data_explore.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b7faad95",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import folium\n",
    "from folium import plugins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7b598b26",
   "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>Neighborhood</th>\n",
       "      <th>Building Address</th>\n",
       "      <th>Contact Info</th>\n",
       "      <th>Tracked Units</th>\n",
       "      <th>Bedroom Sizes</th>\n",
       "      <th>AMI Categories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Allston</td>\n",
       "      <td>31 North Beacon Street</td>\n",
       "      <td>Carol Fiore: cfiore@78clean.com</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Studio, 1BR, 2BR</td>\n",
       "      <td>70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allston</td>\n",
       "      <td>40 Malvern Street</td>\n",
       "      <td>40Malvern@maloneyproperties.com</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1BR, 2BR</td>\n",
       "      <td>70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Allston</td>\n",
       "      <td>450 Cambridge Street</td>\n",
       "      <td>Maloney Properties: 617.209.5406\\r\\n450Cambrid...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1BR, 2BR</td>\n",
       "      <td>70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allston</td>\n",
       "      <td>1505 Commonwealth Avenue</td>\n",
       "      <td>LBC Boston: 617.599.7261</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Studio, 1BR, 2BR</td>\n",
       "      <td>70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Allston</td>\n",
       "      <td>Continuum\\r\\n219 Western Avenue</td>\n",
       "      <td>Continuum@maloneyproperties.com</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Studio, 1BR, 2BR, 3BR</td>\n",
       "      <td>70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Allston</td>\n",
       "      <td>The Eco Allston\\r\\n75 Brainerd Road</td>\n",
       "      <td>National Development: 857.576.6160</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Studio, 1BR, 2BR</td>\n",
       "      <td>70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Allston</td>\n",
       "      <td>The Edge\\r\\n60–66 Brainerd Road</td>\n",
       "      <td>National Development: 857.576.6160</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Studio, 1BR, 2BR</td>\n",
       "      <td>70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Allston</td>\n",
       "      <td>The Element\\r\\n65 Brainerd Road</td>\n",
       "      <td>National Development: 857.576.6160</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Studio, 1BR</td>\n",
       "      <td>70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Allston</td>\n",
       "      <td>The Gateway\\r\\n1298 &amp; 1302 Commonwealth Ave</td>\n",
       "      <td>Mount Vernon: 617.267.0006</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Studio, 1BR</td>\n",
       "      <td>70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Allston</td>\n",
       "      <td>The Radius Apartments\\r\\n530 Western Avenue</td>\n",
       "      <td>Radius@maloneyproperties.com</td>\n",
       "      <td>17.0</td>\n",
       "      <td>micro, studio, 1BD, 2BD</td>\n",
       "      <td>70%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Neighborhood                              Building Address  \\\n",
       "1       Allston                        31 North Beacon Street   \n",
       "2       Allston                             40 Malvern Street   \n",
       "3       Allston                          450 Cambridge Street   \n",
       "4       Allston                      1505 Commonwealth Avenue   \n",
       "5       Allston               Continuum\\r\\n219 Western Avenue   \n",
       "6       Allston           The Eco Allston\\r\\n75 Brainerd Road   \n",
       "7       Allston               The Edge\\r\\n60–66 Brainerd Road   \n",
       "8       Allston               The Element\\r\\n65 Brainerd Road   \n",
       "9       Allston   The Gateway\\r\\n1298 & 1302 Commonwealth Ave   \n",
       "10      Allston  The Radius Apartments\\r\\n530 Western Avenue    \n",
       "\n",
       "                                         Contact Info  Tracked Units  \\\n",
       "1                     Carol Fiore: cfiore@78clean.com            3.0   \n",
       "2                     40Malvern@maloneyproperties.com            6.0   \n",
       "3   Maloney Properties: 617.209.5406\\r\\n450Cambrid...            5.0   \n",
       "4                            LBC Boston: 617.599.7261           10.0   \n",
       "5                     Continuum@maloneyproperties.com           42.0   \n",
       "6                  National Development: 857.576.6160           14.0   \n",
       "7                  National Development: 857.576.6160           10.0   \n",
       "8                  National Development: 857.576.6160            4.0   \n",
       "9                          Mount Vernon: 617.267.0006            8.0   \n",
       "10                       Radius@maloneyproperties.com           17.0   \n",
       "\n",
       "              Bedroom Sizes AMI Categories  \n",
       "1          Studio, 1BR, 2BR            70%  \n",
       "2                  1BR, 2BR            70%  \n",
       "3                  1BR, 2BR            70%  \n",
       "4          Studio, 1BR, 2BR            70%  \n",
       "5     Studio, 1BR, 2BR, 3BR            70%  \n",
       "6          Studio, 1BR, 2BR            70%  \n",
       "7          Studio, 1BR, 2BR            70%  \n",
       "8               Studio, 1BR            70%  \n",
       "9               Studio, 1BR            70%  \n",
       "10  micro, studio, 1BD, 2BD            70%  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Units = Units    1974\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "##This is the data from https://www.bostonplans.org/housing/finding-housing/property-listings\n",
    "##apparently it was an embedded google doc, so I just messed with the url.\n",
    "affordable_db = pd.read_csv('Buildings_Featuring_Existing_Affordable_Rental_Units_Existing_Affordable_Rental_Units.csv')\n",
    "\n",
    "#renaming columns\n",
    "aff_ugly_col_names = affordable_db.columns.tolist()\n",
    "new_names = ['Neighborhood','Building Address','Contact Info','Tracked Units','Bedroom Sizes','AMI Categories']\n",
    "affordable_db = affordable_db.rename(columns =dict(zip(aff_ugly_col_names,new_names)))\n",
    "\n",
    "#what is AMI Categories?\n",
    "#Building addresses don't have area codes.  Not sure that ruins anything, but if we do have to join on address\n",
    "#it isn't ideal\n",
    "affordable_db = affordable_db.drop(labels=0, axis=0)\n",
    "display(affordable_db.head(10))\n",
    "\n",
    "trackedUnits = pd.DataFrame().assign(Units=affordable_db['Tracked Units'])\n",
    "trackedUnits=trackedUnits.dropna()\n",
    "#we dropped about 2 rows\n",
    "trackedUnits[\"Units\"] = trackedUnits.apply(lambda row: int(row),axis = 1)\n",
    "totalUnits = trackedUnits.sum()\n",
    "\n",
    "print(\"Total Units = \" + str(totalUnits))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "796633b7",
   "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>Neighborhood</th>\n",
       "      <th>Building Address</th>\n",
       "      <th>Contact Info</th>\n",
       "      <th>Tracked Units</th>\n",
       "      <th>Bedroom Sizes</th>\n",
       "      <th>AMI Categories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Allston</td>\n",
       "      <td>31 North Beacon Street</td>\n",
       "      <td>Carol Fiore: cfiore@78clean.com</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Studio, 1BR, 2BR</td>\n",
       "      <td>70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allston</td>\n",
       "      <td>40 Malvern Street</td>\n",
       "      <td>40Malvern@maloneyproperties.com</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1BR, 2BR</td>\n",
       "      <td>70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Allston</td>\n",
       "      <td>450 Cambridge Street</td>\n",
       "      <td>Maloney Properties: 617.209.5406\\r\\n450Cambrid...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1BR, 2BR</td>\n",
       "      <td>70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allston</td>\n",
       "      <td>1505 Commonwealth Avenue</td>\n",
       "      <td>LBC Boston: 617.599.7261</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Studio, 1BR, 2BR</td>\n",
       "      <td>70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Allston</td>\n",
       "      <td>Continuum\\r\\n219 Western Avenue</td>\n",
       "      <td>Continuum@maloneyproperties.com</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Studio, 1BR, 2BR, 3BR</td>\n",
       "      <td>70%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Neighborhood                 Building Address  \\\n",
       "1      Allston           31 North Beacon Street   \n",
       "2      Allston                40 Malvern Street   \n",
       "3      Allston             450 Cambridge Street   \n",
       "4      Allston         1505 Commonwealth Avenue   \n",
       "5      Allston  Continuum\\r\\n219 Western Avenue   \n",
       "\n",
       "                                        Contact Info  Tracked Units  \\\n",
       "1                    Carol Fiore: cfiore@78clean.com            3.0   \n",
       "2                    40Malvern@maloneyproperties.com            6.0   \n",
       "3  Maloney Properties: 617.209.5406\\r\\n450Cambrid...            5.0   \n",
       "4                           LBC Boston: 617.599.7261           10.0   \n",
       "5                    Continuum@maloneyproperties.com           42.0   \n",
       "\n",
       "           Bedroom Sizes AMI Categories  \n",
       "1       Studio, 1BR, 2BR            70%  \n",
       "2               1BR, 2BR            70%  \n",
       "3               1BR, 2BR            70%  \n",
       "4       Studio, 1BR, 2BR            70%  \n",
       "5  Studio, 1BR, 2BR, 3BR            70%  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "affordable_db.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b9bd0402",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neighborhood Allston has 11 EXISTING affordable Buildings\n",
      "Total Units in neighborhood Allston is: 130\n",
      "Neighborhood Back Bay has 4 EXISTING affordable Buildings\n",
      "Total Units in neighborhood Back Bay is: 90\n",
      "Neighborhood Brighton has 5 EXISTING affordable Buildings\n",
      "Total Units in neighborhood Brighton is: 106\n",
      "Neighborhood Charlestown has 6 EXISTING affordable Buildings\n",
      "Total Units in neighborhood Charlestown is: 102\n",
      "Neighborhood Dorchester has 8 EXISTING affordable Buildings\n",
      "Total Units in neighborhood Dorchester is: 81\n",
      "Neighborhood Downtown has 9 EXISTING affordable Buildings\n",
      "Total Units in neighborhood Downtown is: 192\n",
      "Neighborhood East Boston has 5 EXISTING affordable Buildings\n",
      "Total Units in neighborhood East Boston is: 103\n",
      "Neighborhood Fenway has 14 EXISTING affordable Buildings\n",
      "Total Units in neighborhood Fenway is: 284\n",
      "Neighborhood Fort Point has 4 EXISTING affordable Buildings\n",
      "Total Units in neighborhood Fort Point is: 67\n",
      "Neighborhood Hyde Park has 2 EXISTING affordable Buildings\n",
      "Total Units in neighborhood Hyde Park is: 10\n",
      "Neighborhood Jamaica Plain has 5 EXISTING affordable Buildings\n",
      "Total Units in neighborhood Jamaica Plain is: 102\n",
      "Neighborhood Mission Hill has 4 EXISTING affordable Buildings\n",
      "Total Units in neighborhood Mission Hill is: 16\n",
      "Neighborhood North End has 1 EXISTING affordable Buildings\n",
      "Total Units in neighborhood North End is: 20\n",
      "Neighborhood Roslindale has 1 EXISTING affordable Buildings\n",
      "Total Units in neighborhood Roslindale is: 6\n",
      "Neighborhood Roxbury has 8 EXISTING affordable Buildings\n",
      "Total Units in neighborhood Roxbury is: 84\n",
      "Neighborhood Seaport has 7 EXISTING affordable Buildings\n",
      "Total Units in neighborhood Seaport is: 246\n",
      "Neighborhood South Boston has 10 EXISTING affordable Buildings\n",
      "Total Units in neighborhood South Boston is: 114\n",
      "Neighborhood South End has 7 EXISTING affordable Buildings\n",
      "Total Units in neighborhood South End is: 145\n",
      "Neighborhood West End has 2 EXISTING affordable Buildings\n",
      "Total Units in neighborhood West End is: 49\n",
      "Neighborhood West Roxbury has 3 EXISTING affordable Buildings\n",
      "Total Units in neighborhood West Roxbury is: 27\n"
     ]
    }
   ],
   "source": [
    "dfGrouped = affordable_db.groupby(['Neighborhood'])\n",
    "dfGroupedKeys = dfGrouped.groups.keys()\n",
    "d = {'neighborhood':[],'totalUnits':[],'buildings':[]}\n",
    "for i in dfGroupedKeys:\n",
    "    groupDf = dfGrouped.get_group(i)\n",
    "    buildings = len(groupDf)\n",
    "    print(\"Neighborhood \"+ i + \" has \" + str(buildings) + \" EXISTING affordable Buildings\")\n",
    "    trackedUnits = pd.DataFrame().assign(Units=groupDf['Tracked Units'])\n",
    "    trackedUnits=trackedUnits.dropna()\n",
    "    trackedUnits[\"Units\"] = trackedUnits.apply(lambda row: int(row),axis = 1)\n",
    "    totalUnits = int(trackedUnits.sum())\n",
    "    d['neighborhood'].append(i)\n",
    "    d['totalUnits'].append(totalUnits)\n",
    "    d['buildings'].append(buildings)\n",
    "    print(\"Total Units in neighborhood \" + i + ' is: ' + str(totalUnits))\n",
    "\n",
    "muniDf = pd.DataFrame(d, columns = ['neighborhood', 'totalUnits','buildings'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "78abde7d",
   "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>neighborhood</th>\n",
       "      <th>totalUnits</th>\n",
       "      <th>buildings</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Allston</td>\n",
       "      <td>130</td>\n",
       "      <td>11</td>\n",
       "      <td>42.3555375</td>\n",
       "      <td>-71.1327494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Back Bay</td>\n",
       "      <td>90</td>\n",
       "      <td>4</td>\n",
       "      <td>42.35054885</td>\n",
       "      <td>-71.08031131584724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brighton</td>\n",
       "      <td>106</td>\n",
       "      <td>5</td>\n",
       "      <td>42.3491615</td>\n",
       "      <td>-71.1533859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Charlestown</td>\n",
       "      <td>102</td>\n",
       "      <td>6</td>\n",
       "      <td>42.3778749</td>\n",
       "      <td>-71.0619957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dorchester</td>\n",
       "      <td>81</td>\n",
       "      <td>8</td>\n",
       "      <td>42.2973205</td>\n",
       "      <td>-71.0744952</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  neighborhood  totalUnits  buildings     latitude           longitude\n",
       "0      Allston         130         11   42.3555375         -71.1327494\n",
       "1     Back Bay          90          4  42.35054885  -71.08031131584724\n",
       "2     Brighton         106          5   42.3491615         -71.1533859\n",
       "3  Charlestown         102          6   42.3778749         -71.0619957\n",
       "4   Dorchester          81          8   42.2973205         -71.0744952"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we will fille muniDf with lat and long and zip\n",
    "hoods = list(muniDf['neighborhood'])\n",
    "bosHoodLat = {}\n",
    "bosHoodLong = {}\n",
    "geolocator = Nominatim(user_agent=\"geoapiExercises\")\n",
    "for i in hoods:\n",
    "    place = i + \", Boston\"\n",
    "    location = geolocator.geocode(place)\n",
    "    bosHoodLat[i] = location.raw['lat']\n",
    "    bosHoodLong[i] = location.raw['lon']\n",
    "\n",
    "\n",
    "muniDf['latitude'] = muniDf.apply(lambda row: bosHoodLat[row[0]],axis=1)\n",
    "muniDf['longitude'] = muniDf.apply(lambda row: bosHoodLong[row[0]],axis=1)\n",
    "muniDf.head()\n",
    "\n",
    "#TODO YAY now we just need to add zip codes, we can do this later tho"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b73058e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
       "&lt;head&gt;    \n",
       "    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n",
       "    \n",
       "        &lt;script&gt;\n",
       "            L_NO_TOUCH = false;\n",
       "            L_DISABLE_3D = false;\n",
       "        &lt;/script&gt;\n",
       "    \n",
       "    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n",
       "    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n",
       "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.6.0/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;script src=&quot;https://code.jquery.com/jquery-1.12.4.min.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;script src=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.6.0/dist/leaflet.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css&quot;/&gt;\n",
       "    \n",
       "            &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
       "                initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
       "            &lt;style&gt;\n",
       "                #map_a4ad858d7fa24df9094eea75d62b1a58 {\n",
       "                    position: relative;\n",
       "                    width: 100.0%;\n",
       "                    height: 100.0%;\n",
       "                    left: 0.0%;\n",
       "                    top: 0.0%;\n",
       "                }\n",
       "            &lt;/style&gt;\n",
       "        \n",
       "    &lt;script src=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium@master/folium/templates/leaflet_heat.min.js&quot;&gt;&lt;/script&gt;\n",
       "&lt;/head&gt;\n",
       "&lt;body&gt;    \n",
       "    \n",
       "            &lt;div class=&quot;folium-map&quot; id=&quot;map_a4ad858d7fa24df9094eea75d62b1a58&quot; &gt;&lt;/div&gt;\n",
       "        \n",
       "&lt;/body&gt;\n",
       "&lt;script&gt;    \n",
       "    \n",
       "            var map_a4ad858d7fa24df9094eea75d62b1a58 = L.map(\n",
       "                &quot;map_a4ad858d7fa24df9094eea75d62b1a58&quot;,\n",
       "                {\n",
       "                    center: [42.35, -71.06],\n",
       "                    crs: L.CRS.EPSG3857,\n",
       "                    zoom: 13,\n",
       "                    zoomControl: true,\n",
       "                    preferCanvas: false,\n",
       "                }\n",
       "            );\n",
       "\n",
       "            \n",
       "\n",
       "        \n",
       "    \n",
       "            var tile_layer_d9ce0095185aeb4936fbeab8d3146d7a = L.tileLayer(\n",
       "                &quot;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,\n",
       "                {&quot;attribution&quot;: &quot;Data by \\u0026copy; \\u003ca href=\\&quot;http://openstreetmap.org\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eODbL\\u003c/a\\u003e.&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
       "            ).addTo(map_a4ad858d7fa24df9094eea75d62b1a58);\n",
       "        \n",
       "    \n",
       "            var feature_group_88be57bd06aa0b7e1503474d2b5abcb6 = L.featureGroup(\n",
       "                {}\n",
       "            ).addTo(map_a4ad858d7fa24df9094eea75d62b1a58);\n",
       "        \n",
       "    \n",
       "            var heat_map_a66950ed551265814ba9deeb9156f7b4 = L.heatLayer(\n",
       "                [[42.3555375, -71.1327494, 130], [42.35054885, -71.08031131584724, 90], [42.3491615, -71.1533859, 106], [42.3778749, -71.0619957, 102], [42.2973205, -71.0744952, 81], [42.3554163, -71.0605179, 192], [42.3750973, -71.0392173, 103], [42.3451868, -71.1045987, 284], [18.9258672, 72.8216228, 67], [42.2556543, -71.1244963, 10], [42.3116046, -71.1143838, 102], [42.3332651, -71.1020285, 16], [42.3650974, -71.0544954, 20], [42.2912093, -71.1244966, 6], [42.3248426, -71.0950158, 84], [42.3502036, -71.0427412, 246], [42.3334312, -71.0494949, 114], [42.34131, -71.0772298, 145], [42.3639186, -71.0638993, 49], [42.2792649, -71.1494972, 27]],\n",
       "                {&quot;blur&quot;: 15, &quot;maxZoom&quot;: 18, &quot;minOpacity&quot;: 0.5, &quot;radius&quot;: 25}\n",
       "            ).addTo(feature_group_88be57bd06aa0b7e1503474d2b5abcb6);\n",
       "        \n",
       "    \n",
       "            var layer_control_e6ffd38a9e7fef1950d33de7bcb18ec3 = {\n",
       "                base_layers : {\n",
       "                    &quot;openstreetmap&quot; : tile_layer_d9ce0095185aeb4936fbeab8d3146d7a,\n",
       "                },\n",
       "                overlays :  {\n",
       "                    &quot;Heat Map&quot; : feature_group_88be57bd06aa0b7e1503474d2b5abcb6,\n",
       "                },\n",
       "            };\n",
       "            L.control.layers(\n",
       "                layer_control_e6ffd38a9e7fef1950d33de7bcb18ec3.base_layers,\n",
       "                layer_control_e6ffd38a9e7fef1950d33de7bcb18ec3.overlays,\n",
       "                {&quot;autoZIndex&quot;: true, &quot;collapsed&quot;: true, &quot;position&quot;: &quot;topright&quot;}\n",
       "            ).addTo(map_a4ad858d7fa24df9094eea75d62b1a58);\n",
       "        \n",
       "&lt;/script&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7fd01a7706d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bare = muniDf[[\"latitude\",\"longitude\",\"totalUnits\"]]\n",
    "map = folium.Map(location = [42.35, -71.06],zoom_start=13)\n",
    "hm = plugins.HeatMap(bare)\n",
    "hm.add_to(folium.FeatureGroup(name='Heat Map').add_to(map))\n",
    "folium.LayerControl().add_to(map)\n",
    "display(map)\n",
    "map.save(\"affordableHousingBoston.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bef91cbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "muniDf\n",
    "\n",
    "#displays each neighborhood and how many existing affordable housing units exist there. \n",
    "\n",
    "\n",
    "\n",
    "types = list(d['neighborhood'])\n",
    "values = list(d['totalUnits'])\n",
    "  \n",
    "fig = plt.figure(figsize = (10, 5))\n",
    " \n",
    "# creating the bar plot\n",
    "plt.bar(types, values, color ='maroon')\n",
    " \n",
    "plt.xlabel(\"Neighborhood\")\n",
    "plt.ylabel(\"Total Units\")\n",
    "plt.title(\"Number of affordable housing units per neighborhood in Boston\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42a6bd2e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3275f998",
   "metadata": {},
   "source": [
    "Helpful Resources:\n",
    "https://data.boston.gov/dataset/property-assessment\n",
    "This holds all the information about the dataset including the data dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9764b7c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "178598"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bostonUnits = pd.read_csv('Property_Assessment_FY2022.csv')\n",
    "#this file holds all properties in Boston\n",
    "len(bostonUnits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4c3720af",
   "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>PID</th>\n",
       "      <th>CM_ID</th>\n",
       "      <th>GIS_ID</th>\n",
       "      <th>ST_NUM</th>\n",
       "      <th>ST_NAME</th>\n",
       "      <th>UNIT_NUM</th>\n",
       "      <th>CITY</th>\n",
       "      <th>ZIPCODE</th>\n",
       "      <th>BLDG_SEQ</th>\n",
       "      <th>NUM_BLDGS</th>\n",
       "      <th>...</th>\n",
       "      <th>KITCHEN_STYLE2</th>\n",
       "      <th>KITCHEN_STYLE3</th>\n",
       "      <th>HEAT_TYPE</th>\n",
       "      <th>HEAT_FUEL</th>\n",
       "      <th>AC_TYPE</th>\n",
       "      <th>FIRE_PLACE</th>\n",
       "      <th>ORIENTATION</th>\n",
       "      <th>NUM_PARKING</th>\n",
       "      <th>PROP_VIEW</th>\n",
       "      <th>CORNER_UNIT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001000</td>\n",
       "      <td></td>\n",
       "      <td>100001000.0</td>\n",
       "      <td>104 A 104</td>\n",
       "      <td>PUTNAM ST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EAST BOSTON</td>\n",
       "      <td>2128</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>S - Semi-Modern</td>\n",
       "      <td>S - Semi-Modern</td>\n",
       "      <td>W - Ht Water/Steam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N - None</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>A - Average</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002000</td>\n",
       "      <td></td>\n",
       "      <td>100002000.0</td>\n",
       "      <td>197</td>\n",
       "      <td>LEXINGTON ST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EAST BOSTON</td>\n",
       "      <td>2128</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>M - Modern</td>\n",
       "      <td>M - Modern</td>\n",
       "      <td>F - Forced Hot Air</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C - Central AC</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>A - Average</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100003000</td>\n",
       "      <td></td>\n",
       "      <td>100003000.0</td>\n",
       "      <td>199</td>\n",
       "      <td>LEXINGTON ST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EAST BOSTON</td>\n",
       "      <td>2128</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>S - Semi-Modern</td>\n",
       "      <td>S - Semi-Modern</td>\n",
       "      <td>S - Space Heat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N - None</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>A - Average</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004000</td>\n",
       "      <td></td>\n",
       "      <td>100004000.0</td>\n",
       "      <td>201</td>\n",
       "      <td>LEXINGTON ST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EAST BOSTON</td>\n",
       "      <td>2128</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>S - Semi-Modern</td>\n",
       "      <td>S - Semi-Modern</td>\n",
       "      <td>W - Ht Water/Steam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N - None</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>A - Average</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100005000</td>\n",
       "      <td></td>\n",
       "      <td>100005000.0</td>\n",
       "      <td>203</td>\n",
       "      <td>LEXINGTON ST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EAST BOSTON</td>\n",
       "      <td>2128</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>S - Semi-Modern</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W - Ht Water/Steam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N - None</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>A - Average</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         PID CM_ID       GIS_ID      ST_NUM       ST_NAME UNIT_NUM  \\\n",
       "0  100001000        100001000.0  104 A 104      PUTNAM ST      NaN   \n",
       "1  100002000        100002000.0       197    LEXINGTON ST      NaN   \n",
       "2  100003000        100003000.0       199    LEXINGTON ST      NaN   \n",
       "3  100004000        100004000.0       201    LEXINGTON ST      NaN   \n",
       "4  100005000        100005000.0       203    LEXINGTON ST      NaN   \n",
       "\n",
       "          CITY  ZIPCODE  BLDG_SEQ  NUM_BLDGS  ...   KITCHEN_STYLE2  \\\n",
       "0  EAST BOSTON     2128       1.0          1  ...  S - Semi-Modern   \n",
       "1  EAST BOSTON     2128       1.0          1  ...       M - Modern   \n",
       "2  EAST BOSTON     2128       1.0          1  ...  S - Semi-Modern   \n",
       "3  EAST BOSTON     2128       1.0          1  ...  S - Semi-Modern   \n",
       "4  EAST BOSTON     2128       1.0          1  ...  S - Semi-Modern   \n",
       "\n",
       "    KITCHEN_STYLE3           HEAT_TYPE HEAT_FUEL         AC_TYPE FIRE_PLACE  \\\n",
       "0  S - Semi-Modern  W - Ht Water/Steam       NaN        N - None          0   \n",
       "1       M - Modern  F - Forced Hot Air       NaN  C - Central AC          0   \n",
       "2  S - Semi-Modern      S - Space Heat       NaN        N - None          0   \n",
       "3  S - Semi-Modern  W - Ht Water/Steam       NaN        N - None          0   \n",
       "4              NaN  W - Ht Water/Steam       NaN        N - None          0   \n",
       "\n",
       "  ORIENTATION NUM_PARKING    PROP_VIEW CORNER_UNIT  \n",
       "0         NaN           3  A - Average         NaN  \n",
       "1         NaN           0  A - Average         NaN  \n",
       "2         NaN           0  A - Average         NaN  \n",
       "3         NaN           0  A - Average         NaN  \n",
       "4         NaN           0  A - Average         NaN  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bostonUnits.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9692d03a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PID', 'CM_ID', 'GIS_ID', 'ST_NUM', 'ST_NAME', 'UNIT_NUM', 'CITY',\n",
       "       'ZIPCODE', 'BLDG_SEQ', 'NUM_BLDGS', 'LUC', 'LU', 'LU_DESC', 'BLDG_TYPE',\n",
       "       'OWN_OCC', 'OWNER', 'MAIL_ADDRESSEE', 'MAIL_ADDRESS', 'MAIL_CITY',\n",
       "       'MAIL_STATE', 'MAIL_ZIPCODE', 'RES_FLOOR', 'CD_FLOOR', 'RES_UNITS',\n",
       "       'COM_UNITS', 'RC_UNITS', 'LAND_SF', 'GROSS_AREA', 'LIVING_AREA',\n",
       "       'LAND_VALUE', 'BLDG_VALUE', 'TOTAL_VALUE', 'GROSS_TAX', 'YR_BUILT',\n",
       "       'YR_REMODEL', 'STRUCTURE_CLASS', 'ROOF_STRUCTURE', 'ROOF_COVER',\n",
       "       'INT_WALL', 'EXT_FINISHED', 'INT_COND', 'EXT_COND', 'OVERALL_COND',\n",
       "       'BED_RMS', 'FULL_BTH', 'HLF_BTH', 'KITCHEN', 'TT_RMS', 'BDRM_COND',\n",
       "       'BTHRM_STYLE1', 'BTHRM_STYLE2', 'BTHRM_STYLE3', 'KITCHEN_TYPE',\n",
       "       'KITCHEN_STYLE1', 'KITCHEN_STYLE2', 'KITCHEN_STYLE3', 'HEAT_TYPE',\n",
       "       'HEAT_FUEL', 'AC_TYPE', 'FIRE_PLACE', 'ORIENTATION', 'NUM_PARKING',\n",
       "       'PROP_VIEW', 'CORNER_UNIT'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bostonUnits.columns\n",
    "#column dictionary can be found at https://data.boston.gov/dataset/property-assessment/resource/db9d1e04-080b-4477-96e0-c440f3ff29f1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7eee9943",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78242"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "owner_occupied = bostonUnits[bostonUnits['OWN_OCC'] == 'Y']\n",
    "len(owner_occupied)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2e29b0c7",
   "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>PID</th>\n",
       "      <th>CM_ID</th>\n",
       "      <th>GIS_ID</th>\n",
       "      <th>ST_NUM</th>\n",
       "      <th>ST_NAME</th>\n",
       "      <th>UNIT_NUM</th>\n",
       "      <th>CITY</th>\n",
       "      <th>ZIPCODE</th>\n",
       "      <th>BLDG_SEQ</th>\n",
       "      <th>NUM_BLDGS</th>\n",
       "      <th>...</th>\n",
       "      <th>KITCHEN_STYLE2</th>\n",
       "      <th>KITCHEN_STYLE3</th>\n",
       "      <th>HEAT_TYPE</th>\n",
       "      <th>HEAT_FUEL</th>\n",
       "      <th>AC_TYPE</th>\n",
       "      <th>FIRE_PLACE</th>\n",
       "      <th>ORIENTATION</th>\n",
       "      <th>NUM_PARKING</th>\n",
       "      <th>PROP_VIEW</th>\n",
       "      <th>CORNER_UNIT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001000</td>\n",
       "      <td></td>\n",
       "      <td>100001000.0</td>\n",
       "      <td>104 A 104</td>\n",
       "      <td>PUTNAM ST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EAST BOSTON</td>\n",
       "      <td>2128</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>S - Semi-Modern</td>\n",
       "      <td>S - Semi-Modern</td>\n",
       "      <td>W - Ht Water/Steam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N - None</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>A - Average</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002000</td>\n",
       "      <td></td>\n",
       "      <td>100002000.0</td>\n",
       "      <td>197</td>\n",
       "      <td>LEXINGTON ST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EAST BOSTON</td>\n",
       "      <td>2128</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>M - Modern</td>\n",
       "      <td>M - Modern</td>\n",
       "      <td>F - Forced Hot Air</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C - Central AC</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>A - Average</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100005000</td>\n",
       "      <td></td>\n",
       "      <td>100005000.0</td>\n",
       "      <td>203</td>\n",
       "      <td>LEXINGTON ST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EAST BOSTON</td>\n",
       "      <td>2128</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>S - Semi-Modern</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W - Ht Water/Steam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N - None</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>A - Average</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100008000</td>\n",
       "      <td></td>\n",
       "      <td>100008000.0</td>\n",
       "      <td>213</td>\n",
       "      <td>LEXINGTON ST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EAST BOSTON</td>\n",
       "      <td>2128</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>S - Semi-Modern</td>\n",
       "      <td>S - Semi-Modern</td>\n",
       "      <td>W - Ht Water/Steam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N - None</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>A - Average</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>100011000</td>\n",
       "      <td></td>\n",
       "      <td>100011000.0</td>\n",
       "      <td>219</td>\n",
       "      <td>LEXINGTON ST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EAST BOSTON</td>\n",
       "      <td>2128</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>M - Modern</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F - Forced Hot Air</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C - Central AC</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>A - Average</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          PID CM_ID       GIS_ID      ST_NUM       ST_NAME UNIT_NUM  \\\n",
       "0   100001000        100001000.0  104 A 104      PUTNAM ST      NaN   \n",
       "1   100002000        100002000.0       197    LEXINGTON ST      NaN   \n",
       "4   100005000        100005000.0       203    LEXINGTON ST      NaN   \n",
       "7   100008000        100008000.0       213    LEXINGTON ST      NaN   \n",
       "10  100011000        100011000.0       219    LEXINGTON ST      NaN   \n",
       "\n",
       "           CITY  ZIPCODE  BLDG_SEQ  NUM_BLDGS  ...   KITCHEN_STYLE2  \\\n",
       "0   EAST BOSTON     2128       1.0          1  ...  S - Semi-Modern   \n",
       "1   EAST BOSTON     2128       1.0          1  ...       M - Modern   \n",
       "4   EAST BOSTON     2128       1.0          1  ...  S - Semi-Modern   \n",
       "7   EAST BOSTON     2128       1.0          1  ...  S - Semi-Modern   \n",
       "10  EAST BOSTON     2128       1.0          1  ...       M - Modern   \n",
       "\n",
       "     KITCHEN_STYLE3           HEAT_TYPE HEAT_FUEL         AC_TYPE FIRE_PLACE  \\\n",
       "0   S - Semi-Modern  W - Ht Water/Steam       NaN        N - None          0   \n",
       "1        M - Modern  F - Forced Hot Air       NaN  C - Central AC          0   \n",
       "4               NaN  W - Ht Water/Steam       NaN        N - None          0   \n",
       "7   S - Semi-Modern  W - Ht Water/Steam       NaN        N - None          0   \n",
       "10              NaN  F - Forced Hot Air       NaN  C - Central AC          0   \n",
       "\n",
       "   ORIENTATION NUM_PARKING    PROP_VIEW CORNER_UNIT  \n",
       "0          NaN           3  A - Average         NaN  \n",
       "1          NaN           0  A - Average         NaN  \n",
       "4          NaN           0  A - Average         NaN  \n",
       "7          NaN           0  A - Average         NaN  \n",
       "10         NaN           0  A - Average         NaN  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "owner_occupied.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ba467e76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of boston housing parcels which are owner occupied: 0.4380900122061837\n",
      "Note each parcel may have multiple units\n"
     ]
    }
   ],
   "source": [
    "print(\"Percentage of boston housing parcels which are owner occupied: \"+ str(len(owner_occupied)/len(bostonUnits)))\n",
    "print(\"Note each parcel may have multiple units\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b3f09cbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78242\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PID</th>\n",
       "      <th>CM_ID</th>\n",
       "      <th>GIS_ID</th>\n",
       "      <th>RES_FLOOR</th>\n",
       "      <th>LU</th>\n",
       "      <th>RES_UNITS</th>\n",
       "      <th>UNIT_NUM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001000</td>\n",
       "      <td></td>\n",
       "      <td>100001000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>R3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002000</td>\n",
       "      <td></td>\n",
       "      <td>100002000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>R3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100005000</td>\n",
       "      <td></td>\n",
       "      <td>100005000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>R2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100008000</td>\n",
       "      <td></td>\n",
       "      <td>100008000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>R3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>100011000</td>\n",
       "      <td></td>\n",
       "      <td>100011000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>R2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PID CM_ID       GIS_ID  RES_FLOOR  LU  RES_UNITS UNIT_NUM\n",
       "0   100001000        100001000.0        3.0  R3          0      NaN\n",
       "1   100002000        100002000.0        3.0  R3          0      NaN\n",
       "4   100005000        100005000.0        3.0  R2          0      NaN\n",
       "7   100008000        100008000.0        3.0  R3          0      NaN\n",
       "10  100011000        100011000.0        3.0  R2          0      NaN"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating a new dataframe with the data of interest\n",
    "ownerData = owner_occupied[['PID', 'CM_ID', 'GIS_ID','RES_FLOOR', 'LU','RES_UNITS','UNIT_NUM']]\n",
    "print(len(ownerData))\n",
    "ownerData.head()\n",
    "#note in owner data all of the condo mains are removed since owneroccupied Y is N for all  mains\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "70edc1da",
   "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>PID</th>\n",
       "      <th>CM_ID</th>\n",
       "      <th>GIS_ID</th>\n",
       "      <th>RES_FLOOR</th>\n",
       "      <th>LU</th>\n",
       "      <th>RES_UNITS</th>\n",
       "      <th>UNIT_NUM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>100244000</td>\n",
       "      <td></td>\n",
       "      <td>100244000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>R4</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>100531000</td>\n",
       "      <td></td>\n",
       "      <td>100531000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>R4</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>100856000</td>\n",
       "      <td></td>\n",
       "      <td>100856000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>R4</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1759</th>\n",
       "      <td>101528000</td>\n",
       "      <td></td>\n",
       "      <td>101528000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>R4</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>101728000</td>\n",
       "      <td></td>\n",
       "      <td>101728000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>R4</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            PID CM_ID       GIS_ID  RES_FLOOR  LU  RES_UNITS UNIT_NUM\n",
       "292   100244000        100244000.0        3.0  R4          0      NaN\n",
       "588   100531000        100531000.0        3.0  R4          0      NaN\n",
       "990   100856000        100856000.0        2.5  R4          0      NaN\n",
       "1759  101528000        101528000.0        4.0  R4          0      NaN\n",
       "2019  101728000        101728000.0        3.0  R4          0      NaN"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = ownerData[ownerData['LU'] == 'R4']\n",
    "temp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "367a2d2c",
   "metadata": {},
   "source": [
    "Notice in the above dataframe head how there are float values for Res_floor and res_units has 0 and unit_num has NaN values. Thus none of those columns are useful since there is garbage data in place. Instead our best way of estimating units per property that is owner occupied is using LU and simply using the number preceding the R as our value. Note for properties of up to 3 units this is not a problem however for units of more than size 4 we will see issues since they could have more. Additionally if some properties have LU as A which signifies that there are more than 7 residential units. We also have issues with condominium units in the data since have a NaN won't allow us to see how many units are in that condominium"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90fdbbb7",
   "metadata": {},
   "source": [
    "Condominimun Units data organization: All condo units contain a 'main entry' and then sub rows for each unit. If you do a group by on GIS_ID then each condominium can be separated from one another. Remember if a sum on this GROUPBy is computed then you will have to subtract 1 from each sum since the main entry isn't a condo unit, it simply is a connector for all. I recommend separating condos from housing units and processing them separately. Additionally to find condo main simply check that PID = CM_ID = GIS_ID, then you can pull RES_UNITS from there to get the residential units of the condo. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5a95486",
   "metadata": {},
   "source": [
    "Important Question is Datasets: Are all affordable housing units just income restricted units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9b7860eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#owner_occupied.groupby(['CITY'])['UNIT_NUM'].count().reset_index().sort_values(by=['UNIT_NUM'],ascending = False)\n",
    "#a useful table for seeing what parts of the city have units\n",
    "# instead of running this query we should instead group by neighborhood/city then group by LU and get the counts \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3a701dbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LU\n",
       "A        55\n",
       "CD    35915\n",
       "R1    25640\n",
       "R2    10479\n",
       "R3     5488\n",
       "R4      419\n",
       "RC      245\n",
       "RL        1\n",
       "Name: LU, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#owner_occupied.groupby(['LU_DESC'])['UNIT_NUM'].count().reset_index().sort_values(by=['UNIT_NUM'],ascending = False)\n",
    "##weird that we getting zeros.  but hey.\n",
    "\n",
    "owner_occupied.groupby(['LU'])['LU'].count()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d9a0bfb",
   "metadata": {},
   "source": [
    "This rrepresents the various building types and the distribution of them amongst the full owner occupied properties. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "02581d41",
   "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>neighborhood</th>\n",
       "      <th>totalUnits</th>\n",
       "      <th>buildings</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Allston</td>\n",
       "      <td>130</td>\n",
       "      <td>11</td>\n",
       "      <td>42.3555375</td>\n",
       "      <td>-71.1327494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Back Bay</td>\n",
       "      <td>90</td>\n",
       "      <td>4</td>\n",
       "      <td>42.35054885</td>\n",
       "      <td>-71.08031131584724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brighton</td>\n",
       "      <td>106</td>\n",
       "      <td>5</td>\n",
       "      <td>42.3491615</td>\n",
       "      <td>-71.1533859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Charlestown</td>\n",
       "      <td>102</td>\n",
       "      <td>6</td>\n",
       "      <td>42.3778749</td>\n",
       "      <td>-71.0619957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dorchester</td>\n",
       "      <td>81</td>\n",
       "      <td>8</td>\n",
       "      <td>42.2973205</td>\n",
       "      <td>-71.0744952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Downtown</td>\n",
       "      <td>192</td>\n",
       "      <td>9</td>\n",
       "      <td>42.3554163</td>\n",
       "      <td>-71.0605179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>East Boston</td>\n",
       "      <td>103</td>\n",
       "      <td>5</td>\n",
       "      <td>42.3750973</td>\n",
       "      <td>-71.0392173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Fenway</td>\n",
       "      <td>284</td>\n",
       "      <td>14</td>\n",
       "      <td>42.3451868</td>\n",
       "      <td>-71.1045987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Fort Point</td>\n",
       "      <td>67</td>\n",
       "      <td>4</td>\n",
       "      <td>18.9258672</td>\n",
       "      <td>72.8216228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Hyde Park</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>42.2556543</td>\n",
       "      <td>-71.1244963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Jamaica Plain</td>\n",
       "      <td>102</td>\n",
       "      <td>5</td>\n",
       "      <td>42.3116046</td>\n",
       "      <td>-71.1143838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Mission Hill</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>42.3332651</td>\n",
       "      <td>-71.1020285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>North End</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>42.3650974</td>\n",
       "      <td>-71.0544954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Roslindale</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>42.2912093</td>\n",
       "      <td>-71.1244966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Roxbury</td>\n",
       "      <td>84</td>\n",
       "      <td>8</td>\n",
       "      <td>42.3248426</td>\n",
       "      <td>-71.0950158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Seaport</td>\n",
       "      <td>246</td>\n",
       "      <td>7</td>\n",
       "      <td>42.3502036</td>\n",
       "      <td>-71.0427412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>South Boston</td>\n",
       "      <td>114</td>\n",
       "      <td>10</td>\n",
       "      <td>42.3334312</td>\n",
       "      <td>-71.0494949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>South End</td>\n",
       "      <td>145</td>\n",
       "      <td>7</td>\n",
       "      <td>42.34131</td>\n",
       "      <td>-71.0772298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>West End</td>\n",
       "      <td>49</td>\n",
       "      <td>2</td>\n",
       "      <td>42.3639186</td>\n",
       "      <td>-71.0638993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>West Roxbury</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>42.2792649</td>\n",
       "      <td>-71.1494972</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     neighborhood  totalUnits  buildings     latitude           longitude\n",
       "0         Allston         130         11   42.3555375         -71.1327494\n",
       "1        Back Bay          90          4  42.35054885  -71.08031131584724\n",
       "2        Brighton         106          5   42.3491615         -71.1533859\n",
       "3     Charlestown         102          6   42.3778749         -71.0619957\n",
       "4      Dorchester          81          8   42.2973205         -71.0744952\n",
       "5        Downtown         192          9   42.3554163         -71.0605179\n",
       "6     East Boston         103          5   42.3750973         -71.0392173\n",
       "7          Fenway         284         14   42.3451868         -71.1045987\n",
       "8      Fort Point          67          4   18.9258672          72.8216228\n",
       "9       Hyde Park          10          2   42.2556543         -71.1244963\n",
       "10  Jamaica Plain         102          5   42.3116046         -71.1143838\n",
       "11   Mission Hill          16          4   42.3332651         -71.1020285\n",
       "12      North End          20          1   42.3650974         -71.0544954\n",
       "13     Roslindale           6          1   42.2912093         -71.1244966\n",
       "14        Roxbury          84          8   42.3248426         -71.0950158\n",
       "15        Seaport         246          7   42.3502036         -71.0427412\n",
       "16   South Boston         114         10   42.3334312         -71.0494949\n",
       "17      South End         145          7     42.34131         -71.0772298\n",
       "18       West End          49          2   42.3639186         -71.0638993\n",
       "19   West Roxbury          27          3   42.2792649         -71.1494972"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "muniDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63092d89",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "vscode": {
   "interpreter": {
    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}