pride_themed_folium_answers.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wigWco2jTLVC"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "588Y5DYiTPYN",
        "outputId": "f2618191-8961-4936-ba83-27ef8dcba157"
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            "text/plain": [
              "      GEOID10  Tax_Mjoint  Mjoint_MF  Mjoint_SS  Mjoint_FF  Mjoint_MM  \\\n",
              "0       90069        2120       1689        431         61        370   \n",
              "1       94114        5080       4036       1044        170        874   \n",
              "2       10011        5790       5166        624         97        527   \n",
              "3       10014        3510       3229        281         74        207   \n",
              "4       94103        2660       2417        243         34        209   \n",
              "...       ...         ...        ...        ...        ...        ...   \n",
              "2323    97208           0          0          0          0          0   \n",
              "2324    98154           0          0          0          0          0   \n",
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              "\n",
              "      TaxRate_SS  TaxRate_FF  TaxRate_MM  Cns_TotHH  ...    FF_Cns  FF_Index  \\\n",
              "0     203.301887   28.773585  174.528302      12551  ...  1.847099  6.724415   \n",
              "1     205.511811   33.464567  172.047244      16456  ...  4.161579  9.834048   \n",
              "2     107.772021   16.753022   91.018998      29762  ...  1.531029  4.370779   \n",
              "3      80.056980   21.082621   58.974359      18786  ...  2.482293  6.055939   \n",
              "4      91.353383   12.781955   78.571429      12728  ...  0.837431  3.004058   \n",
              "...          ...         ...         ...        ...  ...       ...       ...   \n",
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              "\n",
              "         MM_Tax     MM_Cns   MM_Index   SS_Index  SS_Index_Weight  \\\n",
              "0     29.583721  18.704533  48.288254  55.012669        39.429995   \n",
              "1     29.163165  19.415304  48.578469  58.412517        41.866815   \n",
              "2     15.428332  10.932081  26.360413  30.731192        22.026394   \n",
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              "4     13.318386   4.961779  18.280165  21.284224        15.255337   \n",
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              "\n",
              "      Parade_Weight  Bars_Weight   TOTINDEX  \n",
              "0                10    17.647059  67.077054  \n",
              "1                 0    20.000000  61.866815  \n",
              "2                10     5.882353  37.908747  \n",
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              "[2328 rows x 29 columns]"
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          },
          "execution_count": 3,
          "metadata": {},
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        }
      ],
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/joqu86/gayborhood_states_class/main/data.csv')\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting uszipcode\n",
            "  Downloading uszipcode-1.0.1-py2.py3-none-any.whl (35 kB)\n",
            "Collecting sqlalchemy-mate>=1.4.28.3\n",
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            "\u001b[K     |████████████████████████████████| 76 kB 3.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: attrs in /usr/local/lib/python3.7/dist-packages (from uszipcode) (21.4.0)\n",
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            "Collecting fuzzywuzzy\n",
            "  Downloading fuzzywuzzy-0.18.0-py2.py3-none-any.whl (18 kB)\n",
            "Collecting haversine>=2.5.0\n",
            "  Downloading haversine-2.5.1-py2.py3-none-any.whl (6.1 kB)\n",
            "Requirement already satisfied: SQLAlchemy>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from uszipcode) (1.4.37)\n",
            "Collecting pathlib-mate\n",
            "  Downloading pathlib_mate-1.0.3-py2.py3-none-any.whl (120 kB)\n",
            "\u001b[K     |████████████████████████████████| 120 kB 36.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: atomicwrites in /usr/local/lib/python3.7/dist-packages (from uszipcode) (1.4.0)\n",
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            "Collecting autopep8\n",
            "  Downloading autopep8-1.6.0-py2.py3-none-any.whl (45 kB)\n",
            "\u001b[K     |████████████████████████████████| 45 kB 2.9 MB/s \n",
            "\u001b[?25hCollecting toml\n",
            "  Downloading toml-0.10.2-py2.py3-none-any.whl (16 kB)\n",
            "Collecting pycodestyle>=2.8.0\n",
            "  Downloading pycodestyle-2.8.0-py2.py3-none-any.whl (42 kB)\n",
            "\u001b[K     |████████████████████████████████| 42 kB 952 kB/s \n",
            "\u001b[?25hRequirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prettytable->sqlalchemy-mate>=1.4.28.3->uszipcode) (0.2.5)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->uszipcode) (2022.6.15)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->uszipcode) (1.24.3)\n",
            "Installing collected packages: toml, pycodestyle, autopep8, sqlalchemy-mate, pathlib-mate, haversine, fuzzywuzzy, uszipcode\n",
            "Successfully installed autopep8-1.6.0 fuzzywuzzy-0.18.0 haversine-2.5.1 pathlib-mate-1.0.3 pycodestyle-2.8.0 sqlalchemy-mate-1.4.28.3 toml-0.10.2 uszipcode-1.0.1\n"
          ]
        }
      ],
      "source": [
        "pip install uszipcode"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qDQ-SEQTTekb",
        "outputId": "c839a2ec-402a-4baa-e679-71cfd5adc370"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/fuzzywuzzy/fuzz.py:11: UserWarning: Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning\n",
            "  warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')\n"
          ]
        }
      ],
      "source": [
        "from uszipcode import SearchEngine"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "wGbZJD4TTfJV",
        "outputId": "3a763a6b-6f29-4864-99b1-c5e396fbf7ab"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Download /root/.uszipcode/simple_db.sqlite from https://github.com/MacHu-GWU/uszipcode-project/releases/download/1.0.1.db/simple_db.sqlite ...\n",
            "  1.00 MB downloaded ...\n",
            "  2.00 MB downloaded ...\n",
            "  3.00 MB downloaded ...\n",
            "  4.00 MB downloaded ...\n",
            "  5.00 MB downloaded ...\n",
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            "  7.00 MB downloaded ...\n",
            "  8.00 MB downloaded ...\n",
            "  9.00 MB downloaded ...\n",
            "  10.00 MB downloaded ...\n",
            "  11.00 MB downloaded ...\n",
            "  Complete!\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Washington'"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "engine = SearchEngine()\n",
        "engine.by_zipcode(20002).city"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "2jFHLm0yTiPB",
        "outputId": "e0620ce9-08b0-400e-edb9-88734e2849b3"
      },
      "outputs": [
        {
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            "text/plain": [
              "   GEOID10  Tax_Mjoint  Mjoint_MF  Mjoint_SS  Mjoint_FF  Mjoint_MM  \\\n",
              "0    90069        2120       1689        431         61        370   \n",
              "1    94114        5080       4036       1044        170        874   \n",
              "2    10011        5790       5166        624         97        527   \n",
              "3    10014        3510       3229        281         74        207   \n",
              "4    94103        2660       2417        243         34        209   \n",
              "\n",
              "   TaxRate_SS  TaxRate_FF  TaxRate_MM  Cns_TotHH  ...  FF_Index     MM_Tax  \\\n",
              "0  203.301887   28.773585  174.528302      12551  ...  6.724415  29.583721   \n",
              "1  205.511811   33.464567  172.047244      16456  ...  9.834048  29.163165   \n",
              "2  107.772021   16.753022   91.018998      29762  ...  4.370779  15.428332   \n",
              "3   80.056980   21.082621   58.974359      18786  ...  6.055939   9.996551   \n",
              "4   91.353383   12.781955   78.571429      12728  ...  3.004058  13.318386   \n",
              "\n",
              "      MM_Cns   MM_Index   SS_Index  SS_Index_Weight  Parade_Weight  \\\n",
              "0  18.704533  48.288254  55.012669        39.429995             10   \n",
              "1  19.415304  48.578469  58.412517        41.866815              0   \n",
              "2  10.932081  26.360413  30.731192        22.026394             10   \n",
              "3   5.943318  15.939869  21.995808        15.765361             10   \n",
              "4   4.961779  18.280165  21.284224        15.255337             10   \n",
              "\n",
              "   Bars_Weight   TOTINDEX            city  \n",
              "0    17.647059  67.077054  West Hollywood  \n",
              "1    20.000000  61.866815   San Francisco  \n",
              "2     5.882353  37.908747        New York  \n",
              "3    11.764706  37.530067        New York  \n",
              "4    10.588235  35.843573   San Francisco  \n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['city'] = [engine.by_zipcode(i).city for i in df['GEOID10']]\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
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          "height": 424
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        "id": "0W-6aUQFTlys",
        "outputId": "b402f61e-0b0a-4381-dc5c-e2f7c2d4fc4b"
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              "      <th>0</th>\n",
              "      <td>90069</td>\n",
              "      <td>1</td>\n",
              "      <td>West Hollywood</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>94114</td>\n",
              "      <td>0</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10011</td>\n",
              "      <td>1</td>\n",
              "      <td>New York</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10014</td>\n",
              "      <td>1</td>\n",
              "      <td>New York</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>94103</td>\n",
              "      <td>1</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2323</th>\n",
              "      <td>97208</td>\n",
              "      <td>0</td>\n",
              "      <td>Portland</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2324</th>\n",
              "      <td>98154</td>\n",
              "      <td>0</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2325</th>\n",
              "      <td>98158</td>\n",
              "      <td>0</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2326</th>\n",
              "      <td>98174</td>\n",
              "      <td>0</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2327</th>\n",
              "      <td>98195</td>\n",
              "      <td>0</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2328 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-54c15970-4a20-48a2-bb87-63abad813429')\"\n",
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              "\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-54c15970-4a20-48a2-bb87-63abad813429');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      GEOID10  ParadeFlag            city  CountBars\n",
              "0       90069           1  West Hollywood         15\n",
              "1       94114           0   San Francisco         17\n",
              "2       10011           1        New York          5\n",
              "3       10014           1        New York         10\n",
              "4       94103           1   San Francisco          9\n",
              "...       ...         ...             ...        ...\n",
              "2323    97208           0        Portland          0\n",
              "2324    98154           0         Seattle          0\n",
              "2325    98158           0         Seattle          0\n",
              "2326    98174           0         Seattle          0\n",
              "2327    98195           0         Seattle          0\n",
              "\n",
              "[2328 rows x 4 columns]"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "subset= df[['GEOID10', 'ParadeFlag', 'city', 'CountBars']]\n",
        "subset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "FCSj07ziTsfz"
      },
      "outputs": [],
      "source": [
        "towns_by_city = subset.groupby(['city', 'GEOID10']).mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 959
        },
        "id": "mNEIw_LiTwml",
        "outputId": "bfb38a56-e906-4f16-f89a-374b5ab8437b"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>ParadeFlag</th>\n",
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              "    <tr>\n",
              "      <th>GEOID10</th>\n",
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              "      <th>94158</th>\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ],
            "text/plain": [
              "         ParadeFlag  CountBars\n",
              "GEOID10                       \n",
              "94102           1.0        1.0\n",
              "94103           1.0        9.0\n",
              "94104           1.0        0.0\n",
              "94105           1.0        0.0\n",
              "94107           0.0        0.0\n",
              "94108           1.0        1.0\n",
              "94109           0.0        3.0\n",
              "94110           0.0        1.0\n",
              "94111           1.0        0.0\n",
              "94112           0.0        0.0\n",
              "94114           0.0       17.0\n",
              "94115           0.0        0.0\n",
              "94116           0.0        0.0\n",
              "94117           0.0        1.0\n",
              "94118           0.0        0.0\n",
              "94121           0.0        0.0\n",
              "94122           0.0        0.0\n",
              "94123           0.0        0.0\n",
              "94124           0.0        0.0\n",
              "94127           0.0        0.0\n",
              "94128           0.0        0.0\n",
              "94129           0.0        0.0\n",
              "94130           0.0        0.0\n",
              "94131           0.0        0.0\n",
              "94132           0.0        0.0\n",
              "94133           0.0        0.0\n",
              "94134           0.0        0.0\n",
              "94158           0.0        0.0"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "towns_by_city.loc['San Francisco']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "bbeQABHcT89D"
      },
      "outputs": [],
      "source": [
        "import folium"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "NZuGYbnyUNLK"
      },
      "outputs": [],
      "source": [
        "geo_file = 'https://raw.githubusercontent.com/python-visualization/folium/main/examples/data/us-states.json'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qLWTNTXZUXcb",
        "outputId": "7084a983-e824-410d-b0d8-3cf046a1c89b"
      },
      "outputs": [
        {
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              "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.4.0/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://code.jquery.com/jquery-1.12.4.min.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.4.0/dist/leaflet.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://rawcdn.githack.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css&quot;/&gt;\n",
              "    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n",
              "    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n",
              "    \n",
              "    &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
              "        initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
              "    &lt;style&gt;#map_d0deafcc6e55469bc85264e0fcb7fd3c {\n",
              "        position: relative;\n",
              "        width: 100.0%;\n",
              "        height: 100.0%;\n",
              "        left: 0.0%;\n",
              "        top: 0.0%;\n",
              "        }\n",
              "    &lt;/style&gt;\n",
              "&lt;/head&gt;\n",
              "&lt;body&gt;    \n",
              "    \n",
              "    &lt;div class=&quot;folium-map&quot; id=&quot;map_d0deafcc6e55469bc85264e0fcb7fd3c&quot; &gt;&lt;/div&gt;\n",
              "&lt;/body&gt;\n",
              "&lt;script&gt;    \n",
              "    \n",
              "    \n",
              "        var bounds = null;\n",
              "    \n",
              "\n",
              "    var map_d0deafcc6e55469bc85264e0fcb7fd3c = L.map(\n",
              "        &#x27;map_d0deafcc6e55469bc85264e0fcb7fd3c&#x27;, {\n",
              "        center: [40, -95],\n",
              "        zoom: 4,\n",
              "        maxBounds: bounds,\n",
              "        layers: [],\n",
              "        worldCopyJump: false,\n",
              "        crs: L.CRS.EPSG3857,\n",
              "        zoomControl: true,\n",
              "        });\n",
              "\n",
              "\n",
              "    \n",
              "    var tile_layer_21cd2573b02ad887b0b0766ea4c73e43 = L.tileLayer(\n",
              "        &#x27;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&#x27;,\n",
              "        {\n",
              "        &quot;attribution&quot;: null,\n",
              "        &quot;detectRetina&quot;: false,\n",
              "        &quot;maxNativeZoom&quot;: 18,\n",
              "        &quot;maxZoom&quot;: 18,\n",
              "        &quot;minZoom&quot;: 0,\n",
              "        &quot;noWrap&quot;: false,\n",
              "        &quot;opacity&quot;: 1,\n",
              "        &quot;subdomains&quot;: &quot;abc&quot;,\n",
              "        &quot;tms&quot;: false\n",
              "}).addTo(map_d0deafcc6e55469bc85264e0fcb7fd3c);\n",
              "&lt;/script&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
            ],
            "text/plain": [
              "<folium.folium.Map at 0x7fa30d215ad0>"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "m = folium.Map([40, -95], zoom_start= 4)\n",
        "m"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "FchZePWQUZx1",
        "outputId": "7191e863-c404-4998-fe66-8cae7beb715f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
              "&lt;head&gt;    \n",
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              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css&quot;/&gt;\n",
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              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://rawcdn.githack.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css&quot;/&gt;\n",
              "    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n",
              "    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n",
              "    \n",
              "    &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
              "        initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
              "    &lt;style&gt;#map_08251190fda21bd1c28f9db8a33f3f4c {\n",
              "        position: relative;\n",
              "        width: 100.0%;\n",
              "        height: 100.0%;\n",
              "        left: 0.0%;\n",
              "        top: 0.0%;\n",
              "        }\n",
              "    &lt;/style&gt;\n",
              "&lt;/head&gt;\n",
              "&lt;body&gt;    \n",
              "    \n",
              "    &lt;div class=&quot;folium-map&quot; id=&quot;map_08251190fda21bd1c28f9db8a33f3f4c&quot; &gt;&lt;/div&gt;\n",
              "&lt;/body&gt;\n",
              "&lt;script&gt;    \n",
              "    \n",
              "    \n",
              "        var bounds = null;\n",
              "    \n",
              "\n",
              "    var map_08251190fda21bd1c28f9db8a33f3f4c = L.map(\n",
              "        &#x27;map_08251190fda21bd1c28f9db8a33f3f4c&#x27;, {\n",
              "        center: [40, -95],\n",
              "        zoom: 4,\n",
              "        maxBounds: bounds,\n",
              "        layers: [],\n",
              "        worldCopyJump: false,\n",
              "        crs: L.CRS.EPSG3857,\n",
              "        zoomControl: true,\n",
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              "    \n",
              "    var tile_layer_f9f6624089459cf54b44007862720d94 = L.tileLayer(\n",
              "        &#x27;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&#x27;,\n",
              "        {\n",
              "        &quot;attribution&quot;: null,\n",
              "        &quot;detectRetina&quot;: false,\n",
              "        &quot;maxNativeZoom&quot;: 18,\n",
              "        &quot;maxZoom&quot;: 18,\n",
              "        &quot;minZoom&quot;: 0,\n",
              "        &quot;noWrap&quot;: false,\n",
              "        &quot;opacity&quot;: 1,\n",
              "        &quot;subdomains&quot;: &quot;abc&quot;,\n",
              "        &quot;tms&quot;: false\n",
              "}).addTo(map_08251190fda21bd1c28f9db8a33f3f4c);\n",
              "    \n",
              "            var choropleth_1e9ef3c820ce7d7598f4b4563d4656f5 = L.featureGroup(\n",
              "                ).addTo(map_08251190fda21bd1c28f9db8a33f3f4c);\n",
              "        \n",
              "    \n",
              "        var geo_json_e1c6ef2e0513f580cd2441bd20385e2a = L.geoJson(\n",
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              "            {\n",
              "            }\n",
              "        ).addTo(choropleth_1e9ef3c820ce7d7598f4b4563d4656f5 );\n",
              "        geo_json_e1c6ef2e0513f580cd2441bd20385e2a.setStyle(function(feature) {return feature.properties.style;});\n",
              "        \n",
              "&lt;/script&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
            ],
            "text/plain": [
              "<folium.folium.Map at 0x7fa30d2c8a50>"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "m = folium.Map([40, -95], zoom_start= 4)\n",
        "\n",
        "folium.Choropleth(geo_data = geo_file).add_to(m)\n",
        "m"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "yXp4dNGHUsLD",
        "outputId": "60e81cb7-d975-4594-ecf0-c4d8b97664d2"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-57fbfae3-6816-4488-8dac-9daddf725804\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GEOID10</th>\n",
              "      <th>ParadeFlag</th>\n",
              "      <th>city</th>\n",
              "      <th>CountBars</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>90069</td>\n",
              "      <td>1</td>\n",
              "      <td>West Hollywood</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>94114</td>\n",
              "      <td>0</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10011</td>\n",
              "      <td>1</td>\n",
              "      <td>New York</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10014</td>\n",
              "      <td>1</td>\n",
              "      <td>New York</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>94103</td>\n",
              "      <td>1</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2323</th>\n",
              "      <td>97208</td>\n",
              "      <td>0</td>\n",
              "      <td>Portland</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2324</th>\n",
              "      <td>98154</td>\n",
              "      <td>0</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2325</th>\n",
              "      <td>98158</td>\n",
              "      <td>0</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2326</th>\n",
              "      <td>98174</td>\n",
              "      <td>0</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2327</th>\n",
              "      <td>98195</td>\n",
              "      <td>0</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2328 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-57fbfae3-6816-4488-8dac-9daddf725804')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      fill: #1967D2;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-57fbfae3-6816-4488-8dac-9daddf725804 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-57fbfae3-6816-4488-8dac-9daddf725804');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      GEOID10  ParadeFlag            city  CountBars\n",
              "0       90069           1  West Hollywood         15\n",
              "1       94114           0   San Francisco         17\n",
              "2       10011           1        New York          5\n",
              "3       10014           1        New York         10\n",
              "4       94103           1   San Francisco          9\n",
              "...       ...         ...             ...        ...\n",
              "2323    97208           0        Portland          0\n",
              "2324    98154           0         Seattle          0\n",
              "2325    98158           0         Seattle          0\n",
              "2326    98174           0         Seattle          0\n",
              "2327    98195           0         Seattle          0\n",
              "\n",
              "[2328 rows x 4 columns]"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "subset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "sitXWmy7U6ik",
        "outputId": "19f29091-0614-443c-df6a-5436e1aafe03"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'DC'"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "engine = SearchEngine()\n",
        "engine.by_zipcode(20002).state"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "Tw5x5pBCU_vg",
        "outputId": "d7c63f97-56f4-40dd-9377-aa7627acdcdc"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GEOID10</th>\n",
              "      <th>ParadeFlag</th>\n",
              "      <th>city</th>\n",
              "      <th>CountBars</th>\n",
              "      <th>state</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <th>2</th>\n",
              "      <td>10011</td>\n",
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              "      <td>10014</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7fe3bd0e-c453-484b-9139-ea7421efb839')\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7fe3bd0e-c453-484b-9139-ea7421efb839');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   GEOID10  ParadeFlag            city  CountBars state\n",
              "0    90069           1  West Hollywood         15    CA\n",
              "1    94114           0   San Francisco         17    CA\n",
              "2    10011           1        New York          5    NY\n",
              "3    10014           1        New York         10    NY\n",
              "4    94103           1   San Francisco          9    CA"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "subset['state'] = [engine.by_zipcode(i).state for i in subset['GEOID10']]\n",
        "subset.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "2PV_yEh0VHwm",
        "outputId": "3bb73193-abdf-4e4e-8bc9-903d19d0213c"
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      "outputs": [
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              "      <th>3</th>\n",
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              "      <td>3</td>\n",
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              "      <th>4</th>\n",
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              "      <th>13</th>\n",
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              "      <th>17</th>\n",
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              "      <th>18</th>\n",
              "      <td>WA</td>\n",
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              "\n",
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              "  "
            ],
            "text/plain": [
              "   state  ParadeFlag\n",
              "0     CA           9\n",
              "1     CO           2\n",
              "2     CT           0\n",
              "3     DC           3\n",
              "4     FL           1\n",
              "5     GA           2\n",
              "6     IL           3\n",
              "7     IN           0\n",
              "8     LA           3\n",
              "9     MA           3\n",
              "10    MD           0\n",
              "11    MS           0\n",
              "12    NJ           0\n",
              "13    NY           6\n",
              "14    OR           2\n",
              "15    PA           2\n",
              "16    TX           2\n",
              "17    VA           0\n",
              "18    WA           3"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "parade_zipcode = pd.DataFrame(subset.groupby('state')['ParadeFlag'].sum()).reset_index()\n",
        "\n",
        "\n",
        "parade_zipcode"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
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          "height": 1000
        },
        "id": "s8ogFRE9VLo-",
        "outputId": "c760deb2-e5b4-423b-b9ab-bd473cfbc151"
      },
      "outputs": [
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              "    \n",
              "\n",
              "    color_map_8729ee17c292bc732ce751abe4c991e7.x = d3.scale.linear()\n",
              "              .domain([0.0, 9.0])\n",
              "              .range([0, 400]);\n",
              "\n",
              "    color_map_8729ee17c292bc732ce751abe4c991e7.legend = L.control({position: &#x27;topright&#x27;});\n",
              "    color_map_8729ee17c292bc732ce751abe4c991e7.legend.onAdd = function (map) {var div = L.DomUtil.create(&#x27;div&#x27;, &#x27;legend&#x27;); return div};\n",
              "    color_map_8729ee17c292bc732ce751abe4c991e7.legend.addTo(map_32154ac718b562e435c3e69fff5b03a8);\n",
              "\n",
              "    color_map_8729ee17c292bc732ce751abe4c991e7.xAxis = d3.svg.axis()\n",
              "        .scale(color_map_8729ee17c292bc732ce751abe4c991e7.x)\n",
              "        .orient(&quot;top&quot;)\n",
              "        .tickSize(1)\n",
              "        .tickValues([0.0, 1.5, 3.0, 4.5, 6.0, 7.5, 9.0]);\n",
              "\n",
              "    color_map_8729ee17c292bc732ce751abe4c991e7.svg = d3.select(&quot;.legend.leaflet-control&quot;).append(&quot;svg&quot;)\n",
              "        .attr(&quot;id&quot;, &#x27;legend&#x27;)\n",
              "        .attr(&quot;width&quot;, 450)\n",
              "        .attr(&quot;height&quot;, 40);\n",
              "\n",
              "    color_map_8729ee17c292bc732ce751abe4c991e7.g = color_map_8729ee17c292bc732ce751abe4c991e7.svg.append(&quot;g&quot;)\n",
              "        .attr(&quot;class&quot;, &quot;key&quot;)\n",
              "        .attr(&quot;transform&quot;, &quot;translate(25,16)&quot;);\n",
              "\n",
              "    color_map_8729ee17c292bc732ce751abe4c991e7.g.selectAll(&quot;rect&quot;)\n",
              "        .data(color_map_8729ee17c292bc732ce751abe4c991e7.color.range().map(function(d, i) {\n",
              "          return {\n",
              "            x0: i ? color_map_8729ee17c292bc732ce751abe4c991e7.x(color_map_8729ee17c292bc732ce751abe4c991e7.color.domain()[i - 1]) : color_map_8729ee17c292bc732ce751abe4c991e7.x.range()[0],\n",
              "            x1: i &lt; color_map_8729ee17c292bc732ce751abe4c991e7.color.domain().length ? color_map_8729ee17c292bc732ce751abe4c991e7.x(color_map_8729ee17c292bc732ce751abe4c991e7.color.domain()[i]) : color_map_8729ee17c292bc732ce751abe4c991e7.x.range()[1],\n",
              "            z: d\n",
              "          };\n",
              "        }))\n",
              "      .enter().append(&quot;rect&quot;)\n",
              "        .attr(&quot;height&quot;, 10)\n",
              "        .attr(&quot;x&quot;, function(d) { return d.x0; })\n",
              "        .attr(&quot;width&quot;, function(d) { return d.x1 - d.x0; })\n",
              "        .style(&quot;fill&quot;, function(d) { return d.z; });\n",
              "\n",
              "    color_map_8729ee17c292bc732ce751abe4c991e7.g.call(color_map_8729ee17c292bc732ce751abe4c991e7.xAxis).append(&quot;text&quot;)\n",
              "        .attr(&quot;class&quot;, &quot;caption&quot;)\n",
              "        .attr(&quot;y&quot;, 21)\n",
              "        .text(&#x27;&#x27;);\n",
              "    \n",
              "            var layer_control_166311911bec3560c0d329dd685f6ed8 = {\n",
              "                base_layers : { &quot;openstreetmap&quot; : tile_layer_502c6f57f41abc00b999ce217ea546e9, },\n",
              "                overlays : { &quot;choropleth&quot; : choropleth_e9dcb4ee811cce3d808124c7f9151f2f, }\n",
              "                };\n",
              "            L.control.layers(\n",
              "                layer_control_166311911bec3560c0d329dd685f6ed8.base_layers,\n",
              "                layer_control_166311911bec3560c0d329dd685f6ed8.overlays,\n",
              "                {position: &#x27;topright&#x27;,\n",
              "                 collapsed: true,\n",
              "                 autoZIndex: true\n",
              "                }).addTo(map_32154ac718b562e435c3e69fff5b03a8);\n",
              "            \n",
              "        \n",
              "&lt;/script&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
            ],
            "text/plain": [
              "<folium.folium.Map at 0x7fa30d143a90>"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "m = folium.Map([40, -95], zoom_start= 4)\n",
        "\n",
        "folium.Choropleth(geo_data = geo_file,\n",
        "                  name=\"choropleth\",\n",
        "                  data= parade_zipcode,\n",
        "                  columns=['state', 'ParadeFlag'],\n",
        "                  key_on=\"feature.id\",\n",
        "                  fill_color = \"Blues\", \n",
        "                  nan_fill_color = 'White', ).add_to(m)\n",
        "\n",
        "folium.LayerControl().add_to(m)\n",
        "m"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2NWuQMBtVP0r"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "name": "pride_themed_folium_answers",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}