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x=3
x+=1
print(x)

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#open zip code file
f = open('laZips.txt', 'r')
laZips = [z.replace('\n','') for z in f.readlines()]

allStores = []
for idx,z in enumerate(laZips):
    #call request for 100 stores with current zipcode
    r = requests.get('https://www.starbucks.com/store-locator?place='+z)
    
    #if any response fails, quit immediately

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allStores contains a bunch of stores which each have the following format:

[Store Id, Store Location, Latitude, Longitude, City, State, Zip Code]

Here is one example:

['27689-248404', 'Carson & Wilmington', '33.831519', '-118.239858', 'Carson', 'CA', '90745']

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seenStoreIds = []
laStores = []
for store in allStores:
    #if we've already seen this store id, then keep going
    if store[0] in seenStoreIds:
        continue
        
    #if this is the first time seeing this store id,
    #then add it to laStores and add its id to seenStoreIds
    else:

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#needed to call Starbucks API
import requests

#needed to parse through text returned by Starbucks
import re

#needed to put all our nice data into a csv at the end
import pandas as pd

#needed to make fancy maps

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#open up the LA Geojson
with open('laMap.json') as f:
    laArea = json.load(f)
    
#convert the MultiPolygon part of the LA Geojson into a shapely Polygon object for easier inclusiveness checking
laPolygon = Polygon(laArea['features'][0]['geometry']['coordinates'][0][0])

#keep store if and only if it is within the LA polygon
keepLAStores = []
for store in laStores:

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#define columns of csv
dfSbux = pd.DataFrame(columns=['id', 'strLocation', 'latitude', 'longitude', 'city', 'state', 'zip'])

#store data
for i,col in enumerate(dfSbux.columns):
    dfSbux[col] = [item[i] for item in keepLAStores]

#cast the latitude and longitude as floats
dfSbux.latitude = dfSbux.latitude.apply(lambda x: float(x))
dfSbux.longitude = dfSbux.longitude.apply(lambda x: float(x))

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import folium
import pandas as pd
import json
from folium import plugins

df = pd.read_csv('starbucksInLACounty.csv')

with open('laMap.geojson') as f:
    laArea = json.load(f)

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#initialize the map around LA County
laMap = folium.Map(location=[34.0522,-118.2437], tiles='Stamen Toner', zoom_start=9)

#add the shape of LA County to the map
folium.GeoJson(laArea).add_to(laMap)

#for each row in the Starbucks dataset, plot the corresponding latitude and longitude on the map
for i,row in df.iterrows():
    folium.CircleMarker((row.latitude,row.longitude), radius=3, weight=2, color='red', fill_color='red', fill_opacity=.5).add_to(laMap)

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#group the starbucks dataframe by zip code and count the number of stores in each zip code
numStoresSeries = df.groupby('zip').count().id
#initialize an empty dataframe to store this new data
numStoresByZip = pd.DataFrame()
#populate the new dataframe with a 'zipcode' column and a 'numStores' column
numStoresByZip['zipcode'] = [str(i) for i in numStoresSeries.index]
numStoresByZip['numStores'] = numStoresSeries.values

#initialize the LA County map
laMap = folium.Map(location=[34.0522,-118.2437], tiles='Stamen Toner', zoom_start=9)