incomeRestricted.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a file with all the income restricted housing units in Boston MA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#imports\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import pgeocode\n",
    "import folium\n",
    "from folium import plugins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Incoome-Restricted Projects in Boston: 1445\n",
      "Index(['Project Name', 'Neighborhood', 'Zip Code', 'TtlProjUnits', 'RentUnits',\n",
      "       'OwnUnits', 'TtlMarket', 'MarketRent', 'MarketOwn',\n",
      "       'Total Income-Restricted', 'Income-Restricted Rental',\n",
      "       'Income-Restricted Ownership', 'Tenure', 'Public/ Private',\n",
      "       'Includes Senior Units?', 'Section 8'],\n",
      "      dtype='object')\n",
      "Project Name                    object\n",
      "Neighborhood                    object\n",
      "Zip Code                       float64\n",
      "TtlProjUnits                     int64\n",
      "RentUnits                        int64\n",
      "OwnUnits                         int64\n",
      "TtlMarket                      float64\n",
      "MarketRent                     float64\n",
      "MarketOwn                      float64\n",
      "Total Income-Restricted          int64\n",
      "Income-Restricted Rental         int64\n",
      "Income-Restricted Ownership    float64\n",
      "Tenure                          object\n",
      "Public/ Private                 object\n",
      "Includes Senior Units?          object\n",
      "Section 8                       object\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Project Name</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>TtlProjUnits</th>\n",
       "      <th>RentUnits</th>\n",
       "      <th>OwnUnits</th>\n",
       "      <th>TtlMarket</th>\n",
       "      <th>MarketRent</th>\n",
       "      <th>MarketOwn</th>\n",
       "      <th>Total Income-Restricted</th>\n",
       "      <th>Income-Restricted Rental</th>\n",
       "      <th>Income-Restricted Ownership</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Public/ Private</th>\n",
       "      <th>Includes Senior Units?</th>\n",
       "      <th>Section 8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbot Street/ Shawmut Ave</td>\n",
       "      <td>Roxbury</td>\n",
       "      <td>2119.0</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rental</td>\n",
       "      <td>Private</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Academy Homes I</td>\n",
       "      <td>Roxbury</td>\n",
       "      <td>2119.0</td>\n",
       "      <td>202</td>\n",
       "      <td>202</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rental</td>\n",
       "      <td>Private</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Academy Homes II</td>\n",
       "      <td>Roxbury</td>\n",
       "      <td>2119.0</td>\n",
       "      <td>236</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>236</td>\n",
       "      <td>236</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rental</td>\n",
       "      <td>Private</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adams Court Phase A</td>\n",
       "      <td>Mattapan</td>\n",
       "      <td>2126.0</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rental</td>\n",
       "      <td>Private</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adams Court Phase B</td>\n",
       "      <td>Mattapan</td>\n",
       "      <td>2126.0</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rental</td>\n",
       "      <td>Private</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Project Name Neighborhood  Zip Code  TtlProjUnits  RentUnits  \\\n",
       "0  Abbot Street/ Shawmut Ave      Roxbury    2119.0            16         16   \n",
       "1            Academy Homes I      Roxbury    2119.0           202        202   \n",
       "2           Academy Homes II      Roxbury    2119.0           236        236   \n",
       "3        Adams Court Phase A     Mattapan    2126.0            50         50   \n",
       "4        Adams Court Phase B     Mattapan    2126.0            45         45   \n",
       "\n",
       "   OwnUnits  TtlMarket  MarketRent  MarketOwn  Total Income-Restricted  \\\n",
       "0         0        0.0         0.0        0.0                       16   \n",
       "1         0       52.0        52.0        0.0                      150   \n",
       "2         0        0.0         0.0        0.0                      236   \n",
       "3         0        0.0         0.0        0.0                       50   \n",
       "4         0        0.0         0.0        0.0                       45   \n",
       "\n",
       "   Income-Restricted Rental  Income-Restricted Ownership  Tenure  \\\n",
       "0                        16                          0.0  Rental   \n",
       "1                       150                          0.0  Rental   \n",
       "2                       236                          0.0  Rental   \n",
       "3                        50                          0.0  Rental   \n",
       "4                        45                          0.0  Rental   \n",
       "\n",
       "  Public/ Private Includes Senior Units? Section 8  \n",
       "0         Private                    NaN       NaN  \n",
       "1         Private                    NaN       NaN  \n",
       "2         Private                    NaN         Y  \n",
       "3         Private                    NaN       NaN  \n",
       "4         Private                    NaN       NaN  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('income-restricted-inventory-2021.csv')\n",
    "print(\"Total Income-Restricted Projects in Boston: \"+ str(len(df)))\n",
    "\n",
    "print(df.columns)\n",
    "print(df.dtypes)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Problem: The income restricted housing units doesn't contain addresses, instead they contain Project names and neighborhoods, and this is not helpful at all in identifying which owner occupied houses are not affordable right now. Is there a different data set we could be provided?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Current Analysis Goals: See where most of the affordable housing units fall(we will be using neighborhoods for this) and see how much of that area has owner occupied houses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Project Name</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>TtlProjUnits</th>\n",
       "      <th>RentUnits</th>\n",
       "      <th>OwnUnits</th>\n",
       "      <th>TtlMarket</th>\n",
       "      <th>MarketRent</th>\n",
       "      <th>MarketOwn</th>\n",
       "      <th>Total Income-Restricted</th>\n",
       "      <th>Income-Restricted Rental</th>\n",
       "      <th>Income-Restricted Ownership</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Public/ Private</th>\n",
       "      <th>Includes Senior Units?</th>\n",
       "      <th>Section 8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Buildable Lots - Codman Square</td>\n",
       "      <td>Dorchester</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Ownership</td>\n",
       "      <td>Private</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>Buildable Lots - Uphams</td>\n",
       "      <td>Dorchester</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Ownership</td>\n",
       "      <td>Private</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>Buildable Lots- 3&amp;4</td>\n",
       "      <td>Dorchester</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Ownership</td>\n",
       "      <td>Private</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                       Project Name Neighborhood  Zip Code  TtlProjUnits  \\\n",
       "129  Buildable Lots - Codman Square   Dorchester       NaN             6   \n",
       "131         Buildable Lots - Uphams   Dorchester       NaN             6   \n",
       "133             Buildable Lots- 3&4   Dorchester       NaN            18   \n",
       "\n",
       "     RentUnits  OwnUnits  TtlMarket  MarketRent  MarketOwn  \\\n",
       "129          0         6        1.0         0.0        1.0   \n",
       "131          0         6        0.0         0.0        0.0   \n",
       "133          0        18        8.0         0.0        8.0   \n",
       "\n",
       "     Total Income-Restricted  Income-Restricted Rental  \\\n",
       "129                        5                         0   \n",
       "131                        6                         0   \n",
       "133                       10                         0   \n",
       "\n",
       "     Income-Restricted Ownership     Tenure Public/ Private  \\\n",
       "129                          5.0  Ownership         Private   \n",
       "131                          6.0  Ownership         Private   \n",
       "133                         10.0  Ownership         Private   \n",
       "\n",
       "    Includes Senior Units? Section 8  \n",
       "129                    NaN       NaN  \n",
       "131                    NaN       NaN  \n",
       "133                    NaN       NaN  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Zip Code\"].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[129, 'Zip Code'] = 2125\n",
    "df.loc[131, 'Zip Code'] = 2125\n",
    "df.loc[133, 'Zip Code'] = 2125\n",
    "#NANS found in the zips of each of these rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Project Name</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>TtlProjUnits</th>\n",
       "      <th>RentUnits</th>\n",
       "      <th>OwnUnits</th>\n",
       "      <th>TtlMarket</th>\n",
       "      <th>MarketRent</th>\n",
       "      <th>MarketOwn</th>\n",
       "      <th>Total Income-Restricted</th>\n",
       "      <th>Income-Restricted Rental</th>\n",
       "      <th>Income-Restricted Ownership</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Public/ Private</th>\n",
       "      <th>Includes Senior Units?</th>\n",
       "      <th>Section 8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Project Name, Neighborhood, Zip Code, TtlProjUnits, RentUnits, OwnUnits, TtlMarket, MarketRent, MarketOwn, Total Income-Restricted, Income-Restricted Rental, Income-Restricted Ownership, Tenure, Public/ Private, Includes Senior Units?, Section 8]\n",
       "Index: []"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Zip Code\"].isnull()]\n",
    "#all good now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Project Name</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>TtlProjUnits</th>\n",
       "      <th>RentUnits</th>\n",
       "      <th>OwnUnits</th>\n",
       "      <th>TtlMarket</th>\n",
       "      <th>MarketRent</th>\n",
       "      <th>MarketOwn</th>\n",
       "      <th>Total Income-Restricted</th>\n",
       "      <th>Income-Restricted Rental</th>\n",
       "      <th>Income-Restricted Ownership</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Public/ Private</th>\n",
       "      <th>Includes Senior Units?</th>\n",
       "      <th>Section 8</th>\n",
       "      <th>Zip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbot Street/ Shawmut Ave</td>\n",
       "      <td>Roxbury</td>\n",
       "      <td>02119</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rental</td>\n",
       "      <td>Private</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>02119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Academy Homes I</td>\n",
       "      <td>Roxbury</td>\n",
       "      <td>02119</td>\n",
       "      <td>202</td>\n",
       "      <td>202</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rental</td>\n",
       "      <td>Private</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>02119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Academy Homes II</td>\n",
       "      <td>Roxbury</td>\n",
       "      <td>02119</td>\n",
       "      <td>236</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>236</td>\n",
       "      <td>236</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rental</td>\n",
       "      <td>Private</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>02119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adams Court Phase A</td>\n",
       "      <td>Mattapan</td>\n",
       "      <td>02126</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rental</td>\n",
       "      <td>Private</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>02126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adams Court Phase B</td>\n",
       "      <td>Mattapan</td>\n",
       "      <td>02126</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rental</td>\n",
       "      <td>Private</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>02126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Project Name Neighborhood Zip Code  TtlProjUnits  RentUnits  \\\n",
       "0  Abbot Street/ Shawmut Ave      Roxbury    02119            16         16   \n",
       "1            Academy Homes I      Roxbury    02119           202        202   \n",
       "2           Academy Homes II      Roxbury    02119           236        236   \n",
       "3        Adams Court Phase A     Mattapan    02126            50         50   \n",
       "4        Adams Court Phase B     Mattapan    02126            45         45   \n",
       "\n",
       "   OwnUnits  TtlMarket  MarketRent  MarketOwn  Total Income-Restricted  \\\n",
       "0         0        0.0         0.0        0.0                       16   \n",
       "1         0       52.0        52.0        0.0                      150   \n",
       "2         0        0.0         0.0        0.0                      236   \n",
       "3         0        0.0         0.0        0.0                       50   \n",
       "4         0        0.0         0.0        0.0                       45   \n",
       "\n",
       "   Income-Restricted Rental  Income-Restricted Ownership  Tenure  \\\n",
       "0                        16                          0.0  Rental   \n",
       "1                       150                          0.0  Rental   \n",
       "2                       236                          0.0  Rental   \n",
       "3                        50                          0.0  Rental   \n",
       "4                        45                          0.0  Rental   \n",
       "\n",
       "  Public/ Private Includes Senior Units? Section 8    Zip  \n",
       "0         Private                    NaN       NaN  02119  \n",
       "1         Private                    NaN       NaN  02119  \n",
       "2         Private                    NaN         Y  02119  \n",
       "3         Private                    NaN       NaN  02126  \n",
       "4         Private                    NaN       NaN  02126  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#zip codes were messed up being stored as float so this gets cleaned up\n",
    "df['Zip Code'] = df.apply(lambda row: '0'+str(int(row[2])),axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "groupZips = df.groupby('Zip Code')\n",
    "#creates subdata frames using group by \n",
    "groupKeys = list(groupZips.groups.keys()) #list Note the gorup keys are strings\n",
    "#access a specific group using : $groupZips.get_group(<groupName>)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Amount of Income restricted housing projects in Zip Code of 02108 : 1\n",
      "Amount of Income restricted housing projects in Zip Code of 02109 : 5\n",
      "Amount of Income restricted housing projects in Zip Code of 02110 : 4\n",
      "Amount of Income restricted housing projects in Zip Code of 02111 : 25\n",
      "Amount of Income restricted housing projects in Zip Code of 02113 : 5\n",
      "Amount of Income restricted housing projects in Zip Code of 02114 : 22\n",
      "Amount of Income restricted housing projects in Zip Code of 02115 : 35\n",
      "Amount of Income restricted housing projects in Zip Code of 02116 : 31\n",
      "Amount of Income restricted housing projects in Zip Code of 02118 : 110\n",
      "Amount of Income restricted housing projects in Zip Code of 02119 : 175\n",
      "Amount of Income restricted housing projects in Zip Code of 02120 : 43\n",
      "Amount of Income restricted housing projects in Zip Code of 02121 : 134\n",
      "Amount of Income restricted housing projects in Zip Code of 02122 : 42\n",
      "Amount of Income restricted housing projects in Zip Code of 02124 : 165\n",
      "Amount of Income restricted housing projects in Zip Code of 02125 : 108\n",
      "Amount of Income restricted housing projects in Zip Code of 02126 : 50\n",
      "Amount of Income restricted housing projects in Zip Code of 02127 : 99\n",
      "Amount of Income restricted housing projects in Zip Code of 02128 : 95\n",
      "Amount of Income restricted housing projects in Zip Code of 02129 : 36\n",
      "Amount of Income restricted housing projects in Zip Code of 02130 : 79\n",
      "Amount of Income restricted housing projects in Zip Code of 02131 : 27\n",
      "Amount of Income restricted housing projects in Zip Code of 02132 : 16\n",
      "Amount of Income restricted housing projects in Zip Code of 02134 : 32\n",
      "Amount of Income restricted housing projects in Zip Code of 02135 : 43\n",
      "Amount of Income restricted housing projects in Zip Code of 02136 : 35\n",
      "Amount of Income restricted housing projects in Zip Code of 02199 : 1\n",
      "Amount of Income restricted housing projects in Zip Code of 02210 : 6\n",
      "Amount of Income restricted housing projects in Zip Code of 02215 : 21\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfDict = {'zipCode':[],'totalUnits':[],'Projects':[]}\n",
    "d = {}\n",
    "for i in groupKeys:\n",
    "    groupDf = groupZips.get_group(i)\n",
    "    d[i] = len(groupDf)\n",
    "    #3 lines below help create the dict that will be used to create simple dataFrame\n",
    "    dfDict['zipCode'].append(i)\n",
    "    dfDict['Projects'].append(len(groupDf))\n",
    "    dfDict['totalUnits'].append(groupDf['TtlProjUnits'].sum())\n",
    "    print(\"Amount of Income restricted housing projects in Zip Code of \" + i + \" : \" + str(len(groupDf)))\n",
    "\n",
    "\n",
    "types = list(d.keys())\n",
    "values = list(d.values())\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(\"Zip Code\")\n",
    "plt.ylabel(\"Number of ICHP\")\n",
    "plt.title(\"Number of Income restricted housing projects per Zip Code in Boston\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Amount of Income restricted housing UNITS in Zip Code of 02108 : 96\n",
      "Amount of Income restricted housing UNITS in Zip Code of 02109 : 540\n",
      "Amount of Income restricted housing UNITS in Zip Code of 02110 : 609\n",
      "Amount of Income restricted housing UNITS in Zip Code of 02111 : 2570\n",
      "Amount of Income restricted housing UNITS in Zip Code of 02113 : 256\n",
      "Amount of Income restricted housing UNITS in Zip Code of 02114 : 4582\n",
      "Amount of Income restricted housing UNITS in Zip Code of 02115 : 3517\n",
      "Amount of Income restricted housing UNITS in Zip Code of 02116 : 3729\n",
      "Amount of Income restricted housing UNITS in Zip Code of 02118 : 9389\n",
      "Amount of Income restricted housing UNITS in Zip Code of 02119 : 7071\n",
      "Amount of Income restricted housing UNITS in Zip Code of 02120 : 2854\n",
      "Amount of Income restricted housing UNITS in Zip Code of 02121 : 4715\n",
      "Amount of Income restricted housing UNITS in Zip Code of 02122 : 717\n",
      "Amount of Income restricted housing UNITS in Zip Code of 02124 : 4377\n",
      "Amount of Income restricted housing UNITS in Zip Code of 02125 : 3307\n",
      "Amount of Income restricted housing UNITS in Zip Code of 02126 : 1620\n",
      "Amount of Income restricted housing UNITS in Zip Code of 02127 : 7627\n",
      "Amount of Income restricted housing UNITS in Zip Code of 02128 : 3857\n",
      "Amount of Income restricted housing UNITS in Zip Code of 02129 : 3737\n",
      "Amount of Income restricted housing UNITS in Zip Code of 02130 : 4345\n",
      "Amount of Income restricted housing UNITS in Zip Code of 02131 : 1584\n",
      "Amount of Income restricted housing UNITS in Zip Code of 02132 : 877\n",
      "Amount of Income restricted housing UNITS in Zip Code of 02134 : 2160\n",
      "Amount of Income restricted housing UNITS in Zip Code of 02135 : 4978\n",
      "Amount of Income restricted housing UNITS in Zip Code of 02136 : 2276\n",
      "Amount of Income restricted housing UNITS in Zip Code of 02199 : 60\n",
      "Amount of Income restricted housing UNITS in Zip Code of 02210 : 1737\n",
      "Amount of Income restricted housing UNITS in Zip Code of 02215 : 2878\n"
     ]
    },
    {
     "data": {
      "image/png": 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Mqlixoowx6tSpk3N49uzZVbx4cf32228Jfr5du3bKli2b833z5s0VGhqqpUuXSpJ27typQ4cOqXXr1vrnn3905swZnTlzRpcuXVLt2rW1YcOGBKdgde3aNVl9X7p0qR5++GGXUw2zZs2qLl266Pfff9fevXuTtxCSqWPHji5/VY4/EhO/XH7++WcdPXpUvXv3TnB9WPypiydOnNDOnTvVoUMH5cyZ0zn+gQce0OOPP+5cbje728/os88+U8mSJVWiRAnncj9z5owee+wxSdLatWvvOM8+Pj7q2LGjy7DkTjd+GXzzzTd3feF5YvmJ+eKLL5Q7d2699NJLCcbd6bTRs2fPas2aNWrRooXzr9BnzpzRP//8o7p16+rQoUPOu4EuXbpUlStXdjkamydPHrVp0yZF83XrOv7II4+4fHZLly5VpkyZ1LNnT5d2/fr1kzFGy5YtS1GedOPz2LJli44fP57in23ZsqXLKWi3rvvHjx/Xrl271K5dO5db29esWVNlypRJcV5K3GlZpkRq19kqVao4bwohSffdd58aNWqkFStWKDY2VsYYffHFF2rYsKGMMS7bT926dXXhwgWX0zolqX379ik6cipJZ86cUevWrZUrVy4tXLgw0Wu8ktK4cWOXo5EPP/ywKlWqlOi+6WbR0dEu3wW389133+nq1avq3bu3S9+ef/55BQYGOk8F/+mnn3Tq1Cl17drVZd/boUMHBQUFuUzTHfu7xNxp3b+TYcOGadWqVVq1apU++eQTPfPMMxoyZIjLqeXxy7Zv374uP9uvXz9Jci6P+PVz8eLFunbtWorm426+exLbtv75558UncqKjItiC/ekV199VdevX7/jtVspcd9997m8DwoKkq+vr/PUmpuHnzt3LsHPFy1a1OW9w+FQkSJFnNc/HDp0SNKNXxjy5Mnj8pozZ45iYmJ04cIFl2lEREQkq+9//PGHihcvnmB4yZIlnePd6dZlFf8FHL9cjhw5Ikm3vR1/fJ+S6nd8IXq73OR+RocOHdKePXsSLPdixYpJunFKyp3ky5cvwWlLyZ1uy5YtVa1aNXXu3FnBwcFq1aqVPv300xT9EptYfmKOHDmi4sWLJ7gOIjkOHz4sY4yGDh2aYJ6GDx/uMk9//PFHgnVeSvzzTIqvr6/zuop4OXLkcPns/vjjD4WFhSX45TU16/a4ceO0e/duFShQQA8//LBGjBiR7F8Y77Tux/enSJEiCX42sWF369bCOTnLMiVSu84mtm4UK1ZMly9f1unTp3X69GmdP39es2bNSrCuxf9R4dbtMrn7w3jGGLVr104nTpzQ+++/r5CQkBT9fFLzcKdnyAUGBurixYvJykhqP5glSxYVKlTIOT7+31v7lDlzZhUqVMhlmDv2d4m507p/J2XKlFFkZKQiIyPVokULLVy4UA0aNNCgQYN0+vRpSTfm08vLK8G2EhISouzZszuXQ82aNdWsWTONHDlSuXPnVqNGjTRv3rxkXZPmju+elM47Mjau2cI9qVChQmrbtq1mzZqlQYMGJRif1F/wb71Y/GaZMmVK1jBJt71+Kinxv6SMHz8+yVt73/qQ15T+Fdcu7lwuqc1NTl/i4uJUpkwZTZw4MdG2N1/vkJTEPovkTtfPz08bNmzQ2rVrtWTJEi1fvlyffPKJHnvsMa1cuTLJebhTvrvFr6P9+/dX3bp1E23jzoIhOfOdXCnZ5lu0aKFHHnlEX331lVauXKnx48frzTff1Jdffqknn3zytjl2rPu+vr7677//Eh0Xf1H+rXcVdOeylNyzzt5O/LrWtm1btW/fPtE28dfX3NynlHjrrbe0bNkyDRgwIMn12QolSpTQzp07dfXq1TS5W6g79neJsWLdr127thYvXqytW7eqfv36zuF3OgrvcDj0+eef68cff9S3336rFStW6LnnntOECRP0448/uv2B6Wn1nYf0gWIL96xXX31VCxcuTPQC1fi/Ot16Nyd3H+G5WfyRq3jGGB0+fNj5C0PhwoUl3firZ1LPgLlb4eHhOnDgQILh+/fvd463U/y8xt+QITHxfUqq37lz53bLre7j+/PLL7+odu3aKb4Do7um6+Xlpdq1a6t27dqaOHGiXn/9dQ0ZMkRr165VZGSk2/pVuHBhbdmyRdeuXUvyovWksuL/Qp45c+Y7rqPh4eEJ1nkp8c8zNcLDw/Xdd9/p4sWLLke3bl23U7rNh4aG6sUXX9SLL76oU6dOqXz58hozZswdi63k9FdSonfnTGxYUtNI6tTf+OXrrm36duvdndbZ20ls3Th48KD8/f2dR+CyZcum2NhYt+8PpRt3Qh0yZIgqVaqkMWPG3NU0kpqHO91RsmHDhtq8ebO++OILPfPMM7dte/N+8OYjVFevXtXRo0edyya+3aFDh5ynA0rStWvXdPToUZUtW9Y5zKr9nRWuX78uSfr3338l3ZjPuLg4HTp0yHn0WrpxM5Xz588nWO8rV66sypUra8yYMVq0aJHatGmjjz/+WJ07d05y3u387oFn4DRC3LMKFy6stm3b6t1331VUVJTLuMDAQOXOnTvBrYanT59uWX/i71wV7/PPP9eJEyecv7xVqFBBhQsX1ltvveX8YrlZ/GkUd6NevXraunWrNm/e7Bx26dIlzZo1SwULFlSpUqXuetp3o3z58oqIiNDkyZMT/PIb/5fA0NBQlStXTgsWLHBps3v3bq1cuVL16tVzW39atGihv//+O9Fn2/z3338JThlx93TPnj2bYHz80c34017iv9wTu91zSjRr1kxnzpzRO++8k2Bc/LKPfz7YrVl58+ZVrVq19O677yZ6PeTN62i9evX0448/auvWrS7jP/zww1T1/1b16tVTbGxsgvmZNGmSHA6Hc/tK7jYfGxub4HTdvHnzKiwsLFmnIN1JWFiYSpcurffff99lO1+/fr127dqVrGnUq1dPf/31V4K7xsXExDjvgnanuyYmV1LrXXLW2dvZvHmzyzVXx44d0zfffKM6deooU6ZMypQpk5o1a6Yvvvgi0dupp2Z/eP78ebVq1Ur+/v766KOP7vq5UV9//bXzGkVJ2rp1q7Zs2XLHgrxr164KDQ1Vv379dPDgwQTjT506pddee02SFBkZqSxZsmjKlCkuR0nmzp2rCxcuOI/2VKxYUXny5NHMmTN19epVZ7v58+cn+Oys2t9ZYfHixZLkLBbj9/uTJ092aRd/lC5+eZw7dy7BUaVb18+k9nN2fvfAM3BkC/e0IUOG6IMPPtCBAwd0//33u4zr3Lmzxo4dq86dO6tixYrasGFDol987pIzZ05Vr15dHTt21MmTJzV58mQVKVJEzz//vKQbfyWeM2eOnnzySd1///3q2LGj8uXLp7///ltr165VYGCgvv3227vKHjRokD766CM9+eST6tmzp3LmzKkFCxbo6NGj+uKLL1J0Ubg7eHl5acaMGWrYsKHKlSunjh07KjQ0VPv379eePXu0YsUKSTdOqXzyySdVpUoVderUyXn73aCgIJfnoaTWs88+q08//VRdu3bV2rVrVa1aNcXGxmr//v369NNPtWLFClWsWNGy6Y4aNUobNmxQ/fr1FR4erlOnTmn69OnKnz+/86YmhQsXVvbs2TVz5kxly5ZNAQEBqlSpUoqvU2nXrp3ef/999e3bV1u3btUjjzyiS5cu6bvvvtOLL76oRo0ayc/PT6VKldInn3yiYsWKKWfOnCpdurRKly6tadOmqXr16ipTpoyef/55FSpUSCdPntTmzZv1119/6ZdffpEkDRw4UB988IGeeOIJ9erVy3nr9/DwcP36668pXpZJadiwoR599FENGTJEv//+u8qWLauVK1fqm2++Ue/evZ1HUaXkbfMXL15U/vz51bx5c5UtW1ZZs2bVd999p23btiX5DL2Uev3119WoUSNVq1ZNHTt21Llz5/TOO++odOnSif6h5VZdunTRe++9p6efflrPPfecHnzwQf3zzz/65JNPtHv3br3//vtuOz0t/iYWQ4YMUatWrZQ5c2Y1bNgwWevs7ZQuXVp169Z1ufW7dOOZR/HGjh2rtWvXqlKlSnr++edVqlQpnT17Vjt27NB3332XaMGXHF27dtXvv/+uli1bauPGjUnerr5t27a3nU6RIkVUvXp1devWTTExMZo8ebJy5cqlgQMH3vbncuTIoa+++kr16tVTuXLl1LZtW+dy3rFjhz766CPnQ5fz5MmjwYMHa+TIkXriiSf01FNP6cCBA5o+fboeeughZx8zZ86s1157TS+88IIee+wxtWzZUkePHtW8efMSXLNl1f4utb7//ntduXJF0o1i/n//+5/Wr1+vVq1aOZ9HVrZsWbVv316zZs3S+fPnVbNmTW3dulULFixQ48aN9eijj0qSFixYoOnTp6tJkyYqXLiwLl68qNmzZyswMNBZLN1uP2fXdw88hN23PwTSws23fr9V/G2Pb771uzE3bqneqVMnExQUZLJly2ZatGhhTp06leSt32+9xXH79u1NQEBAgrxbbzMff1vbjz76yAwePNjkzZvX+Pn5mfr165s//vgjwc///PPPpmnTpiZXrlzGx8fHhIeHmxYtWpjVq1ffsU+3c+TIEdO8eXOTPXt24+vrax5++GGzePHiBO3khlu/f/bZZy7tkrrt9g8//GAef/xxky1bNhMQEGAeeOABM3XqVJc23333nalWrZrx8/MzgYGBpmHDhmbv3r0ubVL7GRlz49bJb775prn//vuNj4+PyZEjh6lQoYIZOXKkuXDhwm2XQ2LTS8l0V69ebRo1amTCwsJMlixZTFhYmHnmmWfMwYMHXab1zTffmFKlShlvb2+X5Xm7/Ftv/W7MjXV/yJAhJiIiwmTOnNmEhISY5s2bmyNHjjjbbNq0yVSoUMFkyZIlwTZx5MgR065dOxMSEmIyZ85s8uXLZxo0aGA+//xzl5xff/3V1KxZ0/j6+pp8+fKZ0aNHm7lz5yb71u+JfXbxn/XNLl68aPr06WPCwsJM5syZTdGiRc348eNdHiMQP9932uZjYmLMgAEDTNmyZZ3rZdmyZc306dMT9C+x21+PHz8+QZ9vXX7GGPPxxx+bEiVKGB8fH1O6dGnzv//9zzRr1syUKFHitssl3rlz50yfPn2cn2FgYKB59NFHzbJlyxK0TcmyTKyvo0ePNvny5TNeXl7Ozy6562xi4vcbCxcuNEWLFjU+Pj7mwQcfNGvXrk3Q9uTJk6Z79+6mQIECznW1du3aZtasWc42Se13khIeHp7orcZvfcW73Wc9YcIEU6BAAePj42MeeeQR88svvySrD8YYc/z4cdOnTx9TrFgx4+vra/z9/U2FChXMmDFjEuxz3nnnHVOiRAmTOXNmExwcbLp165bgsRnGGDN9+nQTERFhfHx8TMWKFc2GDRsS3QekZn+X2nX/Vond+j1LliymRIkSZsyYMebq1asu7a9du2ZGjhzpXPcLFChgBg8e7PI4ih07dphnnnnG3HfffcbHx8fkzZvXNGjQwOVxA8bcfj+Xmu+e+N9J7rSfg2dwGMPVeQAApHflypVTnjx5tGrVqrTuiqUcDoe6d++e6KmsGcHvv/+uiIgIjR8/Xv3790/r7gBIY1yzBQBAOnLt2jXnhf/x1q1bp19++UW1atVKm04BAO4K12wBAJCO/P3334qMjFTbtm0VFham/fv3a+bMmQoJCUn2g8oBAOkDxRYAAOlIjhw5VKFCBc2ZM0enT59WQECA6tevr7FjxypXrlxp3T0AQApwzRYAAAAAWIBrtgAAAADAAhRbAAAAAGABrtlKhri4OB0/flzZsmWTw+FI6+4AAAAASCPGGF28eFFhYWHy8rr9sSuKrWQ4fvy4ChQokNbdAAAAAJBOHDt2TPnz579tG4qtZMiWLZukGws0MDAwjXsDAAAAIK1ER0erQIECzhrhdii2kiH+1MHAwECKLQAAAADJuryIG2QAAAAAgAUotgAAAADAAhRbAAAAAGABii0AAAAAsADFFgAAAABYgGILAAAAACxAsQUAAAAAFqDYAgAAAAALUGwBAAAAgAUotgAAAADAAhRbAAAAAGABii0AAAAAsADFFgAAAABYgGILAAAAACxAsQUAAAAAFvBO6w4AgFVGOhxun+ZwY9w+TQAA4Jk4sgUAAAAAFqDYAgAAAAALUGwBAAAAgAUotgAAAADAAhRbAAAAAGABii0AAAAAsADFFgAAAABYgGILAAAAACxAsQUAAAAAFqDYAgAAAAALUGwBAAAAgAUotgAAAADAAhRbAAAAAGABii0AAAAAsADFFgAAAABYgGILAAAAACxAsQUAAAAAFqDYAgAAAAALUGwBAAAAgAUotgAAAADAAhRbAAAAAGABii0AAAAAsADFFgAAAABYgGILAAAAACxAsQUAAAAAFqDYAgAAAAALUGwBAAAAgAUotgAAAADAAhRbAAAAAGABii0AAAAAsADFFgAAAABYgGILAAAAACxAsQUAAAAAFqDYAgAAAAALUGwBAAAAgAUotgAAAADAAhRbAAAAAGABii0AAAAAsADFFgAAAABYgGILAAAAACxAsQUAAAAAFqDYAgAAAAALUGwBAAAAgAUotgAAAADAAhRbAAAAAGABii0AAAAAsADFFgAAAABYgGILAAAAACxAsQUAAAAAFkjTYis2NlZDhw5VRESE/Pz8VLhwYY0ePVrGGGcbY4yGDRum0NBQ+fn5KTIyUocOHXKZztmzZ9WmTRsFBgYqe/bs6tSpk/7991+XNr/++qseeeQR+fr6qkCBAho3bpwt8wgAAADg3pSmxdabb76pGTNm6J133tG+ffv05ptvaty4cZo6daqzzbhx4zRlyhTNnDlTW7ZsUUBAgOrWrasrV64427Rp00Z79uzRqlWrtHjxYm3YsEFdunRxjo+OjladOnUUHh6u7du3a/z48RoxYoRmzZpl6/wCAAAAuHc4zM2HkWzWoEEDBQcHa+7cuc5hzZo1k5+fnxYuXChjjMLCwtSvXz/1799fknThwgUFBwdr/vz5atWqlfbt26dSpUpp27ZtqlixoiRp+fLlqlevnv766y+FhYVpxowZGjJkiKKiopQlSxZJ0qBBg/T1119r//79d+xndHS0goKCdOHCBQUGBlqwJABYYaTD4fZpDk+7XSYAAEgHUlIbpOmRrapVq2r16tU6ePCgJOmXX37RDz/8oCeffFKSdPToUUVFRSkyMtL5M0FBQapUqZI2b94sSdq8ebOyZ8/uLLQkKTIyUl5eXtqyZYuzTY0aNZyFliTVrVtXBw4c0Llz5xL0KyYmRtHR0S4vAAAAAEgJ77QMHzRokKKjo1WiRAllypRJsbGxGjNmjNq0aSNJioqKkiQFBwe7/FxwcLBzXFRUlPLmzesy3tvbWzlz5nRpExERkWAa8eNy5MjhMu6NN97QyJEj3TSXAAAAAO5FaXpk69NPP9WHH36oRYsWaceOHVqwYIHeeustLViwIC27pcGDB+vChQvO17Fjx9K0PwAAAAAynjQ9sjVgwAANGjRIrVq1kiSVKVNGf/zxh9544w21b99eISEhkqSTJ08qNDTU+XMnT55UuXLlJEkhISE6deqUy3SvX7+us2fPOn8+JCREJ0+edGkT/z6+zc18fHzk4+PjnpkEAAAAcE9K0yNbly9flpeXaxcyZcqkuLg4SVJERIRCQkK0evVq5/jo6Ght2bJFVapUkSRVqVJF58+f1/bt251t1qxZo7i4OFWqVMnZZsOGDbp27ZqzzapVq1S8ePEEpxACAAAAgDukabHVsGFDjRkzRkuWLNHvv/+ur776ShMnTlSTJk0kSQ6HQ71799Zrr72m//3vf9q1a5fatWunsLAwNW7cWJJUsmRJPfHEE3r++ee1detWbdy4UT169FCrVq0UFhYmSWrdurWyZMmiTp06ac+ePfrkk0/09ttvq2/fvmk16wAAAAA8XJqeRjh16lQNHTpUL774ok6dOqWwsDC98MILGjZsmLPNwIEDdenSJXXp0kXnz59X9erVtXz5cvn6+jrbfPjhh+rRo4dq164tLy8vNWvWTFOmTHGODwoK0sqVK9W9e3dVqFBBuXPn1rBhw1yexQUAAAAA7pSmz9nKKHjOFpAx8ZwtAADgbhnmOVsAAAAA4KkotgAAAADAAhRbAAAAAGABii0AAAAAsADFFgAAAABYgGILAAAAACxAsQUAAAAAFqDYAgAAAAALeKd1BwAAQNpy9wPAefg3ANzAkS0AAAAAsADFFgAAAABYgGILAAAAACxAsQUAAAAAFqDYAgAAAAALUGwBAAAAgAUotgAAAADAAhRbAAAAAGABii0AAAAAsADFFgAAAABYgGILAAAAACxAsQUAAAAAFqDYAgAAAAALUGwBAAAAgAUotgAAAADAAhRbAAAAAGABii0AAAAAsADFFgAAAABYgGILAAAAACxAsQUAAAAAFvBO6w4AQEY30uFw6/SGG+PW6QEAgLTBkS0AAAAAsADFFgAAAABYgGILAAAAACxAsQUAAAAAFqDYAgAAAAALUGwBAAAAgAUotgAAAADAAhRbAAAAAGABii0AAAAAsADFFgAAAABYgGILAAAAACxAsQUAAAAAFqDYAgAAAAALUGwBAAAAgAUotgAAAADAAhRbAAAAAGABii0AAAAAsADFFgAAAABYgGILAAAAACxAsQUAAAAAFqDYAgAAAAALUGwBAAAAgAUotgAAAADAAhRbAAAAAGABii0AAAAAsADFFgAAAABYgGILAAAAACxAsQUAAAAAFqDYAgAAAAALeCe3YXR0dKLDAwIClClTJrd1CAAAAAA8QbKPbGXPnl05cuRI8PLz81Px4sU1e/ZsK/sJAAAAABlKso9srV27NtHh58+f1/bt2zVgwAB5e3urY8eObuscAAAAAGRUyS62atasmeS4Ro0aqWDBgpo6dSrFFgAAAADIjTfIqFmzpg4fPuyuyQEAAABAhua2YuvChQsKCgpy1+QAAAAAIENzS7F17do1jR8/XpUqVXLH5AAAAAAgw0v2NVtNmzZNdPiFCxe0Z88eORwOff/9927rGAAAAABkZMkutpI6RbBAgQJq1qyZ2rRpw2mEAAAAAPB/kl1szZs3z5IO/P3333r55Ze1bNkyXb58WUWKFNG8efNUsWJFSZIxRsOHD9fs2bN1/vx5VatWTTNmzFDRokWd0zh79qxeeuklffvtt/Ly8lKzZs309ttvK2vWrM42v/76q7p3765t27YpT548eumllzRw4EBL5gkAAAAA3HaDjLtx7tw5VatWTZkzZ9ayZcu0d+9eTZgwQTly5HC2GTdunKZMmaKZM2dqy5YtCggIUN26dXXlyhVnmzZt2mjPnj1atWqVFi9erA0bNqhLly7O8dHR0apTp47Cw8O1fft2jR8/XiNGjNCsWbNsnV8AAAAA945kH9l68MEH5XA47thux44dyQ5/8803VaBAAZejZhEREc7/G2M0efJkvfrqq2rUqJEk6f3331dwcLC+/vprtWrVSvv27dPy5cu1bds259GwqVOnql69enrrrbcUFhamDz/8UFevXtV7772nLFmy6P7779fOnTs1ceJEl6IsXkxMjGJiYpzvo6Ojkz1PAAAAACCl4MhW48aN1ahRIzVq1EhPPfWU9uzZo0ceecQ5LP6VEv/73/9UsWJFPf3008qbN68efPBBzZ492zn+6NGjioqKUmRkpHNYUFCQKlWqpM2bN0uSNm/erOzZszsLLUmKjIyUl5eXtmzZ4mxTo0YNZcmSxdmmbt26OnDggM6dO5egX2+88YaCgoKcrwIFCqRovgAAAAAg2Ue2hg8f7vJ+woQJ6tWrlwoVKnTX4b/99ptmzJihvn376pVXXtG2bdvUs2dPZcmSRe3bt1dUVJQkKTg42OXngoODneOioqKUN29el/He3t7KmTOnS5ubj5jdPM2oqCiX0xYlafDgwerbt6/zfXR0NAUXAAAAgBRJdrFlhbi4OFWsWFGvv/66pBunKu7evVszZ85U+/bt06xfPj4+8vHxSbN8AAAAABlfmt4gIzQ0VKVKlXIZVrJkSf3555+SpJCQEEnSyZMnXdqcPHnSOS4kJESnTp1yGX/9+nWdPXvWpU1i07g5AwAAAADcKU2LrWrVqunAgQMuww4ePKjw8HBJN26WERISotWrVzvHR0dHa8uWLapSpYokqUqVKjp//ry2b9/ubLNmzRrFxcWpUqVKzjYbNmzQtWvXnG1WrVql4sWLJziFEAAAAADcIdmnEU6ZMsXl/fXr1zV//nzlzp3bZXjPnj2THd6nTx9VrVpVr7/+ulq0aKGtW7dq1qxZzluyOxwO9e7dW6+99pqKFi2qiIgIDR06VGFhYWrcuLGkG0fCnnjiCT3//POaOXOmrl27ph49eqhVq1YKCwuTJLVu3VojR45Up06d9PLLL2v37t16++23NWnSpGT3FQAAAABSwmGMMclpeOsNJhKdmMOh3377LUUdWLx4sQYPHqxDhw4pIiJCffv21fPPP+8cH/9Q41mzZun8+fOqXr26pk+frmLFijnbnD17Vj169HB5qPGUKVOSfKhx7ty59dJLL+nll19OVh+jo6MVFBSkCxcuKDAwMEXzByDtjEzG4ypSangiu0x35ySWAViJdRgAki8ltUGyi617GcUWkDFRbAHJwzoMAMmXktogTa/ZAgAAAABPlexia82aNSpVqpSio6MTjLtw4YLuv/9+bdiwwa2dAwAAAICMKtnF1uTJk/X8888neqgsKChIL7zwAjecAAAAAID/k+xi65dfftETTzyR5Pg6deq43H4dAAAAAO5lyS62Tp48qcyZMyc53tvbW6dPn3ZLpwAAAAAgo0t2sZUvXz7t3r07yfG//vqrQkND3dIpAAAAAMjokl1s1atXT0OHDtWVK1cSjPvvv/80fPhwNWjQwK2dAwAAAICMyju5DV999VV9+eWXKlasmHr06KHixYtLkvbv369p06YpNjZWQ4YMsayjAAAAAJCRJLvYCg4O1qZNm9StWzcNHjxY8c9Cdjgcqlu3rqZNm6bg4GDLOgoAAAAAGUmyiy1JCg8P19KlS3Xu3DkdPnxYxhgVLVpUOXLksKp/AAAAAJAhpajYipcjRw499NBD7u4LAAAAAHiMZBdbTZs2TVa7L7/88q47AwAAAACeItnFVlBQkJX9AAAAAACPkuxia968eVb2AwAAAAA8SrKfswUAAAAASD6KLQAAAACwAMUWAAAAAFiAYgsAAAAALJCsYqt8+fI6d+6cJGnUqFG6fPmypZ0CAAAAgIwuWcXWvn37dOnSJUnSyJEj9e+//1raKQAAAADI6JJ16/dy5cqpY8eOql69uowxeuutt5Q1a9ZE2w4bNsytHQQAAACAjChZxdb8+fM1fPhwLV68WA6HQ8uWLZO3d8IfdTgcFFsAAAAAoGQWW8WLF9fHH38sSfLy8tLq1auVN29eSzsGAAAAABlZsoqtm8XFxVnRDwAAAADwKCkutiTpyJEjmjx5svbt2ydJKlWqlHr16qXChQu7tXMAAAAAkFGl+DlbK1asUKlSpbR161Y98MADeuCBB7Rlyxbdf//9WrVqlRV9BAAAAIAMJ8VHtgYNGqQ+ffpo7NixCYa//PLLevzxx93WOQAAAADIqFJ8ZGvfvn3q1KlTguHPPfec9u7d65ZOAQAAAEBGl+JiK0+ePNq5c2eC4Tt37uQOhQAAAADwf1J8GuHzzz+vLl266LffflPVqlUlSRs3btSbb76pvn37ur2DAAAAAJARpbjYGjp0qLJly6YJEyZo8ODBkqSwsDCNGDFCPXv2dHsHAQAAACAjSnGx5XA41KdPH/Xp00cXL16UJGXLls3tHQMAAACAjOyunrMVjyILAAAA6cFIh8Ot0xtujFunh3tTim+QAQAAAAC4M4otAAAAALAAxRYAAAAAWCBFxda1a9dUu3ZtHTp0yKr+AAAAAIBHSFGxlTlzZv36669W9QUAAAAAPEaKTyNs27at5s6da0VfAAAAAMBjpPjW79evX9d7772n7777ThUqVFBAQIDL+IkTJ7qtcwAAAACQUaW42Nq9e7fKly8vSTp48KDLOIebn28AAAAAABlViouttWvXWtEPAAAAAPAoKS624h0+fFhHjhxRjRo15OfnJ2MMR7YAIAMbacE+fLgxbp8mAAAZRYpvkPHPP/+odu3aKlasmOrVq6cTJ05Ikjp16qR+/fq5vYMAAAAAkBGluNjq06ePMmfOrD///FP+/v7O4S1bttTy5cvd2jkAAAAAyKhSfBrhypUrtWLFCuXPn99leNGiRfXHH3+4rWMAAAAAkJGl+MjWpUuXXI5oxTt79qx8fHzc0ikAAAAAyOhSXGw98sgjev/9953vHQ6H4uLiNG7cOD366KNu7RwAAAAAZFQpPo1w3Lhxql27tn766SddvXpVAwcO1J49e3T27Flt3LjRij4C+D/cLQ4AACDjSPGRrdKlS+vgwYOqXr26GjVqpEuXLqlp06b6+eefVbhwYSv6CAAAAAAZzl09ZysoKEhDhgxxd18AAAAAwGPcVbF17tw5zZ07V/v27ZMklSpVSh07dlTOnDnd2jkAAAAAyKhSfBrhhg0bVLBgQU2ZMkXnzp3TuXPnNGXKFEVERGjDhg1W9BEAAAAAMpwUH9nq3r27WrZsqRkzZihTpkySpNjYWL344ovq3r27du3a5fZOAgAAAEBGk+IjW4cPH1a/fv2chZYkZcqUSX379tXhw4fd2jkAAAAAyKhSXGyVL1/eea3Wzfbt26eyZcu6pVMAAAAAkNEl6zTCX3/91fn/nj17qlevXjp8+LAqV64sSfrxxx81bdo0jR071ppeAgCQAu5+Jh3PowMA3I1kFVvlypWTw+GQuenLZuDAgQnatW7dWi1btnRf7wAAANIZinkAyZWsYuvo0aNW9wMAAAAAPEqyiq3w8HCr+wEAAAAAHuWuHmp8/Phx/fDDDzp16pTi4uJcxvXs2dMtHQMAAACAjCzFxdb8+fP1wgsvKEuWLMqVK5ccN5237HA4KLYAAAAAQHdRbA0dOlTDhg3T4MGD5eWV4jvHAwAAAMA9IcXV0uXLl9WqVSsKLQAAAAC4jRRXTJ06ddJnn31mRV8AAAAAwGOk+DTCN954Qw0aNNDy5ctVpkwZZc6c2WX8xIkT3dY5AAAAAMio7qrYWrFihYoXLy5JCW6QAQAAAAC4i9MIJ0yYoPfee0/79u3TunXrtHbtWudrzZo1d92RsWPHyuFwqHfv3s5hV65cUffu3ZUrVy5lzZpVzZo108mTJ11+7s8//1T9+vXl7++vvHnzasCAAbp+/bpLm3Xr1ql8+fLy8fFRkSJFNH/+/LvuJwAAAAAkR4qLLR8fH1WrVs2tndi2bZveffddPfDAAy7D+/Tpo2+//VafffaZ1q9fr+PHj6tp06bO8bGxsapfv76uXr2qTZs2acGCBZo/f76GDRvmbHP06FHVr19fjz76qHbu3KnevXurc+fOWrFihVvnAQAAAABuluJiq1evXpo6darbOvDvv/+qTZs2mj17tnLkyOEcfuHCBc2dO1cTJ07UY489pgoVKmjevHnatGmTfvzxR0nSypUrtXfvXi1cuFDlypXTk08+qdGjR2vatGm6evWqJGnmzJmKiIjQhAkTVLJkSfXo0UPNmzfXpEmTkuxTTEyMoqOjXV4AAAAAkBIpLra2bt2qBQsWqFChQmrYsKGaNm3q8kqp7t27q379+oqMjHQZvn37dl27ds1leIkSJXTfffdp8+bNkqTNmzerTJkyCg4OdrapW7euoqOjtWfPHmebW6ddt25d5zQS88YbbygoKMj5KlCgQIrnCwAAAMC9LcU3yMiePftdFVWJ+fjjj7Vjxw5t27YtwbioqChlyZJF2bNndxkeHBysqKgoZ5ubC6348fHjbtcmOjpa//33n/z8/BJkDx48WH379nW+j46OpuACAAAAkCIpLrbmzZvnluBjx46pV69eWrVqlXx9fd0yTXfx8fGRj49PWncDAAAAQAaW4tMI3WX79u06deqUypcvL29vb3l7e2v9+vWaMmWKvL29FRwcrKtXr+r8+fMuP3fy5EmFhIRIkkJCQhLcnTD+/Z3aBAYGJnpUCwAAAADcIcVHtiIiIm77PK3ffvstWdOpXbu2du3a5TKsY8eOKlGihF5++WUVKFBAmTNn1urVq9WsWTNJ0oEDB/Tnn3+qSpUqkqQqVapozJgxOnXqlPLmzStJWrVqlQIDA1WqVClnm6VLl7rkrFq1yjkNAAAAALBCioutm5+DJUnXrl3Tzz//rOXLl2vAgAHJnk62bNlUunRpl2EBAQHKlSuXc3inTp3Ut29f5cyZU4GBgXrppZdUpUoVVa5cWZJUp04dlSpVSs8++6zGjRunqKgovfrqq+revbvzNMCuXbvqnXfe0cCBA/Xcc89pzZo1+vTTT7VkyZKUzjoAAAAAJFuKi61evXolOnzatGn66aefUt2hm02aNEleXl5q1qyZYmJiVLduXU2fPt05PlOmTFq8eLG6deumKlWqKCAgQO3bt9eoUaOcbSIiIrRkyRL16dNHb7/9tvLnz685c+aobt26bu0rAABI2sjbnBVzt4Yb4/ZpAoA7pbjYSsqTTz6pwYMHp+oGGuvWrXN57+vrq2nTpmnatGlJ/kx4eHiC0wRvVatWLf3888933S8AAAAASCm33SDj888/V86cOd01OQAAAADI0FJ8ZOvBBx90uUGGMUZRUVE6ffq0yyl+AAAAAHAvS3Gx1bhxY5f3Xl5eypMnj2rVqqUSJUq4q18AAAAAkKGluNgaPny4Ff0AcI9x98XyXCgPQGLfAiB9SbOHGgMAAACAJ0v2kS0vL6/bPsxYkhwOh65fv57qTgEAAABARpfsYuurr75KctzmzZs1ZcoUxcXFuaVTAAAAAJDRJbvYatSoUYJhBw4c0KBBg/Ttt9+qTZs2Lg8TBgAAAIB72V1ds3X8+HE9//zzKlOmjK5fv66dO3dqwYIFCg8Pd3f/AAAAACBDSlGxdeHCBb388ssqUqSI9uzZo9WrV+vbb79V6dKlreofAAAAAGRIyT6NcNy4cXrzzTcVEhKijz76KNHTCgEAAAAANyS72Bo0aJD8/PxUpEgRLViwQAsWLEi03Zdffum2zgFIGzynBgAAIPWSXWy1a9fujrd+BwAAAADckOxia/78+RZ2AwAAAAA8y13djRAAAAAAcHsUWwAAAABgAYotAAAAALAAxRYAAAAAWIBiCwAAAAAskOy7EQIAgP/P3c+jk3gmHQB4Go5sAQAAAIAFKLYAAAAAwAIUWwAAAABgAYotAAAAALAAxRYAAAAAWIBiCwAAAAAsQLEFAAAAABag2AIAAAAAC1BsAQAAAIAFKLYAAAAAwAIUWwAAAABgAYotAAAAALAAxRYAAAAAWIBiCwAAAAAsQLEFAAAAABag2AIAAAAAC3indQcAAAAAeJ6RDodbpzfcGLdOzw4UWwCQAfCFBQBAxsNphAAAAABgAYotAAAAALAAxRYAAAAAWIBiCwAAAAAsQLEFAAAAABag2AIAAAAAC1BsAQAAAIAFKLYAAAAAwAIUWwAAAABgAYotAAAAALCAd1p3ALDDSIfDrdMbboxbpwcAAADPw5EtAAAAALAAxRYAAAAAWIBiCwAAAAAsQLEFAAAAABag2AIAAAAAC1BsAQAAAIAFKLYAAAAAwAIUWwAAAABgAYotAAAAALAAxRYAAAAAWIBiCwAAAAAs4J3WHQAAAAAygpEOh1unN9wYt04P6Q9HtgAAAADAAhRbAAAAAGABii0AAAAAsADXbAEAAMAyXOeEexlHtgAAAADAAhRbAAAAAGCBNC223njjDT300EPKli2b8ubNq8aNG+vAgQMuba5cuaLu3bsrV65cypo1q5o1a6aTJ0+6tPnzzz9Vv359+fv7K2/evBowYICuX7/u0mbdunUqX768fHx8VKRIEc2fP9/q2QMAAABwD0vTYmv9+vXq3r27fvzxR61atUrXrl1TnTp1dOnSJWebPn366Ntvv9Vnn32m9evX6/jx42ratKlzfGxsrOrXr6+rV69q06ZNWrBggebPn69hw4Y52xw9elT169fXo48+qp07d6p3797q3LmzVqxYYev8AgAAALh3pOkNMpYvX+7yfv78+cqbN6+2b9+uGjVq6MKFC5o7d64WLVqkxx57TJI0b948lSxZUj/++KMqV66slStXau/evfruu+8UHByscuXKafTo0Xr55Zc1YsQIZcmSRTNnzlRERIQmTJggSSpZsqR++OEHTZo0SXXr1k3Qr5iYGMXExDjfR0dHW7gUAAAAAHiidHXN1oULFyRJOXPmlCRt375d165dU2RkpLNNiRIldN9992nz5s2SpM2bN6tMmTIKDg52tqlbt66io6O1Z88eZ5ubpxHfJn4at3rjjTcUFBTkfBUoUMB9MwkAAADgnpBuiq24uDj17t1b1apVU+nSpSVJUVFRypIli7Jnz+7SNjg4WFFRUc42Nxda8ePjx92uTXR0tP77778EfRk8eLAuXLjgfB07dswt8wgAAADg3pFunrPVvXt37d69Wz/88ENad0U+Pj7y8fFJ624AAAAAyMDSxZGtHj16aPHixVq7dq3y58/vHB4SEqKrV6/q/PnzLu1PnjypkJAQZ5tb704Y//5ObQIDA+Xn5+fu2QEAAACAtC22jDHq0aOHvvrqK61Zs0YREREu4ytUqKDMmTNr9erVzmEHDhzQn3/+qSpVqkiSqlSpol27dunUqVPONqtWrVJgYKBKlSrlbHPzNOLbxE8DAAAAANwtTU8j7N69uxYtWqRvvvlG2bJlc15jFRQUJD8/PwUFBalTp07q27evcubMqcDAQL300kuqUqWKKleuLEmqU6eOSpUqpWeffVbjxo1TVFSUXn31VXXv3t15KmDXrl31zjvvaODAgXruuee0Zs0affrpp1qyZEmazTsAAAAAz5amR7ZmzJihCxcuqFatWgoNDXW+PvnkE2ebSZMmqUGDBmrWrJlq1KihkJAQffnll87xmTJl0uLFi5UpUyZVqVJFbdu2Vbt27TRq1Chnm4iICC1ZskSrVq1S2bJlNWHCBM2ZMyfR274DAAAAgDuk6ZEtY8wd2/j6+mratGmaNm1akm3Cw8O1dOnS206nVq1a+vnnn1PcRwAAAAC4G+niBhkAAAAA4GkotgAAAADAAhRbAAAAAGCBdPNQYwAAAAD2GOlwuHV6w5NxL4Z7EUe2AAAAAMACFFsAAAAAYAGKLQAAAACwANdsAQCQjnFdBQBkXBzZAgAAAAALcGQLAAAgnXH3EU2Jo5pAWuDIFgAAAABYgGILAAAAACxAsQUAAAAAFqDYAgAAAAALUGwBAAAAgAUotgAAAADAAhRbAAAAAGABii0AAAAAsADFFgAAAABYgGILAAAAACxAsQUAAAAAFqDYAgAAAAALUGwBAAAAgAUotgAAAADAAhRbAAAAAGABii0AAAAAsADFFgAAAABYgGILAAAAACxAsQUAAAAAFqDYAgAAAAALUGwBAAAAgAUotgAAAADAAhRbAAAAAGABii0AAAAAsADFFgAAAABYgGILAAAAACzgndYdAAAAQNoY6XC4dXrDjXHr9ICMjiNbAAAAAGABii0AAAAAsADFFgAAAABYgGILAAAAACxAsQUAAAAAFqDYAgAAAAALcOt3AICtuNU0AOBewZEtAAAAALAAxRYAAAAAWIBiCwAAAAAswDVbSFPuvnZD4voNAAAApA8c2QIAAAAAC1BsAQAAAIAFOI0QSeL2zAAAAMDd48gWAAAAAFiAYgsAAAAALECxBQAAAAAWoNgCAAAAAAtQbAEAAACABSi2AAAAAMACFFsAAAAAYAGKLQAAAACwAMUWAAAAAFiAYgsAAAAALOCd1h0APMVIh8Ot0xtujFunBwAAAHtxZAsAAAAALECxBQAAAAAWoNgCAAAAAAtQbAEAAACABSi2AAAAAMAC99TdCKdNm6bx48crKipKZcuW1dSpU/Xwww+ndbfuCne+AwAAANK3e6bY+uSTT9S3b1/NnDlTlSpV0uTJk1W3bl0dOHBAefPmTevuAQAAAPxB3cPcM6cRTpw4Uc8//7w6duyoUqVKaebMmfL399d7772X1l0DAAAA4IHuiSNbV69e1fbt2zV48GDnMC8vL0VGRmrz5s0J2sfExCgmJsb5/sKFC5Kk6Oho6zubTFfcPL3E5i0jZtiV4ykZduV4SoZdOZ6SYVeOp2TYleMpGXbleEqGXTlplWFXjqdk2JVj17zYLb4fJhlHDR0mOa0yuOPHjytfvnzatGmTqlSp4hw+cOBArV+/Xlu2bHFpP2LECI0cOdLubgIAAADIII4dO6b8+fPfts09cWQrpQYPHqy+ffs638fFxens2bPKlSuXHG4+j9ZK0dHRKlCggI4dO6bAwEAy0kGOp2TYlcO8pL8Mu3I8JcOuHE/JsCvHUzLsyvGUDLtyPCXDrhy75sWdjDG6ePGiwsLC7tj2nii2cufOrUyZMunkyZMuw0+ePKmQkJAE7X18fOTj4+MyLHv27FZ20VKBgYGWr7yekmFXjqdk2JXDvKS/DLtyPCXDrhxPybArx1My7MrxlAy7cjwlw64cu+bFXYKCgpLV7p64QUaWLFlUoUIFrV692jksLi5Oq1evdjmtEAAAAADc5Z44siVJffv2Vfv27VWxYkU9/PDDmjx5si5duqSOHTumddcAAAAAeKB7pthq2bKlTp8+rWHDhikqKkrlypXT8uXLFRwcnNZds4yPj4+GDx+e4JRIMtIux1My7MphXtJfhl05npJhV46nZNiV4ykZduV4SoZdOZ6SYVeOXfOSVu6JuxECAAAAgN3uiWu2AAAAAMBuFFsAAAAAYAGKLQAAAACwAMUWAAAAAFiAYgsAAAAALECxdQ/IyDecjImJUUxMTFp3I8NZt26d/vvvv7TuRqrFxMToyJEjlq8DJ0+eVFRUlCXTjo2N1cmTJ3X69GlLpo/UOXr0qK5fv57W3XALT5kPKWN/b+HusT3eGd8pGQ/FloeIiYlR//79VaNGDb355puSpNdee01Zs2ZVtmzZ1Lp1a0VHR6c655dfflG7du1UqFAh+fn5KSAgQGXKlNHQoUPdMn1JWrVqlerVq6ccOXLI399f/v7+ypEjh+rVq6fvvvvOLRl3sm/fPhUqVCjV05kzZ47at2+vefPmSZI++eQTlSxZUoUKFdLw4cNTPf2k1KlTR7///rvbpnfq1CmX9zt37lT79u1VrVo1NW/eXOvWrUt1xvz587V582ZJ0pUrV9SpUycFBASoWLFiypo1q7p27Zrqouvs2bNq3ry57rvvPnXr1k2xsbHq3LmzQkNDlS9fPlWtWlUnTpxI9bxI0pIlS1SjRg0FBAQoLCxMISEhyp49u5599ln9+eefbsnYu3evXnzxRT344IMKDQ1VaGioHnzwQb344ovau3evWzJu58iRI3rsscfcMq0TJ05o4cKFWrp0qa5eveoy7tKlSxo1apRbcm5VvHhxHTp0yJJpHz9+XMOHD1ebNm3Uv39/7d+/3y3TXb58uXbt2iVJiouL0+jRo5UvXz75+Pgof/78Gjt2bKqLlYYNG+qDDz6w9I82dn1vSfZ8d6X19ii5b5v0xO1RsmabtGN7lOz5TpHsX4+t2k+mKwYeoU+fPiYsLMz069fPlCxZ0rz44ovmvvvuMwsXLjSLFi0yRYoUMS+99FKqMpYvX278/PxMs2bNTNu2bY2/v7/p0aOHefnll02RIkVM4cKFzYkTJ1KVMX/+fOPt7W1atWpl5s2bZ5YuXWqWLl1q5s2bZ5555hmTOXNm8/7776cqIzl27txpvLy8UjWNSZMmmYCAANO0aVMTGhpqXnvtNZMrVy7z2muvmZEjR5rAwEDz7rvvpirjwQcfTPTlcDhMyZIlne9Ty8vLy5w8edIYY8zGjRtN5syZTc2aNc2AAQPM448/bry9vc369etTlREREWF+/PFHY4wx/fv3NwULFjRffvml2bdvn/n6669NsWLFzIABA1KV8dxzz5nSpUubqVOnmpo1a5pGjRqZBx54wPzwww9m06ZN5qGHHjLt2rVLVYYxxrz//vsmW7Zspl+/fmbIkCEmJCTEDBo0yMyYMcPUrFnT5M6d2xw8eDBVGUuXLjVZsmQxlStXNsOHDzfTp08306dPN8OHDzdVq1Y1Pj4+Zvny5amel9txx3ZijDFbt2412bNnN4GBgcbPz88UKVLE7N692zk+Kioq1TlNmjRJ9OXl5WUiIyOd71PDz8/PnDp1yhhjzJ49e0xQUJApUqSIefrpp02JEiWMv7+/+eWXX1KVYYwxxYsXNxs2bDDGGPP666+bXLlymYkTJ5ply5aZyZMnm+DgYDN27NhUZTgcDuPt7W2CgoJM165dzU8//ZTqft/Kju8tY+z57koP26Mx7tkmPWV7NMaebdKO7dGO7xRj7FmP7dpPpicUWx6iQIECZtWqVcYYY44cOWK8vLzM119/7Ry/cuVKEx4enqqMcuXKmRkzZrhMs0SJEsYYY65evWpq165tOnTokKqMokWLmnfeeSfJ8dOmTTNFihRJVYYxN77kb/dq27Ztqr9MSpQoYT788ENjjDE7duww3t7eZs6cOc7xc+bMMRUqVEhVhre3t3niiSfMiBEjnK/hw4cbLy8v8+KLLzqHpZbD4XAWW48//rh57rnnXMb36tXLPPbYY6nK8PHxMX/88YcxxphixYqZZcuWuYxfv369ue+++1KVERoaajZu3GiMufELg8PhMCtXrnSO/+GHH0y+fPlSlWHMjc/+448/dr7ftm2byZ8/v4mLizPGGNOyZctU/yLxwAMPmKFDhyY5fvjw4aZMmTKpynj77bdv+xo4cKBbiq3IyEjTsWNHExsba6Kjo023bt1Mrly5zI4dO4wx7vnlzuFwmJo1a5oOHTq4vLy8vEzjxo2d71ObEb+dNGrUyDRs2NBcu3bNGGNMbGysadWqlWnQoEGqMoxx3VZKly5tPv30U5fxixcvTvV+0uFwmD179phJkyaZMmXKGC8vL1O2bFkzdepUc/bs2VRNO54d31vG2PPdZcf2aIw926SnbI/xOVZvk3Zsj3Z8pxhjz3ps134yPaHY8hB+fn7Ojd0YYzJnzuzyl6ijR48af3//VGX4+vqao0ePOt/HxcWZzJkzm+PHjxtjjNmwYYPJkydPqjJ8fHzM/v37kxy/f/9+4+vrm6oMY24cqSlfvrypVatWoq+KFSum+svk1s/Ex8fH5TM5dOiQyZ49e6oyfvjhB1O4cGEzbNgwExsb6xzu7e1t9uzZk6pp3+zmnWNoaKjZvHmzy/jdu3eb3LlzpyojPDzcrFmzxhhjTL58+cy2bdtcxu/du9cEBASkKsPf39/8/vvvzveZM2c2u3btcr7/7bffUp1hzI3P/uZtxZgbn8nff/9tjDFmy5Ytqf7sfX19Ld9WHA6HCQsLMwULFkz0FRYW5pZiK0eOHObAgQMuw9544w2TI0cOs3XrVrf8cvfRRx+Z/Pnzm/fee89luDu3lZu3kwIFCjj/2h1vx44dJjQ0NNU5N2+DwcHBzl+C4x08eND4+fmlKuPmeTHmxjrbpUsXExQUZPz8/MwzzzxjVq9enaoMO763jLHnu8uO7dEYe7ZJT9kejbFnm7Rje7TjO8UY+75X7NhPpidcs+Uh7rvvPuf1Ltu2bZPD4dDWrVud47ds2aJ8+fKlKiNfvnw6cOCA8/2RI0cUFxenXLlySZLy58+vf//9N1UZ999/v+bOnZvk+Pfee0+lSpVKVYYkFSlSRH369NHatWsTfc2ePTvVGf7+/rp06ZLzfZ48eZQ1a1aXNqm9gLZatWravn27Dh48qKpVq+rIkSOpmt7tXLx4UdHR0fL19ZWPj4/LOF9fX12+fDlV02/Tpo2GDBmi8+fP69lnn9WoUaOc69Ply5c1YsQIVatWLVUZRYsW1eLFiyVJy5Ytk6+vr1auXOkcv2LFCkVERKQqQ5IKFiyon376yfl+x44d8vLyUnBwsCQpZ86cunbtWqozlixZkuT4JUuWKDw8PFUZ4eHhmjRpko4ePZro63b5KXXlyhWX94MGDdIrr7yiOnXqaNOmTamefqtWrfT9999r7ty5atasmc6dO5fqad7K4XDI4XBIkry8vBQUFOQyPnv27G7JbdKkicaMGaPY2Fg1atRI06dPd7kmZOrUqSpXrlyqc2728MMP691339Xx48c1ffp0HTt2TI8//niqpmnH95Zkz3eXHdujZN826Qnbo2TPNmnH9mjHd0p8jtXrsV37yfTEO607APfo2rWrOnTooDlz5mj79u1666239Morr2j//v3y8vLSjBkz1K9fv1RltGvXTp07d9aQIUPk4+OjiRMn6qmnnlKWLFkk3bhpQmp/UZ0wYYIaNGig5cuXKzIy0rkjOXnypFavXq3ffvvNLV8mFStW1Pbt29W2bdtExzscjlRf0FqiRAn9+uuvKlmypCTp2LFjLuP379+vggULpipDkoKCgvTRRx9p3rx5ql69ukaOHOnckblTsWLFJN24S9hPP/2kBx980Dluz549CgsLS9X0hw8frt27d6tQoUKqWLGivv/+ewUHBytfvnw6fvy4cuXKpVWrVqUqY8CAAWrfvr0mT56sY8eOaeHCherVq5e2bNkiLy8vffnll5o4cWKqMiSpe/fu6ty5s7Zt2yZfX1/NmTNHzz77rDJlyiTpxi+R8cvzbo0aNUqtW7fWunXrEt1Wli9frkWLFqUqo0KFCtq+fbtatGiR6Hh3bCeSVLp0aW3atEkPPPCAy/D+/fsrLi5OzzzzTKozpBu/SGzYsEEjR45U2bJlNXv2bLduK8YYFStWTA6HQ//++69+/fVXl3k6fPiwQkJCUp3z+uuvKzIyUiVKlFCVKlX02WefadWqVSpWrJgOHz6ss2fPasWKFanOSYy/v786dOigDh066ODBg6malh3fW5I93112bI+SPdukp2yPkj3bpB3box3fKZI967Fd+8n0xGHc8U2JdGHRokXavHmzqlatqmeeeUbr1q3TsGHDdPnyZTVs2FBDhw6Vl9fdH8y8fv26hgwZooULFyomJkZ169bV22+/rdy5c0uStm7dqitXrqhGjRqpmo/ff/9dM2bM0I8//ui8HXdISIiqVKmirl27uqVAiYqKUkxMjFv+0piUjRs3KiAgIMm/aE2fPl1xcXHq0aOH2zIPHTqkNm3a6KefftLu3bvdchRQktavX+/yPjQ01GXH/vbbb+vq1asaMGBAqrOWL1+ub7/9Vr/99pvi4uIUGhqqatWqqXXr1goICEj19Ddu3Kgff/xRVapUUdWqVbV3716NHTvWuZ20b98+1RmSNGPGDJdtZejQofL19ZV043OKjY1ViRIlUpWxadMmTZkyRZs3b06wrfTq1UtVqlRJ1fT37t2ry5cvq2LFiomOv3btmo4fP57q7WjOnDlav369Pvjgg0THv/nmm5o5c6aOHj2aqpyb/fDDD2rXrp3++OMP7dq1yy3byoIFC1zeFy9eXJUrV3a+Hz16tM6dO+eWgv7atWuaO3duottKt27dlD9//lRN/9FHH9VXX32l7Nmzp7qvt2P195Zk33eX1dujZM826Snbo2TfNmn19ijZ850iWb8e27mfTC8otgAPExcXp4sXLyowMNCSI1yAp/j333915MgRlSxZ0nmUA0DaiN8eS5QokeBUdSAjo9jyQBcuXHD5a8St58NmlAwAADIyY4zi4uKcp3tl5BxPybArx6qMP//8UydOnJCXl5cKFSrkvPYQ6Rc3yPAgc+bMUalSpZQzZ06VKlXK5f+3u+lEesu4nV9++cXynbBdOZ6SYVeOp2S4M2fp0qXq3LmzBg4cqH379rmMO3funFsebnpzxq0Pm3RXhl05nrq8MvK8eMryun79ul599VXVrFnT+cD68ePHK2vWrPL391f79u0TPBw4veZ4Soanzcv06dMVHh6uiIgIVa1aVZUrV1bevHlVvXp1bd++PdXTT459+/apUKFCGT7Ddrbe+xCWGTdunPH39zeDBg0ya9euNXv37jV79+41a9euNYMHDzYBAQFm/Pjx6T7jTnbu3GkcDoelGXbleEqGXTmekuGunA8//NBkypTJ1K9f31SvXt34+vqahQsXOse74/bMdmTYleMpGXbleEqGXTmvvvqqCQ4ONn379jWlSpUyXbt2NQUKFDALFy40CxYsMPny5TNvvvlmamfFlhxPyfCkeRk/frwJCwszU6dONbNnzzYlS5Y0o0aNMsuWLTPPPvus8ff3T/C4FCu460H2aZ1hN+5G6CHeeecdzZs3L8EdikqWLKlatWqpbNmyGjBggPr375+uM5o2bXrb8RcuXHDLdUh25HhKhl05npJhV8748eM1ceJE9ezZU5L06aef6rnnntOVK1fUqVOnVE3bzgy7cjwlw64cT8mwK2fRokWaM2eOGjRooG7duql48eJatGiRWrZsKenG4zFGjx6tgQMHpvscT8nwpHmZNm2a5syZoyeffFKSVKNGDVWtWlVRUVF64oknlCNHDr3yyisujzK5G3379r3t+NOnT6dq+nZlpDcUWx7i1KlTKlOmTJLjy5QpozNnzqT7jG+//VaPP/6483ajt4qNjU3V9O3M8ZQMu3I8JcOunEOHDqlhw4bO9y1atFCePHn01FNP6dq1a2rSpEmGyLArx1My7MrxlAy7co4fP66yZctKuvEcxyxZsjjfS9JDDz2kP/74I0PkeEqGXTl2ZJw6dcr5GBnpxjMjL1y4oNOnTys0NFTPPfecqlevnqoM6cadhcuVK6fAwMBEx6f2eXR2ZaQ7aX1oDe7xyCOPmHbt2plr164lGHf9+nXTrl07U6NGjXSfUaZMGTNnzpwkx//8889uObxsR46nZNiV4ykZduWEhoaazZs3Jxi+bt06kzVrVjNkyJAMkWFXjqdk2JXjKRl25QQHB5tff/3V+b5q1armr7/+cr7ft2+fCQwMTFWGXTmekmFXjh0Z5cqVM7NmzXK+X716tfH39zdxcXHGGGP2799vsmXLlqoMY4wpVqyY+eCDD5Ic747vLjsy0htukOEh3nnnHa1cuVIhISFq2rSpunXrpm7duqlp06YKDg7WqlWrNG3atHSfUaFCBe3YsSPJ8T4+PrrvvvtSlWFXjqdk2JXjKRl25Tz88MNatmxZguE1a9bUt99+q8mTJ6dq+nZl2JXjKRl25XhKhl05pUqVctnmN27cqHz58jnf79q1S0WLFs0QOZ6SYVeOHRmDBw9W9+7d1bJlS7Vv316NGjVSz549naejr1u3TqVLl05VhiRVrFjxtjfbcMdDs+3ISG+49bsHuXjxohYuXJjow4Bbt26d5CHb9JQRExOj2NhY+fv7p7qvaZ3jKRl25XhKhl0569ev16ZNmzR48OBEx69du1bvv/++5s2bl64z7MrxlAy7cjwlw66cgwcPKnPmzIqIiEh0/KJFi+Tt7Z3gmuf0mOMpGXbl2DUvy5Ytc3mo8fPPP+8c988//0hSqm8DHxUVpZiYmFQ/qD6tM9Ibii0AAAAAsACnEd4jrl27pj///DPDZ1y/ft3yDLtyPCXDrhxPybArx1My7MrxlAy7cjwlw64c5iX9ZdiV4ykZuHsUW/eIvXv3JnmIOyNl7Nmzx/IMu3I8JcOuHE/JsCvHUzLsyvGUDLtyPCXDrhzmJf1l2JWT0TKmT5+uyMhItWjRQqtXr3YZd+bMGbc8cNiOjPSEYgsAAAC4x02ZMkUDBgxQiRIl5OPjo3r16umNN95wjo+NjU31beztyEhveM6Whyhfvvxtx//3339k2JzjKRl25XhKhl05npJhV46nZNiV4ykZduUwL+kvw64cT8mQpHfffVezZ89W69atJUndunVT48aN9d9//2nUqFEZJiO9odjyEHv37lWrVq2SPIx84sQJHTx4kAwbczwlw64cT8mwK8dTMuzK8ZQMu3I8JcOuHOYl/WXYleMpGZJ09OhRVa1a1fm+atWqWrNmjSIjI3Xt2jX17t07Q2SkO2n5kC+4T4UKFcz06dOTHO+Oh8R5SoZdOZ6SYVeOp2TYleMpGXbleEqGXTmekmFXDvOS/jLsyvGUDGOMKVCggNmwYUOC4Xv27DHBwcGmXbt2qc6xIyO94ZotD1GtWjUdOHAgyfHZsmVTjRo1yLAxx1My7MrxlAy7cjwlw64cT8mwK8dTMuzKYV7SX4ZdOZ6SIUnVq1fXl19+mWB4qVKltHr16kQfDp4eM9IbnrMFAAAA3ON+/fVXbd++XR07dkx0/O7du/XFF19o+PDh6TojvaHYAgAAAAALcBqhh4mLi0tyuLseeOcpGXbleEqGXTmekmFXjqdk2JXjKRl25XhKhl05zEv6y7Arx1My7Mqxa17SA4otDxEdHa0WLVooICBAwcHBGjZsmGJjY53jT58+neoH3nlKhl05npJhV46nZNiV4ykZduV4SoZdOZ6SYVcO85L+MuzK8ZQMu3Lsmpd0JW3vzwF36dmzpylWrJj57LPPzOzZs014eLipX7++iYmJMcYYExUVZRwOBxkeNi8sr/SX4UnzwvJKfxmeNC8sr/SZ4ykZnjQvLK+MjWLLQ9x3331m7dq1zvenT582Dz/8sKlTp465cuWKiYqKSvWtND0lw64cT8mwK8dTMuzK8ZQMu3I8JcOuHE/JsCuHeUl/GXbleEqGXTl2zUt6wmmEHuL06dMKDw93vs+dO7e+++47Xbx4UfXq1dPly5fJsDnHUzLsyvGUDLtyPCXDrhxPybArx1My7MphXtJfhl05npJhV45d85KeUGx5iPvuu0/79u1zGZYtWzatXLlS//33n5o0aUKGzTmekmFXjqdk2JXjKRl25XhKhl05npJhVw7zkv4y7MrxlAy7cuyal/SEYstD1KlTR/PmzUswPGvWrFqxYoV8fX3JsDnHUzLsyvGUDLtyPCXDrhxPybArx1My7MphXtJfhl05npJhV45d85KupPV5jHCPs2fPmt27dyc5Pjo62qxbt44MG3M8JcOuHE/JsCvHUzLsyvGUDLtyPCXDrhzmJf1l2JXjKRl25dg1L+kJDzX2IGfOnNF7772nzZs3KyoqSpIUEhKiqlWrqkOHDsqTJw8ZNud4SoZdOZ6SYVeOp2TYleMpGXbleEqGXTnMS/rLsCvHUzLsyrFrXtILii0PsW3bNtWtW1f+/v6KjIxUcHCwJOnkyZNavXq1Ll++rBUrVqhixYr3fIYnzQvLK/1leNK8sLzSX4YnzQvLK33meEqGJ80LyyuDS9sDa3CXSpUqmS5dupi4uLgE4+Li4kyXLl1M5cqVybAxx1My7MrxlAy7cjwlw64cT8mwK8dTMuzKYV7SX4ZdOZ6SYVeOXfOSnlBseQhfX1+zb9++JMfv27fP+Pr6kmFjjqdk2JXjKRl25XhKhl05npJhV46nZNiVw7ykvwy7cjwlw64cu+YlPeFuhB4iJCREW7duTXL81q1bnYdq7/UMu3I8JcOuHE/JsCvHUzLsyvGUDLtyPCXDrhzmJf1l2JXjKRl25dg1L+mJd1p3AO7Rv39/denSRdu3b1ft2rUTnAM7e/ZsvfXWW2R42LywvNJfhifNC8sr/WV40rywvNJnjqdkeNK8sLwyuLQ+tAb3+fjjj02lSpWMt7e3cTgcxuFwGG9vb1OpUiXzySefkJEGOZ6SYVeOp2TYleMpGXbleEqGXTmekmFXDvOS/jLsyvGUDLty7JqX9IK7EXqga9eu6cyZM5Kk3LlzK3PmzGSkcY6nZNiV4ykZduV4SoZdOZ6SYVeOp2TYlcO8pL8Mu3I8JcOuHLvmJa1RbAEAAACABbhBBgAAAABYgGILAAAAACxAsQUAAAAAFqDYAgAAAAALUGwBADzWunXr5HA4dP78+bTuSpIKFiyoyZMnp3U3AAAWoNgCAGQ48UVUUq9HH31UklS1alWdOHFCQUFBqc5cu3at6tWrp1y5csnf31+lSpVSv3799Pfff6d62gAAz0SxBQDIcOKLqFtf7777rhwOh1588UVJUpYsWRQSEiKHw5GqvHfffVeRkZEKCQnRF198ob1792rmzJm6cOGCJkyY4I5ZAgB4IIotAECGE19E3fw6d+6c+vfvr1deeUVPP/20pISnEc6fP1/Zs2fX119/raJFi8rX11d169bVsWPHksz666+/1LNnT/Xs2VPvvfeeatWqpYIFC6pGjRqaM2eOhg0b5mz7xRdf6P7775ePj48KFiyYoBA7deqUGjZsKD8/P0VEROjDDz9MkHf+/Hl17txZefLkUWBgoB577DH98ssvblhqAAC7UWwBADK88+fPq1GjRqpVq5ZGjx5927aXL1/WmDFj9P7772vjxo06f/68WrVqlWT7zz77TFevXtXAgQMTHZ89e3ZJ0vbt29WiRQu1atVKu3bt0ogRIzR06FDNnz/f2bZDhw46duyY1q5dq88//1zTp0/XqVOnXKb39NNP69SpU1q2bJm2b9+u8uXLq3bt2jp79mzyFgYAIN3wTusOAACQGnFxcWrdurW8vb314Ycf3vGUwWvXrumdd95RpUqVJEkLFixQyZIltXXrVj388MMJ2h86dEiBgYEKDQ297XQnTpyo2rVra+jQoZKkYsWKae/evRo/frw6dOiggwcPatmyZdq6daseeughSdLcuXNVsmRJ5zR++OEHbd26VadOnZKPj48k6a233tLXX3+tzz//XF26dEn+ggEApDmObAEAMrRXXnlFmzdv1jfffKNs2bLdsb23t7ez2JGkEiVKKHv27Nq3b1+i7Y0xybrma9++fapWrZrLsGrVqunQoUOKjY3Vvn375O3trQoVKiTIjvfLL7/o33//Va5cuZQ1a1bn6+jRozpy5Mgd+wAASF84sgUAyLA+/vhjvfXWW1qyZImKFi1qSUaxYsV04cIFnThx4o5Ht1Lr33//VWhoqNatW5dg3M1FGQAgY+DIFgAgQ9q5c6c6deqksWPHqm7dusn+uevXr+unn35yvj9w4IDOnz/vcjrfzZo3b64sWbJo3LhxiY6Pv/lGyZIltXHjRpdxGzduVLFixZQpUyaVKFFC169f1/bt2xNkxytfvryioqLk7e2tIkWKuLxy586d7HkEAKQPHNkCAGQ4Z86cUePGjVWrVi21bdtWUVFRLuMzZcqkPHnyJPqzmTNn1ksvvaQpU6bI29tbPXr0UOXKlRO9XkuSChQooEmTJqlHjx6Kjo5Wu3btVLBgQf311196//33lTVrVk2YMEH9+vXTQw89pNGjR6tly5bavHmz3nnnHU2fPl2SVLx4cT3xxBN64YUXNGPGDHl7e6t3797y8/NzZkVGRqpKlSpq3Lixxo0bp2LFiun48eNasmSJmjRpoooVK7ppCQIA7MCRLQBAhrNkyRL98ccfWrp0qUJDQxO8br4m61b+/v56+eWX1bp1a1WrVk1Zs2bVJ598ctu8F198UStXrtTff/+tJk2aqESJEurcubMCAwPVv39/STeOSn366af6+OOPVbp0aQ0bNkyjRo1Shw4dnNOZN2+ewsLCVLNmTTVt2lRdunRR3rx5neMdDoeWLl2qGjVqqGPHjipWrJhatWqlP/74Q8HBwalbaAAA2zmMMSatOwEAgB3mz5+v3r17u5y6BwCAVTiyBQAAAAAWoNgCAAAAAAtwGiEAAAAAWIAjWwAAAABgAYotAAAAALAAxRYAAAAAWIBiCwAAAAAsQLEFAAAAABag2AIAAAAAC1BsAQAAAIAFKLYAAAAAwAL/DxZ76XU+MGA8AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = {}\n",
    "for i in groupKeys:\n",
    "    groupDf = groupZips.get_group(i)\n",
    "    d[i] = groupDf['TtlProjUnits'].sum()\n",
    "    print(\"Amount of Income restricted housing UNITS in Zip Code of \" + i + \" : \" + str(groupDf['TtlProjUnits'].sum()))\n",
    "\n",
    "\n",
    "types = list(d.keys())\n",
    "values = list(d.values())\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(\"Zip Code\")\n",
    "plt.ylabel(\"Number of ICHU\")\n",
    "plt.title(\"Number of Income restricted housing Units per Zip Code in Boston\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "THE HISTOGRAM ABOVE IS PART OF THE ANSWER TO BASE QUESTION 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Income-Restricted Units in Boston: 86065\n"
     ]
    }
   ],
   "source": [
    "print(\"Total Income-Restricted Units in Boston: \" + str(df['TtlProjUnits'].sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the total Income restricuted units in Boston is about 86k. Note this also includes the amount which are ownership units. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>zipCode</th>\n",
       "      <th>totalUnits</th>\n",
       "      <th>Projects</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02108</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02109</td>\n",
       "      <td>540</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>02110</td>\n",
       "      <td>609</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>02111</td>\n",
       "      <td>2570</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>02113</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  zipCode  totalUnits  Projects\n",
       "0   02108          96         1\n",
       "1   02109         540         5\n",
       "2   02110         609         4\n",
       "3   02111        2570        25\n",
       "4   02113         256         5"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zipDf = pd.DataFrame(dfDict, columns = ['zipCode', 'totalUnits','Projects'])\n",
    "zipDf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zipCode</th>\n",
       "      <th>totalUnits</th>\n",
       "      <th>Projects</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
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       "      <th>0</th>\n",
       "      <td>02108</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "      <td>42.3576</td>\n",
       "      <td>-71.0684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02109</td>\n",
       "      <td>540</td>\n",
       "      <td>5</td>\n",
       "      <td>42.3600</td>\n",
       "      <td>-71.0545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>02110</td>\n",
       "      <td>609</td>\n",
       "      <td>4</td>\n",
       "      <td>42.3576</td>\n",
       "      <td>-71.0514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>02111</td>\n",
       "      <td>2570</td>\n",
       "      <td>25</td>\n",
       "      <td>42.3503</td>\n",
       "      <td>-71.0629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>02113</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>42.3657</td>\n",
       "      <td>-71.0560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  zipCode  totalUnits  Projects  latitude  longitude\n",
       "0   02108          96         1   42.3576   -71.0684\n",
       "1   02109         540         5   42.3600   -71.0545\n",
       "2   02110         609         4   42.3576   -71.0514\n",
       "3   02111        2570        25   42.3503   -71.0629\n",
       "4   02113         256         5   42.3657   -71.0560"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets add lat and long so we can plot heatmaps\n",
    "nomi = pgeocode.Nominatim('us')\n",
    "bostonZipslat= {}\n",
    "bostonZipslong = {}\n",
    "zips = list(zipDf['zipCode'])\n",
    "#print(zips)\n",
    "\n",
    "\n",
    "for i in zips:\n",
    "    t = nomi.query_postal_code(i)\n",
    "    bostonZipslat[i] = t['latitude']\n",
    "    bostonZipslong[i] = t['longitude']\n",
    "\n",
    "zipDf['latitude'] = zipDf.apply(lambda row: bostonZipslat[row[0]],axis = 1)\n",
    "zipDf['longitude'] = zipDf.apply(lambda row: bostonZipslong[row[0]],axis =1)\n",
    "zipDf.head()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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",
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       "    \n",
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       "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.6.0/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n",
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       "    &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",
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       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://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_38c15fbfd252b27966268cfac3366beb {\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_38c15fbfd252b27966268cfac3366beb&quot; &gt;&lt;/div&gt;\n",
       "        \n",
       "&lt;/body&gt;\n",
       "&lt;script&gt;    \n",
       "    \n",
       "            var map_38c15fbfd252b27966268cfac3366beb = L.map(\n",
       "                &quot;map_38c15fbfd252b27966268cfac3366beb&quot;,\n",
       "                {\n",
       "                    center: [42.35, -71.06],\n",
       "                    crs: L.CRS.EPSG3857,\n",
       "                    zoom: 13,\n",
       "                    zoomControl: true,\n",
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       "                }\n",
       "            );\n",
       "\n",
       "            \n",
       "\n",
       "        \n",
       "    \n",
       "            var tile_layer_4fffe5398e380a3bb48582229fb7eca9 = 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_38c15fbfd252b27966268cfac3366beb);\n",
       "        \n",
       "    \n",
       "            var feature_group_aa98f82120332139507c49ecbbd5d024 = L.featureGroup(\n",
       "                {}\n",
       "            ).addTo(map_38c15fbfd252b27966268cfac3366beb);\n",
       "        \n",
       "    \n",
       "            var heat_map_7cc76976f02eeb557db2d134c6e7f16a = L.heatLayer(\n",
       "                [[42.3576, -71.0684, 96.0], [42.36, -71.0545, 540.0], [42.3576, -71.0514, 609.0], [42.3503, -71.0629, 2570.0], [42.3657, -71.056, 256.0], [42.3611, -71.0682, 4582.0], [42.3427, -71.0922, 3517.0], [42.3492, -71.0768, 3729.0], [42.3362, -71.0729, 9389.0], [42.3251, -71.0953, 7071.0], [42.3307, -71.0912, 2854.0], [42.2973, -71.0745, 4715.0], [42.2973, -71.0745, 717.0], [42.2918, -71.0717, 4377.0], [42.2973, -71.0745, 3307.0], [42.2739, -71.0939, 1620.0], [42.3347, -71.0375, 7627.0], [42.3642, -71.0257, 3857.0], [42.3778, -71.0627, 3737.0], [42.3126, -71.1115, 4345.0], [42.2836, -71.1295, 1584.0], [42.2787, -71.1589, 877.0], [42.3535, -71.1329, 2160.0], [42.3478, -71.1566, 4978.0], [42.254, -71.1261, 2276.0], [42.3479, -71.0825, 60.0], [42.3489, -71.0465, 1737.0], [42.3471, -71.1027, 2878.0]],\n",
       "                {&quot;blur&quot;: 15, &quot;maxZoom&quot;: 18, &quot;minOpacity&quot;: 0.5, &quot;radius&quot;: 25}\n",
       "            ).addTo(feature_group_aa98f82120332139507c49ecbbd5d024);\n",
       "        \n",
       "    \n",
       "            var layer_control_744a3fefa526b9131537f352653287b0 = {\n",
       "                base_layers : {\n",
       "                    &quot;openstreetmap&quot; : tile_layer_4fffe5398e380a3bb48582229fb7eca9,\n",
       "                },\n",
       "                overlays :  {\n",
       "                    &quot;Heat Map&quot; : feature_group_aa98f82120332139507c49ecbbd5d024,\n",
       "                },\n",
       "            };\n",
       "            L.control.layers(\n",
       "                layer_control_744a3fefa526b9131537f352653287b0.base_layers,\n",
       "                layer_control_744a3fefa526b9131537f352653287b0.overlays,\n",
       "                {&quot;autoZIndex&quot;: true, &quot;collapsed&quot;: true, &quot;position&quot;: &quot;topright&quot;}\n",
       "            ).addTo(map_38c15fbfd252b27966268cfac3366beb);\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 0x7f9ac2f3d7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bare = zipDf[[\"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(\"incomeRestrictedHousing.html\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see visualize the Geogrpahical distribution of income restricted housing units by neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "from geopy.geocoders import Nominatim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'place_id': 298670555, 'licence': 'Data © OpenStreetMap contributors, ODbL 1.0. https://osm.org/copyright', 'osm_type': 'relation', 'osm_id': 4033668, 'boundingbox': ['42.267159', '42.3352405', '-71.0793082', '-71.0326914'], 'lat': '42.2973205', 'lon': '-71.0744952', 'display_name': 'Dorchester, Boston, Suffolk County, Massachusetts, United States', 'class': 'place', 'type': 'suburb', 'importance': 0.6536547140109368, 'icon': 'https://nominatim.openstreetmap.org/ui/mapicons/poi_place_village.p.20.png'}\n"
     ]
    }
   ],
   "source": [
    "geolocator = Nominatim(user_agent=\"geoapiExercises\")\n",
    "place = \"Dorchester, Boston\"\n",
    "location = geolocator.geocode(place)\n",
    "print(location.raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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