=tips, x=col, ax=ax)\n",
        "f.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "BqGKEw1dREqW",
        "outputId": "ca56b648-6133-44a2-e57a-407f74c6e0a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x288 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[['sex','martial_status']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "7MsA61dPUqTZ",
        "outputId": "03aedcca-d485-4b2f-939e-30889af3df12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        sex martial_status\n",
              "0      Male              U\n",
              "1      Male              U\n",
              "2      Male              U\n",
              "3    Female              U\n",
              "4      Male              U\n",
              "..      ...            ...\n",
              "247  Female              U\n",
              "248    Male              U\n",
              "249    Male              U\n",
              "250    Male              U\n",
              "251  Female              U\n",
              "\n",
              "[252 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-65623c9b-2124-46fa-b0b5-b948d42f2997\">\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>sex</th>\n",
              "      <th>martial_status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Male</td>\n",
              "      <td>U</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>U</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Male</td>\n",
              "      <td>U</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Female</td>\n",
              "      <td>U</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Male</td>\n",
              "      <td>U</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>247</th>\n",
              "      <td>Female</td>\n",
              "      <td>U</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248</th>\n",
              "      <td>Male</td>\n",
              "      <td>U</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>Male</td>\n",
              "      <td>U</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>250</th>\n",
              "      <td>Male</td>\n",
              "      <td>U</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>251</th>\n",
              "      <td>Female</td>\n",
              "      <td>U</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>252 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-65623c9b-2124-46fa-b0b5-b948d42f2997')\"\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-65623c9b-2124-46fa-b0b5-b948d42f2997 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-65623c9b-2124-46fa-b0b5-b948d42f2997');\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": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-IjUgAuQV0p8"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}