Statsmodels is a Python package that allows users to explore data, estimate statistical models, and perform statistical tests.StatsmodelsFromWikipedia,thefreeencyclopediaJumptonavigationJumptosearchThisarticlemayrelyexcessivelyonsourcestoocloselyassociatedwiththesubject,potentiallypreventingthearticlefrombeingverifiableandneutral.Pleasehelpimproveitbyreplacingthemwithmoreappropriatecitationstoreliable,independent,third-partysources.(September2019)(Learnhowandwhentoremovethistemplatemessage)StatsmodelsisaPythonpackagethatallowsuserstoexploredata,estimatestatisticalmodels,andperformstatisticaltests.Anextensivelistofdescriptivestatistics,statisticaltests,plottingfunctions,andresultstatisticsareavailablefordifferenttypesofdataandeachestimator.ItcomplementsSciPy'sstatsmodule.[1][2]StatsmodelsispartofthePythonscientificstackthatisorientedtowardsdataanalysis,datascienceandstatistics.StatsmodelsisbuiltontopofthenumericallibrariesNumPyandSciPy,integrateswithPandasfordatahandling,andusesPatsy[3]foranR-likeformulainterface.GraphicalfunctionsarebasedontheMatplotliblibrary.StatsmodelsprovidesthestatisticalbackendforotherPythonlibraries.StatmodelsisfreesoftwarereleasedundertheModifiedBSD(3-clause)license.References[edit]^"StatisticalcomputationsandmodelsforusewithSciPy".^http://www.statsmodels.org/^http://patsy.readthedocs.org/en/latest/index.htmlvteScientificsoftwareinPythonNumPySciPymatplotlibpandasscikit-learnscikit-imagestatsmodelsMayaVimoreRetrievedfrom"https://en.wikipedia.org/w/index.php?title=Statsmodels&oldid=979635404"Categories:FreestatisticalsoftwarePython(programminglanguage)Python(programminglanguage)scientificlibrariesHiddencategories:ArticleslackingreliablereferencesfromSeptember2019AllarticleslackingreliablereferencesNavigationmenuPersonaltoolsNotloggedinTalkContributionsCreateaccountLoginNamespacesArticleTalkVariantsViewsReadEditViewhistoryMoreSearchNavigationMainpageContentsCurrenteventsRandomarticleAboutWikipediaContactusDonateContributeHelpLearntoeditCommunit

BackApplyDocsSettings

Statsmodels is the prominent Python “statistics and econometrics library” and it has a long-standing special relationship with pandas.GettingstartedUserGuideAPIreferenceDevelopmentReleasenotesGitHubTwitterOnthispageDatacleaningandvalidationPyjanitorEngardepandas-pathStatisticsandmachinelearningpandas-tfrecordsStatsmodelssklearn-pandasFeaturetoolsComposeSTUMPYVisualizationAltairBokehSeabornplotnineIPythonvegaPlotlyLuxQtpandasD-TalehvplotIDEIPythonJupyterNotebook/JupyterLabQuantopian/qgridSpyderAPIpandas-datareaderQuandl/PythonPydatastreampandaSDMXfredapidataframe_sqlDomainspecificGeopandasxarrayIOBCPandasDeltalakeOut-of-coreBlazeCylonDaskDask-MLIbisKoalasModinOdoPandarallelVaexExtensiondatatypesCyberpandasPandas-GenomicsPint-PandasTextExtensionsforPandasAccessorspandasecosystem¶Increasingly,packagesarebeingbuiltontopofpandastoaddressspecificneedsindatapreparation,analysisandvisualization.Thisisencouragingbecauseitmeanspandasisnotonlyhelpinguserstohandletheirdatatasksbutalsothatitprovidesabetterstartingpointfordeveloperstobuildpowerfulandmorefocuseddatatools.Thecreationoflibrariesthatcomplementpandas’functionalityalsoallowspandasdevelopmenttoremainfocusedaroundit’soriginalrequirements.ThisisaninexhaustivelistofprojectsthatbuildonpandasinordertoprovidetoolsinthePyDataspace.Foralistofprojectsthatdependonpandas,seethelibraries.iousagepageforpandasorsearchpypiforpandas.We’dliketomakeiteasierforuserstofindtheseprojects,ifyouknowofothersubstantialprojectsthatyoufeelshouldbeonthislist,pleaseletusknow.Datacleaningandvalidation¶Pyjanitor¶PyjanitorprovidesacleanAPIforcleaningdata,usingmethodchaining.Engarde¶Engardeisalightweightlibraryusedtoexplicitlystateassumptionsaboutyourdatasetsandcheckthatthey’reactuallytrue.pandas-path¶SincePython3.4,pathlibhasbeenincludedinthePythonstandardlibrary.Pathobjectsprovideasimpleanddelightfulwaytointeractwiththefilesystem.Thepandas-pathpackageenablesthePathAPIforpandasthroughacustomaccessor.path.Gettingjustthefilenamesfromaseriesoffullfilepath

Let's dive in. Article Resources. Notebook and Data: GitHub; Libraries: numpy, pandas, matplotlib, seaborn, statsmodels ...Theinternet'sbestdatasciencecoursesViewCoursesTogglenavigationDailyLearningCurriculumTutorialsArticlesGlossaryBooksSolutionsCoursesDataScienceMachineLearningTeam4062.6KYouarereadingtutorials240SHARESAuthor:TimDobbinsEngineer&StatisticianAuthor:JohnBurkeResearchAnalystPredictingHousingPriceswithLinearRegressionusingPython,pandas,andstatsmodelsInthispost,we'llwalkthroughbuildinglinearregressionmodelstopredicthousingpricesresultingfromeconomicactivity.Youshouldalreadyknow:PythonfundamentalsSomePandasexperienceLearnbothinteractivelythroughdataquest.ioThispostwillwalkyouthroughbuildinglinearregressionmodelstopredicthousingpricesresultingfromeconomicactivity.Futurepostswillcoverrelatedtopicssuchasexploratoryanalysis,regressiondiagnostics,andadvancedregressionmodeling,butIwantedtojumprightinsoreaderscouldgettheirhandsdirtywithdata.Ifyouwouldliketoseeanythinginparticular,feelfreetoleaveacommentbelow.Let'sdivein.ArticleResourcesNotebookandData:GitHubLibraries:numpy,pandas,matplotlib,seaborn,statsmodelsWhatisRegression?Linearregressionisamodelthatpredictsarelationshipofdirectproportionalitybetweenthedependentvariable(plottedontheverticalorYaxis)andthepredictorvariables(plottedontheXaxis)thatproducesastraightline,likeso:Linearregressionwillbediscussedingreaterdetailaswemovethroughthemodelingprocess.VariableSelectionForourdependentvariablewe'llusehousing_price_index(HPI),whichmeasurespricechangesofresidentialhousing.Forourpredictorvariables,weuseourintuitiontoselectdriversofmacro-(or“bigpicture”)economicactivity,suchasunemployment,interestrates,andgrossdomesticproduct(totalproductivity).Foranexplanationofourvariables,includingassumptionsabouthowtheyimpacthousingprices,andallthesourcesofdatausedinthispost,seehere.ReadingintheDatawithpandasBeforeanything,let'sgetourimportsforthistutorialoutoftheway.Thefirstimportisjusttochangehowtablesappearintheaccompanyingn

Skiptocontentstatsmodelsv0.12.2statsmodelsInstallingstatsmodelsGettingstartedUserGuideExamplesAPIReferenceAPIReferenceContentsAPIReferencestatsmodels.apiRegressionImputationGeneralizedEstimatingEquationsGeneralizedLinearModelsDiscreteandCountModelsMultivariateModelsMiscModelsGraphicsStatisticsToolsstatsmodels.tsa.apiStatisticsandTestsUnivariateTime-SeriesAnalysisExponentialSmoothingMultivariateTimeSeriesModelsFiltersandDecompositionsMarkovRegimeSwitchingModelsForecastingTime-SeriesToolsX12/X13Interfacestatsmodels.formula.apiModelsShowSourcestatsmodels.apiRegressionImputationGeneralizedEstimatingEquationsGeneralizedLinearModelsDiscreteandCountModelsMultivariateModelsMiscModelsGraphicsStatisticsToolsstatsmodels.tsa.apiStatisticsandTestsUnivariateTime-SeriesAnalysisExponentialSmoothingMultivariateTimeSeriesModelsFiltersandDecompositionsMarkovRegimeSwitchingModelsForecastingTime-SeriesToolsX12/X13Interfacestatsmodels.formula.apiModelsAboutstatsmodelsDeveloperPageReleaseNotesContentsAPIReferencestatsmodels.apiRegressionImputationGeneralizedEstimatingEquationsGeneralizedLinearModelsDiscreteandCountModelsMultivariateModelsMiscModelsGraphicsStatisticsToolsstatsmodels.tsa.apiStatisticsandTestsUnivariateTime-SeriesAnalysisExponentialSmoothingMultivariateTimeSeriesModelsFiltersandDecompositionsMarkovRegimeSwitchingModelsForecastingTime-SeriesToolsX12/X13Interfacestatsmodels.formula.apiModelsShowSourceAPIReference¶ThemainstatsmodelsAPIissplitintomodels:statsmodels.api:Cross-sectionalmodelsandmethods.Canonicallyimportedusingimportstatsmodels.apiassm.statsmodels.tsa.api:Time-seriesmodelsandmethods.Canonicallyimportedusingimportstatsmodels.tsa.apiastsa.statsmodels.formula.api:AconvenienceinterfaceforspecifyingmodelsusingformulastringsandDataFrames.ThisAPIdirectlyexposesthefrom_formulaclassmethodofmodelsthatsupporttheformulaAPI.Canonicallyimportedusingimportstatsmodels.formula.apiassmfTheAPIfocusesonmodelsandthemostfrequentlyusedstatisticaltest,andtools.ImportPathsandStructureexpla

The pandas.read_csv function can be used to convert a comma-separated values file to a DataFrame object. patsy is a Python library ...Skiptocontentstatsmodelsv0.13.0.dev0(+600)statsmodelsInstallingstatsmodelsGettingstartedGettingstartedContentsGettingstartedLoadingmodulesandfunctionsDataSubstantivemotivationandmodelDesignmatrices(endog&exog)ModelfitandsummaryDiagnosticsandspecificationtestsDocumentationMoreShowSourceLoadingmodulesandfunctionsDataSubstantivemotivationandmodelDesignmatrices(endog&exog)ModelfitandsummaryDiagnosticsandspecificationtestsDocumentationstatsmodels.tools.web.webdocMoreUserGuideExamplesAPIReferenceAboutstatsmodelsDeveloperPageReleaseNotesContentsGettingstartedLoadingmodulesandfunctionsDataSubstantivemotivationandmodelDesignmatrices(endog&exog)ModelfitandsummaryDiagnosticsandspecificationtestsDocumentationMoreShowSourceGettingstarted¶Thisverysimplecase-studyisdesignedtogetyouup-and-runningquicklywithstatsmodels.Startingfromrawdata,wewillshowthestepsneededtoestimateastatisticalmodelandtodrawadiagnosticplot.Wewillonlyusefunctionsprovidedbystatsmodelsoritspandasandpatsydependencies.Loadingmodulesandfunctions¶Afterinstallingstatsmodelsanditsdependencies,weloadafewmodulesandfunctions:In[1]:importstatsmodels.apiassmIn[2]:importpandasIn[3]:frompatsyimportdmatricespandasbuildsonnumpyarraystoproviderichdatastructuresanddataanalysistools.Thepandas.DataFramefunctionprovideslabelledarraysof(potentiallyheterogenous)data,similartotheR“data.frame”.Thepandas.read_csvfunctioncanbeusedtoconvertacomma-separatedvaluesfiletoaDataFrameobject.patsyisaPythonlibraryfordescribingstatisticalmodelsandbuildingDesignMatricesusingR-likeformulas.NoteThisexampleusestheAPIinterface.SeeImportPathsandStructureforinformationonthedifferencebetweenimportingtheAPIinterfaces(statsmodels.apiandstatsmodels.tsa.api)anddirectlyimportingfromthemodulethatdefinesthemodel.Data¶WedownloadtheGuerrydataset,acollectionofhistoricaldatausedinsupportofAndre-MichelGuerry’s1833EssayontheMoralStatisti

We will use pandas DataFrame to capture the above data in Python. Before we dive into the Python code, make sure that both the statsmodels and pandas ...SkiptocontentInthisguide,I’llshowyouhowtoperformlinearregressioninPythonusingstatsmodels. I’lluseasimpleexampleaboutthestockmarkettodemonstratethisconcept.Herearethetopicstobecovered:BackgroundaboutlinearregressionReviewofanexamplewiththefulldatasetReviewofthePythoncodeInterpretationoftheregressionresultsMakingpredictionsbasedontheregressionresultsAboutLinearRegressionLinearregressionisusedasapredictivemodelthatassumesalinearrelationshipbetweenthe dependentvariable(whichisthevariablewearetryingtopredict/estimate)andtheindependentvariable/s(input variable/susedintheprediction).UnderSimpleLinearRegression,only oneindependent/inputvariableisusedtopredictthedependentvariable.Ithasthefollowingstructure:Y=C+M*XY=Dependentvariable(output/outcome/prediction/estimation)C=Constant(Y-Intercept)M=Slopeoftheregressionline(theeffectthatXhasonY)X=Independentvariable(inputvariableusedinthepredictionofY)Inreality,arelationshipmayexistbetweenthedependentvariableand multipleindependentvariables.Forthesetypesofmodels(assuminglinearity),wecanuseMultipleLinearRegressionwiththefollowingstructure:Y=C+M1*X1+M2*X2+…AnExample(withtheDatasettobeused)Forillustrationpurposes,let’ssupposethatyouhaveafictitiouseconomywiththefollowingparameters:Thegoalhereistopredict/estimatethestockindexpricebasedontwomacroeconomicsvariables:theinterestrateandtheunemploymentrate.WewillusepandasDataFrametocapturetheabovedatainPython. BeforewediveintothePythoncode,makesurethatboththe statsmodels andpandaspackagesareinstalled.YoumayusethePIPmethodtoinstallthosepackages.ThePythonCodeusingStatsmodelsThefollowingPythoncodeincludesanexampleofMultipleLinearRegression,wheretheinputvariablesare:Interest_RateUnemployment_RateThesetwovariablesareusedinthepredictionofthedependentvariableofStock_Index_Price.HereisthecompletesyntaxtoperformthelinearregressioninPythonusingstatsmo

I think you can almost do exactly what you thought would be ideal, using the statsmodels package which was one of pandas ' optional dependencies before ...JoinStackOverflowtolearn,shareknowledge,andbuildyourcareer.SignupwithemailSignupSignupwithGoogleSignupwithGitHubSignupwithFacebookHomePublicQuestionsTagsUsersCollectivesExploreCollectivesFindaJobJobsCompaniesTeamsStackOverflowforTeams–Collaborateandshareknowledgewithaprivategroup.CreateafreeTeamWhatisTeams?TeamsCreatefreeTeamCollectivesonStackOverflowFindcentralized,trustedcontentandcollaboratearoundthetechnologiesyouusemost.LearnmoreTeamsQ&AforworkConnectandshareknowledgewithinasinglelocationthatisstructuredandeasytosearch.LearnmoreRunanOLSregressionwithPandasDataFrameAskQuestionAsked7years,8monthsagoActive5monthsagoViewed221ktimes12257IhaveapandasdataframeandIwouldliketoabletopredictthevaluesofcolumnAfromthevaluesincolumnsBandC.Hereisatoyexample:importpandasaspddf=pd.DataFrame({"A":[10,20,30,40,50],"B":[20,30,10,40,50],"C":[32,234,23,23,42523]})Ideally,Iwouldhavesomethinglikeols(A~B+C,data=df)butwhenIlookattheexamplesfromalgorithmlibrarieslikescikit-learnitappearstofeedthedatatothemodelwithalistofrowsinsteadofcolumns.Thiswouldrequiremetoreformatthedataintolistsinsidelists,whichseemstodefeatthepurposeofusingpandasinthefirstplace.WhatisthemostpythonicwaytorunanOLSregression(oranymachinelearningalgorithmmoregenerally)ondatainapandasdataframe?pythonpandasscikit-learnregressionstatsmodelsShareImprovethisquestionFolloweditedApr4'16at18:33denfromufa5,0721111goldbadges6767silverbadges133133bronzebadgesaskedNov15'13at0:47MichaelMichael10.7k2020goldbadges5959silverbadges104104bronzebadges0Addacomment | 6Answers6ActiveOldestVotes168Ithinkyoucanalmostdoexactlywhatyouthoughtwouldbeideal,usingthestatsmodelspackagewhichwasoneofpandas'optionaldependenciesbeforepandas'version0.20.0(itwasusedforafewthingsinpandas.stats.)>>>importpandasaspd>>>importstatsmodels.formula.apiassm>>>df=pd.DataFrame({"A":[10,20,30,40,50],"B":[20,30,10,4

Statsmodels is the prominent python “statistics and econometrics library” and it has a long-standing special relationship with pandas. Statsmodels provides ...Navigationindexmodules|next|previous|pandas0.18.1documentation»TableOfContentsWhat’sNewInstallationContributingtopandasFrequentlyAskedQuestions(FAQ)Packageoverview10MinutestopandasTutorialsCookbookIntrotoDataStructuresEssentialBasicFunctionalityWorkingwithTextDataOptionsandSettingsIndexingandSelectingDataMultiIndex/AdvancedIndexingComputationaltoolsWorkingwithmissingdataGroupBy:split-apply-combineMerge,join,andconcatenateReshapingandPivotTablesTimeSeries/DatefunctionalityTimeDeltasCategoricalDataVisualizationStyleIOTools(Text,CSV,HDF5,...)RemoteDataAccessEnhancingPerformanceSparsedatastructuresCaveatsandGotchasrpy2/RinterfacepandasEcosystemStatisticsandMachineLearningStatsmodelssklearn-pandasVisualizationBokehyhat/ggplotSeabornVincentPlotlyIDEIPythonquantopian/qgridSpyderAPIpandas-datareaderquandl/PythonpydatastreampandaSDMXfredapiDomainSpecificGeopandasxarrayOut-of-coreDaskBlazeOdoComparisonwithR/RlibrariesComparisonwithSQLComparisonwithSASAPIReferenceInternalsReleaseNotesSearchEntersearchtermsoramodule,classorfunctionname.pandasEcosystem¶Increasingly,packagesarebeingbuiltontopofpandastoaddressspecificneedsindatapreparation,analysisandvisualization.Thisisencouragingbecauseitmeanspandasisnotonlyhelpinguserstohandletheirdatatasksbutalsothatitprovidesabetterstartingpointfordeveloperstobuildpowerfulandmorefocuseddatatools.Thecreationoflibrariesthatcomplementpandas’functionalityalsoallowspandasdevelopmenttoremainfocusedaroundit’soriginalrequirements.Thisisanin-exhaustivelistofprojectsthatbuildonpandasinordertoprovidetoolsinthePyDataspace.We’dliketomakeiteasierforuserstofindtheseproject,ifyouknowofothersubstantialprojectsthatyoufeelshouldbeonthislist,pleaseletusknow.StatisticsandMachineLearning¶Statsmodels¶Statsmodelsistheprominentpython“statisticsandeconometricslibrary”andithasalong-standingspecialrelationshipwith