{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7bc558f7",
   "metadata": {},
   "source": [
    "# Join Tutorial\n",
    "\n",
    "Join points to closest BBL shapes tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "difficult-princeton",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "sexual-hungary",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-12T12:26:47.337270Z",
     "start_time": "2021-01-12T12:26:40.878024Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import geopandas as gpd\n",
    "import fiona\n",
    "from fiona.crs import from_epsg\n",
    "import geoplot\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import matplotlib.dates as mdates\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "import datetime\n",
    "import statsmodels.api as sm\n",
    "\n",
    "plt.rcParams['savefig.facecolor'] = 'white'\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "israeli-surname",
   "metadata": {},
   "source": [
    "Printing versions of Python modules and packages with **watermark** - the IPython magic extension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "driving-interstate",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-12T12:26:47.373246Z",
     "start_time": "2021-01-12T12:26:47.340252Z"
    }
   },
   "outputs": [],
   "source": [
    "%load_ext watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "undefined-polyester",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-12T12:26:47.487466Z",
     "start_time": "2021-01-12T12:26:47.377365Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.8.13\n",
      "IPython version      : 8.4.0\n",
      "\n",
      "numpy            : 1.23.1\n",
      "pandas           : 1.4.3\n",
      "geopandas        : 0.11.1\n",
      "geoplot          : 0.5.1\n",
      "fiona            : 1.8.21\n",
      "matplotlib.pyplot: unknown\n",
      "seaborn          : 0.11.2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark -v -p numpy,pandas,geopandas,geoplot,fiona,matplotlib.pyplot,seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "legitimate-gross",
   "metadata": {},
   "source": [
    "Documention for installing watermark: https://github.com/rasbt/watermark"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "drawn-sponsorship",
   "metadata": {},
   "source": [
    "# Retrieve Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0cb3b300",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26959, 35)\n"
     ]
    },
    {
     "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>unique_key</th>\n",
       "      <th>created_date</th>\n",
       "      <th>agency</th>\n",
       "      <th>agency_name</th>\n",
       "      <th>complaint_type</th>\n",
       "      <th>descriptor</th>\n",
       "      <th>incident_zip</th>\n",
       "      <th>incident_address</th>\n",
       "      <th>street_name</th>\n",
       "      <th>cross_street_1</th>\n",
       "      <th>...</th>\n",
       "      <th>intersection_street_1</th>\n",
       "      <th>intersection_street_2</th>\n",
       "      <th>closed_date</th>\n",
       "      <th>resolution_description</th>\n",
       "      <th>resolution_action_updated_date</th>\n",
       "      <th>location_type</th>\n",
       "      <th>landmark</th>\n",
       "      <th>facility_type</th>\n",
       "      <th>due_date</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48542220</td>\n",
       "      <td>2020-12-31T15:41:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>11420.0</td>\n",
       "      <td>117-17 135 STREET</td>\n",
       "      <td>135 STREET</td>\n",
       "      <td>FOCH BLVD</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-01-01T00:20:00.000</td>\n",
       "      <td>Please call 311 for further information. If yo...</td>\n",
       "      <td>2021-01-01T00:20:00.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (1038500.000 186000.014)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48536430</td>\n",
       "      <td>2020-12-31T14:49:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>11357.0</td>\n",
       "      <td>20-24 150 STREET</td>\n",
       "      <td>150 STREET</td>\n",
       "      <td>20 AVE</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-01-04T10:15:00.000</td>\n",
       "      <td>The Department of Environment Protection inspe...</td>\n",
       "      <td>2021-01-04T10:15:00.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (1035682.000 223771.014)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>48539361</td>\n",
       "      <td>2020-12-31T14:03:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>11228.0</td>\n",
       "      <td>7223 8 AVENUE</td>\n",
       "      <td>8 AVENUE</td>\n",
       "      <td>72 ST</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-01-02T11:25:00.000</td>\n",
       "      <td>The Department of Environmental Protection has...</td>\n",
       "      <td>2021-01-02T11:25:00.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (979584.000 168255.014)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48543132</td>\n",
       "      <td>2020-12-31T13:48:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>10032.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>RIVERSIDE DRIVE</td>\n",
       "      <td>WEST  165 STREET</td>\n",
       "      <td>2020-12-31T14:50:00.000</td>\n",
       "      <td>Please call 311 for further information. If yo...</td>\n",
       "      <td>2020-12-31T14:50:00.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (999557.000 245700.014)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48536441</td>\n",
       "      <td>2020-12-31T13:10:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>11234.0</td>\n",
       "      <td>3123 FILLMORE AVENUE</td>\n",
       "      <td>FILLMORE AVENUE</td>\n",
       "      <td>E 31 ST</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-01-03T10:45:00.000</td>\n",
       "      <td>The Department of Environmental Protection ins...</td>\n",
       "      <td>2021-01-03T10:45:00.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (1001868.000 161232.014)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_key             created_date agency  \\\n",
       "0    48542220  2020-12-31T15:41:00.000    DEP   \n",
       "1    48536430  2020-12-31T14:49:00.000    DEP   \n",
       "2    48539361  2020-12-31T14:03:00.000    DEP   \n",
       "3    48543132  2020-12-31T13:48:00.000    DEP   \n",
       "4    48536441  2020-12-31T13:10:00.000    DEP   \n",
       "\n",
       "                              agency_name complaint_type  \\\n",
       "0  Department of Environmental Protection          Sewer   \n",
       "1  Department of Environmental Protection          Sewer   \n",
       "2  Department of Environmental Protection          Sewer   \n",
       "3  Department of Environmental Protection          Sewer   \n",
       "4  Department of Environmental Protection          Sewer   \n",
       "\n",
       "             descriptor  incident_zip      incident_address      street_name  \\\n",
       "0  Street Flooding (SJ)       11420.0     117-17 135 STREET       135 STREET   \n",
       "1  Street Flooding (SJ)       11357.0      20-24 150 STREET       150 STREET   \n",
       "2  Street Flooding (SJ)       11228.0         7223 8 AVENUE         8 AVENUE   \n",
       "3  Street Flooding (SJ)       10032.0                   NaN              NaN   \n",
       "4  Street Flooding (SJ)       11234.0  3123 FILLMORE AVENUE  FILLMORE AVENUE   \n",
       "\n",
       "  cross_street_1  ... intersection_street_1 intersection_street_2  \\\n",
       "0      FOCH BLVD  ...                   NaN                   NaN   \n",
       "1         20 AVE  ...                   NaN                   NaN   \n",
       "2          72 ST  ...                   NaN                   NaN   \n",
       "3            NaN  ...       RIVERSIDE DRIVE      WEST  165 STREET   \n",
       "4        E 31 ST  ...                   NaN                   NaN   \n",
       "\n",
       "               closed_date                             resolution_description  \\\n",
       "0  2021-01-01T00:20:00.000  Please call 311 for further information. If yo...   \n",
       "1  2021-01-04T10:15:00.000  The Department of Environment Protection inspe...   \n",
       "2  2021-01-02T11:25:00.000  The Department of Environmental Protection has...   \n",
       "3  2020-12-31T14:50:00.000  Please call 311 for further information. If yo...   \n",
       "4  2021-01-03T10:45:00.000  The Department of Environmental Protection ins...   \n",
       "\n",
       "  resolution_action_updated_date  location_type landmark  facility_type  \\\n",
       "0        2021-01-01T00:20:00.000            NaN      NaN            NaN   \n",
       "1        2021-01-04T10:15:00.000            NaN      NaN            NaN   \n",
       "2        2021-01-02T11:25:00.000            NaN      NaN            NaN   \n",
       "3        2020-12-31T14:50:00.000            NaN      NaN            NaN   \n",
       "4        2021-01-03T10:45:00.000            NaN      NaN            NaN   \n",
       "\n",
       "   due_date                        geometry  \n",
       "0       NaN  POINT (1038500.000 186000.014)  \n",
       "1       NaN  POINT (1035682.000 223771.014)  \n",
       "2       NaN   POINT (979584.000 168255.014)  \n",
       "3       NaN   POINT (999557.000 245700.014)  \n",
       "4       NaN  POINT (1001868.000 161232.014)  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = 'https://raw.githubusercontent.com/mebauer/nyc-311-street-flooding/main/data/\\\n",
    "street-flooding-complaints.csv'\n",
    "\n",
    "df = pd.read_csv(path, low_memory=False)\n",
    "gdf = gpd.GeoDataFrame(\n",
    "    df, geometry=gpd.points_from_xy(df.longitude, df.latitude, crs=4263))\n",
    "\n",
    "gdf = gdf.dropna(subset=['longitude']).reset_index(drop=True)\n",
    "gdf = gdf.to_crs(2263)\n",
    "\n",
    "print(gdf.shape)\n",
    "gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "29f15d9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gdf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0adc2eb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 95)\n"
     ]
    },
    {
     "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>Borough</th>\n",
       "      <th>Block</th>\n",
       "      <th>Lot</th>\n",
       "      <th>CD</th>\n",
       "      <th>BCT2020</th>\n",
       "      <th>BCTCB2020</th>\n",
       "      <th>CT2010</th>\n",
       "      <th>CB2010</th>\n",
       "      <th>SchoolDist</th>\n",
       "      <th>Council</th>\n",
       "      <th>...</th>\n",
       "      <th>FIRM07_FLAG</th>\n",
       "      <th>PFIRM15_FLAG</th>\n",
       "      <th>Version</th>\n",
       "      <th>DCPEdited</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Notes</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MN</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>1000500</td>\n",
       "      <td>10005000003</td>\n",
       "      <td>5</td>\n",
       "      <td>1000</td>\n",
       "      <td>02</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22v3.1</td>\n",
       "      <td>None</td>\n",
       "      <td>40.688766</td>\n",
       "      <td>-74.018682</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.478663e+06</td>\n",
       "      <td>MULTIPOLYGON (((980783.787 191526.763, 980898....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MN</td>\n",
       "      <td>1</td>\n",
       "      <td>101</td>\n",
       "      <td>101</td>\n",
       "      <td>1000100</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>1001</td>\n",
       "      <td>02</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>22v3.1</td>\n",
       "      <td>None</td>\n",
       "      <td>40.689920</td>\n",
       "      <td>-74.045337</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.018973e+05</td>\n",
       "      <td>MULTIPOLYGON (((972409.690 190685.560, 972428....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MN</td>\n",
       "      <td>1</td>\n",
       "      <td>201</td>\n",
       "      <td>101</td>\n",
       "      <td>1000100</td>\n",
       "      <td>10001001000</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>02</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>22v3.1</td>\n",
       "      <td>None</td>\n",
       "      <td>40.698188</td>\n",
       "      <td>-74.041329</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.148539e+06</td>\n",
       "      <td>MULTIPOLYGON (((973648.661 193712.461, 973648....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>101</td>\n",
       "      <td>1000900</td>\n",
       "      <td>10009001022</td>\n",
       "      <td>9</td>\n",
       "      <td>1025</td>\n",
       "      <td>02</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22v3.1</td>\n",
       "      <td>t</td>\n",
       "      <td>40.700369</td>\n",
       "      <td>-74.012911</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.008250e+05</td>\n",
       "      <td>MULTIPOLYGON (((980639.785 194217.691, 980609....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>101</td>\n",
       "      <td>1000900</td>\n",
       "      <td>10009001022</td>\n",
       "      <td>9</td>\n",
       "      <td>1025</td>\n",
       "      <td>02</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22v3.1</td>\n",
       "      <td>None</td>\n",
       "      <td>40.700550</td>\n",
       "      <td>-74.011588</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.724425e+04</td>\n",
       "      <td>MULTIPOLYGON (((980915.002 194319.141, 980795....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 95 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Borough  Block  Lot   CD  BCT2020    BCTCB2020 CT2010 CB2010 SchoolDist  \\\n",
       "0      MN      1   10  101  1000500  10005000003      5   1000         02   \n",
       "1      MN      1  101  101  1000100         None      1   1001         02   \n",
       "2      MN      1  201  101  1000100  10001001000      1   1000         02   \n",
       "3      MN      2    1  101  1000900  10009001022      9   1025         02   \n",
       "4      MN      2    2  101  1000900  10009001022      9   1025         02   \n",
       "\n",
       "   Council  ...  FIRM07_FLAG PFIRM15_FLAG  Version  DCPEdited   Latitude  \\\n",
       "0        1  ...            1            1   22v3.1       None  40.688766   \n",
       "1        1  ...         None            1   22v3.1       None  40.689920   \n",
       "2        1  ...         None            1   22v3.1       None  40.698188   \n",
       "3        1  ...            1            1   22v3.1          t  40.700369   \n",
       "4        1  ...            1            1   22v3.1       None  40.700550   \n",
       "\n",
       "   Longitude Notes Shape_Leng    Shape_Area  \\\n",
       "0 -74.018682  None        0.0  7.478663e+06   \n",
       "1 -74.045337  None        0.0  5.018973e+05   \n",
       "2 -74.041329  None        0.0  1.148539e+06   \n",
       "3 -74.012911  None        0.0  1.008250e+05   \n",
       "4 -74.011588  None        0.0  8.724425e+04   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTIPOLYGON (((980783.787 191526.763, 980898....  \n",
       "1  MULTIPOLYGON (((972409.690 190685.560, 972428....  \n",
       "2  MULTIPOLYGON (((973648.661 193712.461, 973648....  \n",
       "3  MULTIPOLYGON (((980639.785 194217.691, 980609....  \n",
       "4  MULTIPOLYGON (((980915.002 194319.141, 980795....  \n",
       "\n",
       "[5 rows x 95 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = '~/Downloads/nyc_mappluto_22v3_1_fgdb/MapPLUTO_22v3_1.gdb'\n",
    "pluto_gdf = gpd.read_file(path, rows=500)\n",
    "\n",
    "pluto_gdf = pluto_gdf.to_crs(2263)\n",
    "\n",
    "print(pluto_gdf.shape)\n",
    "pluto_gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1c8d087c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pluto_gdf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7ec1828c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rtree.index.Index(bounds=[971045.2731347084, 188447.4288892746, 985249.228328228, 201211.1846499443], size=70)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluto_gdf.sindex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "addbb9e5",
   "metadata": {},
   "outputs": [
    {
     "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>minx</th>\n",
       "      <th>miny</th>\n",
       "      <th>maxx</th>\n",
       "      <th>maxy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.038300e+06</td>\n",
       "      <td>185800.014393</td>\n",
       "      <td>1.038700e+06</td>\n",
       "      <td>186200.014393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.035482e+06</td>\n",
       "      <td>223571.014417</td>\n",
       "      <td>1.035882e+06</td>\n",
       "      <td>223971.014417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.793840e+05</td>\n",
       "      <td>168055.014382</td>\n",
       "      <td>9.797840e+05</td>\n",
       "      <td>168455.014382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.993570e+05</td>\n",
       "      <td>245500.014431</td>\n",
       "      <td>9.997570e+05</td>\n",
       "      <td>245900.014431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.001668e+06</td>\n",
       "      <td>161032.014377</td>\n",
       "      <td>1.002068e+06</td>\n",
       "      <td>161432.014377</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           minx           miny          maxx           maxy\n",
       "0  1.038300e+06  185800.014393  1.038700e+06  186200.014393\n",
       "1  1.035482e+06  223571.014417  1.035882e+06  223971.014417\n",
       "2  9.793840e+05  168055.014382  9.797840e+05  168455.014382\n",
       "3  9.993570e+05  245500.014431  9.997570e+05  245900.014431\n",
       "4  1.001668e+06  161032.014377  1.002068e+06  161432.014377"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offset = 200\n",
    "bbox = gdf.bounds + [-offset, -offset, offset, offset]\n",
    "\n",
    "bbox.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "72dee2c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26959,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    []\n",
       "1    []\n",
       "2    []\n",
       "3    []\n",
       "4    []\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hits = bbox.apply(lambda row: list(pluto_gdf.sindex.intersection(row)), axis=1)\n",
    "\n",
    "print(hits.shape)\n",
    "hits.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d44174b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(721, 2)\n"
     ]
    },
    {
     "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>pt_idx</th>\n",
       "      <th>line_i</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>459</td>\n",
       "      <td>128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>459</td>\n",
       "      <td>145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>459</td>\n",
       "      <td>137.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>459</td>\n",
       "      <td>139.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>459</td>\n",
       "      <td>140.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pt_idx  line_i\n",
       "0     459   128.0\n",
       "1     459   145.0\n",
       "2     459   137.0\n",
       "3     459   139.0\n",
       "4     459   140.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = pd.DataFrame(\n",
    "        {\n",
    "        # index of points table\n",
    "        \"pt_idx\": np.repeat(hits.index, hits.apply(len)),\n",
    "\n",
    "        # ordinal position of line - access via iloc later\n",
    "        \"line_i\": np.concatenate(hits.values)\n",
    "        }\n",
    ")\n",
    "\n",
    "print(tmp.shape)\n",
    "tmp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bc450473",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>pt_idx</th>\n",
       "      <th>line_i</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Block</th>\n",
       "      <th>Lot</th>\n",
       "      <th>CD</th>\n",
       "      <th>BCT2020</th>\n",
       "      <th>BCTCB2020</th>\n",
       "      <th>CT2010</th>\n",
       "      <th>CB2010</th>\n",
       "      <th>...</th>\n",
       "      <th>FIRM07_FLAG</th>\n",
       "      <th>PFIRM15_FLAG</th>\n",
       "      <th>Version</th>\n",
       "      <th>DCPEdited</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Notes</th>\n",
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       "      <th>Shape_Area</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>22v3.1</td>\n",
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       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>22v3.1</td>\n",
       "      <td>None</td>\n",
       "      <td>40.710635</td>\n",
       "      <td>-74.007964</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5632.008015</td>\n",
       "      <td>MULTIPOLYGON (((982020.597 198116.096, 981985....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>459</td>\n",
       "      <td>139.0</td>\n",
       "      <td>MN</td>\n",
       "      <td>90</td>\n",
       "      <td>23</td>\n",
       "      <td>101</td>\n",
       "      <td>1001501</td>\n",
       "      <td>10015011009</td>\n",
       "      <td>15.01</td>\n",
       "      <td>1004</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>22v3.1</td>\n",
       "      <td>None</td>\n",
       "      <td>40.710956</td>\n",
       "      <td>-74.007773</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>MULTIPOLYGON (((982110.692 198297.452, 982090....</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>459</td>\n",
       "      <td>140.0</td>\n",
       "      <td>MN</td>\n",
       "      <td>90</td>\n",
       "      <td>24</td>\n",
       "      <td>101</td>\n",
       "      <td>1001501</td>\n",
       "      <td>10015011009</td>\n",
       "      <td>15.01</td>\n",
       "      <td>1004</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>22v3.1</td>\n",
       "      <td>None</td>\n",
       "      <td>40.711005</td>\n",
       "      <td>-74.007817</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1242.680263</td>\n",
       "      <td>MULTIPOLYGON (((982106.447 198327.138, 982072....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 97 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   pt_idx  line_i Borough  Block  Lot   CD  BCT2020    BCTCB2020 CT2010  \\\n",
       "0     459   128.0      MN     79   27  101  1001502  10015021000  15.02   \n",
       "1     459   145.0      MN     92   23  101  1001501  10015011011  15.01   \n",
       "2     459   137.0      MN     89    3  101  1001501  10015011017  15.01   \n",
       "3     459   139.0      MN     90   23  101  1001501  10015011009  15.01   \n",
       "4     459   140.0      MN     90   24  101  1001501  10015011009  15.01   \n",
       "\n",
       "  CB2010  ... FIRM07_FLAG  PFIRM15_FLAG  Version DCPEdited   Latitude  \\\n",
       "0   1000  ...        None          None   22v3.1      None  40.710322   \n",
       "1   1007  ...        None          None   22v3.1      None  40.710594   \n",
       "2   1005  ...        None          None   22v3.1      None  40.710635   \n",
       "3   1004  ...        None          None   22v3.1      None  40.710956   \n",
       "4   1004  ...        None          None   22v3.1      None  40.711005   \n",
       "\n",
       "   Longitude  Notes Shape_Leng   Shape_Area  \\\n",
       "0 -74.008466   None        0.0  5670.917438   \n",
       "1 -74.007185   None        0.0  1702.936740   \n",
       "2 -74.007964   None        0.0  5632.008015   \n",
       "3 -74.007773   None        0.0  1021.570578   \n",
       "4 -74.007817   None        0.0  1242.680263   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTIPOLYGON (((981955.698 198099.048, 981894....  \n",
       "1  MULTIPOLYGON (((982289.308 198169.543, 982267....  \n",
       "2  MULTIPOLYGON (((982020.597 198116.096, 981985....  \n",
       "3  MULTIPOLYGON (((982110.692 198297.452, 982090....  \n",
       "4  MULTIPOLYGON (((982106.447 198327.138, 982072....  \n",
       "\n",
       "[5 rows x 97 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join back to the lines on line_i; we use reset_index() to \n",
    "# give us the ordinal position of each line\n",
    "tmp = tmp.join(pluto_gdf.reset_index(drop=True), on=\"line_i\")\n",
    "\n",
    "tmp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "70023ecf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
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       "      <td>None</td>\n",
       "      <td>22v3.1</td>\n",
       "      <td>None</td>\n",
       "      <td>40.710635</td>\n",
       "      <td>-74.007964</td>\n",
       "      <td>None</td>\n",
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       "      <td>POINT (982027.000 198304.014)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>459</td>\n",
       "      <td>139.0</td>\n",
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       "      <td>90</td>\n",
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       "      <td>...</td>\n",
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       "      <td>MULTIPOLYGON (((982110.692 198297.452, 982090....</td>\n",
       "      <td>POINT (982027.000 198304.014)</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>459</td>\n",
       "      <td>140.0</td>\n",
       "      <td>MN</td>\n",
       "      <td>90</td>\n",
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       "      <td>...</td>\n",
       "      <td>None</td>\n",
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       "      <td>MULTIPOLYGON (((982106.447 198327.138, 982072....</td>\n",
       "      <td>POINT (982027.000 198304.014)</td>\n",
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       "<p>5 rows × 98 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   pt_idx  line_i Borough  Block  Lot   CD  BCT2020    BCTCB2020 CT2010  \\\n",
       "0     459   128.0      MN     79   27  101  1001502  10015021000  15.02   \n",
       "1     459   145.0      MN     92   23  101  1001501  10015011011  15.01   \n",
       "2     459   137.0      MN     89    3  101  1001501  10015011017  15.01   \n",
       "3     459   139.0      MN     90   23  101  1001501  10015011009  15.01   \n",
       "4     459   140.0      MN     90   24  101  1001501  10015011009  15.01   \n",
       "\n",
       "  CB2010  ... PFIRM15_FLAG  Version  DCPEdited   Latitude  Longitude  Notes  \\\n",
       "0   1000  ...         None   22v3.1       None  40.710322 -74.008466   None   \n",
       "1   1007  ...         None   22v3.1       None  40.710594 -74.007185   None   \n",
       "2   1005  ...         None   22v3.1       None  40.710635 -74.007964   None   \n",
       "3   1004  ...         None   22v3.1       None  40.710956 -74.007773   None   \n",
       "4   1004  ...         None   22v3.1       None  40.711005 -74.007817   None   \n",
       "\n",
       "   Shape_Leng   Shape_Area                                           geometry  \\\n",
       "0         0.0  5670.917438  MULTIPOLYGON (((981955.698 198099.048, 981894....   \n",
       "1         0.0  1702.936740  MULTIPOLYGON (((982289.308 198169.543, 982267....   \n",
       "2         0.0  5632.008015  MULTIPOLYGON (((982020.597 198116.096, 981985....   \n",
       "3         0.0  1021.570578  MULTIPOLYGON (((982110.692 198297.452, 982090....   \n",
       "4         0.0  1242.680263  MULTIPOLYGON (((982106.447 198327.138, 982072....   \n",
       "\n",
       "                           point  \n",
       "0  POINT (982027.000 198304.014)  \n",
       "1  POINT (982027.000 198304.014)  \n",
       "2  POINT (982027.000 198304.014)  \n",
       "3  POINT (982027.000 198304.014)  \n",
       "4  POINT (982027.000 198304.014)  \n",
       "\n",
       "[5 rows x 98 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join back to the original points to get their geometry\n",
    "# rename the point geometry as \"point\"\n",
    "tmp = tmp.join(gdf.geometry.rename(\"point\"), on=\"pt_idx\")\n",
    "\n",
    "# Convert back to a GeoDataFrame, so we can do spatial ops\n",
    "tmp = gpd.GeoDataFrame(tmp, geometry=\"geometry\", crs=gdf.crs)\n",
    "\n",
    "tmp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "01b72539",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>...</td>\n",
       "      <td>22v3.1</td>\n",
       "      <td>None</td>\n",
       "      <td>40.710322</td>\n",
       "      <td>-74.008466</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5670.917438</td>\n",
       "      <td>MULTIPOLYGON (((981955.698 198099.048, 981894....</td>\n",
       "      <td>POINT (982027.000 198304.014)</td>\n",
       "      <td>210.186017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>459</td>\n",
       "      <td>145.0</td>\n",
       "      <td>MN</td>\n",
       "      <td>92</td>\n",
       "      <td>23</td>\n",
       "      <td>101</td>\n",
       "      <td>1001501</td>\n",
       "      <td>10015011011</td>\n",
       "      <td>15.01</td>\n",
       "      <td>1007</td>\n",
       "      <td>...</td>\n",
       "      <td>22v3.1</td>\n",
       "      <td>None</td>\n",
       "      <td>40.710594</td>\n",
       "      <td>-74.007185</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1702.936740</td>\n",
       "      <td>MULTIPOLYGON (((982289.308 198169.543, 982267....</td>\n",
       "      <td>POINT (982027.000 198304.014)</td>\n",
       "      <td>244.527951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>459</td>\n",
       "      <td>137.0</td>\n",
       "      <td>MN</td>\n",
       "      <td>89</td>\n",
       "      <td>3</td>\n",
       "      <td>101</td>\n",
       "      <td>1001501</td>\n",
       "      <td>10015011017</td>\n",
       "      <td>15.01</td>\n",
       "      <td>1005</td>\n",
       "      <td>...</td>\n",
       "      <td>22v3.1</td>\n",
       "      <td>None</td>\n",
       "      <td>40.710635</td>\n",
       "      <td>-74.007964</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5632.008015</td>\n",
       "      <td>MULTIPOLYGON (((982020.597 198116.096, 981985....</td>\n",
       "      <td>POINT (982027.000 198304.014)</td>\n",
       "      <td>69.911787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>459</td>\n",
       "      <td>139.0</td>\n",
       "      <td>MN</td>\n",
       "      <td>90</td>\n",
       "      <td>23</td>\n",
       "      <td>101</td>\n",
       "      <td>1001501</td>\n",
       "      <td>10015011009</td>\n",
       "      <td>15.01</td>\n",
       "      <td>1004</td>\n",
       "      <td>...</td>\n",
       "      <td>22v3.1</td>\n",
       "      <td>None</td>\n",
       "      <td>40.710956</td>\n",
       "      <td>-74.007773</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1021.570578</td>\n",
       "      <td>MULTIPOLYGON (((982110.692 198297.452, 982090....</td>\n",
       "      <td>POINT (982027.000 198304.014)</td>\n",
       "      <td>49.550503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>459</td>\n",
       "      <td>140.0</td>\n",
       "      <td>MN</td>\n",
       "      <td>90</td>\n",
       "      <td>24</td>\n",
       "      <td>101</td>\n",
       "      <td>1001501</td>\n",
       "      <td>10015011009</td>\n",
       "      <td>15.01</td>\n",
       "      <td>1004</td>\n",
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       "      <td>22v3.1</td>\n",
       "      <td>None</td>\n",
       "      <td>40.711005</td>\n",
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       "      <td>None</td>\n",
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       "      <td>1242.680263</td>\n",
       "      <td>MULTIPOLYGON (((982106.447 198327.138, 982072....</td>\n",
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       "      <td>30.673000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 99 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   pt_idx  line_i Borough  Block  Lot   CD  BCT2020    BCTCB2020 CT2010  \\\n",
       "0     459   128.0      MN     79   27  101  1001502  10015021000  15.02   \n",
       "1     459   145.0      MN     92   23  101  1001501  10015011011  15.01   \n",
       "2     459   137.0      MN     89    3  101  1001501  10015011017  15.01   \n",
       "3     459   139.0      MN     90   23  101  1001501  10015011009  15.01   \n",
       "4     459   140.0      MN     90   24  101  1001501  10015011009  15.01   \n",
       "\n",
       "  CB2010  ... Version  DCPEdited   Latitude  Longitude  Notes  Shape_Leng  \\\n",
       "0   1000  ...  22v3.1       None  40.710322 -74.008466   None         0.0   \n",
       "1   1007  ...  22v3.1       None  40.710594 -74.007185   None         0.0   \n",
       "2   1005  ...  22v3.1       None  40.710635 -74.007964   None         0.0   \n",
       "3   1004  ...  22v3.1       None  40.710956 -74.007773   None         0.0   \n",
       "4   1004  ...  22v3.1       None  40.711005 -74.007817   None         0.0   \n",
       "\n",
       "    Shape_Area                                           geometry  \\\n",
       "0  5670.917438  MULTIPOLYGON (((981955.698 198099.048, 981894....   \n",
       "1  1702.936740  MULTIPOLYGON (((982289.308 198169.543, 982267....   \n",
       "2  5632.008015  MULTIPOLYGON (((982020.597 198116.096, 981985....   \n",
       "3  1021.570578  MULTIPOLYGON (((982110.692 198297.452, 982090....   \n",
       "4  1242.680263  MULTIPOLYGON (((982106.447 198327.138, 982072....   \n",
       "\n",
       "                           point   snap_dist  \n",
       "0  POINT (982027.000 198304.014)  210.186017  \n",
       "1  POINT (982027.000 198304.014)  244.527951  \n",
       "2  POINT (982027.000 198304.014)   69.911787  \n",
       "3  POINT (982027.000 198304.014)   49.550503  \n",
       "4  POINT (982027.000 198304.014)   30.673000  \n",
       "\n",
       "[5 rows x 99 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp[\"snap_dist\"] = tmp.geometry.distance(gpd.GeoSeries(tmp.point))\n",
    "\n",
    "tmp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "22559048",
   "metadata": {},
   "outputs": [
    {
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       "     pt_idx  line_i Borough  Block   Lot   CD  BCT2020    BCTCB2020  CT2010  \\\n",
       "448   19254   340.0      MN     16     3  101  1031704  10317044000  317.04   \n",
       "483   19475   340.0      MN     16     3  101  1031704  10317044000  317.04   \n",
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       "516   21495    47.0      MN     25    27  101  1000900  10009001000       9   \n",
       "\n",
       "    CB2010  ... Version  DCPEdited   Latitude  Longitude  Notes  Shape_Leng  \\\n",
       "448   4001  ...  22v3.1          t  40.712618 -74.016586   None         0.0   \n",
       "483   4001  ...  22v3.1          t  40.712618 -74.016586   None         0.0   \n",
       "324   4001  ...  22v3.1          t  40.712618 -74.016586   None         0.0   \n",
       "518   1000  ...  22v3.1       None  40.705919 -74.010766   None         0.0   \n",
       "516   1000  ...  22v3.1       None  40.705686 -74.010287   None         0.0   \n",
       "\n",
       "       Shape_Area                                           geometry  \\\n",
       "448  2.052775e+06  MULTIPOLYGON (((980584.841 200985.007, 980594....   \n",
       "483  2.052775e+06  MULTIPOLYGON (((980584.841 200985.007, 980594....   \n",
       "324  2.052775e+06  MULTIPOLYGON (((980584.841 200985.007, 980594....   \n",
       "518  2.585314e+04  MULTIPOLYGON (((981284.442 196374.824, 981236....   \n",
       "516  1.625568e+04  MULTIPOLYGON (((981492.934 196409.111, 981478....   \n",
       "\n",
       "                             point  snap_dist  \n",
       "448  POINT (979355.000 197234.014)   0.000000  \n",
       "483  POINT (979355.000 197234.014)   0.000000  \n",
       "324  POINT (979759.000 199686.014)   0.000000  \n",
       "518  POINT (981399.000 196469.014)  11.267715  \n",
       "516  POINT (981399.000 196469.014)  11.310903  \n",
       "\n",
       "[5 rows x 99 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Discard any lines that are greater than tolerance from points\n",
    "tmp = tmp.loc[tmp.snap_dist <= offset]\n",
    "\n",
    "# Sort on ascending snap distance, so that closest goes to top\n",
    "tmp = tmp.sort_values(by=[\"snap_dist\"])\n",
    "\n",
    "tmp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0e02b9ae",
   "metadata": {},
   "outputs": [
    {
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       "      <td>356.0</td>\n",
       "      <td>MN</td>\n",
       "      <td>144</td>\n",
       "      <td>31</td>\n",
       "      <td>101</td>\n",
       "      <td>1003300</td>\n",
       "      <td>10033002018</td>\n",
       "      <td>33</td>\n",
       "      <td>3022</td>\n",
       "      <td>02</td>\n",
       "      <td>...</td>\n",
       "      <td>22v3.1</td>\n",
       "      <td>t</td>\n",
       "      <td>40.716852</td>\n",
       "      <td>-74.008459</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1353.898306</td>\n",
       "      <td>MULTIPOLYGON (((981934.351 200443.725, 981920....</td>\n",
       "      <td>POINT (981942.000 200380.014)</td>\n",
       "      <td>46.337050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2563</th>\n",
       "      <td>386.0</td>\n",
       "      <td>MN</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>101</td>\n",
       "      <td>1003300</td>\n",
       "      <td>10033003004</td>\n",
       "      <td>33</td>\n",
       "      <td>1010</td>\n",
       "      <td>02</td>\n",
       "      <td>...</td>\n",
       "      <td>22v3.1</td>\n",
       "      <td>t</td>\n",
       "      <td>40.715820</td>\n",
       "      <td>-74.005977</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26217.794000</td>\n",
       "      <td>MULTIPOLYGON (((982672.512 200047.743, 982690....</td>\n",
       "      <td>POINT (982569.000 199967.014)</td>\n",
       "      <td>26.450837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4135</th>\n",
       "      <td>212.0</td>\n",
       "      <td>MN</td>\n",
       "      <td>38</td>\n",
       "      <td>17</td>\n",
       "      <td>101</td>\n",
       "      <td>1000700</td>\n",
       "      <td>10007007002</td>\n",
       "      <td>7</td>\n",
       "      <td>1012</td>\n",
       "      <td>02</td>\n",
       "      <td>...</td>\n",
       "      <td>22v3.1</td>\n",
       "      <td>None</td>\n",
       "      <td>40.705661</td>\n",
       "      <td>-74.006168</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28946.530441</td>\n",
       "      <td>MULTIPOLYGON (((982494.655 196247.048, 982403....</td>\n",
       "      <td>POINT (982649.000 196479.014)</td>\n",
       "      <td>31.676819</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 98 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        line_i Borough  Block   Lot   CD  BCT2020    BCTCB2020 CT2010 CB2010  \\\n",
       "pt_idx                                                                         \n",
       "459      138.0      MN     90     1  101  1001501  10015011009  15.01   1004   \n",
       "1096     353.0      MN    143  7503  101  1003900  10039003007     39   4007   \n",
       "1859     356.0      MN    144    31  101  1003300  10033002018     33   3022   \n",
       "2563     386.0      MN    151     1  101  1003300  10033003004     33   1010   \n",
       "4135     212.0      MN     38    17  101  1000700  10007007002      7   1012   \n",
       "\n",
       "       SchoolDist  ...  Version  DCPEdited   Latitude  Longitude  Notes  \\\n",
       "pt_idx             ...                                                    \n",
       "459            02  ...   22v3.1       None  40.711176 -74.008123   None   \n",
       "1096           02  ...   22v3.1          t  40.717834 -74.009967   None   \n",
       "1859           02  ...   22v3.1          t  40.716852 -74.008459   None   \n",
       "2563           02  ...   22v3.1          t  40.715820 -74.005977   None   \n",
       "4135           02  ...   22v3.1       None  40.705661 -74.006168   None   \n",
       "\n",
       "        Shape_Leng    Shape_Area  \\\n",
       "pt_idx                             \n",
       "459            0.0   6523.195602   \n",
       "1096           0.0   5299.637906   \n",
       "1859           0.0   1353.898306   \n",
       "2563           0.0  26217.794000   \n",
       "4135           0.0  28946.530441   \n",
       "\n",
       "                                                 geometry  \\\n",
       "pt_idx                                                      \n",
       "459     MULTIPOLYGON (((982041.663 198346.135, 982046....   \n",
       "1096    MULTIPOLYGON (((981511.400 200756.768, 981461....   \n",
       "1859    MULTIPOLYGON (((981934.351 200443.725, 981920....   \n",
       "2563    MULTIPOLYGON (((982672.512 200047.743, 982690....   \n",
       "4135    MULTIPOLYGON (((982494.655 196247.048, 982403....   \n",
       "\n",
       "                                point  snap_dist  \n",
       "pt_idx                                            \n",
       "459     POINT (982027.000 198304.014)  13.819315  \n",
       "1096    POINT (981500.000 200877.014)  26.688052  \n",
       "1859    POINT (981942.000 200380.014)  46.337050  \n",
       "2563    POINT (982569.000 199967.014)  26.450837  \n",
       "4135    POINT (982649.000 196479.014)  31.676819  \n",
       "\n",
       "[5 rows x 98 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group by the index of the points and take the first, which is the\n",
    "# closest line \n",
    "closest = tmp.groupby(\"pt_idx\").first()\n",
    "\n",
    "# construct a GeoDataFrame of the closest lines\n",
    "closest = gpd.GeoDataFrame(closest, geometry=\"geometry\")\n",
    "\n",
    "closest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e4d24365",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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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>unique_key</th>\n",
       "      <th>created_date</th>\n",
       "      <th>agency</th>\n",
       "      <th>agency_name</th>\n",
       "      <th>complaint_type</th>\n",
       "      <th>descriptor</th>\n",
       "      <th>incident_zip</th>\n",
       "      <th>incident_address</th>\n",
       "      <th>street_name</th>\n",
       "      <th>cross_street_1</th>\n",
       "      <th>...</th>\n",
       "      <th>PFIRM15_FLAG</th>\n",
       "      <th>Version</th>\n",
       "      <th>DCPEdited</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Notes</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>point</th>\n",
       "      <th>snap_dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48542220</td>\n",
       "      <td>2020-12-31T15:41:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>11420.0</td>\n",
       "      <td>117-17 135 STREET</td>\n",
       "      <td>135 STREET</td>\n",
       "      <td>FOCH BLVD</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48536430</td>\n",
       "      <td>2020-12-31T14:49:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>11357.0</td>\n",
       "      <td>20-24 150 STREET</td>\n",
       "      <td>150 STREET</td>\n",
       "      <td>20 AVE</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>48539361</td>\n",
       "      <td>2020-12-31T14:03:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>11228.0</td>\n",
       "      <td>7223 8 AVENUE</td>\n",
       "      <td>8 AVENUE</td>\n",
       "      <td>72 ST</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48543132</td>\n",
       "      <td>2020-12-31T13:48:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>10032.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48536441</td>\n",
       "      <td>2020-12-31T13:10:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>11234.0</td>\n",
       "      <td>3123 FILLMORE AVENUE</td>\n",
       "      <td>FILLMORE AVENUE</td>\n",
       "      <td>E 31 ST</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 132 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_key             created_date agency  \\\n",
       "0    48542220  2020-12-31T15:41:00.000    DEP   \n",
       "1    48536430  2020-12-31T14:49:00.000    DEP   \n",
       "2    48539361  2020-12-31T14:03:00.000    DEP   \n",
       "3    48543132  2020-12-31T13:48:00.000    DEP   \n",
       "4    48536441  2020-12-31T13:10:00.000    DEP   \n",
       "\n",
       "                              agency_name complaint_type  \\\n",
       "0  Department of Environmental Protection          Sewer   \n",
       "1  Department of Environmental Protection          Sewer   \n",
       "2  Department of Environmental Protection          Sewer   \n",
       "3  Department of Environmental Protection          Sewer   \n",
       "4  Department of Environmental Protection          Sewer   \n",
       "\n",
       "             descriptor  incident_zip      incident_address      street_name  \\\n",
       "0  Street Flooding (SJ)       11420.0     117-17 135 STREET       135 STREET   \n",
       "1  Street Flooding (SJ)       11357.0      20-24 150 STREET       150 STREET   \n",
       "2  Street Flooding (SJ)       11228.0         7223 8 AVENUE         8 AVENUE   \n",
       "3  Street Flooding (SJ)       10032.0                   NaN              NaN   \n",
       "4  Street Flooding (SJ)       11234.0  3123 FILLMORE AVENUE  FILLMORE AVENUE   \n",
       "\n",
       "  cross_street_1  ... PFIRM15_FLAG Version DCPEdited Latitude Longitude  \\\n",
       "0      FOCH BLVD  ...          NaN     NaN       NaN      NaN       NaN   \n",
       "1         20 AVE  ...          NaN     NaN       NaN      NaN       NaN   \n",
       "2          72 ST  ...          NaN     NaN       NaN      NaN       NaN   \n",
       "3            NaN  ...          NaN     NaN       NaN      NaN       NaN   \n",
       "4        E 31 ST  ...          NaN     NaN       NaN      NaN       NaN   \n",
       "\n",
       "   Notes Shape_Leng  Shape_Area  point snap_dist  \n",
       "0    NaN        NaN         NaN   None       NaN  \n",
       "1    NaN        NaN         NaN   None       NaN  \n",
       "2    NaN        NaN         NaN   None       NaN  \n",
       "3    NaN        NaN         NaN   None       NaN  \n",
       "4    NaN        NaN         NaN   None       NaN  \n",
       "\n",
       "[5 rows x 132 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join back to the original points:\n",
    "updated_points = gdf.join(closest.drop(columns=['geometry']))\n",
    "\n",
    "updated_points.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b0ef4791",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_key</th>\n",
       "      <th>incident_zip</th>\n",
       "      <th>bbl</th>\n",
       "      <th>x_coordinate_state_plane</th>\n",
       "      <th>y_coordinate_state_plane</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>location_type</th>\n",
       "      <th>landmark</th>\n",
       "      <th>facility_type</th>\n",
       "      <th>...</th>\n",
       "      <th>BBL</th>\n",
       "      <th>CondoNo</th>\n",
       "      <th>XCoord</th>\n",
       "      <th>YCoord</th>\n",
       "      <th>APPBBL</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>snap_dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.695900e+04</td>\n",
       "      <td>26959.000000</td>\n",
       "      <td>1.615900e+04</td>\n",
       "      <td>2.695900e+04</td>\n",
       "      <td>26959.000000</td>\n",
       "      <td>26959.000000</td>\n",
       "      <td>26959.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.600000e+01</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>6.100000e+01</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.0</td>\n",
       "      <td>8.600000e+01</td>\n",
       "      <td>86.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.254573e+07</td>\n",
       "      <td>10969.384436</td>\n",
       "      <td>3.699429e+09</td>\n",
       "      <td>1.005498e+06</td>\n",
       "      <td>186184.607441</td>\n",
       "      <td>40.677615</td>\n",
       "      <td>-73.923323</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000691e+09</td>\n",
       "      <td>1692.355556</td>\n",
       "      <td>981860.232558</td>\n",
       "      <td>197816.534884</td>\n",
       "      <td>1.000704e+09</td>\n",
       "      <td>40.709637</td>\n",
       "      <td>-74.008620</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.464337e+05</td>\n",
       "      <td>55.102735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.167418e+06</td>\n",
       "      <td>536.377470</td>\n",
       "      <td>1.075864e+09</td>\n",
       "      <td>3.505257e+04</td>\n",
       "      <td>30576.150750</td>\n",
       "      <td>0.083924</td>\n",
       "      <td>0.126329</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4.924506e+05</td>\n",
       "      <td>891.678528</td>\n",
       "      <td>1349.550990</td>\n",
       "      <td>1455.157179</td>\n",
       "      <td>4.997937e+05</td>\n",
       "      <td>0.003994</td>\n",
       "      <td>0.004868</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.086507e+05</td>\n",
       "      <td>41.886663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.563993e+07</td>\n",
       "      <td>10001.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.133570e+05</td>\n",
       "      <td>121032.000000</td>\n",
       "      <td>40.498628</td>\n",
       "      <td>-74.254937</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000078e+09</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>979652.000000</td>\n",
       "      <td>195554.000000</td>\n",
       "      <td>1.000070e+09</td>\n",
       "      <td>40.703427</td>\n",
       "      <td>-74.016586</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.592921e+02</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.455603e+07</td>\n",
       "      <td>10312.000000</td>\n",
       "      <td>3.060255e+09</td>\n",
       "      <td>9.868870e+05</td>\n",
       "      <td>161248.000000</td>\n",
       "      <td>40.609123</td>\n",
       "      <td>-73.990504</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000210e+09</td>\n",
       "      <td>960.000000</td>\n",
       "      <td>980937.250000</td>\n",
       "      <td>196635.250000</td>\n",
       "      <td>1.000250e+09</td>\n",
       "      <td>40.706394</td>\n",
       "      <td>-74.011948</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.331987e+03</td>\n",
       "      <td>28.184895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.291181e+07</td>\n",
       "      <td>11221.000000</td>\n",
       "      <td>4.067910e+09</td>\n",
       "      <td>1.007480e+06</td>\n",
       "      <td>183618.000000</td>\n",
       "      <td>40.670610</td>\n",
       "      <td>-73.916267</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000610e+09</td>\n",
       "      <td>1704.000000</td>\n",
       "      <td>981720.000000</td>\n",
       "      <td>197685.000000</td>\n",
       "      <td>1.000710e+09</td>\n",
       "      <td>40.709276</td>\n",
       "      <td>-74.009125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.725629e+04</td>\n",
       "      <td>38.098773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.043790e+07</td>\n",
       "      <td>11385.000000</td>\n",
       "      <td>4.156250e+09</td>\n",
       "      <td>1.033980e+06</td>\n",
       "      <td>206488.500000</td>\n",
       "      <td>40.733373</td>\n",
       "      <td>-73.820554</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.001055e+09</td>\n",
       "      <td>2442.000000</td>\n",
       "      <td>982535.000000</td>\n",
       "      <td>198845.750000</td>\n",
       "      <td>1.000970e+09</td>\n",
       "      <td>40.712461</td>\n",
       "      <td>-74.006186</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.467984e+04</td>\n",
       "      <td>75.728271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.854313e+07</td>\n",
       "      <td>11697.000000</td>\n",
       "      <td>5.080500e+09</td>\n",
       "      <td>1.067279e+06</td>\n",
       "      <td>271876.000000</td>\n",
       "      <td>40.912869</td>\n",
       "      <td>-73.700384</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.001620e+09</td>\n",
       "      <td>3035.000000</td>\n",
       "      <td>984839.000000</td>\n",
       "      <td>200803.000000</td>\n",
       "      <td>1.001621e+09</td>\n",
       "      <td>40.717834</td>\n",
       "      <td>-73.997875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.052775e+06</td>\n",
       "      <td>165.787876</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         unique_key  incident_zip           bbl  x_coordinate_state_plane  \\\n",
       "count  2.695900e+04  26959.000000  1.615900e+04              2.695900e+04   \n",
       "mean   3.254573e+07  10969.384436  3.699429e+09              1.005498e+06   \n",
       "std    9.167418e+06    536.377470  1.075864e+09              3.505257e+04   \n",
       "min    1.563993e+07  10001.000000  0.000000e+00              9.133570e+05   \n",
       "25%    2.455603e+07  10312.000000  3.060255e+09              9.868870e+05   \n",
       "50%    3.291181e+07  11221.000000  4.067910e+09              1.007480e+06   \n",
       "75%    4.043790e+07  11385.000000  4.156250e+09              1.033980e+06   \n",
       "max    4.854313e+07  11697.000000  5.080500e+09              1.067279e+06   \n",
       "\n",
       "       y_coordinate_state_plane      latitude     longitude  location_type  \\\n",
       "count              26959.000000  26959.000000  26959.000000            0.0   \n",
       "mean              186184.607441     40.677615    -73.923323            NaN   \n",
       "std                30576.150750      0.083924      0.126329            NaN   \n",
       "min               121032.000000     40.498628    -74.254937            NaN   \n",
       "25%               161248.000000     40.609123    -73.990504            NaN   \n",
       "50%               183618.000000     40.670610    -73.916267            NaN   \n",
       "75%               206488.500000     40.733373    -73.820554            NaN   \n",
       "max               271876.000000     40.912869    -73.700384            NaN   \n",
       "\n",
       "       landmark  facility_type  ...           BBL      CondoNo         XCoord  \\\n",
       "count       0.0            0.0  ...  8.600000e+01    45.000000      86.000000   \n",
       "mean        NaN            NaN  ...  1.000691e+09  1692.355556  981860.232558   \n",
       "std         NaN            NaN  ...  4.924506e+05   891.678528    1349.550990   \n",
       "min         NaN            NaN  ...  1.000078e+09    65.000000  979652.000000   \n",
       "25%         NaN            NaN  ...  1.000210e+09   960.000000  980937.250000   \n",
       "50%         NaN            NaN  ...  1.000610e+09  1704.000000  981720.000000   \n",
       "75%         NaN            NaN  ...  1.001055e+09  2442.000000  982535.000000   \n",
       "max         NaN            NaN  ...  1.001620e+09  3035.000000  984839.000000   \n",
       "\n",
       "              YCoord        APPBBL   Latitude  Longitude  Shape_Leng  \\\n",
       "count      86.000000  6.100000e+01  86.000000  86.000000        86.0   \n",
       "mean   197816.534884  1.000704e+09  40.709637 -74.008620         0.0   \n",
       "std      1455.157179  4.997937e+05   0.003994   0.004868         0.0   \n",
       "min    195554.000000  1.000070e+09  40.703427 -74.016586         0.0   \n",
       "25%    196635.250000  1.000250e+09  40.706394 -74.011948         0.0   \n",
       "50%    197685.000000  1.000710e+09  40.709276 -74.009125         0.0   \n",
       "75%    198845.750000  1.000970e+09  40.712461 -74.006186         0.0   \n",
       "max    200803.000000  1.001621e+09  40.717834 -73.997875         0.0   \n",
       "\n",
       "         Shape_Area   snap_dist  \n",
       "count  8.600000e+01   86.000000  \n",
       "mean   1.464337e+05   55.102735  \n",
       "std    4.086507e+05   41.886663  \n",
       "min    6.592921e+02    0.000000  \n",
       "25%    6.331987e+03   28.184895  \n",
       "50%    1.725629e+04   38.098773  \n",
       "75%    4.467984e+04   75.728271  \n",
       "max    2.052775e+06  165.787876  \n",
       "\n",
       "[8 rows x 59 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "updated_points.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "de00c266",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "updated_points['unique_key'].is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "371499a7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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