election_solution_to_25.ipynb

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        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "%matplotlib inline\n"
      ]
    },
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      "id": "3fdf0d28",
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      "source": [
        "df = pd.read_csv('/2004-2019.csv')"
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              "                    Candidate Constituency Party  Criminal Cases  \\\n",
              "0  MADHUSUDHAN REDDY THAKKALA     Adilabad   TRS               0   \n",
              "1          CHENGARA SURENDRAN        Adoor   CPI               0   \n",
              "2                  RAJ BABBAR         Agra    SP               2   \n",
              "3                HARIN PATHAK    Ahmedabad   BJP               1   \n",
              "4    GADAKH TUKARAM GANGADHAR   Ahmednagar   NCP               2   \n",
              "\n",
              "               Education  Total Assets  Liabilities  Year  \n",
              "0  Graduate Professional       7550000       120000  2004  \n",
              "1               Graduate          6274        41438  2004  \n",
              "2               Graduate      22326400      1000000  2004  \n",
              "3  Graduate Professional      10287931            0  2004  \n",
              "4               Graduate      23446680      4680000  2004  "
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              "(2118, 8)"
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            "text/plain": [
              "Candidate         0\n",
              "Constituency      0\n",
              "Party             0\n",
              "Criminal Cases    0\n",
              "Education         0\n",
              "Total Assets      0\n",
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              "Year              0\n",
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        "df.isnull().sum()"
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              "      Candidate  Constituency  Party  Criminal Cases  Education  Total Assets  \\\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          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": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "eecde6b1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eecde6b1",
        "outputId": "ecf0e71a-3b86-4340-a27b-9b149de0fa9a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2118 entries, 0 to 2117\n",
            "Data columns (total 8 columns):\n",
            " #   Column          Non-Null Count  Dtype \n",
            "---  ------          --------------  ----- \n",
            " 0   Candidate       2118 non-null   object\n",
            " 1   Constituency    2118 non-null   object\n",
            " 2   Party           2118 non-null   object\n",
            " 3   Criminal Cases  2118 non-null   int64 \n",
            " 4   Education       2118 non-null   object\n",
            " 5   Total Assets    2118 non-null   int64 \n",
            " 6   Liabilities     2118 non-null   int64 \n",
            " 7   Year            2118 non-null   int64 \n",
            "dtypes: int64(4), object(4)\n",
            "memory usage: 132.5+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "697dbd2d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "697dbd2d",
        "outputId": "5609b16c-26e6-43d9-e2ce-2af704e2b811"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Criminal Cases  Total Assets   Liabilities         Year\n",
              "count     2118.000000  2.118000e+03  2.118000e+03  2118.000000\n",
              "mean         1.278093  1.092130e+08  1.389721e+07  2011.618036\n",
              "std          5.506607  3.699401e+08  7.857403e+07     5.581787\n",
              "min          0.000000  0.000000e+00  0.000000e+00  2004.000000\n",
              "25%          0.000000  6.110977e+06  0.000000e+00  2009.000000\n",
              "50%          0.000000  1.946278e+07  4.827675e+05  2014.000000\n",
              "75%          1.000000  6.580133e+07  3.128824e+06  2019.000000\n",
              "max        204.000000  6.830581e+09  1.547511e+09  2019.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ccba0abc-e2fd-4975-b9d0-983cc6866bda\">\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>Criminal Cases</th>\n",
              "      <th>Total Assets</th>\n",
              "      <th>Liabilities</th>\n",
              "      <th>Year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2118.000000</td>\n",
              "      <td>2.118000e+03</td>\n",
              "      <td>2.118000e+03</td>\n",
              "      <td>2118.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.278093</td>\n",
              "      <td>1.092130e+08</td>\n",
              "      <td>1.389721e+07</td>\n",
              "      <td>2011.618036</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.506607</td>\n",
              "      <td>3.699401e+08</td>\n",
              "      <td>7.857403e+07</td>\n",
              "      <td>5.581787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2004.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.110977e+06</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2009.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.946278e+07</td>\n",
              "      <td>4.827675e+05</td>\n",
              "      <td>2014.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.580133e+07</td>\n",
              "      <td>3.128824e+06</td>\n",
              "      <td>2019.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>204.000000</td>\n",
              "      <td>6.830581e+09</td>\n",
              "      <td>1.547511e+09</td>\n",
              "      <td>2019.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ccba0abc-e2fd-4975-b9d0-983cc6866bda')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .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",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\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-ccba0abc-e2fd-4975-b9d0-983cc6866bda 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-ccba0abc-e2fd-4975-b9d0-983cc6866bda');\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": 11
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "7ec6b64e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        },
        "id": "7ec6b64e",
        "outputId": "68035516-3113-48fb-83f8-ca13e3eb663e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa65ed6fed0>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "sns.set_theme(style=\"white\")\n",
        "corr = df.corr()\n",
        "sns.heatmap(corr, annot=True, annot_kws={\"size\": 7})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "4c17e12c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "id": "4c17e12c",
        "outputId": "bf25448b-d492-4866-d1d6-9265a74c6ec1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.pairplot(df)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "4402cdef",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "4402cdef",
        "outputId": "423a9a1c-a9ee-4c47-f88e-0327722f9e2e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa656c0fe50>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.barplot(x='Party',y='Criminal Cases',data=df)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1= df[['Party', 'Education', 'Criminal Cases']]\n",
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "IOabbvhT_ylW",
        "outputId": "772931f9-09e1-47fc-c6e8-ea9be98d8eb5"
      },
      "id": "IOabbvhT_ylW",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Party              Education  Criminal Cases\n",
              "0      TRS  Graduate Professional               0\n",
              "1      CPI               Graduate               0\n",
              "2       SP               Graduate               2\n",
              "3      BJP  Graduate Professional               1\n",
              "4      NCP               Graduate               2\n",
              "...    ...                    ...             ...\n",
              "2113   BJP              10th Pass               0\n",
              "2114   INC          Post Graduate               5\n",
              "2115   BJP          Post Graduate               0\n",
              "2116   SHS               Graduate               3\n",
              "2117   TRS               Graduate              18\n",
              "\n",
              "[2118 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-aade2fad-8226-41c6-a307-64e735128c6a\">\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>Party</th>\n",
              "      <th>Education</th>\n",
              "      <th>Criminal Cases</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>TRS</td>\n",
              "      <td>Graduate Professional</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CPI</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SP</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BJP</td>\n",
              "      <td>Graduate Professional</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NCP</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2113</th>\n",
              "      <td>BJP</td>\n",
              "      <td>10th Pass</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2114</th>\n",
              "      <td>INC</td>\n",
              "      <td>Post Graduate</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2115</th>\n",
              "      <td>BJP</td>\n",
              "      <td>Post Graduate</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2116</th>\n",
              "      <td>SHS</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2117</th>\n",
              "      <td>TRS</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2118 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aade2fad-8226-41c6-a307-64e735128c6a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      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-aade2fad-8226-41c6-a307-64e735128c6a 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-aade2fad-8226-41c6-a307-64e735128c6a');\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": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "0kwy6DknE2-_",
        "outputId": "09155805-89b8-4394-db51-9caee00120ec"
      },
      "id": "0kwy6DknE2-_",
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Criminal Cases\n",
              "Criminal Cases             1.0"
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Criminal Cases</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Criminal Cases</th>\n",
              "      <td>1.0</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df1['Education'].corr(df1['Criminal Cases']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "VOUyRZJkFC1U",
        "outputId": "86f01402-4718-4e75-8c50-56f9dd94f05e"
      },
      "id": "VOUyRZJkFC1U",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-30-a40939adb4a2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Education'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcorr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Criminal Cases'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mcorr\u001b[0;34m(self, other, method, min_periods)\u001b[0m\n\u001b[1;32m   2507\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"pearson\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"spearman\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"kendall\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2508\u001b[0m             return nanops.nancorr(\n\u001b[0;32m-> 2509\u001b[0;31m                 \u001b[0mthis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_periods\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_periods\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2510\u001b[0m             )\n\u001b[1;32m   2511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36m_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     92\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minvalid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 94\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     95\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m                 \u001b[0;31m# we want to transform an object array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36mnancorr\u001b[0;34m(a, b, method, min_periods)\u001b[0m\n\u001b[1;32m   1525\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m     \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_corr_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1527\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1529\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36mfunc\u001b[0;34m(a, b)\u001b[0m\n\u001b[1;32m   1546\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1547\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1548\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcorrcoef\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1549\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1550\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mcorrcoef\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36mcorrcoef\u001b[0;34m(x, y, rowvar, bias, ddof, dtype)\u001b[0m\n\u001b[1;32m   2681\u001b[0m         warnings.warn('bias and ddof have no effect and are deprecated',\n\u001b[1;32m   2682\u001b[0m                       DeprecationWarning, stacklevel=3)\n\u001b[0;32m-> 2683\u001b[0;31m     \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcov\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrowvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2684\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2685\u001b[0m         \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdiag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mcov\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36mcov\u001b[0;34m(m, y, rowvar, bias, ddof, fweights, aweights, dtype)\u001b[0m\n\u001b[1;32m   2516\u001b[0m             \u001b[0mw\u001b[0m \u001b[0;34m*=\u001b[0m \u001b[0maweights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2517\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2518\u001b[0;31m     \u001b[0mavg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturned\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2519\u001b[0m     \u001b[0mw_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mw_sum\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2520\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36maverage\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36maverage\u001b[0;34m(a, axis, weights, returned)\u001b[0m\n\u001b[1;32m    378\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    379\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mweights\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 380\u001b[0;31m         \u001b[0mavg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    381\u001b[0m         \u001b[0mscl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mavg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mavg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    382\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_mean\u001b[0;34m(a, axis, dtype, out, keepdims, where)\u001b[0m\n\u001b[1;32m    180\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m         ret = um.true_divide(\n\u001b[0;32m--> 182\u001b[0;31m                 ret, rcount, out=ret, casting='unsafe', subok=False)\n\u001b[0m\u001b[1;32m    183\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_float16_result\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m             \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for /: 'str' and 'int'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20, 20))\n",
        "cmap = sns.diverging_palette(222, 10, as_cmap=True)\n",
        "_ = sns.heatmap(df1.corr(), annot=True, vmax=.8, square=True, cmap=cmap)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 956
        },
        "id": "s8QG7wJPAaZp",
        "outputId": "ae9faa6f-d59b-48ad-b2a2-8816338a5f5b"
      },
      "id": "s8QG7wJPAaZp",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1440 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "figure = plt.figure(figsize=(12, 6))\n",
        "sns.heatmap(df.corr(), annot=True,cmap=plt.cm.cool)\n",
        "plt.tight_layout()\n",
        "plt.xlabel('Corr')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "Gh3FAHOJBifw",
        "outputId": "de3d549e-92bb-48dd-e4be-296b5f8e2787"
      },
      "id": "Gh3FAHOJBifw",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ljW1wW2LCJB7"
      },
      "id": "ljW1wW2LCJB7",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    },
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}