Package: instpy
Instpy - Inferencial Stadistics PythonWhat is Instpy?instpyis a stadistic library which permit you analyze measures obtaining main stadistical results
likeHistogramsQqplotsLevenetestShapiro-WilktestParametricorNon parametrictest.With this library you can test multiple or single measures, check if data verify normality condition or not and test measures against stadistic tests.
Instpy has class where you can instance an object with following attributes:#Creating an InferencialStats objectInferencialStats(measures,alpha,is_paired,mean)measures: list lists of data (int or float)
alpha (Optional): Significance level (float) E.g: 0.05 or 0.01
is_paired (Optional) : bool flag to declare your measures as paired or non paired data
mean (Optional): Scalar value to test against single measureMethodsThe main method of this library.inferencial_statistics()It executes an study about its attributes following next workflow¿Single or multiple measures?Obtain[Histogram , Qqplot , Shapiro-Wilk test results]of every measurePerformLevene TestPerformParametricorNon-parametrictest depending on whether measures
follownormal distributionornon normal distributionIf you want to getsome additional featuresabout your analysis you can try with
these methodscrit_diffDisplay a graphical analisys comapring critical differences from each measuresInferencialStats.crit_diff()show_histsPlot aplotly.graph_object.Figurewith all measure histogramsInferencialStats.show_hists();show_qqplotsPlot aplotly.graph_object.Figurewith all measure qqplotsInferencialStats.show_qqplots();get_swtestsReturnPana.DataFramewith Shapiro-Wilk test results confirming if they follow or not a normal distributionInferencialStats.get_swtests();One method that is not vinculate with an InferencialStats object but libraryInstpyget_ranksGet ranks of input measuresInferencialStats.get_ranks(measures);Installation#With pippipinstallinstpyExampleimportinstpy#Lets create some measures such as normal or uniform data distributionx=np.random.normal(size=100).tolist()y=np.random.normal(size=100).tolist()t=np.random.normal(size=100).tolist()z=np.random.normal(size=100).tolist()#---------------------------------------------------------------------xx=np.random.normal(size=100).tolist()yx=np.random.normal(size=104).tolist()tx=np.random.normal(size=110).tolist()zx=np.random.normal(size=108).tolist()#---------------------------------------------------------------------Single measure case#Create measure parameter## Single data -------------single_measure=[x]res=InferecialStats(single_measure,is_paired=True,mean=80)#Now lets analyze measureres.inferencial_statistics()#In this case it will only one plotres.show_hists()res.show_qqplots()print(res.get_swtests())print(res.get_t_res())Multiple measure caseNormal measures## Multiple datadata_measure=[x,y,z,t]res=InferecialStats(data_measure,is_paired=True,alpha=0.05)res.inferencial_statistics()#-------Results-------# [x]-->Histograms# [x]-->Qqplot# [x]-->Shapiro-Wilk test# [x]-->Levene Test# [x]-->Normality Condition# [x]-->Parametric Test#      |#      |- One - Way ANOVA Repeated Measures# [ ]-->Non Parametric TestNon normal measures and unpaired## Multiple datadata_measure=[xx,yx,zx,tx]res=InferecialStats(data_measure,is_paired=False,alpha=0.05)res.inferencial_statistics()#-------Results-------# [x]-->Histograms# [x]-->Qqplot# [x]-->Shapiro-Wilk test# [x]-->Levene Test# [ ]-->Normality Condition# [ ]-->Parametric Test# [x]-->Non Parametric Test#      |#      |- KruskalAuthor - ContactCarlos Enrique -calollikito12000@gmail.comLICENSEMIT