Metropolitan.ipynb

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    "colab": {
      "name": "Metropolitan.ipynb",
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      "authorship_tag": "ABX9TyMQtF29Q8GV2tZI+o2D9S3g",
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      "name": "python3",
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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/hpnightowl/owlsbooks/blob/master/Metropolitan.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "pwd"
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      "outputs": [
        {
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          "data": {
            "text/plain": [
              "'/content'"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 4
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        "id": "rsj7m7xOoLm7",
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      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "data = pd.read_csv('Standard Metropolitan Areas Data - train_data - data.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZdhdOW7ZoUGf",
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        "outputId": "6bd1ff0c-3f32-416d-e5d6-44da8538d592",
        "colab": {
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          "height": 224
        }
      },
      "source": [
        "data.head(5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>land_area</th>\n",
              "      <th>percent_city</th>\n",
              "      <th>percent_senior</th>\n",
              "      <th>physicians</th>\n",
              "      <th>hospital_beds</th>\n",
              "      <th>graduates</th>\n",
              "      <th>work_force</th>\n",
              "      <th>income</th>\n",
              "      <th>region</th>\n",
              "      <th>crime_rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1384</td>\n",
              "      <td>78.1</td>\n",
              "      <td>12.3</td>\n",
              "      <td>25627</td>\n",
              "      <td>69678</td>\n",
              "      <td>50.1</td>\n",
              "      <td>4083.9</td>\n",
              "      <td>72100</td>\n",
              "      <td>1</td>\n",
              "      <td>75.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3719</td>\n",
              "      <td>43.9</td>\n",
              "      <td>9.4</td>\n",
              "      <td>13326</td>\n",
              "      <td>43292</td>\n",
              "      <td>53.9</td>\n",
              "      <td>3305.9</td>\n",
              "      <td>54542</td>\n",
              "      <td>2</td>\n",
              "      <td>56.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3553</td>\n",
              "      <td>37.4</td>\n",
              "      <td>10.7</td>\n",
              "      <td>9724</td>\n",
              "      <td>33731</td>\n",
              "      <td>50.6</td>\n",
              "      <td>2066.3</td>\n",
              "      <td>33216</td>\n",
              "      <td>1</td>\n",
              "      <td>41.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3916</td>\n",
              "      <td>29.9</td>\n",
              "      <td>8.8</td>\n",
              "      <td>6402</td>\n",
              "      <td>24167</td>\n",
              "      <td>52.2</td>\n",
              "      <td>1966.7</td>\n",
              "      <td>32906</td>\n",
              "      <td>2</td>\n",
              "      <td>67.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2480</td>\n",
              "      <td>31.5</td>\n",
              "      <td>10.5</td>\n",
              "      <td>8502</td>\n",
              "      <td>16751</td>\n",
              "      <td>66.1</td>\n",
              "      <td>1514.5</td>\n",
              "      <td>26573</td>\n",
              "      <td>4</td>\n",
              "      <td>80.19</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   land_area  percent_city  percent_senior  ...  income  region  crime_rate\n",
              "0       1384          78.1            12.3  ...   72100       1       75.55\n",
              "1       3719          43.9             9.4  ...   54542       2       56.03\n",
              "2       3553          37.4            10.7  ...   33216       1       41.32\n",
              "3       3916          29.9             8.8  ...   32906       2       67.38\n",
              "4       2480          31.5            10.5  ...   26573       4       80.19\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cN-8iRbdoYXM",
        "colab_type": "code",
        "outputId": "d3931118-f5f9-47fe-f8f8-6d82956374e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 612
        }
      },
      "source": [
        "print('======================================')\n",
        "print('Not Null Values')\n",
        "print('======================================')\n",
        "print(data.info())\n",
        "print('======================================')\n",
        "print('Null Values')\n",
        "print('======================================')\n",
        "print(data.isnull().sum())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "======================================\n",
            "Not Null Values\n",
            "======================================\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 99 entries, 0 to 98\n",
            "Data columns (total 10 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   land_area       99 non-null     int64  \n",
            " 1   percent_city    99 non-null     float64\n",
            " 2   percent_senior  99 non-null     float64\n",
            " 3   physicians      99 non-null     int64  \n",
            " 4   hospital_beds   99 non-null     int64  \n",
            " 5   graduates       99 non-null     float64\n",
            " 6   work_force      99 non-null     float64\n",
            " 7   income          99 non-null     int64  \n",
            " 8   region          99 non-null     int64  \n",
            " 9   crime_rate      99 non-null     float64\n",
            "dtypes: float64(5), int64(5)\n",
            "memory usage: 7.9 KB\n",
            "None\n",
            "======================================\n",
            "Null Values\n",
            "======================================\n",
            "land_area         0\n",
            "percent_city      0\n",
            "percent_senior    0\n",
            "physicians        0\n",
            "hospital_beds     0\n",
            "graduates         0\n",
            "work_force        0\n",
            "income            0\n",
            "region            0\n",
            "crime_rate        0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y0QUTk_1ofoj",
        "colab_type": "code",
        "outputId": "2a580457-604a-4ae0-ae5c-ad119399c370",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(99, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M_5hyT-_OxMz",
        "colab_type": "code",
        "outputId": "02626116-eb08-4bb0-ffb1-4963da600b78",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        }
      },
      "source": [
        "print(data[data.region == 4])\n",
        "print(data[data.region == 4].count())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "    land_area  percent_city  percent_senior  ...  income  region  crime_rate\n",
            "4        2480          31.5            10.5  ...   26573       4       80.19\n",
            "13        782          28.4             7.8  ...   12591       4       63.20\n",
            "14       4226          38.1             9.8  ...   10918       4       64.05\n",
            "18      27293          25.3            12.3  ...    7399       4       76.03\n",
            "20       9155          53.8            11.1  ...    7766       4       85.62\n",
            "25       3434          28.9             8.3  ...    5909       4       77.94\n",
            "33        596         100.0             6.0  ...    5181       4       64.98\n",
            "53       5966          39.5             9.6  ...    3007       4       80.94\n",
            "54       1863          50.4             7.7  ...    2747       4       53.76\n",
            "59       1676          36.6             9.2  ...    2689       4       57.37\n",
            "67       8152          22.3             9.1  ...    2257       4       78.10\n",
            "73       1758          56.8            11.5  ...    1939       4       56.06\n",
            "74       1412          39.2            11.3  ...    2098       4       82.68\n",
            "81       2710          63.7             6.2  ...    1639       4       63.10\n",
            "84       2737          45.0            10.5  ...    1980       4       63.44\n",
            "93       4552          54.4             9.1  ...    1386       4       62.63\n",
            "94       1511          38.7            10.7  ...    1452       4       70.66\n",
            "\n",
            "[17 rows x 10 columns]\n",
            "land_area         17\n",
            "percent_city      17\n",
            "percent_senior    17\n",
            "physicians        17\n",
            "hospital_beds     17\n",
            "graduates         17\n",
            "work_force        17\n",
            "income            17\n",
            "region            17\n",
            "crime_rate        17\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
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        "id": "svvydbp0RIly",
        "colab_type": "code",
        "outputId": "11abf80e-d2ac-4815-c511-012283d8a26f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plt.plot(data.land_area,data.crime_rate)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f6224974748>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WUz4BeUzk5eM",
        "colab_type": "code",
        "outputId": "bdcecf5b-ad14-48f5-fc80-d08a18c74e2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "print(np.mean(data.land_area))\n",
        "print(min(data.income))\n",
        "max(data.crime_rate)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2615.7272727272725\n",
            "769\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "85.62"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2pd0TRUm7vcj",
        "colab_type": "code",
        "outputId": "a9abea4f-3aad-4ff2-c94a-6c451a736420",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        }
      },
      "source": [
        "data.corr()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>land_area</th>\n",
              "      <th>percent_city</th>\n",
              "      <th>percent_senior</th>\n",
              "      <th>physicians</th>\n",
              "      <th>hospital_beds</th>\n",
              "      <th>graduates</th>\n",
              "      <th>work_force</th>\n",
              "      <th>income</th>\n",
              "      <th>region</th>\n",
              "      <th>crime_rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>land_area</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.077320</td>\n",
              "      <td>0.092226</td>\n",
              "      <td>0.085054</td>\n",
              "      <td>0.081034</td>\n",
              "      <td>0.088728</td>\n",
              "      <td>0.135792</td>\n",
              "      <td>0.111404</td>\n",
              "      <td>0.292392</td>\n",
              "      <td>0.293907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>percent_city</th>\n",
              "      <td>-0.077320</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.250995</td>\n",
              "      <td>0.067391</td>\n",
              "      <td>0.052898</td>\n",
              "      <td>0.076720</td>\n",
              "      <td>0.016011</td>\n",
              "      <td>0.019235</td>\n",
              "      <td>0.235880</td>\n",
              "      <td>0.159596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>percent_senior</th>\n",
              "      <td>0.092226</td>\n",
              "      <td>-0.250995</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.056454</td>\n",
              "      <td>0.083775</td>\n",
              "      <td>-0.155695</td>\n",
              "      <td>0.035240</td>\n",
              "      <td>0.046073</td>\n",
              "      <td>-0.242811</td>\n",
              "      <td>-0.177992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>physicians</th>\n",
              "      <td>0.085054</td>\n",
              "      <td>0.067391</td>\n",
              "      <td>0.056454</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.974241</td>\n",
              "      <td>0.049500</td>\n",
              "      <td>0.965597</td>\n",
              "      <td>0.976209</td>\n",
              "      <td>-0.140961</td>\n",
              "      <td>0.187763</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hospital_beds</th>\n",
              "      <td>0.081034</td>\n",
              "      <td>0.052898</td>\n",
              "      <td>0.083775</td>\n",
              "      <td>0.974241</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.003892</td>\n",
              "      <td>0.967913</td>\n",
              "      <td>0.974416</td>\n",
              "      <td>-0.220305</td>\n",
              "      <td>0.109799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>graduates</th>\n",
              "      <td>0.088728</td>\n",
              "      <td>0.076720</td>\n",
              "      <td>-0.155695</td>\n",
              "      <td>0.049500</td>\n",
              "      <td>-0.003892</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.044054</td>\n",
              "      <td>0.045578</td>\n",
              "      <td>0.246226</td>\n",
              "      <td>0.290880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>work_force</th>\n",
              "      <td>0.135792</td>\n",
              "      <td>0.016011</td>\n",
              "      <td>0.035240</td>\n",
              "      <td>0.965597</td>\n",
              "      <td>0.967913</td>\n",
              "      <td>0.044054</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.996735</td>\n",
              "      <td>-0.144022</td>\n",
              "      <td>0.175945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>income</th>\n",
              "      <td>0.111404</td>\n",
              "      <td>0.019235</td>\n",
              "      <td>0.046073</td>\n",
              "      <td>0.976209</td>\n",
              "      <td>0.974416</td>\n",
              "      <td>0.045578</td>\n",
              "      <td>0.996735</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.152016</td>\n",
              "      <td>0.175797</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>region</th>\n",
              "      <td>0.292392</td>\n",
              "      <td>0.235880</td>\n",
              "      <td>-0.242811</td>\n",
              "      <td>-0.140961</td>\n",
              "      <td>-0.220305</td>\n",
              "      <td>0.246226</td>\n",
              "      <td>-0.144022</td>\n",
              "      <td>-0.152016</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.636192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>crime_rate</th>\n",
              "      <td>0.293907</td>\n",
              "      <td>0.159596</td>\n",
              "      <td>-0.177992</td>\n",
              "      <td>0.187763</td>\n",
              "      <td>0.109799</td>\n",
              "      <td>0.290880</td>\n",
              "      <td>0.175945</td>\n",
              "      <td>0.175797</td>\n",
              "      <td>0.636192</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                land_area  percent_city  ...    region  crime_rate\n",
              "land_area        1.000000     -0.077320  ...  0.292392    0.293907\n",
              "percent_city    -0.077320      1.000000  ...  0.235880    0.159596\n",
              "percent_senior   0.092226     -0.250995  ... -0.242811   -0.177992\n",
              "physicians       0.085054      0.067391  ... -0.140961    0.187763\n",
              "hospital_beds    0.081034      0.052898  ... -0.220305    0.109799\n",
              "graduates        0.088728      0.076720  ...  0.246226    0.290880\n",
              "work_force       0.135792      0.016011  ... -0.144022    0.175945\n",
              "income           0.111404      0.019235  ... -0.152016    0.175797\n",
              "region           0.292392      0.235880  ...  1.000000    0.636192\n",
              "crime_rate       0.293907      0.159596  ...  0.636192    1.000000\n",
              "\n",
              "[10 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4MfJZijs7419",
        "colab_type": "code",
        "outputId": "8cf0f2d6-a705-4ad1-8a67-83e41640b209",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plt.scatter(data.hospital_beds,data.physicians)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f6224446320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8bVvqCvDVuM",
        "colab_type": "code",
        "outputId": "017cfb25-08c8-43cf-b694-d78f2547684b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "data.income.hist();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yKkt8c5zr-Mz",
        "colab_type": "code",
        "outputId": "719cd711-ec04-4847-82c0-d01aa35b2728",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "data.region.hist();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J8j2iKuksOzi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 720
        },
        "outputId": "9c4d753b-d6ec-475b-d05f-2b7368e58b98"
      },
      "source": [
        "data[data.region == 1 ]"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>land_area</th>\n",
              "      <th>percent_city</th>\n",
              "      <th>percent_senior</th>\n",
              "      <th>physicians</th>\n",
              "      <th>hospital_beds</th>\n",
              "      <th>graduates</th>\n",
              "      <th>work_force</th>\n",
              "      <th>income</th>\n",
              "      <th>region</th>\n",
              "      <th>crime_rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1384</td>\n",
              "      <td>78.1</td>\n",
              "      <td>12.3</td>\n",
              "      <td>25627</td>\n",
              "      <td>69678</td>\n",
              "      <td>50.1</td>\n",
              "      <td>4083.9</td>\n",
              "      <td>72100</td>\n",
              "      <td>1</td>\n",
              "      <td>75.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3553</td>\n",
              "      <td>37.4</td>\n",
              "      <td>10.7</td>\n",
              "      <td>9724</td>\n",
              "      <td>33731</td>\n",
              "      <td>50.6</td>\n",
              "      <td>2066.3</td>\n",
              "      <td>33216</td>\n",
              "      <td>1</td>\n",
              "      <td>41.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>3049</td>\n",
              "      <td>19.5</td>\n",
              "      <td>12.1</td>\n",
              "      <td>4005</td>\n",
              "      <td>21149</td>\n",
              "      <td>53.4</td>\n",
              "      <td>967.5</td>\n",
              "      <td>15826</td>\n",
              "      <td>1</td>\n",
              "      <td>30.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1008</td>\n",
              "      <td>16.6</td>\n",
              "      <td>10.3</td>\n",
              "      <td>4006</td>\n",
              "      <td>16704</td>\n",
              "      <td>55.9</td>\n",
              "      <td>935.5</td>\n",
              "      <td>15953</td>\n",
              "      <td>1</td>\n",
              "      <td>54.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2966</td>\n",
              "      <td>26.9</td>\n",
              "      <td>10.3</td>\n",
              "      <td>2053</td>\n",
              "      <td>6604</td>\n",
              "      <td>56.3</td>\n",
              "      <td>450.4</td>\n",
              "      <td>6966</td>\n",
              "      <td>1</td>\n",
              "      <td>56.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>2624</td>\n",
              "      <td>30.4</td>\n",
              "      <td>12.2</td>\n",
              "      <td>1532</td>\n",
              "      <td>4730</td>\n",
              "      <td>55.2</td>\n",
              "      <td>356.5</td>\n",
              "      <td>5094</td>\n",
              "      <td>1</td>\n",
              "      <td>38.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2419</td>\n",
              "      <td>27.8</td>\n",
              "      <td>9.9</td>\n",
              "      <td>1250</td>\n",
              "      <td>2870</td>\n",
              "      <td>57.8</td>\n",
              "      <td>286.8</td>\n",
              "      <td>3860</td>\n",
              "      <td>1</td>\n",
              "      <td>47.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>1951</td>\n",
              "      <td>28.4</td>\n",
              "      <td>14.5</td>\n",
              "      <td>696</td>\n",
              "      <td>4843</td>\n",
              "      <td>47.9</td>\n",
              "      <td>271.5</td>\n",
              "      <td>3667</td>\n",
              "      <td>1</td>\n",
              "      <td>23.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>1490</td>\n",
              "      <td>33.1</td>\n",
              "      <td>11.9</td>\n",
              "      <td>827</td>\n",
              "      <td>3818</td>\n",
              "      <td>47.4</td>\n",
              "      <td>300.2</td>\n",
              "      <td>4144</td>\n",
              "      <td>1</td>\n",
              "      <td>30.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>312</td>\n",
              "      <td>19.5</td>\n",
              "      <td>7.5</td>\n",
              "      <td>769</td>\n",
              "      <td>2463</td>\n",
              "      <td>55.0</td>\n",
              "      <td>298.7</td>\n",
              "      <td>4352</td>\n",
              "      <td>1</td>\n",
              "      <td>48.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>47</td>\n",
              "      <td>41.9</td>\n",
              "      <td>11.9</td>\n",
              "      <td>745</td>\n",
              "      <td>3352</td>\n",
              "      <td>36.3</td>\n",
              "      <td>258.9</td>\n",
              "      <td>3915</td>\n",
              "      <td>1</td>\n",
              "      <td>51.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>192</td>\n",
              "      <td>60.5</td>\n",
              "      <td>10.8</td>\n",
              "      <td>617</td>\n",
              "      <td>1789</td>\n",
              "      <td>44.1</td>\n",
              "      <td>212.6</td>\n",
              "      <td>3158</td>\n",
              "      <td>1</td>\n",
              "      <td>58.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>1624</td>\n",
              "      <td>13.4</td>\n",
              "      <td>11.0</td>\n",
              "      <td>832</td>\n",
              "      <td>2938</td>\n",
              "      <td>55.4</td>\n",
              "      <td>207.8</td>\n",
              "      <td>2885</td>\n",
              "      <td>1</td>\n",
              "      <td>39.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>946</td>\n",
              "      <td>16.4</td>\n",
              "      <td>11.1</td>\n",
              "      <td>366</td>\n",
              "      <td>1455</td>\n",
              "      <td>43.9</td>\n",
              "      <td>163.8</td>\n",
              "      <td>2178</td>\n",
              "      <td>1</td>\n",
              "      <td>23.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>2658</td>\n",
              "      <td>39.0</td>\n",
              "      <td>12.2</td>\n",
              "      <td>365</td>\n",
              "      <td>5430</td>\n",
              "      <td>49.9</td>\n",
              "      <td>136.9</td>\n",
              "      <td>1862</td>\n",
              "      <td>1</td>\n",
              "      <td>28.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>2071</td>\n",
              "      <td>19.9</td>\n",
              "      <td>11.3</td>\n",
              "      <td>470</td>\n",
              "      <td>2531</td>\n",
              "      <td>58.9</td>\n",
              "      <td>133.1</td>\n",
              "      <td>1782</td>\n",
              "      <td>1</td>\n",
              "      <td>36.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>862</td>\n",
              "      <td>26.3</td>\n",
              "      <td>13.4</td>\n",
              "      <td>423</td>\n",
              "      <td>1929</td>\n",
              "      <td>43.3</td>\n",
              "      <td>145.5</td>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>25.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>813</td>\n",
              "      <td>46.0</td>\n",
              "      <td>9.8</td>\n",
              "      <td>293</td>\n",
              "      <td>1693</td>\n",
              "      <td>58.4</td>\n",
              "      <td>119.9</td>\n",
              "      <td>1688</td>\n",
              "      <td>1</td>\n",
              "      <td>38.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>1148</td>\n",
              "      <td>45.3</td>\n",
              "      <td>11.1</td>\n",
              "      <td>891</td>\n",
              "      <td>5790</td>\n",
              "      <td>54.0</td>\n",
              "      <td>277.0</td>\n",
              "      <td>3510</td>\n",
              "      <td>1</td>\n",
              "      <td>49.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>1509</td>\n",
              "      <td>37.6</td>\n",
              "      <td>12.0</td>\n",
              "      <td>1087</td>\n",
              "      <td>4900</td>\n",
              "      <td>51.4</td>\n",
              "      <td>319.6</td>\n",
              "      <td>3982</td>\n",
              "      <td>1</td>\n",
              "      <td>45.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>813</td>\n",
              "      <td>13.4</td>\n",
              "      <td>10.9</td>\n",
              "      <td>371</td>\n",
              "      <td>4355</td>\n",
              "      <td>58.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>1589</td>\n",
              "      <td>1</td>\n",
              "      <td>36.33</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    land_area  percent_city  percent_senior  ...  income  region  crime_rate\n",
              "0        1384          78.1            12.3  ...   72100       1       75.55\n",
              "2        3553          37.4            10.7  ...   33216       1       41.32\n",
              "8        3049          19.5            12.1  ...   15826       1       30.51\n",
              "10       1008          16.6            10.3  ...   15953       1       54.16\n",
              "24       2966          26.9            10.3  ...    6966       1       56.55\n",
              "28       2624          30.4            12.2  ...    5094       1       38.75\n",
              "36       2419          27.8             9.9  ...    3860       1       47.65\n",
              "38       1951          28.4            14.5  ...    3667       1       23.64\n",
              "39       1490          33.1            11.9  ...    4144       1       30.59\n",
              "43        312          19.5             7.5  ...    4352       1       48.99\n",
              "46         47          41.9            11.9  ...    3915       1       51.70\n",
              "55        192          60.5            10.8  ...    3158       1       58.79\n",
              "58       1624          13.4            11.0  ...    2885       1       39.59\n",
              "70        946          16.4            11.1  ...    2178       1       23.32\n",
              "72       2658          39.0            12.2  ...    1862       1       28.52\n",
              "75       2071          19.9            11.3  ...    1782       1       36.47\n",
              "76        862          26.3            13.4  ...    2010       1       25.49\n",
              "86        813          46.0             9.8  ...    1688       1       38.24\n",
              "88       1148          45.3            11.1  ...    3510       1       49.64\n",
              "89       1509          37.6            12.0  ...    3982       1       45.19\n",
              "97        813          13.4            10.9  ...    1589       1       36.33\n",
              "\n",
              "[21 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AU7K-WKvFp5T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        },
        "outputId": "215cfaae-a63e-4fd7-f1e0-07631518790f"
      },
      "source": [
        "data[data.region == 3].count"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.count of     land_area  percent_city  percent_senior  ...  income  region  crime_rate\n",
              "5        2815          23.1             6.7  ...   25663       3       58.48\n",
              "6        8360          46.3             8.2  ...   18350       3       72.25\n",
              "7        6794          60.1             6.3  ...   18221       3       64.88\n",
              "12       4326          23.6             7.3  ...   12107       3       58.12\n",
              "16       2045          37.2            21.4  ...    7989       3       64.68\n",
              "22       1967          51.1             8.8  ...    7038       3       61.15\n",
              "26       1392          37.2             9.8  ...    5705       3       51.69\n",
              "27       3358          35.1            11.3  ...    4941       3       53.15\n",
              "30       3214          47.7             9.4  ...    4924       3       45.14\n",
              "31       3491          48.5             9.7  ...    4798       3       57.88\n",
              "32       4080          59.6             9.9  ...    4600       3       43.90\n",
              "34       3199          80.6             8.7  ...    4127       3       61.97\n",
              "40       5677          55.8            10.5  ...    4035       3       52.70\n",
              "41       1525          55.7             8.3  ...    3777       3       62.09\n",
              "42       2528          19.2            10.2  ...    3489       3       74.94\n",
              "47       2115          19.9             9.1  ...    2962       3       58.33\n",
              "49       1165          14.5             8.6  ...    3724       3       60.77\n",
              "50       1553          50.0             8.0  ...    2991       3       49.83\n",
              "51       2023          22.1            21.8  ...    3283       3       76.35\n",
              "52       2766          67.9             7.7  ...    2598       3       63.22\n",
              "57       1057          90.7             6.1  ...    2012       3       58.97\n",
              "60       2109          41.2            10.3  ...    2308       3       40.30\n",
              "62       2618          31.7             6.1  ...    1992       3       65.05\n",
              "63       1465          30.3             6.8  ...    2201       3       70.66\n",
              "66       1489          58.8             9.5  ...    2264       3       70.55\n",
              "68        655          75.2             6.6  ...    2088       3       42.92\n",
              "69       2363          53.1            10.6  ...    1996       3       53.97\n",
              "77       1526          71.7             7.7  ...    1692       3       66.13\n",
              "78       1758          33.2            11.6  ...    1641       3       41.98\n",
              "79       1651          64.6             8.9  ...    1724       3       48.88\n",
              "80       1493          64.8             8.9  ...    1787       3       53.98\n",
              "85       1697          23.8             7.2  ...    1354       3       70.50\n",
              "90       2013          61.7             9.7  ...    1412       3       56.87\n",
              "95       1543          39.6             8.1  ...     769       3       36.36\n",
              "96       1011          37.8            10.5  ...    1337       3       60.16\n",
              "98        654          28.8             3.9  ...    1148       3       68.76\n",
              "\n",
              "[36 rows x 10 columns]>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dzaUKm89Ghai",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "ed91b5ea-3869-4ac3-9783-3965dbd1ad79"
      },
      "source": [
        "data[data.crime_rate == 85.62]"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>land_area</th>\n",
              "      <th>percent_city</th>\n",
              "      <th>percent_senior</th>\n",
              "      <th>physicians</th>\n",
              "      <th>hospital_beds</th>\n",
              "      <th>graduates</th>\n",
              "      <th>work_force</th>\n",
              "      <th>income</th>\n",
              "      <th>region</th>\n",
              "      <th>crime_rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>9155</td>\n",
              "      <td>53.8</td>\n",
              "      <td>11.1</td>\n",
              "      <td>2280</td>\n",
              "      <td>6450</td>\n",
              "      <td>60.1</td>\n",
              "      <td>575.2</td>\n",
              "      <td>7766</td>\n",
              "      <td>4</td>\n",
              "      <td>85.62</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    land_area  percent_city  percent_senior  ...  income  region  crime_rate\n",
              "20       9155          53.8            11.1  ...    7766       4       85.62\n",
              "\n",
              "[1 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    }
  ]
}