GeoPlotting.ipynb

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    "colab": {
      "name": "map.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "ON6vXGVuiqA3",
        "colab_type": "code",
        "outputId": "46716513-bbf1-42ae-a7b6-b200042847fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " datasets\t       EditedData.csv   fig.png     mpcancer.png     test.pdf\n",
            "'EditedData (3).csv'   example.csv      india.png   numarr.csv.npy   zomato.csv\n",
            "'EditedData (4).csv'   fig1.png         map1.png    sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bO1gASQQnnhY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from google.colab import files"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FnjvJ2-Fi5eJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def maximum(a, b, c): \n",
        "  \n",
        "    if (a >= b) and (a >=c): \n",
        "        largest = 1 \n",
        "  \n",
        "    elif (b >= a) and (b >= c): \n",
        "        largest = 2 \n",
        "    else: \n",
        "        largest = 3 \n",
        "          \n",
        "    return largest "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jsfLL6yQjaZp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jStcOcYujeuE",
        "colab_type": "code",
        "outputId": "eebdf70d-1564-4d57-b08d-c7dbdd284cfd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "train= pd.read_csv('EditedData.csv')\n",
        "train.head()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Type_1</th>\n",
              "      <th>Type_2</th>\n",
              "      <th>Type_3</th>\n",
              "      <th>image_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "      <td>0.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>0.301374</td>\n",
              "      <td>0.597676</td>\n",
              "      <td>1.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.169152</td>\n",
              "      <td>0.529468</td>\n",
              "      <td>0.301380</td>\n",
              "      <td>10.jpg</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>0.301382</td>\n",
              "      <td>100.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.169140</td>\n",
              "      <td>0.529502</td>\n",
              "      <td>0.301357</td>\n",
              "      <td>101.jpg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Type_1    Type_2    Type_3 image_name\n",
              "0  0.169131  0.529487  0.301382      0.jpg\n",
              "1  0.169127  0.301374  0.597676      1.jpg\n",
              "2  0.169152  0.529468  0.301380     10.jpg\n",
              "3  0.529500  0.697676  0.301382    100.jpg\n",
              "4  0.169140  0.529502  0.301357    101.jpg"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "metadata": {
        "id": "domrFuY6jmZd",
        "colab_type": "code",
        "outputId": "b5530193-a528-4993-ceb2-7cfff173a71e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "cell_type": "code",
      "source": [
        "a=np.array([])\n",
        "for index, row in train.iterrows():\n",
        "   b= maximum(row[\"Type_1\"], row[\"Type_2\"], row[\"Type_3\"])\n",
        "   a=np.append(a,b)\n",
        "print(a)\n"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[2. 3. 2. 2. 2. 3. 2. 2. 2. 2. 2. 2. 1. 3. 2. 1. 2. 2. 2. 3. 3. 3. 3. 1.\n",
            " 2. 2. 1. 1. 1. 3. 1. 2. 2. 2. 2. 3. 2. 2. 2. 2. 1. 3. 1. 1. 2. 3. 3. 3.\n",
            " 2. 1. 2. 3. 1. 2. 2. 3. 3. 3. 3. 3. 3. 1. 1. 2. 2. 1. 1. 1. 1. 1. 1. 1.\n",
            " 1. 1. 1. 2. 3. 3. 3. 2. 2. 2. 2. 2. 2. 2. 3. 3. 3. 3. 3. 3. 3. 3. 3. 3.\n",
            " 3. 3. 3. 3. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 1. 1. 1. 2. 2. 2.\n",
            " 2. 2. 2. 2. 2. 2. 2. 1. 1. 1. 1. 2. 3. 3. 3. 2. 2. 2. 2. 2. 2. 3. 3. 3.\n",
            " 3. 3. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.\n",
            " 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.\n",
            " 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.\n",
            " 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.\n",
            " 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.\n",
            " 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.\n",
            " 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 2. 2. 3.\n",
            " 3. 3. 3. 3. 2. 2. 2. 2. 2. 2. 2. 2. 3. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.\n",
            " 2. 2. 2. 2. 1. 1. 1. 1. 1. 1. 1. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.\n",
            " 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.\n",
            " 2. 2. 2. 2. 2. 2. 2. 2. 3. 2. 3. 1. 2. 2. 3. 2. 2. 2. 2. 2. 2. 2. 2. 2.\n",
            " 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.\n",
            " 2. 2. 2. 2. 2. 2. 2. 3. 3. 3. 3. 3. 3. 3. 3. 2. 2. 2. 2. 1. 1. 1. 1. 1.\n",
            " 1. 1. 1. 1. 1. 1. 1. 1. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.\n",
            " 2. 2. 2. 2. 2. 2. 2. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 2. 2. 2.\n",
            " 2. 2. 2. 2. 2. 2. 2. 2.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "nYwL50H8lpxc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df= pd.DataFrame(a)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "959BDwvPnqQO",
        "colab_type": "code",
        "outputId": "e49fcf54-6ffe-4759-f8e8-817941d67250",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(512, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "metadata": {
        "id": "piw2WGeCHzgY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_vzHUVwZzQ8I",
        "colab_type": "code",
        "outputId": "4287fb07-d92f-42fd-fcaa-be60509b66d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "DATASETS_URL = \"https://github.com/ageron/handson-ml/raw/master/datasets\"\n",
        "import os\n",
        "import tarfile\n",
        "from six.moves import urllib\n",
        "\n",
        "HOUSING_PATH = \"datasets/housing\"\n",
        "HOUSING_URL = DATASETS_URL + \"/housing/housing.tgz\"\n",
        "\n",
        "def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):\n",
        "    if not os.path.exists(housing_path):\n",
        "        os.makedirs(housing_path)\n",
        "    tgz_path = os.path.join(housing_path, \"housing.tgz\")\n",
        "    urllib.request.urlretrieve(housing_url, tgz_path)\n",
        "    housing_tgz = tarfile.open(tgz_path)\n",
        "    housing_tgz.extractall(path=housing_path)\n",
        "    housing_tgz.close()\n",
        "    \n",
        "fetch_housing_data()\n",
        "import pandas as pd\n",
        "\n",
        "def load_housing_data(housing_path=HOUSING_PATH):\n",
        "    csv_path = os.path.join(housing_path, \"housing.csv\")\n",
        "    return pd.read_csv(csv_path)\n",
        "housing = load_housing_data()\n",
        "housing.head()"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\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>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "      <th>ocean_proximity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-122.23</td>\n",
              "      <td>37.88</td>\n",
              "      <td>41.0</td>\n",
              "      <td>880.0</td>\n",
              "      <td>129.0</td>\n",
              "      <td>322.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>8.3252</td>\n",
              "      <td>452600.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-122.22</td>\n",
              "      <td>37.86</td>\n",
              "      <td>21.0</td>\n",
              "      <td>7099.0</td>\n",
              "      <td>1106.0</td>\n",
              "      <td>2401.0</td>\n",
              "      <td>1138.0</td>\n",
              "      <td>8.3014</td>\n",
              "      <td>358500.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-122.24</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1467.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>496.0</td>\n",
              "      <td>177.0</td>\n",
              "      <td>7.2574</td>\n",
              "      <td>352100.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1274.0</td>\n",
              "      <td>235.0</td>\n",
              "      <td>558.0</td>\n",
              "      <td>219.0</td>\n",
              "      <td>5.6431</td>\n",
              "      <td>341300.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1627.0</td>\n",
              "      <td>280.0</td>\n",
              "      <td>565.0</td>\n",
              "      <td>259.0</td>\n",
              "      <td>3.8462</td>\n",
              "      <td>342200.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0    -122.23     37.88                41.0        880.0           129.0   \n",
              "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
              "2    -122.24     37.85                52.0       1467.0           190.0   \n",
              "3    -122.25     37.85                52.0       1274.0           235.0   \n",
              "4    -122.25     37.85                52.0       1627.0           280.0   \n",
              "\n",
              "   population  households  median_income  median_house_value ocean_proximity  \n",
              "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
              "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
              "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
              "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
              "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "metadata": {
        "id": "tVwGe6xN47c7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "housing.drop(['housing_median_age','total_rooms',\t'total_bedrooms',\t'population',\t'households',\t'median_income',\t'median_house_value',\t'ocean_proximity'], axis=1, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "s3JHQqVp5os_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "housing = housing.sample(frac=1).reset_index(drop=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zwTGhINv6JBC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x = housing[:-512]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UeYM132I7Crh",
        "colab_type": "code",
        "outputId": "68b60e23-f7f4-46cf-bda1-f5be94dd5c6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "x.shape"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20128, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "metadata": {
        "id": "ByqXfueOCeFi",
        "colab_type": "code",
        "outputId": "2a28d154-8217-4125-abf2-aefce717e5fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "y =pd.concat([x, df], axis=1)\n",
        "type(y)"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "metadata": {
        "id": "CM_mDR-QCoIz",
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        "outputId": "283015d8-6d4b-47a9-ee65-86b3f5f9bc94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1969
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "y"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\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>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-117.95</td>\n",
              "      <td>33.99</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-118.13</td>\n",
              "      <td>33.87</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-118.07</td>\n",
              "      <td>33.88</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-118.16</td>\n",
              "      <td>33.96</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-121.51</td>\n",
              "      <td>38.52</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-116.21</td>\n",
              "      <td>33.75</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>-117.47</td>\n",
              "      <td>33.98</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>-117.87</td>\n",
              "      <td>34.07</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>-117.65</td>\n",
              "      <td>34.11</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>-122.32</td>\n",
              "      <td>37.95</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>-121.98</td>\n",
              "      <td>37.80</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>-117.14</td>\n",
              "      <td>33.15</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>-119.69</td>\n",
              "      <td>36.77</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>-118.24</td>\n",
              "      <td>33.88</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>-118.29</td>\n",
              "      <td>34.04</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>-122.31</td>\n",
              "      <td>38.30</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>-120.46</td>\n",
              "      <td>37.30</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>-120.67</td>\n",
              "      <td>38.76</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>-117.19</td>\n",
              "      <td>32.75</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>-118.10</td>\n",
              "      <td>33.81</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>-117.71</td>\n",
              "      <td>33.52</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>-121.33</td>\n",
              "      <td>37.96</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>-118.57</td>\n",
              "      <td>34.29</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>-118.50</td>\n",
              "      <td>34.04</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>-118.46</td>\n",
              "      <td>35.13</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>-121.44</td>\n",
              "      <td>37.70</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>-121.83</td>\n",
              "      <td>37.37</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>-118.47</td>\n",
              "      <td>34.26</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>-118.34</td>\n",
              "      <td>34.16</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>-118.38</td>\n",
              "      <td>33.86</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20098</th>\n",
              "      <td>-117.36</td>\n",
              "      <td>34.10</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20099</th>\n",
              "      <td>-118.09</td>\n",
              "      <td>33.78</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20100</th>\n",
              "      <td>-117.72</td>\n",
              "      <td>34.05</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20101</th>\n",
              "      <td>-117.94</td>\n",
              "      <td>33.89</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20102</th>\n",
              "      <td>-117.86</td>\n",
              "      <td>33.75</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20103</th>\n",
              "      <td>-117.82</td>\n",
              "      <td>33.88</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20104</th>\n",
              "      <td>-118.45</td>\n",
              "      <td>34.24</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20105</th>\n",
              "      <td>-121.94</td>\n",
              "      <td>37.30</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20106</th>\n",
              "      <td>-118.24</td>\n",
              "      <td>33.96</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20107</th>\n",
              "      <td>-119.10</td>\n",
              "      <td>36.40</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20108</th>\n",
              "      <td>-119.01</td>\n",
              "      <td>35.30</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20109</th>\n",
              "      <td>-118.36</td>\n",
              "      <td>34.01</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20110</th>\n",
              "      <td>-118.03</td>\n",
              "      <td>33.72</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20111</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.82</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20112</th>\n",
              "      <td>-117.12</td>\n",
              "      <td>32.77</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20113</th>\n",
              "      <td>-118.30</td>\n",
              "      <td>33.75</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20114</th>\n",
              "      <td>-118.36</td>\n",
              "      <td>34.17</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20115</th>\n",
              "      <td>-121.29</td>\n",
              "      <td>38.03</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20116</th>\n",
              "      <td>-122.20</td>\n",
              "      <td>37.79</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20117</th>\n",
              "      <td>-122.17</td>\n",
              "      <td>39.92</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20118</th>\n",
              "      <td>-121.32</td>\n",
              "      <td>38.01</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20119</th>\n",
              "      <td>-121.97</td>\n",
              "      <td>37.26</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20120</th>\n",
              "      <td>-122.23</td>\n",
              "      <td>37.76</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20121</th>\n",
              "      <td>-121.95</td>\n",
              "      <td>37.24</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20122</th>\n",
              "      <td>-117.86</td>\n",
              "      <td>33.87</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20123</th>\n",
              "      <td>-122.07</td>\n",
              "      <td>37.64</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20124</th>\n",
              "      <td>-117.02</td>\n",
              "      <td>32.69</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20125</th>\n",
              "      <td>-118.09</td>\n",
              "      <td>33.77</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20126</th>\n",
              "      <td>-122.71</td>\n",
              "      <td>38.46</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20127</th>\n",
              "      <td>-122.06</td>\n",
              "      <td>37.37</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>20128 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       longitude  latitude    0\n",
              "0        -117.95     33.99  2.0\n",
              "1        -118.13     33.87  3.0\n",
              "2        -118.07     33.88  2.0\n",
              "3        -118.16     33.96  2.0\n",
              "4        -121.51     38.52  2.0\n",
              "5        -116.21     33.75  3.0\n",
              "6        -117.47     33.98  2.0\n",
              "7        -117.87     34.07  2.0\n",
              "8        -117.65     34.11  2.0\n",
              "9        -122.32     37.95  2.0\n",
              "10       -121.98     37.80  2.0\n",
              "11       -117.14     33.15  2.0\n",
              "12       -119.69     36.77  1.0\n",
              "13       -118.24     33.88  3.0\n",
              "14       -118.29     34.04  2.0\n",
              "15       -122.31     38.30  1.0\n",
              "16       -120.46     37.30  2.0\n",
              "17       -120.67     38.76  2.0\n",
              "18       -117.19     32.75  2.0\n",
              "19       -118.10     33.81  3.0\n",
              "20       -117.71     33.52  3.0\n",
              "21       -121.33     37.96  3.0\n",
              "22       -118.57     34.29  3.0\n",
              "23       -118.50     34.04  1.0\n",
              "24       -118.46     35.13  2.0\n",
              "25       -121.44     37.70  2.0\n",
              "26       -121.83     37.37  1.0\n",
              "27       -118.47     34.26  1.0\n",
              "28       -118.34     34.16  1.0\n",
              "29       -118.38     33.86  3.0\n",
              "...          ...       ...  ...\n",
              "20098    -117.36     34.10  NaN\n",
              "20099    -118.09     33.78  NaN\n",
              "20100    -117.72     34.05  NaN\n",
              "20101    -117.94     33.89  NaN\n",
              "20102    -117.86     33.75  NaN\n",
              "20103    -117.82     33.88  NaN\n",
              "20104    -118.45     34.24  NaN\n",
              "20105    -121.94     37.30  NaN\n",
              "20106    -118.24     33.96  NaN\n",
              "20107    -119.10     36.40  NaN\n",
              "20108    -119.01     35.30  NaN\n",
              "20109    -118.36     34.01  NaN\n",
              "20110    -118.03     33.72  NaN\n",
              "20111    -122.25     37.82  NaN\n",
              "20112    -117.12     32.77  NaN\n",
              "20113    -118.30     33.75  NaN\n",
              "20114    -118.36     34.17  NaN\n",
              "20115    -121.29     38.03  NaN\n",
              "20116    -122.20     37.79  NaN\n",
              "20117    -122.17     39.92  NaN\n",
              "20118    -121.32     38.01  NaN\n",
              "20119    -121.97     37.26  NaN\n",
              "20120    -122.23     37.76  NaN\n",
              "20121    -121.95     37.24  NaN\n",
              "20122    -117.86     33.87  NaN\n",
              "20123    -122.07     37.64  NaN\n",
              "20124    -117.02     32.69  NaN\n",
              "20125    -118.09     33.77  NaN\n",
              "20126    -122.71     38.46  NaN\n",
              "20127    -122.06     37.37  NaN\n",
              "\n",
              "[20128 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "metadata": {
        "id": "VTjK3nBmCsgE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y['Image_name']= train['image_name']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "OtIlDBQIILn1",
        "colab_type": "code",
        "outputId": "2dfd95ec-fa5e-406c-bdfa-bed3c827fd1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "y.columns=['longitude','latitude','predtype','image_name']\n",
        "y.head()"
      ],
      "execution_count": 69,
      "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>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>predtype</th>\n",
              "      <th>image_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-117.95</td>\n",
              "      <td>33.99</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-118.13</td>\n",
              "      <td>33.87</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-118.07</td>\n",
              "      <td>33.88</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-118.16</td>\n",
              "      <td>33.96</td>\n",
              "      <td>2.0</td>\n",
              "      <td>100.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-121.51</td>\n",
              "      <td>38.52</td>\n",
              "      <td>2.0</td>\n",
              "      <td>101.jpg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   longitude  latitude  predtype image_name\n",
              "0    -117.95     33.99       2.0      0.jpg\n",
              "1    -118.13     33.87       3.0      1.jpg\n",
              "2    -118.07     33.88       2.0     10.jpg\n",
              "3    -118.16     33.96       2.0    100.jpg\n",
              "4    -121.51     38.52       2.0    101.jpg"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "metadata": {
        "id": "T2dfV8PvJZi1",
        "colab_type": "code",
        "outputId": "1499104b-899d-4b14-d788-776172e6e609",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "type(y)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "metadata": {
        "id": "xiBuzcFJIS3Z",
        "colab_type": "code",
        "outputId": "6423c966-d24e-4a17-d638-a8cada6af7b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "type(y.latitude.tolist())"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "list"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "metadata": {
        "id": "nDtEtOAaJG-w",
        "colab_type": "code",
        "outputId": "3e17b7e2-6ba0-495e-f2ee-917756f2c6d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 47
        }
      },
      "cell_type": "code",
      "source": [
        "from bokeh.io import output_file, output_notebook, show\n",
        "from bokeh.models import (\n",
        "  GMapPlot, GMapOptions, ColumnDataSource, Circle, LogColorMapper, BasicTicker, ColorBar,\n",
        "    Range1d, PanTool, WheelZoomTool, BoxSelectTool\n",
        ")\n",
        "from bokeh.models.mappers import ColorMapper, LinearColorMapper\n",
        "from bokeh.palettes import Viridis5\n",
        "\n",
        "\n",
        "map_options = GMapOptions(lat=37.88, lng=-122.23, map_type=\"roadmap\", zoom=6)\n",
        "\n",
        "plot = GMapPlot(\n",
        "    x_range=Range1d(), y_range=Range1d(), map_options=map_options\n",
        ")\n",
        "plot.title.text = \"Hey look! It's a scatter plot on a map!\"\n",
        "\n",
        "# For GMaps to function, Google requires you obtain and enable an API key:\n",
        "#\n",
        "#     https://developers.google.com/maps/documentation/javascript/get-api-key\n",
        "#\n",
        "# Replace the value below with your personal API key:\n",
        "plot.api_key = \"AIzaSyBYrbp34OohAHsX1cub8ZeHlMEFajv15fY\"\n",
        "\n",
        "source = ColumnDataSource(\n",
        "    data=dict(\n",
        "        lat=y.latitude.tolist(),\n",
        "        lon=y.longitude.tolist(),\n",
        "        size=y.predtype.tolist(),\n",
        "        color=y.predtype.tolist()\n",
        "    )\n",
        ")\n",
        "max_median_house_value = y.loc[y['predtype'].idxmax()]['predtype']\n",
        "min_median_house_value = y.loc[y['predtype'].idxmin()]['predtype']\n",
        "\n",
        "#color_mapper = CategoricalColorMapper(factors=['hi', 'lo'], palette=[RdBu3[2], RdBu3[0]])\n",
        "#color_mapper = LogColorMapper(palette=\"Viridis5\", low=min_median_house_value, high=max_median_house_value)\n",
        "color_mapper = LinearColorMapper(palette=Viridis5)\n",
        "\n",
        "circle = Circle(x=\"lon\", y=\"lat\", size=\"size\", fill_color={'field': 'color', 'transform': color_mapper}, fill_alpha=0.5, line_color=None)\n",
        "plot.add_glyph(source, circle)\n",
        "\n",
        "color_bar = ColorBar(color_mapper=color_mapper, ticker=BasicTicker(),\n",
        "                     label_standoff=12, border_line_color=None, location=(0,0))\n",
        "plot.add_layout(color_bar, 'right')\n",
        "\n",
        "plot.add_tools(PanTool(), WheelZoomTool(), BoxSelectTool())\n",
        "#output_file(\"gmap_plot.html\")\n",
        "output_notebook()\n",
        "\n",
        "#show(plot)"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div class=\"bk-root\">\n",
              "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
              "        <span id=\"1208\">Loading BokehJS ...</span>\n",
              "    </div>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "(function(root) {\n",
              "  function now() {\n",
              "    return new Date();\n",
              "  }\n",
              "\n",
              "  var force = true;\n",
              "\n",
              "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
              "    root._bokeh_onload_callbacks = [];\n",
              "    root._bokeh_is_loading = undefined;\n",
              "  }\n",
              "\n",
              "  var JS_MIME_TYPE = 'application/javascript';\n",
              "  var HTML_MIME_TYPE = 'text/html';\n",
              "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
              "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
              "\n",
              "  /**\n",
              "   * Render data to the DOM node\n",
              "   */\n",
              "  function render(props, node) {\n",
              "    var script = document.createElement(\"script\");\n",
              "    node.appendChild(script);\n",
              "  }\n",
              "\n",
              "  /**\n",
              "   * Handle when an output is cleared or removed\n",
              "   */\n",
              "  function handleClearOutput(event, handle) {\n",
              "    var cell = handle.cell;\n",
              "\n",
              "    var id = cell.output_area._bokeh_element_id;\n",
              "    var server_id = cell.output_area._bokeh_server_id;\n",
              "    // Clean up Bokeh references\n",
              "    if (id != null && id in Bokeh.index) {\n",
              "      Bokeh.index[id].model.document.clear();\n",
              "      delete Bokeh.index[id];\n",
              "    }\n",
              "\n",
              "    if (server_id !== undefined) {\n",
              "      // Clean up Bokeh references\n",
              "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
              "      cell.notebook.kernel.execute(cmd, {\n",
              "        iopub: {\n",
              "          output: function(msg) {\n",
              "            var id = msg.content.text.trim();\n",
              "            if (id in Bokeh.index) {\n",
              "              Bokeh.index[id].model.document.clear();\n",
              "              delete Bokeh.index[id];\n",
              "            }\n",
              "          }\n",
              "        }\n",
              "      });\n",
              "      // Destroy server and session\n",
              "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
              "      cell.notebook.kernel.execute(cmd);\n",
              "    }\n",
              "  }\n",
              "\n",
              "  /**\n",
              "   * Handle when a new output is added\n",
              "   */\n",
              "  function handleAddOutput(event, handle) {\n",
              "    var output_area = handle.output_area;\n",
              "    var output = handle.output;\n",
              "\n",
              "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
              "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
              "      return\n",
              "    }\n",
              "\n",
              "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
              "\n",
              "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
              "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
              "      // store reference to embed id on output_area\n",
              "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
              "    }\n",
              "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
              "      var bk_div = document.createElement(\"div\");\n",
              "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
              "      var script_attrs = bk_div.children[0].attributes;\n",
              "      for (var i = 0; i < script_attrs.length; i++) {\n",
              "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
              "      }\n",
              "      // store reference to server id on output_area\n",
              "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
              "    }\n",
              "  }\n",
              "\n",
              "  function register_renderer(events, OutputArea) {\n",
              "\n",
              "    function append_mime(data, metadata, element) {\n",
              "      // create a DOM node to render to\n",
              "      var toinsert = this.create_output_subarea(\n",
              "        metadata,\n",
              "        CLASS_NAME,\n",
              "        EXEC_MIME_TYPE\n",
              "      );\n",
              "      this.keyboard_manager.register_events(toinsert);\n",
              "      // Render to node\n",
              "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
              "      render(props, toinsert[toinsert.length - 1]);\n",
              "      element.append(toinsert);\n",
              "      return toinsert\n",
              "    }\n",
              "\n",
              "    /* Handle when an output is cleared or removed */\n",
              "    events.on('clear_output.CodeCell', handleClearOutput);\n",
              "    events.on('delete.Cell', handleClearOutput);\n",
              "\n",
              "    /* Handle when a new output is added */\n",
              "    events.on('output_added.OutputArea', handleAddOutput);\n",
              "\n",
              "    /**\n",
              "     * Register the mime type and append_mime function with output_area\n",
              "     */\n",
              "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
              "      /* Is output safe? */\n",
              "      safe: true,\n",
              "      /* Index of renderer in `output_area.display_order` */\n",
              "      index: 0\n",
              "    });\n",
              "  }\n",
              "\n",
              "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
              "  if (root.Jupyter !== undefined) {\n",
              "    var events = require('base/js/events');\n",
              "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
              "\n",
              "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
              "      register_renderer(events, OutputArea);\n",
              "    }\n",
              "  }\n",
              "\n",
              "  \n",
              "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
              "    root._bokeh_timeout = Date.now() + 5000;\n",
              "    root._bokeh_failed_load = false;\n",
              "  }\n",
              "\n",
              "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
              "     \"<div style='background-color: #fdd'>\\n\"+\n",
              "     \"<p>\\n\"+\n",
              "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
              "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
              "     \"</p>\\n\"+\n",
              "     \"<ul>\\n\"+\n",
              "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
              "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
              "     \"</ul>\\n\"+\n",
              "     \"<code>\\n\"+\n",
              "     \"from bokeh.resources import INLINE\\n\"+\n",
              "     \"output_notebook(resources=INLINE)\\n\"+\n",
              "     \"</code>\\n\"+\n",
              "     \"</div>\"}};\n",
              "\n",
              "  function display_loaded() {\n",
              "    var el = document.getElementById(\"1208\");\n",
              "    if (el != null) {\n",
              "      el.textContent = \"BokehJS is loading...\";\n",
              "    }\n",
              "    if (root.Bokeh !== undefined) {\n",
              "      if (el != null) {\n",
              "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
              "      }\n",
              "    } else if (Date.now() < root._bokeh_timeout) {\n",
              "      setTimeout(display_loaded, 100)\n",
              "    }\n",
              "  }\n",
              "\n",
              "\n",
              "  function run_callbacks() {\n",
              "    try {\n",
              "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
              "    }\n",
              "    finally {\n",
              "      delete root._bokeh_onload_callbacks\n",
              "    }\n",
              "    console.info(\"Bokeh: all callbacks have finished\");\n",
              "  }\n",
              "\n",
              "  function load_libs(js_urls, callback) {\n",
              "    root._bokeh_onload_callbacks.push(callback);\n",
              "    if (root._bokeh_is_loading > 0) {\n",
              "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
              "      return null;\n",
              "    }\n",
              "    if (js_urls == null || js_urls.length === 0) {\n",
              "      run_callbacks();\n",
              "      return null;\n",
              "    }\n",
              "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
              "    root._bokeh_is_loading = js_urls.length;\n",
              "    for (var i = 0; i < js_urls.length; i++) {\n",
              "      var url = js_urls[i];\n",
              "      var s = document.createElement('script');\n",
              "      s.src = url;\n",
              "      s.async = false;\n",
              "      s.onreadystatechange = s.onload = function() {\n",
              "        root._bokeh_is_loading--;\n",
              "        if (root._bokeh_is_loading === 0) {\n",
              "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
              "          run_callbacks()\n",
              "        }\n",
              "      };\n",
              "      s.onerror = function() {\n",
              "        console.warn(\"failed to load library \" + url);\n",
              "      };\n",
              "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
              "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
              "    }\n",
              "  };var element = document.getElementById(\"1208\");\n",
              "  if (element == null) {\n",
              "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1208' but no matching script tag was found. \")\n",
              "    return false;\n",
              "  }\n",
              "\n",
              "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.4.min.js\"];\n",
              "\n",
              "  var inline_js = [\n",
              "    function(Bokeh) {\n",
              "      Bokeh.set_log_level(\"info\");\n",
              "    },\n",
              "    \n",
              "    function(Bokeh) {\n",
              "      \n",
              "    },\n",
              "    function(Bokeh) {\n",
              "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n",
              "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n",
              "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n",
              "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n",
              "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n",
              "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n",
              "    }\n",
              "  ];\n",
              "\n",
              "  function run_inline_js() {\n",
              "    \n",
              "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
              "      for (var i = 0; i < inline_js.length; i++) {\n",
              "        inline_js[i].call(root, root.Bokeh);\n",
              "      }if (force === true) {\n",
              "        display_loaded();\n",
              "      }} else if (Date.now() < root._bokeh_timeout) {\n",
              "      setTimeout(run_inline_js, 100);\n",
              "    } else if (!root._bokeh_failed_load) {\n",
              "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
              "      root._bokeh_failed_load = true;\n",
              "    } else if (force !== true) {\n",
              "      var cell = $(document.getElementById(\"1208\")).parents('.cell').data().cell;\n",
              "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
              "    }\n",
              "\n",
              "  }\n",
              "\n",
              "  if (root._bokeh_is_loading === 0) {\n",
              "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
              "    run_inline_js();\n",
              "  } else {\n",
              "    load_libs(js_urls, function() {\n",
              "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
              "      run_inline_js();\n",
              "    });\n",
              "  }\n",
              "}(window));"
            ],
            "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1208\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"1208\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1208' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.4.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1208\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "FOE-aSEzJOqs",
        "colab_type": "code",
        "outputId": "e42377cc-f6cd-43fd-a0b1-f9341675d38b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        }
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "fig1=plt.figure()\n",
        "y.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4)\n",
        "#plt.show()"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f782ed149b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0TcUXj7uf9Itdj1qadlgv83aG9UVvCrDlBnh+iBeEqKqKEkWoqoInIpQ2h9Fx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\n8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BFh9+b4jBzw6S/8ys5otu3w2BMnV/xZLhTNEYU7Xredm69aN2P+Kv3lALGQVvVlXTlf\nLxwdJuBXkWVPvcMBH1dsamIkqfP0q4MYpkM6b2AYzrw+fUmCWNhHZ3OYeDRQ7m2eNyx6hzMMjukz\nrqEqEq2NQTZ1NBAO+Lj/dy5b1HNUCv30LmhAOWNgeq7/66/fNKWc72IWDxfDgmMpzzDfv+PW1oZZ\nf1eJsMAFgouIUEDjnjdcNu9xc/l/Y6H5fdFrifGszdd+epyv/fQ44PmhW5r8XLm5mbbmMMpkJTSY\nPUDJpyrlrVTLdplIF3AlvKprAZN0a5hXT4+TyZtkc+aMHvCz4bqQzVucG8mSzhlctqER23YJaArh\ngFp1ASDhZQ/A+ZL7cwVaTY+iLzVrSWQK/OjpM3Q0hxkaz9HY4EdRZLoHsri4bGxvIKUbPP5s75Qi\nO0PJPG+8Zl1ZrBYTpV3Pkd21Ug/PIARcIBDMEPTXXB7nuWOJVRzRymG5MJQoMpQYqPr7r/3nUVQF\nHId5y7OqsuffP9Y7gVHN510DruPiuGBYDn6fjOKXmcga+BQZSWJGeV1FBtNyyOkmV036Y+cKtDpw\ncpRjvSlyBZNT51KoCvgUhWy+SNFyCWrKpFUfZt9lLeSLJhNZg6JhkdEtcnqRTN7CnRzrWEpH1w3e\nfvMWfKqyqCjteo7srpV6eAZ5/kMEAsGlxrvfcgVXbYvTGQ+t9lAuOC5e0ZtaaqtbjtfnfbHiDSDJ\nXkpaUFMpmg6NDX78PoVoWKMz7sevSuUXdUiFlsYgOzbEaG4MoExu4+/ZGicW0vApMrGQNiXQ6mSf\nV+3tRF+StG4wmiwykNBJ6TZF0yGTN8nkzMlUuRzZvIXruiTSBrmCyWi6SMGw0QsWRdthPF1kIutZ\nnDAzKruWKO3FnFNv1MMzrNgd8/k8Dz/8MOPj4xSLRT74wQ9y2223AfDUU0/x/ve/n+PHj6/U7QUC\nwRIIBTRu3buORKbAt544OacvV5FqEztBdSwbTMvCtGTODqY43Z/Gp0oE/D5u3NNFoWiXAxVL29wl\nSguHyh2U6f7soQmdsVSeXMErFuOW/+PhuhKOLKHIEo7t0toYwHHcyepwMgGfjGmD6Viokowqy2g+\necH9zSup58juWqmHZ1gxAX/yySfZs2cPH/jAB+jv7+d973sft912G8Vika985Su0tq4tf4dAcKlR\nekF1NgcYGC9UPSbsV9jQ3oAsgV9T6OmfYEJfYDUyAQXDZTBRBLz88eYGH1EXDpwY46a9XeXyruPJ\nfGDcsi0AACAASURBVPnPMH8J1e6BLLYz+Xm44DheZoLrTjZ2kbxAOlWW2L4uxlXbWgDPpyvLMnnD\nIl+0CcuQyXkiHwqqdLWEy/deTJR2PUd210o9PMOKCfjv/u7vlv88ODhIe3s7AI888gj33Xcfn/vc\n51bq1gKBYBkovaC2dkX4+k+P0jOUQS/YyJJEwK+wpSvK/W/cTt9InldOjSErEm+9aRuq4lUqG0vm\neepAP+nC1OxvTQFJkrAnfb+SBLbQ/ClMZEwURSFv2Dx3ZIi2piBdzWH0okVhNIfkwo4N0RlWn2nZ\nHDk7Qb5oocgwnNCxbBdNVWgIKWTz5+vpS3jV3vw+hc7mEFdsPl+u9VB3Aqk9wlgyz6b2CKfOpehq\nDmGYDvv3dOBDWpNW88XGim/a33vvvQwNDfHII49w5swZjh07xkMPPSQEXCBYI8QiQT50zzWz/r6l\n0avTXhlEFQ1pqIpMZ2uEYNbAcV1MyyGgKcTCflK5otdZTPF6dGfyay+IaSVx8JrCqKrCRNoglTU4\nO5hlU2eELe1eipGqKDPSlg51J3Acr7f5yd4JsnkLSYJQQCUUUNgej6CqMo0RjZCm0t4cJjrpM6+8\n1nTL8s3Xn//zpZQOWu/pbisu4N/5znc4evQoH/nIR+js7OTjH/94zec2NYVQ62iyas3NE8yOmMOl\nU49z+PrGEC8dHyGXNwkHfVyzs42Xjo+wqSuGOprFslyCmsymjgYcJGzbwafK9I1kKRg26UyeTN4i\nlTPnv9klgmG5GJaFXpisjS5BV2uA0VQBvWCiFyxcRSYW8bNnazOHusc53p9C0xTGhvLkil41PNeF\ntG6hFy2u29XJe39/D5pvae/VevwOLhbDtGd8d0vz8+yhQRxZJhDUcIDe8Tw37OlclvsuxxyuWCGX\nQ4cO0dzcTGen97C33347AC0tno/lyJEj7Nu3j0cffXTWa9TTKu9SWnWuFGIOl85amkPTsjlwcpST\nfWlcCS5bH521oExaNxhL5hlL6rxyaoyCMMiroshelzXHcVBkr4vahvYII4kcRdNlcDyH68JEWmcs\nbUypBCdL0NkS4u2v3VxuAbsYq3Ip38F6tGjnKoLzzMFBzAr/jk+RuenKpQt43RdyefHFF+nv7+dj\nH/sYY2NjOI7Dz3/+c2TZi6C8/fbb5xRvgUCwtimVfN2/q2POY6YXlDlwcpQfPtXNQKJ64NzFQkCT\nkJBQZRfDBsN05+/G5nq14wtFm3WtwXKN9t6hHNEGjWhYI5EpkNLNGWVcXRcc20sr62oLA/MXIJmt\nwctiqYfiJ9OZK5+7nhuZwArmgd97770kEgnuu+8+/uRP/oT/9b/+V1m8BQKBoBol0f+7B67jLfvX\ns7UjvNpDQpWhJbr81eks28WnyjRGg+ze3EQkOL84BHwyzdEALjCWKpDOGti2g+o7X5Q9lSlQrNIp\nzgWSGZ2jZxOcGUyVq7mldYOXT4zyzMFBXj4xilnRbL4kuKbtlAV3Kax08RPTsmd9ltmYK597rvz6\nekDUQq+RtbR1Wa+IOVw6l9ocmpbNn37+l6s6hpDfK6oylFh8g5dqSHh1zX2qjN8no6oKiWRh1l7i\nqgSbuyIUig6249LY4KcxohFvCNDVEuJkf5rB8RwnelPzWvINQYWO5jDrWiMEfDJt8fMLpfm2kP/g\n9ssW/R2stWb7YlnM9VdjW7/ut9AFAoFgqRw4ObraQ2BzRwOm5TDE3AIu41m5C7GILMfFtWwcx8bO\nm7OKtyKBooDjSERCGuGgj4JhI0kyBdNh7/YWApoP03Q4Tmre+2byNsWBNDndxHUdFDWBYTpYtktz\nzI9l2+zb0brsW8grXfxkMRZ+PeRzLxYh4AKBoG45cS6NDDU3B1kJYiEfKd2iLaowkq4usS1RP5dv\nbmJ0ooDrOgyM5dAL9rzjdl2w7fNtRmfDdsGxPGtxU0eU/rEstu0SDqiMJHR+8FQPV2xqoiGk1lwZ\nz3BgMJGfseBIZIok0yexbIfrLm9fVsFdabGsd5/1cnNxP51AIFjTSC6EgwqZ/Py+zJXiaG/Sq49u\nujQEFYpFG2NSmRUJWhs1fvfGzRw6M85YUietG5g1DNed9v9ajjdNl66WEIPjOWRVIl+wCAZU8kWL\nlG5QMBw2d4Q4PajPe7257j2aNvnafx7jdF+SO2/bTiiwNjrULYeFX4+R8rMhBFwgENQtOzZE6R/P\ncvpcCmOVCq4nZ8lNj/hhXVsjfk0mHNAYnShg2G5N4l2iVMq0lkp0PgX8AYWAprKtK0qswc/ZoQy2\n46L5vABhyYWutiiyojKaLKDnDYxFrn1sF54+PMxIushtV6+vayErsRwW/oGToxztTWKYDppPxrLt\nOTMpVhMh4AKBoG7Zt8N7Gbc1Bnjl5Chpff5t6QtFtgguLqGAt22bL1oszAMOjguaIuH3SeSLzpxn\nR4I+3nTdeq67vI0DJ0c5cS5NRjeIRTS6WrwgNL9fBlOirSmEIsvEG5qYyBToGcqSX4SSm7bLeKpY\nNylfK4Vp2bx4bJhfvHyOU/1ZkCCoKWxfH+XEOVkIuEAgECyUylxy83e8rc1XTo3yzKtDMwK+brsy\nzr1vvtLrf92T5ORAknOjtW0lL5Z4NEBA86xSv6ZSMBa2vNBUCAV8gFevvFC0sBwXq+IyMhAJeUVb\n9m5v4VB3glzRZl1rmI54kGSmSFBTCflVJM4vITRVYXA8R2NDgI2uRCpXIJsrUrS8hUcNGVZIQCn7\ndy327K6VQ90JfnN4hLNDOcCLTcgXbY73pmiOBuc5e/UQAi4QCNYEpe3RPVvj7N4S51hPkvFsgeZo\ngMs3NparvJUF37L5wa9O0z2Q5lR/ekZhk+UgoCm0NwXpG86yviVELm/ik70+4bXcTpVlWhsDZHWT\n7etjGKZnhfcNZ3Fx0Ys2TQ0aqizT1hTmeG8KvWhh2w4DYzqGZRP0qVy/qw2fqkxJo+pqCTGeziNL\nEu3xAC1RP+OZItpkLvnQeJbe4RwFw9vVqJZQrCpeXXvLdoiFVtcPvpK+ab1oUTAtnGmTYDsOfl/9\n1i8RAi4QCNYUtVR4Kx33jtdt41B3gvZTo7xwbJiCsXwqvqHVT9ivMjKex5VhU1cMTfNhOzYSEif6\nJsjoJpIkYTleMxerwo8vS+Ag4QKKLDGSLCBLYNo2igyBgA8JF1WRCWoq7fEgR3smcCUYSehomjx5\nffjR02d4+81bpgRxxUIaN+3pIFe0y4K/rtXHFZu8rmOmZfP4s70kswYFwyLkl+kZylI0bbJ5C1WR\niIU1Ghv8pDJFXru7nZdPjKL0TGAb1gX3ia9kFbeQXyXgU5ElCbu09JJAVWSaY8ICFwgEggtOpdW+\nZ0ucg91jPH1wZMnX3bUxxt4drWg+hYKZY12r54PuagkxMJZjPF0gEtLK4q3IEnrBIps3vcA18ILd\nXJdEqkhTRMN1HFJ5i4Jh0xLzo8gSqiKjKhJNUY2B0ZwXpeZKjKYKGKZFPBYgHgmQzBkcODk6o8Y5\neMJ3tGcCF5f2eGiK+P3h67eXn8m0bP7fJ0+TL1qMp/PEwn58qsy2dTF8iszx3hQp3aBBlslME9AL\nEbm9ElXcKmvxd8YDFIshugdzOHg+8G3rG0hmiku+z0ohBFwgENQlyykKJav9d1+3g/eNZjAtmx89\nfYbxdIFs3uJE3wSGVbt1fro/xa7NTWg+BaniNEWRCflVjJBGKOBDkiQcx0FVFFzX64OuSC5p3RNy\nzacCLrmCRXNjkKADwYBKPBYkp5usb/MT0HwYlk1GL9IY9mPYDrGwRiLtIEsSsiyhqcqsNc6vvqwV\nvWhNqahWTfx8qsIVm5pI6QbasEzBsNEm5zvkV+cU0AtR43w5c7xL362jPRNYrkNXS5jO1gYu39RM\nWjc4O5wpR6G3NNavBV6/m/sCgeCSZrnrcFfiUxXefvMW9l/ezmuuaOe1u9sJ+Wt/HRo2/Py3/YCX\n6lZZL7ulMVgWvlhEAxfyRct72boORcNrNKKqEhIulu2iKBKRgIqiSKiTUWMFw8avqWxoj7BtXYy2\nWAh70kcbi2gE/SquA0FNpaslhCtNHeP0phyVzCZ+pdrfm9obaG7ws7E9Uq4BrqkSZwfTPHXgHM8f\nGaZ/ciE0/V7V/r4cLGdd8tJ3K296Ox4DY17wml60iIY0NrY3sH19jI3tDURX2fc/F8ICFwgEdclK\ni0JlzvD1u9rYtTnOU68OcqIvWZM1nsqZZSGp3Bl4+cTo5Fa6TsGwGBrLkDWmnhvwSfh8Cg4uEhKt\njQFCARW/TwHJixCPhn3l9DDwFgq9w1kmsgaBgMINe9rJ6SYdzWFCfpVoyEeueD60fHpTjtJuhqZK\nWLbNMwcHZ+xszJdHPZ4uYtoutmNzZjDN//P9g7Q1hlBVaI4FURRv8aGpEi+fGF3WLfXlrOJW+i5p\nqkLesDBMb3eiNNaVLPe6nAgBFwgEdclsW6ZjySxf/uFRsgWTSMDHn96xi5bGyJLuVdpi37ejlWcP\nD/KdJ06Qn2e9IEtU9QFrqkQ05CPQFWVoPDdDvMGrge4YNtGwht+nsHtLM9GQb4oPe+fGWDnqvCQk\n+3a0lnPAE8kiOzac77E+V+vPSvGrjFSvZbu7dN3DZyawbIe2phDDiRyJTAFFNskVrHIhmXWtDYT8\nKgXDrOtiKKXvVmmhJcvSlMXYWsl3FwIuEAjqktksoS//8CjJSQFK6gZf/uFRPvae/ctyT5+qcMve\n9dywu5ND3Qn+57e9nO5PYVQR845mf/nPlT5g0/a6YO3f1cozBwer3seyQZW93O/NHRHOjWQZSuj4\nfQrxqJ+AppDWDaIhrZwiVkJVlHLQXK5olwW4VuFZ6M5G6dkURca2HboHkqRzJsWihd+veGlWkvfc\nN13ZCcBjT5ygMFk4pmDYnDiXrisBr/xu7dkSXxNV5qohBFwgENQlswlStmDO+fflvPfOjTG+/tNj\njCR1+kf0chW4dc0BHrrrqvLx1UTRtGz652gZ6QKO4zCeKpLMFdGLDn4VeofS+P0qLY06e7c3z7CQ\nl+paCPlVEplCOY+8MaxhWvasAla6fldLiOGETj5dwHZcXMCwXPSCSTzqw3Gc8rb5aFInoKnIsueY\nl1anCu6srCUrey6EgAsEgjVFJOArW+Clv68UoYDGn7x997zR8KUtWct2OHZ2nFdPJfjqfx6d89qS\nC5m8STpn4EoSigx5Q8KyXVQf6HmTV06N09zgWfql+y41GnvP1jg/evoMRdNG88nEGvxzbqOX7qco\nMo0NfnJFiygwkSlQNB1MGzriQfw+uTyuWEQjmTaIRjQ0VWHHhuiM666lpiH1ihBwgUCwpvjTO3bN\n8IGvJLVYa3u2xvmfF8/y3V/01HxdywXJdJAkkGUJ13Upmg4SYFgOetFEliWUxuAUX/VSgqxKopnR\nTTSfTFdLGFWR57TiS/dL6wZFw8KybSzLpbHBj2k6NEb8XLWthbRulKvPbWhrQFPzbO5oKI9xumBb\ntk2uaGPZDqcHUhztmWDX5iYh5AtACLhAIFhTtDRGls3nvVz4VIUnXuhf8HlmuTLb+ejxoF8h5Fcm\n/+yliMH5reylbP9W+rPzhsXAWI6N7Q1zWvGl+718YpSdm5owXZeRcR3Lcti2PsqezfHy70sWuKrI\nXLGpaco4pwfP9Y96BXAGxrxyrorsXPRNU5YbIeACgUCwDOjF5fHFG4aNHXAIBXzs3tJUTs1aSuGS\nEpX+7IExHXuyxnktVrxetFAVmRv3dHK8J4Fju+zd1lI+d76dgUor3zBtTvQmOHwmQdGwaI756WqO\nzDhOMDdCwAUCgWAZUGUFY0aPtIVju2DYLpd3RMjqJgFNZSyZRwJeODoEeMFj1VLNqqWeVW5HV/qz\nN0wWaanV2i2dqyoKG9sbZpxbuTNQzb9d6bs/3J3AtB0cV8J2XBJpg64Wt3wfQW2ImRIIBIJl4Hf2\nd/HDZ/qW5Vqu4yK5EuOpAsfOToAkERrOEotoqKrMxvYGUrrB48/20jxZ6jORKfD1nw6i+RSyukkk\n5KN7IMXbb95SFvFqVnKtwWSlcxV1/kpo1UqrVt7bdlyCmgqSRNCvYBgOuNKSK6xdaoFxQsAFAoFg\nGdjY3sidtyj86OkeFlBWvSqqIpEtGvQMZMjlzXJDFJ8ic0WFwGV0k+bGIJbt8OqpcYYndFRFxu9T\nMCwHSZKm+JSr+c9rLexSOre1tYHROdLjYOY2eCJT4Ae/Ok3vcA5VkVAVF0WVsW0XWZJoiQXYu71l\nyb7vC1GTvZ4QAi4QCATLQMlyvGxDEyf6JpYk4pLrMJLIoxftirKuLo5r092fxqcoaD65HOw2MJYj\nb3g+astysB2vvrrmmzvCHFamZO30VLdjZyfI5E1My8G2HRLJqRXqrt8ZZ2tXZMnlVy9ETfZ6QjQz\nEQgEgmWgZKF2tobRfEvbtk3lLFI5A8d2yqlZjgsyEqosIUkgIdHVGmY8mWdgNItpOrTHg6iqjDu5\nRd3VEp7Xp1xro5OFML3xSEBTsWwXx3EYTRZmlJc9cDrB/7zYv+TmNSvxLPXMxf10AoFAcIG5bH2U\nF46OeC3LFonlen5wTVMwCxYSXg/xgKbQ0hhg27oYAL1DObrawuSKFnrBIl+w2NQewbQc9mxtJlqD\nT3k58sqnW83Tt+q7B1KMpvJk8/aUtqYlDOu8O6DEYqzntdSIZDkQAi4QCATLiNcQZYgjZ5MUzZli\nVSvZwvkFgCyBIkMk5KOpIVD+eamFaFeLl08d8Cns3d4yJRq9FEA223b0cuSVw9w+57fesBHj6TOM\nJvO4bnXfQq5gYtvOktLmLpYSqbUittAFAoFgGfGpCn/8titoifrnP7hGbBdUBRzX4XR/kp//to+X\njo8iu55P2bPQvRalAIfP1NZL3bRsXj4xyjMHB3n5xGi5v3et1OpzDgU0Lt8YZ8e6KFKV32sSbO5s\n4NjZCXoG0own8+zcGFvQWC5FhAUuEAgEK0AyW1jW6+kG6IkiAJoP0jkTa9JitWxwcWmPh6ZUOSuf\nO4uwLjVqe7a67NO31ndujHHk7AQvHRtj+khCmszlm5o41psi6JcZHMvw7EiOJ17sIxbReN3eLrqa\nIxc0JWytpKMJARcIBIJl5vFne8kbK9eCy7KgKDkk0gU0n0xj2I+snLdtp3f/mm07eqlR27P5nEsL\ng1Kd818e6Md1mSHeALrhBerlixbD40XSuoE9GQOQzBj84uUBfu+1m1c0JWy2Ou1Q3+loQsAFAoFg\nmcnoJrO4epcFxwXbdigYFobpzKhtvmNDFFVRqgZzVYrV0HiOxgZ/TX7n0nlKzwS2YZWt0mrCltYN\neoczDI7ruK6LaTmoyuwe25N9STa2N5DGqw8vAa7rtVw1TE9IVyolzLRsfvDL0/QMZwFoawogSzIb\nOxrKx9RrOpoQcIFAIFhmGkI+VrIFtiSBT5FRZJm2pkDV2uazbflWbpvHGvwkM0U6msPzRm2XzmuQ\nZTLzWKVjyTwFw8ayHBzXJZMzaAhrcz0R8ViAgbHclIWP61JOyVuplLBD3QnODGfLC4XhRAG/b6qA\n12s6Wn2OSiAQCNYwb71hI0++fA69uPgo9GqoMkQCKoGA6uVZ+1TWtzWUa5uHJwu7PH90ZFbfbaU1\nqSoyHc1hbrqyc957T7dC07oxa+GVlsYgetHGp8q4rreg8amzW+DZgsWzh4fY0hlAwiate/MW1GRe\nf3XXkkuszv9c51cNluPQFQsRC2l1n44mBFwgEAiWmVBAY2tXjJFkgZGJ/LJcc2tXhEhAY1+F1Rv2\nK1O2yi3bnrMGecivoqkSZkWw+WyBZ7M1QikxlsyXZW+6nzga0tjQHqGzJcTAWI5EukA8GkBxLY6e\ny1V9PtuGvhGDfTu8a8iSxLWXta647znkV2lrCjKcyGPZLiFN5fKNjXXp856OEHCBQCBYAV67p4Pf\nHBpesoBLQDyq4ZNl2psC9I/mkFzYsSHKvh2tU0T2mYODU84t5YFXinrYr1S1LueLSJ/ezESCKW6C\nSgu9ctFw5Zbmcl66Y7u0NUc52J0glS1iV26XQ7mCnGHZNIZXzuquZM/WOJZto6npKfO6FhACLhAI\nBCvAtTvb8PtUYgGHp48mF3UNRYLLNzUSDfsxTJvWeLgcDKYqyozt8WppXdO3vg3LZf+umQI1X0T6\n9GYmlU1QSveafmwlpb+ndAPbcTlwcoxswbuHBCiKRNCvsrUrekFTt3yqwv5dHezf1bHi91puhIAL\nBALBClASsasva+V9d0DvUILPfPMAtdZKCagSrU1hbtjdScivktaNWS3eEtXSuiota5g9IGu2nO7Z\nWEzZ0tI529bFCPoVTveN0zfm5cs3Rnx86O4r6WwRBVxqRQi4QCAQXAA2dsS5/3d28oNfnSatz5+W\ntL49woaWhnKA2VwWb4lqlm+tQrtQQV5M2dJLrdTpSiMEXCAQCC4Q0ZBGYyRAWs/Oedy2rggb2qJc\nXlFOdLGNOmoVTSGuaw8h4AKBQHCB2LM1TjZf5Ae/OEFylti25jDcfNW6GZ3EhMAKpiMEXCAQCC4Q\nPlXhlr3ruWXvekzL5sDJUQ6fSXB2JEtQU9jc0cDbXruZUGCuoicCgYcQcIFAIFgF1nL0s6A+EO1E\nBQKBQCBYgwgBFwgEAoFgDSIEXCAQCASCNYgQcIFAIBAI1iBCwAUCgUAgWIMIARcIBAKBYA0iBFwg\nEAgEgjWIEHCBQCAQCNYgQsAFAoFAIFiDCAEXCAQCgWANIrmu685/mEAgEAgEgnpCWOACgUAgEKxB\nhIALBAKBQLAGEQIuEAgEAsEaRAi4QCAQCARrECHgAoFAIBCsQYSACwQCgUCwBhECXoXnn3+eG2+8\nkSeffLL8s2PHjnHffffxrne9iw9+8IPk8/ny71zX5d577+Wf/umfVmO4dclC5vDf//3fueuuu/jD\nP/xDHnvssdUact2xkDn813/9V+666y7uvvtufvnLX67WkOuOanPoOA6f//znueGGG8o/s22bj33s\nY9x///3cc889/Md//MdqDLfuqHX+AH7zm99wxx13cOedd/Ld7373Qg+1blnIHMLC9EQI+DR6e3v5\n+te/zjXXXDPl55/5zGd4+OGHefTRR9m0aRPf//73y7/77ne/i2maF3qodctC5rCvr4/vf//7fOc7\n3+Hb3/42X/3qV8lkMqs08vphoXP405/+lG9961t8+ctf5u///u+xbXuVRl4/zDaHX/nKV+js7KSy\nBMavfvUr8vk8jz32GN/4xjf4/Oc/j+M4F3rIdcVC5s+yLD75yU/y5S9/mccee4xnnnnmQg+3LlnI\nHJZYiJ4IAZ9Ga2srX/rSl2hoaJjy80ceeYSrrroKgHg8TjKZBCCRSPDjH/+Ye++994KPtV5ZyByu\nW7eOb33rW6iqiqZpBAIBstnsagy7rljIHD733HPccsstaJpGPB5n3bp1nDp1ajWGXVfMNofvete7\nuP/++6f8rKmpiXQ6jeM46LpOOBxGli/t1+NC5u/w4cNs2rSJjo4OgsEgX/ziFy/kUOuWhcwhLFxP\nLu1vaBWCwSCKosz4eSQSAUDXdX74wx/ylre8BYDPfe5z/NVf/VXVcy5VFjKHsiwTDocBePrpp2lq\naqKzs/OCjrceWcgcjo2NEY/Hy8fE43FGR0cv2FjrlfnmsJJ9+/bR1dXFG97wBt785jfzN3/zNxdi\niHXNQuavv78fn8/HQw89xL333stPfvKTCzHEumchcwgL1xN1SaNb43z3u9+d4at58MEHueWWW6oe\nr+s6f/7nf8773vc+tm3bxgsvvICiKFxzzTX09PRcgBHXH0udwxIHDhzgH/7hH/jKV76youOtR5Y6\nh0888cSU31+K1ZEXOofTefHFFxkcHOSJJ55gfHycBx54gFtvvRVN01ZiuHXHUufPdV0GBwf51re+\nRaFQ4M477+Smm26iqalpJYZblyx1DhejJ5e0gN99993cfffdNR1rWRYf/OAHedvb3sadd94JwM9+\n9jMOHTrEPffcQyKRwDAMNmzYwB/8wR+s5LDriqXOIXiBWR//+Md55JFHLknre6lz2NbWxpkzZ8rH\nDA8P09bWtiJjrVcWMofVeOmll7jxxhtRVZX29nYaGxsZHh5mw4YNyzjK+mWp89fc3MyVV15JMBgk\nGAyyY8cO+vr6LikBX+ocLkZPLmkBXwj/8i//wvXXXz/lA3r44YfLf/7+979Pf3//JSXeC6XaHNq2\nzd/93d/xj//4j6xfv34VR7c2qDaHN9xwA1//+td58MEHmZiYYGRkhO3bt6/iKNcemzZt4vHHHwcg\nm80yPDxMa2vrKo9q7XD11VfzhS98gWKxiCRJnD17Vvx7XiCL0RPRjWwav/jFL/jqV79Kd3c38Xic\n1tZWvva1r3HzzTezfv16fD4fAK95zWv4i7/4i/J5pQl/8MEHV2vodcNC5nDfvn389V//NTt37iyf\n/5GPfKQcqHWpstDv4Te/+U1+/OMfI0kSH/rQh7jxxhtX+QlWn9nm8NOf/jQnTpzgpZde4pprruH2\n22/nPe95D5/61Kc4efIkjuPwwAMP8Hu/93ur/QirykLm773vfS8/+9nP+Od//mckSeLuu+/mj/7o\nj1b7EVadhc5hiVr1RAi4QCAQCARrEBGFLhAIBALBGkQIuEAgEAgEaxAh4AKBQCAQrEGEgAsEAoFA\nsAYRAi4QCAQCwRpECLhAsMZ57rnneOc737ls1zt69Cif/vSnATh16hSHDx9e0Pmve93rOHfu3LKN\nRyAQVEcIuEAgmMKuXbv4xCc+AcATTzzBkSNHVnlEAoGgGqISm0BwkXDmzBk++clP4roulmXx4Q9/\nmOuuu46HH36YtrY2Tpw4wZkzZ7jrrrv4wAc+wMTEBB/+8IfRdZ3NmzczMDDAn/3Zn6EoCl/84hf5\n6Ec/yqOPPkokEiEQCPDMM89w7bXXlqvA7dy5k8OHD5NMJvnQhz6Ebdvs3r17Si32L3zhC7z01OKI\n+QAAAqtJREFU0ksUCgX279/PRz/6USRJWq0pEgguKoQFLhBcJHzmM5/hne98J9/85jf51Kc+xd/+\n7d+Wf9fX18cjjzzC1772NR555BEA/u3f/o0dO3bwne98h/e973289NJLU6539dVXc8stt/D+97+f\n3//935/1vt/4xjfYu3cv3/72t3nHO97ByMgIAI8//jjDw8M8+uijfO9736O3t5cnn3xyBZ5cILg0\nEQIuEFwkvPLKK9x0002AZx1ns1kSiQQA119/PQDr1q0jm81i2zbHjh0r//yyyy5jy5Yti7rviRMn\nuPbaawHYvXt3uffxc889x4EDB3j3u9/Nu9/9bvr7+4VvXCBYRsQWukBwkVBta7r0M1Wd+k/ddV0c\nx0GWz6/hK/883/UNw5hyrcpzbdsGQNM07rnnHv74j/94AU8hEAhqRVjgAsFFwt69e3n66acBOHLk\nCI2NjXO2c9y6dSsvv/wy4EWbd3d3zzhGkiRM0wQgHA4zODgIwG9+85uyoG/btq18nVdeeQVd1wG4\n9tpreeKJJ7AsC4AvfelLNfc5FggE8yMscIHgIuETn/gEn/zkJ/n2t7+NZVl89rOfnfP49773vfzl\nX/4l9913H9u3b2f37t0oijLlmBtuuIHPfvazuK7LXXfdxUMPPcQLL7zAzTffXN4qf8973sNDDz3E\nAw88wI4dO8o9tN/0pjdx4MAB7r33XhRF4Yorrrhk+msLBBcC0Y1MILhE6e7upq+vj1tvvZVCocAb\n3/hGvve979HR0bHaQxMIBDUgBFwguEQZHR3lox/9KLquY1kWd9xxBw888MBqD0sgENSIEHCBQCAQ\nCNYgIohNIBAIBII1iBBwgUAgEAjWIELABQKBQCBYgwgBFwgEAoFgDSIEXCAQCASCNYgQcIFAIBAI\n1iD/PxNHTiKUg7EYAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ZBbO4ntTn7oD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5b117f54-9ba6-4366-873a-95e3b67650b1"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "plt.savefig('fig1.png')\n",
        "files.download('fig1.png')"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "cGQIKTYZQ-nU",
        "colab_type": "code",
        "outputId": "131a8cef-950b-45fe-b761-4c5924d79180",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "y.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\",\n",
        "    s=y['predtype'], label=\"Cancer prone areas\",\n",
        "    c=\"predtype\", cmap=plt.get_cmap(\"jet\"),\n",
        "    colorbar=True, alpha=0.4, figsize=(6,3),\n",
        ")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BFfBVqBhsHY62x/8e/kG8ow9q1OzceRaTKYjhw+O6u0mCELDkRDHC71H06Lv1/g7sFGjz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\niIAvCILQS/x/AgdZ3oChRUIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "6cxyuqxCRokc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5c-i1QzWRYcP",
        "colab_type": "code",
        "outputId": "ffeccb82-cba6-4b35-a077-fd53a19bb0e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        }
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.image as mpimg\n",
        "california_img=mpimg.imread('india.png')\n",
        "ax = y.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", figsize=(10,7),\n",
        "                       s=y['predtype'], label=\"Cancer prone areas\",\n",
        "                       c=\"predtype\", cmap=plt.get_cmap(\"jet\"),\n",
        "                       colorbar=True, alpha=0.4,\n",
        "                      )\n",
        "plt.imshow(california_img, extent=[-124.55, -113.80, 32.45, 42.05], alpha=0.5)\n",
        "plt.ylabel(\"Latitude\", fontsize=14)\n",
        "plt.xlabel(\"Longitude\", fontsize=14)\n",
        "\n",
        "prices = y[\"predtype\"]\n",
        "tick_values = np.linspace(prices.min(), prices.max(), 11)\n",
        "#cbar = plt.colorbar()\n",
        "#cbar.ax.set_yticklabels([\"%d\"%(round(v/1000)) for v in tick_values], fontsize=14)\n",
        "#cbar.set_label('Cancer Risks', fontsize=16)\n",
        "\n",
        "plt.legend(fontsize=16)\n",
        "plt.show()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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B2DzVZJwPs2pV2oza8fBwo7z8KvHxMej1Bi5dqqC9vQuABQvi8PUdfw+0M2cu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\nQ0RERKbHQENERESm9zcV0GuigwawVgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}