Contents

NYC under Covid-19: An Analysis on Consumer and Retail Trends

1. What I did

In Summer 2020, I was fortunate to be selected into the EconEx externship program hosted by Econ department. Mentored by UW-Alumni and Data Scientist Micah Lanier, I completed an independent project analyzing New York City consumer and retail trends under Covid-19. Meanwhile, I self-learnt many useful data analysis techniques in Python, like how to use numpy, pandas, matplotlib … interpolation to fill in missing data and web scraping.

2. Final results

For final deliverables, here is the whitepaper and I also made a video presentation:

Here is the full code: (I attached it here but they’re also available on github repo)

main.py:

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import pandas as pd
import numpy as np
from datetime import datetime
import matplotlib.pyplot as plt

%matplotlib inline

df = pd.read_csv("us-counties.csv",parse_dates=True,index_col=0)

pd.options.display.max_rows = 10

df = df['20200304':'20200611']

df.reset_index(inplace=True)

df

t10 = df.groupby('state').sum().sort_values('cases', ascending=False).head(10)

t10 = t10.drop(columns='fips')

print (t10.index)
t10_list = ['New York', 'New Jersey', 'Illinois', 'California', 'Massachusetts',
       'Pennsylvania', 'Michigan', 'Texas', 'Florida', 'Louisiana']
t10

t_c = t10['cases'].to_list()
t_d = t10['deaths'].to_list()


x = np.arange(len(t10.index))  # the label locations
width = 0.35  # the width of the bars

fig, ax = plt.subplots(figsize=(12,5))
rects1 = ax.bar(x - width/2, t_c, width, label='cases')
rects2 = ax.bar(x + width/2, t_d, width, label='deaths')

# Add some text for labels, title and custom x-axis tick labels, etc.
ax.set_ylabel('number')
ax.set_title('Cases and deaths comparison across top 10 states')

ax.set_xticks(x)
ax.set_xticklabels(t10.index)

#ax.set_yticks(y)
#ax.set_yticklabels(y)
ax.legend()

fig.tight_layout()

plt.show()

# now I have obtained the top 10 states with cases confirmed. Next I want to plot the contrast between
# cases and deaths over time for each state. 

cov19_counties_bystate_cases = pd.Series(df.groupby(['state','date']).cases.sum())
cov19_counties_bystate_deaths = pd.Series(df.groupby(['state','date']).deaths.sum())
cov19_counties_bystate = pd.DataFrame(cov19_counties_bystate_cases)
cov19_counties_bystate['deaths'] = cov19_counties_bystate_deaths

cov19_counties_bystate_deaths = pd.Series(cov19_counties.groupby(['state','date']).deaths.sum())

cov19_counties_bystate = pd.DataFrame(cov19_counties_bystate_cases)

cov19_counties_bystate['deaths'] = cov19_counties_bystate_deaths


cov19_counties_bystate

from matplotlib.dates import DateFormatter
import matplotlib.dates as mdates

cov19_counties.date = pd.to_datetime(cov19_counties.date)

pp.figure(figsize=(12,12))

for i, state in enumerate(t10.index):
    pp.subplot(5,2,i+1) #5 rows, 2 columns
    
    pp.plot(cov19_counties_bystate.loc[state].cases,label='cases')
    pp.plot(cov19_counties_bystate.loc[state].deaths,label='deaths')

    pp.legend()
    pp.title(state)
    #pp.xticks(arange(4), calendar.month_name[3:6+1], rotation=45)

pp.tight_layout()

# drop rows
df = cov19_counties_bystate

ax = cov19_counties_bystate.loc['New York'].cases.plot.line(figsize=(12,5))
ax = cov19_counties_bystate.loc['New Jersey'].cases.plot.line(figsize=(12,5))
ax = cov19_counties_bystate.loc['Illinois'].cases.plot.line(figsize=(12,5))
#ax = cov19_counties_bystate.loc['California'].cases.plot.line(figsize=(12,5))'''


for state in t10.index[:4+1]:
  ax = cov19_counties_bystate.loc[state].cases.plot.line(figsize=(12,5))

ax.legend(t10.index[:4+1])
pp.title('states cases')

for state in t10.index[:4+1]:
  ax = cov19_counties_bystate.loc[state].deaths.plot.line(figsize=(12,5))

ax.legend(t10.index[:4+1])
pp.title('states deaths')

df[df.state == 'New York']

dfc = df[df.state == 'New York'].pivot_table(index = df[df.state == 'New York'].date, values='cases',aggfunc='sum')
dfd = df[df.state == 'New York'].pivot_table(index = df[df.state == 'New York'].date, values='deaths',aggfunc='sum')
ax = dfc.plot.line(figsize=(12,5),grid=True,rot=True)
dfd.plot.line(figsize=(12,5),grid=True,rot=True,ax=ax)
ax.set_xlabel("New York")
ax.set_ylabel("number")

def plotstate(state_name):
    dfc = df[df.state == state_name].pivot_table(index = df[df.state == state_name].date, values='cases',aggfunc='sum')
    dfd = df[df.state == state_name].pivot_table(index = df[df.state == state_name].date, values='deaths',aggfunc='sum')
    ax = dfc.plot.line(figsize=(12,5),grid=True,rot=True)
    dfd.plot.line(figsize=(12,5),grid=True,rot=True,ax=ax)
    ax.set_xlabel(state_name)
    ax.set_ylabel("number")

plotstate('New York')

for i, state in enumerate(t10.index):
    plotstate(state)

# the cases in every county of New York
df_county = df.set_index(['state','county']).sort_index()

df_co = df_county.loc['New York']
df_co

df_co.index.unique()

# get the top ten counties with cases confirmed
def getcountyc(state):
    return (df_county.loc[state].groupby(['county']).sum().sort_values('cases',ascending=False).head(10))
  

getcountyc('New York').index

df_county.loc['New York','New York City'].drop(columns=['fips','deaths'])

df4 = df[df.state == 'New York'].pivot_table(index = 'county', values='cases',aggfunc='sum')
df5 = df[df.state == 'New York'].pivot_table(index = 'county', values='deaths',aggfunc='sum')
df6 = df4.sort_values('cases',ascending=False).head(10)
df6.plot.bar(figsize=(12,5),grid=True,rot=True)

df.columns('New York')

df_county.loc['New York','New York City']

df_county.date

'''def plotcounty(county):
    ax = df_county.loc['New York',county].plot.line(x= 'date',y= 'cases',figsize=(12,5),grid=True,rot=True)
    df_county.loc['New York',county].plot.line(x= 'date',y= 'deaths',figsize=(12,5),grid=True,rot=True,ax=ax)
    ax.set_xlabel(county) 
    ax.set_ylabel("cases")'''  

def plotcounty():
    for i,county in enumerate(getcountyc('New York').index):
        ax = df_county.loc['New York',county].plot.line(x= 'date',y= 'cases',figsize=(12,5),grid=True,rot=True)
        df_county.loc['New York',county].plot.line(x= 'date',y= 'deaths',figsize=(12,5),grid=True,rot=True,ax=ax)
        ax.set_xlabel(county) 
        ax.set_ylabel("cases") 

plotcounty()

for i,county in enumerate(getcountyc('New York').index):
    plotcounty(county)


df3 = df.pivot_table(index='date',values='cases',aggfunc='sum')

ax = df3.plot.line(figsize=(12,5),grid=True,rot=True)

df4 = df.pivot_table(index='date',values='deaths',aggfunc='sum')

ax = df3.plot.line(figsize=(12,5),grid=True,rot=True)
df4.plot.line(figsize=(12,5),grid=True,rot=True,ax=ax)

web scraping.py:

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states = [{"name":"Alabama","data":[{"date":"Feb 3 2020","value":-0.3129734981660876},{"date":"Feb 4 2020","value":-0.2857581142911993},{"date":"Feb 5 2020","value":-0.2795279197609019},{"date":"Feb 6 2020","value":-0.26873101239268987},{"date":"Feb 7 2020","value":-0.2259645118935109},{"date":"Feb 10 2020","value":-0.15580190384504244},{"date":"Feb 11 2020","value":-0.07400771510338015},{"date":"Feb 12 2020","value":0.03655392669497396},{"date":"Feb 13 2020","value":0.10730318395366031},{"date":"Feb 14 2020","value":0.14259768554504487},{"date":"Feb 17 2020","value":0.16108511434435566},{"date":"Feb 18 2020","value":0.15180556469944095},{"date":"Feb 19 2020","value":0.08573038632295868},{"date":"Feb 20 2020","value":0.09484421766130328},{"date":"Feb 21 2020","value":0.08336682634846616},{"date":"Feb 24 2020","value":0.09133853340279452},{"date":"Feb 25 2020","value":0.09990236390574236},{"date":"Feb 26 2020","value":0.11037226017057582},{"date":"Feb 27 2020","value":-0.011089115692379182},{"date":"Feb 28 2020","value":-0.1386906305272325},{"date":"Mar 2 2020","value":-0.23986155661089817},{"date":"Mar 3 2020","value":-0.33990729652479784},{"date":"Mar 4 2020","value":-0.4208233203516656},{"date":"Mar 5 2020","value":-0.410379244077831},{"date":"Mar 6 2020","value":-0.3687928510209387},{"date":"Mar 9 2020","value":-0.3852571183976337},{"date":"Mar 10 2020","value":-0.3954582015352476},{"date":"Mar 11 2020","value":-0.4070338589301073},{"date":"Mar 12 2020","value":-0.4089281205526973},{"date":"Mar 13 2020","value":-0.4304485476357643},{"date":"Mar 16 2020","value":-0.4394210995892514},{"date":"Mar 17 2020","value":-0.4554711462576342},{"date":"Mar 18 2020","value":-0.46860030144038867},{"date":"Mar 19 2020","value":-0.4889931755211813},{"date":"Mar 20 2020","value":-0.5107671238918375},{"date":"Mar 23 2020","value":-0.5249062760597077},{"date":"Mar 24 2020","value":-0.532653184064496},{"date":"Mar 25 2020","value":-0.5369859436819661},{"date":"Mar 26 2020","value":-0.5648982946801647},{"date":"Mar 27 2020","value":-0.5841649768124721},{"date":"Mar 30 2020","value":-0.585027799848894},{"date":"Mar 31 2020","value":-0.5832396954179788},{"date":"Apr 1 2020","value":-0.5854175165851284},{"date":"Apr 2 2020","value":-0.5820484781239451},{"date":"Apr 3 2020","value":-0.5880918521760881},{"date":"Apr 6 2020","value":-0.5988409245916604},{"date":"Apr 7 2020","value":-0.6064841273031438},{"date":"Apr 8 2020","value":-0.6088899049644692},{"date":"Apr 9 2020","value":-0.6162263401627394},{"date":"Apr 10 2020","value":-0.6213958376202983},{"date":"Apr 13 2020","value":-0.6227614199155626},{"date":"Apr 14 2020","value":-0.6349729896623812},{"date":"Apr 15 2020","value":-0.6360625332360453},{"date":"Apr 16 2020","value":-0.6354124499122822},{"date":"Apr 17 2020","value":-0.6341932409458362},{"date":"Apr 20 2020","value":-0.6267716348043891},{"date":"Apr 21 2020","value":-0.5409932463159939},{"date":"Apr 22 2020","value":-0.4735044334400688},{"date":"Apr 23 2020","value":-0.4071169582693552},{"date":"Apr 24 2020","value":-0.4334632967902233},{"date":"Apr 27 2020","value":-0.40864118339303607},{"date":"Apr 28 2020","value":-0.5244941920056894},{"date":"Apr 29 2020","value":-0.5203028969164231},{"date":"Apr 30 2020","value":-0.4847841967404053},{"date":"May 1 2020","value":-0.46345566322997744},{"date":"May 4 2020","value":-0.383531782564992},{"date":"May 5 2020","value":-0.213774658960827},{"date":"May 6 2020","value":-0.1425184942315663},{"date":"May 7 2020","value":-0.12462673162382604},{"date":"May 8 2020","value":-0.041069107551175814},{"date":"May 11 2020","value":-0.020332237132938993},{"date":"May 12 2020","value":-0.01205434301480746},{"date":"May 13 2020","value":-0.04203121745996236},{"date":"May 14 2020","value":-0.07362762582115395},{"date":"May 15 2020","value":-0.14287864807888184},{"date":"May 18 2020","value":-0.13446514896926368},{"date":"May 19 2020","value":-0.1406635330371363},{"date":"May 20 2020","value":-0.0987508573575548},{"date":"May 21 2020","value":-0.054742043753665925},{"date":"May 22 2020","value":0.033990852247398465},{"date":"May 25 2020","value":-0.0017875540047503327},{"date":"May 26 2020","value":0.01892180010912546},{"date":"May 27 2020","value":0.017379641100223796},{"date":"May 28 2020","value":0.0035795664614349542},{"date":"May 29 2020","value":-0.11728953583667376},{"date":"Jun 1 2020","value":-0.0969058538728316},{"date":"Jun 2 2020","value":-0.11468233222231142},{"date":"Jun 3 2020","value":-0.05473949177781139},{"date":"Jun 4 2020","value":-0.05702956985931562},{"date":"Jun 5 2020","value":0.02625065944181944}]},{"name":"Alaska","data":[{"date":"Feb 3 2020","value":-0.07148689834764176},{"date":"Feb 4 2020","value":0.01270227851944354},{"date":"Feb 5 2020","value":0.006839616594322579},{"date":"Feb 6 2020","value":-0.02522868482221517},{"date":"Feb 7 2020","value":-0.00867873991440994},{"date":"Feb 10 2020","value":-0.026893141518425593},{"date":"Feb 11 2020","value":-0.05914981256428077},{"date":"Feb 12 2020","value":-0.0413797143560123},{"date":"Feb 13 2020","value":-0.010464409627150928},{"date":"Feb 14 2020","value":-0.031495721249286276},{"date":"Feb 17 2020","value":0.02079056662691603},{"date":"Feb 18 2020","value":0.05161764233933203},{"date":"Feb 19 2020","value":0.030083751342919945},{"date":"Feb 20 2020","value":0.03380108635797018},{"date":"Feb 21 2020","value":0.040174461163695746},{"date":"Feb 24 2020","value":0.15663808732533122},{"date":"Feb 25 2020","value":0.1098754860364337},{"date":"Feb 26 2020","value":0.10066095351522844},{"date":"Feb 27 2020","value":0.015184560125884185},{"date":"Feb 28 2020","value":-0.10202605812417574},{"date":"Mar 2 2020","value":-0.2859927267096028},{"date":"Mar 3 2020","value":-0.29969084185865735},{"date":"Mar 4 2020","value":-0.35566624369037736},{"date":"Mar 5 2020","value":-0.3110628081584333},{"date":"Mar 6 2020","value":-0.23774225410873673},{"date":"Mar 9 2020","value":-0.2601092267073105},{"date":"Mar 10 2020","value":-0.27850823683072423},{"date":"Mar 11 2020","value":-0.2809471680370522},{"date":"Mar 12 2020","value":-0.3469313673001463},{"date":"Mar 13 2020","value":-0.3789079672533829},{"date":"Mar 16 2020","value":-0.40423631724342407},{"date":"Mar 17 2020","value":-0.4518114201560513},{"date":"Mar 18 2020","value":-0.4923846438721332},{"date":"Mar 19 2020","value":-0.4995001204743787},{"date":"Mar 20 2020","value":-0.517912273257286},{"date":"Mar 23 2020","value":-0.533398503265034},{"date":"Mar 24 2020","value":-0.5297751026069841},{"date":"Mar 25 2020","value":-0.5398973955629771},{"date":"Mar 26 2020","value":-0.5699256217165638},{"date":"Mar 27 2020","value":-0.6112010098452784},{"date":"Mar 30 2020","value":-0.5624900347567309},{"date":"Mar 31 2020","value":-0.4823936756299944},{"date":"Apr 1 2020","value":-0.4273499762871784},{"date":"Apr 2 2020","value":-0.35769377909023253},{"date":"Apr 3 2020","value":-0.3425438585320058},{"date":"Apr 6 2020","value":-0.38466577656561934},{"date":"Apr 7 2020","value":-0.4816615581794463},{"date":"Apr 8 2020","value":-0.5021572809569487},{"date":"Apr 9 2020","value":-0.5582765388755283},{"date":"Apr 10 2020","value":-0.5858377146443358},{"date":"Apr 13 2020","value":-0.6008973685644905},{"date":"Apr 14 2020","value":-0.5815619694224311},{"date":"Apr 15 2020","value":-0.5934355981667518},{"date":"Apr 16 2020","value":-0.595490539577489},{"date":"Apr 17 2020","value":-0.5793833443789406},{"date":"Apr 20 2020","value":-0.5289160413290795},{"date":"Apr 21 2020","value":-0.3306285604254147},{"date":"Apr 22 2020","value":-0.16234556738338324},{"date":"Apr 23 2020","value":-0.08233427895957007},{"date":"Apr 24 2020","value":-0.08145871294648752},{"date":"Apr 27 2020","value":-0.061752364787249575},{"date":"Apr 28 2020","value":-0.19814749629789624},{"date":"Apr 29 2020","value":-0.27295082760549416},{"date":"Apr 30 2020","value":-0.2394125944373949},{"date":"May 1 2020","value":-0.2469487380245157},{"date":"May 4 2020","value":-0.1614712239432768},{"date":"May 5 2020","value":-0.07110402924394571},{"date":"May 6 2020","value":-0.027314929195195563},{"date":"May 7 2020","value":-0.03627791732655913},{"date":"May 8 2020","value":0.011624121976105612},{"date":"May 11 2020","value":0.09681005240253408},{"date":"May 12 2020","value":0.10213048623255468},{"date":"May 13 2020","value":0.09041253178052969},{"date":"May 14 2020","value":0.07645013710646527},{"date":"May 15 2020","value":0.04692601854337548},{"date":"May 18 2020","value":0.008451443221927936},{"date":"May 19 2020","value":0.05679167305545951},{"date":"May 20 2020","value":0.13633593860238513},{"date":"May 21 2020","value":0.19880951956447857},{"date":"May 22 2020","value":0.2814123570544753},{"date":"May 25 2020","value":0.2308629378530495},{"date":"May 26 2020","value":0.20485870876936296},{"date":"May 27 2020","value":0.16063080690325815},{"date":"May 28 2020","value":0.13875785566812832},{"date":"May 29 2020","value":0.07076150504846222},{"date":"Jun 1 2020","value":0.14299264469533202},{"date":"Jun 2 2020","value":0.2029953037915553},{"date":"Jun 3 2020","value":0.30564701363627333},{"date":"Jun 4 2020","value":0.3367594582575378},{"date":"Jun 5 2020","value":0.3542235056974447}]},{"name":"Arizona","data":[{"date":"Feb 3 2020","value":-0.16031091346893933},{"date":"Feb 4 2020","value":-0.1429903937112944},{"date":"Feb 5 2020","value":-0.16009749731099732},{"date":"Feb 6 2020","value":-0.16624245120446773},{"date":"Feb 7 2020","value":-0.13630637136585202},{"date":"Feb 10 2020","value":-0.09052842150018106},{"date":"Feb 11 2020","value":-0.04619597568747775},{"date":"Feb 12 2020","value":0.02479585554798428},{"date":"Feb 13 2020","value":0.09324738477168248},{"date":"Feb 14 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2020","value":-0.5984794102081773},{"date":"Apr 15 2020","value":-0.5930934095801575},{"date":"Apr 16 2020","value":-0.5548382282153044},{"date":"Apr 17 2020","value":-0.5396362331373666},{"date":"Apr 20 2020","value":-0.5539561187865769},{"date":"Apr 21 2020","value":-0.4945694880861816},{"date":"Apr 22 2020","value":-0.47154449948274535},{"date":"Apr 23 2020","value":-0.4882964353320431},{"date":"Apr 24 2020","value":-0.525494568191641},{"date":"Apr 27 2020","value":-0.5192508880244601},{"date":"Apr 28 2020","value":-0.6023924059857092},{"date":"Apr 29 2020","value":-0.6001899481308475},{"date":"Apr 30 2020","value":-0.5818269360154048},{"date":"May 1 2020","value":-0.5724452101994154},{"date":"May 4 2020","value":-0.5127038396763987},{"date":"May 5 2020","value":-0.4132173481223881},{"date":"May 6 2020","value":-0.3641997672988871},{"date":"May 7 2020","value":-0.34907369529399573},{"date":"May 8 2020","value":-0.3008066347096522},{"date":"May 11 2020","value":-0.2758643251713644},{"date":"May 12 2020","value":-0.2789149823097349},{"date":"May 13 2020","value":-0.3049379194506702},{"date":"May 14 2020","value":-0.3156929048632307},{"date":"May 15 2020","value":-0.3475478603008973},{"date":"May 18 2020","value":-0.3437720446329355},{"date":"May 19 2020","value":-0.32714043510538127},{"date":"May 20 2020","value":-0.2796526596384571},{"date":"May 21 2020","value":-0.2600390235412304},{"date":"May 22 2020","value":-0.22082629584156013},{"date":"May 25 2020","value":-0.2360850509116738},{"date":"May 26 2020","value":-0.2415957938779587},{"date":"May 27 2020","value":-0.27212501253905597},{"date":"May 28 2020","value":-0.2821088156168062},{"date":"May 29 2020","value":-0.3391321077136384},{"date":"Jun 1 2020","value":-0.31298706163878565},{"date":"Jun 2 2020","value":-0.2917403502206306},{"date":"Jun 3 2020","value":-0.22529210396259025},{"date":"Jun 4 2020","value":-0.21501793767004704},{"date":"Jun 5 2020","value":-0.17093211548150605}]}]

import pandas as pd

state_data = {
    'state': [],
    'date': [],
    'retail_index': []
}

for state in states:
    for record in state['data']:
        state_data['state'].append(state_name)
        state_data['date'].append(record['date'])
        state_data['retail_index'].append(record['value'])
        
df = pd.DataFrame.from_dict(state_data)

df.to_csv('state_retail_indices.csv', index=False)

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

%matplotlib inline

from jupyterthemes import jtplot
jtplot.style(ticks = True, grid = False)

null = None
WellsFargo=[-0.5930968095197338,-0.45471437398652337,-0.518971579856979,-0.4630332367587038,-0.4932475925715311,null,null,-0.2477954134281337,-0.14290035772868667,0.10157220057663366,0.17434730050264904,0.20443062982995275,null,null,-0.07301663420805479,0.08432770808588756,0.01287870171805916,0.06308382998331533,0.2888169627415782,null,null,0.5956176770264549,0.5747167541544482,0.8052301552033637,0.6636898934317916,0.3867667353358792,null,null,0.16377900316691307,-0.09617226787793393,-0.4486897937695881,-0.5207244621465363,-0.5112835202097362,null,null,-0.5754214245598155,-0.48819120135165406,-0.5679045494037269,-0.4864342153242951,-0.4946053435356164,null,null,-0.4220905378358763,-0.5312399971391086,-0.5206629060795117,-0.5292965841659036,-0.4113446072781549,null,null,-0.48724851415379167,-0.5286511248345315,-0.5597369386819498,-0.6564327262537212,-0.7941301306981865,null,null,-0.8243699883102098,-0.8784021451996233,-0.8995387398710958,-0.925300833293273,-0.9591408414538488,null,null,-0.9924356387350638,-0.98527754751249,-0.9292193166455812,-0.9288921901179648,-0.902452979958517,null,null,-0.89728930530754,-0.758026618015878,-0.6920262029521117,-0.6811606777498853,-0.6304631009484324,null,null,-0.5704213131726465,-0.6460156809683882,-0.6937374616153954,-0.6737510860248969,-0.7203367175491071,null,null,-0.7868999309399552,-0.8033828869443685,-0.8098286865342292,-0.8302178146775459,-0.8624327426686725,null,null,-0.8575170510305659,-0.8314735584448475,-0.8177940416072113,-0.8183585986790655,-0.7354496113760302,null,null,-0.7189244454970858,-0.6910993444571986,-0.6211487886352258,-0.5802298327668222,-0.5776251317592959,null,null,-0.5692640591125843,-0.5785168153587664,-0.6086212496233355,-0.6401801658144571,-0.7262408237491514,null,null,-0.7563276705659993,-0.8428755008025739,-0.8870569282232809,-0.903416772093645,-0.8802136523149184,null,null,-0.835852832927454,-0.8393562525135394,-0.8904618581021269,-0.8907713971820219,-0.8922118091503973]
MexicanGrill=[-0.05516342662968984,-0.0967913855924295,-0.253103288773213,-0.24260979270926936,-0.22324688703740145,null,null,-0.2141153911752685,-0.1774527835465197,0.07895151210820639,0.20637722382642357,0.2224578005411984,null,null,0.11238425267912867,0.20663171984839773,0.007811390765693692,-0.029449505756819025,0.0007890864962028508,null,null,0.14515708952991446,0.08233854162170819,0.2580899225230767,0.16963157012734623,0.058862399805213926,null,null,0.01849307994062354,-0.042902969150021925,-0.24247874958216503,-0.2811166035288329,-0.26014434538116515,null,null,-0.37142607962123847,-0.4321140046928471,-0.44348327961135625,-0.4404007523557253,-0.48042449341152954,null,null,-0.4600862428975518,-0.5195124034812674,-0.5775812519477875,-0.6213786778767948,-0.6798225731125399,null,null,-0.7147662163823405,-0.7231770866664623,-0.6922035813394587,-0.689658621119717,-0.7081966471414143,null,null,-0.7372413405124032,-0.6885468753395141,-0.6766936114950048,-0.7063562390667169,-0.6729243914642927,null,null,-0.702225366794252,-0.758887789876178,-0.8346418794486574,-0.8832166868427938,-0.9164386868481516,null,null,-0.9223715695498932,-0.9468755181077642,-0.8927071560411849,-0.8926634005847752,-0.8674022147404691,null,null,-0.8135535353962294,-0.7296189614121523,-0.7350553536289198,-0.7143447352512113,-0.7474979766822112,null,null,-0.6670325853134661,-0.7170798977610601,-0.6750425127419163,-0.6281196970693668,-0.5796234709030679,null,null,-0.592817973431806,-0.538581745759367,-0.4809683112269059,-0.4592307790763193,-0.46013624913344847,null,null,-0.38046202785406874,-0.24723559277183663,-0.308878101167973,-0.3770437444431315,-0.4094602868631535,null,null,-0.5368654603059132,-0.6856000792956026,-0.6789394272678646,-0.5775946464752598,-0.5118569845465848,null,null,-0.5465827435028355,-0.5129901615707435,-0.5088289283693413,-0.5429421109358578,-0.5990741107298798,null,null,-0.5147680618505701,-0.4953808227871273,-0.5080020395400499,-0.5202696407677029,-0.507126930411858];
Mcdonald=[-0.022277954291717902,-0.14934268660402575,-0.11867978594296716,-0.0788518600430583,-0.02895534337893901,null,null,-0.02015464173180919,0.13149253867308533,0.1738411232832066,0.13941643445439653,0.028392315081263428,null,null,0.043947078882668425,-0.029510274190985192,-0.07015762767963497,-0.05599588445292883,0.0005630282976758748,null,null,-0.006811250646533639,-0.03803844483739736,-0.06557470385976781,-0.03708572504380233,-0.08375912611935087,null,null,-0.17804498355704318,-0.22483756949920075,-0.25453965288103475,-0.2761637352081711,-0.2290636978585187,null,null,-0.24229625460924065,-0.22551016689930067,-0.2472101631542136,-0.31008207813871186,-0.3814958282766222,null,null,-0.3176142580526964,-0.41299631260748143,-0.48754454882328346,-0.5838937468179412,-0.6201889548271642,null,null,-0.7268586513030373,-0.757395028751135,-0.7863895943260918,-0.7898884672573109,-0.8418249809456041,null,null,-0.829507187024589,-0.823007436528365,-0.7491955654119951,-0.7377287666213544,-0.735982746282495,null,null,-0.7777961377017918,-0.7747223827661213,-0.7754503973334979,-0.79528594752222,-0.7548610218419552,null,null,-0.7597339368659288,-0.7830083879829265,-0.8245970741760049,-0.8262284644839218,-0.8363971351095842,null,null,-0.8300856511542459,-0.8150228096048325,-0.7771736434940246,-0.7073389030538655,-0.6740347037990366,null,null,-0.6423383615345141,-0.586352598938622,-0.5137158754242324,-0.5132414133756291,-0.4716398218148287,null,null,-0.4191377493646003,-0.3575730722164134,-0.411446909341563,-0.33815428949118437,-0.4453417189544931,null,null,-0.4987858832484278,-0.6075196794205439,-0.6293570798847323,-0.7338123670873647,-0.6889533088856746,null,null,-0.6711940046216399,-0.5882466514366458,-0.5834663514045595,-0.5801390439501155,-0.6106124128381586,null,null,-0.5537035368805043,-0.5390870692264168,-0.5351334718678167,-0.545912490472802,-0.5353930975008122,null,null,-0.5218325925820958,-0.5556909635096933,-0.4801778612718518,-0.4296981358575895,-0.4273842793389618]
CVS=[-0.39760142015232647,-0.35064414549483514,-0.3497057695766675,-0.3629705459499741,-0.33476264566172614,null,null,-0.1912792661782356,-0.048002492299475424,0.2180573298534312,0.3165974180253785,0.38304993687799943,null,null,0.22938053033715136,0.1782508672885357,-0.024276551769309193,-0.014223655566549295,-0.0482872912162733,null,null,0.13814915756255702,0.1731966133757524,0.2671610446311919,0.14388861407634654,0.02991646909561004,null,null,-0.08712880786543842,-0.18463991249539252,-0.3240730806378642,-0.29520087423717994,-0.22718887066500384,null,null,-0.23572407512534807,-0.27411025054777455,-0.3225412332096434,-0.2965982426502501,-0.323389226197411,null,null,-0.40740018809086653,-0.42585886534089307,-0.4336788706279294,-0.4837010197823463,-0.5221603329147669,null,null,-0.4744452235064825,-0.46190025252170624,-0.4624125535318043,-0.4934226055292334,-0.5360666090178235,null,null,-0.6351826987859926,-0.7029685484254216,-0.712308941775979,-0.7224521647357824,-0.7126948527342437,null,null,-0.6855826699353569,-0.6816403114385693,-0.695436038376289,-0.6868701632633666,-0.7087602461606354,null,null,-0.7166661966829184,-0.7200487315101185,-0.7262977927129002,-0.7435114436923331,-0.7340443232498041,null,null,-0.7233058874058998,-0.6491196455416276,-0.5901901996142689,-0.5504885565323603,-0.5512024075451154,null,null,-0.5187737536059083,-0.5625390896343643,-0.5885740921514925,-0.5620844224879022,-0.5346753073272414,null,null,-0.4666781335211472,-0.4258194316447211,-0.35013504636605014,-0.3882215931055968,-0.38678040947455783,null,null,-0.4040857353715874,-0.418022345087084,-0.45381668184658,-0.42354508479199626,-0.42789829521727696,null,null,-0.41129401280775,-0.35877203694813753,-0.3406308515083165,-0.367372627195297,-0.34324257553940185,null,null,-0.3529584316039392,-0.3880052133368581,-0.4389838824035501,-0.42953395100807024,-0.4619265416524877,null,null,-0.5014374200161623,-0.4680771871349084,-0.3796557179926744,-0.3678026904116687,-0.36621691656133226]
SevenEleven=[-0.1337059119909564,-0.16692644818780947,-0.23696516992244343,-0.23431588549713803,-0.20413566986029075,null,null,-0.17136228260670497,-0.03769455849935648,0.028429710329316047,0.12744076104555324,0.05229986529029232,null,null,0.048846589529739845,0.009561778448706922,0.1356718086522055,0.0570145102267543,0.15183580456999865,null,null,0.3039810089316785,0.42990165840626393,0.2490956680522114,0.1871214090237969,0.08744986379005398,null,null,-0.12770301049114677,-0.3619295636177018,-0.4374445447634085,-0.5100311387120318,-0.5772098607350231,null,null,-0.574887024263724,-0.5842216237721913,-0.5196007106063729,-0.3688723546610065,-0.26234101857996395,null,null,-0.22842550186867328,-0.2092840200463008,-0.28814706543649715,-0.31212360093038155,-0.383160078542345,null,null,-0.41514905549284,-0.4858324900175703,-0.4844530501403015,-0.5502137547301343,-0.5747420661749262,null,null,-0.5829064798214054,-0.6188712830622318,-0.5970971753045969,-0.6058133649019838,-0.6264289769660818,null,null,-0.6847430442523533,-0.6838534224331995,-0.7277839064568308,-0.7885014723767164,-0.7872377651671169,null,null,-0.753615671683957,-0.7455365962671734,-0.6476209143348353,-0.6227594330891777,-0.6307332969898984,null,null,-0.6086377095998493,-0.4261822364773002,-0.32317665520120925,-0.2673397346097958,-0.27221804190457777,null,null,-0.31287177171127306,-0.5104356185404513,-0.6419254642652982,-0.5950104802360168,-0.5258806690984815,null,null,-0.4155126197316794,-0.3718054179254705,-0.32187437043571976,-0.32570406962040854,-0.2656797306896926,null,null,-0.3725933352790968,-0.28834579830017154,-0.26190497529672574,-0.254374168780314,-0.2859645109866151,null,null,-0.17619499824452634,-0.1127582991428379,0.001644027356553039,-0.012357287397725632,-0.018132230612732312,null,null,-0.12771119360906277,-0.20680921138513303,-0.3374199564424324,-0.33894201637480864,-0.3949519514593033,null,null,-0.31099783770850875,-0.2976827358423291,-0.24827540789920263,-0.2589239823416108,-0.17931276617052286]
RiteAid=[-0.7719348423352728,-0.6997286511209654,-0.6682631692272514,-0.550863977739454,-0.4113207867299611,null,null,-0.18320715226683557,-0.04614142863548706,0.12965604499616448,0.2625212540462979,0.29081352551152134,null,null,0.2846036476366957,0.25357607282184685,0.20693412124289676,0.12670623181362617,0.12050726121844003,null,null,0.06787963695725671,0.08433993385340857,0.08312316621361052,0.01149021314036336,-0.1442511970739403,null,null,-0.15952381240933317,-0.26786703298967324,-0.32742199174197734,-0.31584815612437683,-0.2597738314970726,null,null,-0.18249817909026,-0.11651398495037794,-0.17488974558164266,0.01582525083713369,0.06239933489832615,null,null,-0.08778542229956961,-0.06380273821195748,0.13102430263095718,-0.050288618738464096,-0.01941132199864997,null,null,0.1409450794231745,0.11195837782089506,-0.04952753300361026,-0.16462114776091735,-0.30701204769632195,null,null,-0.4830906867586836,-0.4962448660040696,-0.5551066065392777,-0.5620896893485565,-0.5849077183546489,null,null,-0.5847768309989734,-0.5990883938061196,-0.5603772464451358,-0.5324340079283804,-0.5118919646070895,null,null,-0.46062169070105935,-0.3575042689880588,-0.1476603279213028,0.0886883026697386,0.17963925990471885,null,null,0.13841701438670184,0.1764664534495484,0.03462334133604488,-0.08126771675491444,-0.19959715780530976,null,null,-0.12228878355957848,-0.3078482724686931,-0.2718809118970546,-0.26708413269554027,-0.21552542183904003,null,null,-0.2243736494666946,-0.08847500475678573,-0.039420120537559215,-0.053259034560321425,-0.04886340086555314,null,null,-0.09289972453059296,-0.0010289200460044216,0.012790603257398562,0.09283064509287564,0.17633920185375282,null,null,0.3408512768182738,0.3365247225612233,0.4216536163085645,0.5362842776411676,0.5150114465840218,null,null,0.4935907612917616,0.43743402610718,0.3439768183950061,0.1546579537701014,0.05860844926357705,null,null,0.01731591238788235,-0.005672997332564075,0.08644140306397596,0.19994013115397824,0.3338500152095956];
date=["Feb 3","Feb 4","Feb 5","Feb 6","Feb 7","Feb 8","Feb 9","Feb 10","Feb 11","Feb 12","Feb 13","Feb 14","Feb 15","Feb 16","Feb 17","Feb 18","Feb 19","Feb 20","Feb 21","Feb 22","Feb 23","Feb 24","Feb 25","Feb 26","Feb 27","Feb 28","Feb 29","Mar 1","Mar 2","Mar 3","Mar 4","Mar 5","Mar 6","Mar 7","Mar 8","Mar 9","Mar 10","Mar 11","Mar 12","Mar 13","Mar 14","Mar 15","Mar 16","Mar 17","Mar 18","Mar 19","Mar 20","Mar 21","Mar 22","Mar 23","Mar 24","Mar 25","Mar 26","Mar 27","Mar 28","Mar 29","Mar 30","Mar 31","Apr 1","Apr 2","Apr 3","Apr 4","Apr 5","Apr 6","Apr 7","Apr 8","Apr 9","Apr 10","Apr 11","Apr 12","Apr 13","Apr 14","Apr 15","Apr 16","Apr 17","Apr 18","Apr 19","Apr 20","Apr 21","Apr 22","Apr 23","Apr 24","Apr 25","Apr 26","Apr 27","Apr 28","Apr 29","Apr 30","May 1","May 2","May 3","May 4","May 5","May 6","May 7","May 8","May 9","May 10","May 11","May 12","May 13","May 14","May 15","May 16","May 17","May 18","May 19","May 20","May 21","May 22","May 23","May 24","May 25","May 26","May 27","May 28","May 29","May 30","May 31","Jun 1","Jun 2","Jun 3","Jun 4","Jun 5"];
NY_Retail_index = {'date':date,'WellsFargo':WellsFargo,'MexicanGrill':MexicanGrill,
                  'Mcdonald':Mcdonald,'CVS':CVS,'SevenEleven':SevenEleven,'RiteAid':RiteAid}

import pandas as pd

df = pd.DataFrame.from_dict(NY_Retail_index)

df.to_csv('NY_Retail_index.csv', index=False)

NY_Retail_index = pd.read_csv("NY_Retail_index.csv",parse_dates=True)

NY_Retail_index

#create a function that fill missing values using interpolation
def fillnans(array):
    good = ~np.isnan(array) #bool 完好的datapoint
    x = np.arange(len(array)) #total data points number

    return np.interp(x, x[good], array[good])

NY_Retail_index.columns[1:]

#fill all missing values in each brand column
for brand in NY_Retail_index.columns[1:]:
    NY_Retail_index[brand] = fillnans(NY_Retail_index[brand])

# check if all missing values are being filled
np.sum(np.isnan(NY_Retail_index[NY_Retail_index.columns[1:]]))

# now print the NY_Retail_index dataframe again
df = NY_Retail_index
df

# plot (compare the retail index change over time for all six brands in NYC)
ax = df.plot.line(x = 'date',y=NY_Retail_index.columns[1:],figsize = (15,5),grid=True,rot=True)
ax.set_ylabel('retail_index')
plt.axhline(linewidth=1.2,y=0,color='black')
ax.set_title('Comparison:NYC retail brand index change over time')

#
retail_brand_data=[{"brand":"Wells Fargo","change":-0.8922118091503973},{"brand":"Chipotle Mexican Grill","change":-0.507126930411858},{"brand":"McDonald's","change":-0.4273842793389618},{"brand":"CVS","change":-0.36621691656133226},{"brand":"7-Eleven","change":-0.17931276617052286},{"brand":"Rite Aid","change":0.3338500152095956}]
df_new = pd.DataFrame.from_dict(retail_brand_data)
df_new.to_csv('retail_brand_change.csv',index=False)
retail_brand_change = pd.read_csv("retail_brand_change.csv",parse_dates=True)
retail_brand_change

import matplotlib.pyplot as plt
ax = retail_brand_change.plot.bar(x='brand',y='change',figsize = (12,5),rot=0)
plt.axhline(y=0,color='black',linewidth=1.2)
ax.set_title('NYC Retail brand index change since Covid outbreak')

WellsFargo_data

WellsFargo_data.plot.line(x='date',y='WellsFargo_index',figsize = (12,5),grid=True,rot=True)

np.sum(np.isnan(WellsFargo_data['WellsFargo_index']))

def fillnans(array):
    good = ~np.isnan(array) #bool good datapoints
    x = np.arange(len(array)) #all the data points I wish to obtain number

    return np.interp(x, x[good], array[good])

WellsFargo_data['WellsFargo_index'] = fillnans(WellsFargo_data['WellsFargo_index'])

WellsFargo_data.plot.line(x='date',y='WellsFargo_index',figsize = (12,5),grid=True,rot=True)

#2

null = None
Fitness_and_Gyms =[-0.6796311756121224,-0.562671624297108,-0.5718908460795926,-0.5671684671386964,-0.5499909438581663,null,null,-0.29072713532374145,-0.1148068959917725,0.180554485704828,0.31185188732077695,0.35957046823376093,null,null,0.16525689255668413,0.025219793603241128,0.07629643354561504,0.16071216666631974,0.19042047562440553,null,null,0.3468273316442416,0.505549228978821,0.31993787694889225,0.1484999073567101,0.0130325167538623,null,null,-0.12297616048732447,-0.35464059608150106,-0.4719311649945767,-0.5380180997924374,-0.510110609842673,null,null,-0.4742524519243781,-0.36864951065606016,-0.3959532578169077,-0.319336216031661,-0.27380151851720763,null,null,-0.4214312035092029,-0.5671767946254399,-0.6230917043658931,-0.7509914913904197,-0.8664000482994231,null,null,null,-0.8985634044431305,-0.8654616446369945,-0.8560281635601671,-0.8068907870932284,null,null,-0.794402679785234,-0.809385910308804,-0.8525056366675896,-0.8758371726517007,-0.8644347614279142,null,null,-0.9103421139741472,-0.8312934460597455,-0.840867973943294,-0.7921834045680428,-0.8430143836514781,null,null,-0.7402802615663586,-0.6397008766761669,-0.6489860243953725,-0.6476307259278382,-0.6508659545277586,null,null,-0.75354802982073,-0.900555235178635,-0.8951985793307615,-0.8927773625600359,-0.873684517328459,null,null,-0.8788059216758235,-0.9126904652358656,-0.8899939001159602,-0.9138479858932376,-0.8783062724712025,null,null,-0.8770738044331375,-0.860535415760185,-0.8847059460337221,-0.8172324846930383,-0.871956563829145,null,null,-0.867817802917535,-0.841627857108655,-0.8281623110441211,-0.8833568931812457,-0.8792826702918993,null,null,-0.8529844671553514,-0.8920445437265919,-0.9102276110314218,-0.947520178541316,-0.9476450908424712,null,null,-0.8893776660969278,-0.8185732100587713,-0.7615778089133254,-0.7135636022209407,-0.7085608645596737,null,null,-0.7288174760636802,-0.7753972731644657,-0.7472274673822753,-0.7421872560306618,-0.6168169431045287]
Restaurants_Full_Service =[-0.6082316178801704,-0.3025235465146106,-0.3731177061318151,-0.3756759945917801,-0.34038187462327807,null,null,-0.27416117473817253,-0.2302908194179192,0.005082768968562838,0.16814391189832023,0.24973762661867063,null,null,0.2852320291006215,0.32660033292281443,0.20868346049244887,0.16226606410461167,0.0906442480046073,null,null,0.2007992357561313,0.12482211360997604,0.235666941427527,0.09453150466957516,-0.02804060427043105,null,null,-0.20185188644422164,-0.24590298933326243,-0.3315481331828069,-0.24256350040951025,-0.1282639472269719,null,null,-0.041076529362534545,-0.054923845287447905,-0.07783616482599692,-0.15067033143105502,-0.1487484085926394,null,null,-0.24982181762596628,-0.3361804802623392,-0.4734957500642614,-0.553887375058572,-0.6675333955469797,null,null,-0.7403336226522219,-0.7875332061172133,-0.8377597193913726,-0.8492612633755174,-0.8746148590287357,null,null,-0.866102358900476,-0.8735153770928371,-0.8874076825872739,-0.8949730338834602,-0.8931971298233224,null,null,-0.9028422620966032,-0.9010379435715036,-0.9122056175923424,-0.9031713963157488,-0.9084683261591194,null,null,-0.902382894913048,-0.8941561180157969,-0.8680732177618821,-0.8788636108312784,-0.8944639413862208,null,null,-0.9113531836434764,-0.8522787216963645,-0.7898821352207553,-0.7825464675163468,-0.7738200696101772,null,null,-0.7285858199717382,-0.7739660883884552,-0.8399673654607971,-0.7776023506129237,-0.7709399478699805,null,null,-0.807819555518839,-0.8057721354601736,-0.7740576461088889,-0.7717000348077195,-0.7507483133515624,null,null,-0.7058305692811745,-0.6910763583495511,-0.6788139381369741,-0.6881828186675669,-0.6985880378785866,null,null,-0.6760222169544376,-0.7139042237839069,-0.6872372484168802,-0.7247767030854163,-0.6839537991323591,null,null,-0.638427511937364,-0.5459297462877686,-0.47525902547330473,-0.4443109373853062,-0.48863750272634165,null,null,-0.5243592209279901,-0.5127558584443975,-0.5813917878512903,-0.5788510611092533,-0.5698176291233532];
hotel_and_motel=[-0.43113079125938064,-0.397452138234363,-0.4136777723742346,-0.3857785015316906,-0.302639028731321,null,null,-0.1621362769650278,-0.0454532570530501,0.117801927733868,0.19086367597185336,0.2019702154635145,null,null,0.17296088341752405,0.17085168913811735,0.10191366841323128,0.1054444719375457,0.1006688132678062,null,null,0.13431314943538508,0.16817176540889278,0.1775597879877805,0.07884810521031511,-0.05862139289717714,null,null,-0.14602821148739428,-0.2862052650794511,-0.3744453147952185,-0.37557616666619137,-0.31737379596854476,null,null,-0.35992250986186275,-0.38431769530481225,-0.3979758045654078,-0.3880122392631912,-0.4236598776929374,null,null,-0.4816546536867537,-0.5266012691354363,-0.5864438073492626,-0.6494779915742757,-0.671131482944611,null,null,-0.6985558218504078,-0.7319126094930588,-0.7308756164555482,-0.7471725583511365,-0.7983176896001917,null,null,-0.7882610676767647,-0.7677908393778541,-0.7625275247937066,-0.7636007065172437,-0.7517848331255901,null,null,-0.7518773549386325,-0.7424694553228387,-0.746385498997413,-0.7469122850427703,-0.7599040233261931,null,null,-0.7493581410638547,-0.7637263864366944,-0.742305269215301,-0.7334523222083726,-0.7225772655187069,null,null,-0.7026207204650405,-0.6364983756093248,-0.595016782280308,-0.5785570804322961,-0.5554227943798079,null,null,-0.552412715741615,-0.6189186963230869,-0.6407558942990875,-0.6092162956761829,-0.6107273070198207,null,null,-0.5614407232888278,-0.4948832228538505,-0.4488437269141141,-0.4558192298477152,-0.42727625052411206,null,null,-0.42551646424771744,-0.42923133091647586,-0.436869906843548,-0.44235837566637104,-0.48681475029450105,null,null,-0.5162566530136085,-0.4867377379183269,-0.5185488408211231,-0.5196759490349209,-0.4704574642106374,null,null,-0.46055192559463537,-0.5074433709457976,-0.5034112738987944,-0.5287205356353378,-0.5748788481230372,null,null,-0.571106578710927,-0.5427147718294433,-0.5086578311603484,-0.4405107025810556,-0.4052089067665356]
Restaurants_FastCasual_and_QSR =[-0.2490461517115051,-0.12397987277358886,-0.1807028380357007,-0.15639847174396612,-0.11246067352518252,null,null,-0.05432992758944053,-0.0017278725998571035,0.14473407826147025,0.18683527581511797,0.14911369137028543,null,null,0.0992963574349154,0.07921058177676807,-0.029601492693503224,-0.049756702735429155,-0.036653017845103025,null,null,0.015858265242260878,0.007757528835528271,0.052911565605435994,0.03587425672124542,-0.0212492534054768,null,null,-0.08167483195975497,-0.11808520840663247,-0.15082885473784735,-0.1540137417116713,-0.09832124450201822,null,null,-0.11492008487202016,-0.1201316863023334,-0.15472225226252032,-0.15321848190552204,-0.22345529629563785,null,null,-0.2319161470504816,-0.3341896713001924,-0.4022145545778451,-0.4859856519763293,-0.4931396671755218,null,null,-0.5210870940952498,-0.4989104165275987,-0.45932354819611976,-0.4992170121819158,-0.5682417855655185,null,null,-0.6053723907086185,-0.63850361032561,-0.6679299721300527,-0.6586039024922046,-0.6543939401447628,null,null,-0.6492017523582768,-0.630760015006222,-0.6675642610557933,-0.691485543102516,-0.687523585682525,null,null,-0.6946859959709402,-0.7293970664410098,-0.7171675202159871,-0.716843084997072,-0.7132627543413251,null,null,-0.6964466910376884,-0.6360643122133682,-0.6139572439271878,-0.57612967148092,-0.5692121179614974,null,null,-0.5520938314458472,-0.6033755837574846,-0.5928196335485255,-0.5468638657578594,-0.5182701918656832,null,null,-0.475083229439246,-0.39254253730494904,-0.3452264152648374,-0.3471203823729944,-0.34824899718035773,null,null,-0.31816764351885995,-0.32154229448865307,-0.33062455674366154,-0.34843141547325707,-0.3665429459825563,null,null,-0.38078888770023234,-0.3409743025735904,-0.3305308115806758,-0.3074210548254711,-0.30264110089935314,null,null,-0.3095296214018809,-0.33438555671538633,-0.3253540147910979,-0.34608673701246173,-0.35058370647270126,null,null,-0.3387184721404009,-0.37695408090242544,-0.3641561171779558,-0.3493769123969525,-0.35617623507649004]
Pharmacies_and_Drugs =[-0.4568734057692896,-0.415384984427062,-0.4166860550028701,-0.3998243586564818,-0.34349774642433795,null,null,-0.18706792314406925,-0.04612128550612897,0.1982545402671683,0.3024145696129012,0.3501492760366956,null,null,0.22981317569059273,0.20109002075259583,0.03869756233420838,0.022184473933584806,-0.0066515296123580165,null,null,0.12020457100546486,0.12607932178750822,0.1928872261149848,0.09076356645755548,-0.028285855968154652,null,null,-0.10352581847082938,-0.19783108202200264,-0.30829636812591504,-0.284335016863607,-0.20790262922821404,null,null,-0.21289140380611443,-0.2171791414335255,-0.27687783819768286,-0.2226615968290306,-0.2435701767197314,null,null,-0.34200422184558854,-0.3638260348276788,-0.3159175975898459,-0.3770382377615728,-0.3933131468203696,null,null,-0.3120715743781864,-0.30328441245699195,-0.3365510612354419,-0.3962347285010241,-0.4603867421141835,null,null,-0.5729072484639344,-0.6168726075925292,-0.6358530811321204,-0.643407314695623,-0.6476102248141268,null,null,-0.638303908348929,-0.6494079056860754,-0.6650253378665245,-0.6566164607823491,-0.6692814880732014,null,null,-0.6644114210603185,-0.6490270734764165,-0.6007426440369404,-0.5542232890996228,-0.5246854843913988,null,null,-0.5212928935096343,-0.45143476518319015,-0.42829697949534856,-0.43081836893428127,-0.4472435729788297,null,null,-0.38938509820970657,-0.45332542515649366,-0.475653401082124,-0.45010070613169584,-0.41859683922002855,null,null,-0.38109085289432487,-0.33394433481307384,-0.2598140078323219,-0.29828009890598844,-0.3078182262814134,null,null,-0.32481327758549833,-0.31318834254350064,-0.3409628559104141,-0.2962016975691273,-0.2812292601410905,null,null,-0.2435385892989971,-0.20639611304900143,-0.1805178638751992,-0.1822159424769294,-0.1690554512076246,null,null,-0.18583550426090545,-0.2267231277660222,-0.28062425913912403,-0.31355669262722435,-0.3522533209247983,null,null,-0.382011728103634,-0.360873375200818,-0.2746853145665561,-0.24172536945394535,-0.21607077959792273]
Convenience_Stores=[-0.1337059119909564,-0.16692644818780947,-0.23696516992244343,-0.23431588549713803,-0.20413566986029075,null,null,-0.17136228260670497,-0.03769455849935648,0.028429710329316047,0.12744076104555324,0.05229986529029232,null,null,0.048846589529739845,0.009561778448706922,0.1356718086522055,0.0570145102267543,0.15183580456999865,null,null,0.3039810089316785,0.42990165840626393,0.2490956680522114,0.1871214090237969,0.08744986379005398,null,null,-0.12770301049114677,-0.3619295636177018,-0.4374445447634085,-0.5100311387120318,-0.5772098607350231,null,null,-0.574887024263724,-0.5842216237721913,-0.5196007106063729,-0.3688723546610065,-0.26234101857996395,null,null,-0.22842550186867328,-0.2092840200463008,-0.28814706543649715,-0.31212360093038155,-0.383160078542345,null,null,-0.41514905549284,-0.4858324900175703,-0.4844530501403015,-0.5502137547301343,-0.5747420661749262,null,null,-0.5829064798214054,-0.6188712830622318,-0.5970971753045969,-0.6058133649019838,-0.6264289769660818,null,null,-0.6847430442523533,-0.6838534224331995,-0.7277839064568308,-0.7885014723767164,-0.7872377651671169,null,null,-0.753615671683957,-0.7455365962671734,-0.6476209143348353,-0.6227594330891777,-0.6307332969898984,null,null,-0.6086377095998493,-0.4261822364773002,-0.32317665520120925,-0.2673397346097958,-0.27221804190457777,null,null,-0.31287177171127306,-0.5104356185404513,-0.6419254642652982,-0.5950104802360168,-0.5258806690984815,null,null,-0.4155126197316794,-0.3718054179254705,-0.32187437043571976,-0.32570406962040854,-0.2656797306896926,null,null,-0.3725933352790968,-0.28834579830017154,-0.26190497529672574,-0.254374168780314,-0.2859645109866151,null,null,-0.17619499824452634,-0.1127582991428379,0.001644027356553039,-0.012357287397725632,-0.018132230612732312,null,null,-0.12771119360906277,-0.20680921138513303,-0.3374199564424324,-0.33894201637480864,-0.3949519514593033,null,null,-0.31099783770850875,-0.2976827358423291,-0.24827540789920263,-0.2589239823416108,-0.17931276617052286]
Furniture=[-0.5301426477788457,-0.2937611997382127,-0.23197202764923777,-0.21005371753003232,-0.17448676967815654,null,null,-0.07418986843037999,-0.019132903285366543,0.4250715876551064,0.45454245720813896,0.44466360808375854,null,null,0.48694607145742064,0.45599151993326714,-0.010851485083226364,-0.13404901755525497,-0.27017683840560247,null,null,-0.22611148386331012,-0.21268168922138028,-0.2402732282776595,-0.2335224753090593,-0.21749623721766775,null,null,-0.43078046617287535,-0.5393118023603731,-0.4985451674005397,-0.3310622008938361,-0.272820265117483,null,null,-0.2116134382021732,-0.1626370963354705,-0.07685555037182722,-0.18874000781405856,-0.11080219387107468,null,null,-0.060807057050868835,-0.1407552600647216,-0.3933348060250677,-0.4087725169345812,-0.582499311736419,null,null,-0.6935281792419703,-0.7168195133852842,-0.7179990405523253,-0.8325269247064371,-0.8583405182006565,null,null,-0.7805683820777812,-0.7620693407011881,-0.653582514972824,-0.5970987423322492,-0.57055319906561,null,null,-0.548006178711142,-0.5605235089408325,-0.6315671473246741,-0.6187728631270412,-0.6366734861086155,null,null,-0.6817279619649883,-0.6147051017228845,-0.546962655724787,-0.5467005385765555,-0.5113147235653208,null,null,-0.4937627658469599,-0.4615001013849348,-0.44919048663120126,-0.3414727027245348,-0.34304046000735267,null,null,-0.4073852742915832,-0.4243585960412069,-0.31600777449352885,-0.2760002901422523,-0.21586418704943472,null,null,-0.10941989683464706,-0.04866312011724397,-0.136098971478687,-0.3290419206098264,-0.3554934973516277,null,null,-0.1865441584967994,-0.1779833135234316,-0.13348027279965854,0.09955176319116887,0.13664133966590775,null,null,0.08155470088147168,0.3281055524324963,0.4410953529432129,0.3598687106309109,0.39376342526116925,null,null,0.367902848504531,0.15293464051210093,0.094670449506181,0.0999894493726497,-0.0068949998269038715,null,null,-0.08995646217682533,-0.05151673510836656,-0.06210527877559976,-0.008020125321670495,0.024989325732485582]
Groceries_and_supermarkets=[-0.2550104042727685,-0.25140870759919337,-0.28384519739825265,-0.26323694259995156,-0.26003014522976536,null,null,-0.14975456780771648,-0.1254902230769638,0.04529442432079859,0.1189922680937396,0.15617858379664654,null,null,0.165746987551842,0.18345188721738123,0.06593358920249243,0.08852695038618026,0.10385156143311862,null,null,0.04780684116772296,0.0496017479781835,0.05734468850180724,-0.051887160795432694,-0.17835368038888894,null,null,-0.21845247571141763,-0.2639026057459653,-0.29842881523324366,-0.2490674632964569,-0.14752310027712032,null,null,-0.12933637111942878,-0.12893319611865553,-0.14859172622653852,-0.1603222797051796,-0.1913291250979879,null,null,-0.21013490471300736,-0.2282293987477149,-0.1820900516592136,-0.21638227818113095,-0.19741437977632711,null,null,-0.2557245191513313,-0.24402763432187757,-0.282217785044254,-0.32811409541299086,-0.4071550689330226,null,null,-0.3559277947996744,-0.3782837778600999,-0.3925541923787005,-0.3657123569412495,-0.38020034449535506,null,null,-0.4566485490981329,-0.46004427285903227,-0.47912279682545195,-0.5175308035131585,-0.5186537519889968,null,null,-0.44939875525966544,-0.4561845441849621,-0.41077572190487976,-0.39177160848246256,-0.3465863007642123,null,null,-0.33888381920557836,-0.1363485039048089,-0.04335795828784452,0.0777159188391276,0.08995885823103217,null,null,0.1365070389168087,0.03518703924877981,-0.009053848713272906,0.01943463830628375,0.07812503536622796,null,null,0.21908152302256556,0.2770447904319922,0.392469407741111,0.36265878964884274,0.3327644243283434,null,null,0.3220860868692646,0.3412961732043576,0.2830281903732938,0.3130617571677443,0.3686161600462346,null,null,0.2455394226873504,0.25382191038744784,0.2918570155656673,0.227358352126161,0.1944072129752371,null,null,0.21429095522390323,0.16628766644761617,0.11703467663384418,0.14745133041148556,0.1253440426673265,null,null,0.20691129659571297,0.1936201022017975,0.2731160268805967,0.23987064071156483,0.26338635312509195]
date=["Feb 3","Feb 4","Feb 5","Feb 6","Feb 7","Feb 8","Feb 9","Feb 10","Feb 11","Feb 12","Feb 13","Feb 14","Feb 15","Feb 16","Feb 17","Feb 18","Feb 19","Feb 20","Feb 21","Feb 22","Feb 23","Feb 24","Feb 25","Feb 26","Feb 27","Feb 28","Feb 29","Mar 1","Mar 2","Mar 3","Mar 4","Mar 5","Mar 6","Mar 7","Mar 8","Mar 9","Mar 10","Mar 11","Mar 12","Mar 13","Mar 14","Mar 15","Mar 16","Mar 17","Mar 18","Mar 19","Mar 20","Mar 21","Mar 22","Mar 23","Mar 24","Mar 25","Mar 26","Mar 27","Mar 28","Mar 29","Mar 30","Mar 31","Apr 1","Apr 2","Apr 3","Apr 4","Apr 5","Apr 6","Apr 7","Apr 8","Apr 9","Apr 10","Apr 11","Apr 12","Apr 13","Apr 14","Apr 15","Apr 16","Apr 17","Apr 18","Apr 19","Apr 20","Apr 21","Apr 22","Apr 23","Apr 24","Apr 25","Apr 26","Apr 27","Apr 28","Apr 29","Apr 30","May 1","May 2","May 3","May 4","May 5","May 6","May 7","May 8","May 9","May 10","May 11","May 12","May 13","May 14","May 15","May 16","May 17","May 18","May 19","May 20","May 21","May 22","May 23","May 24","May 25","May 26","May 27","May 28","May 29","May 30","May 31","Jun 1","Jun 2","Jun 3","Jun 4","Jun 5"];
NY_RetailCategory_index = {'date':date,'Fitness&Gyms':Fitness_and_Gyms,'Restaurants(Full-Service)':Restaurants_Full_Service,
                  'hotel&motel':hotel_and_motel,'Restaurants(FastCasual&QSR)':Restaurants_FastCasual_and_QSR
                   ,'Pharmacies&Drugs':Pharmacies_and_Drugs,'Convenience_Stores':Convenience_Stores
                   ,'Furniture':Furniture,'Groceries&supermarkets':Groceries_and_supermarkets}

import pandas as pd
df = pd.DataFrame.from_dict(NY_RetailCategory_index)
df.to_csv('NY_RetailCategory_index.csv', index=False)
NY_RetailCategory_index = pd.read_csv("NY_RetailCategory_index.csv",parse_dates=True)

NY_RetailCategory_index

#create a function that fill missing values using interpolation
def fillnans(array):
    good = ~np.isnan(array) #bool good datapoints
    x = np.arange(len(array)) #total data points number

    return np.interp(x, x[good], array[good])

for category in NY_RetailCategory_index.columns[1:]:
    NY_RetailCategory_index[category] = fillnans(NY_RetailCategory_index[category])

df1 = NY_RetailCategory_index
df1

# plot (compare the retail index change over time for all six brands in NYC)
ax = df1.plot.line(x = 'date',y=NY_RetailCategory_index.columns[1:],figsize = (15,5),grid=True,rot=True)
ax.set_ylabel('retail_index')
plt.axhline(linewidth=1.2,y=0,color='black')
ax.set_title('Comparison:NYC retail category index change over time')

retail_industries=[{"industry":"Fitness and Gyms","change":-0.6168169431045287},{"industry":"Restaurants (Full-Service)","change":-0.5698176291233532},{"industry":"Hotels and Motels","change":-0.4052089067665356},{"industry":"Restaurants (Fast Casual and QSR)","change":-0.35617623507649004},{"industry":"Pharmacies and Drugs","change":-0.21607077959792273},{"industry":"Convenience Stores","change":-0.17931276617052286},{"industry":"Furniture","change":0.024989325732485582},{"industry":"Groceries and Supermarkets","change":0.26338635312509195}]
df_neww = pd.DataFrame.from_dict(retail_industries)
df_neww.to_csv('retail_category_change.csv',index=False)
retail_category_change = pd.read_csv("retail_category_change.csv",parse_dates=True)
retail_category_change

import matplotlib.pyplot as plt
ax = retail_category_change.plot.bar(x='industry',y='change',figsize = (15,5),rot=0)
plt.axhline(y=0,color='black',linewidth=1.2)
plt.xticks(rotation=20)
ax.set_title('NYC Retail category index change since Covid outbreak')

import pandas as pd

# make bar plot of retail index change by city
retail_bycity=[{"region":"Minneapolis, MN","change":-0.6806897768158495},{"region":"Boston, MA","change":-0.6734140174579021},{"region":"Chicago, IL","change":-0.6403088581330179},{"region":"Washington, DC","change":-0.5973587662990005},{"region":"Detroit, MI","change":-0.5814492428354163},{"region":"New Orleans, LA","change":-0.5502841070672672},{"region":"San Francisco, CA","change":-0.47335399008701073},{"region":"Oakland, CA","change":-0.4571873479595035},{"region":"Sacramento, CA","change":-0.43901582912356274},{"region":"Philadelphia, PA","change":-0.40804763923178333},{"region":"Charlotte, NC","change":-0.4049781303147214},{"region":"Milwaukee, WI","change":-0.4014166491320813},{"region":"Indianapolis, IN","change":-0.3984184997906135},{"region":"Kansas City, MO","change":-0.3884173744815069},{"region":"Omaha, NE","change":-0.3824757103751875},{"region":"New York, NY","change":-0.3811775234860645},{"region":"Baltimore, MD","change":-0.3761786413506013},{"region":"Atlanta, GA","change":-0.3497014223028972},{"region":"San Jose, CA","change":-0.34062139902927685},{"region":"Nashville, TN","change":-0.33342315819243024},{"region":"Portland, OR","change":-0.3136974420131842},{"region":"Memphis, TN","change":-0.31028505963594427},{"region":"Columbus, OH","change":-0.298131812314005},{"region":"Virginia Beach, VA","change":-0.27202923872182966},{"region":"Denver, CO","change":-0.24722883102606427},{"region":"Miami, FL","change":-0.21717127583436843},{"region":"Louisville, KY","change":-0.2051812001468089},{"region":"Dallas, TX","change":-0.17810143976059273},{"region":"Fresno, CA","change":-0.1667582049878471},{"region":"Raleigh, NC","change":-0.14524857318898488},{"region":"Houston, TX","change":-0.14421342310845162},{"region":"Anaheim, CA","change":-0.14341823845992543},{"region":"El Paso, TX","change":-0.1200152203920371},{"region":"Los Angeles, CA","change":-0.11607884160521348},{"region":"Long Beach, CA","change":-0.10582873280931904},{"region":"San Diego, CA","change":-0.10011207657563632},{"region":"Mesa, AZ","change":-0.0799363060662558},{"region":"San Antonio, TX","change":-0.023987940619324823},{"region":"Tulsa, OK","change":-0.021689850203091336},{"region":"Colorado Springs, CO","change":-0.017476637795675477},{"region":"Austin, TX","change":0.00038372161280009193},{"region":"Tucson, AZ","change":0.044911571685598915},{"region":"Jacksonville, FL","change":0.04797254576120166},{"region":"Oklahoma City, OK","change":0.048278660593059866},{"region":"Fort Worth, TX","change":0.053210001678173934},{"region":"Las Vegas, NV","change":0.056548946602508005},{"region":"Phoenix, AZ","change":0.10053811559778925},{"region":"Arlington, TX","change":0.11715293130690642},{"region":"Seattle, WA","change":0.1434891723123072},{"region":"Tampa, FL","change":0.2025204541112518},{"region":"Albuquerque, NM","change":0.3389196191588688}];
retail_bycity = pd.DataFrame.from_dict(retail_bycity)
retail_bycity.to_csv('retail_bycity.csv',index=False)
df = pd.read_csv("retail_bycity.csv",parse_dates=True)
df