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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 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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
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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
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