main.ipynb

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   "id": "administrative-wagon",
   "metadata": {},
   "source": [
    "# 0010 Final Project: The Best Neighborhood in Pittsburgh"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "conventional-script",
   "metadata": {},
   "source": [
    "## Team All Across the World\n",
    "\n",
    "Keyon Hedayati (keh204@pitt.edu)\n",
    "\n",
    "Minhal Khan (mik172@pitt.edu)\n",
    "\n",
    "Zhen Wu (zhw87@pitt.edu)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "shaped-peoples",
   "metadata": {},
   "source": [
    "## Metrics\n",
    "To determine the best neighborhood in Pittsburgh, we decided to base our metrics on factors that make for a comfortable life in a neighborhood, namely community closeness and cleanliness, opportunities for recreation, and access to public art."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "immediate-neutral",
   "metadata": {},
   "source": [
    "## Cleanliness and Community Closeness - Keyon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "collective-river",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import imports\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import geopandas\n",
    "import plotly.express as px\n",
    "from shapely.geometry import Point, Polygon"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "superb-voltage",
   "metadata": {},
   "source": [
    "**INTRO**\n",
    "\n",
    "The datasets I will be using will be of the Pittsburgh local food gardens, and Smart Trash can locations. The goal of my datasets will be to score and determine a level of cleanliness and signs of a close knit community. The latter of which was determined as number 5 on Forbes top 10 things to look for in a neighborhood. I will be combining these two data sets to come up with an ultimate score (https://www.forbes.com/sites/learnvest/2017/08/02/10-things-you-should-look-for-when-choosing-a-new-neighborhood/?sh=65171b6b5fbc).\n",
    "\n",
    "Additionally, according to research Smart Trash cans are great for the city and neighborhood. They cut on municiple costs, by only sending garbage trucks when they are needed. They also make sure there are no 'overflowing' trash cans. Trash cans that overflow cause more litter than just the overflow. When people see that a can is overflowing, they tend to litter more. That is why I will convert the locations of trash cans to neighborhoods, and develop a metric that looks at trash cans per population density. More information about the benefits of smart trash cans are found at https://www.smartcitiesdive.com/news/pittsburghs-smart-garbage-cans-help-streamline-public-works/559156/#:~:text=The%20smart%20cans%20have%20sensors,most%20efficient%20routes%20for%20collection"
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  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "provincial-aircraft",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the data sources we will be playing with\n",
    "grow = pd.read_csv('https://data.wprdc.org/dataset/0d8c9708-3f01-48c4-adff-04d29825c9a2/resource/dbdfcb3e-8fa5-4468-9b05-f69562798f7a/download/growpghgardens201712.csv')\n",
    "trash = pd.read_csv('https://data.wprdc.org/datastore/dump/75b83ac9-8069-4cf1-bcc3-b9e6b04487d9')\n",
    "grow = grow.drop(['url', 'country', 'state', 'grower_id'], axis = 1) # clean up data by dropping some columns\n",
    "grow = grow[grow.city == 'Pittsburgh'] # limit to pittsburgh city\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "violent-investing",
   "metadata": {},
   "source": [
    "I will be working on the food garden data first. The two main things will be to get pittsburgh divided into neighborhoods since the food garden data doesn't have that information. So I will need to combine a neighborhood map with the food garden locations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "human-yemen",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the neighborhood map\n",
    "neighborhoods = geopandas.read_file(\"Neighborhoods/Neighborhoods_.shp\") # read in the shapefile\n",
    "crs = {'init': 'EPSG:4326'} #this is a coordinate reference system, but not really sure how it works\n",
    "# need to add geometry so we can drop points on the map, and potentially add a column later\n",
    "geometry = [Point(xy) for xy in zip(grow[\"longitude\"], grow['latitude'])]\n",
    "\n",
    "#geo_df = geopandas.GeoDataFrame(grow, crs = crs, geometry=geometry)\n",
    "geo_df = geopandas.GeoDataFrame(grow, geometry=geometry) # i don't think we need crs anymore, should be built in\n",
    "\n",
    "\n"
   ]
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   "source": [
    "# attempt to merge the neighborhood data into the trash can data and map on plotly\n",
    "\n",
    "\n",
    "# convert plain df to geopandas df\n",
    "\n",
    "geo_df_geopanda = geopandas.GeoDataFrame(geo_df, geometry=geopandas.points_from_xy(geo_df.longitude, geo_df.latitude))\n",
    "geo_df_geopanda = geo_df_geopanda.set_crs(\"EPSG:4326\")   # don't think I need this anymore\n",
    "geo_df = geo_df.set_crs(\"EPSG:4326\")\n",
    "\n",
    "point_in_neigh = geopandas.sjoin(geo_df, neighborhoods, how='left', op='within')\n",
    "                \n",
    "# going to try and make map of this data now\n",
    "# plotly version of the data, so can zoom and get neighborhood maps, etc\n",
    "px.set_mapbox_access_token('pk.eyJ1Ijoia2V5b25jYWwiLCJhIjoiY2tuYWM4b25rMWc5djJvbGdyYmFuN3c4MiJ9.CxmqbsOMXQHFuVwsUEFoTg')\n",
    "fig = px.scatter_mapbox(point_in_neigh, lat=\"latitude\", lon=\"longitude\", zoom=11, hover_name='hood')                  \n",
    "fig.show()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "international-tulsa",
   "metadata": {},
   "source": [
    "Now that the food garden map is complete, I can start on the smart trash can information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "orange-baker",
   "metadata": {},
   "outputs": [
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          "Fineview",
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          "Marshall-Shadeland",
          "Brighton Heights",
          "Northview Heights",
          "Marshall-Shadeland",
          "Marshall-Shadeland",
          "Chateau",
          "Marshall-Shadeland",
          "East Allegheny",
          "Troy Hill",
          "Troy Hill",
          "Troy Hill",
          "Brighton Heights",
          "Troy Hill",
          "Allegheny West",
          "Allegheny West",
          "Allegheny West",
          "Allegheny West",
          "East Allegheny",
          "East Allegheny",
          "East Allegheny",
          "Bluff",
          "Bluff",
          "Bluff",
          "East Allegheny",
          "East Allegheny",
          "Brighton Heights",
          "Brighton Heights",
          "Allegheny Center",
          "East Allegheny",
          "East Allegheny",
          "Garfield",
          "Arlington",
          "Greenfield",
          "East Liberty",
          "Morningside",
          "Mt. Oliver",
          "Carrick",
          "East Liberty",
          "Greenfield",
          "Greenfield",
          "East Liberty",
          "East Liberty",
          "East Liberty",
          "Beechview",
          "Point Breeze",
          "Central Business District",
          "East Liberty",
          "Bluff",
          "Beechview",
          "Carrick",
          "Shadyside",
          "Bloomfield",
          "Brookline",
          "Garfield",
          "Shadyside",
          "Shadyside",
          "Shadyside",
          "Brookline",
          "South Oakland",
          "Brighton Heights",
          "Shadyside",
          "Highland Park",
          "Highland Park",
          "Morningside",
          "Central Business District",
          "Brighton Heights",
          "Bluff",
          "South Side Flats",
          "South Side Slopes",
          "Brighton Heights",
          "Allentown",
          "South Side Flats",
          "South Side Slopes",
          "South Side Flats",
          "South Side Flats",
          "South Side Flats",
          "South Side Flats",
          "West End",
          "Brookline",
          "Knoxville",
          "Carrick",
          "Carrick",
          "South Side Flats",
          "Carrick",
          "Carrick",
          "Carrick",
          "South Side Flats",
          "Brookline",
          "Carrick",
          "Carrick",
          "Carrick",
          "South Side Flats",
          "Brookline",
          "South Side Flats",
          "Central Business District",
          "Arlington",
          "South Side Flats",
          "Allentown",
          "South Side Flats",
          "South Side Flats",
          "Brookline",
          "Carrick",
          "Carrick",
          "Carrick",
          "Allentown",
          "South Side Slopes",
          "South Side Flats",
          "South Side Flats",
          "Brookline",
          "Brookline",
          "South Side Slopes",
          "South Side Flats",
          "Beltzhoover",
          "South Side Flats",
          "Allentown",
          "South Side Flats",
          "South Side Flats",
          "South Side Flats",
          "Knoxville",
          "Carrick",
          "Carrick",
          "South Side Slopes",
          "Carrick",
          "Allentown",
          "South Side Flats",
          "South Side Flats",
          "Knoxville",
          "Beltzhoover",
          "South Side Flats",
          "South Side Flats",
          "Brookline",
          "South Side Flats",
          "South Side Flats",
          "Arlington",
          "Allentown",
          "South Side Flats",
          "Carrick",
          "Brighton Heights",
          "Allegheny West",
          "Bloomfield",
          "Shadyside",
          "Bloomfield",
          "Garfield",
          "Shadyside",
          "Manchester",
          "Allegheny West",
          "Allegheny West",
          "Brighton Heights",
          "Brighton Heights",
          "Allegheny West",
          "Allegheny West",
          "East Allegheny",
          "Allegheny West",
          "East Allegheny",
          "Troy Hill",
          "Troy Hill",
          "East Allegheny",
          "East Allegheny",
          "Troy Hill",
          "East Allegheny",
          "East Allegheny",
          "East Allegheny",
          "Troy Hill",
          "East Allegheny",
          "Brighton Heights",
          "Brighton Heights",
          "Brighton Heights",
          "East Allegheny",
          "Knoxville",
          "South Side Flats",
          "South Side Flats",
          "Brookline",
          "Carrick",
          "South Side Flats",
          "South Side Slopes",
          "South Side Flats",
          "South Side Slopes",
          "South Side Flats",
          "South Side Flats",
          "Knoxville",
          "South Side Flats",
          "Carrick",
          "South Side Slopes",
          "Highland Park",
          "Bloomfield",
          "Shadyside",
          "Allegheny Center",
          "Bluff",
          "Allegheny West",
          "Brookline",
          "Bloomfield",
          "Highland Park",
          "Shadyside",
          "Shadyside",
          "Shadyside",
          "Morningside",
          "Central Oakland",
          "East Liberty",
          "Bluff",
          "Bloomfield",
          "Shadyside",
          "Greenfield",
          "East Liberty",
          "Shadyside",
          "Shadyside",
          "Bluff",
          "Highland Park",
          "Bluff",
          "Bluff",
          "Knoxville",
          "Point Breeze",
          "Shadyside",
          "Bloomfield",
          "Shadyside",
          "Larimer",
          "Bluff",
          "East Liberty",
          "Shadyside",
          "Shadyside",
          "Brighton Heights",
          "South Shore",
          "Mount Washington",
          "Sheraden",
          "Crafton Heights",
          "Elliott",
          "Sheraden",
          "Sheraden",
          "Beechview",
          "Crafton Heights",
          "Crafton Heights",
          "Westwood",
          "Beechview",
          "Beechview",
          "Greenfield",
          "Morningside",
          "Elliott",
          "Central Oakland",
          "Elliott",
          "Mount Washington",
          "Beechview",
          "Beechview",
          "Mount Washington",
          "Mount Washington",
          "West End",
          "Sheraden",
          "Crafton Heights",
          "Mount Washington",
          "Elliott",
          "Crafton Heights",
          "Windgap",
          "Sheraden",
          "Mount Washington",
          "West End",
          "Mount Washington",
          "Crafton Heights",
          "Elliott",
          "Sheraden",
          "Central Oakland",
          "Central Oakland",
          "Sheraden",
          "Point Breeze",
          "Central Oakland",
          "Mount Washington",
          "West End",
          "West End",
          "Mount Washington",
          "West End",
          "Crafton Heights",
          "Elliott",
          "Crafton Heights",
          "East Carnegie",
          "Mount Washington",
          "Sheraden",
          "Crafton Heights",
          "Sheraden",
          "Mount Washington",
          "Sheraden",
          "Mount Washington",
          "Elliott",
          "Beechview",
          "Sheraden",
          "Central Oakland",
          "Sheraden",
          "Carrick",
          "Point Breeze",
          "Elliott",
          "Crawford-Roberts",
          "Point Breeze",
          "Central Oakland",
          "Knoxville",
          "Highland Park",
          "Elliott",
          "Shadyside",
          "Shadyside",
          "Central Oakland",
          "Carrick",
          "East Liberty",
          "Shadyside",
          "Shadyside",
          "Bedford Dwellings",
          "Perry North",
          "North Shore",
          "Strip District",
          "Brighton Heights",
          "North Shore",
          "North Shore",
          "North Shore",
          "North Shore",
          "North Shore",
          "Strip District",
          "Strip District",
          "Mount Washington",
          "Strip District",
          "North Shore",
          "Strip District",
          "Strip District",
          "Sheraden",
          "Strip District",
          "Mount Washington",
          "Allegheny West",
          "North Shore",
          "North Shore",
          "Shadyside",
          "Upper Hill",
          "North Shore",
          "Strip District",
          "Polish Hill",
          "Crawford-Roberts",
          "Squirrel Hill South",
          "North Shore",
          "Strip District",
          "Greenfield",
          "North Shore",
          "North Shore",
          "Strip District",
          "Strip District",
          "Crawford-Roberts",
          "Polish Hill",
          "North Shore",
          "North Shore",
          "North Shore",
          "Polish Hill",
          "Central Lawrenceville",
          "North Shore",
          "North Shore",
          "Strip District",
          "North Shore",
          "Duquesne Heights",
          "Crawford-Roberts",
          "North Shore",
          "Strip District",
          "North Shore",
          "Polish Hill",
          "Strip District",
          "Polish Hill",
          "North Shore",
          "Mount Washington",
          "North Shore",
          "North Shore",
          "Strip District",
          "Polish Hill",
          "Crawford-Roberts",
          "North Shore",
          "Bedford Dwellings",
          "Polish Hill",
          "Carrick",
          "North Shore",
          "Chateau",
          "North Shore",
          "Shadyside",
          "Strip District",
          "North Shore",
          "Strip District",
          "Polish Hill",
          "North Shore",
          "Strip District",
          "North Shore",
          "Strip District",
          "North Shore",
          "North Oakland",
          "Perry North",
          "Chateau",
          "North Oakland",
          "Greenfield",
          "North Shore",
          "Central Oakland",
          "North Shore",
          "Middle Hill",
          "Lower Lawrenceville",
          "North Shore",
          "Strip District",
          "Strip District",
          "Polish Hill",
          "North Shore",
          "Strip District",
          "Carrick",
          "Central Oakland",
          "North Shore",
          "North Shore",
          "Polish Hill",
          "North Shore",
          "North Shore",
          "Central Oakland",
          "Strip District",
          "Carrick",
          "Strip District",
          "Strip District",
          "Strip District",
          "Central Oakland",
          "Crawford-Roberts",
          "Strip District",
          "Strip District",
          "Bluff",
          "Polish Hill",
          "Strip District",
          "Strip District",
          "Strip District",
          "Mount Washington",
          "Strip District",
          "Strip District",
          "Strip District",
          "Strip District",
          "Perry North",
          "North Oakland",
          "Allegheny West",
          "Strip District",
          "Strip District",
          "Strip District",
          "North Shore",
          "North Shore",
          "Bluff",
          "North Shore",
          "Strip District",
          "Squirrel Hill South",
          "Polish Hill",
          "North Shore",
          "Mount Washington",
          "Strip District",
          "Crawford-Roberts",
          "North Shore",
          "Middle Hill",
          "Morningside",
          "Central Northside",
          "North Shore",
          "Strip District",
          "Strip District",
          "Chateau",
          "Central Oakland",
          "North Shore",
          "North Shore",
          "Strip District",
          "Strip District",
          "North Shore",
          "Strip District",
          "North Shore",
          "North Shore",
          "Strip District",
          "North Shore",
          "North Oakland",
          "Carrick",
          "North Shore",
          "Carrick",
          "South Oakland",
          "Upper Hill",
          "South Side Flats",
          "Mount Washington",
          "Banksville",
          "South Side Flats",
          "Terrace Village",
          "East Liberty",
          "Point Breeze",
          "Perry North",
          "North Oakland",
          "East Liberty",
          "Hazelwood",
          "South Side Flats",
          "Hazelwood",
          "Crawford-Roberts",
          "Terrace Village",
          "Upper Hill",
          "South Side Slopes",
          "South Side Flats",
          "Point Breeze",
          "Shadyside",
          "Middle Hill",
          "South Side Flats",
          "East Liberty",
          "Squirrel Hill South",
          "Bluff",
          "Bloomfield",
          "Mount Washington",
          "Bedford Dwellings",
          "West Oakland",
          "South Oakland",
          "Hazelwood",
          "Squirrel Hill North",
          "Upper Hill",
          "East Liberty",
          "Lincoln Place",
          "Bloomfield",
          "South Side Flats",
          "Central Oakland",
          "Middle Hill",
          "East Liberty",
          "Central Lawrenceville",
          "Squirrel Hill North",
          "Shadyside",
          "Bluff",
          "Greenfield",
          "Hazelwood",
          "Mount Washington",
          "Hazelwood",
          "West Oakland",
          "Middle Hill",
          "Hazelwood",
          "North Oakland",
          "Middle Hill",
          "Hazelwood",
          "Hazelwood",
          "Middle Hill",
          "Point Breeze",
          "Middle Hill",
          "Bluff",
          "East Liberty",
          "Hazelwood",
          "Hazelwood",
          "Middle Hill",
          "Perry North",
          "Perry North",
          "Perry North",
          "Middle Hill",
          "Allentown",
          "California-Kirkbride",
          "Squirrel Hill South",
          "Middle Hill",
          "Sheraden",
          "Mount Washington",
          "Mount Washington",
          "Mount Washington",
          "Carrick",
          "Homewood South",
          "Central Oakland",
          "West Oakland",
          "Shadyside",
          "Homewood South",
          "Crawford-Roberts",
          "Terrace Village",
          "South Side Flats",
          "Hazelwood",
          "Hazelwood",
          "Central Oakland",
          "East Liberty",
          "Mount Washington",
          "Shadyside",
          "Strip District",
          "Hazelwood",
          "Central Oakland",
          "Central Oakland",
          "Shadyside",
          "Lincoln Place",
          "Hazelwood",
          "Bluff",
          "West Oakland",
          "East Liberty",
          "Central Business District",
          "North Oakland",
          "Terrace Village",
          "Central Oakland",
          "South Oakland",
          "Upper Hill",
          "Squirrel Hill South",
          "Terrace Village",
          "Brookline",
          "Squirrel Hill South",
          "Bluff",
          "Regent Square",
          "North Oakland",
          "Central Oakland",
          "Carrick",
          "Bluff",
          "Bluff",
          "North Oakland",
          "Homewood South",
          "Regent Square",
          "Hazelwood",
          "South Side Flats",
          "Middle Hill",
          "Greenfield",
          "Squirrel Hill South",
          "Central Oakland",
          "Upper Hill",
          "Greenfield",
          "Middle Hill",
          "Middle Hill",
          "Squirrel Hill North",
          "East Liberty",
          "Terrace Village",
          "Lincoln Place",
          "North Oakland",
          "West Oakland",
          "Hazelwood",
          "Squirrel Hill North",
          "Bluff",
          "Middle Hill",
          "Crawford-Roberts",
          "Middle Hill",
          "Upper Hill",
          "Squirrel Hill South",
          "Greenfield",
          "Squirrel Hill South",
          "Central Business District",
          "Central Business District",
          "Crawford-Roberts",
          "Crawford-Roberts",
          "Homewood South",
          "Middle Hill",
          "Bluff",
          "Crawford-Roberts",
          "Upper Hill",
          "Homewood South",
          "Larimer",
          "South Oakland",
          "Homewood South",
          "Greenfield",
          "East Liberty",
          "Carrick",
          "East Liberty",
          "Crawford-Roberts",
          "Homewood South",
          "South Side Flats",
          "North Oakland",
          "Hazelwood",
          "South Side Flats",
          "Allentown",
          "Squirrel Hill South",
          "Squirrel Hill North",
          "South Side Flats",
          "Larimer",
          "Carrick",
          "Point Breeze",
          "Squirrel Hill South",
          "Carrick",
          "Carrick",
          "North Oakland",
          "Squirrel Hill North",
          "South Side Flats",
          "Central Oakland",
          "Squirrel Hill South",
          "Squirrel Hill North",
          "Squirrel Hill South",
          "Squirrel Hill South",
          "Squirrel Hill South",
          "Squirrel Hill North",
          "Middle Hill",
          "Carrick",
          "Squirrel Hill South",
          "Carrick",
          "Squirrel Hill South",
          "Squirrel Hill North",
          "South Side Flats",
          "Carrick",
          "Squirrel Hill South",
          "Central Oakland",
          "East Liberty",
          "Point Breeze",
          "Squirrel Hill North",
          "Squirrel Hill South",
          "Squirrel Hill South",
          "Squirrel Hill South",
          "Squirrel Hill South",
          "Allentown",
          "Squirrel Hill South",
          "Sheraden",
          "Perry North",
          "Squirrel Hill South",
          "Banksville",
          "North Shore",
          "St. Clair",
          "Allentown",
          "Carrick",
          "Carrick",
          "Allentown",
          "Middle Hill",
          "Perry North",
          "South Side Flats",
          "South Side Flats",
          "South Side Flats",
          "South Side Flats",
          "Squirrel Hill South",
          "Spring Hill-City View",
          "Squirrel Hill South",
          "South Side Flats",
          "Crawford-Roberts",
          "Crawford-Roberts",
          "Middle Hill",
          "Middle Hill",
          "Crawford-Roberts",
          "North Oakland",
          "California-Kirkbride",
          "North Oakland",
          "Middle Hill",
          "North Oakland",
          "South Side Flats",
          "Squirrel Hill South",
          "Middle Hill",
          "Crawford-Roberts",
          "Squirrel Hill South",
          "Squirrel Hill North",
          "South Side Flats",
          "South Side Flats",
          "Middle Hill",
          "South Side Flats",
          "South Side Flats",
          "South Side Flats",
          "Carrick",
          "Duquesne Heights",
          "Squirrel Hill South",
          "Squirrel Hill South",
          "Middle Hill",
          "Hazelwood",
          "Squirrel Hill South",
          "Squirrel Hill South",
          "Squirrel Hill South",
          "Squirrel Hill North",
          "Middle Hill",
          "Squirrel Hill South",
          "Middle Hill",
          "Squirrel Hill South",
          "Hazelwood",
          "Squirrel Hill South",
          "Squirrel Hill South",
          "Westwood",
          "Homewood North",
          "Squirrel Hill South",
          "Squirrel Hill South",
          "South Side Flats",
          "Squirrel Hill South",
          "Hazelwood",
          "Squirrel Hill South",
          "Squirrel Hill South",
          "Oakwood",
          "South Side Flats",
          "Squirrel Hill South",
          "South Side Flats",
          "Squirrel Hill South",
          "Westwood",
          "South Side Flats",
          "South Side Flats",
          "Westwood",
          "Duquesne Heights",
          "Middle Hill",
          "Middle Hill",
          "Shadyside",
          "Crawford-Roberts",
          "Squirrel Hill North",
          "Middle Hill",
          "Squirrel Hill North",
          "Squirrel Hill North",
          "South Side Flats",
          "Greenfield",
          "Homewood North",
          "North Shore",
          "Central Oakland",
          "Squirrel Hill South",
          "Squirrel Hill North",
          "Squirrel Hill North",
          "Sheraden",
          "West End",
          "Squirrel Hill South",
          "Central Oakland",
          "Central Oakland",
          "Squirrel Hill South",
          "Central Oakland",
          "Squirrel Hill North",
          "Squirrel Hill South",
          "Middle Hill",
          "South Side Flats",
          "Westwood",
          "West End",
          "West End",
          "Homewood South",
          "Homewood North",
          "Point Breeze North",
          "Brookline",
          "Larimer",
          "Bloomfield",
          "Highland Park",
          "Homewood North",
          "Friendship",
          "Homewood South",
          "Larimer",
          "Larimer",
          "East Liberty",
          "Brookline",
          "Homewood South",
          "East Liberty",
          "East Liberty",
          "Brookline",
          "Bloomfield",
          "Larimer",
          "Lincoln-Lemington-Belmar",
          "Homewood South",
          "Homewood South",
          "Larimer",
          "Homewood West",
          "Larimer",
          "Central Lawrenceville",
          "Shadyside",
          "Larimer",
          "East Liberty",
          "East Liberty",
          "Bloomfield",
          "Larimer",
          "East Liberty",
          "Bloomfield",
          "East Hills",
          "Lower Lawrenceville",
          "East Liberty",
          "Shadyside",
          "Bloomfield",
          "Bloomfield",
          "Lincoln-Lemington-Belmar",
          "Brookline",
          "East Liberty",
          "Lower Lawrenceville",
          "Shadyside",
          "Shadyside",
          "Brookline",
          "East Liberty",
          "Homewood South",
          "Larimer",
          "East Liberty",
          "Friendship",
          "Homewood South",
          "East Liberty",
          "Bloomfield",
          "Lower Lawrenceville",
          "Bloomfield",
          "Friendship",
          "Homewood West",
          "Point Breeze North",
          "Brookline",
          "Larimer",
          "Bluff",
          "Homewood South",
          "Homewood South",
          "Bloomfield",
          "Bluff",
          "Bluff",
          "Homewood North",
          "Bluff",
          "East Liberty",
          "Homewood South",
          "Homewood South",
          "Lower Lawrenceville",
          "Lincoln-Lemington-Belmar",
          "Lincoln-Lemington-Belmar",
          "Central Lawrenceville",
          "Homewood South",
          "Bloomfield",
          "East Liberty",
          "Lower Lawrenceville",
          "Lower Lawrenceville",
          "Highland Park",
          "Homewood North",
          "East Hills",
          "Homewood North",
          "East Liberty",
          "East Liberty",
          "Bloomfield",
          "East Liberty",
          "Friendship",
          "Larimer",
          "Highland Park",
          "Bloomfield",
          "Friendship",
          "Homewood South",
          "Lower Lawrenceville",
          "Lincoln-Lemington-Belmar",
          "Shadyside",
          "Friendship",
          "Shadyside",
          "Friendship",
          "Allegheny Center",
          "Friendship",
          "Lincoln-Lemington-Belmar",
          "East Liberty",
          "Homewood South",
          "Bloomfield",
          "Lincoln-Lemington-Belmar",
          "Bloomfield",
          "Friendship",
          "Bloomfield",
          "East Liberty",
          "East Liberty",
          "Larimer",
          "Shadyside",
          "East Liberty",
          "East Liberty",
          "Middle Hill",
          "Bluff",
          "Central Oakland",
          "Bluff",
          "South Oakland",
          "South Oakland",
          "Central Oakland",
          "Central Oakland",
          "Brighton Heights",
          "Bluff",
          "Bluff",
          "Middle Hill",
          "Crawford-Roberts",
          "West Oakland",
          "Bluff",
          "South Side Flats",
          "Central Oakland",
          "Bluff",
          "South Oakland",
          "West Oakland",
          "Central Oakland",
          "Crawford-Roberts",
          "Central Lawrenceville",
          "West Oakland",
          "North Shore",
          "Central Oakland",
          "Strip District",
          "Bloomfield",
          "Mount Washington",
          "Homewood North",
          "Upper Lawrenceville",
          "Bloomfield",
          "Central Oakland",
          "Stanton Heights",
          "Central Oakland",
          "Central Oakland",
          "Central Oakland",
          "Central Oakland",
          "North Oakland",
          "Central Oakland",
          "Central Lawrenceville",
          "Central Oakland",
          "West Oakland",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Carrick",
          "Central Lawrenceville",
          "Bloomfield",
          "Spring Hill-City View",
          "Allentown",
          "Bloomfield",
          "Central Lawrenceville",
          "Bloomfield",
          "Bloomfield",
          "Greenfield",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Central Lawrenceville",
          "Central Lawrenceville",
          "Friendship",
          "Shadyside",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "East Liberty",
          "Shadyside",
          "Homewood West",
          "Homewood South",
          "Bloomfield",
          "East Liberty",
          "East Liberty",
          "East Liberty",
          "East Liberty",
          "Bloomfield",
          "Friendship",
          "Central Lawrenceville",
          "Lower Lawrenceville",
          "Homewood West",
          "Strip District",
          "Lower Lawrenceville",
          "Highland Park",
          "East Liberty",
          "East Liberty",
          "East Liberty",
          "Lower Lawrenceville",
          "Highland Park",
          "East Liberty",
          "East Liberty",
          "East Liberty",
          "East Liberty",
          "Garfield",
          "East Liberty",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
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     "metadata": {},
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   ],
   "source": [
    "# time to work on trash can data. \n",
    "# gonna try convert it to geo pandas, and maybe helps get the neighborhood name\n",
    "trash_df = trash # copy just in case \n",
    "\n",
    "trash_geo = geopandas.GeoDataFrame(trash_df, geometry=geopandas.points_from_xy(trash_df.x, trash_df.y))\n",
    "trash_geo.set_crs(\"EPSG:4326\")\n",
    "\n",
    "px.set_mapbox_access_token('pk.eyJ1Ijoia2V5b25jYWwiLCJhIjoiY2tuYWM4b25rMWc5djJvbGdyYmFuN3c4MiJ9.CxmqbsOMXQHFuVwsUEFoTg')\n",
    "fig = px.scatter_mapbox(trash_geo, lat=\"y\", lon=\"x\", zoom=11, hover_name=\"neighborhood\")                  \n",
    "fig.show()\n",
    "\n",
    "# this cell works to get the trash cans on the map, and show the neighborhood on hover"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "forbidden-breach",
   "metadata": {},
   "source": [
    "Now for the ultimate goal. Combine the two data sets and come up with a metric that describes my goal. Each neighborhood will end up with a score that is determined by how many smart trash cans they have, and if they have a local food garden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "worthy-connecticut",
   "metadata": {},
   "outputs": [
    {
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper Lawrenceville</th>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West End</th>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Oakland</th>\n",
       "      <td>9</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Westwood</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Windgap</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     smart_trash  food_garden\n",
       "hood                                         \n",
       "Allegheny Center               9          0.0\n",
       "Allegheny West                15          0.0\n",
       "Allentown                     12          0.0\n",
       "Arlington                      3          0.0\n",
       "Banksville                     2          0.0\n",
       "...                          ...          ...\n",
       "Upper Lawrenceville           16          0.0\n",
       "West End                       9          1.0\n",
       "West Oakland                   9          3.0\n",
       "Westwood                       5          0.0\n",
       "Windgap                        1          0.0\n",
       "\n",
       "[79 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# want to merge the two data sets so can do metric of trash_grow / 100 people\n",
    "grow_neigh = point_in_neigh\n",
    "trash_df = trash_geo\n",
    "\n",
    "trash_df=trash_df.rename(columns={'neighborhood':'hood'})   # rename columns so they match for the aggregate\n",
    "\n",
    "# want to know how many trash and food are in each neighborhood. So going to just do a count of neighborhoods in eac\n",
    "grow_neigh_group= grow_neigh.dissolve(by='hood', aggfunc='count')\n",
    "trash_df_group = trash_df.dissolve(by='hood', aggfunc='count')\n",
    "\n",
    "# now need to merge the data\n",
    "merged_df = trash_df_group.merge(grow_neigh_group, on='hood', how='left')   # trash has more entries so merge on left\n",
    "merged_df = merged_df[['container_id', 'page_numbe']]   # since just a count, we only need these two columns (maybe add geometry later)\n",
    "merged_df = merged_df.rename(columns={'container_id':'smart_trash', 'page_numbe': 'food_garden'})  # rename columns to make clear\n",
    "merged_df['food_garden'] = merged_df['food_garden'].fillna(0)   # get rid of the NaN by replacing with 0\n",
    "\n",
    "\n",
    "merged_df\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "instant-progressive",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>hood</th>\n",
       "      <th>smart_trash</th>\n",
       "      <th>food_garden</th>\n",
       "      <th>area</th>\n",
       "      <th>pop</th>\n",
       "      <th>percent_pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Allegheny Center</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.4</td>\n",
       "      <td>933.0</td>\n",
       "      <td>0.003156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Allegheny West</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.2</td>\n",
       "      <td>462.0</td>\n",
       "      <td>0.001563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allentown</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>188.8</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>0.008457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arlington</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.8</td>\n",
       "      <td>1869.0</td>\n",
       "      <td>0.006322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Banksville</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>620.2</td>\n",
       "      <td>4144.0</td>\n",
       "      <td>0.014018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Upper Lawrenceville</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>259.2</td>\n",
       "      <td>2669.0</td>\n",
       "      <td>0.009028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>West End</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.000859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>West Oakland</td>\n",
       "      <td>9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>137.6</td>\n",
       "      <td>2604.0</td>\n",
       "      <td>0.008808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Westwood</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>442.9</td>\n",
       "      <td>3066.0</td>\n",
       "      <td>0.010371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Windgap</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>265.6</td>\n",
       "      <td>1369.0</td>\n",
       "      <td>0.004631</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   hood  smart_trash  food_garden   area     pop  percent_pop\n",
       "0      Allegheny Center            9          0.0  134.4   933.0     0.003156\n",
       "1        Allegheny West           15          0.0   90.2   462.0     0.001563\n",
       "2             Allentown           12          0.0  188.8  2500.0     0.008457\n",
       "3             Arlington            3          0.0  300.8  1869.0     0.006322\n",
       "4            Banksville            2          0.0  620.2  4144.0     0.014018\n",
       "..                  ...          ...          ...    ...     ...          ...\n",
       "74  Upper Lawrenceville           16          0.0  259.2  2669.0     0.009028\n",
       "75             West End            9          1.0  128.0   254.0     0.000859\n",
       "76         West Oakland            9          3.0  137.6  2604.0     0.008808\n",
       "77             Westwood            5          0.0  442.9  3066.0     0.010371\n",
       "78              Windgap            1          0.0  265.6  1369.0     0.004631\n",
       "\n",
       "[79 rows x 6 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now that we just have the breakdown of smart_trash and food_garden by neighborhood lets get population\n",
    "#pop_df = pd.read_csv('https://data.wprdc.org/dataset/3cb2b9c2-66e2-4ffa-b0a1-35fb2c6f7230/resource/eb1f8714-e0eb-4614-acfe-dfe0ebab942f/download/school-enrollment-by-detailed-level-of-school-for-the-population-3-years-and-over.csv')\n",
    "pop_df = pd.read_csv('https://data.wprdc.org/dataset/40188e1c-6d2e-4f20-9391-607bd3054949/resource/8ac5c768-09ba-43a8-829f-ce4a417d20fb/download/population-density.csv')\n",
    "pop_df = pop_df[['Neighborhood', 'Land Area (acres)', 'Pop. 2010']]   # only want the population\n",
    "pop_df = pop_df.rename(columns = {'Neighborhood':'hood', 'Land Area (acres)':'area', 'Pop. 2010':'pop'})  #rename the columns\n",
    "merged_final = merged_df.merge(pop_df, how='left', on='hood')\n",
    "merged_final.replace(',','', regex=True, inplace=True)\n",
    "merged_final['area'] = merged_final['area'].astype(float)\n",
    "merged_final['pop'] = merged_final['pop'].astype(float)\n",
    "merged_final['percent_pop'] = merged_final['pop'] / merged_final['pop'].sum()\n",
    "merged_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "prostate-nylon",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>smart_trash</th>\n",
       "      <th>food_garden</th>\n",
       "      <th>area</th>\n",
       "      <th>pop</th>\n",
       "      <th>percent_pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16.025316</td>\n",
       "      <td>0.708861</td>\n",
       "      <td>396.905063</td>\n",
       "      <td>3742.101266</td>\n",
       "      <td>0.012658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>18.071067</td>\n",
       "      <td>1.099556</td>\n",
       "      <td>309.291995</td>\n",
       "      <td>3290.050384</td>\n",
       "      <td>0.011129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>181.400000</td>\n",
       "      <td>1365.000000</td>\n",
       "      <td>0.004617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>284.800000</td>\n",
       "      <td>2714.000000</td>\n",
       "      <td>0.009181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>532.150000</td>\n",
       "      <td>4742.000000</td>\n",
       "      <td>0.016041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1709.400000</td>\n",
       "      <td>15110.000000</td>\n",
       "      <td>0.051112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       smart_trash  food_garden         area           pop  percent_pop\n",
       "count    79.000000    79.000000    79.000000     79.000000    79.000000\n",
       "mean     16.025316     0.708861   396.905063   3742.101266     0.012658\n",
       "std      18.071067     1.099556   309.291995   3290.050384     0.011129\n",
       "min       1.000000     0.000000    64.000000     11.000000     0.000037\n",
       "25%       4.000000     0.000000   181.400000   1365.000000     0.004617\n",
       "50%       9.000000     0.000000   284.800000   2714.000000     0.009181\n",
       "75%      19.000000     1.000000   532.150000   4742.000000     0.016041\n",
       "max      78.000000     4.000000  1709.400000  15110.000000     0.051112"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now we can add the column for the metric\n",
    "merged_final.describe()    # to help guide us for weighting garden more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "logical-strike",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>hood</th>\n",
       "      <th>smart_trash</th>\n",
       "      <th>food_garden</th>\n",
       "      <th>area</th>\n",
       "      <th>pop</th>\n",
       "      <th>percent_pop</th>\n",
       "      <th>clean_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>South Shore</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135.7</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.000047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Chateau</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>245.1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>St. Clair</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>198.4</td>\n",
       "      <td>209.0</td>\n",
       "      <td>0.000707</td>\n",
       "      <td>0.000356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>East Carnegie</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>276.5</td>\n",
       "      <td>570.0</td>\n",
       "      <td>0.001928</td>\n",
       "      <td>0.000697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Swisshelm Park</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>307.2</td>\n",
       "      <td>1361.0</td>\n",
       "      <td>0.004604</td>\n",
       "      <td>0.001499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bluff</td>\n",
       "      <td>32</td>\n",
       "      <td>1.0</td>\n",
       "      <td>209.3</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>0.022326</td>\n",
       "      <td>0.394670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bloomfield</td>\n",
       "      <td>60</td>\n",
       "      <td>2.0</td>\n",
       "      <td>449.3</td>\n",
       "      <td>8442.0</td>\n",
       "      <td>0.028556</td>\n",
       "      <td>0.444902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>East Liberty</td>\n",
       "      <td>72</td>\n",
       "      <td>4.0</td>\n",
       "      <td>371.8</td>\n",
       "      <td>5869.0</td>\n",
       "      <td>0.019853</td>\n",
       "      <td>0.491247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Central Oakland</td>\n",
       "      <td>44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>179.8</td>\n",
       "      <td>6086.0</td>\n",
       "      <td>0.020587</td>\n",
       "      <td>0.503793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Shadyside</td>\n",
       "      <td>78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>589.4</td>\n",
       "      <td>13915.0</td>\n",
       "      <td>0.047070</td>\n",
       "      <td>0.622910</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               hood  smart_trash  food_garden   area      pop  percent_pop  \\\n",
       "60      South Shore            1          0.0  135.7     19.0     0.000064   \n",
       "18          Chateau            4          0.0  245.1     11.0     0.000037   \n",
       "67        St. Clair            1          0.0  198.4    209.0     0.000707   \n",
       "23    East Carnegie            1          0.0  276.5    570.0     0.001928   \n",
       "70   Swisshelm Park            1          0.0  307.2   1361.0     0.004604   \n",
       "..              ...          ...          ...    ...      ...          ...   \n",
       "9             Bluff           32          1.0  209.3   6600.0     0.022326   \n",
       "8        Bloomfield           60          2.0  449.3   8442.0     0.028556   \n",
       "25     East Liberty           72          4.0  371.8   5869.0     0.019853   \n",
       "17  Central Oakland           44          0.0  179.8   6086.0     0.020587   \n",
       "57        Shadyside           78          0.0  589.4  13915.0     0.047070   \n",
       "\n",
       "    clean_score  \n",
       "60     0.000047  \n",
       "18     0.000061  \n",
       "67     0.000356  \n",
       "23     0.000697  \n",
       "70     0.001499  \n",
       "..          ...  \n",
       "9      0.394670  \n",
       "8      0.444902  \n",
       "25     0.491247  \n",
       "17     0.503793  \n",
       "57     0.622910  \n",
       "\n",
       "[79 rows x 7 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we will make every food garden worth 5 points, and every trash can worth 1 point\n",
    "# then we will figure out how many points per area size and weight by populations percent.\n",
    "# Hopefully giving us our best areas in pitt as judged by level of community and cleanliness\n",
    "merged_final['clean_score'] = ((merged_final['smart_trash'] + (merged_final['food_garden'] * 5)) / (merged_final['area']/100)) * merged_final['percent_pop']\n",
    "\n",
    "# lets see how the data looks on a bar graph\n",
    "\n",
    "\n",
    "merged_final_sort = merged_final.sort_values(by=['clean_score'], ascending=True)\n",
    "\n",
    "graph = merged_final_sort.plot.barh(x='hood', y='clean_score', figsize=(20, 20))\n",
    "merged_final_sort"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "liquid-active",
   "metadata": {},
   "source": [
    "According to my data **Shadyside** is both a clean neighborhood, with lots of local love. In other words, it is a clean, and close-knit community."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "relevant-bacon",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "indoor-norwegian",
   "metadata": {},
   "source": [
    "## Recreation Opportunities in Populated Neighborhoods - Minhal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "going-copying",
   "metadata": {},
   "source": [
    "My metric for determining the best neighborhood in Pittsburgh is the number of recreation facilities per population for each neighborhood. 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 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": 20,
   "id": "trained-harvest",
   "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(\"https://data.wprdc.org/dataset/40188e1c-6d2e-4f20-9391-607bd3054949/resource/8ac5c768-09ba-43a8-829f-ce4a417d20fb/download/population-density.csv\")\n",
    "neighborhoods = gd.read_file(\"Neighborhoods/Neighborhoods_.shp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "computational-lloyd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "orange-cattle",
   "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": 22,
   "id": "integrated-baghdad",
   "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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banksville</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bedford Dwellings</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 22,
     "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": "unable-front",
   "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": 23,
   "id": "greatest-universe",
   "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",
       "    </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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allentown</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banksville</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bedford Dwellings</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beltzhoover</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 23,
     "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": "characteristic-vision",
   "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": 24,
   "id": "russian-hearing",
   "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>facility_num</th>\n",
       "      <th>rec_num</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighborhood</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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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": "historic-check",
   "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": 25,
   "id": "raising-costs",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "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": "green-sucking",
   "metadata": {},
   "source": [
    "As an added bonus, here's a choropleth map of the number of rec facilities in each neighborhood. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eleven-teddy",
   "metadata": {},
   "source": [
    "### The Metric"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adverse-customs",
   "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": 26,
   "id": "derived-single",
   "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": "fiscal-activity",
   "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": 27,
   "id": "returning-edward",
   "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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stretch-humanity",
   "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": 28,
   "id": "weird-perception",
   "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": 29,
   "id": "intermediate-integer",
   "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": 29,
     "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": "injured-tongue",
   "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": "separated-circulation",
   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "polished-support",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "yellow-lounge",
   "metadata": {},
   "source": [
    "## Access to Public Art in Pittsburgh Neighborhoods - Zhen"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "static-maximum",
   "metadata": {},
   "source": [
    "Public art is a spirit of innovation and a tag of the inner-city culture. According to https://www.smartcitiesdive.com/ex/sustainablecitiescollective/how-tough-neighborhood-building-stronger-future-vivid-public-art/91756/, it makes the neighborhoods more vibrant and improves the social environment. Thus, we suppose that more public art pieces in the neighborhood, the better the neighborhood is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "medical-affiliation",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "critical-exclusive",
   "metadata": {},
   "source": [
    "Let's get the public art data from the WPRDC (https://data.wprdc.org/dataset/city-of-pittsburgh-public-art/resource/00d74e83-8a23-486e-841b-286e1332a151?view_id=7c4a2734-ec64-46a0-95ad-39d4fe599ae0).\n",
    "Below shows a sample of what the data looks like on the WPRDC website."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "brutal-label",
   "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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>title</th>\n",
       "      <th>artist_name</th>\n",
       "      <th>art_type</th>\n",
       "      <th>park</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>812728309</td>\n",
       "      <td>1001</td>\n",
       "      <td>Oakwood Road Monument</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oakwood</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4.200356e+10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1-19</td>\n",
       "      <td>40.429192</td>\n",
       "      <td>-80.066225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>188255168</td>\n",
       "      <td>1002</td>\n",
       "      <td>Rooster Waiting For The Streetcar</td>\n",
       "      <td>James Simon</td>\n",
       "      <td>Modern and Contemporary Art</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Elliott</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.200356e+10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1-17</td>\n",
       "      <td>40.442926</td>\n",
       "      <td>-80.039711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2032091917</td>\n",
       "      <td>129</td>\n",
       "      <td>Middle East Memorial Plaque</td>\n",
       "      <td>NaN</td>\n",
       "      <td>War Memorials</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Central Business District</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.200302e+10</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1-4</td>\n",
       "      <td>40.438092</td>\n",
       "      <td>-79.996852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1447629273</td>\n",
       "      <td>143</td>\n",
       "      <td>L'Enfant</td>\n",
       "      <td>Paul Roger-Bloche</td>\n",
       "      <td>Historic Art</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Carrick</td>\n",
       "      <td>4.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>4.200329e+10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4-23</td>\n",
       "      <td>40.388748</td>\n",
       "      <td>-79.986497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1935017944</td>\n",
       "      <td>148</td>\n",
       "      <td>Veterans Memorial (Brookline)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>War Memorials</td>\n",
       "      <td>Brookline Monument</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brookline</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.200319e+10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4-26</td>\n",
       "      <td>40.391721</td>\n",
       "      <td>-80.016981</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  name                              title        artist_name  \\\n",
       "0   812728309  1001              Oakwood Road Monument                NaN   \n",
       "1   188255168  1002  Rooster Waiting For The Streetcar        James Simon   \n",
       "2  2032091917   129        Middle East Memorial Plaque                NaN   \n",
       "3  1447629273   143                           L'Enfant  Paul Roger-Bloche   \n",
       "4  1935017944   148      Veterans Memorial (Brookline)                NaN   \n",
       "\n",
       "                      art_type                park image  \\\n",
       "0                          NaN                 NaN   NaN   \n",
       "1  Modern and Contemporary Art                 NaN   NaN   \n",
       "2                War Memorials                 NaN   NaN   \n",
       "3                 Historic Art                 NaN   NaN   \n",
       "4                War Memorials  Brookline Monument   NaN   \n",
       "\n",
       "                neighborhood  council_district  ward         tract  \\\n",
       "0                    Oakwood               2.0  28.0  4.200356e+10   \n",
       "1                    Elliott               2.0  20.0  4.200356e+10   \n",
       "2  Central Business District               6.0   1.0  4.200302e+10   \n",
       "3                    Carrick               4.0  29.0  4.200329e+10   \n",
       "4                  Brookline               4.0  19.0  4.200319e+10   \n",
       "\n",
       "   public_works_division  pli_division  police_zone fire_zone   latitude  \\\n",
       "0                    5.0          28.0          6.0      1-19  40.429192   \n",
       "1                    5.0          20.0          6.0      1-17  40.442926   \n",
       "2                    6.0           1.0          2.0       1-4  40.438092   \n",
       "3                    3.0          29.0          3.0      4-23  40.388748   \n",
       "4                    5.0          19.0          6.0      4-26  40.391721   \n",
       "\n",
       "   longitude  \n",
       "0 -80.066225  \n",
       "1 -80.039711  \n",
       "2 -79.996852  \n",
       "3 -79.986497  \n",
       "4 -80.016981  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "publicArt = pd.read_csv(\"public-art.csv\")\n",
    "publicArt.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "armed-thriller",
   "metadata": {},
   "source": [
    "Let's group the data by neighborhoods and see the number of public art pieces in each of the neighborhood:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "widespread-biodiversity",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neighborhood\n",
       "Allegheny Center             18\n",
       "Allegheny West                1\n",
       "Allentown                     2\n",
       "Beechview                     8\n",
       "Beltzhoover                   3\n",
       "Bloomfield                    1\n",
       "Brighton Heights              5\n",
       "Brookline                     2\n",
       "Carrick                       3\n",
       "Central Business District    27\n",
       "Central Lawrenceville         2\n",
       "Central Oakland               5\n",
       "Crafton Heights               1\n",
       "Crawford-Roberts              3\n",
       "Duquesne Heights              3\n",
       "East Allegheny                2\n",
       "East Carnegie                 1\n",
       "East Hills                    1\n",
       "East Liberty                  1\n",
       "Elliott                       3\n",
       "Esplen                        1\n",
       "Garfield                      2\n",
       "Greenfield                    3\n",
       "Hays                          1\n",
       "Hazelwood                     4\n",
       "Highland Park                10\n",
       "Knoxville                     1\n",
       "Lincoln-Lemington-Belmar      2\n",
       "Lower Lawrenceville           5\n",
       "Manchester                    1\n",
       "Mount Washington              2\n",
       "North Oakland                 1\n",
       "North Shore                   8\n",
       "Oakwood                       1\n",
       "Overbrook                     1\n",
       "Perry North                   2\n",
       "Perry South                   1\n",
       "Point Breeze                  3\n",
       "Polish Hill                   1\n",
       "Regent Square                 2\n",
       "Shadyside                     6\n",
       "Sheraden                      1\n",
       "South Side Flats             10\n",
       "South Side Slopes             3\n",
       "Spring Garden                 2\n",
       "Spring Hill-City View         4\n",
       "Squirrel Hill North           2\n",
       "Squirrel Hill South          22\n",
       "Stanton Heights               1\n",
       "Strip District                2\n",
       "Swisshelm Park                1\n",
       "Troy Hill                     1\n",
       "Upper Hill                    2\n",
       "Upper Lawrenceville           1\n",
       "West End                      2\n",
       "Name: id, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "length = len(publicArt.groupby(\"neighborhood\"))\n",
    "publicArt.groupby(\"neighborhood\").count().loc[:,\"id\"][:length]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "downtown-weekend",
   "metadata": {},
   "source": [
    "Furthermore, let's sort the data in descending order so we can have a better understanding of which neighborhoods have more pieces of public art."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "stable-verification",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neighborhood\n",
       "Central Business District    27\n",
       "Squirrel Hill South          22\n",
       "Allegheny Center             18\n",
       "South Side Flats             10\n",
       "Highland Park                10\n",
       "North Shore                   8\n",
       "Beechview                     8\n",
       "Shadyside                     6\n",
       "Brighton Heights              5\n",
       "Central Oakland               5\n",
       "Lower Lawrenceville           5\n",
       "Hazelwood                     4\n",
       "Spring Hill-City View         4\n",
       "Greenfield                    3\n",
       "Point Breeze                  3\n",
       "Elliott                       3\n",
       "Duquesne Heights              3\n",
       "South Side Slopes             3\n",
       "Crawford-Roberts              3\n",
       "Carrick                       3\n",
       "Beltzhoover                   3\n",
       "Spring Garden                 2\n",
       "Regent Square                 2\n",
       "Squirrel Hill North           2\n",
       "Perry North                   2\n",
       "Strip District                2\n",
       "Upper Hill                    2\n",
       "Mount Washington              2\n",
       "Lincoln-Lemington-Belmar      2\n",
       "West End                      2\n",
       "Allentown                     2\n",
       "Garfield                      2\n",
       "Brookline                     2\n",
       "Central Lawrenceville         2\n",
       "East Allegheny                2\n",
       "East Carnegie                 1\n",
       "Upper Lawrenceville           1\n",
       "Troy Hill                     1\n",
       "Swisshelm Park                1\n",
       "Bloomfield                    1\n",
       "Stanton Heights               1\n",
       "Crafton Heights               1\n",
       "Manchester                    1\n",
       "Allegheny West                1\n",
       "Sheraden                      1\n",
       "East Liberty                  1\n",
       "Esplen                        1\n",
       "Polish Hill                   1\n",
       "Perry South                   1\n",
       "Hays                          1\n",
       "Overbrook                     1\n",
       "Oakwood                       1\n",
       "Knoxville                     1\n",
       "North Oakland                 1\n",
       "East Hills                    1\n",
       "Name: id, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "art_sort = publicArt.groupby(\"neighborhood\").count().loc[:,\"id\"].sort_values(ascending=False)\n",
    "art_sort[:length]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "supposed-blond",
   "metadata": {},
   "source": [
    "Plot the data as a bar graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "comparative-governor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='neighborhood'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "art_sort[:length].plot.bar(figsize = (30, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "going-inquiry",
   "metadata": {},
   "source": [
    "Now, scale down to figure out how many pieces of public art per 100m^2 for each neighborhood. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "strong-doctor",
   "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>hood</th>\n",
       "      <th>area</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central Oakland</td>\n",
       "      <td>7843108.0</td>\n",
       "      <td>POLYGON ((-79.95304 40.44203, -79.95302 40.442...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>North Oakland</td>\n",
       "      <td>13904629.0</td>\n",
       "      <td>POLYGON ((-79.95455 40.45882, -79.95427 40.458...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>West Oakland</td>\n",
       "      <td>5999801.5</td>\n",
       "      <td>POLYGON ((-79.96230 40.44294, -79.96220 40.442...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Crawford-Roberts</td>\n",
       "      <td>7202139.0</td>\n",
       "      <td>POLYGON ((-79.98275 40.44641, -79.98273 40.446...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Strip District</td>\n",
       "      <td>16947852.0</td>\n",
       "      <td>POLYGON ((-79.97494 40.45629, -79.97484 40.456...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Ridgemont</td>\n",
       "      <td>10278895.0</td>\n",
       "      <td>POLYGON ((-80.03431 40.43366, -80.03424 40.433...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>West End</td>\n",
       "      <td>5579501.0</td>\n",
       "      <td>POLYGON ((-80.02869 40.44454, -80.02845 40.444...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>California-Kirkbride</td>\n",
       "      <td>6050988.0</td>\n",
       "      <td>POLYGON ((-80.02039 40.46285, -80.02022 40.462...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Glen Hazel</td>\n",
       "      <td>12713036.0</td>\n",
       "      <td>POLYGON ((-79.92030 40.41205, -79.92027 40.412...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Perry North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((-80.01783 40.49736, -80.01787 40.497...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    hood        area  \\\n",
       "0        Central Oakland   7843108.0   \n",
       "1          North Oakland  13904629.0   \n",
       "2           West Oakland   5999801.5   \n",
       "3       Crawford-Roberts   7202139.0   \n",
       "4         Strip District  16947852.0   \n",
       "..                   ...         ...   \n",
       "85             Ridgemont  10278895.0   \n",
       "86              West End   5579501.0   \n",
       "87  California-Kirkbride   6050988.0   \n",
       "88            Glen Hazel  12713036.0   \n",
       "89           Perry North         NaN   \n",
       "\n",
       "                                             geometry  \n",
       "0   POLYGON ((-79.95304 40.44203, -79.95302 40.442...  \n",
       "1   POLYGON ((-79.95455 40.45882, -79.95427 40.458...  \n",
       "2   POLYGON ((-79.96230 40.44294, -79.96220 40.442...  \n",
       "3   POLYGON ((-79.98275 40.44641, -79.98273 40.446...  \n",
       "4   POLYGON ((-79.97494 40.45629, -79.97484 40.456...  \n",
       "..                                                ...  \n",
       "85  POLYGON ((-80.03431 40.43366, -80.03424 40.433...  \n",
       "86  POLYGON ((-80.02869 40.44454, -80.02845 40.444...  \n",
       "87  POLYGON ((-80.02039 40.46285, -80.02022 40.462...  \n",
       "88  POLYGON ((-79.92030 40.41205, -79.92027 40.412...  \n",
       "89  POLYGON ((-80.01783 40.49736, -80.01787 40.497...  \n",
       "\n",
       "[90 rows x 3 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoods = gpd.read_file(\"Neighborhoods/Neighborhoods_.shp\")\n",
    "neighborhoods[['hood','area','geometry']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "dramatic-noise",
   "metadata": {},
   "outputs": [],
   "source": [
    "# do the merge\n",
    "area_map = neighborhoods.merge(art_sort, how='left', left_on='hood', right_on='neighborhood')\n",
    "area_map = area_map.rename(columns={'id':'total pieces of public art'})\n",
    "# replace NaN in total pieces of public art by 0 \n",
    "area_map['total pieces of public art'] = area_map['total pieces of public art'].fillna(0)\n",
    "# replace NaN in area by calculation from its polygon\n",
    "area_replace = area_map['geometry'].to_crs(epsg=32129)\n",
    "area_map['area'] = area_map['area'].fillna(pd.to_numeric(area_replace.area))\n",
    "area_map = area_map[['hood','total pieces of public art','area']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "aggressive-suite",
   "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>hood</th>\n",
       "      <th>total pieces of public art</th>\n",
       "      <th>area</th>\n",
       "      <th>art_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central Oakland</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.843108e+06</td>\n",
       "      <td>0.006375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>North Oakland</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.390463e+07</td>\n",
       "      <td>0.000719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>West Oakland</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.999802e+06</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Crawford-Roberts</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.202139e+06</td>\n",
       "      <td>0.004165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Strip District</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.694785e+07</td>\n",
       "      <td>0.001180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Ridgemont</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.027890e+07</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>West End</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.579501e+06</td>\n",
       "      <td>0.003585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>California-Kirkbride</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.050988e+06</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Glen Hazel</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.271304e+07</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Perry North</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.216110e+06</td>\n",
       "      <td>0.006219</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    hood  total pieces of public art          area  \\\n",
       "0        Central Oakland                         5.0  7.843108e+06   \n",
       "1          North Oakland                         1.0  1.390463e+07   \n",
       "2           West Oakland                         0.0  5.999802e+06   \n",
       "3       Crawford-Roberts                         3.0  7.202139e+06   \n",
       "4         Strip District                         2.0  1.694785e+07   \n",
       "..                   ...                         ...           ...   \n",
       "85             Ridgemont                         0.0  1.027890e+07   \n",
       "86              West End                         2.0  5.579501e+06   \n",
       "87  California-Kirkbride                         0.0  6.050988e+06   \n",
       "88            Glen Hazel                         0.0  1.271304e+07   \n",
       "89           Perry North                         2.0  3.216110e+06   \n",
       "\n",
       "    art_percent  \n",
       "0      0.006375  \n",
       "1      0.000719  \n",
       "2      0.000000  \n",
       "3      0.004165  \n",
       "4      0.001180  \n",
       "..          ...  \n",
       "85     0.000000  \n",
       "86     0.003585  \n",
       "87     0.000000  \n",
       "88     0.000000  \n",
       "89     0.006219  \n",
       "\n",
       "[90 rows x 4 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate number of pieces of public art per 100m^2\n",
    "area_map['art_percent'] = (area_map['total pieces of public art']/(area_map['area']/10**4))\n",
    "area_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "temporal-hospital",
   "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>total pieces of public art</th>\n",
       "      <th>area</th>\n",
       "      <th>art_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>9.000000e+01</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.266667</td>\n",
       "      <td>1.601237e+07</td>\n",
       "      <td>0.001655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.362249</td>\n",
       "      <td>1.278851e+07</td>\n",
       "      <td>0.003810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.871538e+06</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.686451e+06</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.185390e+07</td>\n",
       "      <td>0.000623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.088152e+07</td>\n",
       "      <td>0.001763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>7.447013e+07</td>\n",
       "      <td>0.030818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       total pieces of public art          area  art_percent\n",
       "count                   90.000000  9.000000e+01    90.000000\n",
       "mean                     2.266667  1.601237e+07     0.001655\n",
       "std                      4.362249  1.278851e+07     0.003810\n",
       "min                      0.000000  2.871538e+06     0.000000\n",
       "25%                      0.000000  7.686451e+06     0.000000\n",
       "50%                      1.000000  1.185390e+07     0.000623\n",
       "75%                      2.000000  2.088152e+07     0.001763\n",
       "max                     27.000000  7.447013e+07     0.030818"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_map.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nonprofit-conducting",
   "metadata": {},
   "source": [
    "The bar graph showing total number of pieces of public art per 100m^2 for each neighborhood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "diagnostic-unknown",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='hood'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "area_sort = area_map.sort_values(by=['art_percent'], ascending=True)\n",
    "area_sort_graph = area_sort.plot.barh(x='hood', y='art_percent', figsize=(20, 20))\n",
    "area_sort_graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "missing-vertex",
   "metadata": {},
   "source": [
    "Based on our data, we get the conclusion that Allegheny Center has the most access to public art. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "exempt-briefing",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "polar-workplace",
   "metadata": {},
   "source": [
    "We obtain the overall best neighborhood by finding the average rank of neighborhoods present in all three of our final dataframes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "german-fraud",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Minhal's final dataframe\n",
    "df4 = df4.sort_values('rec_ratio',ascending=False).reset_index(drop=True)\n",
    "df4['rec_rank'] = df4.index + 1\n",
    "df4 = df4.rename(columns={'Neighborhood':'hood'})\n",
    "\n",
    "# Zhen's final dataframe\n",
    "area_sort = area_sort.sort_values('art_percent',ascending=False).reset_index(drop=True)\n",
    "area_sort['art_rank'] = area_sort.index + 1\n",
    "\n",
    "# Keyon's final dataframe\n",
    "merged_final_sort = merged_final_sort.sort_values('clean_score',ascending=False).reset_index(drop=True)\n",
    "merged_final_sort['clean_rank'] = merged_final_sort.index + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acoustic-cooperation",
   "metadata": {},
   "source": [
    "We 'rank' each dataframe by sorting their values in descending order, then adding a column of sequential numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "polar-sleep",
   "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>hood</th>\n",
       "      <th>art_rank</th>\n",
       "      <th>clean_rank</th>\n",
       "      <th>rec_rank</th>\n",
       "      <th>average_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Squirrel Hill South</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Side Flats</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shadyside</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Highland Park</td>\n",
       "      <td>11</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brighton Heights</td>\n",
       "      <td>25</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Point Breeze</td>\n",
       "      <td>35</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bloomfield</td>\n",
       "      <td>48</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Greenfield</td>\n",
       "      <td>31</td>\n",
       "      <td>26</td>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mount Washington</td>\n",
       "      <td>45</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>South Side Slopes</td>\n",
       "      <td>28</td>\n",
       "      <td>41</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Carrick</td>\n",
       "      <td>44</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Lincoln-Lemington-Belmar</td>\n",
       "      <td>15</td>\n",
       "      <td>60</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Squirrel Hill North</td>\n",
       "      <td>47</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sheraden</td>\n",
       "      <td>52</td>\n",
       "      <td>27</td>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Brookline</td>\n",
       "      <td>54</td>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        hood  art_rank  clean_rank  rec_rank  average_rank\n",
       "0        Squirrel Hill South        13           8         2             7\n",
       "1           South Side Flats         8           6        10             8\n",
       "2                  Shadyside        19           1        11            10\n",
       "3              Highland Park        11          35         1            15\n",
       "4           Brighton Heights        25          23         7            18\n",
       "5               Point Breeze        35          21         6            20\n",
       "6                 Bloomfield        48           4        14            22\n",
       "7                 Greenfield        31          26        13            23\n",
       "8           Mount Washington        45          20         5            23\n",
       "9          South Side Slopes        28          41         3            24\n",
       "10                   Carrick        44          13        15            24\n",
       "11  Lincoln-Lemington-Belmar        15          60         4            26\n",
       "12       Squirrel Hill North        47          19        12            26\n",
       "13                  Sheraden        52          27         9            29\n",
       "14                 Brookline        54          30         8            30"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating final \n",
    "final_list = area_sort[['hood','art_rank']].merge(merged_final_sort[['hood','clean_rank']], on='hood')\n",
    "final_list = final_list.merge(df4[['hood','rec_rank']], on='hood')\n",
    "\n",
    "final_list['average_rank'] = (final_list['art_rank'] + final_list['rec_rank'] + final_list['clean_rank'])/3\n",
    "final_list.average_rank = final_list.average_rank.astype(int)\n",
    "\n",
    "display(final_list.sort_values('average_rank',ascending=True).reset_index(drop=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "english-triangle",
   "metadata": {},
   "source": [
    "Finally, by merging all of our edited dataframes, we create one last dataframe with a column where each value is the average of the neighborhood's rankings."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "exterior-sailing",
   "metadata": {},
   "source": [
    "From this, it can be seen that based on our metrics, **Squirrel Hill South** is the best neighborhood in Pittsburgh!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "australian-shape",
   "metadata": {},
   "source": [
    "## Reflections\n",
    "**Keyon**: According to my data Shadyside is both a clean neighborhood, with lots of local love. In other words, it is a clean, and close-knit community. However, this is only taking into account community and cleanliness. However, the top 10 in this list, are magnitudes better than the rest of the 69 neighborhoods. At number 8, Squirrel Hill becomes a clear winner when you take into account multiple factors judging the Best Neighborhood.\n",
    "\n",
    "**Minhal**: Because I'm currently studying remotely, I unfortunately haven't had the chance to explore Pittsburgh's neighborhoods. I have heard good things about Squirrel Hill South though, so it seems our answer is on the right track! Even though it falls just outside of the top 10 in terms of art, taking other factors into consideration like community, neatness, and R&R tells that us it is indeed one of Pittsburgh's best neighborhoods.\n",
    "\n",
    "**Zhen**: After doing this project, I get a better understanding of Pittsburgh neighborhoods. I haven't explored those neighborhoods much. By averaging the rankings in all three of our metrics, Squirrel Hill South is the winner. (I hope to visit it someday!) By exploring the dataset of public art that I used, I also know how public art makes the community more vibrant and improves the social environment."
   ]
  }
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