Minhal_rec.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "described-onion",
   "metadata": {},
   "source": [
    "## Recreational Facilities\n",
    "\n",
    "My metric for determining the best neighborhood in Pittsburgh is the number of recreation facilities per neighborhood population. In this context, the term 'recreation facilities' refers to places where people can pursue hobbies and have fun, like pools, gyms, and rec centers. In my view, a good neighborhood is one that has plenty of opportunities to relax and meet new people. The ideal neighborhood should strike a happy medium between a high amount of both people and rec facilities.\n",
    "\n",
    "For my metric, I'll be using the facilities and Pittsburgh population density datasets from the WPRDC, located here:\n",
    "\n",
    "**Facilities:** https://data.wprdc.org/datastore/dump/fbb50b02-2879-47cd-abea-ae697ec05170\n",
    "\n",
    "**Population Density 2010:** https://data.wprdc.org/dataset/40188e1c-6d2e-4f20-9391-607bd3054949/resource/8ac5c768-09ba-43a8-829f-ce4a417d20fb/download/population-density.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "liquid-delay",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import geopandas as gd\n",
    "%matplotlib inline\n",
    "\n",
    "# Reading data\n",
    "df = pd.read_csv(\"fac.csv\")\n",
    "pop = pd.read_csv(\"population-density.csv\")\n",
    "neighborhoods = gd.read_file(\"Neighborhoods/Neighborhoods_.shp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "known-australian",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>parcel_id</th>\n",
       "      <th>inactive</th>\n",
       "      <th>name</th>\n",
       "      <th>rentable</th>\n",
       "      <th>type</th>\n",
       "      <th>primary_user</th>\n",
       "      <th>address_number</th>\n",
       "      <th>street</th>\n",
       "      <th>zip</th>\n",
       "      <th>...</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>650726265</td>\n",
       "      <td>120-J-300</td>\n",
       "      <td>f</td>\n",
       "      <td>57th Street Park Building</td>\n",
       "      <td>f</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Department of Public Works</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57TH ST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Upper Lawrenceville</td>\n",
       "      <td>7</td>\n",
       "      <td>10.0</td>\n",
       "      <td>42003101100</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3-5</td>\n",
       "      <td>40.485658</td>\n",
       "      <td>-79.946446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>783044037</td>\n",
       "      <td>2-H-284</td>\n",
       "      <td>f</td>\n",
       "      <td>Albert Turk Graham Park Shelter</td>\n",
       "      <td>f</td>\n",
       "      <td>Shelter</td>\n",
       "      <td>Department of Public Works</td>\n",
       "      <td>39.0</td>\n",
       "      <td>VINE ST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Crawford-Roberts</td>\n",
       "      <td>6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>42003030500</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2-1</td>\n",
       "      <td>40.440464</td>\n",
       "      <td>-79.984103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1997158435</td>\n",
       "      <td>23-R-157</td>\n",
       "      <td>f</td>\n",
       "      <td>Allegheny Northside Senior Center and Hazlett ...</td>\n",
       "      <td>t</td>\n",
       "      <td>Senior</td>\n",
       "      <td>CitiParks</td>\n",
       "      <td>5.0</td>\n",
       "      <td>ALLEGHENY SQ E</td>\n",
       "      <td>15212.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Allegheny Center</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>42003562700</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1-6</td>\n",
       "      <td>40.453091</td>\n",
       "      <td>-80.005339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>204824684</td>\n",
       "      <td>10-F-198</td>\n",
       "      <td>f</td>\n",
       "      <td>Ammon Recreation Center</td>\n",
       "      <td>f</td>\n",
       "      <td>Pool</td>\n",
       "      <td>CitiParks</td>\n",
       "      <td>2217.0</td>\n",
       "      <td>BEDFORD AVE</td>\n",
       "      <td>15219.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Bedford Dwellings</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>42003050900</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2-5</td>\n",
       "      <td>40.448727</td>\n",
       "      <td>-79.977853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>472140955</td>\n",
       "      <td>013-K-314</td>\n",
       "      <td>f</td>\n",
       "      <td>Arlington Field Lights Building</td>\n",
       "      <td>f</td>\n",
       "      <td>Utility</td>\n",
       "      <td>CitiParks</td>\n",
       "      <td>0.0</td>\n",
       "      <td>STERLING ST</td>\n",
       "      <td>15210.0</td>\n",
       "      <td>...</td>\n",
       "      <td>South Side Slopes</td>\n",
       "      <td>3</td>\n",
       "      <td>16.0</td>\n",
       "      <td>42003160800</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4-22</td>\n",
       "      <td>40.418144</td>\n",
       "      <td>-79.974468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  parcel_id inactive  \\\n",
       "0   650726265  120-J-300        f   \n",
       "1   783044037    2-H-284        f   \n",
       "2  1997158435   23-R-157        f   \n",
       "3   204824684   10-F-198        f   \n",
       "4   472140955  013-K-314        f   \n",
       "\n",
       "                                                name rentable     type  \\\n",
       "0                          57th Street Park Building        f  Storage   \n",
       "1                    Albert Turk Graham Park Shelter        f  Shelter   \n",
       "2  Allegheny Northside Senior Center and Hazlett ...        t   Senior   \n",
       "3                            Ammon Recreation Center        f     Pool   \n",
       "4                    Arlington Field Lights Building        f  Utility   \n",
       "\n",
       "                 primary_user  address_number          street      zip  ...  \\\n",
       "0  Department of Public Works             NaN         57TH ST      NaN  ...   \n",
       "1  Department of Public Works            39.0         VINE ST      NaN  ...   \n",
       "2                   CitiParks             5.0  ALLEGHENY SQ E  15212.0  ...   \n",
       "3                   CitiParks          2217.0     BEDFORD AVE  15219.0  ...   \n",
       "4                   CitiParks             0.0     STERLING ST  15210.0  ...   \n",
       "\n",
       "          neighborhood council_district  ward        tract  \\\n",
       "0  Upper Lawrenceville                7  10.0  42003101100   \n",
       "1     Crawford-Roberts                6   3.0  42003030500   \n",
       "2     Allegheny Center                1  22.0  42003562700   \n",
       "3    Bedford Dwellings                6   5.0  42003050900   \n",
       "4    South Side Slopes                3  16.0  42003160800   \n",
       "\n",
       "   public_works_division  pli_division  police_zone  fire_zone   latitude  \\\n",
       "0                    2.0          10.0          2.0        3-5  40.485658   \n",
       "1                    3.0           3.0          2.0        2-1  40.440464   \n",
       "2                    1.0          22.0          1.0        1-6  40.453091   \n",
       "3                    3.0           5.0          2.0        2-5  40.448727   \n",
       "4                    3.0          16.0          3.0       4-22  40.418144   \n",
       "\n",
       "   longitude  \n",
       "0 -79.946446  \n",
       "1 -79.984103  \n",
       "2 -80.005339  \n",
       "3 -79.977853  \n",
       "4 -79.974468  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "going-child",
   "metadata": {},
   "source": [
    "Facilities.csv is a fairly extensive dataset that has all sorts of information on a variety of facilities beyond recreational. Let's use this data to get an idea of the number of rec facilities in each neighborhood. The *type, neighborhood, inactive* columns should serve our purposes for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "republican-newman",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>facility_num</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighborhood</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Allegheny Center</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allegheny West</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allentown</th>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>Banksville</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bedford Dwellings</th>\n",
       "      <td>6</td>\n",
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      ],
      "text/plain": [
       "                   facility_num\n",
       "neighborhood                   \n",
       "Allegheny Center              4\n",
       "Allegheny West                3\n",
       "Allentown                     3\n",
       "Banksville                    5\n",
       "Bedford Dwellings             6"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Refine data. Remove inactive facilities\n",
    "df = df[(df['inactive'] != 't')].dropna(subset=['inactive']).reset_index(drop=True)\n",
    "\n",
    "#Create subset\n",
    "df2 = df[['neighborhood','id']].groupby('neighborhood').count().rename(columns={'id':'facility_num'})\n",
    "\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "helpful-infrastructure",
   "metadata": {},
   "source": [
    "First, we filter out all facilities that are inactive. We then use .count() to get the total number of facilities for each neighborhood. Finally, we create a subset containing each neighborhood as well as their total facilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "optimum-opening",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rec_num</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighborhood</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Allegheny Center</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allentown</th>\n",
       "      <td>1</td>\n",
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       "      <th>Banksville</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>Bedford Dwellings</th>\n",
       "      <td>1</td>\n",
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       "      <th>Beltzhoover</th>\n",
       "      <td>2</td>\n",
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      "text/plain": [
       "                   rec_num\n",
       "neighborhood              \n",
       "Allegheny Center         2\n",
       "Allentown                1\n",
       "Banksville               2\n",
       "Bedford Dwellings        1\n",
       "Beltzhoover              2"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rec_areas = ['Pool', 'Rec Center', 'Shelter', 'Community', 'Pool/Rec', 'Activity']\n",
    "\n",
    "#Calculate total number of recreational facilities in each neighborhood\n",
    "df['num'] = 1\n",
    "rec = df[['neighborhood','num']].loc[df['type'].isin(rec_areas)].groupby('neighborhood').count().rename(columns={'num':'rec_num'})\n",
    "\n",
    "rec.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "posted-league",
   "metadata": {},
   "source": [
    "Let's calculate the number of *recreational* facilities in each neighborhood. We can do this by filtering out rows in our original dataset that have facilities belonging to our filter, rec_areas. Then, we add them up by grouping by neighborhood and using .count(). \n",
    "\n",
    "The rec dataframe will come in handy when we work on the metric. For now, though, I'd like to use this data for visualization purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "weighted-leadership",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>facility_num</th>\n",
       "      <th>rec_num</th>\n",
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       "      <th>neighborhood</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Squirrel Hill South</th>\n",
       "      <td>29</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Highland Park</th>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perry North</th>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brookline</th>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mount Washington</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
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      ],
      "text/plain": [
       "                     facility_num  rec_num\n",
       "neighborhood                              \n",
       "Squirrel Hill South            29       15\n",
       "Highland Park                  30       12\n",
       "Perry North                    20        9\n",
       "Brookline                      19        7\n",
       "Mount Washington                7        5"
      ]
     },
     "execution_count": 241,
     "metadata": {},
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    },
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3 = df2.merge(rec,on='neighborhood').sort_values('rec_num',ascending=False)\n",
    "df3.plot.bar(figsize=(20,10), title='Facilities in Pittsburgh')\n",
    "\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accessory-bottom",
   "metadata": {},
   "source": [
    "By merging the dataframes containing the total facilities as well as rec facilities on the common column of neighborhood, we can create an entirely new dataframe that helps us put the number of rec facilities in perspective. So far, Squirrel Hill South and Highland Park, among others, have high numbers of recreational facilities. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "known-third",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_hoods = neighborhoods.merge(rec, how='left', left_on='hood', right_on='neighborhood')\n",
    "\n",
    "merged_hoods.plot(column='rec_num',\n",
    "                  legend=True,\n",
    "                  legend_kwds={'label':'Recreational facilities'},\n",
    "                  figsize=(15,10),\n",
    "                  missing_kwds={'color':'lightgrey'})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "different-processor",
   "metadata": {},
   "source": [
    "As an added bonus, here's a choropleth map of the number of rec facilities in each neighborhood. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "spoken-blackberry",
   "metadata": {},
   "source": [
    "### The Metric"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tribal-american",
   "metadata": {},
   "source": [
    "The metric being used to rank Pittsburgh's neighborhoods is based on the ratio of number of rec facilities to neighborhood population. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "wrong-finding",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a subset of Pop density data and merge it with the number of rec facilities\n",
    "pop = pop[['Pop. 2010','Neighborhood']]\n",
    "df4 = rec.merge(pop, left_on='neighborhood',right_on='Neighborhood').rename(columns={'Pop. 2010':'pop'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adjusted-italy",
   "metadata": {},
   "source": [
    "First, we create a subset of the population density data that contains the latest population data for each neighborhood, then merge it with the number of rec facilities in each neighborhood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "outstanding-ideal",
   "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>rec_num</th>\n",
       "      <th>pop</th>\n",
       "      <th>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>933</td>\n",
       "      <td>Allegheny Center</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2,500</td>\n",
       "      <td>Allentown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4,144</td>\n",
       "      <td>Banksville</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1,202</td>\n",
       "      <td>Bedford Dwellings</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1,925</td>\n",
       "      <td>Beltzhoover</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rec_num    pop       Neighborhood\n",
       "0        2    933   Allegheny Center\n",
       "1        1  2,500          Allentown\n",
       "2        2  4,144         Banksville\n",
       "3        1  1,202  Bedford Dwellings\n",
       "4        2  1,925        Beltzhoover"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "agricultural-lloyd",
   "metadata": {},
   "source": [
    "So far, so good! But the values in the *pop* column are of string type, which will cause problems when we use them for calculations later on. Let's convert these values to a usable type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "lovely-soviet",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert pop and num columns to float for calculation purposes\n",
    "df4['pop'].replace(',','', regex=True, inplace=True)\n",
    "df4 = df4.astype({'rec_num':'float','pop':'float'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "contemporary-activation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>rec_num</th>\n",
       "      <th>pop</th>\n",
       "      <th>rec_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Highland Park</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6395.0</td>\n",
       "      <td>0.001876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Squirrel Hill South</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15110.0</td>\n",
       "      <td>0.000993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>South Side Slopes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4423.0</td>\n",
       "      <td>0.000678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lincoln-Lemington-Belmar</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4883.0</td>\n",
       "      <td>0.000614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mount Washington</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8799.0</td>\n",
       "      <td>0.000568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Point Breeze</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5315.0</td>\n",
       "      <td>0.000564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Brighton Heights</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7247.0</td>\n",
       "      <td>0.000552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Brookline</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13214.0</td>\n",
       "      <td>0.000530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sheraden</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5299.0</td>\n",
       "      <td>0.000377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>South Side Flats</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6597.0</td>\n",
       "      <td>0.000303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Shadyside</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13915.0</td>\n",
       "      <td>0.000216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Squirrel Hill North</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11363.0</td>\n",
       "      <td>0.000176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Greenfield</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7294.0</td>\n",
       "      <td>0.000137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bloomfield</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8442.0</td>\n",
       "      <td>0.000118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Carrick</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10113.0</td>\n",
       "      <td>0.000099</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Neighborhood  rec_num      pop  rec_ratio\n",
       "0              Highland Park     12.0   6395.0   0.001876\n",
       "1        Squirrel Hill South     15.0  15110.0   0.000993\n",
       "2          South Side Slopes      3.0   4423.0   0.000678\n",
       "3   Lincoln-Lemington-Belmar      3.0   4883.0   0.000614\n",
       "4           Mount Washington      5.0   8799.0   0.000568\n",
       "5               Point Breeze      3.0   5315.0   0.000564\n",
       "6           Brighton Heights      4.0   7247.0   0.000552\n",
       "7                  Brookline      7.0  13214.0   0.000530\n",
       "8                   Sheraden      2.0   5299.0   0.000377\n",
       "9           South Side Flats      2.0   6597.0   0.000303\n",
       "10                 Shadyside      3.0  13915.0   0.000216\n",
       "11       Squirrel Hill North      2.0  11363.0   0.000176\n",
       "12                Greenfield      1.0   7294.0   0.000137\n",
       "13                Bloomfield      1.0   8442.0   0.000118\n",
       "14                   Carrick      1.0  10113.0   0.000099"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create new column with ratios\n",
    "df4['rec_ratio'] = df4['rec_num']/df4['pop']\n",
    "\n",
    "# Reorganize the dataframe and filter out neighborhoods with low populations\n",
    "df4 = df4[['Neighborhood','rec_num','pop','rec_ratio']].sort_values('rec_ratio')\n",
    "df4 = df4[df4['pop'] > df4['pop'].mean()]\n",
    "\n",
    "# Plot!\n",
    "df4.plot.barh(y='rec_ratio', x='Neighborhood', figsize=(15,20))\n",
    "\n",
    "df4.sort_values('rec_ratio', ascending=False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "indoor-express",
   "metadata": {},
   "source": [
    "We create a column with rec facility ratios for each neighborhood, then filter out neighborhoods with low population levels (i.e., below the mean). Finally, we plot our data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "roman-mayor",
   "metadata": {},
   "source": [
    "It can be seen that **Highland Park** comes out on top, followed by Squirrel Hill South, South Side Slopes, Lincoln-Lemington-Belmar and Mount Washington."
   ]
  }
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