讲义python中英文汇总data science programming

TableofDataScienceProgramminginPython-DataDataMungingandDataScienceProgramminginPython-GrouandAggregatingGrouandqueryingdataGrouandaggregatingDataScienceProgramminginPython-DataDataScienceProgramminginPython-TimeSeriesIntroduction-DataScienceProgramminginTheaimofthisbookistoshowhowtoapplydataysisprinciplestoapracticalusecasescenariousingPythonasthedataysislanguage.We’llgoonthisjourneybylookingatthethedataworkflowfrommungingtogroudatatovisualizingandalsoincludesometime-seriesysisaswell.Theformatincludesaskingquestionsofthedataandshowingtheprogrammingstepsneededtoanswerthequestion.Bytheendofreadingthisbook,youwillbeabletoapplythesetechniquestoyourowndata.AbouttheThisbookiswritteninali teprogrammingstylewheretext,code,andoutputarepresentedtogether.Thiswill izeyourlearningandunderstandingofcodeandthedataysisworkflow.ThebookteachesthetypeofinctivecodinganditiveysisthatisessentialtobesuccessfulindatascienceCodingInthecodesnippets,abackslashcharacter(\)meansthatthesamelineofcodeiswrappedtothenextlineinthebook.Youdonotneedtotypethischaracterintoaninterpreter.UseaREPL(/wiki/Read–eval–print_loop)tohaveaninctiveenvironmentwhereyoucanwritecodeandseetheresultingoutput.Trythemethodsyoulearninthisbookonyourowndatatoreinlearning.UseaPythoninterpretertocodeandyourfavoriteeditortotakenotes.欢迎加入非Python编程学习交 ，群里免费提供500+本Python书籍DataScienceProgramminginPython-DataBayAreaBikeSharecommencedit’spilotphaseofoperationintheSanFranciscobayareainAugust2013withnstoexpand.ItisthefirstbikesharingschemeinCalifornia.Asitismeantforshorttrips,thebikesshouldbereturnedtoadockinthirtyminutesorlessoranadditionalfeewouldbeincurredaccordingtothewebsite.Therearetwotypesofmemberships:customerandsubscriber.Asubscriberisanannualmembershipwhileacustomerisdefinedassomeoneusingeitherthetwenty-fourhourorthreedaypasses.Currently(Sept2014),itcostsninedollarsfortwenty-fourhours,twenty-twodollarsforthreedays,andeight-eightdollarsfortheyear.Overtimefeesarefourdollarsforanextrathirtyminutesandsevendollarsforeachthirtyminutesafterthat.Dataonthefirstsixmonthsofoperationswerereleasedaspartofadatachallenge.Thedataincludedthreefilesfortriphistory,weatherinformation,anddockavailability.Themergeddatawasusedforthefollowing DataMungingandFirst,we’llreadinthedataandinspectthedatacolumnsanddatatypesandthinkaboutwhatquestionswewanttoaskourdataandwhatthingsareweinterestedinlearningaboutthedata.BecuriousandempatheticinthinkingaboutwhatthevariousstakeholdersincludingtheCity,thecustomers,andotherinterestedpeoplewouldbeinterestedingleaningbykeecivicfiscal,civic,andsocialgoalsinmind.Inadditiontothat,therewillbequiteabitofcleaninganddatacarpentryneededtogetthedataintoaformatusefulfor ThedatasetcomesfromthreecsvfilesfromtheBayAreaBikesharedatachallenge.WemergedthedatainRaswestartedasimilar ysistherebutreallywantedtouseIPythonandthesuperbtimeseriesfunctionalityinPandas.Optionally,onecancacheorlogthecodeinIPythonwiththefollowingtwo%load_extActivatingauto-logging.Currentsessionstateplusfutureinputsaved.Filename:Mode:Outputlogging:FalseRawinputlog:FalseTimestam:FalseState:activeImportthelibrariesthatareimportnumpyasnpimportpandasaspd欢迎加入非Python编程学习交 ，群里免费提供500+本Python书籍fromdatetimeimportdatetime,%matplotlibinlineimportseabornassnsfromggplotimport*printpd. printnp. Readthedmerge4=pd.read_csv('finalmerge.csv',parse_dates=['Start.Date'])Formatcolumndmerge4.columns=dmerge4.columns.map(lambdax:x.rece('.','').lower())dmerge4['zip_name']=dmerge4.zip.rece({94107:'SanFrancisco','SanJose',94301:'PaloAlto',94041:'MountainView',94063:'RedwoodInspectInspectingthedatatypesandvariablesarethefirstthingsthatshouldbedoneindatamunging.And,we’lldoitagainafter ngourdatacleaningandformattingoperations.Noticethattheenddateandstartdatearenotcurrentlyindatetimeformatsolet’sremedythatandtakecareofsomeotherthingsforthequestionsrelatedtotime-series.lubstartdate:startstation:tripid:duration:startdate:startterminal:enddate:endstation:endterminal:bike:subscriptiontype:zipcode:date:max_temperature_f:mean_temperature_f:min_temperaturef:max_dew_point_f:>meandew_point_f:>min_dewpoint_f:>max_humidity:>mean_humidity:>min_humidity:>max_sea_level_pressure_in:>mean_sea_level_pressure_in:>min_sea_level_pressure_in: : : :>max_wind_speed_mph:>mean_wind_speed_mph:欢迎加入非Python编程学习交 ，群里免费提供500+本Python书籍>max_gust_speed_mph:>precipitation_in:>cloud_cover:>events:>wind_dir_degrees:>zip:>landmark:>station_id:>name:>lat:>long:>dockcount:>installation:>zip_name:>dtype:WorkingwithdatesandParsedatecolumnsintodatetimedmerge4['enddate']=pd.to_datetime(dmerge4['enddate'])dmerge4['lubstartdate']=pd.to_datetime(dmerge4['lubstartdate'])Setdatetimeindexdmerge4.set_index('startdate',ince=True,drop=False,dmerge4['day']=dmerge4.index.daydmerge4['hour']=dmerge4.index.hourdmerge4['month']=dmerge4.index.monthHavingthedateandtimeisgreat.Howeverforthe ysis,itwouldbereallyusefultogrouptimeintosubjectiveusefulcategoriesthatwouldbeusefulforunderstandingusagealittlebitbetter.Wethinkwewouldbereallyinterestedtolearnaboutthedifferencesandsimilaritiesinusageanddurationpatternsbetweenthemorningcommuteandeveningrushperiod.Itwouldalsobeinterestingtoknowtheprofileandusageofusersinthemiddleofthedayaswellasatnightandinwhatwerefertoastheweehours.So,We’vedecidedtosplitthetimeintothefollowingsegments.morning:evening:midday:night:weehours:dmerge4['timeofday']=dmerge4.hour.rece({15:'evening',17:'evening',20:'night',19:'evening',12:'mid_day',14:'mid_day',13:'mid_day',9:'morning',22:'night',11:'mid_day',18:'evening',16:'evening',10:'morning',21:'night',6:'morning',8:'morning',7:'morning',1:'wee_hours',2:'wee_hours',3:'wee_hours',0:'night',5:'morning',Differencebetweenenddateandstartdmerge4['diff']=dmerge4.apply(lambdax:x['enddate']-x['startdate'],Differencebetweenendhourandstartdmerge4['hourdiff']=dmerge4.apply(lambdax:x['enddate'].hour-.hour,Now,oneofthemostimportantvariablesinthedatasetisduration.Weareveryinterestedinunderstandinghowlongacustomerorsubscriberhasabicycle.Durationisdefinedas欢迎加入非Python编程学习交 ，群里免费提供500+本Python书籍thenumberofsecondsfromtakingabicyclefromadockandreturningittoadock.oftheinterestingquestionssuchasbreakdownofdurationbetweencustomerandsubscriberbylandmarkorbytimeofdayarerelevanttobothcustomerendusersandcivicasitaffectshowmanybikeswillbeavailableandwherewilltheybeneeded.Let’stransformdurationintominutesandthenalsodefineabinaryvariabletocomparetripsunderthirtyminutesandlesstotripsoverthirtyminutes.Durationisinsecondssolet’sconvertittodmerge4['durationminutes']=Convertdurationtoaninteger#roundandconverttodmerge4["duration_i"]=Durationasfloattypetotwodecimal#roundtotwodecimalspacesandkeeepasdmerge4["duration_f"]=We’llwriteafunctionthatmaps‘underthirty’toallvaluesunder30(minutes)and‘overthirty’toallvaluesover30(minutes)defifx<=30:return'in_thirty'elif31<=x:return'over_thirty'else:return'None'dmerge4["thirtymin"]=dmerge4['duration_i'].map(isitthirty) 欢迎加入非Python编程学习交 ，群里免费提供500+本Python书籍 Length:54,dtype:欢迎加入非Python编程学习交 ，群里免费提供500+本Python书籍DataScienceProgramminginPython-GrouandAggregatingDataGrouandqueryingNowwewillsliceanddiceourdatasetusinggrou,aggregationfunctions,summarystatistics,andvariousqueryingandindexingoperations.Toreallyunderstandthebikesharingdata,weneedtolookatnaturalgroussuchasstartinglandmark,subscriptiontype,andthetimeofdayofthestartofabicycletrip.Theworkdoneinthissectionwillsetupsomebeautifulandinformativeplotsinthenextsectionofthe ysis.Mostofthetablesbelowhaveplottingrepresentationsinthevisualizationsection.Grouandaggregatingorreducingdataandobtainingsummarymeasuresandstatisticsistheheartofdata ysisandworktogetherwiththevisualizationprocessincludingthoseofanexploratorynature.Thei tiveprocessofdatadiscoveryatbestleadstonewquestionsandinsightsbeingdiscoveredwhichinformandrefinethedatacarpentryprocess.Thisiswherethein ctivereplexcels.Thinkofitasacircularinquisitiveprocessofdatamunging,grouandaggregating,andvisualizing.WewillexploreusingSQLinspiredmethodsofgrouoperationsinPandasandtheuseofmult-indexesandhierarchicalindexing.importnumpyasnpimportpandasaspdfromdatetimeimportdatetime,%matplotlibinlineimportseabornassnsfromggplotimport*printpd. printnp. %load_extGrouandIt’stimetoexplorevariouswaysofgrouandaggregatingdata.Let’sreviewsometerminology.Dockcountreferstothenumberofdocksinstalled,subscriptiontypeiseithercustomerorsubscriber,andlandmarkreferstoanareacoveredbySanFrancisco,SanJose,RedwoodCity,orPaloAlto.Inthedatamungingsection,wecreatedacolumnindicatingwhetherthedurationofasegmentofabicycletripwasunderthirtyminutesoroverthirtyminutesasthisistheallottedtimebeforebeingchargedovertimefees.So,whatpercentageoftotaltripswereoverthirtyminutes?Whatpercentageoftotaltripswereoverthirty dtype:int64欢迎加入非Python编程学习交 ，群里免费提供500+本Python书籍Only6%oftotalrideshadadurationoverthirtyminutessoitlookslikepeopleareaboutreturningbikesontime.Or,werethey?Whenwelookatthestatisticsbasedonlandmarkandsubscribertype,wewillgetabetterpictureofwhoandwherehavelongerdurationtimes.HowdoesaveragedurationvarybytimeofdayfortripsbothunderthirtyminutesandoverthirtyHereisanotherwaytocheckonhowmanyobservationsoutofthetotalhadtripdurationsoverthirtyminutes.HowmanytripsbeganandendedatthesameOnly6878out144015observationsor4.8%wereroundtripssoitwouldseemthatpeoplearereallyusingthebicyclestogetfrompointAtopointBlen(dmerge4[dmerge4['startstation']==dmerge4['endstation']])Let’slookataveragedurationbysubscriptiontype.Customersaredefinedhashavingeitheratwenty-fourhourorthreedaypasswhileSubscribersaredefinedashavinganannualpass.Averagedurationsforsubscribersisalmosttenminuteswhileitissixtyminutesforcustomers.Perhapscustomersarerecreationalriderswhotakelongertripswhilesubscribersareshorterdistancecommuters.We’lllearnmorewhenwesliceanddicethedatalater.Whatisaveragedurationbysubscriptiondmerge4.groupby(['subscriptiontype']).agg({'duration_f':欢迎加入非Python编程学习交 ，群里免费提供500+本Python书籍Howmanyobservationsinthedatasetbelongtoeachsubscription dtype:int64Whatlandmarkoccursthemostfrequentlyamongsubscriptiontypeofdmerge4[dmerge4['subscriptiontype']SanSanMountainPaloRedwooddtype:So,we’velearnedthatthemajorityoftotalsubscribersareinSanFranciscoandtakeveryshorttrips.WhatarethetopfivestartHarryBridges za(FerryBuilding)EmbarcaderoatSansomeSanFranciscoCaltrain(Townsendat4th)Marketat4thPowellStreetName:duration,dtype:WhatlandmarkhasthehighesttotalThetablebelowindicatesthatSanFranciscohadthehighesttotaldurationbutkeepinmindthatthemajorityoftotalobservationsarealsoinSanFranciscosoofcoursethetotalwouldbehigher.Usingsummarystatisticswillgiveabetterideaofbicyclingbehaviorthanlookingattotalvalues.Although,forsomepurposes,itisgoodtoknowthetotalaswell.Forexample,bicyclewearandtearbasedontotaldurations.%%cachestation.pklstationdataWhatpercentageoftotalbicycledocksarealocatedineach欢迎加入非Python编程学习交 ，群里免费提供500+本Python书籍54%ofalldocksarelocatedinSanFranciscosothismaypartiallyexinwhydurationandbicycletripstakeninSanFranciscoarehigherandwhydurationsarehigherinPaloAltowhereonly6%ofthedocksarelocated.Whatarethedockcountsbylandmarkandstation欢迎加入非Python编程学习交 ，群里免费提供500+本Python书籍WhatarethetotalnumberofbicycletripsbyWhenlookingatnumberofbicycletripsbasedonlandmark,weseethat129,853or90%oftotalobservationsinthedatasetwereinSanFrancisco.6%oftotalobservationswereinSanJosewhileonly1%wereinPaloAlto.WhatareaveragedurationsbyLookingataveragedurationbylandmark,SanFranciscohasthelowestdurationfollowedbySanJosewhilePaloAltohadthehighestduration.WecanhypothesizethatthismaybepartlyduetofewerdocksinPaloAltoorgreaterdistancestotravelaswellasthesubscriptionprofileofPaloAltobicycleusers.Whatareaveragedurationanddockcountbylandmarkandtimeofdayforfirstsixmonthsofthebikesharetimelandmark=dmerge4.groupby(['timeofday','landmark']).agg({'dockcount':np.m\ean,'duration_f':np.mean,}).reset_index()欢迎加入欢迎加入 Python编程学习交WhereandatwhenwasthehighestaverageThehighestdockcountwasinRedwoodCityintheWhatarethetopfivelongestaveragedurationgroupedbytimeofdayandThehighestaveragedurationwasinPaloAltointhewee6duration_f,dtype:Whataretheaveragedockcountanddurationsbytimeofdayandlandmarkforthefirstsixmonthsofthebikesharesystem?Pandasproducesnicereadablemulti-indexnp.round(dmerge4.groupby(['timeofday','landmark']).agg({'dockcount':np.mean,'\duration_i':np.mean,}))欢迎加入 Python编程学习交 ，群里免费提供500+本Python书籍Grouandaggregatingtime-seriesLet’sobtainmeanvaluesbydayofthemonthbycreatingagroupedobjectfromthedaycomponentofthetimestampandapplyinganaggregationfunction.Wewanttoknowifthereishigherdemandortripdurationatcertaindaysofthemonth.day_means=dmerge4.groupby('day').aggregate(np.mean)欢迎加入欢迎加入 Python编程学习交WecanalsocreateagroupedobjectusingPandasgrouper.WecanactuallyspecifyaresamplingandagrouusingtheGrouper.So,inthiscaseIwantthefrequencytobemonthlyandgroupbylandmarkandsubscriptiontype.three_grouper=欢迎加入欢迎加入 Python编程学习交Byunstackingthedatacreatedinthegrouperobject,wenowhavedurationbycustomerandsubscriberbylandmark.Thisisanicewaytopresentthedataintabularformat.three_grouper_unstack=dmerge4.groupby([pd.Grouper(freq='M',key='startdate'),\three_grouper_unstack=dmerge4.groupby([pd.Grouper(freq='M',key='startdate'),"l\欢迎加入欢迎加入 Python编程学习交Averagedurationbymonthfacetedbygrouperl=dmerge4.groupby([pd.Grouper(freq='M',key='startdate'),'landmark']).me\Grouonavariablewithmanyvaluesisalsopossible.Inthiscase,wecanseesummarystatisticsoneverysingledurationasanexample.Wecanbeveryspecificandthelevelofgranularitycanbeverydetailed.Wecanpinpointtheaveragedurationforaveryspecificprofileordemographicwhichcanbeverypowerfulforadata-drivendecisionmakingincludingmarketingorcivicinfrastructuregoalsforthebikesharingsystem.欢迎加入非Python编程学习交 ，群里免费提供500+本Python书籍Thisnexttablegivesanindicationofmonthlydurationsbasedonstartinglandmarkendstation.Inotherwords,wegetanideaofhowlongittakestoreachbybicycleandthedifferencesbetweencustomersandsubscribersallinonetable.Wecanidentifywhichcustomersarekeethebicyclesforlongerperiodsoftime.Let’slookatthebreakdownofaveragedurationbybothlandmarkandsubscriptionWecanseesomecleardifferencesbetweencustomersandsubscribers.Inmostcases,subscribersaretakingshorttrips.欢迎加入 Python编程学习交 ，群里免费提供500+本Python书籍Let’slookateachlevelofdurationonanhourlyInthistable,wearelookingathourlydatabyhourlytry=欢迎加入欢迎加入 Python编程学习交GroupoperationsonadatetimeLet’smakeatableofaveragedockcountbyeachprecipitationlevelandsubscriptioni=pd.DataFrame(dmerge4.groupby(['precipitation_in','subscriptiontype']).dockco\i欢迎加入 Python编程学习交 ，群里免费提供500+本Python书籍WhatarethetotalnumberofobservationsineachoftheinitialsixOtherthanfirstmonthofoperation,thedistributionofobservationsdistributionisprettyevenlyspreadout.12dtype:int6491dtype:Let’screateamulti-levelindexwithoutgrouWe’lloftenwanttoperformqueriesonthefulldmerge4twolevels=dmerge4.set_index(['landmark','subscriptiontype'])欢迎加入欢迎加入 Python编程学习交Createsagroupbyobjectdataframeonwhichaggregateactionscanbeperformedandcangroupbythemulti-indexes.grouplevel0=dmerge4twolevels.groupby(level=0)#sameresultasgrouplevel1=dmerge4twolevels.groupby(level=1)Thelevelcanalsobespecifiedbydmerge4twolevels.sum(level='subscriptiontype')#sameasaboveCreatethelevelwithoutdropitasadmerge4twolevels.xs('Subscriber',欢迎加入欢迎加入 Python编程学习交QueryingBysettinguptheinitialquery,Wecanthenchangetheinputsforin ctivereportingofresults.Wecananswerveryspecificquestionsbyquerying.Let’screatedagroupedobject,applyaggregatefunctions,andthenquerytheInthistable,wehavetheaveragedurationforsubscriptiontypeofcustomergrouped1=dmerge4.groupby(['landmark','subscriptiontype'])group2=grouped1[['duration']].agg([np.mean,np.std])query1=group2.query('subscriptiontype=="Customer"')Returnadatasetfromamulti-indexdataframethatcontainsonlystartterminalnumber62.#subsetingandindexinganon-groupedmultidmerge4twolevels.query('startterminal==欢迎加入欢迎加入 Python编程学习交Queryamulti-indexdataframeforsubscriptiontypeofdmerge4twolevels.query('subscriptiontype==Whatistheaveragedurationindescendingorderbyendstationwhenthestartterminalisnumber62?Wecangetthistypeofgranulardatabyquerying.Bysettinguptheinitialquery,wecanthenchangetheinputsforin ctivereportingofresults.duration62=dmerge4twolevels.query('startterminal==62').groupby('endstation')\2nd2ndatBealeatCivicCenterBART(7thatEmbarcaderoatPowellatPost(UnionEmbarcaderoat za(MarketatGoldenGateatSanFranciscoCaltrain(TownsendatSouthVanNessatCommercialatMarketatTownsendatGrantAvenueatColumbusSanFranciscoCityDavisatBroadwayStatBatteryWashingtonatHarry za(Ferry欢迎加入非Python编程学习交 ，群里免费提供500+本Python书籍5that PowellStreet Clayat Postat Steuartat EmbarcaderoatBryant YerbaBuenaCenteroftheArts(3rd@Howard) Marketat SanFranciscoCaltrain2(330Townsend) EmbarcaderoatFolsom TemporaryTransbayTerminal(HowardatBeale) Spearat 2ndatSouth 2ndat Marketat Howardat Name:duration_f,dtype:MergingandLet’sconcatenatethemorningmonthlyandeveningmonthlycreatedinthetimeseriessectionintoadataframefortheplottingsection.concatenated=pd.concat([morning_monthly,evening_monthly],keys=['morning','e\Mergesamenamedcolumnfromtwodatasetsbutkeepbothcolumnsbutrenamethemwithasuffixmerge=pd.merge(morning_monthly,evening_monthly,how='outer',left_index=True,\r\ight_index=True,suffixes=['_morning','_evening'])欢迎加入欢迎加入 Python编程学习交Ad-hocdatasummaryorstringUsealambdafunctiontogetsummarystatisticsofaveragedurationby umaveragedurationwasinSanJose,thehighestaveragedurationwasinPaloAltoandthelowestwasinSanFrancisco.dmerge4.groupby("landmark")[['duration_f']].apply(lambdax:x.describe()).unstac\AnotheruseofthelambdafunctionistomapaForexamplesake,tocheckthelengthofa#applyalambdaf=lambdax:len(str(x))Hereisanotherstringoperationbywayofmapalambdafunction.Verydmerge4['endstation'].map(lambdax:x.startswith('Grant')).head()2013-08-29 2013-08-29 欢迎加入非Python编程学习交 ，群里免费提供500+本Python书籍2013-08-29 2013-08-29 2013-08-29 Name:endstation,dtype:Makeagroupedobjectwithoutmakingitthedmerge4.groupby('landmark',欢迎加入非Python编程学习交 ，群里免费提供500+本Python书籍DataScienceProgramminginPython-DataLet’smakesomevisualizationsaspartofthei tiveprocessofdatamunging,aggregating,andvisualizing.Visualizationshelpusunderstandthedatabetter.Atbest,exploratorydatavisualizationinspiresquestionsandinformsour ysiswhileidentifyingtrendsandpatternstofurtherlearnfromthedata.Wewillseethatdisyingsimilarinformationinavarietyofwaysandusingdifferenttypesandstylesofplotscanrevealevenmoreaboutthedata.%matplotlibimportnumpyasnpimportpandasaspdfromdatetimeimportdatetime,fromggplotimportimportseabornasprintpd. printnp. %load_extExploratorydataInpracticaldata ysis,wewanttomakeourplotsinthemostefficientandsuccinctwaypossiblefollowingani tivedata ysisprocess.Avisualizationwillformmorequestionsthatleadtofurthervisualizationsusingpandas,ggplot,andseaborn.Inexploratoryvisualization,grou&aggregatingdataandplottingarepartofthat tiveprocess.Wewanttolearnfromourdatawhatfurthervisualizationswillprovideinsightandhopefullyleadtomorestatisticalquestionsandmorevisualizations.MeanPlottingtime-seriesDailyaveragedurationforthefirstsixmonthsofoperationofthebikesharingInthisplot,wecanseethatmosttripswereundersixtyminutesandfluctuatedforthemostpartbetweenundertwentyminutesuptoaroundfiftyminutesdependingonthetimeofday.print(ggplot(aes(x='startdate',y='duration_i'),data=daily_means)+\geom_line())+geom_smooth()DailyaveragemeanSinceSanFranciscodoesn’thavetypicallyharshwinters,temperaturedidnotseemtohavemucheffectonlengthofbicycletripsprint(ggplot(aes(x='startdate',y='mean_temperature_f'),data=daily_means)+\MeantemperaturesfortheentireThisplotisincludedheretocomparetheabovegraphwithplottingtheentiredataset.Takingthedailymeaninthepreviousplotsallowsustoconsolidatemultipleobservationsonasingledaytogivealessnoisygraph.Inthisgraph,everysingleobservationisplotted.Takingthedailymeanisawaytoseethetrendmorecleanlyandalsowillallowforfurther ysisonadailydata.print(ggplot(aes(x='startdate',y='mean_temperature_f'),data=dmerge4)+\AveragedurationonamonthlybasisforthesixmonthThemonthlydurationtakesawaysomeofthenoiseindicatedbythedailydata.WedoactuallyseeadecliningtrendaswegofromAutumntoWinter.print(ggplot(aes(x='startdate',y='duration_i'),data=monthly_means)+\<brRememberthatweconcatenatedtwotimeseriesintervalsforthemorningandeveningcommute.Wecannowseeinthisgraphthattheaveragedurationbymonthismostlyhigherintheeveningthaninthemorning.KeeinmindthatAugustwasthefirstmonthofoperation,theplotalsoindicatesthatonaverage,thebicyclewasreturnedtoadockwithintheinitialthirtyminutefromstarttimefromSeptembertoFebruary.AveragedurationbymonthfacetedbymorningandeveningcommutingAveragedurationat4pmforthesixmonthWhat’sreallyniceaboutPandastimeseriesfunctionalityisthatwecanalsolookatplotsatspecifictimesaswell.Hereisaplotoftotaldurationat4pmfortheentiresixmonthperiod.Theplotindicatesthatauserhadabicycleforareallylongtimeat4pminOctoberbutmostlythetripdurationswereunderfiftyminutes.#totaldurationatparticulardmerge4.duration_i.at_time(time(16,four_pm_df=pd.DataFrame(dmerge4.duration_f.at_time(time(16,0))).reset_index()ggplot(four_pm_df,aes(x='startdate',y='duration_f'))+geom_line()#durationat4pmforeverydaytheentiresixmonthAveragedurationbytimeofDurationsareshorterduringthemorningandeveningcommutinghoursandslightlyhigherduringthemid-dayandatnight.Thedataalsoindicatesthatpeoplearekeebicyclesforalongerperiodoftimeaftermidnightuntilfouram.DodgedbarplotofaveragedurationbylandmarkandtimeofThisplotgivesareallynicevisualofaveragedurationbylandmarkandtimeofday.Acrosstheboard,morningdurationsareshortestwhichwouldmakesenseformorningcommuters.MorningcommutesarehighestinPaloAlto.Perhapsthereismoretrafficorgreaterdistancestotravel.Inaddition,weknowthattherearefewerbicycledocksinPaloAlto.Mid-daystarttimeshavelongertripdurationexceptforMountainViewwhereeveningbicycletripsarethelongest.DurationinSanFranciscoistheshortestandthisisalsotheareathatthehighestnumberofdocksinthedatasetasshowninthegrouandaggregatingsectionof sns.factorplot("landmark","duration_f","timeofday",DockcountsbylandmarkandItisevidentinthisplotthatdockcountswerelowestinPaloAltowhereaveragedurationswerealsothehighest.sns.factorplot("landmark","duration_f","dockcount",data=timelandmark,palette=\AveragedurationbylandmarkfacetedbyThisplotisnicebecauseitallowsustovisuallyseethedifferencesindurationbylandmarkbycategoricaltimeofday.sns.factorplot("landmark","duration_f",data=timelandmark,row="timeofday",margin_titles=True,aspect=3,size=2,palette="BuPu_d");AveragedurationforeachlandmarkfacetedbyPaloAltohadhighestdurationinalmosteverymonthexceptforFebruarywhenMountainViewhadthehighestaverageduration.sns.factorplot("landmark","duration_f",data=dmerge4,row="month",margin_titles=True,aspect=3,size=2,palette="Pas1");Plotofmean,minimum, umtemperaturebyThisplotindicatesplotsfor umsummaryMeanandminimumdurationbyhouroftheDurationislowestinthemorningandeveningcommutehoursandhighestintheeveningandweehours.WhataretheaveragedurationsbydayoftheInwhatmonthdidthehighestdurationoccurandwhatwasthesubscriptionThehighestdurationoccurredinDecemberforasubscriptiontypeofcustomerinRedwoodCityDodgedbarplotsbymonthofaveragedurationbylandmarkandsubscriptionInDecember,January,andFebruary,thehighestaveragedurationwasforacustomerinRedwoodCity.three_grouper_unstack_twice=MonthlyaveragedurationbyFromSeptemberuntilDecember,PaloAltohadthehighestaveragedurationbymonthwhileSanJoseandSanFranciscohadthelowest.AveragedurationbymonthincreasedinRedwoodCityfromSeptembertoFebruary.grouperl=dmerge4.groupby([pd.Grouper(freq='M',key='startdate'),\DodgedbarplotofmonthlyaveragedurationbyThisdodgedbarplotshowsthatdurationwashighestforRedwoodCityinFebruaryandhighestforPaloAltoinNovember.Ineverymonth,durationswerelowestforbothSanFranciscoandSanJose.Wecouldhypothesizethisisduetocommuting,trafficpatterns,anddemandforbicycles.Inanycase,knowingwheredurationsarehigherwillinfluencedemandanddockcount.Averagemonthlydurationfacetedbytimeofgrouperm=AveragedurationbyAveragedurationbyhourofthedayfortheentiresixmonthggplot(dmerge4,aes('hour','duration_f',color='landmark'))+geom_point(stat="i\BoxplotofmonthlyaveragedurationbySubscribersforthemostpartarebelowsixtyminutedurationswhilecustomershavehigherdurationsandthewidestrangeofdurationinRedwoodCityfollowedbyMountainthree_grouper=importseabornassns.factorplot("landmark","duration_f","subscriptiontype",three_grouper,\kind="box",palette="PRGn",aspect=2.25)DistributionofmeanItdoesn’tgettoowarmortoocoldinNorthernCaliforniabetweenAugustandAveragedurationandstandarddeviationforsubscriptiontypeofCustomerbyBarplotofaveragedurationbylandmarkandsubscriptiontype.Inthiscase,weonlywantedtocomparedurationbyonetypeofcustomer.dmerge4.groupby(['landmark','subscriptiontype'])[['duration_f']].agg([np.mean,n\p.std]).query('subscriptiontyp