IBM_CRS9_W2_Capstone.ipynb

{
    "cells": [
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [],
            "source": "# The code was removed by Watson Studio for sharing."
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [],
            "source": "import io\nimport requests\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n\nfrom datetime import timedelta, datetime\n\nfrom PIL import Image\nfrom time import sleep\n\n#!conda install -c conda-forge geocoder --yes    # uncomment line if geocoder needs to be installed\nimport geocoder\n#!conda install -c conda-forge backoff\nimport backoff\n\nfrom geopy.distance import geodesic\nimport folium\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# General Notes  \n\nThis analysis was completed over several days.  To minimize the re-running all the previous steps, incremental versions of the data was saved.  Throughout the notebook you'll see places to load the \"Clean & Enriched\" data frame from storage.  Similarly, you'll see many statements commented out as not all cleansing steps were required on a given run of analysis. \n\nIf something doesn't quite make sense, it is most likely the result of loading the DataFrame from storage and skipping unecessary steps."
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "collapsed": true
            },
            "source": "# Data Collection\n\n\n### Local High School On-Line Schedule\n\n**Notes**  \n  - The 2019-2020 schedule is not online yet, so using last years schedule to test & build the  model.\n  - The school selected is an Independent, College Prepratory school, near to where I live.\n\n\n### Prep the Sporting Event Location Data\n1.  Web-Scrape the Schedule for Location Names  \n1.  Enrich Location Names with Geocode Data  \n1.  Generate high-level travel route data based on location  \n1.  Store Intermediary DataFrame so don't need to continue to web-scrape and call API's  \n\n \n **NOTES** \n - In Future runs, jump down to where DataFrame is loaded from CSV.  \n"
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [],
            "source": "schedule_url = 'https://www.cushing.org/team-detail?fromId=204845&Team=87969&SeasonLabel=2018+-+2019'\n_headers = {\n    'User-Agent': 'My User Agent 1.0'\n}\nr = requests.get(schedule_url, headers=_headers )\n# print (r.text)"
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [],
            "source": "html_soup = BeautifulSoup(r.text, 'html.parser')\ndet_containers = html_soup.find_all('div', class_='game')\nweb_event_data = [[s for s in containers.stripped_strings] for containers in det_containers]"
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [],
            "source": "# Loop through web-data, make a dictionary for each game listed on the website\nsporting_event_list = []\nfor game in web_event_data:\n    idx = 0\n    sporting_event = {}\n    sporting_event['date'] = game[idx]\n#    date_list.append(sporting_event['date'])  # create separate list of values to c\n    idx += 1\n    sporting_event['time'] = game[idx]\n    idx += 1\n    sporting_event['home_away'] = game[idx]\n    idx += 1\n    if game[idx] == 'Scrimmage':\n        idx += 1\n    if game[idx] == 'vs.':\n        idx += 1 # skip 'vs.'\n    sporting_event['opponent_school'] = game[idx]\n    idx += 1\n    sporting_event_list.append(sporting_event)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "From that list of games, make a pandas DataFram"
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "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>date</th>\n      <th>home_away</th>\n      <th>opponent_school</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Wednesday, 11/28/2018</td>\n      <td>Away</td>\n      <td>Middlesex School</td>\n      <td>5:00 PM</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Friday, 11/30/2018</td>\n      <td>Away</td>\n      <td>Dana Hall School</td>\n      <td>5:30 PM</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Saturday, 12/1/2018</td>\n      <td>Home</td>\n      <td>Buckingham Browne &amp; Nichols</td>\n      <td>3:00 PM</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Wednesday, 12/5/2018</td>\n      <td>Away</td>\n      <td>Holderness School</td>\n      <td>3:30 PM</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Saturday, 12/8/2018</td>\n      <td>Away</td>\n      <td>Vermont Academy</td>\n      <td>4:00 PM</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "                    date home_away              opponent_school     time\n0  Wednesday, 11/28/2018      Away             Middlesex School  5:00 PM\n1     Friday, 11/30/2018      Away             Dana Hall School  5:30 PM\n2    Saturday, 12/1/2018      Home  Buckingham Browne & Nichols  3:00 PM\n3   Wednesday, 12/5/2018      Away            Holderness School  3:30 PM\n4    Saturday, 12/8/2018      Away              Vermont Academy  4:00 PM"
                    },
                    "execution_count": 6,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "games_df = pd.DataFrame(sporting_event_list)\ngames_df.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Starting Geocoding based on school name   \n\n\n**NOTE:** \n- It is common to play the same opponent year after year.\n- As a result, the analysis will be completed for all opponents, but final list of analysis, will only include 'travel sites' (i.e. \"Away\" games)\n\n\nFirst step is to set the **HOME** Location, so there is a starting point when calculating distances.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "{'quality': 'school', 'address': 'Cushing Academy, 39, School Street, Ashburnham, Worcester County, Massachusetts, 01430, United States of America', 'state': 'Massachusetts', 'lat': 42.63286695, 'lng': -71.9135057096491}\n"
                }
            ],
            "source": "home_location_name = 'Cushing Academy'\ng = geocoder.osm(home_location_name)\nif not g.ok:\n    print ('GeoCode Failed')\n    \nhome_location = {'quality': g.json['quality'], 'address': g.json['address'] , 'state': g.json['state'], 'lat': g.json['lat'], 'lng': g.json['lng']}\nprint(home_location)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "---  \n### Geocoding helper functions  \n\nDefine functions to make data processing easier\n\n- Function `get_geo_info`  \n  Looks up the latitude and longitude for a school based on school name and returns a Pandas Series, so it's easier to build build a DataFrame while looping through list of school names.   This function also tries multiple geocoding services to find latitude and longitude.  It first starts with Open Street Maps Nominatim, if no results are found, it tries the free version of the ArcGIS service.\n  \n<br>\n  \n- Function `clean_row_geo_info`  \n  A helper function to correct coordinates in the event location list.  Pass in the index of the row to correct in teh DataFrame, along with the preferred search terms, and it will correct the DataFrame.  \n  \n<br>\n\n- Function `get_route_data`  \n  A helper function to get the route information between two sets of coordinates.  The service is basically a \"Demo\" service, so fold int logic to handle multiple retries, and smart backoff logic to increase liklihood the server will be able able to process the routing requests.   \n  \n  Using Project OSRM Routing Service\n\n  `http://router.project-osrm.org/route/v1/driving/-71.91356999999994,42.63187000000005;-71.6747789477679,43.76177855`  \n\n  \n<br> \n\n- Function `convert_meters_to_miles`  \n  The routing service returns driving distance between two coordinate points in meters.  This converts meters to miles.  \n  \n  Meters to Miles conversion = meters * 0.00062137\n\n  \n<br>  \n\n- Function `convert_seconds_to_duration_str`  \n  The routing service returns driving duration between two coordinate points in seconds.  This converts seconds into an `hh:mm` string to represent the duration.  \n  \n  "
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {},
            "outputs": [],
            "source": "## Define Function to look-up Geocode by school name\ndef get_geo_info(_name):\n    g = geocoder.osm(_name)\n    if not g.ok:\n        # Try another Geocoder\n        print ('trying ArcGIS for {}'.format(_name))\n        g = geocoder.arcgis(_name)\n        if not g.ok:\n            print ('Had a problem with {}'.format(_name))\n            return pd.Series({'quality': 'error', 'address': 'error', 'state': 'error', 'lat': 0, 'lng': 0})\n        # arcgis does not have as many fields     \n        return pd.Series({'quality': g.json['quality'], 'address': g.json['address'] , 'state': 'n/a', 'lat': g.json['lat'], 'lng': g.json['lng']})\n\n    \n    return pd.Series({'quality': g.json['quality'], 'address': g.json['address'] , 'state': g.json['state'], 'lat': g.json['lat'], 'lng': g.json['lng']})"
        },
        {
            "cell_type": "code",
            "execution_count": 79,
            "metadata": {},
            "outputs": [],
            "source": "## Clean-Up Function ... some Geocodes matched the wrong school\ndef clean_row_geo_info(_dataframe, _df_index, _better_search_name):\n    g = geocoder.osm(_better_search_name)\n    #print ('old value', _dataframe.iloc[_df_index])\n    geo_data = None\n    if not g.ok:\n        # Try another Geocoder\n        print ('trying ArcGIS for {}'.format(_better_search_name))\n        g = geocoder.arcgis(_better_search_name)\n        if not g.ok:\n            print ('Had a problem with {}'.format(_better_search_name))\n            return \n        # arcgis does not have as many fields     \n        geo_data = {'quality': g.json['quality'], 'address': g.json['address'] , 'state': 'n/a', 'lat': g.json['lat'], 'lng': g.json['lng']}\n    else:\n        geo_data = {'quality': g.json['quality'], 'address': g.json['address'] , 'state': g.json['state'], 'lat': g.json['lat'], 'lng': g.json['lng']}\n    \n    _dataframe.at[_df_index, 'quality'] = geo_data['quality']\n    _dataframe.at[_df_index, 'address'] = geo_data['address']\n    _dataframe.at[_df_index, 'state'] = geo_data['state']\n    _dataframe.at[_df_index, 'lat'] = geo_data['lat']\n    _dataframe.at[_df_index, 'lng'] = geo_data['lng']   \n    #print ('new value', _dataframe.iloc[_df_index])\n\n    \n    return\n    "
        },
        {
            "cell_type": "code",
            "execution_count": 120,
            "metadata": {},
            "outputs": [],
            "source": "@backoff.on_exception(backoff.expo,\n                      requests.exceptions.RequestException,\n                      max_tries=8,\n                     jitter=backoff.full_jitter)    \ndef get_route_data(_row):\n    url = 'http://router.project-osrm.org/route/v1/driving/{},{};{},{}'.format(home_location['lng'], home_location['lat'],\n                                                                              _row['lng'], _row['lat'])\n    \n    r = requests.get(url)\n    r.raise_for_status()  # Ugly, but blowup if web-request doesn't work\n    r_data = r.json()\n#    print (r.text)\n    print ( _row['opponent_school'], r_data['code'])  # make sure this is 'Ok'\n    print (datetime.utcnow())  # Just checking backoff :) \n    if len(r_data['routes']) > 0:\n        r_data = r_data['routes'][0] # Just grab first available route ...\n    else:\n        print (r_data['code'], _row['opponent_school'])\n        return pd.Series({'distance_meters': 0, 'duration_seconds': 0})\n    \n    return pd.Series({'distance_meters': r_data['distance'], 'duration_seconds': r_data['duration']})\n    "
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {},
            "outputs": [],
            "source": "def convert_meters_to_miles(_meters):\n    return _meters * 0.00062137\n\ndef convert_seconds_to_duration_str(_seconds):\n    return str(timedelta(seconds=_seconds))[0:4]  # only show HH:MM  in e "
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Actually do the geocoding work \n\nUsing the helper functions above, add data from geocoding services to the list of sporting events locations (i.e. the games)"
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "trying ArcGIS for Dexter Southfield School\n"
                }
            ],
            "source": "# Get Lat & Long for school names\ngames_df[[\"quality\", \"address\", \"state\", \"lat\", \"lng\"]] = games_df['opponent_school'].apply(get_geo_info)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Data Cleansing \n\nIterative run & re-run the following few cells, to clean-up the data that needs it, due to incorrect GEOCode Searches.  The first cell is to verify the results, the cell after is actually the clean-up functions.\n\nCommenting out the statements after they've cleansed the data, but keeping them in the cell for future reference.  (But after you\nyou fixed the data, don't need to keep fixing it) \n"
        },
        {
            "cell_type": "code",
            "execution_count": 135,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "Massachusetts    14\nNew Hampshire     5\nConnecticut       2\nRhode Island      1\nVermont           1\nName: state, dtype: int64"
                    },
                    "execution_count": 135,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "#  Check the States, make sure all the Geocode results look OK\ngames_df['state'].value_counts()\n\n# Interrogate unexpected  records\n#games_df.loc[games_df['state'] == 'Pennsylvania']\n#games_df.loc[games_df['state'] == 'n/a']\n#games_df.loc[games_df['state'] == 'North Carolina']\n\n"
        },
        {
            "cell_type": "code",
            "execution_count": 132,
            "metadata": {},
            "outputs": [],
            "source": "# Clean-Up Records\n#clean_row_geo_info(games_df, 12, \"Brooks School, North Andover, MA\")\n#clean_row_geo_info(games_df, 14, \"St. George's School, RI, US\")\n#clean_row_geo_info(games_df, 18, \"Lawrence Academy, MA, US\")\n#clean_row_geo_info(games_df, 21, \"Dexter Southfield, Brookline, MA, 02445, US\")\n#clean_row_geo_info(games_df, 22, \"St. Paul's School, NH, US\")\n# games_df.at[21, 'state'] = 'Massachusetts'   # ArcGIS doesn't provide state, but I know it"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Just as a nice to have, add route data\n\nFetch driving routes, based on coordinates, to get distance & duration\n\nAnd then transform from meters & seconds to miles & string of the form \"hh:mm\""
        },
        {
            "cell_type": "code",
            "execution_count": 136,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Middlesex School Ok\n2019-10-29 02:18:11.137362\nMiddlesex School Ok\n2019-10-29 02:18:11.201199\nDana Hall School Ok\n2019-10-29 02:18:11.337350\nBuckingham Browne & Nichols Ok\n2019-10-29 02:18:11.634155\nHolderness School Ok\n2019-10-29 02:18:11.735303\nVermont Academy Ok\n2019-10-29 02:18:11.839715\nMiss Porter's school Ok\n2019-10-29 02:18:11.983652\nPhillips Exeter Academy Ok\n2019-10-29 02:18:12.088022\nWilliston Northampton School Ok\n2019-10-29 02:18:12.182747\nWinchendon School Ok\n2019-10-29 02:18:12.302089\nPomfret School Ok\n2019-10-29 02:18:12.377947\nSt. Mark's School Ok\n2019-10-29 02:18:12.481404\nGovernor's Academy Ok\n2019-10-29 02:18:12.601893\nBrooks School Ok\n2019-10-29 02:18:12.715567\nWilbraham & Monson Academy Ok\n2019-10-29 02:18:12.809741\nSt. George's School Ok\n2019-10-29 02:18:12.930576\nBrewster Academy Ok\n2019-10-29 02:18:13.030945\nKimball Union Academy Ok\n2019-10-29 02:18:13.120090\nNorthfield Mt. Hermon School Ok\n2019-10-29 02:18:13.306882\nLawrence Academy Ok\n2019-10-29 02:18:13.415795\nGroton School Ok\n2019-10-29 02:18:13.517919\nMiss Hall's School Ok\n2019-10-29 02:18:13.642003\nDexter Southfield School Ok\n2019-10-29 02:18:13.727687\nSt. Paul's School Ok\n2019-10-29 02:18:13.832150\nroute data loaded\n"
                },
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>home_away</th>\n      <th>opponent_school</th>\n      <th>time</th>\n      <th>quality</th>\n      <th>address</th>\n      <th>state</th>\n      <th>lat</th>\n      <th>lng</th>\n      <th>distance_meters</th>\n      <th>duration_seconds</th>\n      <th>distance_miles</th>\n      <th>duration_str</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Wednesday, 11/28/2018</td>\n      <td>Away</td>\n      <td>Middlesex School</td>\n      <td>5:00 PM</td>\n      <td>school</td>\n      <td>Middlesex School, 1400, Lowell Road, Concord, ...</td>\n      <td>Massachusetts</td>\n      <td>42.499832</td>\n      <td>-71.368431</td>\n      <td>62722.6</td>\n      <td>3051.1</td>\n      <td>38.973942</td>\n      <td>0:50</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Friday, 11/30/2018</td>\n      <td>Away</td>\n      <td>Dana Hall School</td>\n      <td>5:30 PM</td>\n      <td>school</td>\n      <td>Dana Hall School, 45, Dana Road, Wellesley, No...</td>\n      <td>Massachusetts</td>\n      <td>42.290910</td>\n      <td>-71.288481</td>\n      <td>89181.5</td>\n      <td>4490.4</td>\n      <td>55.414709</td>\n      <td>1:14</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Saturday, 12/1/2018</td>\n      <td>Home</td>\n      <td>Buckingham Browne &amp; Nichols</td>\n      <td>3:00 PM</td>\n      <td>school</td>\n      <td>Buckingham Browne &amp; Nichols School (Middle Sch...</td>\n      <td>Massachusetts</td>\n      <td>42.380804</td>\n      <td>-71.131203</td>\n      <td>82363.5</td>\n      <td>4125.8</td>\n      <td>51.178208</td>\n      <td>1:08</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Wednesday, 12/5/2018</td>\n      <td>Away</td>\n      <td>Holderness School</td>\n      <td>3:30 PM</td>\n      <td>school</td>\n      <td>Holderness School, Chapel Lane, Holderness, Gr...</td>\n      <td>New Hampshire</td>\n      <td>43.761779</td>\n      <td>-71.674779</td>\n      <td>167290.4</td>\n      <td>7161.0</td>\n      <td>103.949236</td>\n      <td>1:59</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Saturday, 12/8/2018</td>\n      <td>Away</td>\n      <td>Vermont Academy</td>\n      <td>4:00 PM</td>\n      <td>school</td>\n      <td>Vermont Academy, Shepard Lane, Saxtons River, ...</td>\n      <td>Vermont</td>\n      <td>43.144794</td>\n      <td>-72.509698</td>\n      <td>90163.1</td>\n      <td>5072.3</td>\n      <td>56.024645</td>\n      <td>1:24</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "                    date home_away              opponent_school     time  \\\n0  Wednesday, 11/28/2018      Away             Middlesex School  5:00 PM   \n1     Friday, 11/30/2018      Away             Dana Hall School  5:30 PM   \n2    Saturday, 12/1/2018      Home  Buckingham Browne & Nichols  3:00 PM   \n3   Wednesday, 12/5/2018      Away            Holderness School  3:30 PM   \n4    Saturday, 12/8/2018      Away              Vermont Academy  4:00 PM   \n\n  quality                                            address          state  \\\n0  school  Middlesex School, 1400, Lowell Road, Concord, ...  Massachusetts   \n1  school  Dana Hall School, 45, Dana Road, Wellesley, No...  Massachusetts   \n2  school  Buckingham Browne & Nichols School (Middle Sch...  Massachusetts   \n3  school  Holderness School, Chapel Lane, Holderness, Gr...  New Hampshire   \n4  school  Vermont Academy, Shepard Lane, Saxtons River, ...        Vermont   \n\n         lat        lng  distance_meters  duration_seconds  distance_miles  \\\n0  42.499832 -71.368431          62722.6            3051.1       38.973942   \n1  42.290910 -71.288481          89181.5            4490.4       55.414709   \n2  42.380804 -71.131203          82363.5            4125.8       51.178208   \n3  43.761779 -71.674779         167290.4            7161.0      103.949236   \n4  43.144794 -72.509698          90163.1            5072.3       56.024645   \n\n  duration_str  \n0         0:50  \n1         1:14  \n2         1:08  \n3         1:59  \n4         1:24  "
                    },
                    "execution_count": 136,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# get Driving Distance & Duration from \"home school\" (Cushing Academy) to each school\ngames_df[['distance_meters', 'duration_seconds']] = games_df.apply(get_route_data, axis=1)\nprint ('route data loaded')\ngames_df.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [],
            "source": "# Convert the Routing information into differt units\n#games_df['distance_miles'] = games_df['distance_meters'].apply(convert_meters_to_miles)\n#games_df['duration_str'] = games_df['duration_seconds'].apply(convert_seconds_to_duration_str)\n"
        },
        {
            "cell_type": "code",
            "execution_count": 71,
            "metadata": {},
            "outputs": [
                {
                    "ename": "NameError",
                    "evalue": "name 'games_df' is not defined",
                    "output_type": "error",
                    "traceback": [
                        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
                        "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
                        "\u001b[0;32m<ipython-input-71-1e9a6bf59981>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgames_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
                        "\u001b[0;31mNameError\u001b[0m: name 'games_df' is not defined"
                    ]
                }
            ],
            "source": "games_df.describe()"
        },
        {
            "cell_type": "code",
            "execution_count": 157,
            "metadata": {},
            "outputs": [
                {
                    "ename": "NameError",
                    "evalue": "name 'games_df' is not defined",
                    "output_type": "error",
                    "traceback": [
                        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
                        "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
                        "\u001b[0;32m<ipython-input-157-822ffa96745d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmax_distance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgames_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'duration_seconds'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midxmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'The Median Travel Distance is: %.1f miles'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mgames_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'distance_miles'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'The Median Travel Duration is:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert_seconds_to_duration_str\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5013.800000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'The game at {} is the farthest drive, at %.1f miles'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgames_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmax_distance\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'opponent_school'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mgames_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmax_distance\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'distance_miles'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
                        "\u001b[0;31mNameError\u001b[0m: name 'games_df' is not defined"
                    ]
                }
            ],
            "source": "max_distance = games_df['duration_seconds'].idxmax()\nprint ('The Median Travel Distance is: %.1f miles' % games_df['distance_miles'].median())\nprint ('The Median Travel Duration is:', convert_seconds_to_duration_str(5013.800000))\nprint ('The game at {} is the farthest drive, at %.1f miles'.format(games_df.iloc[max_distance]['opponent_school']) % games_df.iloc[max_distance]['distance_miles'])\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "##  Save the Games DataFrame\n\nNow that the Games (sporting event location) DataFrame is cleansed & enriched, save it to a CSV, so you can jump right here and just reload it.\n(before getting started on the Foursquare work"
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "{'file_name': 'game_location_data.csv',\n 'message': 'File saved to project storage.',\n 'bucket_name': 'ibmcrs9capstone-donotdelete-pr-afds8o4rva78zh',\n 'asset_id': '66441db3-de56-4445-88d9-a177398bbdef'}"
                    },
                    "execution_count": 7,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# Save games_df form project files\n\n# There are some additional enrichments below, made to the games_loc_df, and I want to persist the refined enrichmetns between runs.\n# --> So choose one of the DataFrames to store... \n\n#project.save_data(\"game_location_data.csv\", games_df.to_csv(index=False))\n\nproject.save_data(\"game_location_data.csv\", games_loc_df.to_csv(index=False), overwrite=True)\n\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": ""
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "---\n\n## Load the Location DataFrame from CSV to get started with Foursquare API analysis\n\nPicking up with analysis, and starting with clean event location data.\n\n\nThe next few cells loads the Games Locations DataFrame and explores and summarizes the data"
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "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>date</th>\n      <th>home_away</th>\n      <th>opponent_school</th>\n      <th>time</th>\n      <th>quality</th>\n      <th>address</th>\n      <th>state</th>\n      <th>lat</th>\n      <th>lng</th>\n      <th>distance_meters</th>\n      <th>duration_seconds</th>\n      <th>distance_miles</th>\n      <th>duration_str</th>\n      <th>Nearby Venue Count</th>\n      <th>duration_minutes</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Wednesday, 11/28/2018</td>\n      <td>Away</td>\n      <td>Middlesex School</td>\n      <td>5:00 PM</td>\n      <td>school</td>\n      <td>Middlesex School, 1400, Lowell Road, Concord, ...</td>\n      <td>Massachusetts</td>\n      <td>42.499832</td>\n      <td>-71.368431</td>\n      <td>62722.6</td>\n      <td>3051.1</td>\n      <td>38.973942</td>\n      <td>0:50</td>\n      <td>16</td>\n      <td>50.851667</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Friday, 11/30/2018</td>\n      <td>Away</td>\n      <td>Dana Hall School</td>\n      <td>5:30 PM</td>\n      <td>school</td>\n      <td>Dana Hall School, 45, Dana Road, Wellesley, No...</td>\n      <td>Massachusetts</td>\n      <td>42.290910</td>\n      <td>-71.288481</td>\n      <td>89181.5</td>\n      <td>4490.4</td>\n      <td>55.414709</td>\n      <td>1:14</td>\n      <td>87</td>\n      <td>74.840000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Saturday, 12/1/2018</td>\n      <td>Home</td>\n      <td>Buckingham Browne &amp; Nichols</td>\n      <td>3:00 PM</td>\n      <td>school</td>\n      <td>Buckingham Browne &amp; Nichols School (Middle Sch...</td>\n      <td>Massachusetts</td>\n      <td>42.380804</td>\n      <td>-71.131203</td>\n      <td>82363.5</td>\n      <td>4125.8</td>\n      <td>51.178208</td>\n      <td>1:08</td>\n      <td>239</td>\n      <td>68.763333</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Wednesday, 12/5/2018</td>\n      <td>Away</td>\n      <td>Holderness School</td>\n      <td>3:30 PM</td>\n      <td>school</td>\n      <td>Holderness School, Chapel Lane, Holderness, Gr...</td>\n      <td>New Hampshire</td>\n      <td>43.761779</td>\n      <td>-71.674779</td>\n      <td>167290.4</td>\n      <td>7161.0</td>\n      <td>103.949236</td>\n      <td>1:59</td>\n      <td>41</td>\n      <td>119.350000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Saturday, 12/8/2018</td>\n      <td>Away</td>\n      <td>Vermont Academy</td>\n      <td>4:00 PM</td>\n      <td>school</td>\n      <td>Vermont Academy, Shepard Lane, Saxtons River, ...</td>\n      <td>Vermont</td>\n      <td>43.144794</td>\n      <td>-72.509698</td>\n      <td>90163.1</td>\n      <td>5072.3</td>\n      <td>56.024645</td>\n      <td>1:24</td>\n      <td>3</td>\n      <td>84.538333</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "                    date home_away              opponent_school     time  \\\n0  Wednesday, 11/28/2018      Away             Middlesex School  5:00 PM   \n1     Friday, 11/30/2018      Away             Dana Hall School  5:30 PM   \n2    Saturday, 12/1/2018      Home  Buckingham Browne & Nichols  3:00 PM   \n3   Wednesday, 12/5/2018      Away            Holderness School  3:30 PM   \n4    Saturday, 12/8/2018      Away              Vermont Academy  4:00 PM   \n\n  quality                                            address          state  \\\n0  school  Middlesex School, 1400, Lowell Road, Concord, ...  Massachusetts   \n1  school  Dana Hall School, 45, Dana Road, Wellesley, No...  Massachusetts   \n2  school  Buckingham Browne & Nichols School (Middle Sch...  Massachusetts   \n3  school  Holderness School, Chapel Lane, Holderness, Gr...  New Hampshire   \n4  school  Vermont Academy, Shepard Lane, Saxtons River, ...        Vermont   \n\n         lat        lng  distance_meters  duration_seconds  distance_miles  \\\n0  42.499832 -71.368431          62722.6            3051.1       38.973942   \n1  42.290910 -71.288481          89181.5            4490.4       55.414709   \n2  42.380804 -71.131203          82363.5            4125.8       51.178208   \n3  43.761779 -71.674779         167290.4            7161.0      103.949236   \n4  43.144794 -72.509698          90163.1            5072.3       56.024645   \n\n  duration_str  Nearby Venue Count  duration_minutes  \n0         0:50                  16         50.851667  \n1         1:14                  87         74.840000  \n2         1:08                 239         68.763333  \n3         1:59                  41        119.350000  \n4         1:24                   3         84.538333  "
                    },
                    "execution_count": 8,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "games_loc_df = pd.read_csv(project.get_file('game_location_data.csv'))\ngames_loc_df.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 36,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "(23, 15)"
                    },
                    "execution_count": 36,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "games_loc_df.shape"
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "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>Unnamed: 0</th>\n      <th>Unnamed: 0.1</th>\n      <th>lat</th>\n      <th>lng</th>\n      <th>distance_meters</th>\n      <th>duration_seconds</th>\n      <th>distance_miles</th>\n      <th>Nearby Venue Count</th>\n      <th>duration_minutes</th>\n    </tr>\n    <tr>\n      <th>home_away</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Away</th>\n      <td>134</td>\n      <td>134</td>\n      <td>554.005476</td>\n      <td>-931.674694</td>\n      <td>1404413.7</td>\n      <td>68683.9</td>\n      <td>872.660541</td>\n      <td>822</td>\n      <td>1144.731667</td>\n    </tr>\n    <tr>\n      <th>Home</th>\n      <td>119</td>\n      <td>119</td>\n      <td>425.923545</td>\n      <td>-719.009241</td>\n      <td>943099.7</td>\n      <td>45297.7</td>\n      <td>586.013861</td>\n      <td>568</td>\n      <td>754.961667</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "           Unnamed: 0  Unnamed: 0.1         lat         lng  distance_meters  \\\nhome_away                                                                      \nAway              134           134  554.005476 -931.674694        1404413.7   \nHome              119           119  425.923545 -719.009241         943099.7   \n\n           duration_seconds  distance_miles  Nearby Venue Count  \\\nhome_away                                                         \nAway                68683.9      872.660541                 822   \nHome                45297.7      586.013861                 568   \n\n           duration_minutes  \nhome_away                    \nAway            1144.731667  \nHome             754.961667  "
                    },
                    "execution_count": 5,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "games_loc_df.groupby(['home_away']).sum()\n"
        },
        {
            "cell_type": "code",
            "execution_count": 159,
            "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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"3\" halign=\"left\">distance_miles</th>\n      <th colspan=\"3\" halign=\"left\">duration_minutes</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>sum</th>\n      <th>mean</th>\n      <th>median</th>\n      <th>sum</th>\n      <th>mean</th>\n      <th>median</th>\n    </tr>\n    <tr>\n      <th>home_away</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Away</th>\n      <td>872.660541</td>\n      <td>67.127734</td>\n      <td>61.506807</td>\n      <td>1144.731667</td>\n      <td>88.056282</td>\n      <td>84.538333</td>\n    </tr>\n    <tr>\n      <th>Home</th>\n      <td>586.013861</td>\n      <td>58.601386</td>\n      <td>58.546569</td>\n      <td>754.961667</td>\n      <td>75.496167</td>\n      <td>76.582500</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "          distance_miles                       duration_minutes             \\\n                     sum       mean     median              sum       mean   \nhome_away                                                                    \nAway          872.660541  67.127734  61.506807      1144.731667  88.056282   \nHome          586.013861  58.601386  58.546569       754.961667  75.496167   \n\n                      \n              median  \nhome_away             \nAway       84.538333  \nHome       76.582500  "
                    },
                    "execution_count": 159,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "games_loc_df.groupby(['home_away']).agg({'distance_miles': ['sum', 'mean', 'median'],\n                                        'duration_minutes': ['sum', 'mean', 'median']})"
        },
        {
            "cell_type": "code",
            "execution_count": 86,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Total roundtrip miles traveled for 13 away games was 1,745.3 miles with an estimated duration of 1 day, 14:09\nThe average one-way trip distance was 67.1 miles for the 13 Away games\n"
                }
            ],
            "source": "print ('Total roundtrip miles traveled for 13 away games was {:,.1f} miles with an estimated duration of {}'.format(872.660541 * 2., str(timedelta(seconds=68683.9 * 2.))[:-10]))\nprint ('The average one-way trip distance was {:,.1f} miles for the {} Away games'.format(games_loc_df.loc[games_loc_df['home_away'] == 'Away']['distance_miles'].mean(),\n                                                                                  games_loc_df.loc[games_loc_df['home_away'] == 'Away']['distance_miles'].count()) )"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "---  \n---  \n# Venue Related Data Collection\n\n\n### Now on to Foursquare / Venue Data Collection\n\n\n-  Limit Foursquare search to 100 venues per sporting event location  \n-  The Search Radius is 2.5 miles around any event location\n\n\n\n### Some Foursquare Helper Functions  \n\n- Function `getNearbyVenues`  \n  Use the Foursquare `Explore` API endpoint to fetch the top 100 most recommended venue with a radius of a given location.  \n<br>  \n\n- Function `get_venue_rating`  \n  Use the Foursquare API to get additional information such as `rating`.  **Note** this is a _premium_ call.  As such Foursquare limits the amount of venue information you can access free-of-charge.  As a result, rating data is only a _nice to have_, and not every venue in the analysis has rating data.    \n  \n  This function tries to be economical with API access, so if it detects an existing rating, it will skip that record and move on to the next, in an attempt to get maximum number of venues with venue rating information.  \n  \n<br>  \n\n- Function `get_parent_category`  \n  For a given category return the top level parent for that category.  Example `Multiplex` is a child of `Movie Theater` which is a child of `Arts & Entertainment`  In this case, you pass in the category id for `Multiplex` and you are returned `Arts & Entertainment`  \n<br>  \n\n  \n- Function `traverse_children`  \n  Categories in Foursquare are stored in a tree structure.  This function traverses the tree and builds a 1:1 mapping of all categories to their top-level parent.  The results of this traversal are used by `get_parent_category` function.  \n\n"
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [],
            "source": "# Helper Function to Process list of locations\n\ndef getNearbyVenues(names, latitudes, longitudes, radius=500):\n    \n    venues_list = []\n    meta_result_list = []\n    for name, lat, lng in zip(names, latitudes, longitudes):       \n            \n        # create the API request URL\n        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n            CLIENT_ID, \n            CLIENT_SECRET, \n            VERSION, \n            lat, \n            lng, \n            radius, \n            LIMIT)\n            \n        # make the GET request\n        r = requests.get(url).json()[\"response\"]\n        results = r['groups'][0]['items']\n        print(name, r['totalResults'])\n        meta_result_list.append((name, r['totalResults']))\n            \n        # return only relevant information for each nearby venue\n        venues_list.append([(\n            name, \n            lat, \n            lng, \n            v['venue']['name'], \n            v['venue']['location']['lat'], \n            v['venue']['location']['lng'],  \n            v['venue']['categories'][0]['name'],\n            get_parent_category(v['venue']['categories'][0]['id']),\n            \n            v['venue']['id']) for v in results])\n\n    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n    nearby_venues.columns = ['Event Location', \n                  'Event Location Latitude', \n                  'Event Location Longitude', \n                  'Venue', \n                  'Venue Latitude', \n                  'Venue Longitude', \n                  'Venue Category',\n                  'Venue Root Category',                             \n                  'Venue Id']\n    \n    return(nearby_venues, meta_result_list)\n\n\ndef get_venue_rating(_venue_info):\n    \"\"\" Foursquare has daily limit of 500 premium calls ... pass in existing rating and skip when it has data, to incrementally build dataset\"\"\"\n    _venue_id = _venue_info['Venue Id']\n    _existing_rating = _venue_info['rating']\n    if _existing_rating > 0:\n        return _existing_rating\n    \n    _params = {\n        'client_id': CLIENT_ID,\n        'client_secret': CLIENT_SECRET,\n        'v': VERSION,\n        'limit': 1\n    }\n    ven_dets_url = 'https://api.foursquare.com/v2/venues/%s' % _venue_id\n    r = requests.get(ven_dets_url, params=_params)\n    if r.status_code != 200:\n        print ('Issue with ', _venue_id)\n        print (r)\n        return 0\n    venue_data = r.json()['response']['venue']\n    \n    if 'rating' in venue_data:\n        return venue_data['rating']\n    else:\n        return 0\n\ndef get_parent_category(_category_id):\n    \"\"\" mapping of all Foursquare Categories to their Parent category \"\"\"\n    if _category_id in parent_category_map:\n        return parent_category_map[_category_id]['my_top_parent'][0]\n    else:\n        'Unkown'\n    \n\ndef traverse_children(_parent_category_map, _eldest_parent, _cat_list):\n    \"\"\" Recursively traverse the Foursquare list of categories mapping to top parent\"\"\"    \n\n    item_count = len(_cat_list)\n\n    if item_count == 0:\n        return\n    \n    else:        \n        c = _cat_list[0]  \n        _parent_category_map[c['id']] = {'my_name': c['name'], 'my_top_parent': _eldest_parent}\n        traverse_children(_parent_category_map, _eldest_parent, _cat_list[1:])\n\n        if len(c['categories']) > 0:\n            traverse_children(_parent_category_map, _eldest_parent, c['categories'])\n\n    "
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "**Build Foursquare category Map**\n\nMap each category (id) to the top-level category, to enable doing analysis of \"Food\" instead of \"Wings Joint\""
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "10 Top Level Categories\n ['Arts & Entertainment', 'College & University', 'Event', 'Food', 'Nightlife Spot', 'Outdoors & Recreation', 'Professional & Other Places', 'Residence', 'Shop & Service', 'Travel & Transport']\n{'my_name': 'Pizza Place', 'my_top_parent': ('Food', '4d4b7105d754a06374d81259')}\nNumber of categories in map 927\n"
                }
            ],
            "source": "_params = {\n    'client_id': CLIENT_ID,\n    'client_secret': CLIENT_SECRET,\n    'v': VERSION,\n    'limit': 1\n}\nfsapi_url = 'https://api.foursquare.com/v2/venues/categories'\nr = requests.get(fsapi_url, params=_params)\nr.raise_for_status()\ncat_data = r.json()['response']\nparent_list = cat_data['categories']\n\nparent_category_map = {}\n\nprint (len(parent_list), 'Top Level Categories\\n', [r['name'] for r in parent_list])\nfor root_node in parent_list:\n    elder_dets = (root_node['name'], root_node['id'])\n    traverse_children(parent_category_map, elder_dets, root_node['categories'])\n    \n# Quick Test, 'Pizza Place'\nprint (parent_category_map['4bf58dd8d48988d1ca941735'])\nprint ('Number of categories in map', len(parent_category_map.keys()))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "#### Get Venue Data from Foursquare  \n\n**Reminder**  The core venue analysis is limited to 100 venues per location.  However, the Foursquare API returns the total number of venues in the result set.  The total number of venues is interesting metadata related to each location.  So store the total number of venues per location and use it to enrich the game location DataFrame"
        },
        {
            "cell_type": "code",
            "execution_count": 48,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Middlesex School 16\nDana Hall School 87\nBuckingham Browne & Nichols 239\nHolderness School 41\nVermont Academy 3\nMiss Porter's school 62\nPhillips Exeter Academy 72\nWilliston Northampton School 60\nWinchendon School 20\nPomfret School 12\nSt. Mark's School 17\nGovernor's Academy 12\nBrooks School 47\nWilbraham & Monson Academy 22\nSt. George's School 191\nBrewster Academy 31\nKimball Union Academy 8\nNorthfield Mt. Hermon School 10\nLawrence Academy 26\nGroton School 41\nMiss Hall's School 95\nDexter Southfield School 232\nSt. Paul's School 55\n"
                }
            ],
            "source": "search_radius = 2.5 * 1609.344  # 2.5 miles x conversion = 4,023.36\nvenues_df, high_level_venue_counts = getNearbyVenues(games_loc_df['opponent_school'], games_loc_df['lat'], games_loc_df['lng'], search_radius)"
        },
        {
            "cell_type": "code",
            "execution_count": 52,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "(1037, 9)"
                    },
                    "execution_count": 52,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "venues_df.shape"
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "[('Middlesex School', 16), ('Dana Hall School', 87), ('Buckingham Browne & Nichols', 239), ('Holderness School', 41), ('Vermont Academy', 3), (\"Miss Porter's school\", 62), ('Phillips Exeter Academy', 72), ('Williston Northampton School', 60), ('Winchendon School', 20), ('Pomfret School', 12), (\"St. Mark's School\", 17), (\"Governor's Academy\", 12), ('Brooks School', 47), ('Wilbraham & Monson Academy', 22), (\"St. George's School\", 191), ('Brewster Academy', 31), ('Kimball Union Academy', 8), ('Northfield Mt. Hermon School', 10), ('Lawrence Academy', 26), ('Groton School', 41), (\"Miss Hall's School\", 95), ('Dexter Southfield School', 232), (\"St. Paul's School\", 55)]\n"
                }
            ],
            "source": "print(high_level_venue_counts)   #  Grab total number of venues b/c result set was limited to top 100 venues at each location"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "**Enrich the Games Location DataFrame**  \n\nEnrich the Event Location dataset with total number of venues near each location."
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "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>date</th>\n      <th>home_away</th>\n      <th>opponent_school</th>\n      <th>time</th>\n      <th>quality</th>\n      <th>address</th>\n      <th>state</th>\n      <th>lat</th>\n      <th>lng</th>\n      <th>distance_meters</th>\n      <th>duration_seconds</th>\n      <th>distance_miles</th>\n      <th>duration_str</th>\n      <th>Nearby Venue Count</th>\n      <th>duration_minutes</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Wednesday, 11/28/2018</td>\n      <td>Away</td>\n      <td>Middlesex School</td>\n      <td>5:00 PM</td>\n      <td>school</td>\n      <td>Middlesex School, 1400, Lowell Road, Concord, ...</td>\n      <td>Massachusetts</td>\n      <td>42.499832</td>\n      <td>-71.368431</td>\n      <td>62722.6</td>\n      <td>3051.1</td>\n      <td>38.973942</td>\n      <td>0:50</td>\n      <td>16</td>\n      <td>50.851667</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Friday, 11/30/2018</td>\n      <td>Away</td>\n      <td>Dana Hall School</td>\n      <td>5:30 PM</td>\n      <td>school</td>\n      <td>Dana Hall School, 45, Dana Road, Wellesley, No...</td>\n      <td>Massachusetts</td>\n      <td>42.290910</td>\n      <td>-71.288481</td>\n      <td>89181.5</td>\n      <td>4490.4</td>\n      <td>55.414709</td>\n      <td>1:14</td>\n      <td>87</td>\n      <td>74.840000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Saturday, 12/1/2018</td>\n      <td>Home</td>\n      <td>Buckingham Browne &amp; Nichols</td>\n      <td>3:00 PM</td>\n      <td>school</td>\n      <td>Buckingham Browne &amp; Nichols School (Middle Sch...</td>\n      <td>Massachusetts</td>\n      <td>42.380804</td>\n      <td>-71.131203</td>\n      <td>82363.5</td>\n      <td>4125.8</td>\n      <td>51.178208</td>\n      <td>1:08</td>\n      <td>239</td>\n      <td>68.763333</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Wednesday, 12/5/2018</td>\n      <td>Away</td>\n      <td>Holderness School</td>\n      <td>3:30 PM</td>\n      <td>school</td>\n      <td>Holderness School, Chapel Lane, Holderness, Gr...</td>\n      <td>New Hampshire</td>\n      <td>43.761779</td>\n      <td>-71.674779</td>\n      <td>167290.4</td>\n      <td>7161.0</td>\n      <td>103.949236</td>\n      <td>1:59</td>\n      <td>41</td>\n      <td>119.350000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Saturday, 12/8/2018</td>\n      <td>Away</td>\n      <td>Vermont Academy</td>\n      <td>4:00 PM</td>\n      <td>school</td>\n      <td>Vermont Academy, Shepard Lane, Saxtons River, ...</td>\n      <td>Vermont</td>\n      <td>43.144794</td>\n      <td>-72.509698</td>\n      <td>90163.1</td>\n      <td>5072.3</td>\n      <td>56.024645</td>\n      <td>1:24</td>\n      <td>3</td>\n      <td>84.538333</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "                    date home_away              opponent_school     time  \\\n0  Wednesday, 11/28/2018      Away             Middlesex School  5:00 PM   \n1     Friday, 11/30/2018      Away             Dana Hall School  5:30 PM   \n2    Saturday, 12/1/2018      Home  Buckingham Browne & Nichols  3:00 PM   \n3   Wednesday, 12/5/2018      Away            Holderness School  3:30 PM   \n4    Saturday, 12/8/2018      Away              Vermont Academy  4:00 PM   \n\n  quality                                            address          state  \\\n0  school  Middlesex School, 1400, Lowell Road, Concord, ...  Massachusetts   \n1  school  Dana Hall School, 45, Dana Road, Wellesley, No...  Massachusetts   \n2  school  Buckingham Browne & Nichols School (Middle Sch...  Massachusetts   \n3  school  Holderness School, Chapel Lane, Holderness, Gr...  New Hampshire   \n4  school  Vermont Academy, Shepard Lane, Saxtons River, ...        Vermont   \n\n         lat        lng  distance_meters  duration_seconds  distance_miles  \\\n0  42.499832 -71.368431          62722.6            3051.1       38.973942   \n1  42.290910 -71.288481          89181.5            4490.4       55.414709   \n2  42.380804 -71.131203          82363.5            4125.8       51.178208   \n3  43.761779 -71.674779         167290.4            7161.0      103.949236   \n4  43.144794 -72.509698          90163.1            5072.3       56.024645   \n\n  duration_str  Nearby Venue Count  duration_minutes  \n0         0:50                  16         50.851667  \n1         1:14                  87         74.840000  \n2         1:08                 239         68.763333  \n3         1:59                  41        119.350000  \n4         1:24                   3         84.538333  "
                    },
                    "execution_count": 6,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# Use Text Output to create hard-coded list, to avoid hitting API ...\nvenue_count_list = [('Middlesex School', 16), ('Dana Hall School', 87), ('Buckingham Browne & Nichols', 239), ('Holderness School', 41), ('Vermont Academy', 3), (\"Miss Porter's school\", 62), ('Phillips Exeter Academy', 72), ('Williston Northampton School', 60), ('Winchendon School', 20), ('Pomfret School', 12), (\"St. Mark's School\", 17), (\"Governor's Academy\", 12), ('Brooks School', 47), ('Wilbraham & Monson Academy', 22), (\"St. George's School\", 191), ('Brewster Academy', 31), ('Kimball Union Academy', 8), ('Northfield Mt. Hermon School', 10), ('Lawrence Academy', 26), ('Groton School', 41), (\"Miss Hall's School\", 95), ('Dexter Southfield School', 232), (\"St. Paul's School\", 55)]\n\ngames_loc_df['Nearby Venue Count'] = [x[1] for x in venue_count_list]\ngames_loc_df['duration_minutes'] = games_loc_df['duration_seconds'].apply(lambda x: x / 60)\n\ngames_loc_df.head()\n\n# --> Jump back a few cells and Save the games_loc_df DataFrame again, with the summary venue counts added"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Additional Enrichement of Venue Data\n\n**How close are the Venues to the Sporting Event Location ?**  \n\nCalculate [Geodesic distance](https://en.wikipedia.org/wiki/Geodesics_on_an_ellipsoid) from each venue to the Sporting Event Location ... i.e. as the 'crow flies'"
        },
        {
            "cell_type": "code",
            "execution_count": 59,
            "metadata": {},
            "outputs": [],
            "source": "venues_df['Geodesic Distance From Event'] = venues_df.apply(lambda x:   geodesic((x['Event Location Latitude'], x['Event Location Longitude']), \n                                                                                 (x['Venue Latitude'], x['Venue Longitude'])).miles, axis=1)\n\n"
        },
        {
            "cell_type": "code",
            "execution_count": 61,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "(1037, 10)\n"
                },
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Event Location</th>\n      <th>Event Location Latitude</th>\n      <th>Event Location Longitude</th>\n      <th>Venue</th>\n      <th>Venue Latitude</th>\n      <th>Venue Longitude</th>\n      <th>Venue Category</th>\n      <th>Venue Root Category</th>\n      <th>Venue Id</th>\n      <th>Geodesic Distance From Event</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Middlesex School</td>\n      <td>42.499832</td>\n      <td>-71.368431</td>\n      <td>Minute Man National Park North Bridge</td>\n      <td>42.468995</td>\n      <td>-71.350601</td>\n      <td>Park</td>\n      <td>Outdoors &amp; Recreation</td>\n      <td>4df8ea0462847bdf4fedc9da</td>\n      <td>2.315207</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Middlesex School</td>\n      <td>42.499832</td>\n      <td>-71.368431</td>\n      <td>Ferns Country Store</td>\n      <td>42.529475</td>\n      <td>-71.348768</td>\n      <td>Sandwich Place</td>\n      <td>Food</td>\n      <td>4b93d4a3f964a520e95334e3</td>\n      <td>2.279200</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Middlesex School</td>\n      <td>42.499832</td>\n      <td>-71.368431</td>\n      <td>Kimball Farm</td>\n      <td>42.525479</td>\n      <td>-71.338571</td>\n      <td>Ice Cream Shop</td>\n      <td>Food</td>\n      <td>5352d0a1498ef37848d305af</td>\n      <td>2.336431</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Middlesex School</td>\n      <td>42.499832</td>\n      <td>-71.368431</td>\n      <td>The Concord Minuteman Statue</td>\n      <td>42.468945</td>\n      <td>-71.351225</td>\n      <td>Historic Site</td>\n      <td>Arts &amp; Entertainment</td>\n      <td>4f01dcd02c5b66f33e0facd3</td>\n      <td>2.306039</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Middlesex School</td>\n      <td>42.499832</td>\n      <td>-71.368431</td>\n      <td>Old North Bridge Visitor Center</td>\n      <td>42.470904</td>\n      <td>-71.352697</td>\n      <td>History Museum</td>\n      <td>Arts &amp; Entertainment</td>\n      <td>4c6b0b9e96eaa593049d21df</td>\n      <td>2.152426</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "     Event Location  Event Location Latitude  Event Location Longitude  \\\n0  Middlesex School                42.499832                -71.368431   \n1  Middlesex School                42.499832                -71.368431   \n2  Middlesex School                42.499832                -71.368431   \n3  Middlesex School                42.499832                -71.368431   \n4  Middlesex School                42.499832                -71.368431   \n\n                                   Venue  Venue Latitude  Venue Longitude  \\\n0  Minute Man National Park North Bridge       42.468995       -71.350601   \n1                    Ferns Country Store       42.529475       -71.348768   \n2                           Kimball Farm       42.525479       -71.338571   \n3           The Concord Minuteman Statue       42.468945       -71.351225   \n4        Old North Bridge Visitor Center       42.470904       -71.352697   \n\n   Venue Category    Venue Root Category                  Venue Id  \\\n0            Park  Outdoors & Recreation  4df8ea0462847bdf4fedc9da   \n1  Sandwich Place                   Food  4b93d4a3f964a520e95334e3   \n2  Ice Cream Shop                   Food  5352d0a1498ef37848d305af   \n3   Historic Site   Arts & Entertainment  4f01dcd02c5b66f33e0facd3   \n4  History Museum   Arts & Entertainment  4c6b0b9e96eaa593049d21df   \n\n   Geodesic Distance From Event  \n0                      2.315207  \n1                      2.279200  \n2                      2.336431  \n3                      2.306039  \n4                      2.152426  "
                    },
                    "execution_count": 61,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "print(venues_df.shape)\nvenues_df.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### What is the quality of these venues ?\n\nFetch ratings for each venue\n\nNeed to do this in multiple passes, so store ratings_df DataFrame and merge in"
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "(1016, 3)\n"
                },
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Venue</th>\n      <th>Venue Id</th>\n      <th>rating</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Minute Man National Park North Bridge</td>\n      <td>4df8ea0462847bdf4fedc9da</td>\n      <td>8.9</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Ferns Country Store</td>\n      <td>4b93d4a3f964a520e95334e3</td>\n      <td>8.3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Kimball Farm</td>\n      <td>5352d0a1498ef37848d305af</td>\n      <td>8.3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>The Concord Minuteman Statue</td>\n      <td>4f01dcd02c5b66f33e0facd3</td>\n      <td>7.6</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Old North Bridge Visitor Center</td>\n      <td>4c6b0b9e96eaa593049d21df</td>\n      <td>7.5</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "                                   Venue                  Venue Id  rating\n0  Minute Man National Park North Bridge  4df8ea0462847bdf4fedc9da     8.9\n1                    Ferns Country Store  4b93d4a3f964a520e95334e3     8.3\n2                           Kimball Farm  5352d0a1498ef37848d305af     8.3\n3           The Concord Minuteman Statue  4f01dcd02c5b66f33e0facd3     7.6\n4        Old North Bridge Visitor Center  4c6b0b9e96eaa593049d21df     7.5"
                    },
                    "execution_count": 4,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# Load previous ratings dataset\n\nratings_df = pd.read_csv(project.get_file(\"venue_ratings.csv\"))\nprint (ratings_df.shape)\nratings_df.head()\n"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [],
            "source": "ratings_df['rating'] = ratings_df[['Venue Id', 'rating']].apply(get_venue_rating, axis=1)"
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "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>Venue</th>\n      <th>Venue Id</th>\n      <th>rating</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>25</th>\n      <td>Alta Strada</td>\n      <td>4abd58d7f964a520da8920e3</td>\n      <td>8.2</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "          Venue                  Venue Id  rating\n25  Alta Strada  4abd58d7f964a520da8920e3     8.2"
                    },
                    "execution_count": 9,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "ratings_df.loc[ratings_df['Venue Id'] == '4abd58d7f964a520da8920e3']"
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "file saved at 2019-11-06 15:57:56.783156\n"
                }
            ],
            "source": "# Save a copy of the venue ratings data\nproject.save_data(\"venue_ratings.csv\", ratings_df.drop_duplicates(subset=['Venue Id']).to_csv(index=False), overwrite=True)\nprint ('file saved at', datetime.utcnow())"
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f30d5c92668>"
                    },
                    "execution_count": 6,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# How Many Ratings are in the dataset  \nratings_df['rating'].hist()"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Save the Venue data .. \nsince there API Limits, and analysis might span several days, save current state so can pick up where left off"
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "2019-11-03 17:05:34.048292\n"
                }
            ],
            "source": "project.save_data(\"venue_data.csv\", venues_df.to_csv(index=False), overwrite=True)\nprint (datetime.utcnow()) "
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "(1037, 10)\n"
                },
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Event Location</th>\n      <th>Event Location Latitude</th>\n      <th>Event Location Longitude</th>\n      <th>Venue</th>\n      <th>Venue Latitude</th>\n      <th>Venue Longitude</th>\n      <th>Venue Category</th>\n      <th>Venue Root Category</th>\n      <th>Venue Id</th>\n      <th>Geodesic Distance From Event</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Middlesex School</td>\n      <td>42.499832</td>\n      <td>-71.368431</td>\n      <td>Minute Man National Park North Bridge</td>\n      <td>42.468995</td>\n      <td>-71.350601</td>\n      <td>Park</td>\n      <td>Outdoors &amp; Recreation</td>\n      <td>4df8ea0462847bdf4fedc9da</td>\n      <td>2.315207</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Middlesex School</td>\n      <td>42.499832</td>\n      <td>-71.368431</td>\n      <td>Ferns Country Store</td>\n      <td>42.529475</td>\n      <td>-71.348768</td>\n      <td>Sandwich Place</td>\n      <td>Food</td>\n      <td>4b93d4a3f964a520e95334e3</td>\n      <td>2.279200</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Middlesex School</td>\n      <td>42.499832</td>\n      <td>-71.368431</td>\n      <td>Kimball Farm</td>\n      <td>42.525479</td>\n      <td>-71.338571</td>\n      <td>Ice Cream Shop</td>\n      <td>Food</td>\n      <td>5352d0a1498ef37848d305af</td>\n      <td>2.336431</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Middlesex School</td>\n      <td>42.499832</td>\n      <td>-71.368431</td>\n      <td>The Concord Minuteman Statue</td>\n      <td>42.468945</td>\n      <td>-71.351225</td>\n      <td>Historic Site</td>\n      <td>Arts &amp; Entertainment</td>\n      <td>4f01dcd02c5b66f33e0facd3</td>\n      <td>2.306039</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Middlesex School</td>\n      <td>42.499832</td>\n      <td>-71.368431</td>\n      <td>Old North Bridge Visitor Center</td>\n      <td>42.470904</td>\n      <td>-71.352697</td>\n      <td>History Museum</td>\n      <td>Arts &amp; Entertainment</td>\n      <td>4c6b0b9e96eaa593049d21df</td>\n      <td>2.152426</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "     Event Location  Event Location Latitude  Event Location Longitude  \\\n0  Middlesex School                42.499832                -71.368431   \n1  Middlesex School                42.499832                -71.368431   \n2  Middlesex School                42.499832                -71.368431   \n3  Middlesex School                42.499832                -71.368431   \n4  Middlesex School                42.499832                -71.368431   \n\n                                   Venue  Venue Latitude  Venue Longitude  \\\n0  Minute Man National Park North Bridge       42.468995       -71.350601   \n1                    Ferns Country Store       42.529475       -71.348768   \n2                           Kimball Farm       42.525479       -71.338571   \n3           The Concord Minuteman Statue       42.468945       -71.351225   \n4        Old North Bridge Visitor Center       42.470904       -71.352697   \n\n   Venue Category    Venue Root Category                  Venue Id  \\\n0            Park  Outdoors & Recreation  4df8ea0462847bdf4fedc9da   \n1  Sandwich Place                   Food  4b93d4a3f964a520e95334e3   \n2  Ice Cream Shop                   Food  5352d0a1498ef37848d305af   \n3   Historic Site   Arts & Entertainment  4f01dcd02c5b66f33e0facd3   \n4  History Museum   Arts & Entertainment  4c6b0b9e96eaa593049d21df   \n\n   Geodesic Distance From Event  \n0                      2.315207  \n1                      2.279200  \n2                      2.336431  \n3                      2.306039  \n4                      2.152426  "
                    },
                    "execution_count": 20,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# Load Venues DataFrame from Project Files\nvenues_df = pd.read_csv(project.get_file(\"venue_data.csv\"))\nprint (venues_df.shape)\nvenues_df.head()\n\n"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        },
        {
            "cell_type": "code",
            "execution_count": 21,
            "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>Event Location</th>\n      <th>Event Location Latitude</th>\n      <th>Event Location Longitude</th>\n      <th>Venue</th>\n      <th>Venue Latitude</th>\n      <th>Venue Longitude</th>\n      <th>Venue Category</th>\n      <th>Venue Root Category</th>\n      <th>Venue Id</th>\n      <th>Geodesic Distance From Event</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>25</th>\n      <td>Dana Hall School</td>\n      <td>42.29091</td>\n      <td>-71.288481</td>\n      <td>Alta Strada</td>\n      <td>42.295855</td>\n      <td>-71.297704</td>\n      <td>Italian Restaurant</td>\n      <td>Food</td>\n      <td>4abd58d7f964a520da8920e3</td>\n      <td>0.582962</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "      Event Location  Event Location Latitude  Event Location Longitude  \\\n25  Dana Hall School                 42.29091                -71.288481   \n\n          Venue  Venue Latitude  Venue Longitude      Venue Category  \\\n25  Alta Strada       42.295855       -71.297704  Italian Restaurant   \n\n   Venue Root Category                  Venue Id  Geodesic Distance From Event  \n25                Food  4abd58d7f964a520da8920e3                      0.582962  "
                    },
                    "execution_count": 21,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# Spot check a venue\nvenues_df.loc[venues_df['Venue Id'] == '4abd58d7f964a520da8920e3']"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "\n#### Merge Ratings with Venue Data\n\ncreate `venues_w_rate_df` for venues with ratings.  Preserving the core venues data and core ratings as their own data entities."
        },
        {
            "cell_type": "code",
            "execution_count": 22,
            "metadata": {},
            "outputs": [],
            "source": "# Merge in ratings from stored ratings DataFrame... to clean the across multiple runs\nvenues_w_rate_df =  pd.merge(venues_df, ratings_df.drop_duplicates(subset=['Venue Id']), on=['Venue Id', 'Venue'], how='left')"
        },
        {
            "cell_type": "code",
            "execution_count": 23,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "(1037, 11)\n"
                },
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Event Location</th>\n      <th>Event Location Latitude</th>\n      <th>Event Location Longitude</th>\n      <th>Venue</th>\n      <th>Venue Latitude</th>\n      <th>Venue Longitude</th>\n      <th>Venue Category</th>\n      <th>Venue Root Category</th>\n      <th>Venue Id</th>\n      <th>Geodesic Distance From Event</th>\n      <th>rating</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Middlesex School</td>\n      <td>42.499832</td>\n      <td>-71.368431</td>\n      <td>Minute Man National Park North Bridge</td>\n      <td>42.468995</td>\n      <td>-71.350601</td>\n      <td>Park</td>\n      <td>Outdoors &amp; Recreation</td>\n      <td>4df8ea0462847bdf4fedc9da</td>\n      <td>2.315207</td>\n      <td>8.9</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Middlesex School</td>\n      <td>42.499832</td>\n      <td>-71.368431</td>\n      <td>Ferns Country Store</td>\n      <td>42.529475</td>\n      <td>-71.348768</td>\n      <td>Sandwich Place</td>\n      <td>Food</td>\n      <td>4b93d4a3f964a520e95334e3</td>\n      <td>2.279200</td>\n      <td>8.3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Middlesex School</td>\n      <td>42.499832</td>\n      <td>-71.368431</td>\n      <td>Kimball Farm</td>\n      <td>42.525479</td>\n      <td>-71.338571</td>\n      <td>Ice Cream Shop</td>\n      <td>Food</td>\n      <td>5352d0a1498ef37848d305af</td>\n      <td>2.336431</td>\n      <td>8.3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Middlesex School</td>\n      <td>42.499832</td>\n      <td>-71.368431</td>\n      <td>The Concord Minuteman Statue</td>\n      <td>42.468945</td>\n      <td>-71.351225</td>\n      <td>Historic Site</td>\n      <td>Arts &amp; Entertainment</td>\n      <td>4f01dcd02c5b66f33e0facd3</td>\n      <td>2.306039</td>\n      <td>7.6</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Middlesex School</td>\n      <td>42.499832</td>\n      <td>-71.368431</td>\n      <td>Old North Bridge Visitor Center</td>\n      <td>42.470904</td>\n      <td>-71.352697</td>\n      <td>History Museum</td>\n      <td>Arts &amp; Entertainment</td>\n      <td>4c6b0b9e96eaa593049d21df</td>\n      <td>2.152426</td>\n      <td>7.5</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "     Event Location  Event Location Latitude  Event Location Longitude  \\\n0  Middlesex School                42.499832                -71.368431   \n1  Middlesex School                42.499832                -71.368431   \n2  Middlesex School                42.499832                -71.368431   \n3  Middlesex School                42.499832                -71.368431   \n4  Middlesex School                42.499832                -71.368431   \n\n                                   Venue  Venue Latitude  Venue Longitude  \\\n0  Minute Man National Park North Bridge       42.468995       -71.350601   \n1                    Ferns Country Store       42.529475       -71.348768   \n2                           Kimball Farm       42.525479       -71.338571   \n3           The Concord Minuteman Statue       42.468945       -71.351225   \n4        Old North Bridge Visitor Center       42.470904       -71.352697   \n\n   Venue Category    Venue Root Category                  Venue Id  \\\n0            Park  Outdoors & Recreation  4df8ea0462847bdf4fedc9da   \n1  Sandwich Place                   Food  4b93d4a3f964a520e95334e3   \n2  Ice Cream Shop                   Food  5352d0a1498ef37848d305af   \n3   Historic Site   Arts & Entertainment  4f01dcd02c5b66f33e0facd3   \n4  History Museum   Arts & Entertainment  4c6b0b9e96eaa593049d21df   \n\n   Geodesic Distance From Event  rating  \n0                      2.315207     8.9  \n1                      2.279200     8.3  \n2                      2.336431     8.3  \n3                      2.306039     7.6  \n4                      2.152426     7.5  "
                    },
                    "execution_count": 23,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "print (venues_w_rate_df.shape)\nvenues_w_rate_df.head()"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "#### Transform venue list \n\nTransform the venue list so that it is grouped at \"per location\" basis \n\n"
        },
        {
            "cell_type": "code",
            "execution_count": 24,
            "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>Event Location</th>\n      <th>Arts &amp; Entertainment</th>\n      <th>College &amp; University</th>\n      <th>Food</th>\n      <th>Nightlife Spot</th>\n      <th>Outdoors &amp; Recreation</th>\n      <th>Professional &amp; Other Places</th>\n      <th>Shop &amp; Service</th>\n      <th>Travel &amp; Transport</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Middlesex School</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Middlesex School</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Middlesex School</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Middlesex School</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Middlesex School</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "     Event Location  Arts & Entertainment  College & University  Food  \\\n0  Middlesex School                     0                     0     0   \n1  Middlesex School                     0                     0     1   \n2  Middlesex School                     0                     0     1   \n3  Middlesex School                     1                     0     0   \n4  Middlesex School                     1                     0     0   \n\n   Nightlife Spot  Outdoors & Recreation  Professional & Other Places  \\\n0               0                      1                            0   \n1               0                      0                            0   \n2               0                      0                            0   \n3               0                      0                            0   \n4               0                      0                            0   \n\n   Shop & Service  Travel & Transport  \n0               0                   0  \n1               0                   0  \n2               0                   0  \n3               0                   0  \n4               0                   0  "
                    },
                    "execution_count": 24,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# One Hot Encode the venues based on Top Parent Category ...\n# one hot encoding\nvenues_onehot = pd.get_dummies(venues_w_rate_df[['Venue Root Category']], prefix=\"\", prefix_sep=\"\")\n\n# add Event Location column back to dataframe\nvenues_onehot['Event Location'] = venues_w_rate_df['Event Location'] \n\n# move neighborhood column to the first column\nfixed_columns = [venues_onehot.columns[-1]] + list(venues_onehot.columns[:-1])\nvenues_onehot = venues_onehot[fixed_columns]\n\nvenues_onehot.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 25,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "(1037, 9)"
                    },
                    "execution_count": 25,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "venues_onehot.shape"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        },
        {
            "cell_type": "code",
            "execution_count": 29,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "(23, 9)\n"
                },
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Event Location</th>\n      <th>Arts &amp; Entertainment</th>\n      <th>College &amp; University</th>\n      <th>Food</th>\n      <th>Nightlife Spot</th>\n      <th>Outdoors &amp; Recreation</th>\n      <th>Professional &amp; Other Places</th>\n      <th>Shop &amp; Service</th>\n      <th>Travel &amp; Transport</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Brewster Academy</td>\n      <td>1</td>\n      <td>0</td>\n      <td>18</td>\n      <td>3</td>\n      <td>3</td>\n      <td>0</td>\n      <td>6</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Brooks School</td>\n      <td>0</td>\n      <td>0</td>\n      <td>26</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0</td>\n      <td>9</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Buckingham Browne &amp; Nichols</td>\n      <td>11</td>\n      <td>1</td>\n      <td>56</td>\n      <td>5</td>\n      <td>10</td>\n      <td>0</td>\n      <td>16</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Dana Hall School</td>\n      <td>1</td>\n      <td>1</td>\n      <td>34</td>\n      <td>2</td>\n      <td>25</td>\n      <td>1</td>\n      <td>22</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Dexter Southfield School</td>\n      <td>5</td>\n      <td>0</td>\n      <td>49</td>\n      <td>5</td>\n      <td>23</td>\n      <td>1</td>\n      <td>15</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Governor's Academy</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>7</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Groton School</td>\n      <td>0</td>\n      <td>0</td>\n      <td>20</td>\n      <td>2</td>\n      <td>7</td>\n      <td>1</td>\n      <td>10</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Holderness School</td>\n      <td>2</td>\n      <td>2</td>\n      <td>17</td>\n      <td>2</td>\n      <td>3</td>\n      <td>0</td>\n      <td>8</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Kimball Union Academy</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Lawrence Academy</td>\n      <td>0</td>\n      <td>0</td>\n      <td>11</td>\n      <td>0</td>\n      <td>8</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Middlesex School</td>\n      <td>2</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>6</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Miss Hall's School</td>\n      <td>6</td>\n      <td>0</td>\n      <td>50</td>\n      <td>6</td>\n      <td>4</td>\n      <td>1</td>\n      <td>20</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Miss Porter's school</td>\n      <td>1</td>\n      <td>0</td>\n      <td>30</td>\n      <td>1</td>\n      <td>10</td>\n      <td>1</td>\n      <td>13</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Northfield Mt. Hermon School</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Phillips Exeter Academy</td>\n      <td>2</td>\n      <td>0</td>\n      <td>31</td>\n      <td>1</td>\n      <td>14</td>\n      <td>1</td>\n      <td>18</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Pomfret School</td>\n      <td>1</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>St. George's School</td>\n      <td>7</td>\n      <td>0</td>\n      <td>55</td>\n      <td>9</td>\n      <td>18</td>\n      <td>1</td>\n      <td>3</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>St. Mark's School</td>\n      <td>0</td>\n      <td>0</td>\n      <td>9</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>St. Paul's School</td>\n      <td>2</td>\n      <td>0</td>\n      <td>31</td>\n      <td>3</td>\n      <td>5</td>\n      <td>0</td>\n      <td>13</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Vermont Academy</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Wilbraham &amp; Monson Academy</td>\n      <td>2</td>\n      <td>0</td>\n      <td>9</td>\n      <td>1</td>\n      <td>5</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Williston Northampton School</td>\n      <td>2</td>\n      <td>0</td>\n      <td>28</td>\n      <td>7</td>\n      <td>10</td>\n      <td>0</td>\n      <td>13</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Winchendon School</td>\n      <td>1</td>\n      <td>0</td>\n      <td>12</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "                  Event Location  Arts & Entertainment  College & University  \\\n0               Brewster Academy                     1                     0   \n1                  Brooks School                     0                     0   \n2    Buckingham Browne & Nichols                    11                     1   \n3               Dana Hall School                     1                     1   \n4       Dexter Southfield School                     5                     0   \n5             Governor's Academy                     0                     0   \n6                  Groton School                     0                     0   \n7              Holderness School                     2                     2   \n8          Kimball Union Academy                     0                     0   \n9               Lawrence Academy                     0                     0   \n10              Middlesex School                     2                     0   \n11            Miss Hall's School                     6                     0   \n12          Miss Porter's school                     1                     0   \n13  Northfield Mt. Hermon School                     0                     0   \n14       Phillips Exeter Academy                     2                     0   \n15                Pomfret School                     1                     0   \n16           St. George's School                     7                     0   \n17             St. Mark's School                     0                     0   \n18             St. Paul's School                     2                     0   \n19               Vermont Academy                     1                     0   \n20    Wilbraham & Monson Academy                     2                     0   \n21  Williston Northampton School                     2                     0   \n22             Winchendon School                     1                     0   \n\n    Food  Nightlife Spot  Outdoors & Recreation  Professional & Other Places  \\\n0     18               3                      3                            0   \n1     26               2                      8                            0   \n2     56               5                     10                            0   \n3     34               2                     25                            1   \n4     49               5                     23                            1   \n5      1               0                      7                            0   \n6     20               2                      7                            1   \n7     17               2                      3                            0   \n8      1               2                      3                            1   \n9     11               0                      8                            1   \n10     4               0                      6                            0   \n11    50               6                      4                            1   \n12    30               1                     10                            1   \n13     4               0                      2                            0   \n14    31               1                     14                            1   \n15     5               0                      5                            0   \n16    55               9                     18                            1   \n17     9               1                      3                            0   \n18    31               3                      5                            0   \n19     0               0                      0                            0   \n20     9               1                      5                            0   \n21    28               7                     10                            0   \n22    12               1                      1                            0   \n\n    Shop & Service  Travel & Transport  \n0                6                   0  \n1                9                   1  \n2               16                   1  \n3               22                   1  \n4               15                   1  \n5                4                   0  \n6               10                   1  \n7                8                   6  \n8                1                   0  \n9                5                   1  \n10               4                   0  \n11              20                   7  \n12              13                   6  \n13               4                   0  \n14              18                   5  \n15               1                   0  \n16               3                   6  \n17               4                   0  \n18              13                   1  \n19               1                   1  \n20               5                   0  \n21              13                   0  \n22               5                   0  "
                    },
                    "execution_count": 29,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# group and find total number of each category\nvenues_grouped = venues_onehot.groupby('Event Location').sum().reset_index()\nprint (venues_grouped.shape)\nvenues_grouped"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "#### Save the Summarized Venue Data \n\n"
        },
        {
            "cell_type": "code",
            "execution_count": 27,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "{'file_name': 'event_location_venue_summary.csv',\n 'message': 'File saved to project storage.',\n 'bucket_name': 'ibmcrs9capstone-donotdelete-pr-afds8o4rva78zh',\n 'asset_id': '34dedc4b-c1c0-4d43-8bdc-89039703e18b'}"
                    },
                    "execution_count": 27,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "## Save Event Lcoation Venue data\n## BE CAREFUL, Overwrite = TRUE\n\nproject.save_data(\"event_location_venue_summary.csv\", venues_grouped.to_csv(index=False), overwrite=True)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "#### Load the Data from the CSV \nto pick up where we left off"
        },
        {
            "cell_type": "code",
            "execution_count": 30,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "(23, 9)\n"
                },
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Event Location</th>\n      <th>Arts &amp; Entertainment</th>\n      <th>College &amp; University</th>\n      <th>Food</th>\n      <th>Nightlife Spot</th>\n      <th>Outdoors &amp; Recreation</th>\n      <th>Professional &amp; Other Places</th>\n      <th>Shop &amp; Service</th>\n      <th>Travel &amp; Transport</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Brewster Academy</td>\n      <td>1</td>\n      <td>0</td>\n      <td>18</td>\n      <td>3</td>\n      <td>3</td>\n      <td>0</td>\n      <td>6</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Brooks School</td>\n      <td>0</td>\n      <td>0</td>\n      <td>26</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0</td>\n      <td>9</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Buckingham Browne &amp; Nichols</td>\n      <td>11</td>\n      <td>1</td>\n      <td>56</td>\n      <td>5</td>\n      <td>10</td>\n      <td>0</td>\n      <td>16</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Dana Hall School</td>\n      <td>1</td>\n      <td>1</td>\n      <td>34</td>\n      <td>2</td>\n      <td>25</td>\n      <td>1</td>\n      <td>22</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Dexter Southfield School</td>\n      <td>5</td>\n      <td>0</td>\n      <td>49</td>\n      <td>5</td>\n      <td>23</td>\n      <td>1</td>\n      <td>15</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "                Event Location  Arts & Entertainment  College & University  \\\n0             Brewster Academy                     1                     0   \n1                Brooks School                     0                     0   \n2  Buckingham Browne & Nichols                    11                     1   \n3             Dana Hall School                     1                     1   \n4     Dexter Southfield School                     5                     0   \n\n   Food  Nightlife Spot  Outdoors & Recreation  Professional & Other Places  \\\n0    18               3                      3                            0   \n1    26               2                      8                            0   \n2    56               5                     10                            0   \n3    34               2                     25                            1   \n4    49               5                     23                            1   \n\n   Shop & Service  Travel & Transport  \n0               6                   0  \n1               9                   1  \n2              16                   1  \n3              22                   1  \n4              15                   1  "
                    },
                    "execution_count": 30,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "venues_grouped = pd.read_csv(project.get_file(\"event_location_venue_summary.csv\"))\nprint (venues_grouped.shape)\nvenues_grouped.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "---  \n---  \n"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Exploratory Data Analysis   \n\nThe different aspects of analysis are in no particular order  "
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Explore Venue Proximity & Venue Rating around locations  \n\nBelow are histograms summarzing   \n\n  1. The Venue's Distance from event location  \n  1. The Venue's Rating distribution  \n  \n**Observations:**  \n\nThe further you get from an event location, the more options there tends to be.  This is not surprising since the data set is list of high schools, and most high school campuses do not have many external venues operating on the campus.  I would expect this pattern to be true of many event locations.  \n\nWith the limited access to the premium Foursquare API, most venue's do not have any rating data.  At this time, due to the large amount of missing data, no analysis will incorporate rating data.\n\n"
        },
        {
            "cell_type": "code",
            "execution_count": 70,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": "/opt/conda/envs/Python36/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3267: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared\n  exec(code_obj, self.user_global_ns, self.user_ns)\n"
                },
                {
                    "data": {
                        "text/plain": "{'file_name': 'venue_radius_and_ratings.png',\n 'message': 'File saved to project storage.',\n 'bucket_name': 'ibmcrs9capstone-donotdelete-pr-afds8o4rva78zh',\n 'asset_id': '2015ff3c-d56e-424d-b270-d8ba7fcc630e'}"
                    },
                    "execution_count": 70,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 720x360 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot distribution of Venue Ratings as well as Geodesic Distance from Sporting Event Location\nfig = plt.figure(figsize=(10,5))\nax = fig.gca()\nvenues_df[['rating', 'Geodesic Distance From Event']].hist(ax=ax)\nbuffer = io.BytesIO()\n#fig.show()\nplt.savefig(buffer, format='jpg')\nbuffer.seek(0)\nproject.save_data('venue_radius_and_ratings.png', buffer, overwrite=True)\n\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Home location to Away locations:  Travel & High Level Venue Analysis\n\n- Plot how far each away game is (distance)?  \n- how long will it take to get there (duration) ? \n- genenerally what's around each school ?\n\n\nThe first analysis is to get a feel for the total population data.  So all schools on the schedule, not just the away games  \n\n**Note**  \nSince athlete schedules tend to remain fairly stable in terms of the same opponents every year, but each year they change locations.  So a game location \"A\" might be home this year will be away next year.  Thus having a general feel all schools is useful.\n\n\n\n**Observations**  \nThe Distance and Duration plots seem like they will cluster relatively smoothly.  In the \"all data\" plots, no driving or duration bin contains more than 5 schools.  \n\nFor the \"Nearby Venue\" histograms, there is a right skew, which might cause some challenges to clustering.  "
        },
        {
            "cell_type": "code",
            "execution_count": 44,
            "metadata": {},
            "outputs": [],
            "source": "school_display_cols = ['home_away', 'date', 'time', 'opponent_school', 'distance_miles', 'duration_minutes', 'duration_str', 'Nearby Venue Count']"
        },
        {
            "cell_type": "code",
            "execution_count": 66,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": "/opt/conda/envs/Python36/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3267: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared\n  exec(code_obj, self.user_global_ns, self.user_ns)\n"
                },
                {
                    "data": {
                        "text/plain": "{'file_name': 'all_event_location_driving_hist_1.png',\n 'message': 'File saved to project storage.',\n 'bucket_name': 'ibmcrs9capstone-donotdelete-pr-afds8o4rva78zh',\n 'asset_id': '8985b348-4abf-40b4-899d-92b1b0f3940d'}"
                    },
                    "execution_count": 66,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 720x288 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Driving Analysis to all schools \n# Plot how far each school is, how long will it take to get there, genenerally what's around each school ?\n\nq_col_list = ['distance_miles', 'duration_minutes']\nfig = plt.figure(figsize=(10,4))\nax = fig.gca()\ngames_loc_df[q_col_list].hist(ax=ax)\nfig.suptitle('Count of Schools at a Distance or Duration; All Schools', fontsize=14)\nplt.subplots_adjust(top=0.80)\n\n\n\nbuffer = io.BytesIO()\nplt.savefig(buffer, format='png')\nbuffer.seek(0)\nproject.save_data('all_event_location_driving_hist_1.png', buffer, overwrite=True)\n"
        },
        {
            "cell_type": "code",
            "execution_count": 67,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "{'file_name': 'all_event_location_venues_hist_1.png',\n 'message': 'File saved to project storage.',\n 'bucket_name': 'ibmcrs9capstone-donotdelete-pr-afds8o4rva78zh',\n 'asset_id': '7d84d7e0-5c3d-4d02-b269-33c9a206c72e'}"
                    },
                    "execution_count": 67,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Venue Analysis to all schools \n# genenerally what's around each school ?\n\nq_col_list = ['Nearby Venue Count']\nfig = plt.figure(figsize=(6,4))\nax = fig.gca()\ngames_loc_df[q_col_list].hist(ax=ax)\nfig.suptitle('Count of Schools with a number of venues; All Schools', fontsize=14)\nplt.subplots_adjust(top=0.80)\n\n\nbuffer = io.BytesIO()\nplt.savefig(buffer, format='png')\nbuffer.seek(0)\nproject.save_data('all_event_location_venues_hist_1.png', buffer, overwrite=True)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Now Repeat this analysis only for the away games"
        },
        {
            "cell_type": "code",
            "execution_count": 38,
            "metadata": {},
            "outputs": [],
            "source": "# Create a DataFrame of just the Away Games\naway_games_df = games_loc_df.loc[games_loc_df['home_away'] == 'Away'] \n"
        },
        {
            "cell_type": "code",
            "execution_count": 63,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": "/opt/conda/envs/Python36/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3267: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared\n  exec(code_obj, self.user_global_ns, self.user_ns)\n"
                },
                {
                    "data": {
                        "text/plain": "{'file_name': 'away_location_driving_hist_1.png',\n 'message': 'File saved to project storage.',\n 'bucket_name': 'ibmcrs9capstone-donotdelete-pr-afds8o4rva78zh',\n 'asset_id': '46af667b-c6b5-474e-805e-df3a2d5da7c7'}"
                    },
                    "execution_count": 63,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 720x288 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Distance Analysis\n#\n# Plot how far each away game is, how long will it take to get there, genenerally what's around each school ?\nfig = plt.figure(figsize=(10,4))\nax = fig.gca()\nq_col_list = ['distance_miles', 'duration_minutes']\nschool_display_cols = ['home_away', 'date', 'time', 'opponent_school', 'distance_miles', 'duration_minutes', 'duration_str', 'Nearby Venue Count']\naway_games_df[q_col_list].hist(ax=ax)\nfig.suptitle('Count of Schools at a Distance or Duration; Away Locations', fontsize=14)\nplt.subplots_adjust(top=0.80)\n\nbuffer = io.BytesIO()\nplt.savefig(buffer, format='png')\nbuffer.seek(0)\nproject.save_data('away_location_driving_hist_1.png', buffer, overwrite=True)\n"
        },
        {
            "cell_type": "code",
            "execution_count": 64,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "{'file_name': 'away_location_venue_hist_1.png',\n 'message': 'File saved to project storage.',\n 'bucket_name': 'ibmcrs9capstone-donotdelete-pr-afds8o4rva78zh',\n 'asset_id': 'e43c3162-448d-4ca9-9e67-5cfc6b79402c'}"
                    },
                    "execution_count": 64,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Venue Analysis\n#\n# Plot how far each away game is, how long will it take to get there, genenerally what's around each school ?\nfig = plt.figure(figsize=(6,4))\nfig.suptitle('Away Game Venue')\nax = fig.gca()\nq_col_list = ['Nearby Venue Count']\nschool_display_cols = ['home_away', 'date', 'time', 'opponent_school', 'distance_miles', 'duration_minutes', 'duration_str', 'Nearby Venue Count']\naway_games_df[q_col_list].hist(ax=ax)\nfig.suptitle('Count of Schools with a number of venues; Away Locations', fontsize=14)\nplt.subplots_adjust(top=0.80)\n\nbuffer = io.BytesIO()\nplt.savefig(buffer, format='png')\nbuffer.seek(0)\nproject.save_data('away_location_venue_hist_1.png', buffer, overwrite=True)\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Numerical Summary of Locations \n\n"
        },
        {
            "cell_type": "code",
            "execution_count": 69,
            "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>lat</th>\n      <th>lng</th>\n      <th>distance_meters</th>\n      <th>duration_seconds</th>\n      <th>distance_miles</th>\n      <th>Nearby Venue Count</th>\n      <th>duration_minutes</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>23.000000</td>\n      <td>23.000000</td>\n      <td>23.00000</td>\n      <td>23.000000</td>\n      <td>23.000000</td>\n      <td>23.000000</td>\n      <td>23.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>42.605610</td>\n      <td>-71.768867</td>\n      <td>102065.80000</td>\n      <td>4955.721739</td>\n      <td>63.420626</td>\n      <td>60.826087</td>\n      <td>82.595362</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.573335</td>\n      <td>0.667594</td>\n      <td>44821.19187</td>\n      <td>1926.154144</td>\n      <td>27.850544</td>\n      <td>68.647752</td>\n      <td>32.102569</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>41.491993</td>\n      <td>-73.245384</td>\n      <td>12850.50000</td>\n      <td>778.900000</td>\n      <td>7.984915</td>\n      <td>3.000000</td>\n      <td>12.981667</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>42.300363</td>\n      <td>-72.340656</td>\n      <td>75961.60000</td>\n      <td>3803.900000</td>\n      <td>47.200259</td>\n      <td>16.500000</td>\n      <td>63.398333</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>42.593118</td>\n      <td>-71.574795</td>\n      <td>98803.80000</td>\n      <td>5013.800000</td>\n      <td>61.393717</td>\n      <td>41.000000</td>\n      <td>83.563333</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>42.864149</td>\n      <td>-71.240521</td>\n      <td>134308.00000</td>\n      <td>6427.250000</td>\n      <td>83.454962</td>\n      <td>67.000000</td>\n      <td>107.120833</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>43.761779</td>\n      <td>-70.902276</td>\n      <td>168934.60000</td>\n      <td>8149.100000</td>\n      <td>104.970892</td>\n      <td>239.000000</td>\n      <td>135.818333</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "             lat        lng  distance_meters  duration_seconds  \\\ncount  23.000000  23.000000         23.00000         23.000000   \nmean   42.605610 -71.768867     102065.80000       4955.721739   \nstd     0.573335   0.667594      44821.19187       1926.154144   \nmin    41.491993 -73.245384      12850.50000        778.900000   \n25%    42.300363 -72.340656      75961.60000       3803.900000   \n50%    42.593118 -71.574795      98803.80000       5013.800000   \n75%    42.864149 -71.240521     134308.00000       6427.250000   \nmax    43.761779 -70.902276     168934.60000       8149.100000   \n\n       distance_miles  Nearby Venue Count  duration_minutes  \ncount       23.000000           23.000000         23.000000  \nmean        63.420626           60.826087         82.595362  \nstd         27.850544           68.647752         32.102569  \nmin          7.984915            3.000000         12.981667  \n25%         47.200259           16.500000         63.398333  \n50%         61.393717           41.000000         83.563333  \n75%         83.454962           67.000000        107.120833  \nmax        104.970892          239.000000        135.818333  "
                    },
                    "execution_count": 69,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# Get numerical summary of All locations\ngames_loc_df.describe()"
        },
        {
            "cell_type": "code",
            "execution_count": 70,
            "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>lat</th>\n      <th>lng</th>\n      <th>distance_meters</th>\n      <th>duration_seconds</th>\n      <th>distance_miles</th>\n      <th>Nearby Venue Count</th>\n      <th>duration_minutes</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>13.000000</td>\n      <td>13.000000</td>\n      <td>13.000000</td>\n      <td>13.000000</td>\n      <td>13.000000</td>\n      <td>13.000000</td>\n      <td>13.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>42.615806</td>\n      <td>-71.667284</td>\n      <td>108031.823077</td>\n      <td>5283.376923</td>\n      <td>67.127734</td>\n      <td>63.615385</td>\n      <td>88.056282</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.628252</td>\n      <td>0.673951</td>\n      <td>42210.327794</td>\n      <td>1770.125290</td>\n      <td>26.228231</td>\n      <td>72.881798</td>\n      <td>29.502088</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>41.491993</td>\n      <td>-73.245384</td>\n      <td>38270.700000</td>\n      <td>2406.800000</td>\n      <td>23.780265</td>\n      <td>3.000000</td>\n      <td>40.113333</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>42.309817</td>\n      <td>-71.964796</td>\n      <td>89181.500000</td>\n      <td>4490.400000</td>\n      <td>55.414709</td>\n      <td>12.000000</td>\n      <td>74.840000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>42.499832</td>\n      <td>-71.530065</td>\n      <td>98985.800000</td>\n      <td>5072.300000</td>\n      <td>61.506807</td>\n      <td>41.000000</td>\n      <td>84.538333</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>42.978211</td>\n      <td>-71.273596</td>\n      <td>134758.800000</td>\n      <td>7006.000000</td>\n      <td>83.735076</td>\n      <td>87.000000</td>\n      <td>116.766667</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>43.761779</td>\n      <td>-70.902276</td>\n      <td>168934.600000</td>\n      <td>7823.200000</td>\n      <td>104.970892</td>\n      <td>232.000000</td>\n      <td>130.386667</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "             lat        lng  distance_meters  duration_seconds  \\\ncount  13.000000  13.000000        13.000000         13.000000   \nmean   42.615806 -71.667284    108031.823077       5283.376923   \nstd     0.628252   0.673951     42210.327794       1770.125290   \nmin    41.491993 -73.245384     38270.700000       2406.800000   \n25%    42.309817 -71.964796     89181.500000       4490.400000   \n50%    42.499832 -71.530065     98985.800000       5072.300000   \n75%    42.978211 -71.273596    134758.800000       7006.000000   \nmax    43.761779 -70.902276    168934.600000       7823.200000   \n\n       distance_miles  Nearby Venue Count  duration_minutes  \ncount       13.000000           13.000000         13.000000  \nmean        67.127734           63.615385         88.056282  \nstd         26.228231           72.881798         29.502088  \nmin         23.780265            3.000000         40.113333  \n25%         55.414709           12.000000         74.840000  \n50%         61.506807           41.000000         84.538333  \n75%         83.735076           87.000000        116.766667  \nmax        104.970892          232.000000        130.386667  "
                    },
                    "execution_count": 70,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# Numerical Summary of Away Games\naway_games_df.describe()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Exploring Clustering  \n\nOne of the goals is to assign labels to location to signal that additional planning is required for this location due to limited number of venues. \n\n\n\nBased on the histograms above lets try simple binning for initial analysis.  \n\n**NOTES**  \n  - Since the actual travel to a location is more important than distance, we'll start with binning by `duration_minutes`.  \n  - To simplify the analysis, we'll focus on `Food` and `Shop & Services` top-level categories, since the analysis is based on travelling for high-school basketball games.   \n\n\nFirst we'll merge the location data with venue data."
        },
        {
            "cell_type": "code",
            "execution_count": 74,
            "metadata": {},
            "outputs": [],
            "source": "# Create a merged DataFrame\n# Location + Venue Merged ... and only save Food + Shop & Services \nloc_ven_merged_df = pd.merge(games_loc_df, venues_grouped[['Event Location','Food', 'Shop & Service']], how='left', \n                             left_on='opponent_school', right_on='Event Location')"
        },
        {
            "cell_type": "code",
            "execution_count": 76,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "(23, 18)\n"
                },
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>home_away</th>\n      <th>opponent_school</th>\n      <th>time</th>\n      <th>quality</th>\n      <th>address</th>\n      <th>state</th>\n      <th>lat</th>\n      <th>lng</th>\n      <th>distance_meters</th>\n      <th>duration_seconds</th>\n      <th>distance_miles</th>\n      <th>duration_str</th>\n      <th>Nearby Venue Count</th>\n      <th>duration_minutes</th>\n      <th>Event Location</th>\n      <th>Food</th>\n      <th>Shop &amp; Service</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Wednesday, 11/28/2018</td>\n      <td>Away</td>\n      <td>Middlesex School</td>\n      <td>5:00 PM</td>\n      <td>school</td>\n      <td>Middlesex School, 1400, Lowell Road, Concord, ...</td>\n      <td>Massachusetts</td>\n      <td>42.499832</td>\n      <td>-71.368431</td>\n      <td>62722.6</td>\n      <td>3051.1</td>\n      <td>38.973942</td>\n      <td>0:50</td>\n      <td>16</td>\n      <td>50.851667</td>\n      <td>Middlesex School</td>\n      <td>4</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Friday, 11/30/2018</td>\n      <td>Away</td>\n      <td>Dana Hall School</td>\n      <td>5:30 PM</td>\n      <td>school</td>\n      <td>Dana Hall School, 45, Dana Road, Wellesley, No...</td>\n      <td>Massachusetts</td>\n      <td>42.290910</td>\n      <td>-71.288481</td>\n      <td>89181.5</td>\n      <td>4490.4</td>\n      <td>55.414709</td>\n      <td>1:14</td>\n      <td>87</td>\n      <td>74.840000</td>\n      <td>Dana Hall School</td>\n      <td>34</td>\n      <td>22</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Saturday, 12/1/2018</td>\n      <td>Home</td>\n      <td>Buckingham Browne &amp; Nichols</td>\n      <td>3:00 PM</td>\n      <td>school</td>\n      <td>Buckingham Browne &amp; Nichols School (Middle Sch...</td>\n      <td>Massachusetts</td>\n      <td>42.380804</td>\n      <td>-71.131203</td>\n      <td>82363.5</td>\n      <td>4125.8</td>\n      <td>51.178208</td>\n      <td>1:08</td>\n      <td>239</td>\n      <td>68.763333</td>\n      <td>Buckingham Browne &amp; Nichols</td>\n      <td>56</td>\n      <td>16</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Wednesday, 12/5/2018</td>\n      <td>Away</td>\n      <td>Holderness School</td>\n      <td>3:30 PM</td>\n      <td>school</td>\n      <td>Holderness School, Chapel Lane, Holderness, Gr...</td>\n      <td>New Hampshire</td>\n      <td>43.761779</td>\n      <td>-71.674779</td>\n      <td>167290.4</td>\n      <td>7161.0</td>\n      <td>103.949236</td>\n      <td>1:59</td>\n      <td>41</td>\n      <td>119.350000</td>\n      <td>Holderness School</td>\n      <td>17</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Saturday, 12/8/2018</td>\n      <td>Away</td>\n      <td>Vermont Academy</td>\n      <td>4:00 PM</td>\n      <td>school</td>\n      <td>Vermont Academy, Shepard Lane, Saxtons River, ...</td>\n      <td>Vermont</td>\n      <td>43.144794</td>\n      <td>-72.509698</td>\n      <td>90163.1</td>\n      <td>5072.3</td>\n      <td>56.024645</td>\n      <td>1:24</td>\n      <td>3</td>\n      <td>84.538333</td>\n      <td>Vermont Academy</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "                    date home_away              opponent_school     time  \\\n0  Wednesday, 11/28/2018      Away             Middlesex School  5:00 PM   \n1     Friday, 11/30/2018      Away             Dana Hall School  5:30 PM   \n2    Saturday, 12/1/2018      Home  Buckingham Browne & Nichols  3:00 PM   \n3   Wednesday, 12/5/2018      Away            Holderness School  3:30 PM   \n4    Saturday, 12/8/2018      Away              Vermont Academy  4:00 PM   \n\n  quality                                            address          state  \\\n0  school  Middlesex School, 1400, Lowell Road, Concord, ...  Massachusetts   \n1  school  Dana Hall School, 45, Dana Road, Wellesley, No...  Massachusetts   \n2  school  Buckingham Browne & Nichols School (Middle Sch...  Massachusetts   \n3  school  Holderness School, Chapel Lane, Holderness, Gr...  New Hampshire   \n4  school  Vermont Academy, Shepard Lane, Saxtons River, ...        Vermont   \n\n         lat        lng  distance_meters  duration_seconds  distance_miles  \\\n0  42.499832 -71.368431          62722.6            3051.1       38.973942   \n1  42.290910 -71.288481          89181.5            4490.4       55.414709   \n2  42.380804 -71.131203          82363.5            4125.8       51.178208   \n3  43.761779 -71.674779         167290.4            7161.0      103.949236   \n4  43.144794 -72.509698          90163.1            5072.3       56.024645   \n\n  duration_str  Nearby Venue Count  duration_minutes  \\\n0         0:50                  16         50.851667   \n1         1:14                  87         74.840000   \n2         1:08                 239         68.763333   \n3         1:59                  41        119.350000   \n4         1:24                   3         84.538333   \n\n                Event Location  Food  Shop & Service  \n0             Middlesex School     4               4  \n1             Dana Hall School    34              22  \n2  Buckingham Browne & Nichols    56              16  \n3            Holderness School    17               8  \n4              Vermont Academy     0               1  "
                    },
                    "execution_count": 76,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "print(loc_ven_merged_df.shape)\nloc_ven_merged_df.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "\n#### Bin based on duration  \n\nLet's try binning on duration minutes\n\nAfter the binning do some simple analysis to check the distrubtion of labels.  3 seems like a good number to start with."
        },
        {
            "cell_type": "code",
            "execution_count": 78,
            "metadata": {},
            "outputs": [],
            "source": "# Try bin'ing\n\nbin_num = 3\ngroup_names = ['Short', 'Medium', 'Long']\nloc_ven_merged_df['drive_label'] = pd.cut(loc_ven_merged_df['duration_minutes'], bin_num, labels=group_names, include_lowest=True)"
        },
        {
            "cell_type": "code",
            "execution_count": 91,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Medium    12\nLong       7\nShort      4\nName: drive_label, dtype: int64\n\n\nMin SHORT Duration duration_minutes    12.981667\ndtype: float64\nMax SHORT Duration duration_minutes    50.851667\ndtype: float64\nSHORT Label Range duration_minutes    37.87\ndtype: float64\n\n\nMin MEDIUM Duration duration_minutes    55.226667\ndtype: float64\nMax MEDIUM Duration duration_minutes    88.476667\ndtype: float64\nMEDIUM Label Range duration_minutes    33.25\ndtype: float64\n\n\nMin LONG Duration duration_minutes    99.045\ndtype: float64\nMax LONG Duration duration_minutes    135.818333\ndtype: float64\nLONG Label Range duration_minutes    36.773333\ndtype: float64\n"
                }
            ],
            "source": "# How many locations for each label \nprint (loc_ven_merged_df['drive_label'].value_counts())\n\n# Range captured in each label\n\nfor l in ['Short', 'Medium', 'Long']:\n    print('\\n')\n\n    label_min = loc_ven_merged_df.loc[loc_ven_merged_df['drive_label'] == l][['duration_minutes']].nsmallest(1, 'duration_minutes').min()\n    label_max = loc_ven_merged_df.loc[loc_ven_merged_df['drive_label'] == l][['duration_minutes']].nlargest(1, 'duration_minutes').max()\n    print('Min {} Duration'.format(l.upper()), label_min)\n    print('Max {} Duration'.format(l.upper()), label_max)\n    print('{} Label Range'.format(l.upper()), label_max - label_min)\n\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "#### Duration Binning Observations  \nThe duration binning results look good.  Each label's range roughly 35 minutes, most of the data is `Medium` which reflects reality, and would help.  This short label for duration will convey useful information. \n\n<br/>  \n####  Binning  based on Food Options  \n\nLets try binning based on food options.  Starting with 3."
        },
        {
            "cell_type": "code",
            "execution_count": 94,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "A Few    12\nSome      7\nLots      4\nName: food_options, dtype: int64\n\n\nMin A FEW Duration Food    0\ndtype: int64\nMax A FEW Duration Food    18\ndtype: int64\nA FEW Label Range Food    18\ndtype: int64\n\n\nMin SOME Duration Food    20\ndtype: int64\nMax SOME Duration Food    34\ndtype: int64\nSOME Label Range Food    14\ndtype: int64\n\n\nMin LOTS Duration Food    49\ndtype: int64\nMax LOTS Duration Food    56\ndtype: int64\nLOTS Label Range Food    7\ndtype: int64\n"
                }
            ],
            "source": "bin_num = 3\ngroup_names = ['A Few', 'Some', 'Lots']\nloc_ven_merged_df['food_options'] = pd.cut(loc_ven_merged_df['Food'], bin_num, labels=group_names, include_lowest=True)\n\n# How many locations for each label \nprint (loc_ven_merged_df['food_options'].value_counts())\n\n# Range captured in each label\n\nfor l in group_names:\n    print('\\n')\n\n    label_min = loc_ven_merged_df.loc[loc_ven_merged_df['food_options'] == l][['Food']].nsmallest(1, 'Food').min()\n    label_max = loc_ven_merged_df.loc[loc_ven_merged_df['food_options'] == l][['Food']].nlargest(1, 'Food').max()\n    print('Min {} Duration'.format(l.upper()), label_min)\n    print('Max {} Duration'.format(l.upper()), label_max)\n    print('{} Label Range'.format(l.upper()), label_max - label_min)\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "#### Observations  for 3 Bins\n\nThe right hand skewness of the data has made the label representing the fewest options ('A Few') very large (over 50% of the data), and the upper bound location in this label has 18 venues.    My intuitions says planning a trip for location with 0 food options is not the same thing as planning a trip to a location with 18 food option.  It is an order of magnitude difference (1 vs 10).  \n\n<br/>  \n**Repeating the analysis with 4 buckets (bins)** \n"
        },
        {
            "cell_type": "code",
            "execution_count": 95,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Slim Pickins    10\nA Few            5\nLots             4\nSome             4\nName: food_options, dtype: int64\n\n\nMin SLIM PICKINS Duration Food    0\ndtype: int64\nMax SLIM PICKINS Duration Food    12\ndtype: int64\nSLIM PICKINS Label Range Food    12\ndtype: int64\n\n\nMin A FEW Duration Food    17\ndtype: int64\nMax A FEW Duration Food    28\ndtype: int64\nA FEW Label Range Food    11\ndtype: int64\n\n\nMin SOME Duration Food    30\ndtype: int64\nMax SOME Duration Food    34\ndtype: int64\nSOME Label Range Food    4\ndtype: int64\n\n\nMin LOTS Duration Food    49\ndtype: int64\nMax LOTS Duration Food    56\ndtype: int64\nLOTS Label Range Food    7\ndtype: int64\n"
                }
            ],
            "source": "bin_num = 4\ngroup_names = ['Slim Pickins', 'A Few', 'Some', 'Lots']\nloc_ven_merged_df['food_options'] = pd.cut(loc_ven_merged_df['Food'], bin_num, labels=group_names, include_lowest=True)\n\n# How many locations for each label \nprint (loc_ven_merged_df['food_options'].value_counts())\n\n# Range captured in each label\n\nfor l in group_names:\n    print('\\n')\n\n    label_min = loc_ven_merged_df.loc[loc_ven_merged_df['food_options'] == l][['Food']].nsmallest(1, 'Food').min()\n    label_max = loc_ven_merged_df.loc[loc_ven_merged_df['food_options'] == l][['Food']].nlargest(1, 'Food').max()\n    print('Min {} Duration'.format(l.upper()), label_min)\n    print('Max {} Duration'.format(l.upper()), label_max)\n    print('{} Label Range'.format(l.upper()), label_max - label_min)\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "#### Observations for 4 Bins  \n\nIntuitively, these results look much more useful.  The distance between max venue count of `Slim Pickins` and the min venue count of `A Few` is 5.  The smallest label, while still the largest label, is now ~43% of the total data (a drop from 53% of total data for the 3-bin cut)\n\n4 Bins for Food options is good choice.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Lets Try K-Means  \n\nThe above binning model solely focused on `Food` options.  Lets try K-Means clustering on all available features \n\nUsing the just the venue summarization data.\n\nFirst step is to find a good `K`. "
        },
        {
            "cell_type": "code",
            "execution_count": 98,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Running KMeans for k= 1\nRunning KMeans for k= 2\nRunning KMeans for k= 3\nRunning KMeans for k= 4\nRunning KMeans for k= 5\nRunning KMeans for k= 6\nRunning KMeans for k= 7\n"
                }
            ],
            "source": "from sklearn.cluster import KMeans\n\nlist_k = range(1, 8)\n_grouped_clustering = venues_grouped.drop('Event Location', axis=1)\n\nsse = []\nfor k in list_k:\n    print('Running KMeans for k=', k)\n    km = KMeans(n_clusters=k)\n    km.fit(_grouped_clustering)\n    sse.append(km.inertia_)\n"
        },
        {
            "cell_type": "code",
            "execution_count": 99,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x432 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot sse against k\nplot_k = list(range(1,8))\nplt.figure(figsize=(6, 6))\nplt.plot(plot_k, sse, '-o')\nplt.xlabel(r'Number of clusters *k*')\nplt.ylabel('Sum of squared distance');"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Based on the **elbow method** for cluser size, it seems 3 is suggested answer.  \n\nLets Run KMeans, for `k=3`, and compare those labels to binning with 4 buckets.  \n\nAnd for comparison to binning, lets also run `k=4`"
        },
        {
            "cell_type": "code",
            "execution_count": 273,
            "metadata": {},
            "outputs": [],
            "source": "# use K = 3\nk_clusters = 3\nkmeans = KMeans(n_clusters=k_clusters, random_state=0).fit(_grouped_clustering)\n\n# check cluster labels generated for each row in the dataframe\nk3_labels = kmeans.labels_ \nk3_centers = kmeans.cluster_centers_"
        },
        {
            "cell_type": "code",
            "execution_count": 271,
            "metadata": {},
            "outputs": [],
            "source": "# Lets also use K = 4\nk_clusters = 4\nkmeans = KMeans(n_clusters=k_clusters, random_state=0).fit(_grouped_clustering)\n\n# check cluster labels generated for each row in the dataframe\nk4_labels = kmeans.labels_ \nk4_centers = kmeans.cluster_centers_"
        },
        {
            "cell_type": "code",
            "execution_count": 114,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "1    12\n0     7\n2     4\nName: K3 Labels, dtype: int64\n1    8\n0    6\n3    5\n2    4\nName: K4 Labels, dtype: int64\n"
                }
            ],
            "source": "# Lets add the labels back to \nvenues_grouped.insert(0,'K3 Labels' , k3_labels)\nvenues_grouped.insert(0,'K4 Labels' , k4_labels)\n\nprint(venues_grouped['K3 Labels'].value_counts())\nprint(venues_grouped['K4 Labels'].value_counts())"
        },
        {
            "cell_type": "code",
            "execution_count": 133,
            "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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"5\" halign=\"left\">Food</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>sum</th>\n      <th>mean</th>\n      <th>count</th>\n      <th>min</th>\n      <th>max</th>\n    </tr>\n    <tr>\n      <th>K3 Labels</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>A Few</th>\n      <td>91</td>\n      <td>7.583333</td>\n      <td>12</td>\n      <td>0</td>\n      <td>18</td>\n    </tr>\n    <tr>\n      <th>Lots</th>\n      <td>210</td>\n      <td>52.500000</td>\n      <td>4</td>\n      <td>49</td>\n      <td>56</td>\n    </tr>\n    <tr>\n      <th>Some</th>\n      <td>200</td>\n      <td>28.571429</td>\n      <td>7</td>\n      <td>20</td>\n      <td>34</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "          Food                         \n           sum       mean count min max\nK3 Labels                              \nA Few       91   7.583333    12   0  18\nLots       210  52.500000     4  49  56\nSome       200  28.571429     7  20  34"
                    },
                    "execution_count": 133,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "venues_grouped.groupby(['K3 Labels']).agg({'Food': ['sum', 'mean', 'count', 'min', 'max']})"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "**Observation for K-MEans(K=3)**  \n\nK-Means with K=3, has the same issue as binning with 3 buckets.  The smallest bucket contains too much data to be practically useful"
        },
        {
            "cell_type": "code",
            "execution_count": 132,
            "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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"5\" halign=\"left\">Food</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>sum</th>\n      <th>mean</th>\n      <th>count</th>\n      <th>min</th>\n      <th>max</th>\n    </tr>\n    <tr>\n      <th>K4 Labels</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>A Few</th>\n      <td>78</td>\n      <td>15.600</td>\n      <td>5</td>\n      <td>11</td>\n      <td>20</td>\n    </tr>\n    <tr>\n      <th>Lots</th>\n      <td>210</td>\n      <td>52.500</td>\n      <td>4</td>\n      <td>49</td>\n      <td>56</td>\n    </tr>\n    <tr>\n      <th>Slim Pickins</th>\n      <td>33</td>\n      <td>4.125</td>\n      <td>8</td>\n      <td>0</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>Some</th>\n      <td>180</td>\n      <td>30.000</td>\n      <td>6</td>\n      <td>26</td>\n      <td>34</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "             Food                      \n              sum    mean count min max\nK4 Labels                              \nA Few          78  15.600     5  11  20\nLots          210  52.500     4  49  56\nSlim Pickins   33   4.125     8   0   9\nSome          180  30.000     6  26  34"
                    },
                    "execution_count": 132,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "venues_grouped.groupby(['K4 Labels']).agg({'Food': ['sum', 'mean', 'count', 'min', 'max']})"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "**Observation for K-Means(K=4)**  \n\nThe clusters are similarly sized (good thing)\nThe cluster for locations with least number of venues are all in same order of magnitude (i.e. < 10) ...(good thing) \n\nRanges are 9, 7, 9, 12 ... so clusters are dense (good thing) \n\n\n\n---  \n---  \n\n## Model Selection\n\nDo a quick model evaluation (since it is not easy to pass in the binning model to classic model scoring, use a custom methodology)\n\n  - Weight Average, determined by the range of values per location in a cluster * items in that cluster.  \n    For example, if location A, with 16 nearby venues is in the same cluster as Location B, with 22 nearby venues, the range is 6. (i.e. 22-16 = 6).  If there 7 items in the cluster, then the \"weight\" of that cluster is 7 * 6 = 42  \n    \n    \n  - The difference between the minimum cluster size and maximum cluster size.  To measure the 'balance' the model has in assigning labels\n"
        },
        {
            "cell_type": "code",
            "execution_count": 155,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "kmeans(k=4) 54.25\nkmeans(k=3) 114.0\nBinning(bins=3) 114.0\nBinning(bins=4) 54.75\nkmeans(k=4) 4\nkmeans(k=3) 8\nBinning(bins=3) 8\nBinning(bins=4) 6\n"
                }
            ],
            "source": "# Used a weighted average of size of cluster * items in cluster)\nprint ('kmeans(k=4)', (9 * 5 + 7*4 + 9*8 + 12*6)/4)\nprint ('kmeans(k=3)', (18 * 12 + 7*4 +  14*7)/3)\nprint ('Binning(bins=3)', (12*18 + 7*14 + 7*4)/3)\nprint ('Binning(bins=4)', (10*12 + 5*11 + 4*4 + 4*7)/4)\n\n# Check Max - Min bucket delta, for items in each cluster\nprint ('kmeans(k=4)', 8 - 4)\nprint ('kmeans(k=3)', 12 - 4)\nprint ('Binning(bins=3)', 12 - 4)\nprint ('Binning(bins=4)', 10 - 4)\n\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "####  KMeans(K=4) is best model\n\nKMeans produced the smallest average cluster weight, as well as the most consistent cluster sizes."
        },
        {
            "cell_type": "code",
            "execution_count": 134,
            "metadata": {},
            "outputs": [],
            "source": "# Use the grouping analysis above, to match KMeans assigned groupings to the original labels from binning\n# Then after udpate is done, re-run the groupby statemetns to see a more human readable version\n# Lets change labels to match binning\nvenues_grouped['K3 Labels'].replace({0: 'Some', 1: 'A Few', 2:'Lots' }, inplace=True)\nvenues_grouped['K4 Labels'].replace({0: 'Some', 1: 'Slim Pickins', 2:'Lots', 3:'A Few'  }, inplace=True)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "#### Lets compare the multiple models by looking at actual data\n\nBinning & Kmeans"
        },
        {
            "cell_type": "code",
            "execution_count": 176,
            "metadata": {},
            "outputs": [],
            "source": "multi_model_df = pd.merge(loc_ven_merged_df[['opponent_school', 'home_away', 'distance_miles', 'duration_minutes', 'Food', 'Nearby Venue Count', 'drive_label', 'food_options']],\n                          venues_grouped[['Event Location', 'K3 Labels', 'K4 Labels']], left_on='opponent_school', right_on='Event Location')\nmulti_model_df.drop(['Event Location'], axis=1, inplace=True)\nmulti_model_df.rename({'food_options': 'Bin Labels'}, axis=1, inplace=True)"
        },
        {
            "cell_type": "code",
            "execution_count": 177,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "(23, 10)\n"
                },
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>opponent_school</th>\n      <th>home_away</th>\n      <th>distance_miles</th>\n      <th>duration_minutes</th>\n      <th>Food</th>\n      <th>Nearby Venue Count</th>\n      <th>drive_label</th>\n      <th>Bin Labels</th>\n      <th>K3 Labels</th>\n      <th>K4 Labels</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Middlesex School</td>\n      <td>Away</td>\n      <td>38.973942</td>\n      <td>50.851667</td>\n      <td>4</td>\n      <td>16</td>\n      <td>Short</td>\n      <td>Slim Pickins</td>\n      <td>A Few</td>\n      <td>Slim Pickins</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Dana Hall School</td>\n      <td>Away</td>\n      <td>55.414709</td>\n      <td>74.840000</td>\n      <td>34</td>\n      <td>87</td>\n      <td>Medium</td>\n      <td>Some</td>\n      <td>Some</td>\n      <td>Some</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Buckingham Browne &amp; Nichols</td>\n      <td>Home</td>\n      <td>51.178208</td>\n      <td>68.763333</td>\n      <td>56</td>\n      <td>239</td>\n      <td>Medium</td>\n      <td>Lots</td>\n      <td>Lots</td>\n      <td>Lots</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Holderness School</td>\n      <td>Away</td>\n      <td>103.949236</td>\n      <td>119.350000</td>\n      <td>17</td>\n      <td>41</td>\n      <td>Long</td>\n      <td>A Few</td>\n      <td>A Few</td>\n      <td>A Few</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Vermont Academy</td>\n      <td>Away</td>\n      <td>56.024645</td>\n      <td>84.538333</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Medium</td>\n      <td>Slim Pickins</td>\n      <td>A Few</td>\n      <td>Slim Pickins</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Miss Porter's school</td>\n      <td>Home</td>\n      <td>102.530959</td>\n      <td>115.196667</td>\n      <td>30</td>\n      <td>62</td>\n      <td>Long</td>\n      <td>Some</td>\n      <td>Some</td>\n      <td>Some</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Phillips Exeter Academy</td>\n      <td>Away</td>\n      <td>83.735076</td>\n      <td>99.045000</td>\n      <td>31</td>\n      <td>72</td>\n      <td>Long</td>\n      <td>Some</td>\n      <td>Some</td>\n      <td>Some</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Williston Northampton School</td>\n      <td>Home</td>\n      <td>74.717195</td>\n      <td>88.476667</td>\n      <td>28</td>\n      <td>60</td>\n      <td>Medium</td>\n      <td>A Few</td>\n      <td>Some</td>\n      <td>Some</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Winchendon School</td>\n      <td>Home</td>\n      <td>7.984915</td>\n      <td>12.981667</td>\n      <td>12</td>\n      <td>20</td>\n      <td>Short</td>\n      <td>Slim Pickins</td>\n      <td>A Few</td>\n      <td>A Few</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Pomfret School</td>\n      <td>Away</td>\n      <td>61.506807</td>\n      <td>77.778333</td>\n      <td>5</td>\n      <td>12</td>\n      <td>Medium</td>\n      <td>Slim Pickins</td>\n      <td>A Few</td>\n      <td>Slim Pickins</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>St. Mark's School</td>\n      <td>Away</td>\n      <td>38.115954</td>\n      <td>58.033333</td>\n      <td>9</td>\n      <td>17</td>\n      <td>Medium</td>\n      <td>Slim Pickins</td>\n      <td>A Few</td>\n      <td>Slim Pickins</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Governor's Academy</td>\n      <td>Away</td>\n      <td>66.162732</td>\n      <td>87.046667</td>\n      <td>1</td>\n      <td>12</td>\n      <td>Medium</td>\n      <td>Slim Pickins</td>\n      <td>A Few</td>\n      <td>Slim Pickins</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Brooks School</td>\n      <td>Home</td>\n      <td>55.699420</td>\n      <td>69.601667</td>\n      <td>26</td>\n      <td>47</td>\n      <td>Medium</td>\n      <td>A Few</td>\n      <td>Some</td>\n      <td>Some</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Wilbraham &amp; Monson Academy</td>\n      <td>Home</td>\n      <td>62.528090</td>\n      <td>83.563333</td>\n      <td>9</td>\n      <td>22</td>\n      <td>Medium</td>\n      <td>Slim Pickins</td>\n      <td>A Few</td>\n      <td>Slim Pickins</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>St. George's School</td>\n      <td>Away</td>\n      <td>104.970892</td>\n      <td>129.490000</td>\n      <td>55</td>\n      <td>191</td>\n      <td>Long</td>\n      <td>Lots</td>\n      <td>Lots</td>\n      <td>Lots</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Brewster Academy</td>\n      <td>Home</td>\n      <td>103.863735</td>\n      <td>135.818333</td>\n      <td>18</td>\n      <td>31</td>\n      <td>Long</td>\n      <td>A Few</td>\n      <td>A Few</td>\n      <td>A Few</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Kimball Union Academy</td>\n      <td>Away</td>\n      <td>83.174848</td>\n      <td>116.766667</td>\n      <td>1</td>\n      <td>8</td>\n      <td>Long</td>\n      <td>Slim Pickins</td>\n      <td>A Few</td>\n      <td>Slim Pickins</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Northfield Mt. Hermon School</td>\n      <td>Home</td>\n      <td>43.222311</td>\n      <td>55.226667</td>\n      <td>4</td>\n      <td>10</td>\n      <td>Medium</td>\n      <td>Slim Pickins</td>\n      <td>A Few</td>\n      <td>Slim Pickins</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Lawrence Academy</td>\n      <td>Home</td>\n      <td>22.895310</td>\n      <td>38.585000</td>\n      <td>11</td>\n      <td>26</td>\n      <td>Short</td>\n      <td>Slim Pickins</td>\n      <td>A Few</td>\n      <td>A Few</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Groton School</td>\n      <td>Away</td>\n      <td>23.780265</td>\n      <td>40.113333</td>\n      <td>20</td>\n      <td>41</td>\n      <td>Short</td>\n      <td>A Few</td>\n      <td>Some</td>\n      <td>A Few</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Miss Hall's School</td>\n      <td>Away</td>\n      <td>99.308596</td>\n      <td>130.386667</td>\n      <td>50</td>\n      <td>95</td>\n      <td>Long</td>\n      <td>Lots</td>\n      <td>Lots</td>\n      <td>Lots</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Dexter Southfield School</td>\n      <td>Away</td>\n      <td>57.542839</td>\n      <td>76.491667</td>\n      <td>49</td>\n      <td>232</td>\n      <td>Medium</td>\n      <td>Lots</td>\n      <td>Lots</td>\n      <td>Lots</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>St. Paul's School</td>\n      <td>Home</td>\n      <td>61.393717</td>\n      <td>86.748333</td>\n      <td>31</td>\n      <td>55</td>\n      <td>Medium</td>\n      <td>Some</td>\n      <td>Some</td>\n      <td>Some</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "                 opponent_school home_away  distance_miles  duration_minutes  \\\n0               Middlesex School      Away       38.973942         50.851667   \n1               Dana Hall School      Away       55.414709         74.840000   \n2    Buckingham Browne & Nichols      Home       51.178208         68.763333   \n3              Holderness School      Away      103.949236        119.350000   \n4                Vermont Academy      Away       56.024645         84.538333   \n5           Miss Porter's school      Home      102.530959        115.196667   \n6        Phillips Exeter Academy      Away       83.735076         99.045000   \n7   Williston Northampton School      Home       74.717195         88.476667   \n8              Winchendon School      Home        7.984915         12.981667   \n9                 Pomfret School      Away       61.506807         77.778333   \n10             St. Mark's School      Away       38.115954         58.033333   \n11            Governor's Academy      Away       66.162732         87.046667   \n12                 Brooks School      Home       55.699420         69.601667   \n13    Wilbraham & Monson Academy      Home       62.528090         83.563333   \n14           St. George's School      Away      104.970892        129.490000   \n15              Brewster Academy      Home      103.863735        135.818333   \n16         Kimball Union Academy      Away       83.174848        116.766667   \n17  Northfield Mt. Hermon School      Home       43.222311         55.226667   \n18              Lawrence Academy      Home       22.895310         38.585000   \n19                 Groton School      Away       23.780265         40.113333   \n20            Miss Hall's School      Away       99.308596        130.386667   \n21      Dexter Southfield School      Away       57.542839         76.491667   \n22             St. Paul's School      Home       61.393717         86.748333   \n\n    Food  Nearby Venue Count drive_label    Bin Labels K3 Labels     K4 Labels  \n0      4                  16       Short  Slim Pickins     A Few  Slim Pickins  \n1     34                  87      Medium          Some      Some          Some  \n2     56                 239      Medium          Lots      Lots          Lots  \n3     17                  41        Long         A Few     A Few         A Few  \n4      0                   3      Medium  Slim Pickins     A Few  Slim Pickins  \n5     30                  62        Long          Some      Some          Some  \n6     31                  72        Long          Some      Some          Some  \n7     28                  60      Medium         A Few      Some          Some  \n8     12                  20       Short  Slim Pickins     A Few         A Few  \n9      5                  12      Medium  Slim Pickins     A Few  Slim Pickins  \n10     9                  17      Medium  Slim Pickins     A Few  Slim Pickins  \n11     1                  12      Medium  Slim Pickins     A Few  Slim Pickins  \n12    26                  47      Medium         A Few      Some          Some  \n13     9                  22      Medium  Slim Pickins     A Few  Slim Pickins  \n14    55                 191        Long          Lots      Lots          Lots  \n15    18                  31        Long         A Few     A Few         A Few  \n16     1                   8        Long  Slim Pickins     A Few  Slim Pickins  \n17     4                  10      Medium  Slim Pickins     A Few  Slim Pickins  \n18    11                  26       Short  Slim Pickins     A Few         A Few  \n19    20                  41       Short         A Few      Some         A Few  \n20    50                  95        Long          Lots      Lots          Lots  \n21    49                 232      Medium          Lots      Lots          Lots  \n22    31                  55      Medium          Some      Some          Some  "
                    },
                    "execution_count": 177,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "print(multi_model_df.shape)\nmulti_model_df"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "**Observations**  \n\nComparing Binning(buckets=4) to Kmeans(k=4):  \n\n2 schools left `Slim Pickins` category in Binning model to `A Few` in KMeans model\n2 schools left `A Few` category in Binning model to `Some` in KMeans model.\n\nBelow is a snapshot of one such school."
        },
        {
            "cell_type": "code",
            "execution_count": 156,
            "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>Event Location</th>\n      <th>Event Location Latitude</th>\n      <th>Event Location Longitude</th>\n      <th>Venue</th>\n      <th>Venue Latitude</th>\n      <th>Venue Longitude</th>\n      <th>Venue Category</th>\n      <th>Venue Root Category</th>\n      <th>Venue Id</th>\n      <th>Geodesic Distance From Event</th>\n      <th>rating</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>720</th>\n      <td>Lawrence Academy</td>\n      <td>42.604433</td>\n      <td>-71.562844</td>\n      <td>Gibbet Hill Grill</td>\n      <td>42.606217</td>\n      <td>-71.563914</td>\n      <td>American Restaurant</td>\n      <td>Food</td>\n      <td>4ad282b3f964a52001e220e3</td>\n      <td>0.134693</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>721</th>\n      <td>Lawrence Academy</td>\n      <td>42.604433</td>\n      <td>-71.562844</td>\n      <td>Filho's Cucina</td>\n      <td>42.610445</td>\n      <td>-71.574478</td>\n      <td>Italian Restaurant</td>\n      <td>Food</td>\n      <td>4b9132bff964a52079a933e3</td>\n      <td>0.723957</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>722</th>\n      <td>Lawrence Academy</td>\n      <td>42.604433</td>\n      <td>-71.562844</td>\n      <td>Salt &amp; Light Cafe Bistro</td>\n      <td>42.607527</td>\n      <td>-71.570545</td>\n      <td>Caf\u00e9</td>\n      <td>Food</td>\n      <td>569ef89d498efee6a1359878</td>\n      <td>0.446994</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>726</th>\n      <td>Lawrence Academy</td>\n      <td>42.604433</td>\n      <td>-71.562844</td>\n      <td>Blackbird Cafe</td>\n      <td>42.619378</td>\n      <td>-71.585972</td>\n      <td>Caf\u00e9</td>\n      <td>Food</td>\n      <td>4d1907fb7c5b8eec72acbcc6</td>\n      <td>1.566735</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>727</th>\n      <td>Lawrence Academy</td>\n      <td>42.604433</td>\n      <td>-71.562844</td>\n      <td>Johnson's Drive-In</td>\n      <td>42.596187</td>\n      <td>-71.556070</td>\n      <td>American Restaurant</td>\n      <td>Food</td>\n      <td>4c461673dd35be9a9acfdcd8</td>\n      <td>0.665770</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>731</th>\n      <td>Lawrence Academy</td>\n      <td>42.604433</td>\n      <td>-71.562844</td>\n      <td>Bruno's Pizza</td>\n      <td>42.608959</td>\n      <td>-71.572354</td>\n      <td>Pizza Place</td>\n      <td>Food</td>\n      <td>4c02e465f423a593d67dcf16</td>\n      <td>0.576827</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>732</th>\n      <td>Lawrence Academy</td>\n      <td>42.604433</td>\n      <td>-71.562844</td>\n      <td>Dunkin'</td>\n      <td>42.606435</td>\n      <td>-71.569390</td>\n      <td>Donut Shop</td>\n      <td>Food</td>\n      <td>4c6ac1892c29d13a6b810e41</td>\n      <td>0.361296</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>733</th>\n      <td>Lawrence Academy</td>\n      <td>42.604433</td>\n      <td>-71.562844</td>\n      <td>Dunkin'</td>\n      <td>42.614150</td>\n      <td>-71.577792</td>\n      <td>Donut Shop</td>\n      <td>Food</td>\n      <td>4bf03907c8d920a180979430</td>\n      <td>1.015270</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>735</th>\n      <td>Lawrence Academy</td>\n      <td>42.604433</td>\n      <td>-71.562844</td>\n      <td>Scudders @GrotonSchool</td>\n      <td>42.592237</td>\n      <td>-71.584551</td>\n      <td>American Restaurant</td>\n      <td>Food</td>\n      <td>4e728fd78877982db9c786e7</td>\n      <td>1.390708</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>736</th>\n      <td>Lawrence Academy</td>\n      <td>42.604433</td>\n      <td>-71.562844</td>\n      <td>Herb Lyceum</td>\n      <td>42.619520</td>\n      <td>-71.582842</td>\n      <td>New American Restaurant</td>\n      <td>Food</td>\n      <td>4bca50730687ef3b48d8dbcc</td>\n      <td>1.457463</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>739</th>\n      <td>Lawrence Academy</td>\n      <td>42.604433</td>\n      <td>-71.562844</td>\n      <td>Main House Chinese Restaurant</td>\n      <td>42.620216</td>\n      <td>-71.586659</td>\n      <td>Chinese Restaurant</td>\n      <td>Food</td>\n      <td>4bd8bfdd11dcc9286ba2f733</td>\n      <td>1.631327</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "       Event Location  Event Location Latitude  Event Location Longitude  \\\n720  Lawrence Academy                42.604433                -71.562844   \n721  Lawrence Academy                42.604433                -71.562844   \n722  Lawrence Academy                42.604433                -71.562844   \n726  Lawrence Academy                42.604433                -71.562844   \n727  Lawrence Academy                42.604433                -71.562844   \n731  Lawrence Academy                42.604433                -71.562844   \n732  Lawrence Academy                42.604433                -71.562844   \n733  Lawrence Academy                42.604433                -71.562844   \n735  Lawrence Academy                42.604433                -71.562844   \n736  Lawrence Academy                42.604433                -71.562844   \n739  Lawrence Academy                42.604433                -71.562844   \n\n                             Venue  Venue Latitude  Venue Longitude  \\\n720              Gibbet Hill Grill       42.606217       -71.563914   \n721                 Filho's Cucina       42.610445       -71.574478   \n722       Salt & Light Cafe Bistro       42.607527       -71.570545   \n726                 Blackbird Cafe       42.619378       -71.585972   \n727             Johnson's Drive-In       42.596187       -71.556070   \n731                  Bruno's Pizza       42.608959       -71.572354   \n732                        Dunkin'       42.606435       -71.569390   \n733                        Dunkin'       42.614150       -71.577792   \n735         Scudders @GrotonSchool       42.592237       -71.584551   \n736                    Herb Lyceum       42.619520       -71.582842   \n739  Main House Chinese Restaurant       42.620216       -71.586659   \n\n              Venue Category Venue Root Category                  Venue Id  \\\n720      American Restaurant                Food  4ad282b3f964a52001e220e3   \n721       Italian Restaurant                Food  4b9132bff964a52079a933e3   \n722                     Caf\u00e9                Food  569ef89d498efee6a1359878   \n726                     Caf\u00e9                Food  4d1907fb7c5b8eec72acbcc6   \n727      American Restaurant                Food  4c461673dd35be9a9acfdcd8   \n731              Pizza Place                Food  4c02e465f423a593d67dcf16   \n732               Donut Shop                Food  4c6ac1892c29d13a6b810e41   \n733               Donut Shop                Food  4bf03907c8d920a180979430   \n735      American Restaurant                Food  4e728fd78877982db9c786e7   \n736  New American Restaurant                Food  4bca50730687ef3b48d8dbcc   \n739       Chinese Restaurant                Food  4bd8bfdd11dcc9286ba2f733   \n\n     Geodesic Distance From Event  rating  \n720                      0.134693     0.0  \n721                      0.723957     0.0  \n722                      0.446994     0.0  \n726                      1.566735     0.0  \n727                      0.665770     0.0  \n731                      0.576827     0.0  \n732                      0.361296     0.0  \n733                      1.015270     0.0  \n735                      1.390708     0.0  \n736                      1.457463     0.0  \n739                      1.631327     0.0  "
                    },
                    "execution_count": 156,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "#  Lawrence Academy is one of the schools that moved from `Slim Pickins` to `A Few` , Looking at data to see if that \"Makes Sense\"\n\nvenues_w_rate_df.loc[(venues_w_rate_df['Event Location'] == 'Lawrence Academy') & (venues_w_rate_df['Venue Root Category'] == 'Food')]"
        },
        {
            "cell_type": "code",
            "execution_count": 178,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "{'file_name': 'multi_model_data.csv',\n 'message': 'File saved to project storage.',\n 'bucket_name': 'ibmcrs9capstone-donotdelete-pr-afds8o4rva78zh',\n 'asset_id': '78085490-4b4c-4d26-9c77-38d58a42d098'}"
                    },
                    "execution_count": 178,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# Lets Save the multi-model DataFrame\n\nproject.save_data(\"multi_model_data.csv\", multi_model_df.to_csv(index=False), overwrite=True)"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "---  \n---  \n\n\n# Reporting & Presentation  Support \n\n\nThis section is miscellaneous data \"slicing & dicing\", as well as additional visualizations to add the final presentation to coaches to make for a more compelling story.\n\n"
        },
        {
            "cell_type": "code",
            "execution_count": 181,
            "metadata": {},
            "outputs": [],
            "source": "multi_model_df['food_options'] = multi_model_df['K4 Labels']"
        },
        {
            "cell_type": "code",
            "execution_count": 215,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "{'file_name': 'table_away_games.png',\n 'message': 'File saved to project storage.',\n 'bucket_name': 'ibmcrs9capstone-donotdelete-pr-afds8o4rva78zh',\n 'asset_id': 'fb34b787-53bf-4a98-b708-843b1d1af018'}"
                    },
                    "execution_count": 215,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 864x432 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "from pandas.plotting import table\n\ntable_df = multi_model_df.loc[multi_model_df['home_away']=='Away'][['opponent_school', 'home_away', 'distance_miles', 'duration_minutes', 'drive_label', 'food_options']]\ntable_df.set_index('opponent_school', inplace=True)\n\nfig = plt.figure(figsize=(12,6))\nax = plt.subplot(111, frame_on=False )\nax.xaxis.set_visible(False)\nax.yaxis.set_visible(False)\n\ntbl = table(ax, np.round(table_df), loc='center' )\ntbl.auto_set_font_size(False)\ntbl.set_fontsize(14)\ntbl.scale(1.5, 1.5)\n\nfig.suptitle('Table of Away Games', fontsize=14)\nplt.subplots_adjust(top=0.96)\n\nbuffer = io.BytesIO()\nplt.savefig(buffer, format='png', bbox_inches='tight')\nbuffer.seek(0)\nproject.save_data('table_away_games.png', buffer, overwrite=True)\n"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": "\n"
        },
        {
            "cell_type": "code",
            "execution_count": 163,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "count    23.000000\nmean     21.782609\nstd      17.946781\nmin       0.000000\n25%       7.000000\n50%      18.000000\n75%      31.000000\nmax      56.000000\nName: Food, dtype: float64"
                    },
                    "execution_count": 163,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "venues_grouped['Food'].describe()"
        },
        {
            "cell_type": "code",
            "execution_count": 277,
            "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>K4 Labels</th>\n      <th>K3 Labels</th>\n      <th>Event Location</th>\n      <th>Arts &amp; Entertainment</th>\n      <th>College &amp; University</th>\n      <th>Food</th>\n      <th>Nightlife Spot</th>\n      <th>Outdoors &amp; Recreation</th>\n      <th>Professional &amp; Other Places</th>\n      <th>Shop &amp; Service</th>\n      <th>Travel &amp; Transport</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>A Few</td>\n      <td>A Few</td>\n      <td>Brewster Academy</td>\n      <td>1</td>\n      <td>0</td>\n      <td>18</td>\n      <td>3</td>\n      <td>3</td>\n      <td>0</td>\n      <td>6</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Some</td>\n      <td>Some</td>\n      <td>Brooks School</td>\n      <td>0</td>\n      <td>0</td>\n      <td>26</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0</td>\n      <td>9</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Lots</td>\n      <td>Lots</td>\n      <td>Buckingham Browne &amp; Nichols</td>\n      <td>11</td>\n      <td>1</td>\n      <td>56</td>\n      <td>5</td>\n      <td>10</td>\n      <td>0</td>\n      <td>16</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Some</td>\n      <td>Some</td>\n      <td>Dana Hall School</td>\n      <td>1</td>\n      <td>1</td>\n      <td>34</td>\n      <td>2</td>\n      <td>25</td>\n      <td>1</td>\n      <td>22</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Lots</td>\n      <td>Lots</td>\n      <td>Dexter Southfield School</td>\n      <td>5</td>\n      <td>0</td>\n      <td>49</td>\n      <td>5</td>\n      <td>23</td>\n      <td>1</td>\n      <td>15</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "  K4 Labels K3 Labels               Event Location  Arts & Entertainment  \\\n0     A Few     A Few             Brewster Academy                     1   \n1      Some      Some                Brooks School                     0   \n2      Lots      Lots  Buckingham Browne & Nichols                    11   \n3      Some      Some             Dana Hall School                     1   \n4      Lots      Lots     Dexter Southfield School                     5   \n\n   College & University  Food  Nightlife Spot  Outdoors & Recreation  \\\n0                     0    18               3                      3   \n1                     0    26               2                      8   \n2                     1    56               5                     10   \n3                     1    34               2                     25   \n4                     0    49               5                     23   \n\n   Professional & Other Places  Shop & Service  Travel & Transport  \n0                            0               6                   0  \n1                            0               9                   1  \n2                            0              16                   1  \n3                            1              22                   1  \n4                            1              15                   1  "
                    },
                    "execution_count": 277,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "venues_grouped.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "#### Just Play with the data  little bit ...\n\nNot yet for inclusion in the final report or presentation, just randomly play with the data \nsee if anything interesting pops out...\n"
        },
        {
            "cell_type": "code",
            "execution_count": 61,
            "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>Unnamed: 0</th>\n      <th>date</th>\n      <th>home_away</th>\n      <th>opponent_school</th>\n      <th>time</th>\n      <th>quality</th>\n      <th>address</th>\n      <th>state</th>\n      <th>lat</th>\n      <th>lng</th>\n      <th>distance_meters</th>\n      <th>duration_seconds</th>\n      <th>distance_miles</th>\n      <th>duration_str</th>\n      <th>Nearby Venue Count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>8</th>\n      <td>8</td>\n      <td>Wednesday, 1/16/2019</td>\n      <td>Home</td>\n      <td>Winchendon School</td>\n      <td>3:00 PM</td>\n      <td>wood</td>\n      <td>Winchendon School, Hall Road, Winchendon, Worc...</td>\n      <td>Massachusetts</td>\n      <td>42.669447</td>\n      <td>-72.031826</td>\n      <td>12850.5</td>\n      <td>778.9</td>\n      <td>7.984915</td>\n      <td>0:12</td>\n      <td>22</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>18</td>\n      <td>Friday, 2/15/2019</td>\n      <td>Home</td>\n      <td>Lawrence Academy</td>\n      <td>6:00 PM</td>\n      <td>school</td>\n      <td>Lawrence Academy, Lowell Road, Groton, Middles...</td>\n      <td>Massachusetts</td>\n      <td>42.604433</td>\n      <td>-71.562844</td>\n      <td>36846.5</td>\n      <td>2315.1</td>\n      <td>22.895310</td>\n      <td>0:38</td>\n      <td>25</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>19</td>\n      <td>Saturday, 2/16/2019</td>\n      <td>Away</td>\n      <td>Groton School</td>\n      <td>6:00 PM</td>\n      <td>school</td>\n      <td>Groton School, Shirley Road, Groton, Middlesex...</td>\n      <td>Massachusetts</td>\n      <td>42.593118</td>\n      <td>-71.586525</td>\n      <td>38270.7</td>\n      <td>2406.8</td>\n      <td>23.780265</td>\n      <td>0:40</td>\n      <td>37</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "    Unnamed: 0                  date home_away    opponent_school     time  \\\n8            8  Wednesday, 1/16/2019      Home  Winchendon School  3:00 PM   \n18          18     Friday, 2/15/2019      Home   Lawrence Academy  6:00 PM   \n19          19   Saturday, 2/16/2019      Away      Groton School  6:00 PM   \n\n   quality                                            address          state  \\\n8     wood  Winchendon School, Hall Road, Winchendon, Worc...  Massachusetts   \n18  school  Lawrence Academy, Lowell Road, Groton, Middles...  Massachusetts   \n19  school  Groton School, Shirley Road, Groton, Middlesex...  Massachusetts   \n\n          lat        lng  distance_meters  duration_seconds  distance_miles  \\\n8   42.669447 -72.031826          12850.5             778.9        7.984915   \n18  42.604433 -71.562844          36846.5            2315.1       22.895310   \n19  42.593118 -71.586525          38270.7            2406.8       23.780265   \n\n   duration_str  Nearby Venue Count  \n8          0:12                  22  \n18         0:38                  25  \n19         0:40                  37  "
                    },
                    "execution_count": 61,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "games_loc_df.loc[games_loc_df['distance_miles'] < 25]"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        },
        {
            "cell_type": "code",
            "execution_count": 83,
            "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>home_away</th>\n      <th>date</th>\n      <th>time</th>\n      <th>opponent_school</th>\n      <th>distance_miles</th>\n      <th>duration_minutes</th>\n      <th>duration_str</th>\n      <th>Nearby Venue Count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>3</th>\n      <td>Away</td>\n      <td>Wednesday, 12/5/2018</td>\n      <td>3:30 PM</td>\n      <td>Holderness School</td>\n      <td>103.949236</td>\n      <td>119.350000</td>\n      <td>1:59</td>\n      <td>43</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Away</td>\n      <td>Saturday, 12/8/2018</td>\n      <td>4:00 PM</td>\n      <td>Vermont Academy</td>\n      <td>56.024645</td>\n      <td>84.538333</td>\n      <td>1:24</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Away</td>\n      <td>Wednesday, 1/9/2019</td>\n      <td>3:30 PM</td>\n      <td>Phillips Exeter Academy</td>\n      <td>83.735076</td>\n      <td>99.045000</td>\n      <td>1:39</td>\n      <td>67</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Away</td>\n      <td>Saturday, 1/19/2019</td>\n      <td>2:00 PM</td>\n      <td>Pomfret School</td>\n      <td>61.506807</td>\n      <td>77.778333</td>\n      <td>1:17</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Away</td>\n      <td>Friday, 1/25/2019</td>\n      <td>6:30 PM</td>\n      <td>Governor's Academy</td>\n      <td>66.162732</td>\n      <td>87.046667</td>\n      <td>1:27</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Away</td>\n      <td>Friday, 2/1/2019</td>\n      <td>6:00 PM</td>\n      <td>St. George's School</td>\n      <td>104.970892</td>\n      <td>129.490000</td>\n      <td>2:09</td>\n      <td>192</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Away</td>\n      <td>Saturday, 2/9/2019</td>\n      <td>4:30 PM</td>\n      <td>Kimball Union Academy</td>\n      <td>83.174848</td>\n      <td>116.766667</td>\n      <td>1:56</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Away</td>\n      <td>Wednesday, 2/20/2019</td>\n      <td>4:00 PM</td>\n      <td>Miss Hall's School</td>\n      <td>99.308596</td>\n      <td>130.386667</td>\n      <td>2:10</td>\n      <td>93</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Away</td>\n      <td>Friday, 2/22/2019</td>\n      <td>6:00 PM</td>\n      <td>Dexter Southfield School</td>\n      <td>57.542839</td>\n      <td>76.491667</td>\n      <td>1:16</td>\n      <td>234</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   home_away                  date     time           opponent_school  \\\n3       Away  Wednesday, 12/5/2018  3:30 PM         Holderness School   \n4       Away   Saturday, 12/8/2018  4:00 PM           Vermont Academy   \n6       Away   Wednesday, 1/9/2019  3:30 PM   Phillips Exeter Academy   \n9       Away   Saturday, 1/19/2019  2:00 PM            Pomfret School   \n11      Away     Friday, 1/25/2019  6:30 PM        Governor's Academy   \n14      Away      Friday, 2/1/2019  6:00 PM       St. George's School   \n16      Away    Saturday, 2/9/2019  4:30 PM     Kimball Union Academy   \n20      Away  Wednesday, 2/20/2019  4:00 PM        Miss Hall's School   \n21      Away     Friday, 2/22/2019  6:00 PM  Dexter Southfield School   \n\n    distance_miles  duration_minutes duration_str  Nearby Venue Count  \n3       103.949236        119.350000         1:59                  43  \n4        56.024645         84.538333         1:24                   3  \n6        83.735076         99.045000         1:39                  67  \n9        61.506807         77.778333         1:17                  12  \n11       66.162732         87.046667         1:27                  11  \n14      104.970892        129.490000         2:09                 192  \n16       83.174848        116.766667         1:56                  10  \n20       99.308596        130.386667         2:10                  93  \n21       57.542839         76.491667         1:16                 234  "
                    },
                    "execution_count": 83,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "long_trip_duration = 75  # Define 75 minutes as a \"Long Trip\", since duration is only one-way.  This is a 2hr 30 minute total driving time  \ngames_loc_df.loc[(games_loc_df['duration_minutes'] > long_trip_duration) & (games_loc_df['home_away'] == 'Away')][school_display_cols]"
        },
        {
            "cell_type": "code",
            "execution_count": 263,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "{'file_name': 'three_closest_games.png',\n 'message': 'File saved to project storage.',\n 'bucket_name': 'ibmcrs9capstone-donotdelete-pr-afds8o4rva78zh',\n 'asset_id': '6f610cb3-bb69-4778-af70-1447647021e4'}"
                    },
                    "execution_count": 263,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 720x216 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# 3 Closest Trips\n_cols = ['opponent_school', 'date', 'distance_miles', 'duration_minutes', 'duration_str']\ntable_df = games_loc_df.loc[games_loc_df['home_away'] == 'Away'][_cols].nsmallest(3, 'duration_minutes')\ntable_df.set_index('opponent_school', inplace=True)\n\nfig = plt.figure(figsize=(10,3))\nax = plt.subplot(111, frame_on=False )\nax.xaxis.set_visible(False)\nax.yaxis.set_visible(False)\n\ntbl = table(ax, np.round(table_df), loc='center' )\ntbl.auto_set_font_size(False)\ntbl.set_fontsize(14)\ntbl.scale(1.5, 1.5)\n\nfig.suptitle('Three Closest Games', fontsize=14)\nplt.subplots_adjust(top=0.96)\n\nbuffer = io.BytesIO()\nplt.savefig(buffer, format='png', bbox_inches='tight')\nbuffer.seek(0)\nproject.save_data('three_closest_games.png', buffer, overwrite=True)\n\n\n"
        },
        {
            "cell_type": "code",
            "execution_count": 266,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "{'file_name': 'three_farthest_games.png',\n 'message': 'File saved to project storage.',\n 'bucket_name': 'ibmcrs9capstone-donotdelete-pr-afds8o4rva78zh',\n 'asset_id': 'ade3633c-fecc-4f01-aa07-f183fd132066'}"
                    },
                    "execution_count": 266,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 720x216 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# 3 Furthest Trips\n\n_cols = ['opponent_school', 'date', 'distance_miles', 'duration_minutes', 'duration_str']\ntable_df = games_loc_df.loc[games_loc_df['home_away'] == 'Away'][_cols].nlargest(3, 'duration_minutes')\n\ntable_df.set_index('opponent_school', inplace=True)\n\nfig = plt.figure(figsize=(10,3))\nax = plt.subplot(111, frame_on=False )\nax.xaxis.set_visible(False)\nax.yaxis.set_visible(False)\n\ntbl = table(ax, np.round(table_df), loc='center' )\ntbl.auto_set_font_size(False)\ntbl.set_fontsize(14)\ntbl.scale(1.5, 1.5)\n\nfig.suptitle('Three Farthest Games', fontsize=14)\nplt.subplots_adjust(top=0.96)\n\nbuffer = io.BytesIO()\nplt.savefig(buffer, format='png', bbox_inches='tight')\nbuffer.seek(0)\nproject.save_data('three_farthest_games.png', buffer, overwrite=True)\n"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": "\n"
        },
        {
            "cell_type": "code",
            "execution_count": 257,
            "metadata": {
                "scrolled": true
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Middlesex School\n               Venue     Venue Category  crow_distance\n Ferns Country Store     Sandwich Place           2.28\n        Kimball Farm     Ice Cream Shop           2.34\n             Monsoon  Indian Restaurant           2.46\n\n\n\nDana Hall School\n              Venue      Venue Category  crow_distance\n       Truly Yogurt  Frozen Yogurt Shop           0.40\n Amarin of Thailand     Thai Restaurant           0.42\n     Tutto Italiano  Italian Restaurant           0.44\n\n\n\nHolderness School\n                       Venue   Venue Category  crow_distance\n Annie's Overflow Restaurant   Breakfast Spot           0.47\n                  Thai Smile  Thai Restaurant           0.64\n         Rise and Shine Caf\u00e9             Caf\u00e9           0.68\n\n\n\nVermont Academy\nEmpty DataFrame\nColumns: [Venue, Venue Category, crow_distance]\nIndex: []\n\n\n\nPhillips Exeter Academy\n                  Venue           Venue Category  crow_distance\n    Blue Moon Evolution  New American Restaurant           0.23\n Seadog Brewing Company               Restaurant           0.24\n             Laney & Lu                     Caf\u00e9           0.26\n\n\n\nPomfret School\n             Venue       Venue Category  crow_distance\n Vanilla Bean Cafe                 Caf\u00e9           0.80\n          Grill 37  American Restaurant           0.90\n     Baker's Dozen          Coffee Shop           1.92\n\n\n\nSt. Mark's School\n                    Venue  Venue Category  crow_distance\n                  Mauro's  Breakfast Spot           0.40\n Red Barn Coffee Roasters     Coffee Shop           1.26\n              The Vin Bin          Bistro           1.32\n\n\n\nGovernor's Academy\n     Venue Venue Category  crow_distance\n Rusty Can      BBQ Joint            1.9\n\n\n\nSt. George's School\n                  Venue      Venue Category  crow_distance\n        Atlantic Grille               Diner           0.54\n  Empire Tea and Coffee         Coffee Shop           0.55\n Diego\u2019s Barrio Cantina  Mexican Restaurant           0.56\n\n\n\nKimball Union Academy\n        Venue Venue Category  crow_distance\n Meriden Deli  Deli / Bodega           0.22\n\n\n\nGroton School\n                    Venue Venue Category  crow_distance\n                  Dunkin'     Donut Shop           1.27\n    Groton House Of Pizza    Pizza Place           1.27\n Salt & Light Cafe Bistro           Caf\u00e9           1.29\n\n\n\nMiss Hall's School\n                            Venue Venue Category  crow_distance\n        Sarah's Cheesecake & Cafe           Caf\u00e9           1.19\n Berkshire Mountain Bakery & Cafe         Bakery           1.19\n                          Samel's  Deli / Bodega           1.22\n\n\n\nDexter Southfield School\n      Venue      Venue Category  crow_distance\n Pizza Stop         Pizza Place           0.37\n   La Morra  Italian Restaurant           0.75\n    Cutty's      Sandwich Place           0.81\n\n\n\n"
                }
            ],
            "source": "# Find the 3 closest Food Venues for each away game \nrpt_df = venues_w_rate_df.loc[venues_w_rate_df['Venue Root Category'] == 'Food'].rename({'Geodesic Distance From Event': 'crow_distance'}, axis=1)\n#rpt_df = rpt_df.loc(rpt_df['Venue Root Category'] == 'Food']\n#rpt_df.set_index('Event Location')\n_cols = ['Venue', 'Venue Category', 'crow_distance']\nfor _s in away_games_df['opponent_school']:\n    print (_s)\n    print (np.round(rpt_df.loc[rpt_df['Event Location'] == _s][_cols], 2).nsmallest(3, 'crow_distance').to_string(index=False))\n    print ('\\n\\n')\n"
        },
        {
            "cell_type": "code",
            "execution_count": 222,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "Index(['date', 'home_away', 'opponent_school', 'time', 'quality', 'address',\n       'state', 'lat', 'lng', 'distance_meters', 'duration_seconds',\n       'distance_miles', 'duration_str', 'Nearby Venue Count',\n       'duration_minutes'],\n      dtype='object')"
                    },
                    "execution_count": 222,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": ""
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        },
        {
            "cell_type": "code",
            "execution_count": 215,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "MinMaxScaler(copy=True, feature_range=(0, 1))\n"
                },
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": "/opt/conda/envs/Python36/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n  warnings.warn(msg, DataConversionWarning)\n"
                }
            ],
            "source": "from sklearn.preprocessing import MinMaxScaler\nscaler = MinMaxScaler()\nprint(scaler.fit(test_df['sub_total'].to_numpy().reshape(-1, 1)))"
        },
        {
            "cell_type": "code",
            "execution_count": 220,
            "metadata": {},
            "outputs": [],
            "source": "test_df['location_score'] = scaler.transform(test_df['sub_total'].to_numpy().reshape(-1, 1))\n"
        },
        {
            "cell_type": "code",
            "execution_count": 222,
            "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>date</th>\n      <th>home_away</th>\n      <th>opponent_school</th>\n      <th>time</th>\n      <th>quality</th>\n      <th>address</th>\n      <th>state</th>\n      <th>lat</th>\n      <th>lng</th>\n      <th>distance_meters</th>\n      <th>...</th>\n      <th>duration_minutes</th>\n      <th>dur-bin</th>\n      <th>Loc-opts</th>\n      <th>Event Location</th>\n      <th>Food</th>\n      <th>Shop &amp; Service</th>\n      <th>drive_label</th>\n      <th>food_options</th>\n      <th>sub_total</th>\n      <th>location_score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4</th>\n      <td>Saturday, 12/8/2018</td>\n      <td>Away</td>\n      <td>Vermont Academy</td>\n      <td>4:00 PM</td>\n      <td>school</td>\n      <td>Vermont Academy, Shepard Lane, Saxtons River, ...</td>\n      <td>Vermont</td>\n      <td>43.144794</td>\n      <td>-72.509698</td>\n      <td>90163.1</td>\n      <td>...</td>\n      <td>84.538333</td>\n      <td>Medium</td>\n      <td>Slim Pickins</td>\n      <td>Vermont Academy</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Medium</td>\n      <td>Slim Pickins</td>\n      <td>1</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Saturday, 2/9/2019</td>\n      <td>Away</td>\n      <td>Kimball Union Academy</td>\n      <td>4:30 PM</td>\n      <td>school</td>\n      <td>Kimball Union Academy, Main Street, Plainfield...</td>\n      <td>New Hampshire</td>\n      <td>43.546183</td>\n      <td>-72.258146</td>\n      <td>133857.2</td>\n      <td>...</td>\n      <td>116.766667</td>\n      <td>Long</td>\n      <td>Slim Pickins</td>\n      <td>Kimball Union Academy</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Long</td>\n      <td>Slim Pickins</td>\n      <td>2</td>\n      <td>0.014085</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Friday, 1/25/2019</td>\n      <td>Away</td>\n      <td>Governor's Academy</td>\n      <td>6:30 PM</td>\n      <td>school</td>\n      <td>The Governor's Academy, 1, Middle Road, Newbur...</td>\n      <td>Massachusetts</td>\n      <td>42.750087</td>\n      <td>-70.902276</td>\n      <td>106478.8</td>\n      <td>...</td>\n      <td>87.046667</td>\n      <td>Medium</td>\n      <td>Slim Pickins</td>\n      <td>Governor's Academy</td>\n      <td>1</td>\n      <td>4</td>\n      <td>Medium</td>\n      <td>Slim Pickins</td>\n      <td>5</td>\n      <td>0.056338</td>\n    </tr>\n  </tbody>\n</table>\n<p>3 rows \u00d7 24 columns</p>\n</div>",
                        "text/plain": "                   date home_away        opponent_school     time quality  \\\n4   Saturday, 12/8/2018      Away        Vermont Academy  4:00 PM  school   \n16   Saturday, 2/9/2019      Away  Kimball Union Academy  4:30 PM  school   \n11    Friday, 1/25/2019      Away     Governor's Academy  6:30 PM  school   \n\n                                              address          state  \\\n4   Vermont Academy, Shepard Lane, Saxtons River, ...        Vermont   \n16  Kimball Union Academy, Main Street, Plainfield...  New Hampshire   \n11  The Governor's Academy, 1, Middle Road, Newbur...  Massachusetts   \n\n          lat        lng  distance_meters  ...  duration_minutes  dur-bin  \\\n4   43.144794 -72.509698          90163.1  ...         84.538333   Medium   \n16  43.546183 -72.258146         133857.2  ...        116.766667     Long   \n11  42.750087 -70.902276         106478.8  ...         87.046667   Medium   \n\n        Loc-opts         Event Location  Food Shop & Service drive_label  \\\n4   Slim Pickins        Vermont Academy     0              1      Medium   \n16  Slim Pickins  Kimball Union Academy     1              1        Long   \n11  Slim Pickins     Governor's Academy     1              4      Medium   \n\n    food_options  sub_total  location_score  \n4   Slim Pickins          1        0.000000  \n16  Slim Pickins          2        0.014085  \n11  Slim Pickins          5        0.056338  \n\n[3 rows x 24 columns]"
                    },
                    "execution_count": 222,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "test_df.nsmallest(3, 'location_score')"
        },
        {
            "cell_type": "code",
            "execution_count": 223,
            "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>date</th>\n      <th>home_away</th>\n      <th>opponent_school</th>\n      <th>time</th>\n      <th>quality</th>\n      <th>address</th>\n      <th>state</th>\n      <th>lat</th>\n      <th>lng</th>\n      <th>distance_meters</th>\n      <th>...</th>\n      <th>duration_minutes</th>\n      <th>dur-bin</th>\n      <th>Loc-opts</th>\n      <th>Event Location</th>\n      <th>Food</th>\n      <th>Shop &amp; Service</th>\n      <th>drive_label</th>\n      <th>food_options</th>\n      <th>sub_total</th>\n      <th>location_score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>Saturday, 12/1/2018</td>\n      <td>Home</td>\n      <td>Buckingham Browne &amp; Nichols</td>\n      <td>3:00 PM</td>\n      <td>school</td>\n      <td>Buckingham Browne &amp; Nichols School (Middle Sch...</td>\n      <td>Massachusetts</td>\n      <td>42.380804</td>\n      <td>-71.131203</td>\n      <td>82363.5</td>\n      <td>...</td>\n      <td>68.763333</td>\n      <td>Medium</td>\n      <td>Lots</td>\n      <td>Buckingham Browne &amp; Nichols</td>\n      <td>56</td>\n      <td>16</td>\n      <td>Medium</td>\n      <td>Lots</td>\n      <td>72</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Wednesday, 2/20/2019</td>\n      <td>Away</td>\n      <td>Miss Hall's School</td>\n      <td>4:00 PM</td>\n      <td>school</td>\n      <td>Miss Hall's School, Pomeroy Avenue, Pittsfield...</td>\n      <td>Massachusetts</td>\n      <td>42.427160</td>\n      <td>-73.245384</td>\n      <td>159822.0</td>\n      <td>...</td>\n      <td>130.386667</td>\n      <td>Long</td>\n      <td>A Few</td>\n      <td>Miss Hall's School</td>\n      <td>50</td>\n      <td>20</td>\n      <td>Long</td>\n      <td>Lots</td>\n      <td>70</td>\n      <td>0.971831</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Friday, 2/22/2019</td>\n      <td>Away</td>\n      <td>Dexter Southfield School</td>\n      <td>6:00 PM</td>\n      <td>Postal</td>\n      <td>02445, Brookline, Massachusetts</td>\n      <td>Massachusetts</td>\n      <td>42.325383</td>\n      <td>-71.131117</td>\n      <td>92606.4</td>\n      <td>...</td>\n      <td>76.491667</td>\n      <td>Medium</td>\n      <td>Lots</td>\n      <td>Dexter Southfield School</td>\n      <td>49</td>\n      <td>15</td>\n      <td>Medium</td>\n      <td>Lots</td>\n      <td>64</td>\n      <td>0.887324</td>\n    </tr>\n  </tbody>\n</table>\n<p>3 rows \u00d7 24 columns</p>\n</div>",
                        "text/plain": "                    date home_away              opponent_school     time  \\\n2    Saturday, 12/1/2018      Home  Buckingham Browne & Nichols  3:00 PM   \n20  Wednesday, 2/20/2019      Away           Miss Hall's School  4:00 PM   \n21     Friday, 2/22/2019      Away     Dexter Southfield School  6:00 PM   \n\n   quality                                            address          state  \\\n2   school  Buckingham Browne & Nichols School (Middle Sch...  Massachusetts   \n20  school  Miss Hall's School, Pomeroy Avenue, Pittsfield...  Massachusetts   \n21  Postal                    02445, Brookline, Massachusetts  Massachusetts   \n\n          lat        lng  distance_meters  ...  duration_minutes  dur-bin  \\\n2   42.380804 -71.131203          82363.5  ...         68.763333   Medium   \n20  42.427160 -73.245384         159822.0  ...        130.386667     Long   \n21  42.325383 -71.131117          92606.4  ...         76.491667   Medium   \n\n   Loc-opts               Event Location  Food Shop & Service drive_label  \\\n2      Lots  Buckingham Browne & Nichols    56             16      Medium   \n20    A Few           Miss Hall's School    50             20        Long   \n21     Lots     Dexter Southfield School    49             15      Medium   \n\n   food_options  sub_total  location_score  \n2          Lots         72        1.000000  \n20         Lots         70        0.971831  \n21         Lots         64        0.887324  \n\n[3 rows x 24 columns]"
                    },
                    "execution_count": 223,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "test_df.nlargest(3, 'location_score')"
        },
        {
            "cell_type": "code",
            "execution_count": 204,
            "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>home_away</th>\n      <th>date</th>\n      <th>time</th>\n      <th>opponent_school</th>\n      <th>distance_miles</th>\n      <th>duration_minutes</th>\n      <th>duration_str</th>\n      <th>Nearby Venue Count</th>\n      <th>food_options</th>\n      <th>drive_label</th>\n      <th>Food</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>Away</td>\n      <td>Friday, 11/30/2018</td>\n      <td>5:30 PM</td>\n      <td>Dana Hall School</td>\n      <td>55.414709</td>\n      <td>74.840000</td>\n      <td>1:14</td>\n      <td>87</td>\n      <td>Some</td>\n      <td>Medium</td>\n      <td>34</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Home</td>\n      <td>Saturday, 12/1/2018</td>\n      <td>3:00 PM</td>\n      <td>Buckingham Browne &amp; Nichols</td>\n      <td>51.178208</td>\n      <td>68.763333</td>\n      <td>1:08</td>\n      <td>239</td>\n      <td>Lots</td>\n      <td>Medium</td>\n      <td>56</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Away</td>\n      <td>Saturday, 12/8/2018</td>\n      <td>4:00 PM</td>\n      <td>Vermont Academy</td>\n      <td>56.024645</td>\n      <td>84.538333</td>\n      <td>1:24</td>\n      <td>3</td>\n      <td>Slim Pickins</td>\n      <td>Medium</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Home</td>\n      <td>Saturday, 1/12/2019</td>\n      <td>1:00 PM</td>\n      <td>Williston Northampton School</td>\n      <td>74.717195</td>\n      <td>88.476667</td>\n      <td>1:28</td>\n      <td>60</td>\n      <td>A Few</td>\n      <td>Medium</td>\n      <td>28</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Away</td>\n      <td>Saturday, 1/19/2019</td>\n      <td>2:00 PM</td>\n      <td>Pomfret School</td>\n      <td>61.506807</td>\n      <td>77.778333</td>\n      <td>1:17</td>\n      <td>12</td>\n      <td>Slim Pickins</td>\n      <td>Medium</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Away</td>\n      <td>Wednesday, 1/23/2019</td>\n      <td>5:00 PM</td>\n      <td>St. Mark's School</td>\n      <td>38.115954</td>\n      <td>58.033333</td>\n      <td>0:58</td>\n      <td>17</td>\n      <td>Slim Pickins</td>\n      <td>Medium</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Away</td>\n      <td>Friday, 1/25/2019</td>\n      <td>6:30 PM</td>\n      <td>Governor's Academy</td>\n      <td>66.162732</td>\n      <td>87.046667</td>\n      <td>1:27</td>\n      <td>12</td>\n      <td>Slim Pickins</td>\n      <td>Medium</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Home</td>\n      <td>Saturday, 1/26/2019</td>\n      <td>3:00 PM</td>\n      <td>Brooks School</td>\n      <td>55.699420</td>\n      <td>69.601667</td>\n      <td>1:09</td>\n      <td>47</td>\n      <td>A Few</td>\n      <td>Medium</td>\n      <td>26</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Home</td>\n      <td>Wednesday, 1/30/2019</td>\n      <td>4:30 PM</td>\n      <td>Wilbraham &amp; Monson Academy</td>\n      <td>62.528090</td>\n      <td>83.563333</td>\n      <td>1:23</td>\n      <td>22</td>\n      <td>Slim Pickins</td>\n      <td>Medium</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Home</td>\n      <td>Wednesday, 2/13/2019</td>\n      <td>4:15 PM</td>\n      <td>Northfield Mt. Hermon School</td>\n      <td>43.222311</td>\n      <td>55.226667</td>\n      <td>0:55</td>\n      <td>10</td>\n      <td>Slim Pickins</td>\n      <td>Medium</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Away</td>\n      <td>Friday, 2/22/2019</td>\n      <td>6:00 PM</td>\n      <td>Dexter Southfield School</td>\n      <td>57.542839</td>\n      <td>76.491667</td>\n      <td>1:16</td>\n      <td>232</td>\n      <td>Lots</td>\n      <td>Medium</td>\n      <td>49</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Home</td>\n      <td>Saturday, 2/23/2019</td>\n      <td>3:30 PM</td>\n      <td>St. Paul's School</td>\n      <td>61.393717</td>\n      <td>86.748333</td>\n      <td>1:26</td>\n      <td>55</td>\n      <td>Some</td>\n      <td>Medium</td>\n      <td>31</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   home_away                  date     time               opponent_school  \\\n1       Away    Friday, 11/30/2018  5:30 PM              Dana Hall School   \n2       Home   Saturday, 12/1/2018  3:00 PM   Buckingham Browne & Nichols   \n4       Away   Saturday, 12/8/2018  4:00 PM               Vermont Academy   \n7       Home   Saturday, 1/12/2019  1:00 PM  Williston Northampton School   \n9       Away   Saturday, 1/19/2019  2:00 PM                Pomfret School   \n10      Away  Wednesday, 1/23/2019  5:00 PM             St. Mark's School   \n11      Away     Friday, 1/25/2019  6:30 PM            Governor's Academy   \n12      Home   Saturday, 1/26/2019  3:00 PM                 Brooks School   \n13      Home  Wednesday, 1/30/2019  4:30 PM    Wilbraham & Monson Academy   \n17      Home  Wednesday, 2/13/2019  4:15 PM  Northfield Mt. Hermon School   \n21      Away     Friday, 2/22/2019  6:00 PM      Dexter Southfield School   \n22      Home   Saturday, 2/23/2019  3:30 PM             St. Paul's School   \n\n    distance_miles  duration_minutes duration_str  Nearby Venue Count  \\\n1        55.414709         74.840000         1:14                  87   \n2        51.178208         68.763333         1:08                 239   \n4        56.024645         84.538333         1:24                   3   \n7        74.717195         88.476667         1:28                  60   \n9        61.506807         77.778333         1:17                  12   \n10       38.115954         58.033333         0:58                  17   \n11       66.162732         87.046667         1:27                  12   \n12       55.699420         69.601667         1:09                  47   \n13       62.528090         83.563333         1:23                  22   \n17       43.222311         55.226667         0:55                  10   \n21       57.542839         76.491667         1:16                 232   \n22       61.393717         86.748333         1:26                  55   \n\n    food_options drive_label  Food  \n1           Some      Medium    34  \n2           Lots      Medium    56  \n4   Slim Pickins      Medium     0  \n7          A Few      Medium    28  \n9   Slim Pickins      Medium     5  \n10  Slim Pickins      Medium     9  \n11  Slim Pickins      Medium     1  \n12         A Few      Medium    26  \n13  Slim Pickins      Medium     9  \n17  Slim Pickins      Medium     4  \n21          Lots      Medium    49  \n22          Some      Medium    31  "
                    },
                    "execution_count": 204,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "col_list = school_display_cols + ['food_options', 'drive_label', 'Food']\ntest_df.loc[test_df['drive_label'] == 'Medium'][col_list]"
        },
        {
            "cell_type": "code",
            "execution_count": 205,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Medium    6\nLong      5\nShort     2\nName: dur-bin, dtype: int64\nSlim Pickins    8\nA Few           3\nLots            2\nSome            0\nName: Loc-opts, dtype: int64\n"
                }
            ],
            "source": "print(test_df.loc[games_loc_df['home_away'] == 'Away']['dur-bin'].value_counts())\nprint(test_df.loc[games_loc_df['home_away'] == 'Away']['Loc-opts'].value_counts())"
        },
        {
            "cell_type": "code",
            "execution_count": 163,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "x = test_df.loc[games_loc_df['home_away'] == 'Away']['duration_minutes']\ny = test_df.loc[games_loc_df['home_away'] == 'Away']['Nearby Venue Count']\narea = y\nplt.scatter(x, y, s=area,  alpha=0.5)\nplt.show()"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        },
        {
            "cell_type": "code",
            "execution_count": 207,
            "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>opponent_school</th>\n      <th>dur-bin</th>\n      <th>Loc-opts</th>\n      <th>Nearby Venue Count</th>\n      <th>Food</th>\n      <th>Shop &amp; Service</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Middlesex School</td>\n      <td>Short</td>\n      <td>Slim Pickins</td>\n      <td>16</td>\n      <td>4</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Dana Hall School</td>\n      <td>Medium</td>\n      <td>A Few</td>\n      <td>87</td>\n      <td>34</td>\n      <td>22</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Holderness School</td>\n      <td>Long</td>\n      <td>Slim Pickins</td>\n      <td>41</td>\n      <td>17</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Vermont Academy</td>\n      <td>Medium</td>\n      <td>Slim Pickins</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Phillips Exeter Academy</td>\n      <td>Long</td>\n      <td>A Few</td>\n      <td>72</td>\n      <td>31</td>\n      <td>18</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Pomfret School</td>\n      <td>Medium</td>\n      <td>Slim Pickins</td>\n      <td>12</td>\n      <td>5</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>St. Mark's School</td>\n      <td>Medium</td>\n      <td>Slim Pickins</td>\n      <td>17</td>\n      <td>9</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Governor's Academy</td>\n      <td>Medium</td>\n      <td>Slim Pickins</td>\n      <td>12</td>\n      <td>1</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>St. George's School</td>\n      <td>Long</td>\n      <td>Lots</td>\n      <td>191</td>\n      <td>55</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Kimball Union Academy</td>\n      <td>Long</td>\n      <td>Slim Pickins</td>\n      <td>8</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Groton School</td>\n      <td>Short</td>\n      <td>Slim Pickins</td>\n      <td>41</td>\n      <td>20</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Miss Hall's School</td>\n      <td>Long</td>\n      <td>A Few</td>\n      <td>95</td>\n      <td>50</td>\n      <td>20</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Dexter Southfield School</td>\n      <td>Medium</td>\n      <td>Lots</td>\n      <td>232</td>\n      <td>49</td>\n      <td>15</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "             opponent_school dur-bin      Loc-opts  Nearby Venue Count  Food  \\\n0           Middlesex School   Short  Slim Pickins                  16     4   \n1           Dana Hall School  Medium         A Few                  87    34   \n3          Holderness School    Long  Slim Pickins                  41    17   \n4            Vermont Academy  Medium  Slim Pickins                   3     0   \n6    Phillips Exeter Academy    Long         A Few                  72    31   \n9             Pomfret School  Medium  Slim Pickins                  12     5   \n10         St. Mark's School  Medium  Slim Pickins                  17     9   \n11        Governor's Academy  Medium  Slim Pickins                  12     1   \n14       St. George's School    Long          Lots                 191    55   \n16     Kimball Union Academy    Long  Slim Pickins                   8     1   \n19             Groton School   Short  Slim Pickins                  41    20   \n20        Miss Hall's School    Long         A Few                  95    50   \n21  Dexter Southfield School  Medium          Lots                 232    49   \n\n    Shop & Service  \n0                4  \n1               22  \n3                8  \n4                1  \n6               18  \n9                1  \n10               4  \n11               4  \n14               3  \n16               1  \n19              10  \n20              20  \n21              15  "
                    },
                    "execution_count": 207,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "merged_test_df.loc[merged_test_df['home_away'] == 'Away'][['opponent_school','dur-bin','Loc-opts', 'Nearby Venue Count', 'Food', 'Shop & Service']]\n"
        },
        {
            "cell_type": "code",
            "execution_count": 189,
            "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>date</th>\n      <th>home_away</th>\n      <th>opponent_school</th>\n      <th>time</th>\n      <th>quality</th>\n      <th>address</th>\n      <th>state</th>\n      <th>lat</th>\n      <th>lng</th>\n      <th>distance_meters</th>\n      <th>duration_seconds</th>\n      <th>distance_miles</th>\n      <th>duration_str</th>\n      <th>Nearby Venue Count</th>\n      <th>duration_minutes</th>\n      <th>dur-bin</th>\n      <th>Loc-opts</th>\n      <th>Event Location</th>\n      <th>Food</th>\n      <th>Shop &amp; Service</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Wednesday, 11/28/2018</td>\n      <td>Away</td>\n      <td>Middlesex School</td>\n      <td>5:00 PM</td>\n      <td>school</td>\n      <td>Middlesex School, 1400, Lowell Road, Concord, ...</td>\n      <td>Massachusetts</td>\n      <td>42.499832</td>\n      <td>-71.368431</td>\n      <td>62722.6</td>\n      <td>3051.1</td>\n      <td>38.973942</td>\n      <td>0:50</td>\n      <td>16</td>\n      <td>50.851667</td>\n      <td>Short</td>\n      <td>Slim Pickins</td>\n      <td>Middlesex School</td>\n      <td>4</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Friday, 11/30/2018</td>\n      <td>Away</td>\n      <td>Dana Hall School</td>\n      <td>5:30 PM</td>\n      <td>school</td>\n      <td>Dana Hall School, 45, Dana Road, Wellesley, No...</td>\n      <td>Massachusetts</td>\n      <td>42.290910</td>\n      <td>-71.288481</td>\n      <td>89181.5</td>\n      <td>4490.4</td>\n      <td>55.414709</td>\n      <td>1:14</td>\n      <td>87</td>\n      <td>74.840000</td>\n      <td>Medium</td>\n      <td>A Few</td>\n      <td>Dana Hall School</td>\n      <td>34</td>\n      <td>22</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Saturday, 12/1/2018</td>\n      <td>Home</td>\n      <td>Buckingham Browne &amp; Nichols</td>\n      <td>3:00 PM</td>\n      <td>school</td>\n      <td>Buckingham Browne &amp; Nichols School (Middle Sch...</td>\n      <td>Massachusetts</td>\n      <td>42.380804</td>\n      <td>-71.131203</td>\n      <td>82363.5</td>\n      <td>4125.8</td>\n      <td>51.178208</td>\n      <td>1:08</td>\n      <td>239</td>\n      <td>68.763333</td>\n      <td>Medium</td>\n      <td>Lots</td>\n      <td>Buckingham Browne &amp; Nichols</td>\n      <td>56</td>\n      <td>16</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Wednesday, 12/5/2018</td>\n      <td>Away</td>\n      <td>Holderness School</td>\n      <td>3:30 PM</td>\n      <td>school</td>\n      <td>Holderness School, Chapel Lane, Holderness, Gr...</td>\n      <td>New Hampshire</td>\n      <td>43.761779</td>\n      <td>-71.674779</td>\n      <td>167290.4</td>\n      <td>7161.0</td>\n      <td>103.949236</td>\n      <td>1:59</td>\n      <td>41</td>\n      <td>119.350000</td>\n      <td>Long</td>\n      <td>Slim Pickins</td>\n      <td>Holderness School</td>\n      <td>17</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Saturday, 12/8/2018</td>\n      <td>Away</td>\n      <td>Vermont Academy</td>\n      <td>4:00 PM</td>\n      <td>school</td>\n      <td>Vermont Academy, Shepard Lane, Saxtons River, ...</td>\n      <td>Vermont</td>\n      <td>43.144794</td>\n      <td>-72.509698</td>\n      <td>90163.1</td>\n      <td>5072.3</td>\n      <td>56.024645</td>\n      <td>1:24</td>\n      <td>3</td>\n      <td>84.538333</td>\n      <td>Medium</td>\n      <td>Slim Pickins</td>\n      <td>Vermont Academy</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "                    date home_away              opponent_school     time  \\\n0  Wednesday, 11/28/2018      Away             Middlesex School  5:00 PM   \n1     Friday, 11/30/2018      Away             Dana Hall School  5:30 PM   \n2    Saturday, 12/1/2018      Home  Buckingham Browne & Nichols  3:00 PM   \n3   Wednesday, 12/5/2018      Away            Holderness School  3:30 PM   \n4    Saturday, 12/8/2018      Away              Vermont Academy  4:00 PM   \n\n  quality                                            address          state  \\\n0  school  Middlesex School, 1400, Lowell Road, Concord, ...  Massachusetts   \n1  school  Dana Hall School, 45, Dana Road, Wellesley, No...  Massachusetts   \n2  school  Buckingham Browne & Nichols School (Middle Sch...  Massachusetts   \n3  school  Holderness School, Chapel Lane, Holderness, Gr...  New Hampshire   \n4  school  Vermont Academy, Shepard Lane, Saxtons River, ...        Vermont   \n\n         lat        lng  distance_meters  duration_seconds  distance_miles  \\\n0  42.499832 -71.368431          62722.6            3051.1       38.973942   \n1  42.290910 -71.288481          89181.5            4490.4       55.414709   \n2  42.380804 -71.131203          82363.5            4125.8       51.178208   \n3  43.761779 -71.674779         167290.4            7161.0      103.949236   \n4  43.144794 -72.509698          90163.1            5072.3       56.024645   \n\n  duration_str  Nearby Venue Count  duration_minutes dur-bin      Loc-opts  \\\n0         0:50                  16         50.851667   Short  Slim Pickins   \n1         1:14                  87         74.840000  Medium         A Few   \n2         1:08                 239         68.763333  Medium          Lots   \n3         1:59                  41        119.350000    Long  Slim Pickins   \n4         1:24                   3         84.538333  Medium  Slim Pickins   \n\n                Event Location  Food  Shop & Service  \n0             Middlesex School     4               4  \n1             Dana Hall School    34              22  \n2  Buckingham Browne & Nichols    56              16  \n3            Holderness School    17               8  \n4              Vermont Academy     0               1  "
                    },
                    "execution_count": 189,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "merged_test_df = pd.merge(test_df, venues_grouped[['Event Location','Food', 'Shop & Service']], how='left', \n         left_on='opponent_school', right_on='Event Location')\nmerged_test_df.head()\n"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": "# Come up with a School Location Score ... \n# what % of venues are Food & Shops \n"
        },
        {
            "cell_type": "code",
            "execution_count": 88,
            "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>Event Location</th>\n      <th>Event Location Latitude</th>\n      <th>Event Location Longitude</th>\n      <th>Venue</th>\n      <th>Venue Latitude</th>\n      <th>Venue Longitude</th>\n      <th>Venue Category</th>\n      <th>Venue Root Category</th>\n      <th>Venue Id</th>\n      <th>Geodesic Distance From Event</th>\n      <th>rating</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>203</th>\n      <td>Holderness School</td>\n      <td>43.761779</td>\n      <td>-71.674779</td>\n      <td>Biederman's Deli and Pub</td>\n      <td>43.757824</td>\n      <td>-71.687622</td>\n      <td>Sandwich Place</td>\n      <td>Food</td>\n      <td>4ba4036af964a520c47738e3</td>\n      <td>0.698237</td>\n      <td>8.8</td>\n    </tr>\n    <tr>\n      <th>204</th>\n      <td>Holderness School</td>\n      <td>43.761779</td>\n      <td>-71.674779</td>\n      <td>Burrito Me</td>\n      <td>43.756996</td>\n      <td>-71.687864</td>\n      <td>Burrito Place</td>\n      <td>Food</td>\n      <td>4f722ff5e4b07e78e2f98071</td>\n      <td>0.733296</td>\n      <td>8.5</td>\n    </tr>\n    <tr>\n      <th>207</th>\n      <td>Holderness School</td>\n      <td>43.761779</td>\n      <td>-71.674779</td>\n      <td>Six Burner Bistro</td>\n      <td>43.753565</td>\n      <td>-71.687654</td>\n      <td>American Restaurant</td>\n      <td>Food</td>\n      <td>4be88e7b947820a1470fb5db</td>\n      <td>0.858289</td>\n      <td>8.2</td>\n    </tr>\n    <tr>\n      <th>208</th>\n      <td>Holderness School</td>\n      <td>43.761779</td>\n      <td>-71.674779</td>\n      <td>M 'n M Scoops</td>\n      <td>43.757273</td>\n      <td>-71.687683</td>\n      <td>Ice Cream Shop</td>\n      <td>Food</td>\n      <td>4fb81878754acd863d3fb4bd</td>\n      <td>0.716740</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>209</th>\n      <td>Holderness School</td>\n      <td>43.761779</td>\n      <td>-71.674779</td>\n      <td>Rise and Shine Caf\u00e9</td>\n      <td>43.769122</td>\n      <td>-71.683850</td>\n      <td>Caf\u00e9</td>\n      <td>Food</td>\n      <td>4c3cf90b17f2ef3b2a4081f4</td>\n      <td>0.680454</td>\n      <td>7.9</td>\n    </tr>\n    <tr>\n      <th>212</th>\n      <td>Holderness School</td>\n      <td>43.761779</td>\n      <td>-71.674779</td>\n      <td>Annie's Overflow Restaurant</td>\n      <td>43.760108</td>\n      <td>-71.683941</td>\n      <td>Breakfast Spot</td>\n      <td>Food</td>\n      <td>4d29a8d38292236a89f924bb</td>\n      <td>0.472745</td>\n      <td>7.6</td>\n    </tr>\n    <tr>\n      <th>213</th>\n      <td>Holderness School</td>\n      <td>43.761779</td>\n      <td>-71.674779</td>\n      <td>Cafe Monte Alto</td>\n      <td>43.757877</td>\n      <td>-71.687606</td>\n      <td>Coffee Shop</td>\n      <td>Food</td>\n      <td>4bdaed213904a59397e2479e</td>\n      <td>0.696072</td>\n      <td>7.6</td>\n    </tr>\n    <tr>\n      <th>214</th>\n      <td>Holderness School</td>\n      <td>43.761779</td>\n      <td>-71.674779</td>\n      <td>Phat Fish</td>\n      <td>43.756660</td>\n      <td>-71.687560</td>\n      <td>Seafood Restaurant</td>\n      <td>Food</td>\n      <td>5869b437424f937f62ac8cb6</td>\n      <td>0.730686</td>\n      <td>7.5</td>\n    </tr>\n    <tr>\n      <th>215</th>\n      <td>Holderness School</td>\n      <td>43.761779</td>\n      <td>-71.674779</td>\n      <td>Foster's Boiler Room</td>\n      <td>43.769060</td>\n      <td>-71.684031</td>\n      <td>American Restaurant</td>\n      <td>Food</td>\n      <td>4b42a4e2f964a52017d825e3</td>\n      <td>0.683376</td>\n      <td>7.5</td>\n    </tr>\n    <tr>\n      <th>217</th>\n      <td>Holderness School</td>\n      <td>43.761779</td>\n      <td>-71.674779</td>\n      <td>Thai Smile</td>\n      <td>43.759605</td>\n      <td>-71.687296</td>\n      <td>Thai Restaurant</td>\n      <td>Food</td>\n      <td>4ceaa65c595cb1f7a61ad514</td>\n      <td>0.644027</td>\n      <td>7.3</td>\n    </tr>\n    <tr>\n      <th>220</th>\n      <td>Holderness School</td>\n      <td>43.761779</td>\n      <td>-71.674779</td>\n      <td>The Main Street Station</td>\n      <td>43.758527</td>\n      <td>-71.687528</td>\n      <td>Diner</td>\n      <td>Food</td>\n      <td>4b8fba46f964a520215f33e3</td>\n      <td>0.676271</td>\n      <td>6.9</td>\n    </tr>\n    <tr>\n      <th>221</th>\n      <td>Holderness School</td>\n      <td>43.761779</td>\n      <td>-71.674779</td>\n      <td>Italian Farmhouse</td>\n      <td>43.732940</td>\n      <td>-71.674370</td>\n      <td>Italian Restaurant</td>\n      <td>Food</td>\n      <td>4b42a4b3f964a52012d825e3</td>\n      <td>1.991094</td>\n      <td>7.5</td>\n    </tr>\n    <tr>\n      <th>225</th>\n      <td>Holderness School</td>\n      <td>43.761779</td>\n      <td>-71.674779</td>\n      <td>SUBWAY</td>\n      <td>43.756551</td>\n      <td>-71.687711</td>\n      <td>Sandwich Place</td>\n      <td>Food</td>\n      <td>4bd6120b6f6495210a4070ec</td>\n      <td>0.740921</td>\n      <td>6.5</td>\n    </tr>\n    <tr>\n      <th>230</th>\n      <td>Holderness School</td>\n      <td>43.761779</td>\n      <td>-71.674779</td>\n      <td>McDonald's</td>\n      <td>43.772992</td>\n      <td>-71.684136</td>\n      <td>Fast Food Restaurant</td>\n      <td>Food</td>\n      <td>4b74261ff964a52000ca2de3</td>\n      <td>0.904708</td>\n      <td>6.0</td>\n    </tr>\n    <tr>\n      <th>232</th>\n      <td>Holderness School</td>\n      <td>43.761779</td>\n      <td>-71.674779</td>\n      <td>Burger King</td>\n      <td>43.769970</td>\n      <td>-71.719290</td>\n      <td>Fast Food Restaurant</td>\n      <td>Food</td>\n      <td>4bc0bf5574a9a5933194d0f6</td>\n      <td>2.297751</td>\n      <td>6.3</td>\n    </tr>\n    <tr>\n      <th>235</th>\n      <td>Holderness School</td>\n      <td>43.761779</td>\n      <td>-71.674779</td>\n      <td>Hong Kong Garden</td>\n      <td>43.757703</td>\n      <td>-71.687669</td>\n      <td>Chinese Restaurant</td>\n      <td>Food</td>\n      <td>4d40d5d622518eecb70cb1f3</td>\n      <td>0.703684</td>\n      <td>5.6</td>\n    </tr>\n    <tr>\n      <th>241</th>\n      <td>Holderness School</td>\n      <td>43.761779</td>\n      <td>-71.674779</td>\n      <td>Dunkin'</td>\n      <td>43.770285</td>\n      <td>-71.715200</td>\n      <td>Donut Shop</td>\n      <td>Food</td>\n      <td>4b73328cf964a5204da12de3</td>\n      <td>2.105943</td>\n      <td>5.7</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "        Event Location  Event Location Latitude  Event Location Longitude  \\\n203  Holderness School                43.761779                -71.674779   \n204  Holderness School                43.761779                -71.674779   \n207  Holderness School                43.761779                -71.674779   \n208  Holderness School                43.761779                -71.674779   \n209  Holderness School                43.761779                -71.674779   \n212  Holderness School                43.761779                -71.674779   \n213  Holderness School                43.761779                -71.674779   \n214  Holderness School                43.761779                -71.674779   \n215  Holderness School                43.761779                -71.674779   \n217  Holderness School                43.761779                -71.674779   \n220  Holderness School                43.761779                -71.674779   \n221  Holderness School                43.761779                -71.674779   \n225  Holderness School                43.761779                -71.674779   \n230  Holderness School                43.761779                -71.674779   \n232  Holderness School                43.761779                -71.674779   \n235  Holderness School                43.761779                -71.674779   \n241  Holderness School                43.761779                -71.674779   \n\n                           Venue  Venue Latitude  Venue Longitude  \\\n203     Biederman's Deli and Pub       43.757824       -71.687622   \n204                   Burrito Me       43.756996       -71.687864   \n207            Six Burner Bistro       43.753565       -71.687654   \n208                M 'n M Scoops       43.757273       -71.687683   \n209          Rise and Shine Caf\u00e9       43.769122       -71.683850   \n212  Annie's Overflow Restaurant       43.760108       -71.683941   \n213              Cafe Monte Alto       43.757877       -71.687606   \n214                    Phat Fish       43.756660       -71.687560   \n215         Foster's Boiler Room       43.769060       -71.684031   \n217                   Thai Smile       43.759605       -71.687296   \n220      The Main Street Station       43.758527       -71.687528   \n221            Italian Farmhouse       43.732940       -71.674370   \n225                       SUBWAY       43.756551       -71.687711   \n230                   McDonald's       43.772992       -71.684136   \n232                  Burger King       43.769970       -71.719290   \n235             Hong Kong Garden       43.757703       -71.687669   \n241                      Dunkin'       43.770285       -71.715200   \n\n           Venue Category Venue Root Category                  Venue Id  \\\n203        Sandwich Place                Food  4ba4036af964a520c47738e3   \n204         Burrito Place                Food  4f722ff5e4b07e78e2f98071   \n207   American Restaurant                Food  4be88e7b947820a1470fb5db   \n208        Ice Cream Shop                Food  4fb81878754acd863d3fb4bd   \n209                  Caf\u00e9                Food  4c3cf90b17f2ef3b2a4081f4   \n212        Breakfast Spot                Food  4d29a8d38292236a89f924bb   \n213           Coffee Shop                Food  4bdaed213904a59397e2479e   \n214    Seafood Restaurant                Food  5869b437424f937f62ac8cb6   \n215   American Restaurant                Food  4b42a4e2f964a52017d825e3   \n217       Thai Restaurant                Food  4ceaa65c595cb1f7a61ad514   \n220                 Diner                Food  4b8fba46f964a520215f33e3   \n221    Italian Restaurant                Food  4b42a4b3f964a52012d825e3   \n225        Sandwich Place                Food  4bd6120b6f6495210a4070ec   \n230  Fast Food Restaurant                Food  4b74261ff964a52000ca2de3   \n232  Fast Food Restaurant                Food  4bc0bf5574a9a5933194d0f6   \n235    Chinese Restaurant                Food  4d40d5d622518eecb70cb1f3   \n241            Donut Shop                Food  4b73328cf964a5204da12de3   \n\n     Geodesic Distance From Event  rating  \n203                      0.698237     8.8  \n204                      0.733296     8.5  \n207                      0.858289     8.2  \n208                      0.716740     8.0  \n209                      0.680454     7.9  \n212                      0.472745     7.6  \n213                      0.696072     7.6  \n214                      0.730686     7.5  \n215                      0.683376     7.5  \n217                      0.644027     7.3  \n220                      0.676271     6.9  \n221                      1.991094     7.5  \n225                      0.740921     6.5  \n230                      0.904708     6.0  \n232                      2.297751     6.3  \n235                      0.703684     5.6  \n241                      2.105943     5.7  "
                    },
                    "execution_count": 88,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "\nvenues_df.loc[(venues_df['Event Location'] == 'Holderness School') & (venues_df['Venue Root Category'] == 'Food' )]\n"
        },
        {
            "cell_type": "code",
            "execution_count": 86,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "{'file_name': 'venue_school_heatmap.png',\n 'message': 'File saved to project storage.',\n 'bucket_name': 'ibmcrs9capstone-donotdelete-pr-afds8o4rva78zh',\n 'asset_id': '486fe450-4d19-4452-9b73-0dd1e54210e1'}"
                    },
                    "execution_count": 86,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 1008x1152 with 2 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "import seaborn as sns\nsns.set(font_scale=1.2)\nplt.figure(figsize=(14,16))\nax = plt.axes()\nsns.heatmap(venues_grouped.set_index('Event Location'), linewidths=1., cmap='YlGnBu', annot=True, fmt=\"d\", ax=ax)\nax.set_title(\"Venue Count Per School by Category\")\n\nbuffer = io.BytesIO()\nplt.savefig(buffer, format='png', bbox_inches='tight')\nbuffer.seek(0)\nproject.save_data('venue_school_heatmap.png', buffer, overwrite=True)\n"
        },
        {
            "cell_type": "code",
            "execution_count": 178,
            "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>Event Location</th>\n      <th>Food</th>\n      <th>Shop &amp; Service</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Brewster Academy</td>\n      <td>18</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Brooks School</td>\n      <td>26</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Buckingham Browne &amp; Nichols</td>\n      <td>56</td>\n      <td>16</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Dana Hall School</td>\n      <td>34</td>\n      <td>22</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Dexter Southfield School</td>\n      <td>49</td>\n      <td>15</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "                Event Location  Food  Shop & Service\n0             Brewster Academy    18               6\n1                Brooks School    26               9\n2  Buckingham Browne & Nichols    56              16\n3             Dana Hall School    34              22\n4     Dexter Southfield School    49              15"
                    },
                    "execution_count": 178,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "venues_grouped[['Event Location','Food', 'Shop & Service']].head()"
        },
        {
            "cell_type": "code",
            "execution_count": 91,
            "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>Event Location</th>\n      <th>Arts &amp; Entertainment</th>\n      <th>College &amp; University</th>\n      <th>Food</th>\n      <th>Nightlife Spot</th>\n      <th>Outdoors &amp; Recreation</th>\n      <th>Professional &amp; Other Places</th>\n      <th>Shop &amp; Service</th>\n      <th>Travel &amp; Transport</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Brewster Academy</td>\n      <td>1</td>\n      <td>0</td>\n      <td>18</td>\n      <td>3</td>\n      <td>3</td>\n      <td>0</td>\n      <td>6</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Brooks School</td>\n      <td>0</td>\n      <td>0</td>\n      <td>26</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0</td>\n      <td>9</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Buckingham Browne &amp; Nichols</td>\n      <td>11</td>\n      <td>1</td>\n      <td>56</td>\n      <td>5</td>\n      <td>10</td>\n      <td>0</td>\n      <td>16</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Dana Hall School</td>\n      <td>1</td>\n      <td>1</td>\n      <td>34</td>\n      <td>2</td>\n      <td>25</td>\n      <td>1</td>\n      <td>22</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Dexter Southfield School</td>\n      <td>5</td>\n      <td>0</td>\n      <td>49</td>\n      <td>5</td>\n      <td>23</td>\n      <td>1</td>\n      <td>15</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "                Event Location  Arts & Entertainment  College & University  \\\n0             Brewster Academy                     1                     0   \n1                Brooks School                     0                     0   \n2  Buckingham Browne & Nichols                    11                     1   \n3             Dana Hall School                     1                     1   \n4     Dexter Southfield School                     5                     0   \n\n   Food  Nightlife Spot  Outdoors & Recreation  Professional & Other Places  \\\n0    18               3                      3                            0   \n1    26               2                      8                            0   \n2    56               5                     10                            0   \n3    34               2                     25                            1   \n4    49               5                     23                            1   \n\n   Shop & Service  Travel & Transport  \n0               6                   0  \n1               9                   1  \n2              16                   1  \n3              22                   1  \n4              15                   1  "
                    },
                    "execution_count": 91,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "venues_grouped.head()"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        },
        {
            "cell_type": "code",
            "execution_count": 185,
            "metadata": {},
            "outputs": [],
            "source": ""
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "---  \n---\n"
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "Index(['date', 'home_away', 'opponent_school', 'time', 'quality', 'address',\n       'state', 'lat', 'lng', 'distance_meters', 'duration_seconds',\n       'distance_miles', 'duration_str', 'Nearby Venue Count',\n       'duration_minutes'],\n      dtype='object')"
                    },
                    "execution_count": 14,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "games_loc_df.columns"
        },
        {
            "cell_type": "code",
            "execution_count": 39,
            "metadata": {},
            "outputs": [],
            "source": "away_games_df = games_loc_df.loc[games_loc_df['home_away'] == 'Away']\n\nhome_base_map = folium.Map(location=[42.63286695, -71.9135057096491], zoom_start=8)\nfolium.Marker(location=[42.63286695, -71.9135057096491], \n              popup='Cushing Academy',\n             icon=folium.Icon(icon='home', color='purple', prefix='fa')).add_to(home_base_map)\n# add markers to map\nfor lat, lng, opponent_school, _date in zip(away_games_df['lat'], away_games_df['lng'], away_games_df['opponent_school'], away_games_df['date']):\n    label = '{}; {}'.format( opponent_school, _date)\n    label = folium.Popup(label, parse_html=True)\n    folium.CircleMarker(\n        [lat, lng],\n        radius=5,\n        popup=label,\n        color='blue',\n        fill=True,\n        fill_color='#3186cc',\n        fill_opacity=0.7, \n        parse_html=False).add_to(home_base_map)  "
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {
                "scrolled": true
            },
            "outputs": [],
            "source": ""
        },
        {
            "cell_type": "code",
            "execution_count": 43,
            "metadata": {},
            "outputs": [
                {
                    "data": {
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    <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.1/jquery.min.js"></script>
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                    iconColor: 'white',
                    markerColor: 'purple',
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                    extraClasses: 'fa-rotate-0'
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                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
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  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
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                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
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            circle_marker_71bf9ed8b93f4f048b0348c6b49ea3aa.bindPopup(popup_2ce42722d47d4d59bda458af29cd2432);

            
        
    
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                {
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  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
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  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
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                {
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  "color": "blue",
  "dashArray": null,
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  "fill": true,
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                popup_cd92f9842cd146e4b205c19d6020fb16.setContent(html_de26fb5f00b841479ab58547468806c0);
            

            circle_marker_eec3fdea698c4051935319966145548b.bindPopup(popup_cd92f9842cd146e4b205c19d6020fb16);

            
        
    
            var circle_marker_815e33244d164d17a7d88f1763e63b84 = L.circleMarker(
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