parks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this project our goal was to answer the question: \"What is the best neighborhood in Pittburgh?\" and support our argument with data that came from the WPRDC."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_why parks?:_ Our overall metric for defining the \"best\" neighborhood is **promoting citizen well being**. I believe parks contribute a lot to promoting good health in the community especially with children. If a neighborhood has more parks the more opportunities there are to be active and overall just get outside contributing to the overall quality of living and individual health. Therefore, **More parks is equal to a better neighborhood.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_data using the city of parks dataset:_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>_id</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>maintenance_responsibility</th>\n",
       "      <th>image</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>council_district</th>\n",
       "      <th>ward</th>\n",
       "      <th>tract</th>\n",
       "      <th>public_works_division</th>\n",
       "      <th>pli_division</th>\n",
       "      <th>police_zone</th>\n",
       "      <th>fire_zone</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2111959181</td>\n",
       "      <td>Able Long Park</td>\n",
       "      <td>Park</td>\n",
       "      <td>Parks - Southern</td>\n",
       "      <td>https://tools.wprdc.org/images/pittsburgh/park...</td>\n",
       "      <td>Beechview</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>42003192000</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>4-28</td>\n",
       "      <td>40.408118</td>\n",
       "      <td>-80.028197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1109513049</td>\n",
       "      <td>Albert Turk Graham Park</td>\n",
       "      <td>Park</td>\n",
       "      <td>Parks - Schenley</td>\n",
       "      <td>https://tools.wprdc.org/images/pittsburgh/park...</td>\n",
       "      <td>Crawford-Roberts</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>42003030500</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2-1</td>\n",
       "      <td>40.440507</td>\n",
       "      <td>-79.984018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1544096765</td>\n",
       "      <td>Alcoa Park</td>\n",
       "      <td>Park</td>\n",
       "      <td>Parks - Northern</td>\n",
       "      <td>NaN</td>\n",
       "      <td>North Shore</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>42003563200</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1-20</td>\n",
       "      <td>40.447481</td>\n",
       "      <td>-80.001017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1212681573</td>\n",
       "      <td>Allegheny Commons</td>\n",
       "      <td>Park</td>\n",
       "      <td>Parks - Northern</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Allegheny Center</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>42003562700</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1-7</td>\n",
       "      <td>40.453126</td>\n",
       "      <td>-80.009158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>954863286</td>\n",
       "      <td>Allegheny Landing Park</td>\n",
       "      <td>Park</td>\n",
       "      <td>Parks - Northern</td>\n",
       "      <td>NaN</td>\n",
       "      <td>North Shore</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>42003563200</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1-20</td>\n",
       "      <td>40.447184</td>\n",
       "      <td>-80.002907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>223959093</td>\n",
       "      <td>Allegheny Riverfront Park</td>\n",
       "      <td>Park</td>\n",
       "      <td>Parks - Northern</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Central Business District</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>42003020100</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1-3</td>\n",
       "      <td>40.444591</td>\n",
       "      <td>-80.002416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>1596467340</td>\n",
       "      <td>Alpine Gardens Park</td>\n",
       "      <td>Park</td>\n",
       "      <td>Parks - Northern</td>\n",
       "      <td>https://tools.wprdc.org/images/pittsburgh/park...</td>\n",
       "      <td>Central Northside</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>42003250300</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1-21</td>\n",
       "      <td>40.457673</td>\n",
       "      <td>-80.012852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>1829922263</td>\n",
       "      <td>Alton Park</td>\n",
       "      <td>Park</td>\n",
       "      <td>Parks - Southern</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Beechview</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>42003191600</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>4-28</td>\n",
       "      <td>40.414580</td>\n",
       "      <td>-80.021855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2049886968</td>\n",
       "      <td>Amber Triangle</td>\n",
       "      <td>Traffic Island/Median</td>\n",
       "      <td>2nd Division</td>\n",
       "      <td>NaN</td>\n",
       "      <td>East Liberty</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>42003111500</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>3-23</td>\n",
       "      <td>40.459099</td>\n",
       "      <td>-79.931846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1047118140</td>\n",
       "      <td>Ammon Park</td>\n",
       "      <td>Park</td>\n",
       "      <td>Parks - Schenley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bedford Dwellings</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>42003050900</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2-5</td>\n",
       "      <td>40.449526</td>\n",
       "      <td>-79.978247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1928748922</td>\n",
       "      <td>Arlington Park</td>\n",
       "      <td>Park</td>\n",
       "      <td>Parks - Southern</td>\n",
       "      <td>NaN</td>\n",
       "      <td>South Side Slopes</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>42003160800</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>4-22</td>\n",
       "      <td>40.418283</td>\n",
       "      <td>-79.975050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1334547409</td>\n",
       "      <td>Arlington Gym</td>\n",
       "      <td>Building Grounds</td>\n",
       "      <td>Parks - Southern</td>\n",
       "      <td>NaN</td>\n",
       "      <td>South Side Slopes</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>42003160800</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>4-22</td>\n",
       "      <td>40.419241</td>\n",
       "      <td>-79.975076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>1848662473</td>\n",
       "      <td>Armstrong Park</td>\n",
       "      <td>Park</td>\n",
       "      <td>Parks - Southern</td>\n",
       "      <td>NaN</td>\n",
       "      <td>South Side Flats</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>42003170200</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>4-24</td>\n",
       "      <td>40.428056</td>\n",
       "      <td>-79.985842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>485438954</td>\n",
       "      <td>Arsenal Park</td>\n",
       "      <td>Park</td>\n",
       "      <td>Parks - Northeast</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lower Lawrenceville</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>42003060300</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3-3</td>\n",
       "      <td>40.466696</td>\n",
       "      <td>-79.961229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>1366899437</td>\n",
       "      <td>August Wilson Park</td>\n",
       "      <td>Park</td>\n",
       "      <td>Parks - Schenley</td>\n",
       "      <td>https://tools.wprdc.org/images/pittsburgh/park...</td>\n",
       "      <td>Crawford-Roberts</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>42003030500</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2-1</td>\n",
       "      <td>40.446779</td>\n",
       "      <td>-79.985602</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    _id          id                       name                   type  \\\n",
       "0     1  2111959181             Able Long Park                   Park   \n",
       "1     2  1109513049    Albert Turk Graham Park                   Park   \n",
       "2     3  1544096765                 Alcoa Park                   Park   \n",
       "3     4  1212681573          Allegheny Commons                   Park   \n",
       "4     5   954863286     Allegheny Landing Park                   Park   \n",
       "5     6   223959093  Allegheny Riverfront Park                   Park   \n",
       "6     7  1596467340        Alpine Gardens Park                   Park   \n",
       "7     8  1829922263                 Alton Park                   Park   \n",
       "8     9  2049886968             Amber Triangle  Traffic Island/Median   \n",
       "9    10  1047118140                 Ammon Park                   Park   \n",
       "10   11  1928748922             Arlington Park                   Park   \n",
       "11   12  1334547409              Arlington Gym       Building Grounds   \n",
       "12   13  1848662473             Armstrong Park                   Park   \n",
       "13   14   485438954               Arsenal Park                   Park   \n",
       "14   15  1366899437         August Wilson Park                   Park   \n",
       "\n",
       "   maintenance_responsibility  \\\n",
       "0            Parks - Southern   \n",
       "1            Parks - Schenley   \n",
       "2            Parks - Northern   \n",
       "3            Parks - Northern   \n",
       "4            Parks - Northern   \n",
       "5            Parks - Northern   \n",
       "6            Parks - Northern   \n",
       "7            Parks - Southern   \n",
       "8                2nd Division   \n",
       "9            Parks - Schenley   \n",
       "10           Parks - Southern   \n",
       "11           Parks - Southern   \n",
       "12           Parks - Southern   \n",
       "13          Parks - Northeast   \n",
       "14           Parks - Schenley   \n",
       "\n",
       "                                                image  \\\n",
       "0   https://tools.wprdc.org/images/pittsburgh/park...   \n",
       "1   https://tools.wprdc.org/images/pittsburgh/park...   \n",
       "2                                                 NaN   \n",
       "3                                                 NaN   \n",
       "4                                                 NaN   \n",
       "5                                                 NaN   \n",
       "6   https://tools.wprdc.org/images/pittsburgh/park...   \n",
       "7                                                 NaN   \n",
       "8                                                 NaN   \n",
       "9                                                 NaN   \n",
       "10                                                NaN   \n",
       "11                                                NaN   \n",
       "12                                                NaN   \n",
       "13                                                NaN   \n",
       "14  https://tools.wprdc.org/images/pittsburgh/park...   \n",
       "\n",
       "                 neighborhood  council_district  ward        tract  \\\n",
       "0                   Beechview                 4    19  42003192000   \n",
       "1            Crawford-Roberts                 6     3  42003030500   \n",
       "2                 North Shore                 1    22  42003563200   \n",
       "3            Allegheny Center                 1    22  42003562700   \n",
       "4                 North Shore                 1    22  42003563200   \n",
       "5   Central Business District                 6     2  42003020100   \n",
       "6           Central Northside                 6    25  42003250300   \n",
       "7                   Beechview                 4    19  42003191600   \n",
       "8                East Liberty                 7     8  42003111500   \n",
       "9           Bedford Dwellings                 6     5  42003050900   \n",
       "10          South Side Slopes                 3    16  42003160800   \n",
       "11          South Side Slopes                 3    16  42003160800   \n",
       "12           South Side Flats                 3    17  42003170200   \n",
       "13        Lower Lawrenceville                 7     6  42003060300   \n",
       "14           Crawford-Roberts                 6     3  42003030500   \n",
       "\n",
       "    public_works_division  pli_division  police_zone fire_zone   latitude  \\\n",
       "0                       5            19            6      4-28  40.408118   \n",
       "1                       3             3            2       2-1  40.440507   \n",
       "2                       6            22            1      1-20  40.447481   \n",
       "3                       1            22            1       1-7  40.453126   \n",
       "4                       6            22            1      1-20  40.447184   \n",
       "5                       6             2            2       1-3  40.444591   \n",
       "6                       1            25            1      1-21  40.457673   \n",
       "7                       5            19            6      4-28  40.414580   \n",
       "8                       2             8            5      3-23  40.459099   \n",
       "9                       3             5            2       2-5  40.449526   \n",
       "10                      3            16            3      4-22  40.418283   \n",
       "11                      3            16            3      4-22  40.419241   \n",
       "12                      3            17            3      4-24  40.428056   \n",
       "13                      2             6            2       3-3  40.466696   \n",
       "14                      3             3            2       2-1  40.446779   \n",
       "\n",
       "    longitude  \n",
       "0  -80.028197  \n",
       "1  -79.984018  \n",
       "2  -80.001017  \n",
       "3  -80.009158  \n",
       "4  -80.002907  \n",
       "5  -80.002416  \n",
       "6  -80.012852  \n",
       "7  -80.021855  \n",
       "8  -79.931846  \n",
       "9  -79.978247  \n",
       "10 -79.975050  \n",
       "11 -79.975076  \n",
       "12 -79.985842  \n",
       "13 -79.961229  \n",
       "14 -79.985602  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "park = pd.read_csv(\"parks.csv\")\n",
    "park.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "breif overview of the data set: essentially shows name of parks and the neighborhood it is in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Bedford Dwellings': 1, 'Bon Air': 1, 'Windgap': 1, 'Homewood West': 1, 'Arlington': 1, 'Stanton Heights': 1, 'East Carnegie': 1, 'East Hills': 1, 'Esplen': 1, 'Fairywood': 1, 'North Oakland': 1, 'Hays': 1, 'Homewood South': 1, 'Morningside': 1, 'Central Lawrenceville': 1, 'Oakwood': 1, 'Regent Square': 1, 'Upper Hill': 1, 'New Homestead': 1, 'West Oakland': 1, 'Bluff': 1, 'Middle Hill': 1, 'Squirrel Hill North': 1, 'Allegheny Center': 2, 'Banksville': 2, 'Homewood North': 2, 'Spring Garden': 2, 'Perry South': 2, 'Strip District': 2, 'Crafton Heights': 2, 'Allentown': 2, 'Duquesne Heights': 2, 'Fineview': 2, 'Perry North': 2, 'Highland Park': 2, 'Terrace Village': 2, 'Lincoln Place': 2, 'Polish Hill': 2, 'West End': 2, 'Crawford-Roberts': 3, 'North Shore': 3, 'Central Northside': 3, 'Lower Lawrenceville': 3, 'Brookline': 3, 'Lincoln-Lemington-Belmar': 3, 'Westwood': 3, 'Upper Lawrenceville': 3, 'Garfield': 3, 'Greenfield': 3, 'Larimer': 3, 'Bloomfield': 3, 'Spring Hill-City View': 3, 'Carrick': 3, 'Manchester': 3, 'Beltzhoover': 3, 'Point Breeze North': 3, 'Swisshelm Park': 3, 'Central Oakland': 4, 'Brighton Heights': 4, 'Elliott': 4, 'Marshall-Shadeland': 4, 'South Oakland': 5, 'Troy Hill': 5, 'South Side Flats': 6, 'Hazelwood': 6, 'Squirrel Hill South': 6, 'Mount Washington': 6, 'Sheraden': 6, 'South Side Slopes': 7, 'Point Breeze': 7, 'Beechview': 8, 'Central Business District': 10, 'East Liberty': 12}\n"
     ]
    }
   ],
   "source": [
    "count ={}\n",
    "\n",
    "for index, row in park.iterrows():\n",
    "    neighborhood = row[\"neighborhood\"]\n",
    "    \n",
    "    if neighborhood in count:\n",
    "        count[neighborhood]+=1\n",
    "    else:\n",
    "        count[neighborhood]=1\n",
    "\n",
    "sort = dict(sorted(count.items(), key=lambda item: item[1])) \n",
    "print(sort)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "counts the amount of parks in each neighborhood (east liberty has the most with 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3d49c88250>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "neighborhoods = pd.DataFrame.from_dict(sort, orient='index', columns=['Count'])\n",
    "\n",
    "neighborhoods.plot.bar(figsize=(16,12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "shows the same data in a bar graph again we can see east liberty has the most with 12 parks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_conclusion:_ The East Liberty neighborhood is the best neighborhood according to this submetric. Looking at the dataset it has the most number of parks promoting a healthy and active lifestyle which supports residents wellbeing living in Pittsburgh."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**things to note:** this data hasn't been updated since 2021 so it's not a complete accurate representation but it gives a good general idea."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}