src/preprocess.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "72e7105c-5181-4eec-9e89-d958a02a5f09",
   "metadata": {},
   "source": [
    "# Preprocess\n",
    "\n",
    "This notebook preprocesses the downloaded data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bd3e619-c49b-4805-8c7a-4c8ae452e61e",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7d272929-1e2d-4f00-a96b-b0ff2f87e530",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "folder = '../tmp/downloads' # stores downloads\n",
    "dist_folder = '../tmp/dist' # stores outputs\n",
    "data_sources_file = 'data.csv'\n",
    "study_area_dataset = 'toronto' # bbox will be used as study area\n",
    "force = False # whether to force processing\n",
    "processed = {} # stores processed datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8fc248e-69b6-4421-8ff8-cd4b071e790f",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "33e7fd0d-55f2-4cc0-966d-3be42aee9496",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "\n",
    "from datetime import datetime, timedelta\n",
    "from IPython.display import display, HTML\n",
    "from pathlib import Path\n",
    "from shapely.geometry import box, Point\n",
    "from time import time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ef5f143-32c2-4b23-bf0e-68a79cdb396b",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8602a104-3590-49c3-8220-79151840909e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For timing processes and displaying processing messages\n",
    "class Timer:\n",
    "    \n",
    "    def __init__(self, name, process_name=None, start_prefix=None, end_prefix=None):\n",
    "        self.name = name\n",
    "        self.process_name = process_name\n",
    "        self.start_prefix = start_prefix if start_prefix else 'Loading' if process_name.startswith('already') else 'Processing'\n",
    "        self.end_prefix = end_prefix if end_prefix else 'Loaded' if process_name.startswith('already') else 'Processed'\n",
    "    \n",
    "    def start(self):\n",
    "        self.start = time()\n",
    "        self.start_dt = datetime.now()\n",
    "        print(f'{self.start_prefix} {self.name} - {self.process_name} ({self.start_dt})...')\n",
    "    \n",
    "    def end(self):\n",
    "        self.end = time()\n",
    "        self.end_dt = datetime.now()\n",
    "        self.elapsed = str(timedelta(seconds=self.end - self.start))\n",
    "        print(f'{self.end_prefix} {self.name} - {self.process_name} ({self.end_dt}) [Elapsed: {self.elapsed}]')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64ba832f-d332-44a2-882a-add13fd27b96",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8dc3d67c-a617-4eae-bb3e-ced1dc521499",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Formats human readable sizes in bytes\n",
    "# https://stackoverflow.com/questions/1094841/get-human-readable-version-of-file-size\n",
    "def format_size(num, suffix=\"B\"):\n",
    "    for unit in [\"\", \"Ki\", \"Mi\", \"Gi\", \"Ti\", \"Pi\", \"Ei\", \"Zi\"]:\n",
    "        if abs(num) < 1024.0:\n",
    "            return f\"{num:3.1f} {unit}{suffix}\"\n",
    "        num /= 1024.0\n",
    "    return f\"{num:.1f} Yi{suffix}\"\n",
    "\n",
    "# Extracts file info for summary\n",
    "def file_info(file):\n",
    "    stats = Path(file).stat()\n",
    "    modified = datetime.fromtimestamp(stats.st_mtime)\n",
    "    created = datetime.fromtimestamp(stats.st_birthtime)\n",
    "    size = format_size(stats.st_size)\n",
    "    out = {'created': created, 'modified': modified, 'size': size}\n",
    "    return out\n",
    "\n",
    "# Get basic info on a pandas dataframe\n",
    "def df_info(df, name):\n",
    "    memory = format_size(df.memory_usage().sum())\n",
    "    cols = len(df.columns)\n",
    "    rows = len(df)\n",
    "    geometry = str(pd.unique(df.geometry.geom_type)) if 'geometry' in df else None\n",
    "    out = {\n",
    "        'dataset': name,\n",
    "        'columns': cols,\n",
    "        'rows': rows,\n",
    "        'memory_usage': memory,\n",
    "        'geometry': geometry\n",
    "    }\n",
    "    return out\n",
    "\n",
    "# Read csv, shp, or geojson file into pandas or geopandas\n",
    "def read_data(file, sheet_name=None, *args, **kwargs):\n",
    "    path = Path(file)\n",
    "    ext = path.suffix if path.suffix != '.zip' else path.suffixes[0]\n",
    "    ext = ext.lower()\n",
    "    if ext == '.csv':\n",
    "        out = pd.read_csv(file, *args, **kwargs)\n",
    "    elif ext in ['.xlsx', '.xls']:\n",
    "        out = pd.read_excel(file, sheet_name=sheet_name, *args, **kwargs)\n",
    "    elif ext in ['.geojson', '.shp']:\n",
    "        out = gpd.read_file(file, *args, **kwargs)\n",
    "    else:\n",
    "        raise ValueError('File format not supported')\n",
    "    return out\n",
    "\n",
    "# Read data sources into pandas or geopandas datasets\n",
    "def read_datasets(sources, folder=folder, *args, **kwargs):\n",
    "    out = {}\n",
    "    for row in sources.itertuples():\n",
    "        \n",
    "        # Define vars\n",
    "        dataset = Path(row.file).with_suffix('').stem\n",
    "        file = f'{folder}/{row.file}'\n",
    "        \n",
    "        # Start loading\n",
    "        timer = Timer(file, 'read dataset', 'Loading', 'Loaded')\n",
    "        timer.start()\n",
    "        out[dataset] = read_data(file, *args, **kwargs)\n",
    "        \n",
    "        # End loading\n",
    "        timer.end()\n",
    "    return out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "045c60e3-9212-4b6f-93f2-86d019f497e1",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "Read datasets from downloaded data files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2384929e-eec3-4b5f-af00-27da5f37324d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_sources = read_data(data_sources_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "756ce4ee-1602-4081-8cf0-1405ce9543e2",
   "metadata": {},
   "source": [
    "### Non-Excel Spreadsheets\n",
    "\n",
    "Read non-excel spreadsheet datasets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0fdfc7c2-809d-458e-bc7e-4cfcbc2e6ec7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading ../tmp/downloads/toronto.geojson - read dataset (2022-02-17 21:32:34.448683)...\n",
      "Loaded ../tmp/downloads/toronto.geojson - read dataset (2022-02-17 21:32:34.585769) [Elapsed: 0:00:00.137086]\n",
      "Loading ../tmp/downloads/centrelines.geojson.zip - read dataset (2022-02-17 21:32:34.585983)...\n",
      "Loaded ../tmp/downloads/centrelines.geojson.zip - read dataset (2022-02-17 21:32:43.192651) [Elapsed: 0:00:08.606663]\n",
      "Loading ../tmp/downloads/collisions.geojson - read dataset (2022-02-17 21:32:43.192825)...\n",
      "Loaded ../tmp/downloads/collisions.geojson - read dataset (2022-02-17 21:33:14.048838) [Elapsed: 0:00:30.856011]\n",
      "Loading ../tmp/downloads/traffic.csv.zip - read dataset (2022-02-17 21:33:14.048993)...\n",
      "Loaded ../tmp/downloads/traffic.csv.zip - read dataset (2022-02-17 21:33:15.150469) [Elapsed: 0:00:01.101474]\n",
      "Loading ../tmp/downloads/autospeed_enforcement.geojson - read dataset (2022-02-17 21:33:15.150653)...\n",
      "Loaded ../tmp/downloads/autospeed_enforcement.geojson - read dataset (2022-02-17 21:33:15.158795) [Elapsed: 0:00:00.008142]\n",
      "Loading ../tmp/downloads/watch_your_speed.geojson - read dataset (2022-02-17 21:33:15.158904)...\n",
      "Loaded ../tmp/downloads/watch_your_speed.geojson - read dataset (2022-02-17 21:33:15.199176) [Elapsed: 0:00:00.040270]\n",
      "Loading ../tmp/downloads/red_light_cams.geojson - read dataset (2022-02-17 21:33:15.199278)...\n",
      "Loaded ../tmp/downloads/red_light_cams.geojson - read dataset (2022-02-17 21:33:15.220206) [Elapsed: 0:00:00.020928]\n",
      "Loading ../tmp/downloads/police.geojson - read dataset (2022-02-17 21:33:15.220305)...\n",
      "Loaded ../tmp/downloads/police.geojson - read dataset (2022-02-17 21:33:15.224984) [Elapsed: 0:00:00.004678]\n",
      "Loading ../tmp/downloads/ambulance.geojson - read dataset (2022-02-17 21:33:15.225060)...\n",
      "Loaded ../tmp/downloads/ambulance.geojson - read dataset (2022-02-17 21:33:15.232302) [Elapsed: 0:00:00.007242]\n",
      "Loading ../tmp/downloads/fire_hydrants.geojson - read dataset (2022-02-17 21:33:15.232370)...\n",
      "Loaded ../tmp/downloads/fire_hydrants.geojson - read dataset (2022-02-17 21:33:16.608515) [Elapsed: 0:00:01.376144]\n",
      "Loading ../tmp/downloads/fire_stations.geojson - read dataset (2022-02-17 21:33:16.608682)...\n",
      "Loaded ../tmp/downloads/fire_stations.geojson - read dataset (2022-02-17 21:33:16.621292) [Elapsed: 0:00:00.012609]\n",
      "Loading ../tmp/downloads/renewables.geojson - read dataset (2022-02-17 21:33:16.621400)...\n",
      "Loaded ../tmp/downloads/renewables.geojson - read dataset (2022-02-17 21:33:16.635716) [Elapsed: 0:00:00.014315]\n",
      "Loading ../tmp/downloads/bicycle_parking.geojson - read dataset (2022-02-17 21:33:16.635819)...\n",
      "Loaded ../tmp/downloads/bicycle_parking.geojson - read dataset (2022-02-17 21:33:17.625989) [Elapsed: 0:00:00.990169]\n",
      "Loading ../tmp/downloads/transit_shelters.geojson - read dataset (2022-02-17 21:33:17.626164)...\n",
      "Loaded ../tmp/downloads/transit_shelters.geojson - read dataset (2022-02-17 21:33:18.029288) [Elapsed: 0:00:00.403122]\n",
      "Loading ../tmp/downloads/wayfind.geojson - read dataset (2022-02-17 21:33:18.029465)...\n",
      "Loaded ../tmp/downloads/wayfind.geojson - read dataset (2022-02-17 21:33:18.054490) [Elapsed: 0:00:00.025023]\n",
      "Loading ../tmp/downloads/litter.geojson - read dataset (2022-02-17 21:33:18.054612)...\n",
      "Loaded ../tmp/downloads/litter.geojson - read dataset (2022-02-17 21:33:18.699240) [Elapsed: 0:00:00.644625]\n",
      "Loading ../tmp/downloads/schools.geojson - read dataset (2022-02-17 21:33:18.699402)...\n",
      "Loaded ../tmp/downloads/schools.geojson - read dataset (2022-02-17 21:33:18.795045) [Elapsed: 0:00:00.095641]\n",
      "Loading ../tmp/downloads/childcare.geojson - read dataset (2022-02-17 21:33:18.795182)...\n",
      "Loaded ../tmp/downloads/childcare.geojson - read dataset (2022-02-17 21:33:18.856731) [Elapsed: 0:00:00.061547]\n",
      "Loading ../tmp/downloads/art.geojson - read dataset (2022-02-17 21:33:18.856862)...\n",
      "Loaded ../tmp/downloads/art.geojson - read dataset (2022-02-17 21:33:18.894626) [Elapsed: 0:00:00.037763]\n",
      "Loading ../tmp/downloads/culture.geojson - read dataset (2022-02-17 21:33:18.894740)...\n",
      "Loaded ../tmp/downloads/culture.geojson - read dataset (2022-02-17 21:33:18.984710) [Elapsed: 0:00:00.089968]\n",
      "Loading ../tmp/downloads/religious.geojson - read dataset (2022-02-17 21:33:18.984870)...\n",
      "Loaded ../tmp/downloads/religious.geojson - read dataset (2022-02-17 21:33:19.146848) [Elapsed: 0:00:00.161977]\n",
      "Loading ../tmp/downloads/crime.geojson - read dataset (2022-02-17 21:33:19.147016)...\n",
      "Loaded ../tmp/downloads/crime.geojson - read dataset (2022-02-17 21:33:19.306540) [Elapsed: 0:00:00.159523]\n"
     ]
    }
   ],
   "source": [
    "non_excel = data_sources[~data_sources['file'].str.endswith('.xlsx')]\n",
    "datasets = read_datasets(non_excel)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff29c951-f161-4f5a-bf12-40ade25e973b",
   "metadata": {},
   "source": [
    "### Excel Spreadsheets\n",
    "\n",
    "Combine/remove sheets in excel spreadsheets:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "888317ca-06a2-427e-a5ff-becd8242b564",
   "metadata": {},
   "source": [
    "#### education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d587f112-7b51-41b3-a63c-98310a4468d2",
   "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>Neighbourhood 2008</th>\n",
       "      <th>Neighbourhood Id</th>\n",
       "      <th>Catholic School Graduation 2008</th>\n",
       "      <th>Catholic School Literacy 2008</th>\n",
       "      <th>Catholic University Applicants 2008</th>\n",
       "      <th>Early Development Instrument 2008</th>\n",
       "      <th>Library Activity 2008</th>\n",
       "      <th>Library Open Hours 2008</th>\n",
       "      <th>Library Program Attendance 2008</th>\n",
       "      <th>Library Programs 2008</th>\n",
       "      <th>Library Space 2008</th>\n",
       "      <th>Neighbourhood 2011</th>\n",
       "      <th>Early Development Instrument 2011</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>West Humber-Clairville</td>\n",
       "      <td>1</td>\n",
       "      <td>0.810055</td>\n",
       "      <td>68.518516</td>\n",
       "      <td>33.110366</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>West Humber-Clairville</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mount Olive-Silverstone-Jamestown</td>\n",
       "      <td>2</td>\n",
       "      <td>0.792899</td>\n",
       "      <td>59.200000</td>\n",
       "      <td>19.122257</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>Mount Olive-Silverstone-Jamestown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Thistletown-Beaumond Heights</td>\n",
       "      <td>3</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>68.888885</td>\n",
       "      <td>33.116882</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>Thistletown-Beaumond Heights</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rexdale-Kipling</td>\n",
       "      <td>4</td>\n",
       "      <td>0.864197</td>\n",
       "      <td>74.074073</td>\n",
       "      <td>32.330825</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>Rexdale-Kipling</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Elms-Old Rexdale</td>\n",
       "      <td>5</td>\n",
       "      <td>0.653333</td>\n",
       "      <td>59.200000</td>\n",
       "      <td>28.695652</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Elms-Old Rexdale</td>\n",
       "      <td>1</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>West Hill</td>\n",
       "      <td>136</td>\n",
       "      <td>0.865030</td>\n",
       "      <td>80.213905</td>\n",
       "      <td>38.425926</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>West Hill</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Woburn</td>\n",
       "      <td>137</td>\n",
       "      <td>0.829999</td>\n",
       "      <td>78.787879</td>\n",
       "      <td>37.049179</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Eglinton East</td>\n",
       "      <td>138</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>72.727272</td>\n",
       "      <td>42.424243</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Eglinton East</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Scarborough Village</td>\n",
       "      <td>139</td>\n",
       "      <td>0.788461</td>\n",
       "      <td>76.190475</td>\n",
       "      <td>32.258064</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>Scarborough Village</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Guildwood</td>\n",
       "      <td>140</td>\n",
       "      <td>0.894736</td>\n",
       "      <td>93.333335</td>\n",
       "      <td>42.500000</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Guildwood</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Neighbourhood 2008  Neighbourhood Id  \\\n",
       "0               West Humber-Clairville                 1   \n",
       "1    Mount Olive-Silverstone-Jamestown                 2   \n",
       "2         Thistletown-Beaumond Heights                 3   \n",
       "3                      Rexdale-Kipling                 4   \n",
       "4                     Elms-Old Rexdale                 5   \n",
       "..                                 ...               ...   \n",
       "135                          West Hill               136   \n",
       "136                             Woburn               137   \n",
       "137                      Eglinton East               138   \n",
       "138                Scarborough Village               139   \n",
       "139                          Guildwood               140   \n",
       "\n",
       "     Catholic School Graduation 2008  Catholic School Literacy 2008  \\\n",
       "0                           0.810055                      68.518516   \n",
       "1                           0.792899                      59.200000   \n",
       "2                           0.730000                      68.888885   \n",
       "3                           0.864197                      74.074073   \n",
       "4                           0.653333                      59.200000   \n",
       "..                               ...                            ...   \n",
       "135                         0.865030                      80.213905   \n",
       "136                         0.829999                      78.787879   \n",
       "137                         0.833333                      72.727272   \n",
       "138                         0.788461                      76.190475   \n",
       "139                         0.894736                      93.333335   \n",
       "\n",
       "     Catholic University Applicants 2008  Early Development Instrument 2008  \\\n",
       "0                              33.110366                                 50   \n",
       "1                              19.122257                                 50   \n",
       "2                              33.116882                                100   \n",
       "3                              32.330825                                 50   \n",
       "4                              28.695652                                 50   \n",
       "..                                   ...                                ...   \n",
       "135                            38.425926                                 50   \n",
       "136                            37.049179                                 50   \n",
       "137                            42.424243                                100   \n",
       "138                            32.258064                                 50   \n",
       "139                            42.500000                                100   \n",
       "\n",
       "     Library Activity 2008  Library Open Hours 2008  \\\n",
       "0                        1                        4   \n",
       "1                        5                        8   \n",
       "2                        5                        9   \n",
       "3                        5                        9   \n",
       "4                        1                        4   \n",
       "..                     ...                      ...   \n",
       "135                      2                        4   \n",
       "136                      5                        7   \n",
       "137                      2                        4   \n",
       "138                      4                        7   \n",
       "139                      1                        4   \n",
       "\n",
       "     Library Program Attendance 2008  Library Programs 2008  \\\n",
       "0                                  2                      3   \n",
       "1                                  7                      6   \n",
       "2                                  6                      6   \n",
       "3                                  6                      6   \n",
       "4                                  1                      2   \n",
       "..                               ...                    ...   \n",
       "135                                2                      2   \n",
       "136                                6                      6   \n",
       "137                                2                      3   \n",
       "138                                5                      6   \n",
       "139                                2                      2   \n",
       "\n",
       "     Library Space 2008                 Neighbourhood 2011  \\\n",
       "0                     2             West Humber-Clairville   \n",
       "1                     6  Mount Olive-Silverstone-Jamestown   \n",
       "2                     6       Thistletown-Beaumond Heights   \n",
       "3                     6                    Rexdale-Kipling   \n",
       "4                     2                   Elms-Old Rexdale   \n",
       "..                  ...                                ...   \n",
       "135                   3                          West Hill   \n",
       "136                   5                             Woburn   \n",
       "137                   2                      Eglinton East   \n",
       "138                   5                Scarborough Village   \n",
       "139                   2                          Guildwood   \n",
       "\n",
       "     Early Development Instrument 2011  \n",
       "0                                  100  \n",
       "1                                    1  \n",
       "2                                   50  \n",
       "3                                  100  \n",
       "4                                    1  \n",
       "..                                 ...  \n",
       "135                                  1  \n",
       "136                                100  \n",
       "137                                 50  \n",
       "138                                 50  \n",
       "139                                100  \n",
       "\n",
       "[140 rows x 13 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read selected sheets\n",
    "name = 'education'\n",
    "datasets[name] = read_data(f'{folder}/{name}.xlsx', sheet_name=[1,2])\n",
    "\n",
    "# Rename cols for ref year\n",
    "datasets[name][1].columns = [\n",
    "    f'{c} 2008' if c != 'Neighbourhood Id' else c for c in datasets[name][1].columns\n",
    "]\n",
    "datasets[name][2].columns = [\n",
    "    f'{c} 2011' if c != 'Neighbourhood Id' else c for c in datasets[name][2].columns\n",
    "]\n",
    "\n",
    "# Join sheets together\n",
    "datasets[name] = pd.merge(\n",
    "    datasets[name][1],\n",
    "    datasets[name][2],\n",
    "    on='Neighbourhood Id',\n",
    "    how='left'\n",
    ")\n",
    "\n",
    "datasets['education']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42f74a76-6f18-46df-aea8-8142fadd31fc",
   "metadata": {},
   "source": [
    "#### environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f54c01e6-943f-4d5e-9dac-c5ddcd46f5d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighbourhood 2008</th>\n",
       "      <th>Neighbourhood Id</th>\n",
       "      <th>City Green Retrofits 2008</th>\n",
       "      <th>Green Rebate Programs 2008</th>\n",
       "      <th>Green Spaces 2008</th>\n",
       "      <th>Polluting Facilities 2008</th>\n",
       "      <th>Tree Cover 2008</th>\n",
       "      <th>Neighbourhood 2011</th>\n",
       "      <th>Green Rebate Programs 2011</th>\n",
       "      <th>Green Spaces 2011</th>\n",
       "      <th>Pollutant Carcinogenic TEP Score 2011</th>\n",
       "      <th>Pollutant Non-Carcinogenic TEP Score 2011</th>\n",
       "      <th>Pollutants Released to Air 2011</th>\n",
       "      <th>Tree Cover 2011</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>West Humber-Clairville</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>339</td>\n",
       "      <td>2.09802</td>\n",
       "      <td>81</td>\n",
       "      <td>2.895861e+06</td>\n",
       "      <td>West Humber-Clairville</td>\n",
       "      <td>428</td>\n",
       "      <td>2.078836</td>\n",
       "      <td>5737.87</td>\n",
       "      <td>18658529.73</td>\n",
       "      <td>1585690</td>\n",
       "      <td>2.895861e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mount Olive-Silverstone-Jamestown</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>152</td>\n",
       "      <td>1.03964</td>\n",
       "      <td>0</td>\n",
       "      <td>1.303945e+06</td>\n",
       "      <td>Mount Olive-Silverstone-Jamestown</td>\n",
       "      <td>250</td>\n",
       "      <td>1.048870</td>\n",
       "      <td>29.76</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>31</td>\n",
       "      <td>1.303945e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Thistletown-Beaumond Heights</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>0.93849</td>\n",
       "      <td>0</td>\n",
       "      <td>1.499130e+06</td>\n",
       "      <td>Thistletown-Beaumond Heights</td>\n",
       "      <td>118</td>\n",
       "      <td>0.939108</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1.499130e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rexdale-Kipling</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>103</td>\n",
       "      <td>0.24065</td>\n",
       "      <td>0</td>\n",
       "      <td>7.563823e+05</td>\n",
       "      <td>Rexdale-Kipling</td>\n",
       "      <td>121</td>\n",
       "      <td>0.240663</td>\n",
       "      <td>0.00</td>\n",
       "      <td>37632.00</td>\n",
       "      <td>35920</td>\n",
       "      <td>7.563823e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Elms-Old Rexdale</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>0.70749</td>\n",
       "      <td>1</td>\n",
       "      <td>9.975108e+05</td>\n",
       "      <td>Elms-Old Rexdale</td>\n",
       "      <td>73</td>\n",
       "      <td>0.730090</td>\n",
       "      <td>0.00</td>\n",
       "      <td>309.00</td>\n",
       "      <td>309</td>\n",
       "      <td>9.975108e+05</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>West Hill</td>\n",
       "      <td>136</td>\n",
       "      <td>2</td>\n",
       "      <td>271</td>\n",
       "      <td>1.96760</td>\n",
       "      <td>10</td>\n",
       "      <td>3.403338e+06</td>\n",
       "      <td>West Hill</td>\n",
       "      <td>338</td>\n",
       "      <td>1.993607</td>\n",
       "      <td>104759.13</td>\n",
       "      <td>54076528.30</td>\n",
       "      <td>371626</td>\n",
       "      <td>3.403338e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Woburn</td>\n",
       "      <td>137</td>\n",
       "      <td>3</td>\n",
       "      <td>512</td>\n",
       "      <td>1.28579</td>\n",
       "      <td>11</td>\n",
       "      <td>2.746208e+06</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>600</td>\n",
       "      <td>1.354012</td>\n",
       "      <td>0.00</td>\n",
       "      <td>17463.80</td>\n",
       "      <td>2357</td>\n",
       "      <td>2.746208e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Eglinton East</td>\n",
       "      <td>138</td>\n",
       "      <td>0</td>\n",
       "      <td>173</td>\n",
       "      <td>0.23025</td>\n",
       "      <td>3</td>\n",
       "      <td>6.563212e+05</td>\n",
       "      <td>Eglinton East</td>\n",
       "      <td>209</td>\n",
       "      <td>0.233049</td>\n",
       "      <td>27469.60</td>\n",
       "      <td>901516.80</td>\n",
       "      <td>4410</td>\n",
       "      <td>6.563212e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Scarborough Village</td>\n",
       "      <td>139</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>0.28573</td>\n",
       "      <td>0</td>\n",
       "      <td>1.127497e+06</td>\n",
       "      <td>Scarborough Village</td>\n",
       "      <td>115</td>\n",
       "      <td>0.286328</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1.127497e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Guildwood</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>171</td>\n",
       "      <td>0.66435</td>\n",
       "      <td>0</td>\n",
       "      <td>1.681205e+06</td>\n",
       "      <td>Guildwood</td>\n",
       "      <td>179</td>\n",
       "      <td>0.697039</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1.681205e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Neighbourhood 2008  Neighbourhood Id  \\\n",
       "0               West Humber-Clairville                 1   \n",
       "1    Mount Olive-Silverstone-Jamestown                 2   \n",
       "2         Thistletown-Beaumond Heights                 3   \n",
       "3                      Rexdale-Kipling                 4   \n",
       "4                     Elms-Old Rexdale                 5   \n",
       "..                                 ...               ...   \n",
       "135                          West Hill               136   \n",
       "136                             Woburn               137   \n",
       "137                      Eglinton East               138   \n",
       "138                Scarborough Village               139   \n",
       "139                          Guildwood               140   \n",
       "\n",
       "     City Green Retrofits 2008  Green Rebate Programs 2008  Green Spaces 2008  \\\n",
       "0                            5                         339            2.09802   \n",
       "1                            7                         152            1.03964   \n",
       "2                            0                         110            0.93849   \n",
       "3                            1                         103            0.24065   \n",
       "4                            1                          60            0.70749   \n",
       "..                         ...                         ...                ...   \n",
       "135                          2                         271            1.96760   \n",
       "136                          3                         512            1.28579   \n",
       "137                          0                         173            0.23025   \n",
       "138                          1                         100            0.28573   \n",
       "139                          0                         171            0.66435   \n",
       "\n",
       "     Polluting Facilities 2008  Tree Cover 2008  \\\n",
       "0                           81     2.895861e+06   \n",
       "1                            0     1.303945e+06   \n",
       "2                            0     1.499130e+06   \n",
       "3                            0     7.563823e+05   \n",
       "4                            1     9.975108e+05   \n",
       "..                         ...              ...   \n",
       "135                         10     3.403338e+06   \n",
       "136                         11     2.746208e+06   \n",
       "137                          3     6.563212e+05   \n",
       "138                          0     1.127497e+06   \n",
       "139                          0     1.681205e+06   \n",
       "\n",
       "                    Neighbourhood 2011  Green Rebate Programs 2011  \\\n",
       "0               West Humber-Clairville                         428   \n",
       "1    Mount Olive-Silverstone-Jamestown                         250   \n",
       "2         Thistletown-Beaumond Heights                         118   \n",
       "3                      Rexdale-Kipling                         121   \n",
       "4                     Elms-Old Rexdale                          73   \n",
       "..                                 ...                         ...   \n",
       "135                          West Hill                         338   \n",
       "136                             Woburn                         600   \n",
       "137                      Eglinton East                         209   \n",
       "138                Scarborough Village                         115   \n",
       "139                          Guildwood                         179   \n",
       "\n",
       "     Green Spaces 2011  Pollutant Carcinogenic TEP Score 2011  \\\n",
       "0             2.078836                                5737.87   \n",
       "1             1.048870                                  29.76   \n",
       "2             0.939108                                   0.00   \n",
       "3             0.240663                                   0.00   \n",
       "4             0.730090                                   0.00   \n",
       "..                 ...                                    ...   \n",
       "135           1.993607                              104759.13   \n",
       "136           1.354012                                   0.00   \n",
       "137           0.233049                               27469.60   \n",
       "138           0.286328                                   0.00   \n",
       "139           0.697039                                   0.00   \n",
       "\n",
       "     Pollutant Non-Carcinogenic TEP Score 2011  \\\n",
       "0                                  18658529.73   \n",
       "1                                      2015.00   \n",
       "2                                         0.00   \n",
       "3                                     37632.00   \n",
       "4                                       309.00   \n",
       "..                                         ...   \n",
       "135                                54076528.30   \n",
       "136                                   17463.80   \n",
       "137                                  901516.80   \n",
       "138                                       0.00   \n",
       "139                                       0.00   \n",
       "\n",
       "     Pollutants Released to Air 2011  Tree Cover 2011  \n",
       "0                            1585690     2.895861e+06  \n",
       "1                                 31     1.303945e+06  \n",
       "2                                  0     1.499130e+06  \n",
       "3                              35920     7.563823e+05  \n",
       "4                                309     9.975108e+05  \n",
       "..                               ...              ...  \n",
       "135                           371626     3.403338e+06  \n",
       "136                             2357     2.746208e+06  \n",
       "137                             4410     6.563212e+05  \n",
       "138                                0     1.127497e+06  \n",
       "139                                0     1.681205e+06  \n",
       "\n",
       "[140 rows x 14 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read selected sheets\n",
    "name = 'environment'\n",
    "datasets[name] = read_data(f'{folder}/{name}.xlsx', sheet_name=[1,2])\n",
    "\n",
    "# Rename cols for ref year\n",
    "datasets[name][1].columns = [\n",
    "    f'{c} 2008' if c != 'Neighbourhood Id' else c for c in datasets[name][1].columns\n",
    "]\n",
    "datasets[name][2].columns = [\n",
    "    f'{c} 2011' if c != 'Neighbourhood Id' else c for c in datasets[name][2].columns\n",
    "]\n",
    "\n",
    "# Join sheets together\n",
    "datasets[name] = pd.merge(\n",
    "    datasets[name][1],\n",
    "    datasets[name][2],\n",
    "    on='Neighbourhood Id',\n",
    "    how='left'\n",
    ")\n",
    "\n",
    "datasets['environment']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93175bee-b4aa-4903-99cf-e321d907d595",
   "metadata": {},
   "source": [
    "#### demographics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0f4601f0-e407-4925-b639-1932f9471c2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighbourhood Id</th>\n",
       "      <th>Neighbourhood 2008</th>\n",
       "      <th>Pop2016 2008</th>\n",
       "      <th>Pop2011 2008</th>\n",
       "      <th>PopChg11t16 2008</th>\n",
       "      <th>Neighbourhood 2016</th>\n",
       "      <th>0 to 04 years 2016</th>\n",
       "      <th>0 to 14 years 2016</th>\n",
       "      <th>05 to 09 years 2016</th>\n",
       "      <th>10 to 14 years 2016</th>\n",
       "      <th>...</th>\n",
       "      <th>65 to 69 years 2016</th>\n",
       "      <th>65 years and over 2016</th>\n",
       "      <th>70 to 74 years 2016</th>\n",
       "      <th>75 to 79 years 2016</th>\n",
       "      <th>80 to 84 years 2016</th>\n",
       "      <th>85 to 89 years 2016</th>\n",
       "      <th>85 years and over 2016</th>\n",
       "      <th>90 to 94 years 2016</th>\n",
       "      <th>95 to 99 years 2016</th>\n",
       "      <th>Total Population - All Age Groups - 100% data 2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>West Humber-Clairville</td>\n",
       "      <td>33312</td>\n",
       "      <td>34100</td>\n",
       "      <td>-0.023109</td>\n",
       "      <td>West Humber-Clairville</td>\n",
       "      <td>1540.0</td>\n",
       "      <td>5060.0</td>\n",
       "      <td>1720.0</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1595.0</td>\n",
       "      <td>4980.0</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>885.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>615.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>33320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Mount Olive-Silverstone-Jamestown</td>\n",
       "      <td>32954</td>\n",
       "      <td>32788</td>\n",
       "      <td>0.005063</td>\n",
       "      <td>Mount Olive-Silverstone-Jamestown</td>\n",
       "      <td>2190.0</td>\n",
       "      <td>7090.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2415.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1285.0</td>\n",
       "      <td>3560.0</td>\n",
       "      <td>885.0</td>\n",
       "      <td>630.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>32950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Thistletown-Beaumond Heights</td>\n",
       "      <td>10360</td>\n",
       "      <td>10138</td>\n",
       "      <td>0.021898</td>\n",
       "      <td>Thistletown-Beaumond Heights</td>\n",
       "      <td>540.0</td>\n",
       "      <td>1730.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>595.0</td>\n",
       "      <td>...</td>\n",
       "      <td>490.0</td>\n",
       "      <td>1880.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>335.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Rexdale-Kipling</td>\n",
       "      <td>10529</td>\n",
       "      <td>10488</td>\n",
       "      <td>0.003909</td>\n",
       "      <td>Rexdale-Kipling</td>\n",
       "      <td>560.0</td>\n",
       "      <td>1640.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>...</td>\n",
       "      <td>520.0</td>\n",
       "      <td>1730.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>295.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>10530.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Elms-Old Rexdale</td>\n",
       "      <td>9456</td>\n",
       "      <td>9550</td>\n",
       "      <td>-0.009843</td>\n",
       "      <td>Elms-Old Rexdale</td>\n",
       "      <td>540.0</td>\n",
       "      <td>1805.0</td>\n",
       "      <td>605.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>...</td>\n",
       "      <td>415.0</td>\n",
       "      <td>1275.0</td>\n",
       "      <td>305.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9460.0</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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>136</td>\n",
       "      <td>West Hill</td>\n",
       "      <td>27392</td>\n",
       "      <td>26547</td>\n",
       "      <td>0.031830</td>\n",
       "      <td>West Hill</td>\n",
       "      <td>1425.0</td>\n",
       "      <td>4635.0</td>\n",
       "      <td>1590.0</td>\n",
       "      <td>1630.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>4240.0</td>\n",
       "      <td>975.0</td>\n",
       "      <td>775.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>27390.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>137</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>53485</td>\n",
       "      <td>53350</td>\n",
       "      <td>0.002530</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>3140.0</td>\n",
       "      <td>9625.0</td>\n",
       "      <td>3365.0</td>\n",
       "      <td>3125.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>8010.0</td>\n",
       "      <td>1810.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>1330.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>305.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>53490.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>138</td>\n",
       "      <td>Eglinton East</td>\n",
       "      <td>22776</td>\n",
       "      <td>22829</td>\n",
       "      <td>-0.002322</td>\n",
       "      <td>Eglinton East</td>\n",
       "      <td>1315.0</td>\n",
       "      <td>4180.0</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>3505.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>560.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>22775.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>139</td>\n",
       "      <td>Scarborough Village</td>\n",
       "      <td>16724</td>\n",
       "      <td>16609</td>\n",
       "      <td>0.006924</td>\n",
       "      <td>Scarborough Village</td>\n",
       "      <td>1055.0</td>\n",
       "      <td>3365.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>675.0</td>\n",
       "      <td>2225.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>430.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>16730.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>140</td>\n",
       "      <td>Guildwood</td>\n",
       "      <td>9917</td>\n",
       "      <td>9816</td>\n",
       "      <td>0.010289</td>\n",
       "      <td>Guildwood</td>\n",
       "      <td>1295.0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2635.0</td>\n",
       "      <td>510.0</td>\n",
       "      <td>510.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>9910.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Neighbourhood Id                 Neighbourhood 2008  Pop2016 2008  \\\n",
       "0                   1             West Humber-Clairville         33312   \n",
       "1                   2  Mount Olive-Silverstone-Jamestown         32954   \n",
       "2                   3       Thistletown-Beaumond Heights         10360   \n",
       "3                   4                    Rexdale-Kipling         10529   \n",
       "4                   5                   Elms-Old Rexdale          9456   \n",
       "..                ...                                ...           ...   \n",
       "135               136                          West Hill         27392   \n",
       "136               137                             Woburn         53485   \n",
       "137               138                      Eglinton East         22776   \n",
       "138               139                Scarborough Village         16724   \n",
       "139               140                          Guildwood          9917   \n",
       "\n",
       "     Pop2011 2008  PopChg11t16 2008                 Neighbourhood 2016  \\\n",
       "0           34100         -0.023109             West Humber-Clairville   \n",
       "1           32788          0.005063  Mount Olive-Silverstone-Jamestown   \n",
       "2           10138          0.021898       Thistletown-Beaumond Heights   \n",
       "3           10488          0.003909                    Rexdale-Kipling   \n",
       "4            9550         -0.009843                   Elms-Old Rexdale   \n",
       "..            ...               ...                                ...   \n",
       "135         26547          0.031830                          West Hill   \n",
       "136         53350          0.002530                             Woburn   \n",
       "137         22829         -0.002322                      Eglinton East   \n",
       "138         16609          0.006924                Scarborough Village   \n",
       "139          9816          0.010289                          Guildwood   \n",
       "\n",
       "     0 to 04 years 2016  0 to 14 years 2016  05 to 09 years 2016  \\\n",
       "0                1540.0              5060.0               1720.0   \n",
       "1                2190.0              7090.0               2500.0   \n",
       "2                 540.0              1730.0                600.0   \n",
       "3                 560.0              1640.0                515.0   \n",
       "4                 540.0              1805.0                605.0   \n",
       "..                  ...                 ...                  ...   \n",
       "135              1425.0              4635.0               1590.0   \n",
       "136              3140.0              9625.0               3365.0   \n",
       "137              1315.0              4180.0               1480.0   \n",
       "138              1055.0              3365.0               1200.0   \n",
       "139              1295.0               425.0                455.0   \n",
       "\n",
       "     10 to 14 years 2016  ...  65 to 69 years 2016  65 years and over 2016  \\\n",
       "0                 1790.0  ...               1595.0                  4980.0   \n",
       "1                 2415.0  ...               1285.0                  3560.0   \n",
       "2                  595.0  ...                490.0                  1880.0   \n",
       "3                  565.0  ...                520.0                  1730.0   \n",
       "4                  660.0  ...                415.0                  1275.0   \n",
       "..                   ...  ...                  ...                     ...   \n",
       "135               1630.0  ...               1350.0                  4240.0   \n",
       "136               3125.0  ...               2220.0                  8010.0   \n",
       "137               1395.0  ...               1015.0                  3505.0   \n",
       "138               1100.0  ...                675.0                  2225.0   \n",
       "139                  5.0  ...               2635.0                   510.0   \n",
       "\n",
       "     70 to 74 years 2016  75 to 79 years 2016  80 to 84 years 2016  \\\n",
       "0                 1185.0                885.0                700.0   \n",
       "1                  885.0                630.0                465.0   \n",
       "2                  375.0                335.0                320.0   \n",
       "3                  350.0                295.0                270.0   \n",
       "4                  305.0                235.0                180.0   \n",
       "..                   ...                  ...                  ...   \n",
       "135                975.0                775.0                520.0   \n",
       "136               1810.0               1550.0               1330.0   \n",
       "137                815.0                625.0                480.0   \n",
       "138                455.0                365.0                300.0   \n",
       "139                510.0                490.0                350.0   \n",
       "\n",
       "     85 to 89 years 2016  85 years and over 2016  90 to 94 years 2016  \\\n",
       "0                  400.0                   615.0                160.0   \n",
       "1                  225.0                   300.0                 70.0   \n",
       "2                  225.0                   350.0                100.0   \n",
       "3                  205.0                   300.0                 85.0   \n",
       "4                  105.0                   145.0                 40.0   \n",
       "..                   ...                     ...                  ...   \n",
       "135                365.0                   625.0                210.0   \n",
       "136                740.0                  1130.0                305.0   \n",
       "137                345.0                   560.0                170.0   \n",
       "138                245.0                   430.0                135.0   \n",
       "139                540.0                   165.0                 30.0   \n",
       "\n",
       "     95 to 99 years 2016  Total Population - All Age Groups - 100% data 2016  \n",
       "0                   50.0                                            33320.0   \n",
       "1                   10.0                                            32950.0   \n",
       "2                   20.0                                            10360.0   \n",
       "3                   15.0                                            10530.0   \n",
       "4                    5.0                                             9460.0   \n",
       "..                   ...                                                ...   \n",
       "135                 60.0                                            27390.0   \n",
       "136                 60.0                                            53490.0   \n",
       "137                 45.0                                            22775.0   \n",
       "138                 25.0                                            16730.0   \n",
       "139               9910.0                                                NaN   \n",
       "\n",
       "[140 rows x 31 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read selected sheets\n",
    "name = 'demographics'\n",
    "datasets[name] = read_data(f'{folder}/{name}.xlsx', sheet_name=[1,2])\n",
    "\n",
    "# Rename id cols\n",
    "datasets[name][1] = datasets[name][1].rename(columns={\n",
    "    'HoodID': 'Neighbourhood Id'\n",
    "})\n",
    "datasets[name][2] = datasets[name][2].rename(columns={\n",
    "    'NeighbourhoodID': 'Neighbourhood Id'\n",
    "})\n",
    "\n",
    "# Rename cols for ref year\n",
    "datasets[name][1].columns = [\n",
    "    f'{c} 2008' if c != 'Neighbourhood Id' else c for c in datasets[name][1].columns\n",
    "]\n",
    "datasets[name][2].columns = [\n",
    "    f'{c} 2016' if c != 'Neighbourhood Id' else c for c in datasets[name][2].columns\n",
    "]\n",
    "\n",
    "# Join sheets together\n",
    "datasets[name] = pd.merge(\n",
    "    datasets[name][1],\n",
    "    datasets[name][2],\n",
    "    on='Neighbourhood Id',\n",
    "    how='left'\n",
    ")\n",
    "\n",
    "datasets['demographics']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "780054ae-753e-4df2-bbc3-35f4c5c3363c",
   "metadata": {},
   "source": [
    "#### housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "863a8b9c-f627-447b-a385-7fe88bbec933",
   "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>Neighbourhood 2008</th>\n",
       "      <th>Neighbourhood Id</th>\n",
       "      <th>Households Assisted 2008</th>\n",
       "      <th>Rent Bank Applicants 2008</th>\n",
       "      <th>Social Housing Turnover 2008</th>\n",
       "      <th>Social Housing Units 2008</th>\n",
       "      <th>Social Housing Waiting List 2008</th>\n",
       "      <th>Neighbourhood 2011</th>\n",
       "      <th>Home Prices 2011</th>\n",
       "      <th>Mid-Century Highrise Households 2011</th>\n",
       "      <th>Mid-Century Highrise Population 2011</th>\n",
       "      <th>Percent Mid-Century Highrise Households 2011</th>\n",
       "      <th>Percent Mid-Century Highrise Population 2011</th>\n",
       "      <th>Rent Bank Applicants 2011</th>\n",
       "      <th>Social Housing Turnover 2011</th>\n",
       "      <th>Social Housing Units 2011</th>\n",
       "      <th>Social Housing Waiting List 2011</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>West Humber-Clairville</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>1.214285</td>\n",
       "      <td>960</td>\n",
       "      <td>445</td>\n",
       "      <td>West Humber-Clairville</td>\n",
       "      <td>317508</td>\n",
       "      <td>690</td>\n",
       "      <td>1810</td>\n",
       "      <td>6.8</td>\n",
       "      <td>5.3</td>\n",
       "      <td>38</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>952</td>\n",
       "      <td>557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mount Olive-Silverstone-Jamestown</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>8.363636</td>\n",
       "      <td>1138</td>\n",
       "      <td>914</td>\n",
       "      <td>Mount Olive-Silverstone-Jamestown</td>\n",
       "      <td>251119</td>\n",
       "      <td>4110</td>\n",
       "      <td>13395</td>\n",
       "      <td>42.9</td>\n",
       "      <td>40.9</td>\n",
       "      <td>41</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1146</td>\n",
       "      <td>1049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Thistletown-Beaumond Heights</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>447</td>\n",
       "      <td>179</td>\n",
       "      <td>Thistletown-Beaumond Heights</td>\n",
       "      <td>414216</td>\n",
       "      <td>430</td>\n",
       "      <td>1260</td>\n",
       "      <td>13.4</td>\n",
       "      <td>12.4</td>\n",
       "      <td>7</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>447</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rexdale-Kipling</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>432</td>\n",
       "      <td>176</td>\n",
       "      <td>Rexdale-Kipling</td>\n",
       "      <td>392271</td>\n",
       "      <td>600</td>\n",
       "      <td>1050</td>\n",
       "      <td>15.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>19</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>432</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Elms-Old Rexdale</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>9.399999</td>\n",
       "      <td>571</td>\n",
       "      <td>191</td>\n",
       "      <td>Elms-Old Rexdale</td>\n",
       "      <td>233832</td>\n",
       "      <td>870</td>\n",
       "      <td>2305</td>\n",
       "      <td>27.3</td>\n",
       "      <td>24.1</td>\n",
       "      <td>14</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>463</td>\n",
       "      <td>239</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",
       "      <td>...</td>\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>135</th>\n",
       "      <td>West Hill</td>\n",
       "      <td>136</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2089</td>\n",
       "      <td>658</td>\n",
       "      <td>West Hill</td>\n",
       "      <td>308229</td>\n",
       "      <td>3010</td>\n",
       "      <td>5880</td>\n",
       "      <td>31.1</td>\n",
       "      <td>22.1</td>\n",
       "      <td>22</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>2089</td>\n",
       "      <td>725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Woburn</td>\n",
       "      <td>137</td>\n",
       "      <td>12</td>\n",
       "      <td>42</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2594</td>\n",
       "      <td>1144</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>316584</td>\n",
       "      <td>5090</td>\n",
       "      <td>14870</td>\n",
       "      <td>27.8</td>\n",
       "      <td>27.9</td>\n",
       "      <td>26</td>\n",
       "      <td>4.911765</td>\n",
       "      <td>2660</td>\n",
       "      <td>1306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Eglinton East</td>\n",
       "      <td>138</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>4.666666</td>\n",
       "      <td>865</td>\n",
       "      <td>600</td>\n",
       "      <td>Eglinton East</td>\n",
       "      <td>274020</td>\n",
       "      <td>3935</td>\n",
       "      <td>10105</td>\n",
       "      <td>48.8</td>\n",
       "      <td>44.3</td>\n",
       "      <td>33</td>\n",
       "      <td>2.588235</td>\n",
       "      <td>865</td>\n",
       "      <td>688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Scarborough Village</td>\n",
       "      <td>139</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>922</td>\n",
       "      <td>565</td>\n",
       "      <td>Scarborough Village</td>\n",
       "      <td>356096</td>\n",
       "      <td>2905</td>\n",
       "      <td>7860</td>\n",
       "      <td>49.2</td>\n",
       "      <td>47.3</td>\n",
       "      <td>22</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>922</td>\n",
       "      <td>683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Guildwood</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.666666</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Guildwood</td>\n",
       "      <td>444309</td>\n",
       "      <td>605</td>\n",
       "      <td>1020</td>\n",
       "      <td>15.3</td>\n",
       "      <td>10.4</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Neighbourhood 2008  Neighbourhood Id  \\\n",
       "0               West Humber-Clairville                 1   \n",
       "1    Mount Olive-Silverstone-Jamestown                 2   \n",
       "2         Thistletown-Beaumond Heights                 3   \n",
       "3                      Rexdale-Kipling                 4   \n",
       "4                     Elms-Old Rexdale                 5   \n",
       "..                                 ...               ...   \n",
       "135                          West Hill               136   \n",
       "136                             Woburn               137   \n",
       "137                      Eglinton East               138   \n",
       "138                Scarborough Village               139   \n",
       "139                          Guildwood               140   \n",
       "\n",
       "     Households Assisted 2008  Rent Bank Applicants 2008  \\\n",
       "0                           3                         38   \n",
       "1                           1                         62   \n",
       "2                           0                         14   \n",
       "3                           0                         25   \n",
       "4                           0                         14   \n",
       "..                        ...                        ...   \n",
       "135                         5                         30   \n",
       "136                        12                         42   \n",
       "137                         5                         31   \n",
       "138                         0                         30   \n",
       "139                         0                          5   \n",
       "\n",
       "     Social Housing Turnover 2008  Social Housing Units 2008  \\\n",
       "0                        1.214285                        960   \n",
       "1                        8.363636                       1138   \n",
       "2                        0.000000                        447   \n",
       "3                        0.000000                        432   \n",
       "4                        9.399999                        571   \n",
       "..                            ...                        ...   \n",
       "135                      0.000000                       2089   \n",
       "136                      6.000000                       2594   \n",
       "137                      4.666666                        865   \n",
       "138                      1.100000                        922   \n",
       "139                      1.666666                          0   \n",
       "\n",
       "     Social Housing Waiting List 2008                 Neighbourhood 2011  \\\n",
       "0                                 445             West Humber-Clairville   \n",
       "1                                 914  Mount Olive-Silverstone-Jamestown   \n",
       "2                                 179       Thistletown-Beaumond Heights   \n",
       "3                                 176                    Rexdale-Kipling   \n",
       "4                                 191                   Elms-Old Rexdale   \n",
       "..                                ...                                ...   \n",
       "135                               658                          West Hill   \n",
       "136                              1144                             Woburn   \n",
       "137                               600                      Eglinton East   \n",
       "138                               565                Scarborough Village   \n",
       "139                                40                          Guildwood   \n",
       "\n",
       "     Home Prices 2011  Mid-Century Highrise Households 2011  \\\n",
       "0              317508                                   690   \n",
       "1              251119                                  4110   \n",
       "2              414216                                   430   \n",
       "3              392271                                   600   \n",
       "4              233832                                   870   \n",
       "..                ...                                   ...   \n",
       "135            308229                                  3010   \n",
       "136            316584                                  5090   \n",
       "137            274020                                  3935   \n",
       "138            356096                                  2905   \n",
       "139            444309                                   605   \n",
       "\n",
       "     Mid-Century Highrise Population 2011  \\\n",
       "0                                    1810   \n",
       "1                                   13395   \n",
       "2                                    1260   \n",
       "3                                    1050   \n",
       "4                                    2305   \n",
       "..                                    ...   \n",
       "135                                  5880   \n",
       "136                                 14870   \n",
       "137                                 10105   \n",
       "138                                  7860   \n",
       "139                                  1020   \n",
       "\n",
       "     Percent Mid-Century Highrise Households 2011  \\\n",
       "0                                             6.8   \n",
       "1                                            42.9   \n",
       "2                                            13.4   \n",
       "3                                            15.2   \n",
       "4                                            27.3   \n",
       "..                                            ...   \n",
       "135                                          31.1   \n",
       "136                                          27.8   \n",
       "137                                          48.8   \n",
       "138                                          49.2   \n",
       "139                                          15.3   \n",
       "\n",
       "     Percent Mid-Century Highrise Population 2011  Rent Bank Applicants 2011  \\\n",
       "0                                             5.3                         38   \n",
       "1                                            40.9                         41   \n",
       "2                                            12.4                          7   \n",
       "3                                            10.0                         19   \n",
       "4                                            24.1                         14   \n",
       "..                                            ...                        ...   \n",
       "135                                          22.1                         22   \n",
       "136                                          27.9                         26   \n",
       "137                                          44.3                         33   \n",
       "138                                          47.3                         22   \n",
       "139                                          10.4                          3   \n",
       "\n",
       "     Social Housing Turnover 2011  Social Housing Units 2011  \\\n",
       "0                        0.312500                        952   \n",
       "1                        7.000000                       1146   \n",
       "2                        1.333333                        447   \n",
       "3                        4.200000                        432   \n",
       "4                        1.000000                        463   \n",
       "..                            ...                        ...   \n",
       "135                      8.400000                       2089   \n",
       "136                      4.911765                       2660   \n",
       "137                      2.588235                        865   \n",
       "138                      4.400000                        922   \n",
       "139                      0.000000                          0   \n",
       "\n",
       "     Social Housing Waiting List 2011  \n",
       "0                                 557  \n",
       "1                                1049  \n",
       "2                                 270  \n",
       "3                                 186  \n",
       "4                                 239  \n",
       "..                                ...  \n",
       "135                               725  \n",
       "136                              1306  \n",
       "137                               688  \n",
       "138                               683  \n",
       "139                                62  \n",
       "\n",
       "[140 rows x 17 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read selected sheets\n",
    "name = 'housing'\n",
    "datasets[name] = read_data(f'{folder}/{name}.xlsx', sheet_name=[1,2])\n",
    "\n",
    "# Rename cols for ref year\n",
    "datasets[name][1].columns = [\n",
    "    f'{c} 2008' if c != 'Neighbourhood Id' else c for c in datasets[name][1].columns\n",
    "]\n",
    "datasets[name][2].columns = [\n",
    "    f'{c} 2011' if c != 'Neighbourhood Id' else c for c in datasets[name][2].columns\n",
    "]\n",
    "\n",
    "# Join sheets together\n",
    "datasets[name] = pd.merge(\n",
    "    datasets[name][1],\n",
    "    datasets[name][2],\n",
    "    on='Neighbourhood Id',\n",
    "    how='left'\n",
    ")\n",
    "\n",
    "datasets['housing']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb5d50ef-9171-4085-bf08-bafb9f28ce2b",
   "metadata": {},
   "source": [
    "#### economics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5ad13955-07a5-4a36-801b-2526ff4c1cac",
   "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>Neighbourhood 2008</th>\n",
       "      <th>Neighbourhood Id</th>\n",
       "      <th>Access to Child Care 2008</th>\n",
       "      <th>Business Licensing 2008</th>\n",
       "      <th>Businesses 2008</th>\n",
       "      <th>Child Care Spaces 2008</th>\n",
       "      <th>Inequality (Gini coeff.) 2008</th>\n",
       "      <th>Local Employment 2008</th>\n",
       "      <th>Social Assistance Recipients 2008</th>\n",
       "      <th>Neighbourhood 2011</th>\n",
       "      <th>Businesses 2011</th>\n",
       "      <th>Child Care Spaces 2011</th>\n",
       "      <th>Debt Risk Score 2011</th>\n",
       "      <th>Home Prices 2011</th>\n",
       "      <th>Local Employment 2011</th>\n",
       "      <th>Social Assistance Recipients 2011</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>West Humber-Clairville</td>\n",
       "      <td>1</td>\n",
       "      <td>0.383582</td>\n",
       "      <td>695</td>\n",
       "      <td>2550</td>\n",
       "      <td>180.00000</td>\n",
       "      <td>0.359380</td>\n",
       "      <td>63385</td>\n",
       "      <td>2702.0</td>\n",
       "      <td>West Humber-Clairville</td>\n",
       "      <td>2463</td>\n",
       "      <td>195</td>\n",
       "      <td>719</td>\n",
       "      <td>317508</td>\n",
       "      <td>58271</td>\n",
       "      <td>2912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mount Olive-Silverstone-Jamestown</td>\n",
       "      <td>2</td>\n",
       "      <td>0.245736</td>\n",
       "      <td>106</td>\n",
       "      <td>273</td>\n",
       "      <td>45.00000</td>\n",
       "      <td>0.420150</td>\n",
       "      <td>3346</td>\n",
       "      <td>6406.0</td>\n",
       "      <td>Mount Olive-Silverstone-Jamestown</td>\n",
       "      <td>271</td>\n",
       "      <td>60</td>\n",
       "      <td>687</td>\n",
       "      <td>251119</td>\n",
       "      <td>3244</td>\n",
       "      <td>6561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Thistletown-Beaumond Heights</td>\n",
       "      <td>3</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>116</td>\n",
       "      <td>236</td>\n",
       "      <td>25.00000</td>\n",
       "      <td>0.382010</td>\n",
       "      <td>1350</td>\n",
       "      <td>1082.0</td>\n",
       "      <td>Thistletown-Beaumond Heights</td>\n",
       "      <td>217</td>\n",
       "      <td>25</td>\n",
       "      <td>718</td>\n",
       "      <td>414216</td>\n",
       "      <td>1311</td>\n",
       "      <td>1276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rexdale-Kipling</td>\n",
       "      <td>4</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>49</td>\n",
       "      <td>155</td>\n",
       "      <td>60.00000</td>\n",
       "      <td>0.388429</td>\n",
       "      <td>1190</td>\n",
       "      <td>1231.0</td>\n",
       "      <td>Rexdale-Kipling</td>\n",
       "      <td>144</td>\n",
       "      <td>75</td>\n",
       "      <td>721</td>\n",
       "      <td>392271</td>\n",
       "      <td>1178</td>\n",
       "      <td>1323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Elms-Old Rexdale</td>\n",
       "      <td>5</td>\n",
       "      <td>0.321622</td>\n",
       "      <td>31</td>\n",
       "      <td>70</td>\n",
       "      <td>60.00000</td>\n",
       "      <td>0.388130</td>\n",
       "      <td>831</td>\n",
       "      <td>1759.0</td>\n",
       "      <td>Elms-Old Rexdale</td>\n",
       "      <td>67</td>\n",
       "      <td>60</td>\n",
       "      <td>692</td>\n",
       "      <td>233832</td>\n",
       "      <td>903</td>\n",
       "      <td>1683</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",
       "      <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>135</th>\n",
       "      <td>West Hill</td>\n",
       "      <td>136</td>\n",
       "      <td>0.490647</td>\n",
       "      <td>225</td>\n",
       "      <td>439</td>\n",
       "      <td>134.00000</td>\n",
       "      <td>0.435829</td>\n",
       "      <td>4551</td>\n",
       "      <td>4448.0</td>\n",
       "      <td>West Hill</td>\n",
       "      <td>424</td>\n",
       "      <td>153</td>\n",
       "      <td>706</td>\n",
       "      <td>308229</td>\n",
       "      <td>4500</td>\n",
       "      <td>4974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Woburn</td>\n",
       "      <td>137</td>\n",
       "      <td>0.214933</td>\n",
       "      <td>333</td>\n",
       "      <td>1020</td>\n",
       "      <td>210.00000</td>\n",
       "      <td>0.414480</td>\n",
       "      <td>14374</td>\n",
       "      <td>6647.0</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>1073</td>\n",
       "      <td>214</td>\n",
       "      <td>723</td>\n",
       "      <td>316584</td>\n",
       "      <td>16190</td>\n",
       "      <td>6747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Eglinton East</td>\n",
       "      <td>138</td>\n",
       "      <td>0.268571</td>\n",
       "      <td>130</td>\n",
       "      <td>313</td>\n",
       "      <td>153.00000</td>\n",
       "      <td>0.399830</td>\n",
       "      <td>2440</td>\n",
       "      <td>4405.0</td>\n",
       "      <td>Eglinton East</td>\n",
       "      <td>296</td>\n",
       "      <td>143</td>\n",
       "      <td>705</td>\n",
       "      <td>274020</td>\n",
       "      <td>2351</td>\n",
       "      <td>4171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Scarborough Village</td>\n",
       "      <td>139</td>\n",
       "      <td>0.350667</td>\n",
       "      <td>127</td>\n",
       "      <td>220</td>\n",
       "      <td>220.00000</td>\n",
       "      <td>0.432119</td>\n",
       "      <td>1926</td>\n",
       "      <td>3981.0</td>\n",
       "      <td>Scarborough Village</td>\n",
       "      <td>228</td>\n",
       "      <td>217</td>\n",
       "      <td>699</td>\n",
       "      <td>356096</td>\n",
       "      <td>1851</td>\n",
       "      <td>4133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Guildwood</td>\n",
       "      <td>140</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>66</td>\n",
       "      <td>145</td>\n",
       "      <td>0.34834</td>\n",
       "      <td>792.000000</td>\n",
       "      <td>300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Guildwood</td>\n",
       "      <td>67</td>\n",
       "      <td>171</td>\n",
       "      <td>774</td>\n",
       "      <td>444309</td>\n",
       "      <td>924</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Neighbourhood 2008  Neighbourhood Id  \\\n",
       "0               West Humber-Clairville                 1   \n",
       "1    Mount Olive-Silverstone-Jamestown                 2   \n",
       "2         Thistletown-Beaumond Heights                 3   \n",
       "3                      Rexdale-Kipling                 4   \n",
       "4                     Elms-Old Rexdale                 5   \n",
       "..                                 ...               ...   \n",
       "135                          West Hill               136   \n",
       "136                             Woburn               137   \n",
       "137                      Eglinton East               138   \n",
       "138                Scarborough Village               139   \n",
       "139                          Guildwood               140   \n",
       "\n",
       "     Access to Child Care 2008  Business Licensing 2008  Businesses 2008  \\\n",
       "0                     0.383582                      695             2550   \n",
       "1                     0.245736                      106              273   \n",
       "2                     0.325000                      116              236   \n",
       "3                     0.240000                       49              155   \n",
       "4                     0.321622                       31               70   \n",
       "..                         ...                      ...              ...   \n",
       "135                   0.490647                      225              439   \n",
       "136                   0.214933                      333             1020   \n",
       "137                   0.268571                      130              313   \n",
       "138                   0.350667                      127              220   \n",
       "139                  41.000000                       66              145   \n",
       "\n",
       "     Child Care Spaces 2008  Inequality (Gini coeff.) 2008  \\\n",
       "0                 180.00000                       0.359380   \n",
       "1                  45.00000                       0.420150   \n",
       "2                  25.00000                       0.382010   \n",
       "3                  60.00000                       0.388429   \n",
       "4                  60.00000                       0.388130   \n",
       "..                      ...                            ...   \n",
       "135               134.00000                       0.435829   \n",
       "136               210.00000                       0.414480   \n",
       "137               153.00000                       0.399830   \n",
       "138               220.00000                       0.432119   \n",
       "139                 0.34834                     792.000000   \n",
       "\n",
       "     Local Employment 2008  Social Assistance Recipients 2008  \\\n",
       "0                    63385                             2702.0   \n",
       "1                     3346                             6406.0   \n",
       "2                     1350                             1082.0   \n",
       "3                     1190                             1231.0   \n",
       "4                      831                             1759.0   \n",
       "..                     ...                                ...   \n",
       "135                   4551                             4448.0   \n",
       "136                  14374                             6647.0   \n",
       "137                   2440                             4405.0   \n",
       "138                   1926                             3981.0   \n",
       "139                    300                                NaN   \n",
       "\n",
       "                    Neighbourhood 2011  Businesses 2011  \\\n",
       "0               West Humber-Clairville             2463   \n",
       "1    Mount Olive-Silverstone-Jamestown              271   \n",
       "2         Thistletown-Beaumond Heights              217   \n",
       "3                      Rexdale-Kipling              144   \n",
       "4                     Elms-Old Rexdale               67   \n",
       "..                                 ...              ...   \n",
       "135                          West Hill              424   \n",
       "136                             Woburn             1073   \n",
       "137                      Eglinton East              296   \n",
       "138                Scarborough Village              228   \n",
       "139                          Guildwood               67   \n",
       "\n",
       "     Child Care Spaces 2011  Debt Risk Score 2011  Home Prices 2011  \\\n",
       "0                       195                   719            317508   \n",
       "1                        60                   687            251119   \n",
       "2                        25                   718            414216   \n",
       "3                        75                   721            392271   \n",
       "4                        60                   692            233832   \n",
       "..                      ...                   ...               ...   \n",
       "135                     153                   706            308229   \n",
       "136                     214                   723            316584   \n",
       "137                     143                   705            274020   \n",
       "138                     217                   699            356096   \n",
       "139                     171                   774            444309   \n",
       "\n",
       "     Local Employment 2011  Social Assistance Recipients 2011  \n",
       "0                    58271                               2912  \n",
       "1                     3244                               6561  \n",
       "2                     1311                               1276  \n",
       "3                     1178                               1323  \n",
       "4                      903                               1683  \n",
       "..                     ...                                ...  \n",
       "135                   4500                               4974  \n",
       "136                  16190                               6747  \n",
       "137                   2351                               4171  \n",
       "138                   1851                               4133  \n",
       "139                    924                                309  \n",
       "\n",
       "[140 rows x 16 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read selected sheets\n",
    "name = 'economics'\n",
    "datasets[name] = read_data(f'{folder}/{name}.xlsx', sheet_name=[1,2], skiprows=1)\n",
    "\n",
    "# Rename cols for ref year\n",
    "datasets[name][1].columns = [\n",
    "    f'{c} 2008' if c != 'Neighbourhood Id' else c for c in datasets[name][1].columns\n",
    "]\n",
    "datasets[name][2].columns = [\n",
    "    f'{c} 2011' if c != 'Neighbourhood Id' else c for c in datasets[name][2].columns\n",
    "]\n",
    "\n",
    "# Join sheets together\n",
    "datasets[name] = pd.merge(\n",
    "    datasets[name][1],\n",
    "    datasets[name][2],\n",
    "    on='Neighbourhood Id',\n",
    "    how='left'\n",
    ")\n",
    "\n",
    "datasets['economics']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b4df4f2-8d80-435a-a247-48e8c72230fb",
   "metadata": {},
   "source": [
    "#### civics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eb9b075f-fa52-43b0-8be0-e43830037e08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Neighbourhood 2008</th>\n",
       "      <th>Neighbourhood Id</th>\n",
       "      <th>City Beautification 2008</th>\n",
       "      <th>City Grants Funding $ 2008</th>\n",
       "      <th>Diversity Index 2008</th>\n",
       "      <th>Voter Turnout 2008</th>\n",
       "      <th>Watermain Breaks 2008</th>\n",
       "      <th>Neighbourhood 2011</th>\n",
       "      <th>City Grants Funding $ 2011</th>\n",
       "      <th>Neighbourhood Equity Score 2011</th>\n",
       "      <th>Salvation Army Donors 2011</th>\n",
       "      <th>Walk Score 2011</th>\n",
       "      <th>Watermain Breaks 2011</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>West Humber-Clairville</td>\n",
       "      <td>1</td>\n",
       "      <td>191</td>\n",
       "      <td>520390.000</td>\n",
       "      <td>4.769540</td>\n",
       "      <td>34.7571</td>\n",
       "      <td>30</td>\n",
       "      <td>West Humber-Clairville</td>\n",
       "      <td>123283.0</td>\n",
       "      <td>45.78</td>\n",
       "      <td>640</td>\n",
       "      <td>57</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mount Olive-Silverstone-Jamestown</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>10040.000</td>\n",
       "      <td>4.969163</td>\n",
       "      <td>28.1441</td>\n",
       "      <td>8</td>\n",
       "      <td>Mount Olive-Silverstone-Jamestown</td>\n",
       "      <td>238172.0</td>\n",
       "      <td>29.29</td>\n",
       "      <td>222</td>\n",
       "      <td>61</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Thistletown-Beaumond Heights</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>158658.000</td>\n",
       "      <td>5.106365</td>\n",
       "      <td>40.5373</td>\n",
       "      <td>9</td>\n",
       "      <td>Thistletown-Beaumond Heights</td>\n",
       "      <td>7296.0</td>\n",
       "      <td>39.78</td>\n",
       "      <td>218</td>\n",
       "      <td>54</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rexdale-Kipling</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>49210.000</td>\n",
       "      <td>5.208610</td>\n",
       "      <td>39.7091</td>\n",
       "      <td>5</td>\n",
       "      <td>Rexdale-Kipling</td>\n",
       "      <td>35100.0</td>\n",
       "      <td>43.76</td>\n",
       "      <td>336</td>\n",
       "      <td>58</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Elms-Old Rexdale</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>42870.000</td>\n",
       "      <td>5.502152</td>\n",
       "      <td>34.0128</td>\n",
       "      <td>10</td>\n",
       "      <td>Elms-Old Rexdale</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.54</td>\n",
       "      <td>152</td>\n",
       "      <td>48</td>\n",
       "      <td>10</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>West Hill</td>\n",
       "      <td>136</td>\n",
       "      <td>79</td>\n",
       "      <td>297563.000</td>\n",
       "      <td>5.301753</td>\n",
       "      <td>32.5398</td>\n",
       "      <td>8</td>\n",
       "      <td>West Hill</td>\n",
       "      <td>55370.0</td>\n",
       "      <td>37.25</td>\n",
       "      <td>765</td>\n",
       "      <td>66</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Woburn</td>\n",
       "      <td>137</td>\n",
       "      <td>105</td>\n",
       "      <td>371010.000</td>\n",
       "      <td>5.021696</td>\n",
       "      <td>37.2474</td>\n",
       "      <td>22</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>144255.0</td>\n",
       "      <td>39.01</td>\n",
       "      <td>1429</td>\n",
       "      <td>66</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Eglinton East</td>\n",
       "      <td>138</td>\n",
       "      <td>46</td>\n",
       "      <td>94295.000</td>\n",
       "      <td>5.184124</td>\n",
       "      <td>35.3157</td>\n",
       "      <td>5</td>\n",
       "      <td>Eglinton East</td>\n",
       "      <td>5800.0</td>\n",
       "      <td>36.28</td>\n",
       "      <td>396</td>\n",
       "      <td>62</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Scarborough Village</td>\n",
       "      <td>139</td>\n",
       "      <td>32</td>\n",
       "      <td>253268.125</td>\n",
       "      <td>5.178616</td>\n",
       "      <td>32.9390</td>\n",
       "      <td>6</td>\n",
       "      <td>Scarborough Village</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>33.94</td>\n",
       "      <td>410</td>\n",
       "      <td>70</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Guildwood</td>\n",
       "      <td>140</td>\n",
       "      <td>18</td>\n",
       "      <td>15000.000</td>\n",
       "      <td>4.513205</td>\n",
       "      <td>43.0591</td>\n",
       "      <td>14</td>\n",
       "      <td>Guildwood</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>66.19</td>\n",
       "      <td>903</td>\n",
       "      <td>59</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Neighbourhood 2008  Neighbourhood Id  \\\n",
       "0               West Humber-Clairville                 1   \n",
       "1    Mount Olive-Silverstone-Jamestown                 2   \n",
       "2         Thistletown-Beaumond Heights                 3   \n",
       "3                      Rexdale-Kipling                 4   \n",
       "4                     Elms-Old Rexdale                 5   \n",
       "..                                 ...               ...   \n",
       "135                          West Hill               136   \n",
       "136                             Woburn               137   \n",
       "137                      Eglinton East               138   \n",
       "138                Scarborough Village               139   \n",
       "139                          Guildwood               140   \n",
       "\n",
       "     City Beautification 2008  City Grants Funding $ 2008  \\\n",
       "0                         191                  520390.000   \n",
       "1                          55                   10040.000   \n",
       "2                          25                  158658.000   \n",
       "3                          19                   49210.000   \n",
       "4                          17                   42870.000   \n",
       "..                        ...                         ...   \n",
       "135                        79                  297563.000   \n",
       "136                       105                  371010.000   \n",
       "137                        46                   94295.000   \n",
       "138                        32                  253268.125   \n",
       "139                        18                   15000.000   \n",
       "\n",
       "     Diversity Index 2008  Voter Turnout 2008  Watermain Breaks 2008  \\\n",
       "0                4.769540             34.7571                     30   \n",
       "1                4.969163             28.1441                      8   \n",
       "2                5.106365             40.5373                      9   \n",
       "3                5.208610             39.7091                      5   \n",
       "4                5.502152             34.0128                     10   \n",
       "..                    ...                 ...                    ...   \n",
       "135              5.301753             32.5398                      8   \n",
       "136              5.021696             37.2474                     22   \n",
       "137              5.184124             35.3157                      5   \n",
       "138              5.178616             32.9390                      6   \n",
       "139              4.513205             43.0591                     14   \n",
       "\n",
       "                    Neighbourhood 2011  City Grants Funding $ 2011  \\\n",
       "0               West Humber-Clairville                    123283.0   \n",
       "1    Mount Olive-Silverstone-Jamestown                    238172.0   \n",
       "2         Thistletown-Beaumond Heights                      7296.0   \n",
       "3                      Rexdale-Kipling                     35100.0   \n",
       "4                     Elms-Old Rexdale                         0.0   \n",
       "..                                 ...                         ...   \n",
       "135                          West Hill                     55370.0   \n",
       "136                             Woburn                    144255.0   \n",
       "137                      Eglinton East                      5800.0   \n",
       "138                Scarborough Village                      3200.0   \n",
       "139                          Guildwood                     11500.0   \n",
       "\n",
       "     Neighbourhood Equity Score 2011  Salvation Army Donors 2011  \\\n",
       "0                              45.78                         640   \n",
       "1                              29.29                         222   \n",
       "2                              39.78                         218   \n",
       "3                              43.76                         336   \n",
       "4                              29.54                         152   \n",
       "..                               ...                         ...   \n",
       "135                            37.25                         765   \n",
       "136                            39.01                        1429   \n",
       "137                            36.28                         396   \n",
       "138                            33.94                         410   \n",
       "139                            66.19                         903   \n",
       "\n",
       "     Walk Score 2011  Watermain Breaks 2011  \n",
       "0                 57                     30  \n",
       "1                 61                      8  \n",
       "2                 54                      9  \n",
       "3                 58                      5  \n",
       "4                 48                     10  \n",
       "..               ...                    ...  \n",
       "135               66                      8  \n",
       "136               66                     22  \n",
       "137               62                      5  \n",
       "138               70                      6  \n",
       "139               59                     14  \n",
       "\n",
       "[140 rows x 13 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read selected sheets\n",
    "name = 'civics'\n",
    "datasets[name] = read_data(f'{folder}/{name}.xlsx', sheet_name=[1,2], skiprows=1)\n",
    "\n",
    "# Rename cols for ref year\n",
    "datasets[name][1].columns = [\n",
    "    f'{c} 2008' if c != 'Neighbourhood Id' else c for c in datasets[name][1].columns\n",
    "]\n",
    "datasets[name][2].columns = [\n",
    "    f'{c} 2011' if c != 'Neighbourhood Id' else c for c in datasets[name][2].columns\n",
    "]\n",
    "\n",
    "# Join sheets together\n",
    "datasets[name] = pd.merge(\n",
    "    datasets[name][1],\n",
    "    datasets[name][2],\n",
    "    on='Neighbourhood Id',\n",
    "    how='left'\n",
    ")\n",
    "\n",
    "datasets['civics']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34e489aa-e897-4198-abfa-6e606b973934",
   "metadata": {},
   "source": [
    "#### health"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b9a32998-0e6c-4db5-bb86-0701db459b6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Neighbourhood 2008</th>\n",
       "      <th>Neighbourhood Id</th>\n",
       "      <th>Breast Cancer Screenings 2008</th>\n",
       "      <th>Cervical Cancer Screenings 2008</th>\n",
       "      <th>Colorectal Cancer Screenings 2008</th>\n",
       "      <th>Community Food Programs 2008</th>\n",
       "      <th>Diabetes Prevalence 2008</th>\n",
       "      <th>DineSafe Inspections 2008</th>\n",
       "      <th>Female Fertility 2008</th>\n",
       "      <th>Health Providers 2008</th>\n",
       "      <th>...</th>\n",
       "      <th>Student Nutrition 2008</th>\n",
       "      <th>Teen Pregnancy 2008</th>\n",
       "      <th>Neighbourhood 2011</th>\n",
       "      <th>Breast Cancer Screenings 2011</th>\n",
       "      <th>Cervical Cancer Screenings 2011</th>\n",
       "      <th>DineSafe Inspections 2011</th>\n",
       "      <th>Female Fertility 2011</th>\n",
       "      <th>Health Providers 2011</th>\n",
       "      <th>Premature Mortality 2011</th>\n",
       "      <th>Student Nutrition 2011</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>West Humber-Clairville</td>\n",
       "      <td>1</td>\n",
       "      <td>52.3</td>\n",
       "      <td>60.4</td>\n",
       "      <td>38.7</td>\n",
       "      <td>2</td>\n",
       "      <td>14.0</td>\n",
       "      <td>45</td>\n",
       "      <td>60.286693</td>\n",
       "      <td>94</td>\n",
       "      <td>...</td>\n",
       "      <td>1190</td>\n",
       "      <td>30.917874</td>\n",
       "      <td>West Humber-Clairville</td>\n",
       "      <td>57.02</td>\n",
       "      <td>60.52</td>\n",
       "      <td>10</td>\n",
       "      <td>59.923279</td>\n",
       "      <td>101</td>\n",
       "      <td>223.8</td>\n",
       "      <td>1105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mount Olive-Silverstone-Jamestown</td>\n",
       "      <td>2</td>\n",
       "      <td>47.8</td>\n",
       "      <td>54.6</td>\n",
       "      <td>36.8</td>\n",
       "      <td>3</td>\n",
       "      <td>14.9</td>\n",
       "      <td>2</td>\n",
       "      <td>71.103462</td>\n",
       "      <td>39</td>\n",
       "      <td>...</td>\n",
       "      <td>5026</td>\n",
       "      <td>37.106918</td>\n",
       "      <td>Mount Olive-Silverstone-Jamestown</td>\n",
       "      <td>50.14</td>\n",
       "      <td>55.75</td>\n",
       "      <td>2</td>\n",
       "      <td>64.410021</td>\n",
       "      <td>44</td>\n",
       "      <td>242.6</td>\n",
       "      <td>5132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Thistletown-Beaumond Heights</td>\n",
       "      <td>3</td>\n",
       "      <td>54.8</td>\n",
       "      <td>60.1</td>\n",
       "      <td>38.2</td>\n",
       "      <td>2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>60.681976</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>950</td>\n",
       "      <td>38.418079</td>\n",
       "      <td>Thistletown-Beaumond Heights</td>\n",
       "      <td>55.22</td>\n",
       "      <td>61.15</td>\n",
       "      <td>4</td>\n",
       "      <td>57.480863</td>\n",
       "      <td>14</td>\n",
       "      <td>226.1</td>\n",
       "      <td>950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rexdale-Kipling</td>\n",
       "      <td>4</td>\n",
       "      <td>56.0</td>\n",
       "      <td>62.6</td>\n",
       "      <td>37.2</td>\n",
       "      <td>2</td>\n",
       "      <td>11.9</td>\n",
       "      <td>0</td>\n",
       "      <td>52.042160</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>125</td>\n",
       "      <td>33.879781</td>\n",
       "      <td>Rexdale-Kipling</td>\n",
       "      <td>54.28</td>\n",
       "      <td>64.69</td>\n",
       "      <td>0</td>\n",
       "      <td>45.586298</td>\n",
       "      <td>6</td>\n",
       "      <td>282.7</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Elms-Old Rexdale</td>\n",
       "      <td>5</td>\n",
       "      <td>54.7</td>\n",
       "      <td>61.1</td>\n",
       "      <td>35.6</td>\n",
       "      <td>1</td>\n",
       "      <td>12.7</td>\n",
       "      <td>0</td>\n",
       "      <td>65.449630</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1975</td>\n",
       "      <td>39.344262</td>\n",
       "      <td>Elms-Old Rexdale</td>\n",
       "      <td>55.15</td>\n",
       "      <td>61.54</td>\n",
       "      <td>1</td>\n",
       "      <td>61.392833</td>\n",
       "      <td>1</td>\n",
       "      <td>163.6</td>\n",
       "      <td>2030</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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>West Hill</td>\n",
       "      <td>136</td>\n",
       "      <td>55.0</td>\n",
       "      <td>63.4</td>\n",
       "      <td>38.9</td>\n",
       "      <td>5</td>\n",
       "      <td>13.3</td>\n",
       "      <td>2</td>\n",
       "      <td>48.014076</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>1540</td>\n",
       "      <td>55.121043</td>\n",
       "      <td>West Hill</td>\n",
       "      <td>58.00</td>\n",
       "      <td>61.50</td>\n",
       "      <td>1</td>\n",
       "      <td>48.366013</td>\n",
       "      <td>33</td>\n",
       "      <td>248.9</td>\n",
       "      <td>1725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Woburn</td>\n",
       "      <td>137</td>\n",
       "      <td>55.0</td>\n",
       "      <td>60.4</td>\n",
       "      <td>39.4</td>\n",
       "      <td>5</td>\n",
       "      <td>13.6</td>\n",
       "      <td>33</td>\n",
       "      <td>56.722587</td>\n",
       "      <td>90</td>\n",
       "      <td>...</td>\n",
       "      <td>6172</td>\n",
       "      <td>40.305011</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>56.83</td>\n",
       "      <td>60.67</td>\n",
       "      <td>6</td>\n",
       "      <td>55.628495</td>\n",
       "      <td>93</td>\n",
       "      <td>187.8</td>\n",
       "      <td>5690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Eglinton East</td>\n",
       "      <td>138</td>\n",
       "      <td>55.0</td>\n",
       "      <td>60.3</td>\n",
       "      <td>36.2</td>\n",
       "      <td>0</td>\n",
       "      <td>14.6</td>\n",
       "      <td>2</td>\n",
       "      <td>55.619995</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
       "      <td>935</td>\n",
       "      <td>36.888889</td>\n",
       "      <td>Eglinton East</td>\n",
       "      <td>56.70</td>\n",
       "      <td>59.10</td>\n",
       "      <td>8</td>\n",
       "      <td>53.686827</td>\n",
       "      <td>19</td>\n",
       "      <td>214.7</td>\n",
       "      <td>1005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Scarborough Village</td>\n",
       "      <td>139</td>\n",
       "      <td>53.0</td>\n",
       "      <td>60.6</td>\n",
       "      <td>35.7</td>\n",
       "      <td>4</td>\n",
       "      <td>14.6</td>\n",
       "      <td>3</td>\n",
       "      <td>48.911224</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>2047</td>\n",
       "      <td>38.364780</td>\n",
       "      <td>Scarborough Village</td>\n",
       "      <td>54.67</td>\n",
       "      <td>59.76</td>\n",
       "      <td>4</td>\n",
       "      <td>55.695142</td>\n",
       "      <td>24</td>\n",
       "      <td>239.2</td>\n",
       "      <td>2157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Guildwood</td>\n",
       "      <td>140</td>\n",
       "      <td>61.3</td>\n",
       "      <td>67.8</td>\n",
       "      <td>38.4</td>\n",
       "      <td>2</td>\n",
       "      <td>8.7</td>\n",
       "      <td>0</td>\n",
       "      <td>35.176651</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>150</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>Guildwood</td>\n",
       "      <td>64.99</td>\n",
       "      <td>71.48</td>\n",
       "      <td>0</td>\n",
       "      <td>31.797235</td>\n",
       "      <td>7</td>\n",
       "      <td>188.6</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Neighbourhood 2008  Neighbourhood Id  \\\n",
       "0               West Humber-Clairville                 1   \n",
       "1    Mount Olive-Silverstone-Jamestown                 2   \n",
       "2         Thistletown-Beaumond Heights                 3   \n",
       "3                      Rexdale-Kipling                 4   \n",
       "4                     Elms-Old Rexdale                 5   \n",
       "..                                 ...               ...   \n",
       "135                          West Hill               136   \n",
       "136                             Woburn               137   \n",
       "137                      Eglinton East               138   \n",
       "138                Scarborough Village               139   \n",
       "139                          Guildwood               140   \n",
       "\n",
       "     Breast Cancer Screenings 2008  Cervical Cancer Screenings 2008  \\\n",
       "0                             52.3                             60.4   \n",
       "1                             47.8                             54.6   \n",
       "2                             54.8                             60.1   \n",
       "3                             56.0                             62.6   \n",
       "4                             54.7                             61.1   \n",
       "..                             ...                              ...   \n",
       "135                           55.0                             63.4   \n",
       "136                           55.0                             60.4   \n",
       "137                           55.0                             60.3   \n",
       "138                           53.0                             60.6   \n",
       "139                           61.3                             67.8   \n",
       "\n",
       "     Colorectal Cancer Screenings 2008  Community Food Programs 2008  \\\n",
       "0                                 38.7                             2   \n",
       "1                                 36.8                             3   \n",
       "2                                 38.2                             2   \n",
       "3                                 37.2                             2   \n",
       "4                                 35.6                             1   \n",
       "..                                 ...                           ...   \n",
       "135                               38.9                             5   \n",
       "136                               39.4                             5   \n",
       "137                               36.2                             0   \n",
       "138                               35.7                             4   \n",
       "139                               38.4                             2   \n",
       "\n",
       "     Diabetes Prevalence 2008  DineSafe Inspections 2008  \\\n",
       "0                        14.0                         45   \n",
       "1                        14.9                          2   \n",
       "2                        12.0                          0   \n",
       "3                        11.9                          0   \n",
       "4                        12.7                          0   \n",
       "..                        ...                        ...   \n",
       "135                      13.3                          2   \n",
       "136                      13.6                         33   \n",
       "137                      14.6                          2   \n",
       "138                      14.6                          3   \n",
       "139                       8.7                          0   \n",
       "\n",
       "     Female Fertility 2008  Health Providers 2008  ...  \\\n",
       "0                60.286693                     94  ...   \n",
       "1                71.103462                     39  ...   \n",
       "2                60.681976                     10  ...   \n",
       "3                52.042160                      6  ...   \n",
       "4                65.449630                      1  ...   \n",
       "..                     ...                    ...  ...   \n",
       "135              48.014076                     28  ...   \n",
       "136              56.722587                     90  ...   \n",
       "137              55.619995                     22  ...   \n",
       "138              48.911224                     25  ...   \n",
       "139              35.176651                      6  ...   \n",
       "\n",
       "     Student Nutrition 2008  Teen Pregnancy 2008  \\\n",
       "0                      1190            30.917874   \n",
       "1                      5026            37.106918   \n",
       "2                       950            38.418079   \n",
       "3                       125            33.879781   \n",
       "4                      1975            39.344262   \n",
       "..                      ...                  ...   \n",
       "135                    1540            55.121043   \n",
       "136                    6172            40.305011   \n",
       "137                     935            36.888889   \n",
       "138                    2047            38.364780   \n",
       "139                     150            16.666667   \n",
       "\n",
       "                    Neighbourhood 2011 Breast Cancer Screenings 2011  \\\n",
       "0               West Humber-Clairville                         57.02   \n",
       "1    Mount Olive-Silverstone-Jamestown                         50.14   \n",
       "2         Thistletown-Beaumond Heights                         55.22   \n",
       "3                      Rexdale-Kipling                         54.28   \n",
       "4                     Elms-Old Rexdale                         55.15   \n",
       "..                                 ...                           ...   \n",
       "135                          West Hill                         58.00   \n",
       "136                             Woburn                         56.83   \n",
       "137                      Eglinton East                         56.70   \n",
       "138                Scarborough Village                         54.67   \n",
       "139                          Guildwood                         64.99   \n",
       "\n",
       "     Cervical Cancer Screenings 2011  DineSafe Inspections 2011  \\\n",
       "0                              60.52                         10   \n",
       "1                              55.75                          2   \n",
       "2                              61.15                          4   \n",
       "3                              64.69                          0   \n",
       "4                              61.54                          1   \n",
       "..                               ...                        ...   \n",
       "135                            61.50                          1   \n",
       "136                            60.67                          6   \n",
       "137                            59.10                          8   \n",
       "138                            59.76                          4   \n",
       "139                            71.48                          0   \n",
       "\n",
       "     Female Fertility 2011  Health Providers 2011  Premature Mortality 2011  \\\n",
       "0                59.923279                    101                     223.8   \n",
       "1                64.410021                     44                     242.6   \n",
       "2                57.480863                     14                     226.1   \n",
       "3                45.586298                      6                     282.7   \n",
       "4                61.392833                      1                     163.6   \n",
       "..                     ...                    ...                       ...   \n",
       "135              48.366013                     33                     248.9   \n",
       "136              55.628495                     93                     187.8   \n",
       "137              53.686827                     19                     214.7   \n",
       "138              55.695142                     24                     239.2   \n",
       "139              31.797235                      7                     188.6   \n",
       "\n",
       "     Student Nutrition 2011  \n",
       "0                      1105  \n",
       "1                      5132  \n",
       "2                       950  \n",
       "3                       180  \n",
       "4                      2030  \n",
       "..                      ...  \n",
       "135                    1725  \n",
       "136                    5690  \n",
       "137                    1005  \n",
       "138                    2157  \n",
       "139                     150  \n",
       "\n",
       "[140 rows x 21 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read selected sheets\n",
    "name = 'health'\n",
    "datasets[name] = read_data(f'{folder}/{name}.xlsx', sheet_name=[1,2], skiprows=1)\n",
    "\n",
    "# Rename cols for ref year\n",
    "datasets[name][1].columns = [\n",
    "    f'{c} 2008' if c != 'Neighbourhood Id' else c for c in datasets[name][1].columns\n",
    "]\n",
    "datasets[name][2].columns = [\n",
    "    f'{c} 2011' if c != 'Neighbourhood Id' else c for c in datasets[name][2].columns\n",
    "]\n",
    "\n",
    "# Join sheets together\n",
    "datasets[name] = pd.merge(\n",
    "    datasets[name][1],\n",
    "    datasets[name][2],\n",
    "    on='Neighbourhood Id',\n",
    "    how='left'\n",
    ")\n",
    "\n",
    "datasets['health']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19802579-7783-43fe-9975-6249a2e7732e",
   "metadata": {},
   "source": [
    "### Study Area\n",
    "\n",
    "Get the dataset for the study area:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aeb6cf1b-79b0-4c4a-8351-3ba4ed34b332",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "study_area = datasets[study_area_dataset]\n",
    "study_area.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad3390d4-258a-4f50-b2a5-52d5cab14eb4",
   "metadata": {},
   "source": [
    "### Neighbourhoods\n",
    "\n",
    "Format neighbourhoods for spatial binning and save in clean data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "45518ff3-a045-47b4-97d1-f4336e1ac39c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename neighbourhood id\n",
    "nbhoods = study_area\n",
    "nbhoods = nbhoods.rename(columns={\n",
    "    'AREA_SHORT_CODE': 'Neighbourhood Id'\n",
    "})\n",
    "\n",
    "# Format neighbourhood ids to be ints\n",
    "nbhoods['Neighbourhood Id'] = nbhoods['Neighbourhood Id'].astype(int)\n",
    "\n",
    "# Keep only id and geometry\n",
    "nbhoods = nbhoods[['Neighbourhood Id', 'geometry']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3c4f221-d43e-4043-9f9f-477d7a670fca",
   "metadata": {},
   "source": [
    "## Run\n",
    "\n",
    "Create folders to store processed data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f1607086-e618-4e47-9703-d37b742fe5cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "Path(dist_folder).mkdir(parents=True, exist_ok=True)\n",
    "Path(f'{dist_folder}/clean-datasets').mkdir(parents=True, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0730b81-65b8-44ff-8bc5-923dfdd64ab6",
   "metadata": {},
   "source": [
    "The following datasets will be preprocessed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ac35844a-f20a-41d2-944e-948f0fd89273",
   "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>dataset</th>\n",
       "      <th>columns</th>\n",
       "      <th>rows</th>\n",
       "      <th>memory_usage</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>centrelines</td>\n",
       "      <td>41</td>\n",
       "      <td>70827</td>\n",
       "      <td>22.2 MiB</td>\n",
       "      <td>['LineString']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>collisions</td>\n",
       "      <td>18</td>\n",
       "      <td>499538</td>\n",
       "      <td>68.6 MiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>traffic</td>\n",
       "      <td>59</td>\n",
       "      <td>226110</td>\n",
       "      <td>101.8 MiB</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>autospeed_enforcement</td>\n",
       "      <td>6</td>\n",
       "      <td>100</td>\n",
       "      <td>4.8 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>watch_your_speed</td>\n",
       "      <td>14</td>\n",
       "      <td>783</td>\n",
       "      <td>85.8 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>red_light_cams</td>\n",
       "      <td>32</td>\n",
       "      <td>200</td>\n",
       "      <td>50.1 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>police</td>\n",
       "      <td>6</td>\n",
       "      <td>32</td>\n",
       "      <td>1.6 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ambulance</td>\n",
       "      <td>30</td>\n",
       "      <td>46</td>\n",
       "      <td>10.9 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>fire_hydrants</td>\n",
       "      <td>6</td>\n",
       "      <td>41936</td>\n",
       "      <td>1.9 MiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>fire_stations</td>\n",
       "      <td>30</td>\n",
       "      <td>92</td>\n",
       "      <td>21.7 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>renewables</td>\n",
       "      <td>41</td>\n",
       "      <td>100</td>\n",
       "      <td>32.2 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>bicycle_parking</td>\n",
       "      <td>20</td>\n",
       "      <td>16998</td>\n",
       "      <td>2.6 MiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>transit_shelters</td>\n",
       "      <td>24</td>\n",
       "      <td>5852</td>\n",
       "      <td>1.1 MiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>wayfind</td>\n",
       "      <td>21</td>\n",
       "      <td>330</td>\n",
       "      <td>54.3 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>litter</td>\n",
       "      <td>21</td>\n",
       "      <td>10337</td>\n",
       "      <td>1.7 MiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>schools</td>\n",
       "      <td>29</td>\n",
       "      <td>1194</td>\n",
       "      <td>270.6 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>childcare</td>\n",
       "      <td>19</td>\n",
       "      <td>1038</td>\n",
       "      <td>154.2 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>art</td>\n",
       "      <td>28</td>\n",
       "      <td>405</td>\n",
       "      <td>88.7 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>culture</td>\n",
       "      <td>32</td>\n",
       "      <td>895</td>\n",
       "      <td>223.9 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>religious</td>\n",
       "      <td>45</td>\n",
       "      <td>1407</td>\n",
       "      <td>494.8 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>crime</td>\n",
       "      <td>104</td>\n",
       "      <td>140</td>\n",
       "      <td>113.9 KiB</td>\n",
       "      <td>['Polygon']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>education</td>\n",
       "      <td>13</td>\n",
       "      <td>140</td>\n",
       "      <td>15.3 KiB</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>environment</td>\n",
       "      <td>14</td>\n",
       "      <td>140</td>\n",
       "      <td>16.4 KiB</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>demographics</td>\n",
       "      <td>31</td>\n",
       "      <td>140</td>\n",
       "      <td>35.0 KiB</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>housing</td>\n",
       "      <td>17</td>\n",
       "      <td>140</td>\n",
       "      <td>19.7 KiB</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>economics</td>\n",
       "      <td>16</td>\n",
       "      <td>140</td>\n",
       "      <td>18.6 KiB</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>civics</td>\n",
       "      <td>13</td>\n",
       "      <td>140</td>\n",
       "      <td>15.3 KiB</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>health</td>\n",
       "      <td>21</td>\n",
       "      <td>140</td>\n",
       "      <td>24.1 KiB</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  dataset  columns    rows memory_usage        geometry\n",
       "0             centrelines       41   70827     22.2 MiB  ['LineString']\n",
       "1              collisions       18  499538     68.6 MiB       ['Point']\n",
       "2                 traffic       59  226110    101.8 MiB            None\n",
       "3   autospeed_enforcement        6     100      4.8 KiB       ['Point']\n",
       "4        watch_your_speed       14     783     85.8 KiB       ['Point']\n",
       "5          red_light_cams       32     200     50.1 KiB       ['Point']\n",
       "6                  police        6      32      1.6 KiB       ['Point']\n",
       "7               ambulance       30      46     10.9 KiB       ['Point']\n",
       "8           fire_hydrants        6   41936      1.9 MiB       ['Point']\n",
       "9           fire_stations       30      92     21.7 KiB       ['Point']\n",
       "10             renewables       41     100     32.2 KiB       ['Point']\n",
       "11        bicycle_parking       20   16998      2.6 MiB       ['Point']\n",
       "12       transit_shelters       24    5852      1.1 MiB       ['Point']\n",
       "13                wayfind       21     330     54.3 KiB       ['Point']\n",
       "14                 litter       21   10337      1.7 MiB       ['Point']\n",
       "15                schools       29    1194    270.6 KiB       ['Point']\n",
       "16              childcare       19    1038    154.2 KiB       ['Point']\n",
       "17                    art       28     405     88.7 KiB       ['Point']\n",
       "18                culture       32     895    223.9 KiB       ['Point']\n",
       "19              religious       45    1407    494.8 KiB       ['Point']\n",
       "20                  crime      104     140    113.9 KiB     ['Polygon']\n",
       "21              education       13     140     15.3 KiB            None\n",
       "22            environment       14     140     16.4 KiB            None\n",
       "23           demographics       31     140     35.0 KiB            None\n",
       "24                housing       17     140     19.7 KiB            None\n",
       "25              economics       16     140     18.6 KiB            None\n",
       "26                 civics       13     140     15.3 KiB            None\n",
       "27                 health       21     140     24.1 KiB            None"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info = pd.DataFrame([df_info(df, name) for name, df in datasets.items() if name != study_area_dataset])\n",
    "info"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f14aeae6-3e77-4af3-b56f-a474407f6fab",
   "metadata": {},
   "source": [
    "Track processing time for all datasets and procedures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "97fbe44b-0889-44c1-ac08-0efe9ffdec3b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing all datasets - preprocessing (2022-02-17 21:33:20.546540)...\n"
     ]
    }
   ],
   "source": [
    "gtimer = Timer('all datasets', 'preprocessing')\n",
    "gtimer.start()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d79b23a8-a384-4d19-8aae-2f37ce8404be",
   "metadata": {},
   "source": [
    "### Geodata Conversion\n",
    "\n",
    "Convert data without geometries to geospatial data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55f831ea-b524-4750-9849-0e91cfbe92d7",
   "metadata": {},
   "source": [
    "#### traffic\n",
    "\n",
    "Convert longitude/latitude to point geometries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "30c80520-8d8a-4c40-8071-cbc31d35fdc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing traffic - geoconversion (2022-02-17 21:33:20.550721)...\n",
      "Processed traffic - geoconversion (2022-02-17 21:33:20.590139) [Elapsed: 0:00:00.039416]\n"
     ]
    }
   ],
   "source": [
    "# Start processing\n",
    "timer = Timer('traffic', 'geoconversion')\n",
    "timer.start()\n",
    "\n",
    "# Convert coords to points\n",
    "df = datasets['traffic']\n",
    "datasets['traffic'] = gpd.GeoDataFrame(\n",
    "    df,\n",
    "    geometry=gpd.points_from_xy(df.lng, df.lat),\n",
    "    crs='EPSG:4326'\n",
    ")\n",
    "\n",
    "# End processing\n",
    "timer.end()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f73970aa-e603-4bf2-b5bb-e4e91820d006",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         POINT (-79.39326 43.70528)\n",
       "1         POINT (-79.39326 43.70528)\n",
       "2         POINT (-79.39326 43.70528)\n",
       "3         POINT (-79.39326 43.70528)\n",
       "4         POINT (-79.39326 43.70528)\n",
       "                     ...            \n",
       "226105    POINT (-79.42188 43.65904)\n",
       "226106    POINT (-79.42188 43.65904)\n",
       "226107    POINT (-79.42188 43.65904)\n",
       "226108    POINT (-79.42188 43.65904)\n",
       "226109    POINT (-79.42188 43.65904)\n",
       "Name: geometry, Length: 226110, dtype: geometry"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets['traffic'].geometry"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc44d57f-7d61-4a50-a4be-fb79b1e1bf40",
   "metadata": {},
   "source": [
    "#### Wellbeing Toronto\n",
    "\n",
    "Join wellbeing toronto datasets to neighbourhood polygons:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "03df8b63-4c34-4852-8e8f-b99d4bd534b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing education - geoconversion (2022-02-17 21:33:20.602969)...\n",
      "Processed education - geoconversion (2022-02-17 21:33:20.605782) [Elapsed: 0:00:00.002813]\n",
      "Processing environment - geoconversion (2022-02-17 21:33:20.605917)...\n",
      "Processed environment - geoconversion (2022-02-17 21:33:20.608146) [Elapsed: 0:00:00.002229]\n",
      "Processing demographics - geoconversion (2022-02-17 21:33:20.608268)...\n",
      "Processed demographics - geoconversion (2022-02-17 21:33:20.610210) [Elapsed: 0:00:00.001940]\n",
      "Processing housing - geoconversion (2022-02-17 21:33:20.610298)...\n",
      "Processed housing - geoconversion (2022-02-17 21:33:20.612233) [Elapsed: 0:00:00.001934]\n",
      "Processing economics - geoconversion (2022-02-17 21:33:20.612282)...\n",
      "Processed economics - geoconversion (2022-02-17 21:33:20.613843) [Elapsed: 0:00:00.001560]\n",
      "Processing civics - geoconversion (2022-02-17 21:33:20.613900)...\n",
      "Processed civics - geoconversion (2022-02-17 21:33:20.615554) [Elapsed: 0:00:00.001632]\n",
      "Processing health - geoconversion (2022-02-17 21:33:20.615645)...\n",
      "Processed health - geoconversion (2022-02-17 21:33:20.618647) [Elapsed: 0:00:00.003001]\n"
     ]
    }
   ],
   "source": [
    "wellbeing = [\n",
    "    'education',\n",
    "    'environment',\n",
    "    'demographics',\n",
    "    'housing',\n",
    "    'economics',\n",
    "    'civics',\n",
    "    'health'\n",
    "]\n",
    "\n",
    "# Join to polygons based on nbhood id\n",
    "for name in wellbeing:\n",
    "    \n",
    "    # Start processing\n",
    "    timer = Timer(name, 'geoconversion')\n",
    "    timer.start()\n",
    "    \n",
    "    # Join by nbhood id\n",
    "    datasets[name] = pd.merge(\n",
    "        nbhoods,\n",
    "        datasets[name],\n",
    "        on='Neighbourhood Id',\n",
    "        how='left'\n",
    "    )\n",
    "\n",
    "    # End processing\n",
    "    timer.end()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6801a09d-806a-4557-9aaf-169675ccfb1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "datasets['health'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17cab1e1-e673-46a8-b7dc-af4f6c0479cd",
   "metadata": {},
   "source": [
    "### Extract Bounding Box\n",
    "\n",
    "Get the bounding box from the `toronto` dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2cfb3ede-59de-4dfc-86be-7a07dee2905e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing bbox - bbox extraction (2022-02-17 21:33:20.722237)...\n",
      "Processed bbox - bbox extraction (2022-02-17 21:33:20.725676) [Elapsed: 0:00:00.003438]\n"
     ]
    }
   ],
   "source": [
    "# Start processing\n",
    "timer = Timer('bbox', 'bbox extraction')\n",
    "timer.start()\n",
    "\n",
    "# Extract bbox\n",
    "bbox = gpd.GeoSeries(box(*study_area.total_bounds), crs='EPSG:4326')\n",
    "\n",
    "# End processing\n",
    "timer.end()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "40b26c12-4031-4fac-97a4-6c106547f9f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bbox.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd71d3e5-6b9c-41de-bc21-998219a97625",
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "source": [
    "### Normalize Projections\n",
    "\n",
    "Ensure that all datasets have the same projections as the study area `bbox`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6a042d2f-5be8-4841-b981-c0676f00fc4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing toronto - reprojection (2022-02-17 21:33:20.992757)...\n",
      "Processed toronto - reprojection (2022-02-17 21:33:21.031287) [Elapsed: 0:00:00.038531]\n",
      "Processing centrelines - reprojection (2022-02-17 21:33:21.031379)...\n",
      "Processed centrelines - reprojection (2022-02-17 21:33:21.488322) [Elapsed: 0:00:00.456941]\n",
      "Processing collisions - reprojection (2022-02-17 21:33:21.520522)...\n",
      "Processed collisions - reprojection (2022-02-17 21:33:22.111027) [Elapsed: 0:00:00.590496]\n",
      "Processing traffic - reprojection (2022-02-17 21:33:22.262454)...\n",
      "Processed traffic - reprojection (2022-02-17 21:33:22.503959) [Elapsed: 0:00:00.241504]\n",
      "Processing autospeed_enforcement - reprojection (2022-02-17 21:33:22.506567)...\n",
      "Processed autospeed_enforcement - reprojection (2022-02-17 21:33:22.509417) [Elapsed: 0:00:00.002851]\n",
      "Processing watch_your_speed - reprojection (2022-02-17 21:33:22.509639)...\n",
      "Processed watch_your_speed - reprojection (2022-02-17 21:33:22.513372) [Elapsed: 0:00:00.003731]\n",
      "Processing red_light_cams - reprojection (2022-02-17 21:33:22.513754)...\n",
      "Processed red_light_cams - reprojection (2022-02-17 21:33:22.517557) [Elapsed: 0:00:00.003803]\n",
      "Processing police - reprojection (2022-02-17 21:33:22.517912)...\n",
      "Processed police - reprojection (2022-02-17 21:33:22.521373) [Elapsed: 0:00:00.003460]\n",
      "Processing ambulance - reprojection (2022-02-17 21:33:22.521876)...\n",
      "Processed ambulance - reprojection (2022-02-17 21:33:22.524907) [Elapsed: 0:00:00.003030]\n",
      "Processing fire_hydrants - reprojection (2022-02-17 21:33:22.525099)...\n",
      "Processed fire_hydrants - reprojection (2022-02-17 21:33:22.572573) [Elapsed: 0:00:00.047472]\n",
      "Processing fire_stations - reprojection (2022-02-17 21:33:22.581566)...\n",
      "Processed fire_stations - reprojection (2022-02-17 21:33:22.584531) [Elapsed: 0:00:00.002965]\n",
      "Processing renewables - reprojection (2022-02-17 21:33:22.584649)...\n",
      "Processed renewables - reprojection (2022-02-17 21:33:22.586837) [Elapsed: 0:00:00.002188]\n",
      "Processing bicycle_parking - reprojection (2022-02-17 21:33:22.586941)...\n",
      "Processed bicycle_parking - reprojection (2022-02-17 21:33:22.609636) [Elapsed: 0:00:00.022694]\n",
      "Processing transit_shelters - reprojection (2022-02-17 21:33:22.614403)...\n",
      "Processed transit_shelters - reprojection (2022-02-17 21:33:22.624561) [Elapsed: 0:00:00.010158]\n",
      "Processing wayfind - reprojection (2022-02-17 21:33:22.626466)...\n",
      "Processed wayfind - reprojection (2022-02-17 21:33:22.629514) [Elapsed: 0:00:00.003049]\n",
      "Processing litter - reprojection (2022-02-17 21:33:22.629783)...\n",
      "Processed litter - reprojection (2022-02-17 21:33:22.644504) [Elapsed: 0:00:00.014720]\n",
      "Processing schools - reprojection (2022-02-17 21:33:22.647567)...\n",
      "Processed schools - reprojection (2022-02-17 21:33:22.651750) [Elapsed: 0:00:00.004183]\n",
      "Processing childcare - reprojection (2022-02-17 21:33:22.652192)...\n",
      "Processed childcare - reprojection (2022-02-17 21:33:22.655736) [Elapsed: 0:00:00.003543]\n",
      "Processing art - reprojection (2022-02-17 21:33:22.656122)...\n",
      "Processed art - reprojection (2022-02-17 21:33:22.658943) [Elapsed: 0:00:00.002821]\n",
      "Processing culture - reprojection (2022-02-17 21:33:22.659161)...\n",
      "Processed culture - reprojection (2022-02-17 21:33:22.662368) [Elapsed: 0:00:00.003206]\n",
      "Processing religious - reprojection (2022-02-17 21:33:22.662736)...\n",
      "Processed religious - reprojection (2022-02-17 21:33:22.667014) [Elapsed: 0:00:00.004277]\n",
      "Processing crime - reprojection (2022-02-17 21:33:22.667618)...\n",
      "Processed crime - reprojection (2022-02-17 21:33:22.703402) [Elapsed: 0:00:00.035782]\n",
      "Processing education - reprojection (2022-02-17 21:33:22.703609)...\n",
      "Processed education - reprojection (2022-02-17 21:33:22.740270) [Elapsed: 0:00:00.036660]\n",
      "Processing environment - reprojection (2022-02-17 21:33:22.740384)...\n",
      "Processed environment - reprojection (2022-02-17 21:33:22.776998) [Elapsed: 0:00:00.036612]\n",
      "Processing demographics - reprojection (2022-02-17 21:33:22.777096)...\n",
      "Processed demographics - reprojection (2022-02-17 21:33:22.813801) [Elapsed: 0:00:00.036704]\n",
      "Processing housing - reprojection (2022-02-17 21:33:22.813901)...\n",
      "Processed housing - reprojection (2022-02-17 21:33:22.849968) [Elapsed: 0:00:00.036065]\n",
      "Processing economics - reprojection (2022-02-17 21:33:22.850061)...\n",
      "Processed economics - reprojection (2022-02-17 21:33:22.886377) [Elapsed: 0:00:00.036314]\n",
      "Processing civics - reprojection (2022-02-17 21:33:22.886473)...\n",
      "Processed civics - reprojection (2022-02-17 21:33:22.922491) [Elapsed: 0:00:00.036017]\n",
      "Processing health - reprojection (2022-02-17 21:33:22.922592)...\n",
      "Processed health - reprojection (2022-02-17 21:33:22.959253) [Elapsed: 0:00:00.036660]\n"
     ]
    }
   ],
   "source": [
    "# Normalize projections\n",
    "crs = bbox.estimate_utm_crs()\n",
    "bbox = bbox.to_crs(crs)\n",
    "for name, df in datasets.items():\n",
    "    \n",
    "    # Start processing\n",
    "    timer = Timer(name, 'reprojection')\n",
    "    timer.start()\n",
    "    datasets[name] = df.to_crs(crs)\n",
    "    \n",
    "    # End processing\n",
    "    timer.end()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bc6a1fc-fd8b-4625-8562-198794c7d433",
   "metadata": {},
   "source": [
    "Reproject neighbourhood polygons:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d12d3585-8273-4f74-91c6-0f8845605c07",
   "metadata": {},
   "outputs": [],
   "source": [
    "nbhoods = nbhoods.to_crs(crs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd5c7dff-06d9-4e16-972e-d2342c59f9b5",
   "metadata": {},
   "source": [
    "Check crs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7d1acdac-4e2a-46ec-abf9-146d68e6f4cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Derived Projected CRS: EPSG:32617>\n",
       "Name: WGS 84 / UTM zone 17N\n",
       "Axis Info [cartesian]:\n",
       "- E[east]: Easting (metre)\n",
       "- N[north]: Northing (metre)\n",
       "Area of Use:\n",
       "- name: Between 84°W and 78°W, northern hemisphere between equator and 84°N, onshore and offshore. Bahamas. Ecuador - north of equator. Canada - Nunavut; Ontario; Quebec. Cayman Islands. Colombia. Costa Rica. Cuba. Jamaica. Nicaragua. Panama. United States (USA).\n",
       "- bounds: (-84.0, 0.0, -78.0, 84.0)\n",
       "Coordinate Operation:\n",
       "- name: UTM zone 17N\n",
       "- method: Transverse Mercator\n",
       "Datum: World Geodetic System 1984 ensemble\n",
       "- Ellipsoid: WGS 84\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b163d84b-8f23-4d7d-9d1a-f1bc7f2cf3fc",
   "metadata": {},
   "source": [
    "Display projected crs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4d6f93e1-caab-417b-b887-2c5b718056bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset</th>\n",
       "      <th>projected_crs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>centrelines</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>collisions</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>traffic</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>autospeed_enforcement</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>watch_your_speed</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>red_light_cams</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>police</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ambulance</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>fire_hydrants</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>fire_stations</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>renewables</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>bicycle_parking</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>transit_shelters</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>wayfind</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>litter</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>schools</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>childcare</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>art</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>culture</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>religious</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>crime</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>education</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>environment</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>demographics</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>housing</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>economics</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>civics</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>health</td>\n",
       "      <td>epsg:32617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  dataset projected_crs\n",
       "0             centrelines    epsg:32617\n",
       "1              collisions    epsg:32617\n",
       "2                 traffic    epsg:32617\n",
       "3   autospeed_enforcement    epsg:32617\n",
       "4        watch_your_speed    epsg:32617\n",
       "5          red_light_cams    epsg:32617\n",
       "6                  police    epsg:32617\n",
       "7               ambulance    epsg:32617\n",
       "8           fire_hydrants    epsg:32617\n",
       "9           fire_stations    epsg:32617\n",
       "10             renewables    epsg:32617\n",
       "11        bicycle_parking    epsg:32617\n",
       "12       transit_shelters    epsg:32617\n",
       "13                wayfind    epsg:32617\n",
       "14                 litter    epsg:32617\n",
       "15                schools    epsg:32617\n",
       "16              childcare    epsg:32617\n",
       "17                    art    epsg:32617\n",
       "18                culture    epsg:32617\n",
       "19              religious    epsg:32617\n",
       "20                  crime    epsg:32617\n",
       "21              education    epsg:32617\n",
       "22            environment    epsg:32617\n",
       "23           demographics    epsg:32617\n",
       "24                housing    epsg:32617\n",
       "25              economics    epsg:32617\n",
       "26                 civics    epsg:32617\n",
       "27                 health    epsg:32617"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proj = pd.DataFrame(\n",
    "    [{'dataset': name, 'projected_crs': str(df.crs)} for name, df in datasets.items() if name != study_area_dataset]\n",
    ")\n",
    "proj"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05b91ee7-7738-4e0a-935a-c11d12ae8c52",
   "metadata": {},
   "source": [
    "### Clip Data\n",
    "\n",
    "Clip data to study area bounds:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "18774a1f-95b6-4106-9771-bd82a4fc6b58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing toronto - clip (2022-02-17 21:33:23.015692)...\n",
      "Processed toronto - clip (2022-02-17 21:33:23.035974) [Elapsed: 0:00:00.020281]\n",
      "Processing centrelines - clip (2022-02-17 21:33:23.036184)...\n",
      "Processed centrelines - clip (2022-02-17 21:33:23.949044) [Elapsed: 0:00:00.912859]\n",
      "Processing collisions - clip (2022-02-17 21:33:23.974839)...\n",
      "Processed collisions - clip (2022-02-17 21:33:24.534195) [Elapsed: 0:00:00.559354]\n",
      "Processing traffic - clip (2022-02-17 21:33:24.663193)...\n",
      "Processed traffic - clip (2022-02-17 21:33:24.892222) [Elapsed: 0:00:00.229029]\n",
      "Processing autospeed_enforcement - clip (2022-02-17 21:33:24.897734)...\n",
      "Processed autospeed_enforcement - clip (2022-02-17 21:33:24.900520) [Elapsed: 0:00:00.002787]\n",
      "Processing watch_your_speed - clip (2022-02-17 21:33:24.900562)...\n",
      "Processed watch_your_speed - clip (2022-02-17 21:33:24.903904) [Elapsed: 0:00:00.003342]\n",
      "Processing red_light_cams - clip (2022-02-17 21:33:24.903988)...\n",
      "Processed red_light_cams - clip (2022-02-17 21:33:24.906395) [Elapsed: 0:00:00.002407]\n",
      "Processing police - clip (2022-02-17 21:33:24.906449)...\n",
      "Processed police - clip (2022-02-17 21:33:24.908200) [Elapsed: 0:00:00.001751]\n",
      "Processing ambulance - clip (2022-02-17 21:33:24.908321)...\n",
      "Processed ambulance - clip (2022-02-17 21:33:24.910486) [Elapsed: 0:00:00.002164]\n",
      "Processing fire_hydrants - clip (2022-02-17 21:33:24.910526)...\n",
      "Processed fire_hydrants - clip (2022-02-17 21:33:24.953853) [Elapsed: 0:00:00.043326]\n",
      "Processing fire_stations - clip (2022-02-17 21:33:24.954786)...\n",
      "Processed fire_stations - clip (2022-02-17 21:33:24.957195) [Elapsed: 0:00:00.002410]\n",
      "Processing renewables - clip (2022-02-17 21:33:24.957386)...\n",
      "Processed renewables - clip (2022-02-17 21:33:24.959329) [Elapsed: 0:00:00.001942]\n",
      "Processing bicycle_parking - clip (2022-02-17 21:33:24.959369)...\n",
      "Processed bicycle_parking - clip (2022-02-17 21:33:24.979136) [Elapsed: 0:00:00.019765]\n",
      "Processing transit_shelters - clip (2022-02-17 21:33:24.980543)...\n",
      "Processed transit_shelters - clip (2022-02-17 21:33:24.989900) [Elapsed: 0:00:00.009356]\n",
      "Processing wayfind - clip (2022-02-17 21:33:24.990571)...\n",
      "Processed wayfind - clip (2022-02-17 21:33:24.993177) [Elapsed: 0:00:00.002606]\n",
      "Processing litter - clip (2022-02-17 21:33:24.993298)...\n",
      "Processed litter - clip (2022-02-17 21:33:25.007543) [Elapsed: 0:00:00.014244]\n",
      "Processing schools - clip (2022-02-17 21:33:25.008593)...\n",
      "Processed schools - clip (2022-02-17 21:33:25.012230) [Elapsed: 0:00:00.003637]\n",
      "Processing childcare - clip (2022-02-17 21:33:25.012376)...\n",
      "Processed childcare - clip (2022-02-17 21:33:25.015191) [Elapsed: 0:00:00.002814]\n",
      "Processing art - clip (2022-02-17 21:33:25.015247)...\n",
      "Processed art - clip (2022-02-17 21:33:25.017936) [Elapsed: 0:00:00.002689]\n",
      "Processing culture - clip (2022-02-17 21:33:25.018023)...\n",
      "Processed culture - clip (2022-02-17 21:33:25.020890) [Elapsed: 0:00:00.002867]\n",
      "Processing religious - clip (2022-02-17 21:33:25.021000)...\n",
      "Processed religious - clip (2022-02-17 21:33:25.024672) [Elapsed: 0:00:00.003671]\n",
      "Processing crime - clip (2022-02-17 21:33:25.024939)...\n",
      "Processed crime - clip (2022-02-17 21:33:25.040637) [Elapsed: 0:00:00.015697]\n",
      "Processing education - clip (2022-02-17 21:33:25.040780)...\n",
      "Processed education - clip (2022-02-17 21:33:25.055846) [Elapsed: 0:00:00.015065]\n",
      "Processing environment - clip (2022-02-17 21:33:25.055971)...\n",
      "Processed environment - clip (2022-02-17 21:33:25.071788) [Elapsed: 0:00:00.015816]\n",
      "Processing demographics - clip (2022-02-17 21:33:25.071930)...\n",
      "Processed demographics - clip (2022-02-17 21:33:25.087367) [Elapsed: 0:00:00.015436]\n",
      "Processing housing - clip (2022-02-17 21:33:25.087489)...\n",
      "Processed housing - clip (2022-02-17 21:33:25.103804) [Elapsed: 0:00:00.016314]\n",
      "Processing economics - clip (2022-02-17 21:33:25.103933)...\n",
      "Processed economics - clip (2022-02-17 21:33:25.120099) [Elapsed: 0:00:00.016166]\n",
      "Processing civics - clip (2022-02-17 21:33:25.120281)...\n",
      "Processed civics - clip (2022-02-17 21:33:25.136071) [Elapsed: 0:00:00.015789]\n",
      "Processing health - clip (2022-02-17 21:33:25.136224)...\n",
      "Processed health - clip (2022-02-17 21:33:25.151994) [Elapsed: 0:00:00.015769]\n"
     ]
    }
   ],
   "source": [
    "# Clip datasets to study area bbox\n",
    "for name, df in datasets.items():\n",
    "        \n",
    "    # Start processing\n",
    "    timer = Timer(name, 'clip')\n",
    "    timer.start()\n",
    "\n",
    "    # Clip data to study area\n",
    "    datasets[name] = df.clip(bbox, keep_geom_type=True)\n",
    "\n",
    "    # End processing\n",
    "    timer.end()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7e597e09-23f8-4011-8ad1-ce8b3d610eb6",
   "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>dataset</th>\n",
       "      <th>columns</th>\n",
       "      <th>rows</th>\n",
       "      <th>memory_usage</th>\n",
       "      <th>geometry</th>\n",
       "      <th>columns_orig</th>\n",
       "      <th>rows_orig</th>\n",
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       "      <th>rows_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>centrelines</td>\n",
       "      <td>41</td>\n",
       "      <td>70827</td>\n",
       "      <td>22.7 MiB</td>\n",
       "      <td>['LineString']</td>\n",
       "      <td>41</td>\n",
       "      <td>70827</td>\n",
       "      <td>22.2 MiB</td>\n",
       "      <td>['LineString']</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>collisions</td>\n",
       "      <td>18</td>\n",
       "      <td>429631</td>\n",
       "      <td>62.3 MiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>18</td>\n",
       "      <td>499538</td>\n",
       "      <td>68.6 MiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>0</td>\n",
       "      <td>69907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>traffic</td>\n",
       "      <td>60</td>\n",
       "      <td>219497</td>\n",
       "      <td>102.2 MiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>59</td>\n",
       "      <td>226110</td>\n",
       "      <td>101.8 MiB</td>\n",
       "      <td>None</td>\n",
       "      <td>-1</td>\n",
       "      <td>6613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>autospeed_enforcement</td>\n",
       "      <td>6</td>\n",
       "      <td>100</td>\n",
       "      <td>5.5 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>6</td>\n",
       "      <td>100</td>\n",
       "      <td>4.8 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>watch_your_speed</td>\n",
       "      <td>14</td>\n",
       "      <td>783</td>\n",
       "      <td>91.8 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>14</td>\n",
       "      <td>783</td>\n",
       "      <td>85.8 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>red_light_cams</td>\n",
       "      <td>32</td>\n",
       "      <td>200</td>\n",
       "      <td>51.6 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>32</td>\n",
       "      <td>200</td>\n",
       "      <td>50.1 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>police</td>\n",
       "      <td>6</td>\n",
       "      <td>32</td>\n",
       "      <td>1.8 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>6</td>\n",
       "      <td>32</td>\n",
       "      <td>1.6 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ambulance</td>\n",
       "      <td>30</td>\n",
       "      <td>46</td>\n",
       "      <td>11.1 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>30</td>\n",
       "      <td>46</td>\n",
       "      <td>10.9 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>fire_hydrants</td>\n",
       "      <td>6</td>\n",
       "      <td>41936</td>\n",
       "      <td>2.2 MiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>6</td>\n",
       "      <td>41936</td>\n",
       "      <td>1.9 MiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>fire_stations</td>\n",
       "      <td>30</td>\n",
       "      <td>92</td>\n",
       "      <td>22.3 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>30</td>\n",
       "      <td>92</td>\n",
       "      <td>21.7 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>renewables</td>\n",
       "      <td>41</td>\n",
       "      <td>100</td>\n",
       "      <td>32.8 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>41</td>\n",
       "      <td>100</td>\n",
       "      <td>32.2 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>bicycle_parking</td>\n",
       "      <td>20</td>\n",
       "      <td>16998</td>\n",
       "      <td>2.7 MiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>20</td>\n",
       "      <td>16998</td>\n",
       "      <td>2.6 MiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>transit_shelters</td>\n",
       "      <td>24</td>\n",
       "      <td>5852</td>\n",
       "      <td>1.1 MiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>24</td>\n",
       "      <td>5852</td>\n",
       "      <td>1.1 MiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>wayfind</td>\n",
       "      <td>21</td>\n",
       "      <td>330</td>\n",
       "      <td>56.7 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>21</td>\n",
       "      <td>330</td>\n",
       "      <td>54.3 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>litter</td>\n",
       "      <td>21</td>\n",
       "      <td>10337</td>\n",
       "      <td>1.7 MiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>21</td>\n",
       "      <td>10337</td>\n",
       "      <td>1.7 MiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>schools</td>\n",
       "      <td>29</td>\n",
       "      <td>1194</td>\n",
       "      <td>279.8 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>29</td>\n",
       "      <td>1194</td>\n",
       "      <td>270.6 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>childcare</td>\n",
       "      <td>19</td>\n",
       "      <td>1038</td>\n",
       "      <td>162.2 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>19</td>\n",
       "      <td>1038</td>\n",
       "      <td>154.2 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>art</td>\n",
       "      <td>28</td>\n",
       "      <td>405</td>\n",
       "      <td>91.8 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>28</td>\n",
       "      <td>405</td>\n",
       "      <td>88.7 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>culture</td>\n",
       "      <td>32</td>\n",
       "      <td>893</td>\n",
       "      <td>230.2 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>32</td>\n",
       "      <td>895</td>\n",
       "      <td>223.9 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>religious</td>\n",
       "      <td>45</td>\n",
       "      <td>1407</td>\n",
       "      <td>505.6 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>45</td>\n",
       "      <td>1407</td>\n",
       "      <td>494.8 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>crime</td>\n",
       "      <td>104</td>\n",
       "      <td>140</td>\n",
       "      <td>114.8 KiB</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>104</td>\n",
       "      <td>140</td>\n",
       "      <td>113.9 KiB</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>education</td>\n",
       "      <td>14</td>\n",
       "      <td>140</td>\n",
       "      <td>16.4 KiB</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>13</td>\n",
       "      <td>140</td>\n",
       "      <td>15.3 KiB</td>\n",
       "      <td>None</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>environment</td>\n",
       "      <td>15</td>\n",
       "      <td>140</td>\n",
       "      <td>17.5 KiB</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>14</td>\n",
       "      <td>140</td>\n",
       "      <td>16.4 KiB</td>\n",
       "      <td>None</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>demographics</td>\n",
       "      <td>32</td>\n",
       "      <td>140</td>\n",
       "      <td>36.1 KiB</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>31</td>\n",
       "      <td>140</td>\n",
       "      <td>35.0 KiB</td>\n",
       "      <td>None</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>housing</td>\n",
       "      <td>18</td>\n",
       "      <td>140</td>\n",
       "      <td>20.8 KiB</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>17</td>\n",
       "      <td>140</td>\n",
       "      <td>19.7 KiB</td>\n",
       "      <td>None</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>economics</td>\n",
       "      <td>17</td>\n",
       "      <td>140</td>\n",
       "      <td>19.7 KiB</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>16</td>\n",
       "      <td>140</td>\n",
       "      <td>18.6 KiB</td>\n",
       "      <td>None</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>civics</td>\n",
       "      <td>14</td>\n",
       "      <td>140</td>\n",
       "      <td>16.4 KiB</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>13</td>\n",
       "      <td>140</td>\n",
       "      <td>15.3 KiB</td>\n",
       "      <td>None</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>health</td>\n",
       "      <td>22</td>\n",
       "      <td>140</td>\n",
       "      <td>25.2 KiB</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>21</td>\n",
       "      <td>140</td>\n",
       "      <td>24.1 KiB</td>\n",
       "      <td>None</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  dataset  columns    rows memory_usage        geometry  \\\n",
       "0             centrelines       41   70827     22.7 MiB  ['LineString']   \n",
       "1              collisions       18  429631     62.3 MiB       ['Point']   \n",
       "2                 traffic       60  219497    102.2 MiB       ['Point']   \n",
       "3   autospeed_enforcement        6     100      5.5 KiB       ['Point']   \n",
       "4        watch_your_speed       14     783     91.8 KiB       ['Point']   \n",
       "5          red_light_cams       32     200     51.6 KiB       ['Point']   \n",
       "6                  police        6      32      1.8 KiB       ['Point']   \n",
       "7               ambulance       30      46     11.1 KiB       ['Point']   \n",
       "8           fire_hydrants        6   41936      2.2 MiB       ['Point']   \n",
       "9           fire_stations       30      92     22.3 KiB       ['Point']   \n",
       "10             renewables       41     100     32.8 KiB       ['Point']   \n",
       "11        bicycle_parking       20   16998      2.7 MiB       ['Point']   \n",
       "12       transit_shelters       24    5852      1.1 MiB       ['Point']   \n",
       "13                wayfind       21     330     56.7 KiB       ['Point']   \n",
       "14                 litter       21   10337      1.7 MiB       ['Point']   \n",
       "15                schools       29    1194    279.8 KiB       ['Point']   \n",
       "16              childcare       19    1038    162.2 KiB       ['Point']   \n",
       "17                    art       28     405     91.8 KiB       ['Point']   \n",
       "18                culture       32     893    230.2 KiB       ['Point']   \n",
       "19              religious       45    1407    505.6 KiB       ['Point']   \n",
       "20                  crime      104     140    114.8 KiB     ['Polygon']   \n",
       "21              education       14     140     16.4 KiB     ['Polygon']   \n",
       "22            environment       15     140     17.5 KiB     ['Polygon']   \n",
       "23           demographics       32     140     36.1 KiB     ['Polygon']   \n",
       "24                housing       18     140     20.8 KiB     ['Polygon']   \n",
       "25              economics       17     140     19.7 KiB     ['Polygon']   \n",
       "26                 civics       14     140     16.4 KiB     ['Polygon']   \n",
       "27                 health       22     140     25.2 KiB     ['Polygon']   \n",
       "\n",
       "    columns_orig  rows_orig memory_usage_orig   geometry_orig  columns_diff  \\\n",
       "0             41      70827          22.2 MiB  ['LineString']             0   \n",
       "1             18     499538          68.6 MiB       ['Point']             0   \n",
       "2             59     226110         101.8 MiB            None            -1   \n",
       "3              6        100           4.8 KiB       ['Point']             0   \n",
       "4             14        783          85.8 KiB       ['Point']             0   \n",
       "5             32        200          50.1 KiB       ['Point']             0   \n",
       "6              6         32           1.6 KiB       ['Point']             0   \n",
       "7             30         46          10.9 KiB       ['Point']             0   \n",
       "8              6      41936           1.9 MiB       ['Point']             0   \n",
       "9             30         92          21.7 KiB       ['Point']             0   \n",
       "10            41        100          32.2 KiB       ['Point']             0   \n",
       "11            20      16998           2.6 MiB       ['Point']             0   \n",
       "12            24       5852           1.1 MiB       ['Point']             0   \n",
       "13            21        330          54.3 KiB       ['Point']             0   \n",
       "14            21      10337           1.7 MiB       ['Point']             0   \n",
       "15            29       1194         270.6 KiB       ['Point']             0   \n",
       "16            19       1038         154.2 KiB       ['Point']             0   \n",
       "17            28        405          88.7 KiB       ['Point']             0   \n",
       "18            32        895         223.9 KiB       ['Point']             0   \n",
       "19            45       1407         494.8 KiB       ['Point']             0   \n",
       "20           104        140         113.9 KiB     ['Polygon']             0   \n",
       "21            13        140          15.3 KiB            None            -1   \n",
       "22            14        140          16.4 KiB            None            -1   \n",
       "23            31        140          35.0 KiB            None            -1   \n",
       "24            17        140          19.7 KiB            None            -1   \n",
       "25            16        140          18.6 KiB            None            -1   \n",
       "26            13        140          15.3 KiB            None            -1   \n",
       "27            21        140          24.1 KiB            None            -1   \n",
       "\n",
       "    rows_diff  \n",
       "0           0  \n",
       "1       69907  \n",
       "2        6613  \n",
       "3           0  \n",
       "4           0  \n",
       "5           0  \n",
       "6           0  \n",
       "7           0  \n",
       "8           0  \n",
       "9           0  \n",
       "10          0  \n",
       "11          0  \n",
       "12          0  \n",
       "13          0  \n",
       "14          0  \n",
       "15          0  \n",
       "16          0  \n",
       "17          0  \n",
       "18          2  \n",
       "19          0  \n",
       "20          0  \n",
       "21          0  \n",
       "22          0  \n",
       "23          0  \n",
       "24          0  \n",
       "25          0  \n",
       "26          0  \n",
       "27          0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate info for clipped datasets\n",
    "info_clip = pd.DataFrame([df_info(df, name) for name, df in datasets.items() if name != study_area_dataset])\n",
    "\n",
    "# Calculate difference in cols and rows from clip\n",
    "info_diff = info[['columns', 'rows']] - info_clip[['columns', 'rows']]\n",
    "\n",
    "# Add original datasets info\n",
    "info_diff = pd.concat([info[['columns', 'rows', 'memory_usage', 'geometry']], info_diff], axis=1)\n",
    "info_diff.columns = ['columns_orig', 'rows_orig', 'memory_usage_orig', 'geometry_orig', 'columns_diff', 'rows_diff']\n",
    "\n",
    "# Add clipped datasets info\n",
    "info_diff = pd.concat([info_clip, info_diff], axis=1)\n",
    "info_diff"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "681195a3-996a-41b6-a194-81aa1476e989",
   "metadata": {},
   "source": [
    "**Note**: the `-1` in `columns_diff` resulted from an added `geometry` column by converting the non-spatial coordinates to point geometries "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9596c41-8d54-451e-b685-e2d8416140ae",
   "metadata": {},
   "source": [
    "### Manual Geometry Extraction\n",
    "\n",
    "Extract only the geometries for selected datasets - discarding all other attributes as only counts are needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b57bc7f6-8410-4607-a141-1f28b54d832f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing autospeed_enforcement - geometry extract (2022-02-17 21:33:25.253748)...\n",
      "Processed autospeed_enforcement - geometry extract (2022-02-17 21:33:25.278957) [Elapsed: 0:00:00.025208]\n",
      "Processing red_light_cams - geometry extract (2022-02-17 21:33:25.279234)...\n",
      "Processed red_light_cams - geometry extract (2022-02-17 21:33:25.294972) [Elapsed: 0:00:00.015737]\n",
      "Processing police - geometry extract (2022-02-17 21:33:25.295238)...\n",
      "Processed police - geometry extract (2022-02-17 21:33:25.300628) [Elapsed: 0:00:00.005389]\n",
      "Processing ambulance - geometry extract (2022-02-17 21:33:25.300794)...\n",
      "Processed ambulance - geometry extract (2022-02-17 21:33:25.307695) [Elapsed: 0:00:00.006899]\n",
      "Processing fire_hydrants - geometry extract (2022-02-17 21:33:25.308012)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n",
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n",
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n",
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n",
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed fire_hydrants - geometry extract (2022-02-17 21:33:27.333536) [Elapsed: 0:00:02.025522]\n",
      "Processing fire_stations - geometry extract (2022-02-17 21:33:27.333713)...\n",
      "Processed fire_stations - geometry extract (2022-02-17 21:33:27.342069) [Elapsed: 0:00:00.008355]\n",
      "Processing renewables - geometry extract (2022-02-17 21:33:27.342163)...\n",
      "Processed renewables - geometry extract (2022-02-17 21:33:27.349714) [Elapsed: 0:00:00.007551]\n",
      "Processing bicycle_parking - geometry extract (2022-02-17 21:33:27.349845)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n",
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n",
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed bicycle_parking - geometry extract (2022-02-17 21:33:28.122925) [Elapsed: 0:00:00.773079]\n",
      "Processing transit_shelters - geometry extract (2022-02-17 21:33:28.123112)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed transit_shelters - geometry extract (2022-02-17 21:33:28.402166) [Elapsed: 0:00:00.279053]\n",
      "Processing wayfind - geometry extract (2022-02-17 21:33:28.402389)...\n",
      "Processed wayfind - geometry extract (2022-02-17 21:33:28.422629) [Elapsed: 0:00:00.020238]\n",
      "Processing litter - geometry extract (2022-02-17 21:33:28.422779)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n",
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed litter - geometry extract (2022-02-17 21:33:28.958705) [Elapsed: 0:00:00.535923]\n",
      "Processing art - geometry extract (2022-02-17 21:33:28.958877)...\n",
      "Processed art - geometry extract (2022-02-17 21:33:29.001544) [Elapsed: 0:00:00.042664]\n",
      "Processing culture - geometry extract (2022-02-17 21:33:29.001891)...\n",
      "Processed culture - geometry extract (2022-02-17 21:33:29.053643) [Elapsed: 0:00:00.051749]\n",
      "Processing religious - geometry extract (2022-02-17 21:33:29.053813)...\n",
      "Processed religious - geometry extract (2022-02-17 21:33:29.138484) [Elapsed: 0:00:00.084669]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n",
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n",
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>POINT (617655.986 4827315.786)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>POINT (618384.811 4827785.108)</td>\n",
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       "      <th>281</th>\n",
       "      <td>POINT (618702.504 4827845.270)</td>\n",
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       "      <th>115</th>\n",
       "      <td>POINT (618011.257 4827850.830)</td>\n",
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       "      <th>287</th>\n",
       "      <td>POINT (618804.006 4827997.771)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>502</th>\n",
       "      <td>POINT (638386.092 4853990.843)</td>\n",
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       "      <th>503</th>\n",
       "      <td>POINT (638386.092 4853990.843)</td>\n",
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       "      <th>1382</th>\n",
       "      <td>POINT (639356.271 4854162.590)</td>\n",
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       "      <th>1383</th>\n",
       "      <td>POINT (639467.195 4854295.261)</td>\n",
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       "      <th>504</th>\n",
       "      <td>POINT (640049.356 4854377.015)</td>\n",
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       "</table>\n",
       "<p>1407 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            geometry\n",
       "279   POINT (617655.986 4827315.786)\n",
       "280   POINT (618384.811 4827785.108)\n",
       "281   POINT (618702.504 4827845.270)\n",
       "115   POINT (618011.257 4827850.830)\n",
       "287   POINT (618804.006 4827997.771)\n",
       "...                              ...\n",
       "502   POINT (638386.092 4853990.843)\n",
       "503   POINT (638386.092 4853990.843)\n",
       "1382  POINT (639356.271 4854162.590)\n",
       "1383  POINT (639467.195 4854295.261)\n",
       "504   POINT (640049.356 4854377.015)\n",
       "\n",
       "[1407 rows x 1 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_extract = [\n",
    "    'autospeed_enforcement',\n",
    "    'red_light_cams',\n",
    "    'police',\n",
    "    'ambulance',\n",
    "    'fire_hydrants',\n",
    "    'fire_stations',\n",
    "    'renewables',\n",
    "    'bicycle_parking',\n",
    "    'transit_shelters',\n",
    "    'wayfind',\n",
    "    'litter',\n",
    "    'art',\n",
    "    'culture',\n",
    "    'religious'\n",
    "]\n",
    "\n",
    "for name in to_extract:\n",
    "    \n",
    "    file = Path(f'{dist_folder}/clean-datasets/{name}.geojson')\n",
    "    if not file.is_file() or force:\n",
    "\n",
    "        # Start processing\n",
    "        timer = Timer(name, 'geometry extract')\n",
    "        timer.start()\n",
    "        gdf = datasets[name]\n",
    "\n",
    "        # Extract geometry only\n",
    "        gdf = gdf[['geometry']]\n",
    "\n",
    "        # Save data\n",
    "        gdf.to_file(file)\n",
    "\n",
    "        # End processing\n",
    "        timer.end()\n",
    "\n",
    "    else:\n",
    "\n",
    "        # Start loading\n",
    "        timer = Timer(name, 'already extracted')\n",
    "        timer.start()\n",
    "\n",
    "        # Read clean data\n",
    "        gdf = read_data(file)\n",
    "\n",
    "        # End processing\n",
    "        timer.end()\n",
    "\n",
    "    processed[name] = gdf\n",
    "    \n",
    "processed['religious']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccd241ce-5845-4a9d-96e2-92c007692067",
   "metadata": {},
   "source": [
    "### Manual Cleaning (Non-neighbourhood)\n",
    "\n",
    "Clean each dataset that is not at the neighbourhood geography level based on context - removing unnecessary variables and converting data types.\n",
    "\n",
    "**Note**: Will perform cleaning only if processed data does not exist in the `dist/clean-datasets` folder."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88f389fe-abc4-44f1-9b7c-61b6e18e6525",
   "metadata": {},
   "source": [
    "#### centrelines\n",
    "\n",
    "1. Filter out needed columns\n",
    "2. Rename cols to identify dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fb7b846b-bbe2-49a7-b725-bb0486fefc1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing centrelines - clean (2022-02-17 21:33:29.149685)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed centrelines - clean (2022-02-17 21:33:35.392449) [Elapsed: 0:00:06.242764]\n"
     ]
    }
   ],
   "source": [
    "name = 'centrelines'\n",
    "file = Path(f'{dist_folder}/clean-datasets/{name}.geojson')\n",
    "if not file.is_file() or force:\n",
    "    \n",
    "    # Start processing\n",
    "    timer = Timer(name, 'clean')\n",
    "    timer.start()\n",
    "    gdf = datasets[name]\n",
    "\n",
    "    # Lowercase col names\n",
    "    gdf = gdf.rename(str.lower, axis='columns')\n",
    "\n",
    "    # 1. Filter cols needed\n",
    "    gdf = gdf[[\n",
    "        'feature_code_desc',\n",
    "        'oneway_dir_code_desc',\n",
    "        'geometry'\n",
    "    ]]\n",
    "\n",
    "    # 2. Rename cols\n",
    "    gdf.columns = [f'centrelines_{c.replace(\"_desc\", \"\")}' if c != 'geometry' else c for c in gdf.columns]\n",
    "\n",
    "    # Save data\n",
    "    gdf.to_file(file)\n",
    "    \n",
    "    # End processing\n",
    "    timer.end()\n",
    "\n",
    "else:\n",
    "    \n",
    "    # Start loading\n",
    "    timer = Timer(name, 'already cleaned')\n",
    "    timer.start()\n",
    "\n",
    "    # Read clean data\n",
    "    gdf = read_data(file)\n",
    "\n",
    "    # End processing\n",
    "    timer.end()\n",
    "    \n",
    "processed[name] = gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "614327d5-c07b-4e5c-8810-a69b774d55c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>centrelines_feature_code</th>\n",
       "      <th>centrelines_oneway_dir_code</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6701</th>\n",
       "      <td>Trail</td>\n",
       "      <td>Not One-Way</td>\n",
       "      <td>LINESTRING (618678.172 4827207.870, 618678.656...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68876</th>\n",
       "      <td>Trail</td>\n",
       "      <td>Not One-Way</td>\n",
       "      <td>LINESTRING (618306.630 4827201.438, 618308.192...</td>\n",
       "    </tr>\n",
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       "      <th>29895</th>\n",
       "      <td>Trail</td>\n",
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       "      <td>LINESTRING (618678.172 4827207.870, 618685.440...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15899</th>\n",
       "      <td>Trail</td>\n",
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       "      <td>LINESTRING (618724.442 4827235.183, 618719.953...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2504</th>\n",
       "      <td>Trail</td>\n",
       "      <td>Not One-Way</td>\n",
       "      <td>LINESTRING (618732.369 4827232.078, 618724.442...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29889</th>\n",
       "      <td>Minor Arterial</td>\n",
       "      <td>Not One-Way</td>\n",
       "      <td>LINESTRING (644671.915 4856599.284, 644681.040...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68814</th>\n",
       "      <td>Trail</td>\n",
       "      <td>Not One-Way</td>\n",
       "      <td>LINESTRING (644750.044 4856560.792, 644765.203...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70766</th>\n",
       "      <td>Local</td>\n",
       "      <td>Not One-Way</td>\n",
       "      <td>LINESTRING (645745.075 4856239.846, 645744.766...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70771</th>\n",
       "      <td>Minor Arterial</td>\n",
       "      <td>Not One-Way</td>\n",
       "      <td>LINESTRING (644734.702 4856623.232, 644785.673...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70783</th>\n",
       "      <td>Minor Arterial</td>\n",
       "      <td>Not One-Way</td>\n",
       "      <td>LINESTRING (645500.290 4856893.002, 645597.209...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70827 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      centrelines_feature_code centrelines_oneway_dir_code  \\\n",
       "6701                     Trail                 Not One-Way   \n",
       "68876                    Trail                 Not One-Way   \n",
       "29895                    Trail                 Not One-Way   \n",
       "15899                    Trail                 Not One-Way   \n",
       "2504                     Trail                 Not One-Way   \n",
       "...                        ...                         ...   \n",
       "29889           Minor Arterial                 Not One-Way   \n",
       "68814                    Trail                 Not One-Way   \n",
       "70766                    Local                 Not One-Way   \n",
       "70771           Minor Arterial                 Not One-Way   \n",
       "70783           Minor Arterial                 Not One-Way   \n",
       "\n",
       "                                                geometry  \n",
       "6701   LINESTRING (618678.172 4827207.870, 618678.656...  \n",
       "68876  LINESTRING (618306.630 4827201.438, 618308.192...  \n",
       "29895  LINESTRING (618678.172 4827207.870, 618685.440...  \n",
       "15899  LINESTRING (618724.442 4827235.183, 618719.953...  \n",
       "2504   LINESTRING (618732.369 4827232.078, 618724.442...  \n",
       "...                                                  ...  \n",
       "29889  LINESTRING (644671.915 4856599.284, 644681.040...  \n",
       "68814  LINESTRING (644750.044 4856560.792, 644765.203...  \n",
       "70766  LINESTRING (645745.075 4856239.846, 645744.766...  \n",
       "70771  LINESTRING (644734.702 4856623.232, 644785.673...  \n",
       "70783  LINESTRING (645500.290 4856893.002, 645597.209...  \n",
       "\n",
       "[70827 rows x 3 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed['centrelines']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd9e8736-55b7-4bb6-b34b-f02b01da89aa",
   "metadata": {},
   "source": [
    "#### collisions\n",
    "\n",
    "1. Filter out needed columns\n",
    "2. Convert yes/no to 1/0\n",
    "3. Extract day of month\n",
    "4. Rename columns to identify dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8cf78b7d-a89e-494c-b61d-cbe8c3c568dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing collisions - clean (2022-02-17 21:33:35.409957)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed collisions - clean (2022-02-17 21:34:16.709877) [Elapsed: 0:00:41.299917]\n"
     ]
    }
   ],
   "source": [
    "name = 'collisions'\n",
    "file = Path(f'{dist_folder}/clean-datasets/{name}.geojson')\n",
    "if not file.is_file() or force:\n",
    "    \n",
    "    # Start processing\n",
    "    timer = Timer(name, 'clean')\n",
    "    timer.start()\n",
    "    gdf = datasets[name]\n",
    "\n",
    "    # Lowercase col names\n",
    "    gdf = gdf.rename(str.lower, axis='columns')\n",
    "\n",
    "    # 1. Filter cols needed\n",
    "    gdf = gdf[[\n",
    "        'month',\n",
    "        'day_of_week',\n",
    "        'year',\n",
    "        'hour',\n",
    "        'fatalities',\n",
    "        'injury_collisions',\n",
    "        'ftr_collisions', # fail to remain\n",
    "        'pd_collisions', # property damage\n",
    "        'geometry'\n",
    "    ]]\n",
    "\n",
    "    # 2a. Convert yes/no to 1/0\n",
    "    convert_cols = [\n",
    "        'injury_collisions',\n",
    "        'ftr_collisions',\n",
    "        'pd_collisions'\n",
    "    ]\n",
    "    convert = {c: gdf[c].apply(lambda x: 1 if x.strip().lower() == 'yes' else 0) for c in convert_cols}\n",
    "\n",
    "    # 2b. Assign conversions to data\n",
    "    gdf = gdf.assign(**convert)\n",
    "\n",
    "    # 3. Extract day of month\n",
    "    occurrence_date = pd.to_datetime(\n",
    "        datasets[name]['OccurrenceDate'],\n",
    "        infer_datetime_format=True\n",
    "    )\n",
    "    gdf.insert(1, 'day_of_month' , occurrence_date.dt.day)\n",
    "\n",
    "    # 4. Rename cols\n",
    "    gdf.columns = [f'collisions_{c.replace(\"_collisions\", \"\")}' if c != 'geometry' else c for c in gdf.columns]\n",
    "\n",
    "    # Save data\n",
    "    gdf.to_file(file)\n",
    "    \n",
    "    # End processing\n",
    "    timer.end()\n",
    "\n",
    "else:\n",
    "    \n",
    "    # Start loading\n",
    "    timer = Timer(name, 'already cleaned')\n",
    "    timer.start()\n",
    "\n",
    "    # Read clean data\n",
    "    gdf = read_data(file)\n",
    "\n",
    "    # End processing\n",
    "    timer.end()\n",
    "    \n",
    "processed[name] = gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "15315f55-77be-4e8b-a2b0-4aefd494dd60",
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>collisions_month</th>\n",
       "      <th>collisions_day_of_month</th>\n",
       "      <th>collisions_day_of_week</th>\n",
       "      <th>collisions_year</th>\n",
       "      <th>collisions_hour</th>\n",
       "      <th>collisions_fatalities</th>\n",
       "      <th>collisions_injury</th>\n",
       "      <th>collisions_ftr</th>\n",
       "      <th>collisions_pd</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>498520</th>\n",
       "      <td>August</td>\n",
       "      <td>23</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2018</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (610468.436 4826364.782)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498965</th>\n",
       "      <td>January</td>\n",
       "      <td>30</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (610641.711 4826705.142)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496310</th>\n",
       "      <td>September</td>\n",
       "      <td>12</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2019</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (612238.418 4828523.139)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497080</th>\n",
       "      <td>March</td>\n",
       "      <td>31</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2017</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (611788.704 4829640.113)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496198</th>\n",
       "      <td>November</td>\n",
       "      <td>26</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (612054.109 4833348.199)</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72562</th>\n",
       "      <td>December</td>\n",
       "      <td>12</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2016</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (647055.491 4857445.800)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72561</th>\n",
       "      <td>May</td>\n",
       "      <td>24</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2016</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (647055.491 4857445.800)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72092</th>\n",
       "      <td>November</td>\n",
       "      <td>22</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (647055.491 4857445.800)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72560</th>\n",
       "      <td>September</td>\n",
       "      <td>12</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (647055.491 4857445.800)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72556</th>\n",
       "      <td>May</td>\n",
       "      <td>27</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2014</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (647055.491 4857445.800)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>429631 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       collisions_month  collisions_day_of_month collisions_day_of_week  \\\n",
       "498520           August                       23               Thursday   \n",
       "498965          January                       30                Tuesday   \n",
       "496310        September                       12               Thursday   \n",
       "497080            March                       31                 Friday   \n",
       "496198         November                       26              Wednesday   \n",
       "...                 ...                      ...                    ...   \n",
       "72562          December                       12                 Monday   \n",
       "72561               May                       24                Tuesday   \n",
       "72092          November                       22               Thursday   \n",
       "72560         September                       12                 Monday   \n",
       "72556               May                       27                Tuesday   \n",
       "\n",
       "        collisions_year  collisions_hour  collisions_fatalities  \\\n",
       "498520             2018               19                      0   \n",
       "498965             2018               11                      0   \n",
       "496310             2019               16                      0   \n",
       "497080             2017               10                      0   \n",
       "496198             2014                9                      0   \n",
       "...                 ...              ...                    ...   \n",
       "72562              2016               16                      0   \n",
       "72561              2016               15                      0   \n",
       "72092              2018               10                      0   \n",
       "72560              2016                3                      0   \n",
       "72556              2014               17                      0   \n",
       "\n",
       "        collisions_injury  collisions_ftr  collisions_pd  \\\n",
       "498520                  0               0              1   \n",
       "498965                  1               0              0   \n",
       "496310                  0               0              1   \n",
       "497080                  0               0              1   \n",
       "496198                  0               0              1   \n",
       "...                   ...             ...            ...   \n",
       "72562                   0               0              1   \n",
       "72561                   0               0              1   \n",
       "72092                   0               0              1   \n",
       "72560                   0               0              1   \n",
       "72556                   1               0              0   \n",
       "\n",
       "                              geometry  \n",
       "498520  POINT (610468.436 4826364.782)  \n",
       "498965  POINT (610641.711 4826705.142)  \n",
       "496310  POINT (612238.418 4828523.139)  \n",
       "497080  POINT (611788.704 4829640.113)  \n",
       "496198  POINT (612054.109 4833348.199)  \n",
       "...                                ...  \n",
       "72562   POINT (647055.491 4857445.800)  \n",
       "72561   POINT (647055.491 4857445.800)  \n",
       "72092   POINT (647055.491 4857445.800)  \n",
       "72560   POINT (647055.491 4857445.800)  \n",
       "72556   POINT (647055.491 4857445.800)  \n",
       "\n",
       "[429631 rows x 10 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed['collisions']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6bd6bfa-82e1-4c6c-b17c-c4bacda49d53",
   "metadata": {},
   "source": [
    "#### traffic\n",
    "\n",
    "1. Filter out needed columns\n",
    "2. Extract month, day, year of traffic counts\n",
    "3. Rename columns to identify dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "88d40571-850a-4d9c-a921-a49429fd83b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing traffic - clean (2022-02-17 21:34:16.731721)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed traffic - clean (2022-02-17 21:36:39.937382) [Elapsed: 0:02:23.205659]\n"
     ]
    }
   ],
   "source": [
    "name = 'traffic'\n",
    "file = Path(f'{dist_folder}/clean-datasets/{name}.geojson')\n",
    "if not file.is_file() or force:\n",
    "    \n",
    "    # Start processing\n",
    "    timer = Timer(name, 'clean')\n",
    "    timer.start()\n",
    "    gdf = datasets[name]\n",
    "\n",
    "    # Lowercase col names\n",
    "    gdf = gdf.rename(str.lower, axis='columns')\n",
    "\n",
    "    # 1. Filter cols needed\n",
    "    gdf = gdf[[\n",
    "        'sb_cars_r', # southbound cars, turn right\n",
    "        'sb_cars_t', # straight\n",
    "        'sb_cars_l', # turn left\n",
    "        'nb_cars_r', # northbound cars\n",
    "        'nb_cars_t',\n",
    "        'nb_cars_l',\n",
    "        'wb_cars_r', # westbound cars\n",
    "        'wb_cars_t',\n",
    "        'wb_cars_l',\n",
    "        'eb_cars_r', # eastbound cars\n",
    "        'eb_cars_t',\n",
    "        'eb_cars_l',\n",
    "        'sb_truck_r', # southbound trucks\n",
    "        'sb_truck_t',\n",
    "        'sb_truck_l',\n",
    "        'nb_truck_r', # northbound trucks\n",
    "        'nb_truck_t',\n",
    "        'nb_truck_l',\n",
    "        'wb_truck_r', # westbound trucks\n",
    "        'wb_truck_t',\n",
    "        'wb_truck_l',\n",
    "        'eb_truck_r', # eastbound trucks\n",
    "        'eb_truck_t',\n",
    "        'eb_truck_l',\n",
    "        'sb_bus_r', # southbound buses\n",
    "        'sb_bus_t',\n",
    "        'sb_bus_l',\n",
    "        'nb_bus_r', # northbound buses\n",
    "        'nb_bus_t',\n",
    "        'nb_bus_l',\n",
    "        'wb_bus_r', # westbound buses\n",
    "        'wb_bus_t',\n",
    "        'wb_bus_l',\n",
    "        'eb_bus_r', # eastbound buses\n",
    "        'eb_bus_t',\n",
    "        'eb_bus_l',\n",
    "        'nx_peds', # north leg pedestrians\n",
    "        'sx_peds', # south leg pedestrians\n",
    "        'ex_peds', # east leg pedestrians\n",
    "        'wx_peds', # west leg pedestrians\n",
    "        'nx_bike', # bikes\n",
    "        'sx_bike',\n",
    "        'ex_bike',\n",
    "        'wx_bike',\n",
    "        'nx_other', # non-vehicle (skateboard, scooters)\n",
    "        'sx_other',\n",
    "        'ex_other',\n",
    "        'wx_other',\n",
    "        'geometry'\n",
    "    ]]\n",
    "\n",
    "    # 2. Extract month, day, year\n",
    "    count_date = pd.to_datetime(\n",
    "        datasets[name]['count_date'],\n",
    "        infer_datetime_format=True\n",
    "    )\n",
    "    gdf.insert(0, 'year' , count_date.dt.year)\n",
    "    gdf.insert(0, 'day_of_month' , count_date.dt.day)\n",
    "    gdf.insert(0, 'month' , count_date.dt.month)\n",
    "\n",
    "    # 3. Rename cols\n",
    "    gdf.columns = [f'traffic_{c.replace(\"_collisions\", \"\")}' if c != 'geometry' else c for c in gdf.columns]\n",
    "\n",
    "    # Save data\n",
    "    gdf.to_file(file)\n",
    "    \n",
    "    # End processing\n",
    "    timer.end()\n",
    "\n",
    "else:\n",
    "    \n",
    "    # Start loading\n",
    "    timer = Timer(name, 'already cleaned')\n",
    "    timer.start()\n",
    "\n",
    "    # Read clean data\n",
    "    gdf = read_data(file)\n",
    "\n",
    "    # End processing\n",
    "    timer.end()\n",
    "    \n",
    "processed[name] = gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4650b3ea-b3ca-4adf-81ff-6775f2b6602c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>traffic_month</th>\n",
       "      <th>traffic_day_of_month</th>\n",
       "      <th>traffic_year</th>\n",
       "      <th>traffic_sb_cars_r</th>\n",
       "      <th>traffic_sb_cars_t</th>\n",
       "      <th>traffic_sb_cars_l</th>\n",
       "      <th>traffic_nb_cars_r</th>\n",
       "      <th>traffic_nb_cars_t</th>\n",
       "      <th>traffic_nb_cars_l</th>\n",
       "      <th>traffic_wb_cars_r</th>\n",
       "      <th>...</th>\n",
       "      <th>traffic_wx_peds</th>\n",
       "      <th>traffic_nx_bike</th>\n",
       "      <th>traffic_sx_bike</th>\n",
       "      <th>traffic_ex_bike</th>\n",
       "      <th>traffic_wx_bike</th>\n",
       "      <th>traffic_nx_other</th>\n",
       "      <th>traffic_sx_other</th>\n",
       "      <th>traffic_ex_other</th>\n",
       "      <th>traffic_wx_other</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>53626</th>\n",
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       "      <td>4</td>\n",
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       "      <td>4</td>\n",
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       "      <th>53622</th>\n",
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       "    <tr>\n",
       "      <th>193737</th>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>2018</td>\n",
       "      <td>15.0</td>\n",
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       "      <td>28.0</td>\n",
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       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>POINT (647051.956 4857446.075)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193736</th>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>2018</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>POINT (647051.956 4857446.075)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193735</th>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>2018</td>\n",
       "      <td>25.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>POINT (647051.956 4857446.075)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193734</th>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>2018</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>POINT (647051.956 4857446.075)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193733</th>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>2018</td>\n",
       "      <td>39.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>POINT (647051.956 4857446.075)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>219497 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        traffic_month  traffic_day_of_month  traffic_year  traffic_sb_cars_r  \\\n",
       "53626               4                    25          2012               15.0   \n",
       "53625               4                    25          2012                1.0   \n",
       "53624               4                    25          2012                2.0   \n",
       "53623               4                    25          2012                2.0   \n",
       "53622               4                    25          2012                4.0   \n",
       "...               ...                   ...           ...                ...   \n",
       "193737              8                    28          2018               15.0   \n",
       "193736              8                    28          2018               17.0   \n",
       "193735              8                    28          2018               25.0   \n",
       "193734              8                    28          2018               27.0   \n",
       "193733              8                    28          2018               39.0   \n",
       "\n",
       "        traffic_sb_cars_t  traffic_sb_cars_l  traffic_nb_cars_r  \\\n",
       "53626                 0.0               17.0                9.0   \n",
       "53625                 1.0                2.0                5.0   \n",
       "53624                 0.0                1.0                6.0   \n",
       "53623                 0.0                1.0                4.0   \n",
       "53622                 1.0                8.0                6.0   \n",
       "...                   ...                ...                ...   \n",
       "193737                7.0               28.0                0.0   \n",
       "193736                4.0               32.0                2.0   \n",
       "193735                7.0               46.0                0.0   \n",
       "193734                6.0               51.0                0.0   \n",
       "193733                4.0               46.0                0.0   \n",
       "\n",
       "        traffic_nb_cars_t  traffic_nb_cars_l  traffic_wb_cars_r  ...  \\\n",
       "53626                 1.0                4.0                6.0  ...   \n",
       "53625                 0.0                3.0                2.0  ...   \n",
       "53624                 0.0                4.0                3.0  ...   \n",
       "53623                 0.0                1.0                0.0  ...   \n",
       "53622                 0.0                0.0                1.0  ...   \n",
       "...                   ...                ...                ...  ...   \n",
       "193737                7.0                1.0               29.0  ...   \n",
       "193736                7.0                4.0               59.0  ...   \n",
       "193735               11.0                2.0               53.0  ...   \n",
       "193734               14.0                5.0               86.0  ...   \n",
       "193733               20.0                4.0               98.0  ...   \n",
       "\n",
       "        traffic_wx_peds  traffic_nx_bike  traffic_sx_bike  traffic_ex_bike  \\\n",
       "53626               1.0              0.0              0.0              5.0   \n",
       "53625               1.0              1.0              0.0              3.0   \n",
       "53624               0.0              0.0              0.0              3.0   \n",
       "53623               0.0              0.0              0.0              0.0   \n",
       "53622               1.0              0.0              0.0              3.0   \n",
       "...                 ...              ...              ...              ...   \n",
       "193737              0.0              0.0              0.0              0.0   \n",
       "193736              0.0              0.0              3.0              0.0   \n",
       "193735              0.0              0.0              0.0              1.0   \n",
       "193734              0.0              0.0              0.0              0.0   \n",
       "193733              0.0              0.0              0.0              0.0   \n",
       "\n",
       "        traffic_wx_bike  traffic_nx_other  traffic_sx_other  traffic_ex_other  \\\n",
       "53626               5.0               0.0               0.0               0.0   \n",
       "53625               3.0               0.0               0.0               0.0   \n",
       "53624               4.0               0.0               0.0               0.0   \n",
       "53623               0.0               0.0               0.0               0.0   \n",
       "53622               2.0               0.0               0.0               0.0   \n",
       "...                 ...               ...               ...               ...   \n",
       "193737              0.0               0.0               0.0               0.0   \n",
       "193736              0.0               0.0               0.0               0.0   \n",
       "193735              0.0               0.0               0.0               0.0   \n",
       "193734              0.0               0.0               0.0               0.0   \n",
       "193733              1.0               0.0               0.0               0.0   \n",
       "\n",
       "        traffic_wx_other                        geometry  \n",
       "53626                0.0  POINT (617452.805 4827414.957)  \n",
       "53625                0.0  POINT (617452.805 4827414.957)  \n",
       "53624                0.0  POINT (617452.805 4827414.957)  \n",
       "53623                0.0  POINT (617452.805 4827414.957)  \n",
       "53622                0.0  POINT (617452.805 4827414.957)  \n",
       "...                  ...                             ...  \n",
       "193737               0.0  POINT (647051.956 4857446.075)  \n",
       "193736               0.0  POINT (647051.956 4857446.075)  \n",
       "193735               0.0  POINT (647051.956 4857446.075)  \n",
       "193734               0.0  POINT (647051.956 4857446.075)  \n",
       "193733               0.0  POINT (647051.956 4857446.075)  \n",
       "\n",
       "[219497 rows x 52 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed['traffic']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef013bc5-6cee-4bee-94c6-62fa6a2a04d0",
   "metadata": {},
   "source": [
    "#### watch_your_speed\n",
    "\n",
    "1. Filter out needed columns\n",
    "2. Rename cols to identify dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3128f1f2-904a-451e-8966-a83804bcaf85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing watch_your_speed - clean (2022-02-17 21:36:39.995979)...\n",
      "Processed watch_your_speed - clean (2022-02-17 21:36:40.053866) [Elapsed: 0:00:00.057886]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    }
   ],
   "source": [
    "name = 'watch_your_speed'\n",
    "file = Path(f'{dist_folder}/clean-datasets/{name}.geojson')\n",
    "if not file.is_file() or force:\n",
    "    \n",
    "    # Start processing\n",
    "    timer = Timer(name, 'clean')\n",
    "    timer.start()\n",
    "    gdf = datasets[name]\n",
    "\n",
    "    # Lowercase col names\n",
    "    gdf = gdf.rename(str.lower, axis='columns')\n",
    "\n",
    "    # 1. Filter cols needed\n",
    "    gdf = gdf[[\n",
    "        'dir', # dir of travel\n",
    "        'min_speed', # speed at which sign starts recording\n",
    "        'speed_limit',\n",
    "        'geometry'\n",
    "    ]]\n",
    "\n",
    "    # 2. Rename cols\n",
    "    gdf.columns = [f'wys_{c}' if c != 'geometry' else c for c in gdf.columns]\n",
    "\n",
    "    # Save data\n",
    "    gdf.to_file(file)\n",
    "    \n",
    "    # End processing\n",
    "    timer.end()\n",
    "    \n",
    "else:\n",
    "    \n",
    "    # Start loading\n",
    "    timer = Timer(name, 'already cleaned')\n",
    "    timer.start()\n",
    "\n",
    "    # Read clean data\n",
    "    gdf = read_data(file)\n",
    "\n",
    "    # End processing\n",
    "    timer.end()\n",
    "    \n",
    "processed[name] = gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2745d452-30f3-4e5f-b820-38e51ed1a686",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>wys_dir</th>\n",
       "      <th>wys_min_speed</th>\n",
       "      <th>wys_speed_limit</th>\n",
       "      <th>geometry</th>\n",
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       "      <td>POINT (634510.486 4852632.280)</td>\n",
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       "  </tbody>\n",
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       "<p>783 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    wys_dir  wys_min_speed  wys_speed_limit                        geometry\n",
       "107      NB              5               40  POINT (619788.800 4828082.946)\n",
       "106      EB             30               50  POINT (619589.983 4828274.595)\n",
       "105      WB             30               50  POINT (619596.527 4828297.042)\n",
       "104      SB             30               50  POINT (619656.943 4828426.993)\n",
       "93       WB             20               30  POINT (617066.766 4829371.128)\n",
       "..      ...            ...              ...                             ...\n",
       "507      SB              5               40  POINT (634510.486 4852632.280)\n",
       "508      SB              5               40  POINT (634510.486 4852632.280)\n",
       "759      SB             20               30  POINT (635262.125 4852901.409)\n",
       "582      NB              5               40  POINT (634584.345 4854392.927)\n",
       "583      WB              5               40  POINT (633927.281 4855480.335)\n",
       "\n",
       "[783 rows x 4 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed['watch_your_speed']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b900a5f-7c8f-445a-985e-aa5b4b4b466f",
   "metadata": {},
   "source": [
    "#### schools\n",
    "\n",
    "1. Filter out needed columns\n",
    "2. Rename columns to identify dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "08991666-214e-4ae3-a990-6ce4124d0722",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing schools - clean (2022-02-17 21:36:40.070258)...\n",
      "Processed schools - clean (2022-02-17 21:36:40.147178) [Elapsed: 0:00:00.076918]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    }
   ],
   "source": [
    "name = 'schools'\n",
    "file = Path(f'{dist_folder}/clean-datasets/{name}.geojson')\n",
    "if not file.is_file() or force:\n",
    "    \n",
    "    # Start processing\n",
    "    timer = Timer(name, 'clean')\n",
    "    timer.start()\n",
    "    gdf = datasets[name]\n",
    "    \n",
    "    # Lowercase col names\n",
    "    gdf = gdf.rename(str.lower, axis='columns')\n",
    "\n",
    "    # 1. Filter cols needed\n",
    "    gdf = gdf[[\n",
    "        'school_level',\n",
    "        'school_type_desc',\n",
    "        'geometry'\n",
    "    ]]\n",
    "    \n",
    "    # 2. Rename cols\n",
    "    gdf = gdf.rename(columns={\n",
    "        'school_level': 'schools_level',\n",
    "        'school_type_desc': 'schools_type'\n",
    "    })\n",
    "\n",
    "    # Save data\n",
    "    gdf.to_file(file)\n",
    "    \n",
    "    # End processing\n",
    "    timer.end()\n",
    "    \n",
    "else:\n",
    "    \n",
    "    # Start loading\n",
    "    timer = Timer(name, 'already cleaned')\n",
    "    timer.start()\n",
    "\n",
    "    # Read clean data\n",
    "    gdf = read_data(file)\n",
    "\n",
    "    # End processing\n",
    "    timer.end()\n",
    "    \n",
    "processed[name] = gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "75f3e537-f7d3-4983-9bf9-a05ba617a9bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>363</th>\n",
       "      <td>None</td>\n",
       "      <td>English Separate</td>\n",
       "      <td>POINT (619741.624 4827976.711)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POINT (619434.035 4828177.362)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>None</td>\n",
       "      <td>English Separate</td>\n",
       "      <td>POINT (619676.909 4828220.336)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>None</td>\n",
       "      <td>Private</td>\n",
       "      <td>POINT (633994.306 4851998.156)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>None</td>\n",
       "      <td>English Public</td>\n",
       "      <td>POINT (635294.270 4852056.133)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>None</td>\n",
       "      <td>English Public</td>\n",
       "      <td>POINT (635163.526 4852382.972)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>931</th>\n",
       "      <td>None</td>\n",
       "      <td>English Separate</td>\n",
       "      <td>POINT (634484.334 4852539.452)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041</th>\n",
       "      <td>None</td>\n",
       "      <td>English Public</td>\n",
       "      <td>POINT (635214.778 4852799.263)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1194 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     schools_level      schools_type                        geometry\n",
       "675           None           Private  POINT (618384.811 4827785.108)\n",
       "515           None    English Public  POINT (618641.124 4827869.702)\n",
       "363           None  English Separate  POINT (619741.624 4827976.711)\n",
       "477           None              None  POINT (619434.035 4828177.362)\n",
       "472           None  English Separate  POINT (619676.909 4828220.336)\n",
       "...            ...               ...                             ...\n",
       "50            None           Private  POINT (633994.306 4851998.156)\n",
       "265           None    English Public  POINT (635294.270 4852056.133)\n",
       "295           None    English Public  POINT (635163.526 4852382.972)\n",
       "931           None  English Separate  POINT (634484.334 4852539.452)\n",
       "1041          None    English Public  POINT (635214.778 4852799.263)\n",
       "\n",
       "[1194 rows x 3 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed['schools']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "035822b3-c0df-42bc-9ef1-a5004603f1f5",
   "metadata": {},
   "source": [
    "#### childcare\n",
    "\n",
    "1. Filter out needed columns\n",
    "2. Convert yes/no to 1/0\n",
    "3. Rename columns to identify dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7580b28d-699d-4ec8-9364-072f5a68dfac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing childcare - clean (2022-02-17 21:36:40.170109)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed childcare - clean (2022-02-17 21:36:40.279275) [Elapsed: 0:00:00.109162]\n"
     ]
    }
   ],
   "source": [
    "name = 'childcare'\n",
    "file = Path(f'{dist_folder}/clean-datasets/{name}.geojson')\n",
    "if not file.is_file() or force:\n",
    "    \n",
    "    # Start processing\n",
    "    timer = Timer(name, 'clean')\n",
    "    timer.start()\n",
    "    gdf = datasets[name]\n",
    "    \n",
    "    # Lowercase col names\n",
    "    gdf = gdf.rename(str.lower, axis='columns')\n",
    "\n",
    "    # 1. Filter cols needed\n",
    "    gdf = gdf[[\n",
    "        'auspice', # operating auspice: commercial, nonprofit, public\n",
    "        'bldg_type', # building type\n",
    "        'igspace', # spaces for infants 0-18 months\n",
    "        'tgspace', # spaces for toddlers 18-30 months\n",
    "        'pgspace', # spaces for preschoolers 30 months+\n",
    "        'kgspace', # spaces for children in full day kindergarten\n",
    "        'sgspace', # spaces for children grade 1+\n",
    "        'totspace', # total spaces for all age groups\n",
    "        'subsidy', # centre has fee subsidy contract (y/n)\n",
    "        'geometry'\n",
    "    ]]\n",
    "    \n",
    "    # 2. Convert yes/no to 1/0\n",
    "    gdf = gdf.assign(\n",
    "        subsidy=gdf['subsidy'].apply(lambda x: 1 if x.strip().lower() == 'y' else 0)\n",
    "    )\n",
    "    \n",
    "    # 3. Rename cols\n",
    "    gdf.columns = [f'childcare_{c.replace(\"_desc\", \"\")}' if c != 'geometry' else c for c in gdf.columns]\n",
    "\n",
    "    # Save data\n",
    "    gdf.to_file(file)\n",
    "    \n",
    "    # End processing\n",
    "    timer.end()\n",
    "    \n",
    "else:\n",
    "    \n",
    "    # Start loading\n",
    "    timer = Timer(name, 'already cleaned')\n",
    "    timer.start()\n",
    "\n",
    "    # Read clean data\n",
    "    gdf = read_data(file)\n",
    "\n",
    "    # End processing\n",
    "    timer.end()\n",
    "    \n",
    "processed[name] = gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "937b4c81-f4eb-44ff-9f94-12703f0145ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\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>childcare_auspice</th>\n",
       "      <th>childcare_bldg_type</th>\n",
       "      <th>childcare_igspace</th>\n",
       "      <th>childcare_tgspace</th>\n",
       "      <th>childcare_pgspace</th>\n",
       "      <th>childcare_kgspace</th>\n",
       "      <th>childcare_sgspace</th>\n",
       "      <th>childcare_totspace</th>\n",
       "      <th>childcare_subsidy</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>Public (City Operated) Agency</td>\n",
       "      <td>Public (school closed)</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>POINT (618589.937 4827866.979)</td>\n",
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       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>Non Profit Agency</td>\n",
       "      <td>Public Middle School</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>30</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (618641.125 4827869.702)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>Non Profit Agency</td>\n",
       "      <td>Other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (620628.977 4827920.202)</td>\n",
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       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>Public (City Operated) Agency</td>\n",
       "      <td>Other</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (619574.314 4828218.620)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>935</th>\n",
       "      <td>Non Profit Agency</td>\n",
       "      <td>Catholic Elementary School</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>60</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (619676.910 4828220.335)</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>Non Profit Agency</td>\n",
       "      <td>Catholic Elementary School</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (639089.617 4853537.022)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>Non Profit Agency</td>\n",
       "      <td>Public Elementary (French)</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>26</td>\n",
       "      <td>30</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (638518.197 4853596.489)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>Non Profit Agency</td>\n",
       "      <td>Public Elementary School</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>39</td>\n",
       "      <td>60</td>\n",
       "      <td>131</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (637516.539 4853855.179)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>Non Profit Agency</td>\n",
       "      <td>Catholic Elementary School</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>30</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (638388.746 4853919.924)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>Non Profit Agency</td>\n",
       "      <td>Public Elementary School</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>52</td>\n",
       "      <td>60</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (638314.100 4854082.125)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1038 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 childcare_auspice         childcare_bldg_type  \\\n",
       "270  Public (City Operated) Agency      Public (school closed)   \n",
       "801              Non Profit Agency        Public Middle School   \n",
       "434              Non Profit Agency                       Other   \n",
       "331  Public (City Operated) Agency                       Other   \n",
       "935              Non Profit Agency  Catholic Elementary School   \n",
       "..                             ...                         ...   \n",
       "625              Non Profit Agency  Catholic Elementary School   \n",
       "388              Non Profit Agency  Public Elementary (French)   \n",
       "374              Non Profit Agency    Public Elementary School   \n",
       "509              Non Profit Agency  Catholic Elementary School   \n",
       "467              Non Profit Agency    Public Elementary School   \n",
       "\n",
       "     childcare_igspace  childcare_tgspace  childcare_pgspace  \\\n",
       "270                 10                 20                 32   \n",
       "801                  0                  0                  0   \n",
       "434                  0                  0                 16   \n",
       "331                 10                 20                 32   \n",
       "935                  0                  0                  0   \n",
       "..                 ...                ...                ...   \n",
       "625                  0                 10                 24   \n",
       "388                  0                 10                 24   \n",
       "374                  0                  0                 32   \n",
       "509                  0                  0                  0   \n",
       "467                  0                  0                 32   \n",
       "\n",
       "     childcare_kgspace  childcare_sgspace  childcare_totspace  \\\n",
       "270                  0                  0                  62   \n",
       "801                 13                 30                  43   \n",
       "434                  0                  0                  16   \n",
       "331                  0                  0                  62   \n",
       "935                 39                 60                  99   \n",
       "..                 ...                ...                 ...   \n",
       "625                  0                 30                  64   \n",
       "388                 26                 30                  90   \n",
       "374                 39                 60                 131   \n",
       "509                 13                 30                  43   \n",
       "467                 52                 60                 144   \n",
       "\n",
       "     childcare_subsidy                        geometry  \n",
       "270                  1  POINT (618589.937 4827866.979)  \n",
       "801                  1  POINT (618641.125 4827869.702)  \n",
       "434                  0  POINT (620628.977 4827920.202)  \n",
       "331                  1  POINT (619574.314 4828218.620)  \n",
       "935                  1  POINT (619676.910 4828220.335)  \n",
       "..                 ...                             ...  \n",
       "625                  1  POINT (639089.617 4853537.022)  \n",
       "388                  1  POINT (638518.197 4853596.489)  \n",
       "374                  1  POINT (637516.539 4853855.179)  \n",
       "509                  1  POINT (638388.746 4853919.924)  \n",
       "467                  1  POINT (638314.100 4854082.125)  \n",
       "\n",
       "[1038 rows x 10 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed['childcare']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e43cfa71",
   "metadata": {},
   "source": [
    "### Manual Cleaning (Neighbourhood)\n",
    "\n",
    "Clean each dataset at the neighbourhood geography level based on context - removing unnecessary variables and converting data types.\n",
    "\n",
    "**Note**: Will perform cleaning only if processed data does not exist in the `dist/clean-datasets` folder."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99284ad5-6164-48c7-b5bf-84fff7ffa6ef",
   "metadata": {},
   "source": [
    "#### nbhoods\n",
    "\n",
    "Rename and save neighbourhoods polygons:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "03d9aebd-2316-4154-921b-c79bca13fde1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <td>96</td>\n",
       "      <td>POLYGON ((627795.397 4836876.867, 627781.902 4...</td>\n",
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       "      <th>1</th>\n",
       "      <td>95</td>\n",
       "      <td>POLYGON ((629463.304 4836332.300, 629324.313 4...</td>\n",
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       "      <td>64</td>\n",
       "      <td>POLYGON ((635860.212 4836239.139, 635719.852 4...</td>\n",
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       "      <th>...</th>\n",
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       "      <th>135</th>\n",
       "      <td>126</td>\n",
       "      <td>POLYGON ((637264.757 4848044.207, 637331.396 4...</td>\n",
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       "      <th>136</th>\n",
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       "      <th>137</th>\n",
       "      <td>22</td>\n",
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       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>37</td>\n",
       "      <td>POLYGON ((625533.338 4846776.345, 625525.472 4...</td>\n",
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       "      <th>139</th>\n",
       "      <td>51</td>\n",
       "      <td>POLYGON ((627795.153 4847184.892, 627788.963 4...</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 2 columns</p>\n",
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       "     neighbourhood_id                                           geometry\n",
       "0                  96  POLYGON ((627795.397 4836876.867, 627781.902 4...\n",
       "1                  95  POLYGON ((629463.304 4836332.300, 629324.313 4...\n",
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       "3                  64  POLYGON ((635860.212 4836239.139, 635719.852 4...\n",
       "4                 103  POLYGON ((628032.302 4840233.689, 627977.197 4...\n",
       "..                ...                                                ...\n",
       "135               126  POLYGON ((637264.757 4848044.207, 637331.396 4...\n",
       "136               133  POLYGON ((649297.450 4847596.814, 649298.794 4...\n",
       "137                22  POLYGON ((618676.633 4843651.507, 618660.054 4...\n",
       "138                37  POLYGON ((625533.338 4846776.345, 625525.472 4...\n",
       "139                51  POLYGON ((627795.153 4847184.892, 627788.963 4...\n",
       "\n",
       "[140 rows x 2 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbhoods = nbhoods.rename(columns={'Neighbourhood Id': 'neighbourhood_id'})\n",
    "nbhoods.to_file(f'{dist_folder}/nbhoods.geojson')\n",
    "nbhoods"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9be16113-3165-43be-9b54-5e3bd22f42e8",
   "metadata": {},
   "source": [
    "#### crime\n",
    "\n",
    "1. Filter out needed columns\n",
    "2. Add totals for each crime from 2014 to 2020\n",
    "3. Rename columns to identify dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "873f07d4-831d-42c8-bf03-7cb4a7c60650",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing crime - clean (2022-02-17 21:36:40.516657)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed crime - clean (2022-02-17 21:36:41.108695) [Elapsed: 0:00:00.592036]\n"
     ]
    }
   ],
   "source": [
    "name = 'crime'\n",
    "file = Path(f'{dist_folder}/clean-datasets/{name}.geojson')\n",
    "if not file.is_file() or force:\n",
    "    \n",
    "    # Start processing\n",
    "    timer = Timer(name, 'clean')\n",
    "    timer.start()\n",
    "    gdf = datasets[name]\n",
    "\n",
    "    # Lowercase col names and rename cols\n",
    "    gdf = gdf.rename(str.lower, axis='columns')\n",
    "    gdf = gdf.rename(columns={\n",
    "        'hood_id': 'neighbourhood_id',\n",
    "        'robberyrate_2015': 'robbery_rate2015',\n",
    "        'shooting_rate2014': 'shootings_rate2014'\n",
    "    })\n",
    "\n",
    "    # 1. Filter cols needed\n",
    "    gdf = gdf[[\n",
    "        'neighbourhood_id',\n",
    "        'f2020_population_projection',\n",
    "        'assault_2014', # assault\n",
    "        'assault_2015',\n",
    "        'assault_2016',\n",
    "        'assault_2017',\n",
    "        'assault_2018',\n",
    "        'assault_2019',\n",
    "        'assault_2020',\n",
    "        'assault_rate2014',\n",
    "        'assault_rate2015',\n",
    "        'assault_rate2016',\n",
    "        'assault_rate2017',\n",
    "        'assault_rate2018',\n",
    "        'assault_rate2019',\n",
    "        'assault_rate2020',\n",
    "        'autotheft_2014', # autotheft\n",
    "        'autotheft_2015',\n",
    "        'autotheft_2016',\n",
    "        'autotheft_2017',\n",
    "        'autotheft_2018',\n",
    "        'autotheft_2019',\n",
    "        'autotheft_2020',\n",
    "        'autotheft_rate2014',\n",
    "        'autotheft_rate2015',\n",
    "        'autotheft_rate2016',\n",
    "        'autotheft_rate2017',\n",
    "        'autotheft_rate2018',\n",
    "        'autotheft_rate2019',\n",
    "        'autotheft_rate2020',\n",
    "        'breakandenter_2014', # breakandenter\n",
    "        'breakandenter_2015',\n",
    "        'breakandenter_2016',\n",
    "        'breakandenter_2017',\n",
    "        'breakandenter_2018',\n",
    "        'breakandenter_2019',\n",
    "        'breakandenter_2020',\n",
    "        'breakandenter_rate2014',\n",
    "        'breakandenter_rate2015',\n",
    "        'breakandenter_rate2016',\n",
    "        'breakandenter_rate2017',\n",
    "        'breakandenter_rate2018',\n",
    "        'breakandenter_rate2019',\n",
    "        'breakandenter_rate2020',\n",
    "        'robbery_2014', # robbery\n",
    "        'robbery_2015',\n",
    "        'robbery_2016',\n",
    "        'robbery_2017',\n",
    "        'robbery_2018',\n",
    "        'robbery_2019',\n",
    "        'robbery_2020',\n",
    "        'robbery_rate2014',\n",
    "        'robbery_rate2015',\n",
    "        'robbery_rate2016',\n",
    "        'robbery_rate2017',\n",
    "        'robbery_rate2018',\n",
    "        'robbery_rate2019',\n",
    "        'robbery_rate2020',\n",
    "        'theftover_2014', # theftover\n",
    "        'theftover_2015',\n",
    "        'theftover_2016',\n",
    "        'theftover_2017',\n",
    "        'theftover_2018',\n",
    "        'theftover_2019',\n",
    "        'theftover_2020',\n",
    "        'theftover_rate2014',\n",
    "        'theftover_rate2015',\n",
    "        'theftover_rate2016',\n",
    "        'theftover_rate2017',\n",
    "        'theftover_rate2018',\n",
    "        'theftover_rate2019',\n",
    "        'theftover_rate2020',\n",
    "        'homicide_2014', # homicide\n",
    "        'homicide_2015',\n",
    "        'homicide_2016',\n",
    "        'homicide_2017',\n",
    "        'homicide_2018',\n",
    "        'homicide_2019',\n",
    "        'homicide_2020',\n",
    "        'homicide_rate2014',\n",
    "        'homicide_rate2015',\n",
    "        'homicide_rate2016',\n",
    "        'homicide_rate2017',\n",
    "        'homicide_rate2018',\n",
    "        'homicide_rate2019',\n",
    "        'homicide_rate2020',\n",
    "        'shootings_2014', # shootings\n",
    "        'shootings_2015',\n",
    "        'shootings_2016',\n",
    "        'shootings_2017',\n",
    "        'shootings_2018',\n",
    "        'shootings_2019',\n",
    "        'shootings_2020',\n",
    "        'shootings_rate2014',\n",
    "        'shootings_rate2015',\n",
    "        'shootings_rate2016',\n",
    "        'shootings_rate2017',\n",
    "        'shootings_rate2018',\n",
    "        'shootings_rate2019',\n",
    "        'shootings_rate2020',\n",
    "        'geometry',\n",
    "    ]]\n",
    "    \n",
    "    # 2a. Get unique crime types\n",
    "    crime_type = [\n",
    "        c.split('_')[0] for c in gdf.columns if c not in ['geometry', 'f2020_population_projection', 'neighbourhood_id']\n",
    "    ]\n",
    "    crime_type = list(dict.fromkeys(crime_type)) # get unique crime types\n",
    "    \n",
    "    # 2b. Add totals for each crime\n",
    "    years = [2014, 2015, 2016, 2017, 2018, 2019, 2020]\n",
    "    for ctype in crime_type:\n",
    "        \n",
    "        # 2b1. Add totals for counts\n",
    "        counts = [f'{ctype}_{year}' for year in years]\n",
    "        gdf.insert(\n",
    "            len(gdf.columns) - 2, # insert before geometry\n",
    "            f'{ctype}_{min(years)}to{max(years)}',\n",
    "            gdf[counts].sum(axis=1)\n",
    "        )\n",
    "        \n",
    "        # 2b2. Add totals for rates\n",
    "        rates = [f'{ctype}_rate{year}' for year in years]\n",
    "        gdf.insert(\n",
    "            len(gdf.columns) - 1, # insert before geometry\n",
    "            f'{ctype}_rate{min(years)}to{max(years)}',\n",
    "            gdf[rates].sum(axis=1)\n",
    "        )\n",
    "\n",
    "    # 3. Rename cols\n",
    "    gdf.columns = [f'crime_{c}' if c not in ['geometry', 'neighbourhood_id'] else c for c in gdf.columns]\n",
    "\n",
    "    # Save data\n",
    "    gdf.to_file(file)\n",
    "    \n",
    "    # End processing\n",
    "    timer.end()\n",
    "    \n",
    "else:\n",
    "    \n",
    "    # Start loading\n",
    "    timer = Timer(name, 'already cleaned')\n",
    "    timer.start()\n",
    "\n",
    "    # Read clean data\n",
    "    gdf = read_data(file)\n",
    "\n",
    "    # End processing\n",
    "    timer.end()\n",
    "    \n",
    "processed[name] = gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b0a57a47-0cc0-4772-941b-bc4764a0830e",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neighbourhood_id</th>\n",
       "      <th>crime_f2020_population_projection</th>\n",
       "      <th>crime_assault_2014</th>\n",
       "      <th>crime_assault_2015</th>\n",
       "      <th>crime_assault_2016</th>\n",
       "      <th>crime_assault_2017</th>\n",
       "      <th>crime_assault_2018</th>\n",
       "      <th>crime_assault_2019</th>\n",
       "      <th>crime_assault_2020</th>\n",
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       "      <th>...</th>\n",
       "      <th>crime_robbery_2014to2020</th>\n",
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       "      <th>crime_theftover_2014to2020</th>\n",
       "      <th>crime_robbery_rate2014to2020</th>\n",
       "      <th>crime_homicide_2014to2020</th>\n",
       "      <th>crime_theftover_rate2014to2020</th>\n",
       "      <th>crime_shootings_2014to2020</th>\n",
       "      <th>crime_homicide_rate2014to2020</th>\n",
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       "      <td>2</td>\n",
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       "      <td>POLYGON ((641205.530 4852569.487, 641140.779 4...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 115 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     neighbourhood_id  crime_f2020_population_projection  crime_assault_2014  \\\n",
       "121                19                              11564                  63   \n",
       "105                18                              12539                  84   \n",
       "86                 20                              13242                  45   \n",
       "139                17                              38739                 208   \n",
       "11                 14                              51481                 230   \n",
       "..                ...                                ...                 ...   \n",
       "122               132                              47548                 219   \n",
       "79                129                              31618                  67   \n",
       "126               116                              26911                  44   \n",
       "97                131                              50790                 183   \n",
       "134               130                              29166                  72   \n",
       "\n",
       "     crime_assault_2015  crime_assault_2016  crime_assault_2017  \\\n",
       "121                  67                  60                  66   \n",
       "105                 103                  98                  99   \n",
       "86                   47                  39                  21   \n",
       "139                 206                 242                 339   \n",
       "11                  209                 214                 211   \n",
       "..                  ...                 ...                 ...   \n",
       "122                 286                 279                 277   \n",
       "79                   77                  78                  73   \n",
       "126                  35                  45                  43   \n",
       "97                  174                 148                 196   \n",
       "134                  95                  91                  83   \n",
       "\n",
       "     crime_assault_2018  crime_assault_2019  crime_assault_2020  \\\n",
       "121                  57                  61                  67   \n",
       "105                 105                 111                  87   \n",
       "86                   30                  36                  37   \n",
       "139                 487                 324                 286   \n",
       "11                  236                 217                 222   \n",
       "..                  ...                 ...                 ...   \n",
       "122                 271                 333                 231   \n",
       "79                   80                  81                  69   \n",
       "126                  31                  47                  42   \n",
       "97                  195                 188                 150   \n",
       "134                  74                 101                  86   \n",
       "\n",
       "     crime_assault_rate2014  ...  crime_robbery_2014to2020  \\\n",
       "121                615.1743  ...                       104   \n",
       "105                722.7050  ...                       127   \n",
       "86                 363.8422  ...                        55   \n",
       "139                647.9146  ...                       125   \n",
       "11                 534.1260  ...                       290   \n",
       "..                      ...  ...                       ...   \n",
       "122                478.6676  ...                       331   \n",
       "79                 219.9462  ...                       203   \n",
       "126                172.3328  ...                        61   \n",
       "97                 383.5111  ...                       249   \n",
       "134                260.2002  ...                       248   \n",
       "\n",
       "     crime_breakandenter_rate2014to2020  crime_theftover_2014to2020  \\\n",
       "121                          1981.35580                          18   \n",
       "105                          1837.33640                          20   \n",
       "86                           1466.15480                          46   \n",
       "139                          1154.51830                         106   \n",
       "11                           1705.50980                         269   \n",
       "..                                  ...                         ...   \n",
       "122                           734.84410                          60   \n",
       "79                           1108.93624                          34   \n",
       "126                          1411.14821                          23   \n",
       "97                            971.34368                          80   \n",
       "134                          2538.03390                         113   \n",
       "\n",
       "     crime_robbery_rate2014to2020  crime_homicide_2014to2020  \\\n",
       "121                     956.46373                          0   \n",
       "105                    1057.88110                          3   \n",
       "86                      432.37176                          1   \n",
       "139                     349.45112                          5   \n",
       "11                      619.38531                          5   \n",
       "..                            ...                        ...   \n",
       "122                     720.10202                         10   \n",
       "79                      662.07605                          1   \n",
       "126                     236.33259                          0   \n",
       "97                      508.57115                          6   \n",
       "134                     885.05838                          2   \n",
       "\n",
       "     crime_theftover_rate2014to2020  crime_shootings_2014to2020  \\\n",
       "121                      165.966920                           3   \n",
       "105                      166.582427                          13   \n",
       "86                       362.660840                           3   \n",
       "139                      294.649130                          11   \n",
       "11                       574.890940                          24   \n",
       "..                              ...                         ...   \n",
       "122                      129.955653                          72   \n",
       "79                       111.315010                           5   \n",
       "126                       88.860765                           5   \n",
       "97                       163.542330                          30   \n",
       "134                      401.986850                           7   \n",
       "\n",
       "     crime_homicide_rate2014to2020  crime_shootings_rate2014to2020  \\\n",
       "121                       0.000000                       28.047870   \n",
       "105                       8.443807                      106.830366   \n",
       "86                        8.085382                       23.833008   \n",
       "139                      16.521209                       30.342647   \n",
       "11                        8.711522                       50.484814   \n",
       "..                             ...                             ...   \n",
       "122                      19.614071                      155.500707   \n",
       "79                        0.000000                       16.027049   \n",
       "126                       0.000000                       19.093110   \n",
       "97                       12.292325                       61.239236   \n",
       "134                      10.630690                       24.906718   \n",
       "\n",
       "                                              geometry  \n",
       "121  POLYGON ((619551.935 4827460.816, 619551.804 4...  \n",
       "105  POLYGON ((619543.073 4827511.205, 619533.657 4...  \n",
       "86   POLYGON ((617153.051 4827388.332, 617141.535 4...  \n",
       "139  POLYGON ((622614.670 4830909.791, 622620.320 4...  \n",
       "11   POLYGON ((619814.903 4830752.853, 619682.860 4...  \n",
       "..                                                 ...  \n",
       "122  POLYGON ((645015.999 4850845.445, 644790.392 4...  \n",
       "79   POLYGON ((641322.747 4851384.649, 641274.088 4...  \n",
       "126  POLYGON ((633513.806 4852778.044, 633577.106 4...  \n",
       "97   POLYGON ((645046.841 4850864.164, 645044.549 4...  \n",
       "134  POLYGON ((641205.530 4852569.487, 641140.779 4...  \n",
       "\n",
       "[140 rows x 115 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed['crime']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7cc91d9-c255-45d1-8215-c99b239bd739",
   "metadata": {},
   "source": [
    "#### education\n",
    "\n",
    "1. Format column names\n",
    "2. Filter out and rearrange needed columns\n",
    "3. Rename columns to identify dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "75fe9c51-ba85-4dc2-9db7-435d5768f9e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing education - clean (2022-02-17 21:36:41.142579)...\n",
      "Processed education - clean (2022-02-17 21:36:41.353430) [Elapsed: 0:00:00.210850]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    }
   ],
   "source": [
    "name = 'education'\n",
    "file = Path(f'{dist_folder}/clean-datasets/{name}.geojson')\n",
    "if not file.is_file() or force:\n",
    "    \n",
    "    # Start processing\n",
    "    timer = Timer(name, 'clean')\n",
    "    timer.start()\n",
    "    gdf = datasets[name]\n",
    "\n",
    "    # 1. Lowercase col names and remove spaces\n",
    "    gdf = gdf.rename(str.lower, axis='columns')\n",
    "    gdf.columns = [c.replace(' ', '_') for c in gdf.columns]\n",
    "    gdf = gdf.rename(columns={'neighbourhood_2008': 'neighbourhood'})\n",
    "\n",
    "    # 2. Filter and arrange cols needed\n",
    "    gdf = gdf.drop(['neighbourhood_2011'], axis=1)\n",
    "    gdf = gdf[[c for c in gdf.columns if c != 'geometry'] + ['geometry']] # move geom to end\n",
    "\n",
    "    # 3. Rename cols\n",
    "    gdf.columns = [f'{name}_{c}' if c not in ['neighbourhood_id', 'neighbourhood', 'geometry'] else c for c in gdf.columns]\n",
    "\n",
    "    # Save data\n",
    "    gdf.to_file(file)\n",
    "    \n",
    "    # End processing\n",
    "    timer.end()\n",
    "\n",
    "else:\n",
    "    \n",
    "    # Start loading\n",
    "    timer = Timer(name, 'already cleaned')\n",
    "    timer.start()\n",
    "\n",
    "    # Read clean data\n",
    "    gdf = read_data(file)\n",
    "\n",
    "    # End processing\n",
    "    timer.end()\n",
    "    \n",
    "processed[name] = gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a3f98524-d9a9-4888-8197-ac54d07831d1",
   "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>neighbourhood_id</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>education_catholic_school_graduation_2008</th>\n",
       "      <th>education_catholic_school_literacy_2008</th>\n",
       "      <th>education_catholic_university_applicants_2008</th>\n",
       "      <th>education_early_development_instrument_2008</th>\n",
       "      <th>education_library_activity_2008</th>\n",
       "      <th>education_library_open_hours_2008</th>\n",
       "      <th>education_library_program_attendance_2008</th>\n",
       "      <th>education_library_programs_2008</th>\n",
       "      <th>education_library_space_2008</th>\n",
       "      <th>education_early_development_instrument_2011</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>19</td>\n",
       "      <td>Long Branch</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>85.542167</td>\n",
       "      <td>51.315788</td>\n",
       "      <td>100</td>\n",
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       "      <td>POLYGON ((619548.814 4827460.764, 619548.682 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>18</td>\n",
       "      <td>New Toronto</td>\n",
       "      <td>0.830508</td>\n",
       "      <td>87.209304</td>\n",
       "      <td>48.863636</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
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       "      <td>POLYGON ((619539.953 4827511.152, 619530.538 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>20</td>\n",
       "      <td>Alderwood</td>\n",
       "      <td>0.867256</td>\n",
       "      <td>91.216217</td>\n",
       "      <td>55.172412</td>\n",
       "      <td>50</td>\n",
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       "      <td>100</td>\n",
       "      <td>POLYGON ((617149.946 4827388.284, 617138.431 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>17</td>\n",
       "      <td>Mimico</td>\n",
       "      <td>0.921985</td>\n",
       "      <td>86.124404</td>\n",
       "      <td>53.535354</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>POLYGON ((622611.632 4830909.658, 622617.283 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>14</td>\n",
       "      <td>Islington-City Centre West</td>\n",
       "      <td>0.876288</td>\n",
       "      <td>86.428573</td>\n",
       "      <td>62.551441</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>POLYGON ((619811.872 4830752.687, 619679.828 4...</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>132</td>\n",
       "      <td>Malvern</td>\n",
       "      <td>0.807162</td>\n",
       "      <td>73.198196</td>\n",
       "      <td>32.812500</td>\n",
       "      <td>100</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>100</td>\n",
       "      <td>POLYGON ((645012.663 4850845.571, 644787.054 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>129</td>\n",
       "      <td>Agincourt North</td>\n",
       "      <td>0.798507</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>50.877193</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
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       "      <td>4</td>\n",
       "      <td>100</td>\n",
       "      <td>POLYGON ((641319.328 4851384.746, 641270.670 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>116</td>\n",
       "      <td>Steeles</td>\n",
       "      <td>0.840425</td>\n",
       "      <td>91.025642</td>\n",
       "      <td>63.380283</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
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       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>POLYGON ((633510.381 4852777.985, 633573.677 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>131</td>\n",
       "      <td>Rouge</td>\n",
       "      <td>0.858433</td>\n",
       "      <td>79.629631</td>\n",
       "      <td>45.127117</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>POLYGON ((645043.505 4850864.291, 645041.213 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>130</td>\n",
       "      <td>Milliken</td>\n",
       "      <td>0.823008</td>\n",
       "      <td>82.692306</td>\n",
       "      <td>52.666667</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>POLYGON ((641202.087 4852569.586, 641137.336 4...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     neighbourhood_id               neighbourhood  \\\n",
       "99                 19                 Long Branch   \n",
       "75                 18                 New Toronto   \n",
       "51                 20                   Alderwood   \n",
       "92                 17                      Mimico   \n",
       "115                14  Islington-City Centre West   \n",
       "..                ...                         ...   \n",
       "98                132                     Malvern   \n",
       "56                129             Agincourt North   \n",
       "11                116                     Steeles   \n",
       "116               131                       Rouge   \n",
       "5                 130                    Milliken   \n",
       "\n",
       "     education_catholic_school_graduation_2008  \\\n",
       "99                                    0.875000   \n",
       "75                                    0.830508   \n",
       "51                                    0.867256   \n",
       "92                                    0.921985   \n",
       "115                                   0.876288   \n",
       "..                                         ...   \n",
       "98                                    0.807162   \n",
       "56                                    0.798507   \n",
       "11                                    0.840425   \n",
       "116                                   0.858433   \n",
       "5                                     0.823008   \n",
       "\n",
       "     education_catholic_school_literacy_2008  \\\n",
       "99                                 85.542167   \n",
       "75                                 87.209304   \n",
       "51                                 91.216217   \n",
       "92                                 86.124404   \n",
       "115                                86.428573   \n",
       "..                                       ...   \n",
       "98                                 73.198196   \n",
       "56                                 80.000000   \n",
       "11                                 91.025642   \n",
       "116                                79.629631   \n",
       "5                                  82.692306   \n",
       "\n",
       "     education_catholic_university_applicants_2008  \\\n",
       "99                                       51.315788   \n",
       "75                                       48.863636   \n",
       "51                                       55.172412   \n",
       "92                                       53.535354   \n",
       "115                                      62.551441   \n",
       "..                                             ...   \n",
       "98                                       32.812500   \n",
       "56                                       50.877193   \n",
       "11                                       63.380283   \n",
       "116                                      45.127117   \n",
       "5                                        52.666667   \n",
       "\n",
       "     education_early_development_instrument_2008  \\\n",
       "99                                           100   \n",
       "75                                            50   \n",
       "51                                            50   \n",
       "92                                           100   \n",
       "115                                          100   \n",
       "..                                           ...   \n",
       "98                                           100   \n",
       "56                                            50   \n",
       "11                                           100   \n",
       "116                                          100   \n",
       "5                                            100   \n",
       "\n",
       "     education_library_activity_2008  education_library_open_hours_2008  \\\n",
       "99                                 1                                  4   \n",
       "75                                 1                                  4   \n",
       "51                                 1                                  4   \n",
       "92                                 1                                  4   \n",
       "115                                3                                  5   \n",
       "..                               ...                                ...   \n",
       "98                                 6                                  9   \n",
       "56                                 4                                  5   \n",
       "11                                 2                                  4   \n",
       "116                                2                                  4   \n",
       "5                                  3                                  5   \n",
       "\n",
       "     education_library_program_attendance_2008  \\\n",
       "99                                           2   \n",
       "75                                           2   \n",
       "51                                           2   \n",
       "92                                           2   \n",
       "115                                          2   \n",
       "..                                         ...   \n",
       "98                                           7   \n",
       "56                                           3   \n",
       "11                                           2   \n",
       "116                                          2   \n",
       "5                                            2   \n",
       "\n",
       "     education_library_programs_2008  education_library_space_2008  \\\n",
       "99                                 2                             2   \n",
       "75                                 2                             2   \n",
       "51                                 2                             3   \n",
       "92                                 2                             3   \n",
       "115                                3                             3   \n",
       "..                               ...                           ...   \n",
       "98                                 9                             8   \n",
       "56                                 3                             4   \n",
       "11                                 2                             2   \n",
       "116                                2                             2   \n",
       "5                                  3                             3   \n",
       "\n",
       "     education_early_development_instrument_2011  \\\n",
       "99                                           100   \n",
       "75                                           100   \n",
       "51                                           100   \n",
       "92                                           100   \n",
       "115                                          100   \n",
       "..                                           ...   \n",
       "98                                           100   \n",
       "56                                           100   \n",
       "11                                           100   \n",
       "116                                          100   \n",
       "5                                             50   \n",
       "\n",
       "                                              geometry  \n",
       "99   POLYGON ((619548.814 4827460.764, 619548.682 4...  \n",
       "75   POLYGON ((619539.953 4827511.152, 619530.538 4...  \n",
       "51   POLYGON ((617149.946 4827388.284, 617138.431 4...  \n",
       "92   POLYGON ((622611.632 4830909.658, 622617.283 4...  \n",
       "115  POLYGON ((619811.872 4830752.687, 619679.828 4...  \n",
       "..                                                 ...  \n",
       "98   POLYGON ((645012.663 4850845.571, 644787.054 4...  \n",
       "56   POLYGON ((641319.328 4851384.746, 641270.670 4...  \n",
       "11   POLYGON ((633510.381 4852777.985, 633573.677 4...  \n",
       "116  POLYGON ((645043.505 4850864.291, 645041.213 4...  \n",
       "5    POLYGON ((641202.087 4852569.586, 641137.336 4...  \n",
       "\n",
       "[140 rows x 13 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed['education']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09ec1ece-ef80-475c-be0e-45925acf6940",
   "metadata": {},
   "source": [
    "#### environment\n",
    "\n",
    "1. Format column names\n",
    "2. Filter out and rearrange needed columns\n",
    "3. Rename columns to identify dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b774c0a9-044a-444c-9439-27009c7ae71f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing environment - clean (2022-02-17 21:36:41.382134)...\n",
      "Processed environment - clean (2022-02-17 21:36:41.594102) [Elapsed: 0:00:00.211967]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    }
   ],
   "source": [
    "name = 'environment'\n",
    "file = Path(f'{dist_folder}/clean-datasets/{name}.geojson')\n",
    "if not file.is_file() or force:\n",
    "    \n",
    "    # Start processing\n",
    "    timer = Timer(name, 'clean')\n",
    "    timer.start()\n",
    "    gdf = datasets[name]\n",
    "\n",
    "    # 1. Lowercase col names and remove spaces\n",
    "    gdf = gdf.rename(str.lower, axis='columns')\n",
    "    gdf.columns = [c.replace(' ', '_') for c in gdf.columns]\n",
    "    gdf = gdf.rename(columns={'neighbourhood_2008': 'neighbourhood'})\n",
    "\n",
    "    # 2. Filter and arrange cols needed\n",
    "    gdf = gdf.drop(['neighbourhood_2011'], axis=1)\n",
    "    gdf = gdf[[c for c in gdf.columns if c != 'geometry'] + ['geometry']] # move geom to end\n",
    "\n",
    "    # 3. Rename cols\n",
    "    gdf.columns = [f'{name}_{c}' if c not in ['neighbourhood_id', 'neighbourhood', 'geometry'] else c for c in gdf.columns]\n",
    "\n",
    "    # Save data\n",
    "    gdf.to_file(file)\n",
    "    \n",
    "    # End processing\n",
    "    timer.end()\n",
    "\n",
    "else:\n",
    "    \n",
    "    # Start loading\n",
    "    timer = Timer(name, 'already cleaned')\n",
    "    timer.start()\n",
    "\n",
    "    # Read clean data\n",
    "    gdf = read_data(file)\n",
    "\n",
    "    # End processing\n",
    "    timer.end()\n",
    "    \n",
    "processed[name] = gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3ffca323-1874-4e7f-b8ec-0d9179fa508e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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>neighbourhood_id</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>environment_city_green_retrofits_2008</th>\n",
       "      <th>environment_green_rebate_programs_2008</th>\n",
       "      <th>environment_green_spaces_2008</th>\n",
       "      <th>environment_polluting_facilities_2008</th>\n",
       "      <th>environment_tree_cover_2008</th>\n",
       "      <th>environment_green_rebate_programs_2011</th>\n",
       "      <th>environment_green_spaces_2011</th>\n",
       "      <th>environment_pollutant_carcinogenic_tep_score_2011</th>\n",
       "      <th>environment_pollutant_non-carcinogenic_tep_score_2011</th>\n",
       "      <th>environment_pollutants_released_to_air_2011</th>\n",
       "      <th>environment_tree_cover_2011</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>19</td>\n",
       "      <td>Long Branch</td>\n",
       "      <td>2</td>\n",
       "      <td>98</td>\n",
       "      <td>0.32317</td>\n",
       "      <td>2</td>\n",
       "      <td>7.503634e+05</td>\n",
       "      <td>92</td>\n",
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       "      <td>0.00</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>18</td>\n",
       "      <td>New Toronto</td>\n",
       "      <td>4</td>\n",
       "      <td>99</td>\n",
       "      <td>0.58133</td>\n",
       "      <td>6</td>\n",
       "      <td>5.225084e+05</td>\n",
       "      <td>109</td>\n",
       "      <td>0.593630</td>\n",
       "      <td>0.00</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>20</td>\n",
       "      <td>Alderwood</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>0.29232</td>\n",
       "      <td>15</td>\n",
       "      <td>1.086910e+06</td>\n",
       "      <td>192</td>\n",
       "      <td>0.292452</td>\n",
       "      <td>107370.00</td>\n",
       "      <td>3099019.00</td>\n",
       "      <td>64042</td>\n",
       "      <td>1.086910e+06</td>\n",
       "      <td>POLYGON ((617149.946 4827388.284, 617138.431 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>17</td>\n",
       "      <td>Mimico</td>\n",
       "      <td>5</td>\n",
       "      <td>281</td>\n",
       "      <td>0.73398</td>\n",
       "      <td>12</td>\n",
       "      <td>1.058931e+06</td>\n",
       "      <td>241</td>\n",
       "      <td>0.716898</td>\n",
       "      <td>114.85</td>\n",
       "      <td>266439.90</td>\n",
       "      <td>96927</td>\n",
       "      <td>1.058931e+06</td>\n",
       "      <td>POLYGON ((622611.632 4830909.658, 622617.283 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>14</td>\n",
       "      <td>Islington-City Centre West</td>\n",
       "      <td>5</td>\n",
       "      <td>379</td>\n",
       "      <td>0.46354</td>\n",
       "      <td>21</td>\n",
       "      <td>2.980850e+06</td>\n",
       "      <td>391</td>\n",
       "      <td>0.510250</td>\n",
       "      <td>1822.49</td>\n",
       "      <td>1163882.03</td>\n",
       "      <td>895785</td>\n",
       "      <td>2.980850e+06</td>\n",
       "      <td>POLYGON ((619811.872 4830752.687, 619679.828 4...</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>132</td>\n",
       "      <td>Malvern</td>\n",
       "      <td>4</td>\n",
       "      <td>763</td>\n",
       "      <td>0.68761</td>\n",
       "      <td>5</td>\n",
       "      <td>1.249251e+06</td>\n",
       "      <td>549</td>\n",
       "      <td>0.690798</td>\n",
       "      <td>399.64</td>\n",
       "      <td>79469.00</td>\n",
       "      <td>66889</td>\n",
       "      <td>1.249251e+06</td>\n",
       "      <td>POLYGON ((645012.663 4850845.571, 644787.054 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>129</td>\n",
       "      <td>Agincourt North</td>\n",
       "      <td>3</td>\n",
       "      <td>546</td>\n",
       "      <td>0.24951</td>\n",
       "      <td>3</td>\n",
       "      <td>1.254144e+06</td>\n",
       "      <td>775</td>\n",
       "      <td>0.250098</td>\n",
       "      <td>607.86</td>\n",
       "      <td>82592.30</td>\n",
       "      <td>16092</td>\n",
       "      <td>1.254144e+06</td>\n",
       "      <td>POLYGON ((641319.328 4851384.746, 641270.670 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>116</td>\n",
       "      <td>Steeles</td>\n",
       "      <td>1</td>\n",
       "      <td>538</td>\n",
       "      <td>0.44764</td>\n",
       "      <td>2</td>\n",
       "      <td>1.022877e+06</td>\n",
       "      <td>692</td>\n",
       "      <td>0.450219</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2596.00</td>\n",
       "      <td>2596</td>\n",
       "      <td>1.022877e+06</td>\n",
       "      <td>POLYGON ((633510.381 4852777.985, 633573.677 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>131</td>\n",
       "      <td>Rouge</td>\n",
       "      <td>2</td>\n",
       "      <td>592</td>\n",
       "      <td>14.25833</td>\n",
       "      <td>12</td>\n",
       "      <td>1.288804e+07</td>\n",
       "      <td>725</td>\n",
       "      <td>14.271455</td>\n",
       "      <td>171.26</td>\n",
       "      <td>93419.88</td>\n",
       "      <td>56778</td>\n",
       "      <td>1.288804e+07</td>\n",
       "      <td>POLYGON ((645043.505 4850864.291, 645041.213 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>130</td>\n",
       "      <td>Milliken</td>\n",
       "      <td>1</td>\n",
       "      <td>616</td>\n",
       "      <td>0.50885</td>\n",
       "      <td>14</td>\n",
       "      <td>1.043903e+06</td>\n",
       "      <td>815</td>\n",
       "      <td>0.512022</td>\n",
       "      <td>8791.76</td>\n",
       "      <td>3546500.60</td>\n",
       "      <td>222837</td>\n",
       "      <td>1.043903e+06</td>\n",
       "      <td>POLYGON ((641202.087 4852569.586, 641137.336 4...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     neighbourhood_id               neighbourhood  \\\n",
       "99                 19                 Long Branch   \n",
       "75                 18                 New Toronto   \n",
       "51                 20                   Alderwood   \n",
       "92                 17                      Mimico   \n",
       "115                14  Islington-City Centre West   \n",
       "..                ...                         ...   \n",
       "98                132                     Malvern   \n",
       "56                129             Agincourt North   \n",
       "11                116                     Steeles   \n",
       "116               131                       Rouge   \n",
       "5                 130                    Milliken   \n",
       "\n",
       "     environment_city_green_retrofits_2008  \\\n",
       "99                                       2   \n",
       "75                                       4   \n",
       "51                                       2   \n",
       "92                                       5   \n",
       "115                                      5   \n",
       "..                                     ...   \n",
       "98                                       4   \n",
       "56                                       3   \n",
       "11                                       1   \n",
       "116                                      2   \n",
       "5                                        1   \n",
       "\n",
       "     environment_green_rebate_programs_2008  environment_green_spaces_2008  \\\n",
       "99                                       98                        0.32317   \n",
       "75                                       99                        0.58133   \n",
       "51                                      160                        0.29232   \n",
       "92                                      281                        0.73398   \n",
       "115                                     379                        0.46354   \n",
       "..                                      ...                            ...   \n",
       "98                                      763                        0.68761   \n",
       "56                                      546                        0.24951   \n",
       "11                                      538                        0.44764   \n",
       "116                                     592                       14.25833   \n",
       "5                                       616                        0.50885   \n",
       "\n",
       "     environment_polluting_facilities_2008  environment_tree_cover_2008  \\\n",
       "99                                       2                 7.503634e+05   \n",
       "75                                       6                 5.225084e+05   \n",
       "51                                      15                 1.086910e+06   \n",
       "92                                      12                 1.058931e+06   \n",
       "115                                     21                 2.980850e+06   \n",
       "..                                     ...                          ...   \n",
       "98                                       5                 1.249251e+06   \n",
       "56                                       3                 1.254144e+06   \n",
       "11                                       2                 1.022877e+06   \n",
       "116                                     12                 1.288804e+07   \n",
       "5                                       14                 1.043903e+06   \n",
       "\n",
       "     environment_green_rebate_programs_2011  environment_green_spaces_2011  \\\n",
       "99                                       92                       0.323677   \n",
       "75                                      109                       0.593630   \n",
       "51                                      192                       0.292452   \n",
       "92                                      241                       0.716898   \n",
       "115                                     391                       0.510250   \n",
       "..                                      ...                            ...   \n",
       "98                                      549                       0.690798   \n",
       "56                                      775                       0.250098   \n",
       "11                                      692                       0.450219   \n",
       "116                                     725                      14.271455   \n",
       "5                                       815                       0.512022   \n",
       "\n",
       "     environment_pollutant_carcinogenic_tep_score_2011  \\\n",
       "99                                                0.00   \n",
       "75                                                0.00   \n",
       "51                                           107370.00   \n",
       "92                                              114.85   \n",
       "115                                            1822.49   \n",
       "..                                                 ...   \n",
       "98                                              399.64   \n",
       "56                                              607.86   \n",
       "11                                                0.00   \n",
       "116                                             171.26   \n",
       "5                                              8791.76   \n",
       "\n",
       "     environment_pollutant_non-carcinogenic_tep_score_2011  \\\n",
       "99                                             3475.80       \n",
       "75                                            98637.00       \n",
       "51                                          3099019.00       \n",
       "92                                           266439.90       \n",
       "115                                         1163882.03       \n",
       "..                                                 ...       \n",
       "98                                            79469.00       \n",
       "56                                            82592.30       \n",
       "11                                             2596.00       \n",
       "116                                           93419.88       \n",
       "5                                           3546500.60       \n",
       "\n",
       "     environment_pollutants_released_to_air_2011  environment_tree_cover_2011  \\\n",
       "99                                          2405                 7.503634e+05   \n",
       "75                                         34919                 5.225084e+05   \n",
       "51                                         64042                 1.086910e+06   \n",
       "92                                         96927                 1.058931e+06   \n",
       "115                                       895785                 2.980850e+06   \n",
       "..                                           ...                          ...   \n",
       "98                                         66889                 1.249251e+06   \n",
       "56                                         16092                 1.254144e+06   \n",
       "11                                          2596                 1.022877e+06   \n",
       "116                                        56778                 1.288804e+07   \n",
       "5                                         222837                 1.043903e+06   \n",
       "\n",
       "                                              geometry  \n",
       "99   POLYGON ((619548.814 4827460.764, 619548.682 4...  \n",
       "75   POLYGON ((619539.953 4827511.152, 619530.538 4...  \n",
       "51   POLYGON ((617149.946 4827388.284, 617138.431 4...  \n",
       "92   POLYGON ((622611.632 4830909.658, 622617.283 4...  \n",
       "115  POLYGON ((619811.872 4830752.687, 619679.828 4...  \n",
       "..                                                 ...  \n",
       "98   POLYGON ((645012.663 4850845.571, 644787.054 4...  \n",
       "56   POLYGON ((641319.328 4851384.746, 641270.670 4...  \n",
       "11   POLYGON ((633510.381 4852777.985, 633573.677 4...  \n",
       "116  POLYGON ((645043.505 4850864.291, 645041.213 4...  \n",
       "5    POLYGON ((641202.087 4852569.586, 641137.336 4...  \n",
       "\n",
       "[140 rows x 14 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed['environment']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc19df14-e534-49a2-b51b-f17fc020a946",
   "metadata": {},
   "source": [
    "#### demographics\n",
    "\n",
    "1. Format column names\n",
    "2. Filter out and rearrange needed columns\n",
    "3. Rename columns to identify dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "70a8f775-e629-4127-807c-88ef8742dfe4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing demographics - clean (2022-02-17 21:36:41.626167)...\n",
      "Processed demographics - clean (2022-02-17 21:36:41.866175) [Elapsed: 0:00:00.240008]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    }
   ],
   "source": [
    "name = 'demographics'\n",
    "file = Path(f'{dist_folder}/clean-datasets/{name}.geojson')\n",
    "if not file.is_file() or force:\n",
    "    \n",
    "    # Start processing\n",
    "    timer = Timer(name, 'clean')\n",
    "    timer.start()\n",
    "    gdf = datasets[name]\n",
    "\n",
    "    # 1. Lowercase col names and remove spaces\n",
    "    gdf = gdf.rename(str.lower, axis='columns')\n",
    "    gdf.columns = [c.replace(' ', '_') for c in gdf.columns]\n",
    "    gdf = gdf.rename(columns={\n",
    "        'neighbourhood_2008': 'neighbourhood',\n",
    "        'total_population_-_all_age_groups_-_100%_data_2016': 'all_ages_2016'\n",
    "    })\n",
    "\n",
    "    # 2. Filter and arrange cols needed\n",
    "    gdf = gdf.drop(['neighbourhood_2016'], axis=1)\n",
    "    gdf = gdf[[c for c in gdf.columns if c != 'geometry'] + ['geometry']] # move geom to end\n",
    "\n",
    "    # 3. Rename cols\n",
    "    gdf.columns = [f'{name}_{c}' if c not in ['neighbourhood_id', 'neighbourhood', 'geometry'] else c for c in gdf.columns]\n",
    "\n",
    "    # Save data\n",
    "    gdf.to_file(file)\n",
    "    \n",
    "    # End processing\n",
    "    timer.end()\n",
    "\n",
    "else:\n",
    "    \n",
    "    # Start loading\n",
    "    timer = Timer(name, 'already cleaned')\n",
    "    timer.start()\n",
    "\n",
    "    # Read clean data\n",
    "    gdf = read_data(file)\n",
    "\n",
    "    # End processing\n",
    "    timer.end()\n",
    "    \n",
    "processed[name] = gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c12ce5b0-a560-4c39-8221-ebd6f10bb9c2",
   "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>neighbourhood_id</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>demographics_pop2016_2008</th>\n",
       "      <th>demographics_pop2011_2008</th>\n",
       "      <th>demographics_popchg11t16_2008</th>\n",
       "      <th>demographics_0_to_04_years_2016</th>\n",
       "      <th>demographics_0_to_14_years_2016</th>\n",
       "      <th>demographics_05_to_09_years_2016</th>\n",
       "      <th>demographics_10_to_14_years_2016</th>\n",
       "      <th>demographics_100_years_and_over_2016</th>\n",
       "      <th>...</th>\n",
       "      <th>demographics_65_years_and_over_2016</th>\n",
       "      <th>demographics_70_to_74_years_2016</th>\n",
       "      <th>demographics_75_to_79_years_2016</th>\n",
       "      <th>demographics_80_to_84_years_2016</th>\n",
       "      <th>demographics_85_to_89_years_2016</th>\n",
       "      <th>demographics_85_years_and_over_2016</th>\n",
       "      <th>demographics_90_to_94_years_2016</th>\n",
       "      <th>demographics_95_to_99_years_2016</th>\n",
       "      <th>demographics_all_ages_2016</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>19</td>\n",
       "      <td>Long Branch</td>\n",
       "      <td>10084</td>\n",
       "      <td>9632</td>\n",
       "      <td>0.046927</td>\n",
       "      <td>505.0</td>\n",
       "      <td>1335.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1405.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10085.0</td>\n",
       "      <td>POLYGON ((619548.814 4827460.764, 619548.682 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>18</td>\n",
       "      <td>New Toronto</td>\n",
       "      <td>11463</td>\n",
       "      <td>10900</td>\n",
       "      <td>0.051651</td>\n",
       "      <td>620.0</td>\n",
       "      <td>1610.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11315.0</td>\n",
       "      <td>POLYGON ((619539.953 4827511.152, 619530.538 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>20</td>\n",
       "      <td>Alderwood</td>\n",
       "      <td>12054</td>\n",
       "      <td>11904</td>\n",
       "      <td>0.012601</td>\n",
       "      <td>685.0</td>\n",
       "      <td>1760.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12050.0</td>\n",
       "      <td>POLYGON ((617149.946 4827388.284, 617138.431 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>17</td>\n",
       "      <td>Mimico (includes Humber Bay Shores)</td>\n",
       "      <td>33964</td>\n",
       "      <td>26541</td>\n",
       "      <td>0.279680</td>\n",
       "      <td>1475.0</td>\n",
       "      <td>3505.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>930.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5160.0</td>\n",
       "      <td>1285.0</td>\n",
       "      <td>930.0</td>\n",
       "      <td>605.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>33960.0</td>\n",
       "      <td>POLYGON ((622611.632 4830909.658, 622617.283 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>14</td>\n",
       "      <td>Islington-City Centre West</td>\n",
       "      <td>43965</td>\n",
       "      <td>38084</td>\n",
       "      <td>0.154422</td>\n",
       "      <td>2225.0</td>\n",
       "      <td>5820.0</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>1705.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7405.0</td>\n",
       "      <td>1545.0</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>43975.0</td>\n",
       "      <td>POLYGON ((619811.872 4830752.687, 619679.828 4...</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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>132</td>\n",
       "      <td>Malvern</td>\n",
       "      <td>43794</td>\n",
       "      <td>45086</td>\n",
       "      <td>-0.028656</td>\n",
       "      <td>2405.0</td>\n",
       "      <td>7910.0</td>\n",
       "      <td>2680.0</td>\n",
       "      <td>2815.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5890.0</td>\n",
       "      <td>1535.0</td>\n",
       "      <td>1045.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>305.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>43790.0</td>\n",
       "      <td>POLYGON ((645012.663 4850845.571, 644787.054 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>129</td>\n",
       "      <td>Agincourt North</td>\n",
       "      <td>29113</td>\n",
       "      <td>30279</td>\n",
       "      <td>-0.038509</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>3840.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6045.0</td>\n",
       "      <td>1290.0</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>865.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>925.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>29110.0</td>\n",
       "      <td>POLYGON ((641319.328 4851384.746, 641270.670 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>116</td>\n",
       "      <td>Steeles</td>\n",
       "      <td>24623</td>\n",
       "      <td>25017</td>\n",
       "      <td>-0.015749</td>\n",
       "      <td>1045.0</td>\n",
       "      <td>3150.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>980.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5755.0</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>860.0</td>\n",
       "      <td>585.0</td>\n",
       "      <td>955.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>24610.0</td>\n",
       "      <td>POLYGON ((633510.381 4852777.985, 633573.677 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>131</td>\n",
       "      <td>Rouge</td>\n",
       "      <td>46496</td>\n",
       "      <td>45912</td>\n",
       "      <td>0.012720</td>\n",
       "      <td>2370.0</td>\n",
       "      <td>7960.0</td>\n",
       "      <td>2810.0</td>\n",
       "      <td>2775.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6625.0</td>\n",
       "      <td>1670.0</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>680.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>685.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>46495.0</td>\n",
       "      <td>POLYGON ((645043.505 4850864.291, 645041.213 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>130</td>\n",
       "      <td>Milliken</td>\n",
       "      <td>26572</td>\n",
       "      <td>27167</td>\n",
       "      <td>-0.021902</td>\n",
       "      <td>1385.0</td>\n",
       "      <td>3675.0</td>\n",
       "      <td>1260.0</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5115.0</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>835.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>26570.0</td>\n",
       "      <td>POLYGON ((641202.087 4852569.586, 641137.336 4...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     neighbourhood_id                        neighbourhood  \\\n",
       "99                 19                          Long Branch   \n",
       "75                 18                          New Toronto   \n",
       "51                 20                            Alderwood   \n",
       "92                 17  Mimico (includes Humber Bay Shores)   \n",
       "115                14           Islington-City Centre West   \n",
       "..                ...                                  ...   \n",
       "98                132                              Malvern   \n",
       "56                129                      Agincourt North   \n",
       "11                116                              Steeles   \n",
       "116               131                                Rouge   \n",
       "5                 130                             Milliken   \n",
       "\n",
       "     demographics_pop2016_2008  demographics_pop2011_2008  \\\n",
       "99                       10084                       9632   \n",
       "75                       11463                      10900   \n",
       "51                       12054                      11904   \n",
       "92                       33964                      26541   \n",
       "115                      43965                      38084   \n",
       "..                         ...                        ...   \n",
       "98                       43794                      45086   \n",
       "56                       29113                      30279   \n",
       "11                       24623                      25017   \n",
       "116                      46496                      45912   \n",
       "5                        26572                      27167   \n",
       "\n",
       "     demographics_popchg11t16_2008  demographics_0_to_04_years_2016  \\\n",
       "99                        0.046927                            505.0   \n",
       "75                        0.051651                            620.0   \n",
       "51                        0.012601                            685.0   \n",
       "92                        0.279680                           1475.0   \n",
       "115                       0.154422                           2225.0   \n",
       "..                             ...                              ...   \n",
       "98                       -0.028656                           2405.0   \n",
       "56                       -0.038509                           1280.0   \n",
       "11                       -0.015749                           1045.0   \n",
       "116                       0.012720                           2370.0   \n",
       "5                        -0.021902                           1385.0   \n",
       "\n",
       "     demographics_0_to_14_years_2016  demographics_05_to_09_years_2016  \\\n",
       "99                            1335.0                             450.0   \n",
       "75                            1610.0                             570.0   \n",
       "51                            1760.0                             600.0   \n",
       "92                            3505.0                            1090.0   \n",
       "115                           5820.0                            1930.0   \n",
       "..                               ...                               ...   \n",
       "98                            7910.0                            2680.0   \n",
       "56                            3840.0                            1300.0   \n",
       "11                            3150.0                            1125.0   \n",
       "116                           7960.0                            2810.0   \n",
       "5                             3675.0                            1260.0   \n",
       "\n",
       "     demographics_10_to_14_years_2016  demographics_100_years_and_over_2016  \\\n",
       "99                              380.0                                   0.0   \n",
       "75                              425.0                                   5.0   \n",
       "51                              480.0                                   0.0   \n",
       "92                              930.0                                   0.0   \n",
       "115                            1705.0                                  30.0   \n",
       "..                                ...                                   ...   \n",
       "98                             2815.0                                   0.0   \n",
       "56                             1240.0                                  10.0   \n",
       "11                              980.0                                  15.0   \n",
       "116                            2775.0                                  10.0   \n",
       "5                              1030.0                                  15.0   \n",
       "\n",
       "     ...  demographics_65_years_and_over_2016  \\\n",
       "99   ...                               1405.0   \n",
       "75   ...                               1525.0   \n",
       "51   ...                               2015.0   \n",
       "92   ...                               5160.0   \n",
       "115  ...                               7405.0   \n",
       "..   ...                                  ...   \n",
       "98   ...                               5890.0   \n",
       "56   ...                               6045.0   \n",
       "11   ...                               5755.0   \n",
       "116  ...                               6625.0   \n",
       "5    ...                               5115.0   \n",
       "\n",
       "     demographics_70_to_74_years_2016  demographics_75_to_79_years_2016  \\\n",
       "99                              340.0                             225.0   \n",
       "75                              350.0                             300.0   \n",
       "51                              420.0                             340.0   \n",
       "92                             1285.0                             930.0   \n",
       "115                            1545.0                            1240.0   \n",
       "..                                ...                               ...   \n",
       "98                             1535.0                            1045.0   \n",
       "56                             1290.0                            1065.0   \n",
       "11                             1180.0                            1010.0   \n",
       "116                            1670.0                            1005.0   \n",
       "5                              1010.0                             835.0   \n",
       "\n",
       "     demographics_80_to_84_years_2016  demographics_85_to_89_years_2016  \\\n",
       "99                              165.0                              95.0   \n",
       "75                              205.0                              80.0   \n",
       "51                              315.0                             190.0   \n",
       "92                              605.0                             375.0   \n",
       "115                            1080.0                             870.0   \n",
       "..                                ...                               ...   \n",
       "98                              590.0                             305.0   \n",
       "56                              865.0                             565.0   \n",
       "11                              860.0                             585.0   \n",
       "116                             680.0                             415.0   \n",
       "5                               750.0                             520.0   \n",
       "\n",
       "     demographics_85_years_and_over_2016  demographics_90_to_94_years_2016  \\\n",
       "99                                 140.0                              35.0   \n",
       "75                                 140.0                              45.0   \n",
       "51                                 320.0                             100.0   \n",
       "92                                 565.0                             150.0   \n",
       "115                               1480.0                             450.0   \n",
       "..                                   ...                               ...   \n",
       "98                                 445.0                             110.0   \n",
       "56                                 925.0                             270.0   \n",
       "11                                 955.0                             275.0   \n",
       "116                                685.0                             205.0   \n",
       "5                                  880.0                             245.0   \n",
       "\n",
       "     demographics_95_to_99_years_2016  demographics_all_ages_2016  \\\n",
       "99                                5.0                     10085.0   \n",
       "75                               15.0                     11315.0   \n",
       "51                               15.0                     12050.0   \n",
       "92                               25.0                     33960.0   \n",
       "115                             115.0                     43975.0   \n",
       "..                                ...                         ...   \n",
       "98                               30.0                     43790.0   \n",
       "56                               90.0                     29110.0   \n",
       "11                               70.0                     24610.0   \n",
       "116                              65.0                     46495.0   \n",
       "5                                90.0                     26570.0   \n",
       "\n",
       "                                              geometry  \n",
       "99   POLYGON ((619548.814 4827460.764, 619548.682 4...  \n",
       "75   POLYGON ((619539.953 4827511.152, 619530.538 4...  \n",
       "51   POLYGON ((617149.946 4827388.284, 617138.431 4...  \n",
       "92   POLYGON ((622611.632 4830909.658, 622617.283 4...  \n",
       "115  POLYGON ((619811.872 4830752.687, 619679.828 4...  \n",
       "..                                                 ...  \n",
       "98   POLYGON ((645012.663 4850845.571, 644787.054 4...  \n",
       "56   POLYGON ((641319.328 4851384.746, 641270.670 4...  \n",
       "11   POLYGON ((633510.381 4852777.985, 633573.677 4...  \n",
       "116  POLYGON ((645043.505 4850864.291, 645041.213 4...  \n",
       "5    POLYGON ((641202.087 4852569.586, 641137.336 4...  \n",
       "\n",
       "[140 rows x 31 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed['demographics']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a7b1db9-51f7-479e-9787-02dd3e0bdd02",
   "metadata": {},
   "source": [
    "#### housing\n",
    "\n",
    "1. Format column names\n",
    "2. Filter out and rearrange needed columns\n",
    "3. Rename columns to identify dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "65b77f3a-53e4-4fd6-a2cc-fa8250e4874f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing housing - clean (2022-02-17 21:36:41.905359)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed housing - clean (2022-02-17 21:36:42.123184) [Elapsed: 0:00:00.217824]\n"
     ]
    }
   ],
   "source": [
    "name = 'housing'\n",
    "file = Path(f'{dist_folder}/clean-datasets/{name}.geojson')\n",
    "if not file.is_file() or force:\n",
    "    \n",
    "    # Start processing\n",
    "    timer = Timer(name, 'clean')\n",
    "    timer.start()\n",
    "    gdf = datasets[name]\n",
    "\n",
    "    # 1. Lowercase col names and remove spaces\n",
    "    gdf = gdf.rename(str.lower, axis='columns')\n",
    "    gdf.columns = [c.replace(' ', '_').replace('-', '_') for c in gdf.columns]\n",
    "    gdf = gdf.rename(columns={'neighbourhood_2008': 'neighbourhood'})\n",
    "\n",
    "    # 2. Filter and arrange cols needed\n",
    "    gdf = gdf.drop(['neighbourhood_2011'], axis=1)\n",
    "    gdf = gdf[[c for c in gdf.columns if c != 'geometry'] + ['geometry']] # move geom to end\n",
    "\n",
    "    # 3. Rename cols\n",
    "    gdf.columns = [f'{name}_{c}' if c not in ['neighbourhood_id', 'neighbourhood', 'geometry'] else c for c in gdf.columns]\n",
    "\n",
    "    # Save data\n",
    "    gdf.to_file(file)\n",
    "    \n",
    "    # End processing\n",
    "    timer.end()\n",
    "\n",
    "else:\n",
    "    \n",
    "    # Start loading\n",
    "    timer = Timer(name, 'already cleaned')\n",
    "    timer.start()\n",
    "\n",
    "    # Read clean data\n",
    "    gdf = read_data(file)\n",
    "\n",
    "    # End processing\n",
    "    timer.end()\n",
    "    \n",
    "processed[name] = gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "99467c85-fb69-4636-8ae8-1db87a5bf6db",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neighbourhood_id</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>housing_households_assisted_2008</th>\n",
       "      <th>housing_rent_bank_applicants_2008</th>\n",
       "      <th>housing_social_housing_turnover_2008</th>\n",
       "      <th>housing_social_housing_units_2008</th>\n",
       "      <th>housing_social_housing_waiting_list_2008</th>\n",
       "      <th>housing_home_prices_2011</th>\n",
       "      <th>housing_mid_century_highrise_households_2011</th>\n",
       "      <th>housing_mid_century_highrise_population_2011</th>\n",
       "      <th>housing_percent_mid_century_highrise_households_2011</th>\n",
       "      <th>housing_percent_mid_century_highrise_population_2011</th>\n",
       "      <th>housing_rent_bank_applicants_2011</th>\n",
       "      <th>housing_social_housing_turnover_2011</th>\n",
       "      <th>housing_social_housing_units_2011</th>\n",
       "      <th>housing_social_housing_waiting_list_2011</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>19</td>\n",
       "      <td>Long Branch</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>106</td>\n",
       "      <td>176</td>\n",
       "      <td>459088</td>\n",
       "      <td>690</td>\n",
       "      <td>1220</td>\n",
       "      <td>15.3</td>\n",
       "      <td>12.7</td>\n",
       "      <td>20</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>106</td>\n",
       "      <td>177</td>\n",
       "      <td>POLYGON ((619548.814 4827460.764, 619548.682 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>18</td>\n",
       "      <td>New Toronto</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>1.684210</td>\n",
       "      <td>1189</td>\n",
       "      <td>342</td>\n",
       "      <td>460811</td>\n",
       "      <td>385</td>\n",
       "      <td>490</td>\n",
       "      <td>7.4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>18</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>1189</td>\n",
       "      <td>389</td>\n",
       "      <td>POLYGON ((619539.953 4827511.152, 619530.538 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>20</td>\n",
       "      <td>Alderwood</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>85</td>\n",
       "      <td>62</td>\n",
       "      <td>504233</td>\n",
       "      <td>350</td>\n",
       "      <td>630</td>\n",
       "      <td>7.5</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>84</td>\n",
       "      <td>69</td>\n",
       "      <td>POLYGON ((617149.946 4827388.284, 617138.431 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>17</td>\n",
       "      <td>Mimico</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>5.533333</td>\n",
       "      <td>547</td>\n",
       "      <td>444</td>\n",
       "      <td>429941</td>\n",
       "      <td>2430</td>\n",
       "      <td>3880</td>\n",
       "      <td>17.8</td>\n",
       "      <td>14.6</td>\n",
       "      <td>36</td>\n",
       "      <td>8.111111</td>\n",
       "      <td>947</td>\n",
       "      <td>547</td>\n",
       "      <td>POLYGON ((622611.632 4830909.658, 622617.283 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>14</td>\n",
       "      <td>Islington-City Centre West</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>3.904761</td>\n",
       "      <td>1245</td>\n",
       "      <td>418</td>\n",
       "      <td>491678</td>\n",
       "      <td>4085</td>\n",
       "      <td>8890</td>\n",
       "      <td>25.1</td>\n",
       "      <td>23.4</td>\n",
       "      <td>16</td>\n",
       "      <td>5.181818</td>\n",
       "      <td>1245</td>\n",
       "      <td>489</td>\n",
       "      <td>POLYGON ((619811.872 4830752.687, 619679.828 4...</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",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>132</td>\n",
       "      <td>Malvern</td>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>1.923076</td>\n",
       "      <td>927</td>\n",
       "      <td>755</td>\n",
       "      <td>256488</td>\n",
       "      <td>1060</td>\n",
       "      <td>3175</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.0</td>\n",
       "      <td>32</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>927</td>\n",
       "      <td>851</td>\n",
       "      <td>POLYGON ((645012.663 4850845.571, 644787.054 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>129</td>\n",
       "      <td>Agincourt North</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>77</td>\n",
       "      <td>426</td>\n",
       "      <td>375307</td>\n",
       "      <td>540</td>\n",
       "      <td>1905</td>\n",
       "      <td>5.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>77</td>\n",
       "      <td>531</td>\n",
       "      <td>POLYGON ((641319.328 4851384.746, 641270.670 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>116</td>\n",
       "      <td>Steeles</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>339</td>\n",
       "      <td>440688</td>\n",
       "      <td>1175</td>\n",
       "      <td>2985</td>\n",
       "      <td>15.3</td>\n",
       "      <td>11.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>428</td>\n",
       "      <td>POLYGON ((633510.381 4852777.985, 633573.677 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>131</td>\n",
       "      <td>Rouge</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>0.727272</td>\n",
       "      <td>336</td>\n",
       "      <td>355</td>\n",
       "      <td>426850</td>\n",
       "      <td>590</td>\n",
       "      <td>1165</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>23</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>522</td>\n",
       "      <td>411</td>\n",
       "      <td>POLYGON ((645043.505 4850864.291, 645041.213 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>130</td>\n",
       "      <td>Milliken</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>247</td>\n",
       "      <td>329</td>\n",
       "      <td>387879</td>\n",
       "      <td>310</td>\n",
       "      <td>825</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>247</td>\n",
       "      <td>404</td>\n",
       "      <td>POLYGON ((641202.087 4852569.586, 641137.336 4...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     neighbourhood_id               neighbourhood  \\\n",
       "99                 19                 Long Branch   \n",
       "75                 18                 New Toronto   \n",
       "51                 20                   Alderwood   \n",
       "92                 17                      Mimico   \n",
       "115                14  Islington-City Centre West   \n",
       "..                ...                         ...   \n",
       "98                132                     Malvern   \n",
       "56                129             Agincourt North   \n",
       "11                116                     Steeles   \n",
       "116               131                       Rouge   \n",
       "5                 130                    Milliken   \n",
       "\n",
       "     housing_households_assisted_2008  housing_rent_bank_applicants_2008  \\\n",
       "99                                  1                                  8   \n",
       "75                                  0                                 23   \n",
       "51                                  1                                  4   \n",
       "92                                  0                                 31   \n",
       "115                                 0                                 21   \n",
       "..                                ...                                ...   \n",
       "98                                  2                                 38   \n",
       "56                                  0                                  2   \n",
       "11                                  0                                  7   \n",
       "116                                 0                                 26   \n",
       "5                                   0                                  1   \n",
       "\n",
       "     housing_social_housing_turnover_2008  housing_social_housing_units_2008  \\\n",
       "99                               0.000000                                106   \n",
       "75                               1.684210                               1189   \n",
       "51                               0.750000                                 85   \n",
       "92                               5.533333                                547   \n",
       "115                              3.904761                               1245   \n",
       "..                                    ...                                ...   \n",
       "98                               1.923076                                927   \n",
       "56                               0.000000                                 77   \n",
       "11                               0.000000                                  0   \n",
       "116                              0.727272                                336   \n",
       "5                                0.000000                                247   \n",
       "\n",
       "     housing_social_housing_waiting_list_2008  housing_home_prices_2011  \\\n",
       "99                                        176                    459088   \n",
       "75                                        342                    460811   \n",
       "51                                         62                    504233   \n",
       "92                                        444                    429941   \n",
       "115                                       418                    491678   \n",
       "..                                        ...                       ...   \n",
       "98                                        755                    256488   \n",
       "56                                        426                    375307   \n",
       "11                                        339                    440688   \n",
       "116                                       355                    426850   \n",
       "5                                         329                    387879   \n",
       "\n",
       "     housing_mid_century_highrise_households_2011  \\\n",
       "99                                            690   \n",
       "75                                            385   \n",
       "51                                            350   \n",
       "92                                           2430   \n",
       "115                                          4085   \n",
       "..                                            ...   \n",
       "98                                           1060   \n",
       "56                                            540   \n",
       "11                                           1175   \n",
       "116                                           590   \n",
       "5                                             310   \n",
       "\n",
       "     housing_mid_century_highrise_population_2011  \\\n",
       "99                                           1220   \n",
       "75                                            490   \n",
       "51                                            630   \n",
       "92                                           3880   \n",
       "115                                          8890   \n",
       "..                                            ...   \n",
       "98                                           3175   \n",
       "56                                           1905   \n",
       "11                                           2985   \n",
       "116                                          1165   \n",
       "5                                             825   \n",
       "\n",
       "     housing_percent_mid_century_highrise_households_2011  \\\n",
       "99                                                15.3      \n",
       "75                                                 7.4      \n",
       "51                                                 7.5      \n",
       "92                                                17.8      \n",
       "115                                               25.1      \n",
       "..                                                 ...      \n",
       "98                                                 7.9      \n",
       "56                                                 5.9      \n",
       "11                                                15.3      \n",
       "116                                                4.5      \n",
       "5                                                  4.0      \n",
       "\n",
       "     housing_percent_mid_century_highrise_population_2011  \\\n",
       "99                                                12.7      \n",
       "75                                                 4.5      \n",
       "51                                                 5.3      \n",
       "92                                                14.6      \n",
       "115                                               23.4      \n",
       "..                                                 ...      \n",
       "98                                                 7.0      \n",
       "56                                                 6.3      \n",
       "11                                                11.9      \n",
       "116                                                2.5      \n",
       "5                                                  3.0      \n",
       "\n",
       "     housing_rent_bank_applicants_2011  housing_social_housing_turnover_2011  \\\n",
       "99                                  20                              0.000000   \n",
       "75                                  18                              0.960000   \n",
       "51                                   2                              2.500000   \n",
       "92                                  36                              8.111111   \n",
       "115                                 16                              5.181818   \n",
       "..                                 ...                                   ...   \n",
       "98                                  32                              1.166667   \n",
       "56                                   6                              0.000000   \n",
       "11                                   0                              0.000000   \n",
       "116                                 23                              0.090909   \n",
       "5                                    1                              6.666667   \n",
       "\n",
       "     housing_social_housing_units_2011  \\\n",
       "99                                 106   \n",
       "75                                1189   \n",
       "51                                  84   \n",
       "92                                 947   \n",
       "115                               1245   \n",
       "..                                 ...   \n",
       "98                                 927   \n",
       "56                                  77   \n",
       "11                                   0   \n",
       "116                                522   \n",
       "5                                  247   \n",
       "\n",
       "     housing_social_housing_waiting_list_2011  \\\n",
       "99                                        177   \n",
       "75                                        389   \n",
       "51                                         69   \n",
       "92                                        547   \n",
       "115                                       489   \n",
       "..                                        ...   \n",
       "98                                        851   \n",
       "56                                        531   \n",
       "11                                        428   \n",
       "116                                       411   \n",
       "5                                         404   \n",
       "\n",
       "                                              geometry  \n",
       "99   POLYGON ((619548.814 4827460.764, 619548.682 4...  \n",
       "75   POLYGON ((619539.953 4827511.152, 619530.538 4...  \n",
       "51   POLYGON ((617149.946 4827388.284, 617138.431 4...  \n",
       "92   POLYGON ((622611.632 4830909.658, 622617.283 4...  \n",
       "115  POLYGON ((619811.872 4830752.687, 619679.828 4...  \n",
       "..                                                 ...  \n",
       "98   POLYGON ((645012.663 4850845.571, 644787.054 4...  \n",
       "56   POLYGON ((641319.328 4851384.746, 641270.670 4...  \n",
       "11   POLYGON ((633510.381 4852777.985, 633573.677 4...  \n",
       "116  POLYGON ((645043.505 4850864.291, 645041.213 4...  \n",
       "5    POLYGON ((641202.087 4852569.586, 641137.336 4...  \n",
       "\n",
       "[140 rows x 17 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed['housing']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc1ce080-94c2-447d-b00b-0098f800cb5e",
   "metadata": {},
   "source": [
    "#### economics\n",
    "\n",
    "1. Format column names\n",
    "2. Filter out and rearrange needed columns\n",
    "3. Rename columns to identify dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "68060640-7125-49b2-9391-7cb96259edb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing economics - clean (2022-02-17 21:36:42.155586)...\n",
      "Processed economics - clean (2022-02-17 21:36:42.370691) [Elapsed: 0:00:00.215104]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    }
   ],
   "source": [
    "name = 'economics'\n",
    "file = Path(f'{dist_folder}/clean-datasets/{name}.geojson')\n",
    "if not file.is_file() or force:\n",
    "    \n",
    "    # Start processing\n",
    "    timer = Timer(name, 'clean')\n",
    "    timer.start()\n",
    "    gdf = datasets[name]\n",
    "\n",
    "    # 1. Lowercase col names and remove spaces\n",
    "    gdf = gdf.rename(str.lower, axis='columns')\n",
    "    gdf.columns = [c.replace(' ', '_').translate({ord(x): '' for x in ['(', '.', ')']}) for c in gdf.columns]\n",
    "    gdf = gdf.rename(columns={'neighbourhood_2008': 'neighbourhood'})\n",
    "\n",
    "    # 2. Filter and arrange cols needed\n",
    "    gdf = gdf.drop(['neighbourhood_2011'], axis=1)\n",
    "    gdf = gdf[[c for c in gdf.columns if c != 'geometry'] + ['geometry']] # move geom to end\n",
    "\n",
    "    # 3. Rename cols\n",
    "    gdf.columns = [f'{name}_{c}' if c not in ['neighbourhood_id', 'neighbourhood', 'geometry'] else c for c in gdf.columns]\n",
    "\n",
    "    # Save data\n",
    "    gdf.to_file(file)\n",
    "    \n",
    "    # End processing\n",
    "    timer.end()\n",
    "\n",
    "else:\n",
    "    \n",
    "    # Start loading\n",
    "    timer = Timer(name, 'already cleaned')\n",
    "    timer.start()\n",
    "\n",
    "    # Read clean data\n",
    "    gdf = read_data(file)\n",
    "\n",
    "    # End processing\n",
    "    timer.end()\n",
    "    \n",
    "processed[name] = gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "152f68d5-8605-41c7-9dd5-32f6052337cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\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>neighbourhood_id</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>economics_access_to_child_care_2008</th>\n",
       "      <th>economics_business_licensing_2008</th>\n",
       "      <th>economics_businesses_2008</th>\n",
       "      <th>economics_child_care_spaces_2008</th>\n",
       "      <th>economics_inequality_gini_coeff_2008</th>\n",
       "      <th>economics_local_employment_2008</th>\n",
       "      <th>economics_social_assistance_recipients_2008</th>\n",
       "      <th>economics_businesses_2011</th>\n",
       "      <th>economics_child_care_spaces_2011</th>\n",
       "      <th>economics_debt_risk_score_2011</th>\n",
       "      <th>economics_home_prices_2011</th>\n",
       "      <th>economics_local_employment_2011</th>\n",
       "      <th>economics_social_assistance_recipients_2011</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>19</td>\n",
       "      <td>Long Branch</td>\n",
       "      <td>0.442105</td>\n",
       "      <td>105</td>\n",
       "      <td>168</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.390909</td>\n",
       "      <td>1546</td>\n",
       "      <td>1074.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>18</td>\n",
       "      <td>New Toronto</td>\n",
       "      <td>0.389189</td>\n",
       "      <td>148</td>\n",
       "      <td>342</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.430070</td>\n",
       "      <td>4398</td>\n",
       "      <td>1551.0</td>\n",
       "      <td>368</td>\n",
       "      <td>90</td>\n",
       "      <td>719</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>20</td>\n",
       "      <td>Alderwood</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>128</td>\n",
       "      <td>312</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0.353179</td>\n",
       "      <td>5432</td>\n",
       "      <td>494.0</td>\n",
       "      <td>309</td>\n",
       "      <td>120</td>\n",
       "      <td>764</td>\n",
       "      <td>504233</td>\n",
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       "      <td>516</td>\n",
       "      <td>POLYGON ((617149.946 4827388.284, 617138.431 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>17</td>\n",
       "      <td>Mimico</td>\n",
       "      <td>0.236620</td>\n",
       "      <td>281</td>\n",
       "      <td>635</td>\n",
       "      <td>147.0</td>\n",
       "      <td>0.405239</td>\n",
       "      <td>11586</td>\n",
       "      <td>2147.0</td>\n",
       "      <td>619</td>\n",
       "      <td>195</td>\n",
       "      <td>741</td>\n",
       "      <td>429941</td>\n",
       "      <td>11549</td>\n",
       "      <td>2634</td>\n",
       "      <td>POLYGON ((622611.632 4830909.658, 622617.283 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>14</td>\n",
       "      <td>Islington-City Centre West</td>\n",
       "      <td>0.318812</td>\n",
       "      <td>763</td>\n",
       "      <td>2291</td>\n",
       "      <td>189.0</td>\n",
       "      <td>0.398129</td>\n",
       "      <td>52812</td>\n",
       "      <td>2441.0</td>\n",
       "      <td>2263</td>\n",
       "      <td>204</td>\n",
       "      <td>755</td>\n",
       "      <td>491678</td>\n",
       "      <td>45794</td>\n",
       "      <td>2534</td>\n",
       "      <td>POLYGON ((619811.872 4830752.687, 619679.828 4...</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",
       "      <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>98</th>\n",
       "      <td>132</td>\n",
       "      <td>Malvern</td>\n",
       "      <td>0.314943</td>\n",
       "      <td>141</td>\n",
       "      <td>665</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.352910</td>\n",
       "      <td>12435</td>\n",
       "      <td>5275.0</td>\n",
       "      <td>625</td>\n",
       "      <td>81</td>\n",
       "      <td>724</td>\n",
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       "      <td>11173</td>\n",
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       "      <td>POLYGON ((645012.663 4850845.571, 644787.054 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>129</td>\n",
       "      <td>Agincourt North</td>\n",
       "      <td>0.271154</td>\n",
       "      <td>188</td>\n",
       "      <td>295</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0.366070</td>\n",
       "      <td>7542</td>\n",
       "      <td>1563.0</td>\n",
       "      <td>353</td>\n",
       "      <td>130</td>\n",
       "      <td>765</td>\n",
       "      <td>375307</td>\n",
       "      <td>7449</td>\n",
       "      <td>1930</td>\n",
       "      <td>POLYGON ((641319.328 4851384.746, 641270.670 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>116</td>\n",
       "      <td>Steeles</td>\n",
       "      <td>0.330435</td>\n",
       "      <td>160</td>\n",
       "      <td>257</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.375050</td>\n",
       "      <td>5870</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>237</td>\n",
       "      <td>105</td>\n",
       "      <td>778</td>\n",
       "      <td>440688</td>\n",
       "      <td>5835</td>\n",
       "      <td>1549</td>\n",
       "      <td>POLYGON ((633510.381 4852777.985, 633573.677 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>131</td>\n",
       "      <td>Rouge</td>\n",
       "      <td>0.396694</td>\n",
       "      <td>159</td>\n",
       "      <td>832</td>\n",
       "      <td>192.0</td>\n",
       "      <td>0.322259</td>\n",
       "      <td>13923</td>\n",
       "      <td>2528.0</td>\n",
       "      <td>877</td>\n",
       "      <td>210</td>\n",
       "      <td>738</td>\n",
       "      <td>426850</td>\n",
       "      <td>14207</td>\n",
       "      <td>2994</td>\n",
       "      <td>POLYGON ((645043.505 4850864.291, 645041.213 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>130</td>\n",
       "      <td>Milliken</td>\n",
       "      <td>0.283517</td>\n",
       "      <td>408</td>\n",
       "      <td>1871</td>\n",
       "      <td>179.0</td>\n",
       "      <td>0.372999</td>\n",
       "      <td>16704</td>\n",
       "      <td>1348.0</td>\n",
       "      <td>2090</td>\n",
       "      <td>220</td>\n",
       "      <td>770</td>\n",
       "      <td>387879</td>\n",
       "      <td>16901</td>\n",
       "      <td>1921</td>\n",
       "      <td>POLYGON ((641202.087 4852569.586, 641137.336 4...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     neighbourhood_id               neighbourhood  \\\n",
       "99                 19                 Long Branch   \n",
       "75                 18                 New Toronto   \n",
       "51                 20                   Alderwood   \n",
       "92                 17                      Mimico   \n",
       "115                14  Islington-City Centre West   \n",
       "..                ...                         ...   \n",
       "98                132                     Malvern   \n",
       "56                129             Agincourt North   \n",
       "11                116                     Steeles   \n",
       "116               131                       Rouge   \n",
       "5                 130                    Milliken   \n",
       "\n",
       "     economics_access_to_child_care_2008  economics_business_licensing_2008  \\\n",
       "99                              0.442105                                105   \n",
       "75                              0.389189                                148   \n",
       "51                              0.240000                                128   \n",
       "92                              0.236620                                281   \n",
       "115                             0.318812                                763   \n",
       "..                                   ...                                ...   \n",
       "98                              0.314943                                141   \n",
       "56                              0.271154                                188   \n",
       "11                              0.330435                                160   \n",
       "116                             0.396694                                159   \n",
       "5                               0.283517                                408   \n",
       "\n",
       "     economics_businesses_2008  economics_child_care_spaces_2008  \\\n",
       "99                         168                              69.0   \n",
       "75                         342                              90.0   \n",
       "51                         312                             120.0   \n",
       "92                         635                             147.0   \n",
       "115                       2291                             189.0   \n",
       "..                         ...                               ...   \n",
       "98                         665                              85.0   \n",
       "56                         295                             115.0   \n",
       "11                         257                              90.0   \n",
       "116                        832                             192.0   \n",
       "5                         1871                             179.0   \n",
       "\n",
       "     economics_inequality_gini_coeff_2008  economics_local_employment_2008  \\\n",
       "99                               0.390909                             1546   \n",
       "75                               0.430070                             4398   \n",
       "51                               0.353179                             5432   \n",
       "92                               0.405239                            11586   \n",
       "115                              0.398129                            52812   \n",
       "..                                    ...                              ...   \n",
       "98                               0.352910                            12435   \n",
       "56                               0.366070                             7542   \n",
       "11                               0.375050                             5870   \n",
       "116                              0.322259                            13923   \n",
       "5                                0.372999                            16704   \n",
       "\n",
       "     economics_social_assistance_recipients_2008  economics_businesses_2011  \\\n",
       "99                                        1074.0                        170   \n",
       "75                                        1551.0                        368   \n",
       "51                                         494.0                        309   \n",
       "92                                        2147.0                        619   \n",
       "115                                       2441.0                       2263   \n",
       "..                                           ...                        ...   \n",
       "98                                        5275.0                        625   \n",
       "56                                        1563.0                        353   \n",
       "11                                        1007.0                        237   \n",
       "116                                       2528.0                        877   \n",
       "5                                         1348.0                       2090   \n",
       "\n",
       "     economics_child_care_spaces_2011  economics_debt_risk_score_2011  \\\n",
       "99                                 54                             724   \n",
       "75                                 90                             719   \n",
       "51                                120                             764   \n",
       "92                                195                             741   \n",
       "115                               204                             755   \n",
       "..                                ...                             ...   \n",
       "98                                 81                             724   \n",
       "56                                130                             765   \n",
       "11                                105                             778   \n",
       "116                               210                             738   \n",
       "5                                 220                             770   \n",
       "\n",
       "     economics_home_prices_2011  economics_local_employment_2011  \\\n",
       "99                       459088                             1143   \n",
       "75                       460811                             5289   \n",
       "51                       504233                             4839   \n",
       "92                       429941                            11549   \n",
       "115                      491678                            45794   \n",
       "..                          ...                              ...   \n",
       "98                       256488                            11173   \n",
       "56                       375307                             7449   \n",
       "11                       440688                             5835   \n",
       "116                      426850                            14207   \n",
       "5                        387879                            16901   \n",
       "\n",
       "     economics_social_assistance_recipients_2011  \\\n",
       "99                                          1024   \n",
       "75                                          1600   \n",
       "51                                           516   \n",
       "92                                          2634   \n",
       "115                                         2534   \n",
       "..                                           ...   \n",
       "98                                          5078   \n",
       "56                                          1930   \n",
       "11                                          1549   \n",
       "116                                         2994   \n",
       "5                                           1921   \n",
       "\n",
       "                                              geometry  \n",
       "99   POLYGON ((619548.814 4827460.764, 619548.682 4...  \n",
       "75   POLYGON ((619539.953 4827511.152, 619530.538 4...  \n",
       "51   POLYGON ((617149.946 4827388.284, 617138.431 4...  \n",
       "92   POLYGON ((622611.632 4830909.658, 622617.283 4...  \n",
       "115  POLYGON ((619811.872 4830752.687, 619679.828 4...  \n",
       "..                                                 ...  \n",
       "98   POLYGON ((645012.663 4850845.571, 644787.054 4...  \n",
       "56   POLYGON ((641319.328 4851384.746, 641270.670 4...  \n",
       "11   POLYGON ((633510.381 4852777.985, 633573.677 4...  \n",
       "116  POLYGON ((645043.505 4850864.291, 645041.213 4...  \n",
       "5    POLYGON ((641202.087 4852569.586, 641137.336 4...  \n",
       "\n",
       "[140 rows x 16 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed['economics']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "567c42d1-ebb8-411d-b7f3-6df833d527a5",
   "metadata": {},
   "source": [
    "#### civics\n",
    "\n",
    "1. Format column names\n",
    "2. Filter out and rearrange needed columns\n",
    "3. Rename columns to identify dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "671a7b71-eb01-422c-8c97-43d7f4783522",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing civics - clean (2022-02-17 21:36:42.402184)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed civics - clean (2022-02-17 21:36:42.613036) [Elapsed: 0:00:00.210853]\n"
     ]
    }
   ],
   "source": [
    "name = 'civics'\n",
    "file = Path(f'{dist_folder}/clean-datasets/{name}.geojson')\n",
    "if not file.is_file() or force:\n",
    "    \n",
    "    # Start processing\n",
    "    timer = Timer(name, 'clean')\n",
    "    timer.start()\n",
    "    gdf = datasets[name]\n",
    "\n",
    "    # 1. Lowercase col names and remove spaces\n",
    "    gdf = gdf.rename(str.lower, axis='columns')\n",
    "    gdf.columns = [c.replace('$ ', '').replace(' ', '_') for c in gdf.columns]\n",
    "    gdf = gdf.rename(columns={'neighbourhood_2008': 'neighbourhood'})\n",
    "\n",
    "    # 2. Filter and arrange cols needed\n",
    "    gdf = gdf.drop(['neighbourhood_2011'], axis=1)\n",
    "    gdf = gdf[[c for c in gdf.columns if c != 'geometry'] + ['geometry']] # move geom to end\n",
    "\n",
    "    # 3. Rename cols\n",
    "    gdf.columns = [f'{name}_{c}' if c not in ['neighbourhood_id', 'neighbourhood', 'geometry'] else c for c in gdf.columns]\n",
    "\n",
    "    # Save data\n",
    "    gdf.to_file(file)\n",
    "    \n",
    "    # End processing\n",
    "    timer.end()\n",
    "\n",
    "else:\n",
    "    \n",
    "    # Start loading\n",
    "    timer = Timer(name, 'already cleaned')\n",
    "    timer.start()\n",
    "\n",
    "    # Read clean data\n",
    "    gdf = read_data(file)\n",
    "\n",
    "    # End processing\n",
    "    timer.end()\n",
    "    \n",
    "processed[name] = gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3bf2d3ce-f68c-451f-b1a9-24974a375228",
   "metadata": {},
   "outputs": [
    {
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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>neighbourhood_id</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>civics_city_beautification_2008</th>\n",
       "      <th>civics_city_grants_funding_2008</th>\n",
       "      <th>civics_diversity_index_2008</th>\n",
       "      <th>civics_voter_turnout_2008</th>\n",
       "      <th>civics_watermain_breaks_2008</th>\n",
       "      <th>civics_city_grants_funding_2011</th>\n",
       "      <th>civics_neighbourhood_equity_score_2011</th>\n",
       "      <th>civics_salvation_army_donors_2011</th>\n",
       "      <th>civics_walk_score_2011</th>\n",
       "      <th>civics_watermain_breaks_2011</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>19</td>\n",
       "      <td>Long Branch</td>\n",
       "      <td>82</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.791108</td>\n",
       "      <td>35.5761</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.26</td>\n",
       "      <td>308</td>\n",
       "      <td>72</td>\n",
       "      <td>11</td>\n",
       "      <td>POLYGON ((619548.814 4827460.764, 619548.682 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>18</td>\n",
       "      <td>New Toronto</td>\n",
       "      <td>142</td>\n",
       "      <td>149954.0</td>\n",
       "      <td>4.930158</td>\n",
       "      <td>34.5868</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.21</td>\n",
       "      <td>279</td>\n",
       "      <td>79</td>\n",
       "      <td>6</td>\n",
       "      <td>POLYGON ((619539.953 4827511.152, 619530.538 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>20</td>\n",
       "      <td>Alderwood</td>\n",
       "      <td>32</td>\n",
       "      <td>25500.0</td>\n",
       "      <td>4.510898</td>\n",
       "      <td>38.2793</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.88</td>\n",
       "      <td>599</td>\n",
       "      <td>70</td>\n",
       "      <td>20</td>\n",
       "      <td>POLYGON ((617149.946 4827388.284, 617138.431 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>17</td>\n",
       "      <td>Mimico</td>\n",
       "      <td>125</td>\n",
       "      <td>145616.0</td>\n",
       "      <td>5.036239</td>\n",
       "      <td>34.0997</td>\n",
       "      <td>6</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>62.36</td>\n",
       "      <td>873</td>\n",
       "      <td>71</td>\n",
       "      <td>6</td>\n",
       "      <td>POLYGON ((622611.632 4830909.658, 622617.283 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>14</td>\n",
       "      <td>Islington-City Centre West</td>\n",
       "      <td>153</td>\n",
       "      <td>360895.0</td>\n",
       "      <td>5.258512</td>\n",
       "      <td>33.6886</td>\n",
       "      <td>32</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>62.51</td>\n",
       "      <td>2012</td>\n",
       "      <td>72</td>\n",
       "      <td>32</td>\n",
       "      <td>POLYGON ((619811.872 4830752.687, 619679.828 4...</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>132</td>\n",
       "      <td>Malvern</td>\n",
       "      <td>125</td>\n",
       "      <td>188369.0</td>\n",
       "      <td>4.775605</td>\n",
       "      <td>29.8452</td>\n",
       "      <td>0</td>\n",
       "      <td>101775.0</td>\n",
       "      <td>43.89</td>\n",
       "      <td>498</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((645012.663 4850845.571, 644787.054 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>129</td>\n",
       "      <td>Agincourt North</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.576297</td>\n",
       "      <td>33.7874</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.67</td>\n",
       "      <td>467</td>\n",
       "      <td>66</td>\n",
       "      <td>4</td>\n",
       "      <td>POLYGON ((641319.328 4851384.746, 641270.670 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>116</td>\n",
       "      <td>Steeles</td>\n",
       "      <td>49</td>\n",
       "      <td>223135.0</td>\n",
       "      <td>2.886918</td>\n",
       "      <td>30.2621</td>\n",
       "      <td>2</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>51.66</td>\n",
       "      <td>574</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>POLYGON ((633510.381 4852777.985, 633573.677 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>131</td>\n",
       "      <td>Rouge</td>\n",
       "      <td>47</td>\n",
       "      <td>5060.0</td>\n",
       "      <td>4.982575</td>\n",
       "      <td>33.8903</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.81</td>\n",
       "      <td>805</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>POLYGON ((645043.505 4850864.291, 645041.213 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>130</td>\n",
       "      <td>Milliken</td>\n",
       "      <td>80</td>\n",
       "      <td>112135.0</td>\n",
       "      <td>3.039185</td>\n",
       "      <td>30.9314</td>\n",
       "      <td>1</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>56.85</td>\n",
       "      <td>337</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((641202.087 4852569.586, 641137.336 4...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     neighbourhood_id               neighbourhood  \\\n",
       "99                 19                 Long Branch   \n",
       "75                 18                 New Toronto   \n",
       "51                 20                   Alderwood   \n",
       "92                 17                      Mimico   \n",
       "115                14  Islington-City Centre West   \n",
       "..                ...                         ...   \n",
       "98                132                     Malvern   \n",
       "56                129             Agincourt North   \n",
       "11                116                     Steeles   \n",
       "116               131                       Rouge   \n",
       "5                 130                    Milliken   \n",
       "\n",
       "     civics_city_beautification_2008  civics_city_grants_funding_2008  \\\n",
       "99                                82                              0.0   \n",
       "75                               142                         149954.0   \n",
       "51                                32                          25500.0   \n",
       "92                               125                         145616.0   \n",
       "115                              153                         360895.0   \n",
       "..                               ...                              ...   \n",
       "98                               125                         188369.0   \n",
       "56                                75                              0.0   \n",
       "11                                49                         223135.0   \n",
       "116                               47                           5060.0   \n",
       "5                                 80                         112135.0   \n",
       "\n",
       "     civics_diversity_index_2008  civics_voter_turnout_2008  \\\n",
       "99                      4.791108                    35.5761   \n",
       "75                      4.930158                    34.5868   \n",
       "51                      4.510898                    38.2793   \n",
       "92                      5.036239                    34.0997   \n",
       "115                     5.258512                    33.6886   \n",
       "..                           ...                        ...   \n",
       "98                      4.775605                    29.8452   \n",
       "56                      3.576297                    33.7874   \n",
       "11                      2.886918                    30.2621   \n",
       "116                     4.982575                    33.8903   \n",
       "5                       3.039185                    30.9314   \n",
       "\n",
       "     civics_watermain_breaks_2008  civics_city_grants_funding_2011  \\\n",
       "99                             11                              0.0   \n",
       "75                              6                              0.0   \n",
       "51                             20                              0.0   \n",
       "92                              6                           3000.0   \n",
       "115                            32                          12500.0   \n",
       "..                            ...                              ...   \n",
       "98                              0                         101775.0   \n",
       "56                              4                              0.0   \n",
       "11                              2                          35000.0   \n",
       "116                             2                              0.0   \n",
       "5                               1                           2000.0   \n",
       "\n",
       "     civics_neighbourhood_equity_score_2011  \\\n",
       "99                                    56.26   \n",
       "75                                    53.21   \n",
       "51                                    63.88   \n",
       "92                                    62.36   \n",
       "115                                   62.51   \n",
       "..                                      ...   \n",
       "98                                    43.89   \n",
       "56                                    54.67   \n",
       "11                                    51.66   \n",
       "116                                   48.81   \n",
       "5                                     56.85   \n",
       "\n",
       "     civics_salvation_army_donors_2011  civics_walk_score_2011  \\\n",
       "99                                 308                      72   \n",
       "75                                 279                      79   \n",
       "51                                 599                      70   \n",
       "92                                 873                      71   \n",
       "115                               2012                      72   \n",
       "..                                 ...                     ...   \n",
       "98                                 498                      61   \n",
       "56                                 467                      66   \n",
       "11                                 574                      61   \n",
       "116                                805                      42   \n",
       "5                                  337                      65   \n",
       "\n",
       "     civics_watermain_breaks_2011  \\\n",
       "99                             11   \n",
       "75                              6   \n",
       "51                             20   \n",
       "92                              6   \n",
       "115                            32   \n",
       "..                            ...   \n",
       "98                              0   \n",
       "56                              4   \n",
       "11                              2   \n",
       "116                             2   \n",
       "5                               1   \n",
       "\n",
       "                                              geometry  \n",
       "99   POLYGON ((619548.814 4827460.764, 619548.682 4...  \n",
       "75   POLYGON ((619539.953 4827511.152, 619530.538 4...  \n",
       "51   POLYGON ((617149.946 4827388.284, 617138.431 4...  \n",
       "92   POLYGON ((622611.632 4830909.658, 622617.283 4...  \n",
       "115  POLYGON ((619811.872 4830752.687, 619679.828 4...  \n",
       "..                                                 ...  \n",
       "98   POLYGON ((645012.663 4850845.571, 644787.054 4...  \n",
       "56   POLYGON ((641319.328 4851384.746, 641270.670 4...  \n",
       "11   POLYGON ((633510.381 4852777.985, 633573.677 4...  \n",
       "116  POLYGON ((645043.505 4850864.291, 645041.213 4...  \n",
       "5    POLYGON ((641202.087 4852569.586, 641137.336 4...  \n",
       "\n",
       "[140 rows x 13 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed['civics']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06dcf8da-f208-49a8-8e57-c1baa63febf1",
   "metadata": {},
   "source": [
    "#### health\n",
    "\n",
    "1. Format column names\n",
    "2. Filter out and rearrange needed columns\n",
    "3. Rename columns to identify dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d1298eeb-7e91-4d6f-8cfe-baac9fb95ede",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing health - clean (2022-02-17 21:36:42.644497)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed health - clean (2022-02-17 21:36:42.895267) [Elapsed: 0:00:00.250771]\n"
     ]
    }
   ],
   "source": [
    "name = 'health'\n",
    "file = Path(f'{dist_folder}/clean-datasets/{name}.geojson')\n",
    "if not file.is_file() or force:\n",
    "    \n",
    "    # Start processing\n",
    "    timer = Timer(name, 'clean')\n",
    "    timer.start()\n",
    "    gdf = datasets[name]\n",
    "\n",
    "    # 1. Lowercase col names and remove spaces\n",
    "    gdf = gdf.rename(str.lower, axis='columns')\n",
    "    gdf.columns = [c.replace(' ', '_') for c in gdf.columns]\n",
    "    gdf = gdf.rename(columns={'neighbourhood_2008': 'neighbourhood'})\n",
    "\n",
    "    # 2. Filter and arrange cols needed\n",
    "    gdf = gdf.drop(['neighbourhood_2011'], axis=1)\n",
    "    gdf = gdf[[c for c in gdf.columns if c != 'geometry'] + ['geometry']] # move geom to end\n",
    "\n",
    "    # 3. Rename cols\n",
    "    gdf.columns = [f'{name}_{c}' if c not in ['neighbourhood_id', 'neighbourhood', 'geometry'] else c for c in gdf.columns]\n",
    "\n",
    "    # Save data\n",
    "    gdf.to_file(file)\n",
    "    \n",
    "    # End processing\n",
    "    timer.end()\n",
    "\n",
    "else:\n",
    "    \n",
    "    # Start loading\n",
    "    timer = Timer(name, 'already cleaned')\n",
    "    timer.start()\n",
    "\n",
    "    # Read clean data\n",
    "    gdf = read_data(file)\n",
    "\n",
    "    # End processing\n",
    "    timer.end()\n",
    "    \n",
    "processed[name] = gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f2acd5b5-7f40-4f53-b864-e90f9a3ac084",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neighbourhood_id</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>health_breast_cancer_screenings_2008</th>\n",
       "      <th>health_cervical_cancer_screenings_2008</th>\n",
       "      <th>health_colorectal_cancer_screenings_2008</th>\n",
       "      <th>health_community_food_programs_2008</th>\n",
       "      <th>health_diabetes_prevalence_2008</th>\n",
       "      <th>health_dinesafe_inspections_2008</th>\n",
       "      <th>health_female_fertility_2008</th>\n",
       "      <th>health_health_providers_2008</th>\n",
       "      <th>...</th>\n",
       "      <th>health_student_nutrition_2008</th>\n",
       "      <th>health_teen_pregnancy_2008</th>\n",
       "      <th>health_breast_cancer_screenings_2011</th>\n",
       "      <th>health_cervical_cancer_screenings_2011</th>\n",
       "      <th>health_dinesafe_inspections_2011</th>\n",
       "      <th>health_female_fertility_2011</th>\n",
       "      <th>health_health_providers_2011</th>\n",
       "      <th>health_premature_mortality_2011</th>\n",
       "      <th>health_student_nutrition_2011</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>19</td>\n",
       "      <td>Long Branch</td>\n",
       "      <td>49.6</td>\n",
       "      <td>64.5</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>5</td>\n",
       "      <td>42.903018</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>220</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>49.90</td>\n",
       "      <td>62.88</td>\n",
       "      <td>2</td>\n",
       "      <td>43.031471</td>\n",
       "      <td>4</td>\n",
       "      <td>300.8</td>\n",
       "      <td>170</td>\n",
       "      <td>POLYGON ((619548.814 4827460.764, 619548.682 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>18</td>\n",
       "      <td>New Toronto</td>\n",
       "      <td>58.2</td>\n",
       "      <td>65.8</td>\n",
       "      <td>35.0</td>\n",
       "      <td>4</td>\n",
       "      <td>10.2</td>\n",
       "      <td>16</td>\n",
       "      <td>44.909832</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>274</td>\n",
       "      <td>42.528736</td>\n",
       "      <td>52.45</td>\n",
       "      <td>64.08</td>\n",
       "      <td>10</td>\n",
       "      <td>46.073298</td>\n",
       "      <td>14</td>\n",
       "      <td>333.6</td>\n",
       "      <td>1005</td>\n",
       "      <td>POLYGON ((619539.953 4827511.152, 619530.538 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>20</td>\n",
       "      <td>Alderwood</td>\n",
       "      <td>60.5</td>\n",
       "      <td>68.6</td>\n",
       "      <td>36.3</td>\n",
       "      <td>1</td>\n",
       "      <td>9.4</td>\n",
       "      <td>3</td>\n",
       "      <td>33.038349</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>16.438356</td>\n",
       "      <td>57.97</td>\n",
       "      <td>66.16</td>\n",
       "      <td>1</td>\n",
       "      <td>40.235988</td>\n",
       "      <td>13</td>\n",
       "      <td>271.0</td>\n",
       "      <td>75</td>\n",
       "      <td>POLYGON ((617149.946 4827388.284, 617138.431 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>17</td>\n",
       "      <td>Mimico</td>\n",
       "      <td>62.5</td>\n",
       "      <td>70.9</td>\n",
       "      <td>38.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8.9</td>\n",
       "      <td>16</td>\n",
       "      <td>39.518901</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>515</td>\n",
       "      <td>32.380952</td>\n",
       "      <td>56.12</td>\n",
       "      <td>65.23</td>\n",
       "      <td>8</td>\n",
       "      <td>44.526264</td>\n",
       "      <td>22</td>\n",
       "      <td>250.7</td>\n",
       "      <td>525</td>\n",
       "      <td>POLYGON ((622611.632 4830909.658, 622617.283 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>14</td>\n",
       "      <td>Islington-City Centre West</td>\n",
       "      <td>60.9</td>\n",
       "      <td>67.4</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6</td>\n",
       "      <td>9.4</td>\n",
       "      <td>11</td>\n",
       "      <td>39.870471</td>\n",
       "      <td>121</td>\n",
       "      <td>...</td>\n",
       "      <td>485</td>\n",
       "      <td>16.936937</td>\n",
       "      <td>61.02</td>\n",
       "      <td>66.48</td>\n",
       "      <td>12</td>\n",
       "      <td>46.913580</td>\n",
       "      <td>128</td>\n",
       "      <td>222.4</td>\n",
       "      <td>744</td>\n",
       "      <td>POLYGON ((619811.872 4830752.687, 619679.828 4...</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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>132</td>\n",
       "      <td>Malvern</td>\n",
       "      <td>58.4</td>\n",
       "      <td>63.4</td>\n",
       "      <td>41.6</td>\n",
       "      <td>2</td>\n",
       "      <td>16.8</td>\n",
       "      <td>0</td>\n",
       "      <td>53.438060</td>\n",
       "      <td>73</td>\n",
       "      <td>...</td>\n",
       "      <td>1814</td>\n",
       "      <td>42.020202</td>\n",
       "      <td>60.42</td>\n",
       "      <td>62.57</td>\n",
       "      <td>13</td>\n",
       "      <td>51.564007</td>\n",
       "      <td>84</td>\n",
       "      <td>205.9</td>\n",
       "      <td>2512</td>\n",
       "      <td>POLYGON ((645012.663 4850845.571, 644787.054 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>129</td>\n",
       "      <td>Agincourt North</td>\n",
       "      <td>61.3</td>\n",
       "      <td>62.4</td>\n",
       "      <td>48.5</td>\n",
       "      <td>0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>7</td>\n",
       "      <td>40.243644</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>327</td>\n",
       "      <td>15.566836</td>\n",
       "      <td>65.87</td>\n",
       "      <td>64.22</td>\n",
       "      <td>9</td>\n",
       "      <td>42.260029</td>\n",
       "      <td>20</td>\n",
       "      <td>138.7</td>\n",
       "      <td>500</td>\n",
       "      <td>POLYGON ((641319.328 4851384.746, 641270.670 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>116</td>\n",
       "      <td>Steeles</td>\n",
       "      <td>67.4</td>\n",
       "      <td>63.3</td>\n",
       "      <td>53.6</td>\n",
       "      <td>0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>0</td>\n",
       "      <td>32.397296</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>13.333333</td>\n",
       "      <td>68.59</td>\n",
       "      <td>65.21</td>\n",
       "      <td>13</td>\n",
       "      <td>39.001560</td>\n",
       "      <td>9</td>\n",
       "      <td>143.8</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((633510.381 4852777.985, 633573.677 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>131</td>\n",
       "      <td>Rouge</td>\n",
       "      <td>63.6</td>\n",
       "      <td>67.6</td>\n",
       "      <td>44.9</td>\n",
       "      <td>4</td>\n",
       "      <td>14.8</td>\n",
       "      <td>3</td>\n",
       "      <td>35.910579</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>22.580645</td>\n",
       "      <td>64.72</td>\n",
       "      <td>66.77</td>\n",
       "      <td>15</td>\n",
       "      <td>48.042147</td>\n",
       "      <td>25</td>\n",
       "      <td>215.7</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((645043.505 4850864.291, 645041.213 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>130</td>\n",
       "      <td>Milliken</td>\n",
       "      <td>62.5</td>\n",
       "      <td>63.0</td>\n",
       "      <td>50.3</td>\n",
       "      <td>0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>14</td>\n",
       "      <td>38.884952</td>\n",
       "      <td>124</td>\n",
       "      <td>...</td>\n",
       "      <td>150</td>\n",
       "      <td>16.962525</td>\n",
       "      <td>67.02</td>\n",
       "      <td>64.82</td>\n",
       "      <td>74</td>\n",
       "      <td>42.192299</td>\n",
       "      <td>137</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((641202.087 4852569.586, 641137.336 4...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     neighbourhood_id               neighbourhood  \\\n",
       "99                 19                 Long Branch   \n",
       "75                 18                 New Toronto   \n",
       "51                 20                   Alderwood   \n",
       "92                 17                      Mimico   \n",
       "115                14  Islington-City Centre West   \n",
       "..                ...                         ...   \n",
       "98                132                     Malvern   \n",
       "56                129             Agincourt North   \n",
       "11                116                     Steeles   \n",
       "116               131                       Rouge   \n",
       "5                 130                    Milliken   \n",
       "\n",
       "     health_breast_cancer_screenings_2008  \\\n",
       "99                                   49.6   \n",
       "75                                   58.2   \n",
       "51                                   60.5   \n",
       "92                                   62.5   \n",
       "115                                  60.9   \n",
       "..                                    ...   \n",
       "98                                   58.4   \n",
       "56                                   61.3   \n",
       "11                                   67.4   \n",
       "116                                  63.6   \n",
       "5                                    62.5   \n",
       "\n",
       "     health_cervical_cancer_screenings_2008  \\\n",
       "99                                     64.5   \n",
       "75                                     65.8   \n",
       "51                                     68.6   \n",
       "92                                     70.9   \n",
       "115                                    67.4   \n",
       "..                                      ...   \n",
       "98                                     63.4   \n",
       "56                                     62.4   \n",
       "11                                     63.3   \n",
       "116                                    67.6   \n",
       "5                                      63.0   \n",
       "\n",
       "     health_colorectal_cancer_screenings_2008  \\\n",
       "99                                       33.0   \n",
       "75                                       35.0   \n",
       "51                                       36.3   \n",
       "92                                       38.0   \n",
       "115                                      38.0   \n",
       "..                                        ...   \n",
       "98                                       41.6   \n",
       "56                                       48.5   \n",
       "11                                       53.6   \n",
       "116                                      44.9   \n",
       "5                                        50.3   \n",
       "\n",
       "     health_community_food_programs_2008  health_diabetes_prevalence_2008  \\\n",
       "99                                     0                              9.2   \n",
       "75                                     4                             10.2   \n",
       "51                                     1                              9.4   \n",
       "92                                     3                              8.9   \n",
       "115                                    6                              9.4   \n",
       "..                                   ...                              ...   \n",
       "98                                     2                             16.8   \n",
       "56                                     0                             11.6   \n",
       "11                                     0                             11.6   \n",
       "116                                    4                             14.8   \n",
       "5                                      0                             11.6   \n",
       "\n",
       "     health_dinesafe_inspections_2008  health_female_fertility_2008  \\\n",
       "99                                  5                     42.903018   \n",
       "75                                 16                     44.909832   \n",
       "51                                  3                     33.038349   \n",
       "92                                 16                     39.518901   \n",
       "115                                11                     39.870471   \n",
       "..                                ...                           ...   \n",
       "98                                  0                     53.438060   \n",
       "56                                  7                     40.243644   \n",
       "11                                  0                     32.397296   \n",
       "116                                 3                     35.910579   \n",
       "5                                  14                     38.884952   \n",
       "\n",
       "     health_health_providers_2008  ...  health_student_nutrition_2008  \\\n",
       "99                              4  ...                            220   \n",
       "75                             15  ...                            274   \n",
       "51                             12  ...                              0   \n",
       "92                             25  ...                            515   \n",
       "115                           121  ...                            485   \n",
       "..                            ...  ...                            ...   \n",
       "98                             73  ...                           1814   \n",
       "56                             17  ...                            327   \n",
       "11                             10  ...                              0   \n",
       "116                            16  ...                              0   \n",
       "5                             124  ...                            150   \n",
       "\n",
       "     health_teen_pregnancy_2008  health_breast_cancer_screenings_2011  \\\n",
       "99                    37.500000                                 49.90   \n",
       "75                    42.528736                                 52.45   \n",
       "51                    16.438356                                 57.97   \n",
       "92                    32.380952                                 56.12   \n",
       "115                   16.936937                                 61.02   \n",
       "..                          ...                                   ...   \n",
       "98                    42.020202                                 60.42   \n",
       "56                    15.566836                                 65.87   \n",
       "11                    13.333333                                 68.59   \n",
       "116                   22.580645                                 64.72   \n",
       "5                     16.962525                                 67.02   \n",
       "\n",
       "     health_cervical_cancer_screenings_2011  health_dinesafe_inspections_2011  \\\n",
       "99                                    62.88                                 2   \n",
       "75                                    64.08                                10   \n",
       "51                                    66.16                                 1   \n",
       "92                                    65.23                                 8   \n",
       "115                                   66.48                                12   \n",
       "..                                      ...                               ...   \n",
       "98                                    62.57                                13   \n",
       "56                                    64.22                                 9   \n",
       "11                                    65.21                                13   \n",
       "116                                   66.77                                15   \n",
       "5                                     64.82                                74   \n",
       "\n",
       "     health_female_fertility_2011  health_health_providers_2011  \\\n",
       "99                      43.031471                             4   \n",
       "75                      46.073298                            14   \n",
       "51                      40.235988                            13   \n",
       "92                      44.526264                            22   \n",
       "115                     46.913580                           128   \n",
       "..                            ...                           ...   \n",
       "98                      51.564007                            84   \n",
       "56                      42.260029                            20   \n",
       "11                      39.001560                             9   \n",
       "116                     48.042147                            25   \n",
       "5                       42.192299                           137   \n",
       "\n",
       "     health_premature_mortality_2011  health_student_nutrition_2011  \\\n",
       "99                             300.8                            170   \n",
       "75                             333.6                           1005   \n",
       "51                             271.0                             75   \n",
       "92                             250.7                            525   \n",
       "115                            222.4                            744   \n",
       "..                               ...                            ...   \n",
       "98                             205.9                           2512   \n",
       "56                             138.7                            500   \n",
       "11                             143.8                              0   \n",
       "116                            215.7                              0   \n",
       "5                              120.0                              0   \n",
       "\n",
       "                                              geometry  \n",
       "99   POLYGON ((619548.814 4827460.764, 619548.682 4...  \n",
       "75   POLYGON ((619539.953 4827511.152, 619530.538 4...  \n",
       "51   POLYGON ((617149.946 4827388.284, 617138.431 4...  \n",
       "92   POLYGON ((622611.632 4830909.658, 622617.283 4...  \n",
       "115  POLYGON ((619811.872 4830752.687, 619679.828 4...  \n",
       "..                                                 ...  \n",
       "98   POLYGON ((645012.663 4850845.571, 644787.054 4...  \n",
       "56   POLYGON ((641319.328 4851384.746, 641270.670 4...  \n",
       "11   POLYGON ((633510.381 4852777.985, 633573.677 4...  \n",
       "116  POLYGON ((645043.505 4850864.291, 645041.213 4...  \n",
       "5    POLYGON ((641202.087 4852569.586, 641137.336 4...  \n",
       "\n",
       "[140 rows x 21 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed['health']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e7184cc-8483-4166-b7d7-7b7549deab5a",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9ab869e9-7b90-4acf-906d-57290592aecf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed all datasets - preprocessing (2022-02-17 21:36:42.931636) [Elapsed: 0:03:22.385096]\n"
     ]
    }
   ],
   "source": [
    "gtimer.end()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fe5765b-1f2b-479d-89de-c5e257260d6d",
   "metadata": {},
   "source": [
    "The following datasets were processed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "38913f7c-b881-4a98-9cfc-04f88194b725",
   "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>dataset</th>\n",
       "      <th>columns</th>\n",
       "      <th>rows</th>\n",
       "      <th>memory_usage</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>autospeed_enforcement</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1.6 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>red_light_cams</td>\n",
       "      <td>1</td>\n",
       "      <td>200</td>\n",
       "      <td>3.1 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>police</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>512.0 B</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ambulance</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>736.0 B</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fire_hydrants</td>\n",
       "      <td>1</td>\n",
       "      <td>41936</td>\n",
       "      <td>655.2 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fire_stations</td>\n",
       "      <td>1</td>\n",
       "      <td>92</td>\n",
       "      <td>1.4 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>renewables</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1.6 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>bicycle_parking</td>\n",
       "      <td>1</td>\n",
       "      <td>16998</td>\n",
       "      <td>265.6 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>transit_shelters</td>\n",
       "      <td>1</td>\n",
       "      <td>5852</td>\n",
       "      <td>91.4 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>wayfind</td>\n",
       "      <td>1</td>\n",
       "      <td>330</td>\n",
       "      <td>5.2 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>litter</td>\n",
       "      <td>1</td>\n",
       "      <td>10337</td>\n",
       "      <td>161.5 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>art</td>\n",
       "      <td>1</td>\n",
       "      <td>405</td>\n",
       "      <td>6.3 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>culture</td>\n",
       "      <td>1</td>\n",
       "      <td>893</td>\n",
       "      <td>14.0 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>religious</td>\n",
       "      <td>1</td>\n",
       "      <td>1407</td>\n",
       "      <td>22.0 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>centrelines</td>\n",
       "      <td>3</td>\n",
       "      <td>70827</td>\n",
       "      <td>2.2 MiB</td>\n",
       "      <td>['LineString']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>collisions</td>\n",
       "      <td>10</td>\n",
       "      <td>429631</td>\n",
       "      <td>36.1 MiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>traffic</td>\n",
       "      <td>52</td>\n",
       "      <td>219497</td>\n",
       "      <td>88.8 MiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>watch_your_speed</td>\n",
       "      <td>4</td>\n",
       "      <td>783</td>\n",
       "      <td>30.6 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>schools</td>\n",
       "      <td>3</td>\n",
       "      <td>1194</td>\n",
       "      <td>37.3 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>childcare</td>\n",
       "      <td>10</td>\n",
       "      <td>1038</td>\n",
       "      <td>89.2 KiB</td>\n",
       "      <td>['Point']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>crime</td>\n",
       "      <td>115</td>\n",
       "      <td>140</td>\n",
       "      <td>126.9 KiB</td>\n",
       "      <td>['Polygon']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>education</td>\n",
       "      <td>13</td>\n",
       "      <td>140</td>\n",
       "      <td>15.3 KiB</td>\n",
       "      <td>['Polygon']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>environment</td>\n",
       "      <td>14</td>\n",
       "      <td>140</td>\n",
       "      <td>16.4 KiB</td>\n",
       "      <td>['Polygon']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>demographics</td>\n",
       "      <td>31</td>\n",
       "      <td>140</td>\n",
       "      <td>35.0 KiB</td>\n",
       "      <td>['Polygon']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>housing</td>\n",
       "      <td>17</td>\n",
       "      <td>140</td>\n",
       "      <td>19.7 KiB</td>\n",
       "      <td>['Polygon']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>economics</td>\n",
       "      <td>16</td>\n",
       "      <td>140</td>\n",
       "      <td>18.6 KiB</td>\n",
       "      <td>['Polygon']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>civics</td>\n",
       "      <td>13</td>\n",
       "      <td>140</td>\n",
       "      <td>15.3 KiB</td>\n",
       "      <td>['Polygon']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>health</td>\n",
       "      <td>21</td>\n",
       "      <td>140</td>\n",
       "      <td>24.1 KiB</td>\n",
       "      <td>['Polygon']</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  dataset  columns    rows memory_usage        geometry\n",
       "0   autospeed_enforcement        1     100      1.6 KiB       ['Point']\n",
       "1          red_light_cams        1     200      3.1 KiB       ['Point']\n",
       "2                  police        1      32      512.0 B       ['Point']\n",
       "3               ambulance        1      46      736.0 B       ['Point']\n",
       "4           fire_hydrants        1   41936    655.2 KiB       ['Point']\n",
       "5           fire_stations        1      92      1.4 KiB       ['Point']\n",
       "6              renewables        1     100      1.6 KiB       ['Point']\n",
       "7         bicycle_parking        1   16998    265.6 KiB       ['Point']\n",
       "8        transit_shelters        1    5852     91.4 KiB       ['Point']\n",
       "9                 wayfind        1     330      5.2 KiB       ['Point']\n",
       "10                 litter        1   10337    161.5 KiB       ['Point']\n",
       "11                    art        1     405      6.3 KiB       ['Point']\n",
       "12                culture        1     893     14.0 KiB       ['Point']\n",
       "13              religious        1    1407     22.0 KiB       ['Point']\n",
       "14            centrelines        3   70827      2.2 MiB  ['LineString']\n",
       "15             collisions       10  429631     36.1 MiB       ['Point']\n",
       "16                traffic       52  219497     88.8 MiB       ['Point']\n",
       "17       watch_your_speed        4     783     30.6 KiB       ['Point']\n",
       "18                schools        3    1194     37.3 KiB       ['Point']\n",
       "19              childcare       10    1038     89.2 KiB       ['Point']\n",
       "20                  crime      115     140    126.9 KiB     ['Polygon']\n",
       "21              education       13     140     15.3 KiB     ['Polygon']\n",
       "22            environment       14     140     16.4 KiB     ['Polygon']\n",
       "23           demographics       31     140     35.0 KiB     ['Polygon']\n",
       "24                housing       17     140     19.7 KiB     ['Polygon']\n",
       "25              economics       16     140     18.6 KiB     ['Polygon']\n",
       "26                 civics       13     140     15.3 KiB     ['Polygon']\n",
       "27                 health       21     140     24.1 KiB     ['Polygon']"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info = pd.DataFrame([df_info(df, name) for name, df in processed.items()])\n",
    "info"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5674cf31-3885-4bc4-a431-b849dc09c379",
   "metadata": {},
   "source": [
    "Each clean dataset was saved to file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c526f0a8-3f77-49cc-89de-9286a1bf6970",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file</th>\n",
       "      <th>size</th>\n",
       "      <th>created</th>\n",
       "      <th>modified</th>\n",
       "      <th>source</th>\n",
       "      <th>source_url</th>\n",
       "      <th>raw_url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>../tmp/dist/clean-datasets/centrelines.geojson</td>\n",
       "      <td>37.9 MiB</td>\n",
       "      <td>2022-02-17 21:33:29.193725</td>\n",
       "      <td>2022-02-17 21:33:35.391961</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/toronto-centreline-tcl/\" target=\"_blank\">https://open.toronto.ca/dataset/toronto-centreline-tcl/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/ba71b78e-464b-46dd-8185-a0d249fe4857\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/ba71b78e-464b-46dd-8185-a0d249fe4857</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>../tmp/dist/clean-datasets/collisions.geojson</td>\n",
       "      <td>156.0 MiB</td>\n",
       "      <td>2022-02-17 21:33:36.620256</td>\n",
       "      <td>2022-02-17 21:34:16.709532</td>\n",
       "      <td>Toronto Police Service Public Safety Data Portal</td>\n",
       "      <td><a href=\"https://data.torontopolice.on.ca/datasets/TorontoPS::traffic-collisions-asr-t-tbl-001/about\" target=\"_blank\">https://data.torontopolice.on.ca/datasets/TorontoPS::traffic-collisions-asr-t-tbl-001/about</a></td>\n",
       "      <td><a href=\"https://opendata.arcgis.com/api/v3/datasets/2a36d61277a84c3eb9d0beebef4c023d_0/downloads/data?format=geojson&spatialRefId=4326\" target=\"_blank\">https://opendata.arcgis.com/api/v3/datasets/2a36d61277a84c3eb9d0beebef4c023d_0/downloads/data?format=geojson&amp;spatialRefId=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>../tmp/dist/clean-datasets/traffic.geojson</td>\n",
       "      <td>303.3 MiB</td>\n",
       "      <td>2022-02-17 21:34:16.877143</td>\n",
       "      <td>2022-02-17 21:36:39.937125</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/traffic-volumes-at-intersections-for-all-modes/\" target=\"_blank\">https://open.toronto.ca/dataset/traffic-volumes-at-intersections-for-all-modes/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/0ffadfc9-b017-44df-a1b1-905591e54caa\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/0ffadfc9-b017-44df-a1b1-905591e54caa</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>../tmp/dist/clean-datasets/autospeed_enforcement.geojson</td>\n",
       "      <td>14.1 KiB</td>\n",
       "      <td>2022-02-17 21:33:25.272296</td>\n",
       "      <td>2022-02-17 21:33:25.278645</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/automated-speed-enforcement-locations/\" target=\"_blank\">https://open.toronto.ca/dataset/automated-speed-enforcement-locations/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/e25e9460-a0e8-469c-b9fb-9a4837ac6c1c?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/e25e9460-a0e8-469c-b9fb-9a4837ac6c1c?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>../tmp/dist/clean-datasets/watch_your_speed.geojson</td>\n",
       "      <td>154.9 KiB</td>\n",
       "      <td>2022-02-17 21:36:40.001553</td>\n",
       "      <td>2022-02-17 21:36:40.053722</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/school-safety-zone-watch-your-speed-program-locations/\" target=\"_blank\">https://open.toronto.ca/dataset/school-safety-zone-watch-your-speed-program-locations/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/4e2221b9-da3a-4ef8-b8eb-17e95b7abaa0?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/4e2221b9-da3a-4ef8-b8eb-17e95b7abaa0?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>../tmp/dist/clean-datasets/red_light_cams.geojson</td>\n",
       "      <td>28.0 KiB</td>\n",
       "      <td>2022-02-17 21:33:25.283033</td>\n",
       "      <td>2022-02-17 21:33:25.294870</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/red-light-cameras/\" target=\"_blank\">https://open.toronto.ca/dataset/red-light-cameras/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/b57a31a1-5ee6-43e3-bfb9-206ebe93066d?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/b57a31a1-5ee6-43e3-bfb9-206ebe93066d?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>../tmp/dist/clean-datasets/police.geojson</td>\n",
       "      <td>4.6 KiB</td>\n",
       "      <td>2022-02-17 21:33:25.298477</td>\n",
       "      <td>2022-02-17 21:33:25.300551</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/police-facility-locations/\" target=\"_blank\">https://open.toronto.ca/dataset/police-facility-locations/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/4afc3c66-5614-466a-b714-e8d6336fc6d3?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/4afc3c66-5614-466a-b714-e8d6336fc6d3?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>../tmp/dist/clean-datasets/ambulance.geojson</td>\n",
       "      <td>6.5 KiB</td>\n",
       "      <td>2022-02-17 21:33:25.303596</td>\n",
       "      <td>2022-02-17 21:33:25.307559</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/ambulance-station-locations/\" target=\"_blank\">https://open.toronto.ca/dataset/ambulance-station-locations/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/e49245ba-395c-46bf-bcf8-22fc7024d649?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/e49245ba-395c-46bf-bcf8-22fc7024d649?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>../tmp/dist/clean-datasets/fire_hydrants.geojson</td>\n",
       "      <td>5.7 MiB</td>\n",
       "      <td>2022-02-17 21:33:25.319984</td>\n",
       "      <td>2022-02-17 21:33:27.333411</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/fire-hydrants/\" target=\"_blank\">https://open.toronto.ca/dataset/fire-hydrants/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/beaaa552-6338-4c81-95be-411e6cef6b89?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/beaaa552-6338-4c81-95be-411e6cef6b89?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>../tmp/dist/clean-datasets/fire_stations.geojson</td>\n",
       "      <td>13.0 KiB</td>\n",
       "      <td>2022-02-17 21:33:27.336470</td>\n",
       "      <td>2022-02-17 21:33:27.341984</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/fire-station-locations/\" target=\"_blank\">https://open.toronto.ca/dataset/fire-station-locations/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/9d1b7352-32ce-4af2-8681-595ce9e47b6e?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/9d1b7352-32ce-4af2-8681-595ce9e47b6e?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>../tmp/dist/clean-datasets/renewables.geojson</td>\n",
       "      <td>14.1 KiB</td>\n",
       "      <td>2022-02-17 21:33:27.344618</td>\n",
       "      <td>2022-02-17 21:33:27.349653</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/renewable-energy-installations/\" target=\"_blank\">https://open.toronto.ca/dataset/renewable-energy-installations/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/05a84a1c-a707-4792-9e89-0d8176f1fd7b?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/05a84a1c-a707-4792-9e89-0d8176f1fd7b?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>../tmp/dist/clean-datasets/bicycle_parking.geojson</td>\n",
       "      <td>2.3 MiB</td>\n",
       "      <td>2022-02-17 21:33:27.354919</td>\n",
       "      <td>2022-02-17 21:33:28.122732</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/street-furniture-bicycle-parking/\" target=\"_blank\">https://open.toronto.ca/dataset/street-furniture-bicycle-parking/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/e0700d94-0c4d-4058-9354-a4b397c0b249?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/e0700d94-0c4d-4058-9354-a4b397c0b249?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>../tmp/dist/clean-datasets/transit_shelters.geojson</td>\n",
       "      <td>816.1 KiB</td>\n",
       "      <td>2022-02-17 21:33:28.127333</td>\n",
       "      <td>2022-02-17 21:33:28.402024</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/street-furniture-transit-shelter/\" target=\"_blank\">https://open.toronto.ca/dataset/street-furniture-transit-shelter/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/b9214fd7-60d1-45f3-8463-a6bd9828f8bf?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/b9214fd7-60d1-45f3-8463-a6bd9828f8bf?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>../tmp/dist/clean-datasets/wayfind.geojson</td>\n",
       "      <td>46.1 KiB</td>\n",
       "      <td>2022-02-17 21:33:28.405402</td>\n",
       "      <td>2022-02-17 21:33:28.422492</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/street-furniture-information-pillar-wayfinding-structure/\" target=\"_blank\">https://open.toronto.ca/dataset/street-furniture-information-pillar-wayfinding-structure/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/2ee41858-5bbd-4073-98bf-7d46430bb1b9?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/2ee41858-5bbd-4073-98bf-7d46430bb1b9?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>../tmp/dist/clean-datasets/litter.geojson</td>\n",
       "      <td>1.4 MiB</td>\n",
       "      <td>2022-02-17 21:33:28.427375</td>\n",
       "      <td>2022-02-17 21:33:28.958537</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/street-furniture-litter-receptacle/\" target=\"_blank\">https://open.toronto.ca/dataset/street-furniture-litter-receptacle/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/6c74cc93-3814-4970-84ab-0755e845b25f?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/6c74cc93-3814-4970-84ab-0755e845b25f?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>../tmp/dist/clean-datasets/schools.geojson</td>\n",
       "      <td>229.9 KiB</td>\n",
       "      <td>2022-02-17 21:36:40.077711</td>\n",
       "      <td>2022-02-17 21:36:40.147007</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/school-locations-all-types/\" target=\"_blank\">https://open.toronto.ca/dataset/school-locations-all-types/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/02ef7447-54d9-4aa7-b76d-8ef8138ac546?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/02ef7447-54d9-4aa7-b76d-8ef8138ac546?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>../tmp/dist/clean-datasets/childcare.geojson</td>\n",
       "      <td>407.5 KiB</td>\n",
       "      <td>2022-02-17 21:36:40.177532</td>\n",
       "      <td>2022-02-17 21:36:40.279127</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/licensed-child-care-centres/\" target=\"_blank\">https://open.toronto.ca/dataset/licensed-child-care-centres/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/69403dde-a0b3-491d-9451-2338806a3bf0?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/69403dde-a0b3-491d-9451-2338806a3bf0?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>../tmp/dist/clean-datasets/art.geojson</td>\n",
       "      <td>56.6 KiB</td>\n",
       "      <td>2022-02-17 21:33:28.962275</td>\n",
       "      <td>2022-02-17 21:33:29.001401</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/public-art/\" target=\"_blank\">https://open.toronto.ca/dataset/public-art/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/978ac0af-4bbb-4485-98b8-451b8e5e5229?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/978ac0af-4bbb-4485-98b8-451b8e5e5229?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>../tmp/dist/clean-datasets/culture.geojson</td>\n",
       "      <td>124.6 KiB</td>\n",
       "      <td>2022-02-17 21:33:29.007904</td>\n",
       "      <td>2022-02-17 21:33:29.053510</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/cultural-hotspot-points-of-interest/\" target=\"_blank\">https://open.toronto.ca/dataset/cultural-hotspot-points-of-interest/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/a95fcc72-a575-4826-aadd-6eef52774e50?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/a95fcc72-a575-4826-aadd-6eef52774e50?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>../tmp/dist/clean-datasets/religious.geojson</td>\n",
       "      <td>196.3 KiB</td>\n",
       "      <td>2022-02-17 21:33:29.057376</td>\n",
       "      <td>2022-02-17 21:33:29.138300</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/places-of-worship/\" target=\"_blank\">https://open.toronto.ca/dataset/places-of-worship/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/eafc9b0f-ba16-4be1-a196-304e35751b45?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/eafc9b0f-ba16-4be1-a196-304e35751b45?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>../tmp/dist/clean-datasets/crime.geojson</td>\n",
       "      <td>2.7 MiB</td>\n",
       "      <td>2022-02-17 21:36:40.532051</td>\n",
       "      <td>2022-02-17 21:36:41.108499</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/neighbourhood-crime-rates/\" target=\"_blank\">https://open.toronto.ca/dataset/neighbourhood-crime-rates/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/4754baf5-3715-4166-a347-eda9813521de?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/4754baf5-3715-4166-a347-eda9813521de?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>../tmp/dist/clean-datasets/education.geojson</td>\n",
       "      <td>2.3 MiB</td>\n",
       "      <td>2022-02-17 21:36:41.149695</td>\n",
       "      <td>2022-02-17 21:36:41.353254</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/wellbeing-toronto-education/\" target=\"_blank\">https://open.toronto.ca/dataset/wellbeing-toronto-education/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/64a5b3dd-5ce3-4c98-83a5-11986eb54e34\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/64a5b3dd-5ce3-4c98-83a5-11986eb54e34</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>../tmp/dist/clean-datasets/environment.geojson</td>\n",
       "      <td>2.3 MiB</td>\n",
       "      <td>2022-02-17 21:36:41.388286</td>\n",
       "      <td>2022-02-17 21:36:41.593922</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/wellbeing-toronto-environment/\" target=\"_blank\">https://open.toronto.ca/dataset/wellbeing-toronto-environment/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/87cb3a08-0929-4c60-bd87-1dac04e41689\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/87cb3a08-0929-4c60-bd87-1dac04e41689</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>../tmp/dist/clean-datasets/demographics.geojson</td>\n",
       "      <td>2.4 MiB</td>\n",
       "      <td>2022-02-17 21:36:41.632499</td>\n",
       "      <td>2022-02-17 21:36:41.865999</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/wellbeing-toronto-demographics/\" target=\"_blank\">https://open.toronto.ca/dataset/wellbeing-toronto-demographics/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/2fd345a6-4107-49c1-b439-5ba0abe011d7\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/2fd345a6-4107-49c1-b439-5ba0abe011d7</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>../tmp/dist/clean-datasets/housing.geojson</td>\n",
       "      <td>2.3 MiB</td>\n",
       "      <td>2022-02-17 21:36:41.912231</td>\n",
       "      <td>2022-02-17 21:36:42.122971</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/wellbeing-toronto-housing/\" target=\"_blank\">https://open.toronto.ca/dataset/wellbeing-toronto-housing/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/2fc77a12-fadc-447a-9ab9-1b170a508937\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/2fc77a12-fadc-447a-9ab9-1b170a508937</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>../tmp/dist/clean-datasets/economics.geojson</td>\n",
       "      <td>2.3 MiB</td>\n",
       "      <td>2022-02-17 21:36:42.162314</td>\n",
       "      <td>2022-02-17 21:36:42.370517</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/wellbeing-toronto-economics/\" target=\"_blank\">https://open.toronto.ca/dataset/wellbeing-toronto-economics/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/78cf2678-8cd9-442b-a8e7-ed9ae6e82e01\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/78cf2678-8cd9-442b-a8e7-ed9ae6e82e01</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>../tmp/dist/clean-datasets/civics.geojson</td>\n",
       "      <td>2.3 MiB</td>\n",
       "      <td>2022-02-17 21:36:42.409361</td>\n",
       "      <td>2022-02-17 21:36:42.612846</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/wellbeing-toronto-civics-equity-indicators/\" target=\"_blank\">https://open.toronto.ca/dataset/wellbeing-toronto-civics-equity-indicators/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/f62b0d1d-dc2d-4e0e-a9d3-aee112b9c400\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/f62b0d1d-dc2d-4e0e-a9d3-aee112b9c400</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>../tmp/dist/clean-datasets/health.geojson</td>\n",
       "      <td>2.3 MiB</td>\n",
       "      <td>2022-02-17 21:36:42.667484</td>\n",
       "      <td>2022-02-17 21:36:42.895076</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/wellbeing-toronto-health/\" target=\"_blank\">https://open.toronto.ca/dataset/wellbeing-toronto-health/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/bcab8834-3743-49f5-a956-9a98b7130e27\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/bcab8834-3743-49f5-a956-9a98b7130e27</a></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
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   ],
   "source": [
    "# Copy data sources to use in summary\n",
    "summary = data_sources.copy().drop(0).reset_index()\n",
    "\n",
    "# Get clean dataset file paths\n",
    "clean_files = [Path(row.file).with_suffix('').stem for row in summary.itertuples() if Path(row.file).stem != study_area_dataset]\n",
    "clean_files = [f'{dist_folder}/clean-datasets/{f}.geojson' for f in clean_files]\n",
    "summary['file'] = clean_files\n",
    "\n",
    "# Get file infos and add to summary\n",
    "info = [file_info(f) for f in clean_files]\n",
    "info = pd.DataFrame(info)\n",
    "summary = pd.concat([summary, info], axis=1)\n",
    "\n",
    "# Rearrange summary columns and display\n",
    "summary = summary.rename(columns={'url': 'raw_url'})\n",
    "summary = summary[['file', 'size', 'created', 'modified', 'source', 'source_url', 'raw_url']]\n",
    "display(HTML(summary.to_html(render_links=True)))"
   ]
  }
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