In [96]:
# Import quandl to retrieve data
# Pandas and matplotlib standard imports for data analysis
import quandl
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

# Use Plotly for better graphs
import plotly.offline as plotly
import plotly.graph_objs as go

# Offline Plotting
In [97]:
# Constants

# Populate your own quandl API Token
# Populate your own mapbox API Token
quandl.ApiConfig.api_key = {YOUR API KEY}
mapbox_access_token = {YOUR API KEY}

# Start date and end date should be the same
start_date = '2018-06-30'
end_date = '2018-06-30'

# Specify Zillow Indicator we want to graph
indicator = 'MRPFAH'

# Specify the county and state we want to generate the map for
county = 'Alameda'
state = 'CA'
In [98]:
# We are going to fetch the data for each zip code in the county and add that to a single dataframe
def get_city_data(cities, indicator):
    df_array = []
    for city, code in cities.items():
        quandl_db_code = f'ZILLOW/Z{code}_{indicator}'
            data = quandl.get(quandl_db_code, start_date=start_date, end_date=end_date)
            data.rename(index=str, columns={'Value': city}, inplace=True)
            data.index = pd.to_datetime(data.index, format='%Y/%m/%d')
            print(f'Error fetching for: {quandl_db_code}')
    ret = pd.concat(df_array, axis=1)
    return ret
In [99]:
# Here we are going to figure out what zip codes are in the specified county
# We will also use the logitude / latitude data for plotting

ZIP_CODE_DTYPE = {'zip_code': str}
ZIP_CODES = pd.read_csv('zip_codes_states.csv', dtype=ZIP_CODE_DTYPE)
county_df = ZIP_CODES['county'] == county
state_df = ZIP_CODES['state'] == state
data = ZIP_CODES[county_df & state_df]
In [100]:
data.index = data['zip_code']
In [101]:
# We will call our function to fetch the data from quandl
# There will be errors for data we cannot retrieve

real_estate_data = get_city_data(data['zip_code'], indicator)
Error fetching for: ZILLOW/Z94502_MRPFAH
Error fetching for: ZILLOW/Z94537_MRPFAH
Error fetching for: ZILLOW/Z94540_MRPFAH
Error fetching for: ZILLOW/Z94542_MRPFAH
Error fetching for: ZILLOW/Z94543_MRPFAH
Error fetching for: ZILLOW/Z94544_MRPFAH
Error fetching for: ZILLOW/Z94545_MRPFAH
Error fetching for: ZILLOW/Z94552_MRPFAH
Error fetching for: ZILLOW/Z94557_MRPFAH
Error fetching for: ZILLOW/Z94577_MRPFAH
Error fetching for: ZILLOW/Z94578_MRPFAH
Error fetching for: ZILLOW/Z94579_MRPFAH
Error fetching for: ZILLOW/Z94580_MRPFAH
Error fetching for: ZILLOW/Z94586_MRPFAH
Error fetching for: ZILLOW/Z94601_MRPFAH
Error fetching for: ZILLOW/Z94602_MRPFAH
Error fetching for: ZILLOW/Z94603_MRPFAH
Error fetching for: ZILLOW/Z94604_MRPFAH
Error fetching for: ZILLOW/Z94606_MRPFAH
Error fetching for: ZILLOW/Z94609_MRPFAH
Error fetching for: ZILLOW/Z94610_MRPFAH
Error fetching for: ZILLOW/Z94612_MRPFAH
Error fetching for: ZILLOW/Z94613_MRPFAH
Error fetching for: ZILLOW/Z94614_MRPFAH
Error fetching for: ZILLOW/Z94615_MRPFAH
Error fetching for: ZILLOW/Z94617_MRPFAH
Error fetching for: ZILLOW/Z94618_MRPFAH
Error fetching for: ZILLOW/Z94619_MRPFAH
Error fetching for: ZILLOW/Z94620_MRPFAH
Error fetching for: ZILLOW/Z94621_MRPFAH
Error fetching for: ZILLOW/Z94622_MRPFAH
Error fetching for: ZILLOW/Z94623_MRPFAH
Error fetching for: ZILLOW/Z94624_MRPFAH
Error fetching for: ZILLOW/Z94625_MRPFAH
Error fetching for: ZILLOW/Z94626_MRPFAH
Error fetching for: ZILLOW/Z94627_MRPFAH
Error fetching for: ZILLOW/Z94643_MRPFAH
Error fetching for: ZILLOW/Z94649_MRPFAH
Error fetching for: ZILLOW/Z94659_MRPFAH
Error fetching for: ZILLOW/Z94660_MRPFAH
Error fetching for: ZILLOW/Z94661_MRPFAH
Error fetching for: ZILLOW/Z94662_MRPFAH
Error fetching for: ZILLOW/Z94666_MRPFAH
Error fetching for: ZILLOW/Z94701_MRPFAH
Error fetching for: ZILLOW/Z94702_MRPFAH
Error fetching for: ZILLOW/Z94703_MRPFAH
Error fetching for: ZILLOW/Z94704_MRPFAH
Error fetching for: ZILLOW/Z94705_MRPFAH
Error fetching for: ZILLOW/Z94706_MRPFAH
Error fetching for: ZILLOW/Z94707_MRPFAH
Error fetching for: ZILLOW/Z94708_MRPFAH
Error fetching for: ZILLOW/Z94709_MRPFAH
Error fetching for: ZILLOW/Z94710_MRPFAH
Error fetching for: ZILLOW/Z94712_MRPFAH
Error fetching for: ZILLOW/Z94720_MRPFAH
In [102]:
# Here we are just doing some transformation and massaging of the data we get back
# If you want more information please run the notebook on your own

transformed_real_estate_data = real_estate_data.transpose()
column_name = f'{indicator} {end_date}'
In [103]:
transformed_real_estate_data[column_name] = transformed_real_estate_data[end_date].astype('float')
In [104]:
concat_data = pd.concat([data, transformed_real_estate_data[column_name]], axis=1, sort=True)
In [105]:
In [106]:
concat_data['text'] = concat_data['zip_code'] + ' ' + concat_data[column_name].astype(str)
In [107]:
df = concat_data
In [108]:
# Here is our final result dataframe
# We can see for each zip code we have the longitude and latitude data
# As well as the Zillow Indicator and text label 

zip_code latitude longitude city state county MRPFAH 2018-06-30 text
94501 94501 37.770563 -122.264779 Alameda CA Alameda 2.698115 94501 2.6981150835926
94536 94536 37.565285 -121.982721 Fremont CA Alameda 2.397260 94536 2.3972602739726
94538 94538 37.509453 -121.958320 Fremont CA Alameda 2.460908 94538 2.4609079743008
94539 94539 37.520339 -121.912568 Fremont CA Alameda 2.297297 94539 2.2972972972973
94541 94541 37.675130 -121.974120 Hayward CA Alameda 1.966925 94541 1.9669247009149
In [109]:
# Now it's just a matter of plotting using the Plotly Mapbox graph

plot_data = [
        lon = df['longitude'],
        lat = df['latitude'],
            reversescale = True,
            autocolorscale = False,
            colorscale = 'YlOrRd',
            cmin = df[column_name].min(),
            color = df[column_name],
            cmax = df[column_name].max(),
        text = df['text'],
        hoverinfo= 'text'

layout = go.Layout(
    title= f'{indicator} in {county}, {state} {end_date}',
In [110]:
# Finally calling our plot function after declaring the layout

fig = dict( data=plot_data, layout=layout )
plotly.iplot( fig, filename=f'{indicator} {county} {state} {end_date}')
In [111]:
# Output to div for blog post
# plotly.plot(fig, filename=f'{indicator} {county} {state} {end_date}', include_plotlyjs=False, output_type='div')