playground.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from shapely.wkt import loads\n",
    "import geopandas as gp\n",
    "import pandas as p\n",
    "from shapely.geometry import Point\n",
    "import matplotlib.pyplot as plt\n",
    "import geodatasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>Store Name</th>\n",
       "      <th>Address</th>\n",
       "      <th>Zip</th>\n",
       "      <th>New status</th>\n",
       "      <th>Last updated</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jewel - Osco</td>\n",
       "      <td>87 W 87th St</td>\n",
       "      <td>60620</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/3/2020 17:00</td>\n",
       "      <td>POINT (-87.626243 41.736172)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Farm on Ogden</td>\n",
       "      <td>3555 W OGDEN AVE</td>\n",
       "      <td>60623</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/10/2020 0:00</td>\n",
       "      <td>POINT (-87.71437 41.854608)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jewel - Osco</td>\n",
       "      <td>5343 N Broadway St</td>\n",
       "      <td>60640-2311</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/3/2020 17:00</td>\n",
       "      <td>POINT (-87.659887 41.978998)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>International Foods NW</td>\n",
       "      <td>4404 W FULLERTON AVE</td>\n",
       "      <td>60639</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/10/2020 0:00</td>\n",
       "      <td>POINT (-87.737127 41.924425)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jewel - Osco</td>\n",
       "      <td>2520 N Narragansett Ave</td>\n",
       "      <td>60639-1041</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/3/2020 17:00</td>\n",
       "      <td>POINT (-87.785559 41.926236)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>Fat Lee Supermarket</td>\n",
       "      <td>2707 S WELLS ST</td>\n",
       "      <td>60616</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/10/2020 0:00</td>\n",
       "      <td>POINT (-87.633033 41.843813)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>Aldi</td>\n",
       "      <td>6220 N California Ave</td>\n",
       "      <td>60659-2608</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/4/2020 15:00</td>\n",
       "      <td>POINT (-87.699541 41.994349)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>Fair Share Finer Foods</td>\n",
       "      <td>6422 W 63rd Street</td>\n",
       "      <td>60638</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/4/2020 15:00</td>\n",
       "      <td>POINT (-87.782075 41.777702)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>Avenue Food Mart</td>\n",
       "      <td>6850 W BELMONT AVE</td>\n",
       "      <td>60634</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/10/2020 0:00</td>\n",
       "      <td>POINT (-87.79769200000001 41.938134)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>Lindo Michoacan</td>\n",
       "      <td>3142 W LAWRENCE  AVE</td>\n",
       "      <td>60625</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/10/2020 0:00</td>\n",
       "      <td>POINT (-87.707908 41.968473)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>254 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Store Name                  Address         Zip New status  \\\n",
       "0              Jewel - Osco             87 W 87th St       60620       OPEN   \n",
       "1             Farm on Ogden         3555 W OGDEN AVE       60623       OPEN   \n",
       "2              Jewel - Osco       5343 N Broadway St  60640-2311       OPEN   \n",
       "3    International Foods NW     4404 W FULLERTON AVE       60639       OPEN   \n",
       "4              Jewel - Osco  2520 N Narragansett Ave  60639-1041       OPEN   \n",
       "..                      ...                      ...         ...        ...   \n",
       "259     Fat Lee Supermarket          2707 S WELLS ST       60616       OPEN   \n",
       "260                    Aldi    6220 N California Ave  60659-2608       OPEN   \n",
       "261  Fair Share Finer Foods       6422 W 63rd Street       60638       OPEN   \n",
       "262        Avenue Food Mart       6850 W BELMONT AVE       60634       OPEN   \n",
       "263         Lindo Michoacan     3142 W LAWRENCE  AVE       60625       OPEN   \n",
       "\n",
       "       Last updated                              Location  \n",
       "0    6/3/2020 17:00          POINT (-87.626243 41.736172)  \n",
       "1    6/10/2020 0:00           POINT (-87.71437 41.854608)  \n",
       "2    6/3/2020 17:00          POINT (-87.659887 41.978998)  \n",
       "3    6/10/2020 0:00          POINT (-87.737127 41.924425)  \n",
       "4    6/3/2020 17:00          POINT (-87.785559 41.926236)  \n",
       "..              ...                                   ...  \n",
       "259  6/10/2020 0:00          POINT (-87.633033 41.843813)  \n",
       "260  6/4/2020 15:00          POINT (-87.699541 41.994349)  \n",
       "261  6/4/2020 15:00          POINT (-87.782075 41.777702)  \n",
       "262  6/10/2020 0:00  POINT (-87.79769200000001 41.938134)  \n",
       "263  6/10/2020 0:00          POINT (-87.707908 41.968473)  \n",
       "\n",
       "[254 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('GroceryStore.csv')\n",
    "df = df.dropna()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store Name</th>\n",
       "      <th>Address</th>\n",
       "      <th>Zip</th>\n",
       "      <th>New status</th>\n",
       "      <th>Last updated</th>\n",
       "      <th>Location</th>\n",
       "      <th>points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jewel - Osco</td>\n",
       "      <td>87 W 87th St</td>\n",
       "      <td>60620</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/3/2020 17:00</td>\n",
       "      <td>POINT (-87.626243 41.736172)</td>\n",
       "      <td>POINT (-87.626243 41.736172)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Farm on Ogden</td>\n",
       "      <td>3555 W OGDEN AVE</td>\n",
       "      <td>60623</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/10/2020 0:00</td>\n",
       "      <td>POINT (-87.71437 41.854608)</td>\n",
       "      <td>POINT (-87.71437 41.854608)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jewel - Osco</td>\n",
       "      <td>5343 N Broadway St</td>\n",
       "      <td>60640-2311</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/3/2020 17:00</td>\n",
       "      <td>POINT (-87.659887 41.978998)</td>\n",
       "      <td>POINT (-87.659887 41.978998)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>International Foods NW</td>\n",
       "      <td>4404 W FULLERTON AVE</td>\n",
       "      <td>60639</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/10/2020 0:00</td>\n",
       "      <td>POINT (-87.737127 41.924425)</td>\n",
       "      <td>POINT (-87.737127 41.924425)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jewel - Osco</td>\n",
       "      <td>2520 N Narragansett Ave</td>\n",
       "      <td>60639-1041</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/3/2020 17:00</td>\n",
       "      <td>POINT (-87.785559 41.926236)</td>\n",
       "      <td>POINT (-87.785559 41.926236)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>Fat Lee Supermarket</td>\n",
       "      <td>2707 S WELLS ST</td>\n",
       "      <td>60616</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/10/2020 0:00</td>\n",
       "      <td>POINT (-87.633033 41.843813)</td>\n",
       "      <td>POINT (-87.633033 41.843813)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>Aldi</td>\n",
       "      <td>6220 N California Ave</td>\n",
       "      <td>60659-2608</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/4/2020 15:00</td>\n",
       "      <td>POINT (-87.699541 41.994349)</td>\n",
       "      <td>POINT (-87.699541 41.994349)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>Fair Share Finer Foods</td>\n",
       "      <td>6422 W 63rd Street</td>\n",
       "      <td>60638</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/4/2020 15:00</td>\n",
       "      <td>POINT (-87.782075 41.777702)</td>\n",
       "      <td>POINT (-87.782075 41.777702)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>Avenue Food Mart</td>\n",
       "      <td>6850 W BELMONT AVE</td>\n",
       "      <td>60634</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/10/2020 0:00</td>\n",
       "      <td>POINT (-87.79769200000001 41.938134)</td>\n",
       "      <td>POINT (-87.79769200000001 41.938134)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>Lindo Michoacan</td>\n",
       "      <td>3142 W LAWRENCE  AVE</td>\n",
       "      <td>60625</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/10/2020 0:00</td>\n",
       "      <td>POINT (-87.707908 41.968473)</td>\n",
       "      <td>POINT (-87.707908 41.968473)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>254 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Store Name                  Address         Zip New status  \\\n",
       "0              Jewel - Osco             87 W 87th St       60620       OPEN   \n",
       "1             Farm on Ogden         3555 W OGDEN AVE       60623       OPEN   \n",
       "2              Jewel - Osco       5343 N Broadway St  60640-2311       OPEN   \n",
       "3    International Foods NW     4404 W FULLERTON AVE       60639       OPEN   \n",
       "4              Jewel - Osco  2520 N Narragansett Ave  60639-1041       OPEN   \n",
       "..                      ...                      ...         ...        ...   \n",
       "259     Fat Lee Supermarket          2707 S WELLS ST       60616       OPEN   \n",
       "260                    Aldi    6220 N California Ave  60659-2608       OPEN   \n",
       "261  Fair Share Finer Foods       6422 W 63rd Street       60638       OPEN   \n",
       "262        Avenue Food Mart       6850 W BELMONT AVE       60634       OPEN   \n",
       "263         Lindo Michoacan     3142 W LAWRENCE  AVE       60625       OPEN   \n",
       "\n",
       "       Last updated                              Location  \\\n",
       "0    6/3/2020 17:00          POINT (-87.626243 41.736172)   \n",
       "1    6/10/2020 0:00           POINT (-87.71437 41.854608)   \n",
       "2    6/3/2020 17:00          POINT (-87.659887 41.978998)   \n",
       "3    6/10/2020 0:00          POINT (-87.737127 41.924425)   \n",
       "4    6/3/2020 17:00          POINT (-87.785559 41.926236)   \n",
       "..              ...                                   ...   \n",
       "259  6/10/2020 0:00          POINT (-87.633033 41.843813)   \n",
       "260  6/4/2020 15:00          POINT (-87.699541 41.994349)   \n",
       "261  6/4/2020 15:00          POINT (-87.782075 41.777702)   \n",
       "262  6/10/2020 0:00  POINT (-87.79769200000001 41.938134)   \n",
       "263  6/10/2020 0:00          POINT (-87.707908 41.968473)   \n",
       "\n",
       "                                   points  \n",
       "0            POINT (-87.626243 41.736172)  \n",
       "1             POINT (-87.71437 41.854608)  \n",
       "2            POINT (-87.659887 41.978998)  \n",
       "3            POINT (-87.737127 41.924425)  \n",
       "4            POINT (-87.785559 41.926236)  \n",
       "..                                    ...  \n",
       "259          POINT (-87.633033 41.843813)  \n",
       "260          POINT (-87.699541 41.994349)  \n",
       "261          POINT (-87.782075 41.777702)  \n",
       "262  POINT (-87.79769200000001 41.938134)  \n",
       "263          POINT (-87.707908 41.968473)  \n",
       "\n",
       "[254 rows x 7 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['points'] = df['Location'].apply(loads)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store Name</th>\n",
       "      <th>Address</th>\n",
       "      <th>Zip</th>\n",
       "      <th>New status</th>\n",
       "      <th>Last updated</th>\n",
       "      <th>Location</th>\n",
       "      <th>points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jewel - Osco</td>\n",
       "      <td>87 W 87th St</td>\n",
       "      <td>60620</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/3/2020 17:00</td>\n",
       "      <td>POINT (-87.626243 41.736172)</td>\n",
       "      <td>POINT (-87.62624 41.73617)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Farm on Ogden</td>\n",
       "      <td>3555 W OGDEN AVE</td>\n",
       "      <td>60623</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/10/2020 0:00</td>\n",
       "      <td>POINT (-87.71437 41.854608)</td>\n",
       "      <td>POINT (-87.71437 41.85461)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jewel - Osco</td>\n",
       "      <td>5343 N Broadway St</td>\n",
       "      <td>60640-2311</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/3/2020 17:00</td>\n",
       "      <td>POINT (-87.659887 41.978998)</td>\n",
       "      <td>POINT (-87.65989 41.97900)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>International Foods NW</td>\n",
       "      <td>4404 W FULLERTON AVE</td>\n",
       "      <td>60639</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/10/2020 0:00</td>\n",
       "      <td>POINT (-87.737127 41.924425)</td>\n",
       "      <td>POINT (-87.73713 41.92442)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jewel - Osco</td>\n",
       "      <td>2520 N Narragansett Ave</td>\n",
       "      <td>60639-1041</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/3/2020 17:00</td>\n",
       "      <td>POINT (-87.785559 41.926236)</td>\n",
       "      <td>POINT (-87.78556 41.92624)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>Fat Lee Supermarket</td>\n",
       "      <td>2707 S WELLS ST</td>\n",
       "      <td>60616</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/10/2020 0:00</td>\n",
       "      <td>POINT (-87.633033 41.843813)</td>\n",
       "      <td>POINT (-87.63303 41.84381)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>Aldi</td>\n",
       "      <td>6220 N California Ave</td>\n",
       "      <td>60659-2608</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/4/2020 15:00</td>\n",
       "      <td>POINT (-87.699541 41.994349)</td>\n",
       "      <td>POINT (-87.69954 41.99435)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>Fair Share Finer Foods</td>\n",
       "      <td>6422 W 63rd Street</td>\n",
       "      <td>60638</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/4/2020 15:00</td>\n",
       "      <td>POINT (-87.782075 41.777702)</td>\n",
       "      <td>POINT (-87.78208 41.77770)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>Avenue Food Mart</td>\n",
       "      <td>6850 W BELMONT AVE</td>\n",
       "      <td>60634</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/10/2020 0:00</td>\n",
       "      <td>POINT (-87.79769200000001 41.938134)</td>\n",
       "      <td>POINT (-87.79769 41.93813)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>Lindo Michoacan</td>\n",
       "      <td>3142 W LAWRENCE  AVE</td>\n",
       "      <td>60625</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>6/10/2020 0:00</td>\n",
       "      <td>POINT (-87.707908 41.968473)</td>\n",
       "      <td>POINT (-87.70791 41.96847)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>254 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Store Name                  Address         Zip New status  \\\n",
       "0              Jewel - Osco             87 W 87th St       60620       OPEN   \n",
       "1             Farm on Ogden         3555 W OGDEN AVE       60623       OPEN   \n",
       "2              Jewel - Osco       5343 N Broadway St  60640-2311       OPEN   \n",
       "3    International Foods NW     4404 W FULLERTON AVE       60639       OPEN   \n",
       "4              Jewel - Osco  2520 N Narragansett Ave  60639-1041       OPEN   \n",
       "..                      ...                      ...         ...        ...   \n",
       "259     Fat Lee Supermarket          2707 S WELLS ST       60616       OPEN   \n",
       "260                    Aldi    6220 N California Ave  60659-2608       OPEN   \n",
       "261  Fair Share Finer Foods       6422 W 63rd Street       60638       OPEN   \n",
       "262        Avenue Food Mart       6850 W BELMONT AVE       60634       OPEN   \n",
       "263         Lindo Michoacan     3142 W LAWRENCE  AVE       60625       OPEN   \n",
       "\n",
       "       Last updated                              Location  \\\n",
       "0    6/3/2020 17:00          POINT (-87.626243 41.736172)   \n",
       "1    6/10/2020 0:00           POINT (-87.71437 41.854608)   \n",
       "2    6/3/2020 17:00          POINT (-87.659887 41.978998)   \n",
       "3    6/10/2020 0:00          POINT (-87.737127 41.924425)   \n",
       "4    6/3/2020 17:00          POINT (-87.785559 41.926236)   \n",
       "..              ...                                   ...   \n",
       "259  6/10/2020 0:00          POINT (-87.633033 41.843813)   \n",
       "260  6/4/2020 15:00          POINT (-87.699541 41.994349)   \n",
       "261  6/4/2020 15:00          POINT (-87.782075 41.777702)   \n",
       "262  6/10/2020 0:00  POINT (-87.79769200000001 41.938134)   \n",
       "263  6/10/2020 0:00          POINT (-87.707908 41.968473)   \n",
       "\n",
       "                         points  \n",
       "0    POINT (-87.62624 41.73617)  \n",
       "1    POINT (-87.71437 41.85461)  \n",
       "2    POINT (-87.65989 41.97900)  \n",
       "3    POINT (-87.73713 41.92442)  \n",
       "4    POINT (-87.78556 41.92624)  \n",
       "..                          ...  \n",
       "259  POINT (-87.63303 41.84381)  \n",
       "260  POINT (-87.69954 41.99435)  \n",
       "261  POINT (-87.78208 41.77770)  \n",
       "262  POINT (-87.79769 41.93813)  \n",
       "263  POINT (-87.70791 41.96847)  \n",
       "\n",
       "[254 rows x 7 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapPoints = gp.GeoDataFrame(df, geometry=\"points\")\n",
    "mapPoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "shapefile=r\"geo_export_2de1f329-0bd0-4922-a95b-6cc4d6a6d59e.shp\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>area_num_1</th>\n",
       "      <th>area_numbe</th>\n",
       "      <th>comarea</th>\n",
       "      <th>comarea_id</th>\n",
       "      <th>community</th>\n",
       "      <th>perimeter</th>\n",
       "      <th>shape_area</th>\n",
       "      <th>shape_len</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0.0</td>\n",
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       "      <td>35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>DOUGLAS</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.600462e+07</td>\n",
       "      <td>31027.054510</td>\n",
       "      <td>POLYGON ((-87.60914 41.84469, -87.60915 41.844...</td>\n",
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       "      <th>1</th>\n",
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       "      <td>36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>OAKLAND</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.691396e+07</td>\n",
       "      <td>19565.506153</td>\n",
       "      <td>POLYGON ((-87.59215 41.81693, -87.59231 41.816...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>37</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>FULLER PARK</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.991670e+07</td>\n",
       "      <td>25339.089750</td>\n",
       "      <td>POLYGON ((-87.62880 41.80189, -87.62879 41.801...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>GRAND BOULEVARD</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.849250e+07</td>\n",
       "      <td>28196.837157</td>\n",
       "      <td>POLYGON ((-87.60671 41.81681, -87.60670 41.816...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>KENWOOD</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.907174e+07</td>\n",
       "      <td>23325.167906</td>\n",
       "      <td>POLYGON ((-87.59215 41.81693, -87.59215 41.816...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>72</th>\n",
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       "      <td>74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>MOUNT GREENWOOD</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.558429e+07</td>\n",
       "      <td>48665.130539</td>\n",
       "      <td>POLYGON ((-87.69646 41.70714, -87.69644 41.706...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>MORGAN PARK</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.187734e+07</td>\n",
       "      <td>46396.419362</td>\n",
       "      <td>POLYGON ((-87.64215 41.68508, -87.64249 41.685...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>0.0</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>OHARE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.718356e+08</td>\n",
       "      <td>173625.984660</td>\n",
       "      <td>MULTIPOLYGON (((-87.83658 41.98640, -87.83658 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>0.0</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>EDGEWATER</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.844999e+07</td>\n",
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       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>EDISON PARK</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.163631e+07</td>\n",
       "      <td>25937.226841</td>\n",
       "      <td>POLYGON ((-87.80676 42.00084, -87.80676 42.000...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    area area_num_1 area_numbe  comarea  comarea_id        community  \\\n",
       "0    0.0         35         35      0.0         0.0          DOUGLAS   \n",
       "1    0.0         36         36      0.0         0.0          OAKLAND   \n",
       "2    0.0         37         37      0.0         0.0      FULLER PARK   \n",
       "3    0.0         38         38      0.0         0.0  GRAND BOULEVARD   \n",
       "4    0.0         39         39      0.0         0.0          KENWOOD   \n",
       "..   ...        ...        ...      ...         ...              ...   \n",
       "72   0.0         74         74      0.0         0.0  MOUNT GREENWOOD   \n",
       "73   0.0         75         75      0.0         0.0      MORGAN PARK   \n",
       "74   0.0         76         76      0.0         0.0            OHARE   \n",
       "75   0.0         77         77      0.0         0.0        EDGEWATER   \n",
       "76   0.0          9          9      0.0         0.0      EDISON PARK   \n",
       "\n",
       "    perimeter    shape_area      shape_len  \\\n",
       "0         0.0  4.600462e+07   31027.054510   \n",
       "1         0.0  1.691396e+07   19565.506153   \n",
       "2         0.0  1.991670e+07   25339.089750   \n",
       "3         0.0  4.849250e+07   28196.837157   \n",
       "4         0.0  2.907174e+07   23325.167906   \n",
       "..        ...           ...            ...   \n",
       "72        0.0  7.558429e+07   48665.130539   \n",
       "73        0.0  9.187734e+07   46396.419362   \n",
       "74        0.0  3.718356e+08  173625.984660   \n",
       "75        0.0  4.844999e+07   31004.830946   \n",
       "76        0.0  3.163631e+07   25937.226841   \n",
       "\n",
       "                                             geometry  \n",
       "0   POLYGON ((-87.60914 41.84469, -87.60915 41.844...  \n",
       "1   POLYGON ((-87.59215 41.81693, -87.59231 41.816...  \n",
       "2   POLYGON ((-87.62880 41.80189, -87.62879 41.801...  \n",
       "3   POLYGON ((-87.60671 41.81681, -87.60670 41.816...  \n",
       "4   POLYGON ((-87.59215 41.81693, -87.59215 41.816...  \n",
       "..                                                ...  \n",
       "72  POLYGON ((-87.69646 41.70714, -87.69644 41.706...  \n",
       "73  POLYGON ((-87.64215 41.68508, -87.64249 41.685...  \n",
       "74  MULTIPOLYGON (((-87.83658 41.98640, -87.83658 ...  \n",
       "75  POLYGON ((-87.65456 41.99817, -87.65456 41.998...  \n",
       "76  POLYGON ((-87.80676 42.00084, -87.80676 42.000...  \n",
       "\n",
       "[77 rows x 10 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base = gp.read_file(shapefile)\n",
    "base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "base.plot(ax=ax, color='grey')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alexg\\AppData\\Roaming\\Python\\Python312\\site-packages\\pyproj\\crs\\crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n"
     ]
    }
   ],
   "source": [
    "mapPoints.crs={'init' : 'epsg:4326'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mapPoints.plot(ax=ax, color=\"RED\", markersize=1)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
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