Package: randan
RandanA python package for the analysis of social dataCurrent version: 0.2.1Documentation:https://randan.readthedocs.io/en/latest/If you want to contribute or report a bug, do not hesitate toopen an issueon this page or contact us:alexey.n.rotmistrov@gmail.com(Aleksei Rotmistrov),lana_lob@mail.ru(Svetlana Zhuchkova).OverviewRandan is a python package that aims to provide most of the functions presented in SPSS. Unlike the other python packages for data analysis, it has three main features, which make it attractive for social scientists:it provides the results of the analysis in a readable and understandable form, similar to SPSSit gives you information about statistical significance of the parameters whenever possibleit unites all the necessary methods so you do not need to switch between different packages and software anymoreAs we emphasize the importance of the way your results look like, we highly suggest to userandanin Jupyter Notebook and store your data inpandasDataFrames.N.B.:You should understand that this project is under development now, which means it is constantly updating. But you can use all the modules and classes presented in the last release.InstallationYou can easily install the package from the PyPi by running:pip install randanIf something goes wrong during the installation, consider using this code:pip install --user randanTo upgrade package's version, run this code:pip install --upgrade randanOnce you install the package, you can import it as any python package:# like thisimportrandan# or like thisfromrandan.treeimportCHAIDRegressor# etc.StructureBy now,sevenmodules have been included in the package. These modules correspond to the SPSS functions as follows:ModuleClass or functionCorresponding SPSS functionDescriptiondescriptive_statisticsNominalStatisticsAnalyze -> Descriptive statistics -> Frequencies, Descriptives, ExploreDescriptive statistics relevant for nominal variablesdescriptive_statisticsOrdinalStatisticsAnalyze -> Descriptive statistics -> Frequencies, Descriptives, ExploreDescriptive statistics relevant for ordinal variablesdescriptive_statisticsScaleStatisticsAnalyze -> Descriptive statistics -> Frequencies, Descriptives, ExploreDescriptive statistics relevant for scale (interval) variablesbivariate_associationCrosstabAnalyze -> Descriptive statistics -> CrosstabsAnalysis of contingency tablesbivariate_associationCorrelationAnalyze -> Correlate -> BivariateCorrelation coefficientscomparison_of_central_tendencyANOVAAnalyze -> Compare means -> One-Way ANOVAAnalysis of varianceclusteringKMeansAnalyze -> Classify -> K-Means ClusterCluster analysis with k-means algorithmdimension_reductionCAAnalyze -> Dimension Reduction -> Correspondence AnalysisCorrespondence analysisdimension_reductionPCAAnalyze -> Dimension Reduction -> Factor (extraction method: principal components)Principal component analysisregressionLinearRegressionAnalyze -> Regression -> LinearOLS regressionregressionBinaryLogisticRegressionAnalyze -> Regression -> Binary LogisticBinary logistic regressiontreeCHAIDRegressor, CHAIDClassifierAnalyze -> Classify -> Tree -> CHAIDCHAID decision tree for scale and categorical dependent variables, respectivelyQuick startAlthoughrandanis built to be similar to SPSS, it reproduces the fit-predict and fit-transform approach, which is now being used in the most popular machine learning python packages. This approach means that you should, firstly, initialize your model and then, secondly, fit it to your data (i.e., use thefitfunction) if necessary.If the method you use belongs to theunsupervised methods(i.e., youdo not havea dependent variable in your data), you can then usetransformfunction to get values of the obtained, hidden, dependent variable such as cluster membership, factor scores etc.If the method you use belongs to thesupervised methods(i.e., youhavea dependent variable in your data), you can then usepredictfunction to get values of the given dependent variable.If the method does not assume to estimate new values for your data (such methods are crosstabs, t-tests etc.), then it does not require to usefitandtransform/predictfunctions.If you want to see the full list of the availiable functions associated with some class, please visit ourdocumentation pageor literally ask for help:fromrandan.bivariate_associationimportCrosstabhelp(Crosstab)Modulebivariate_associationThis module aggregates methods devoted to searching for statistical relationships between two variables. These methods do not require to usefitfunction, i.e. you only need to call the necessary class:fromrandan.bivariate_associationimportCrosstab# with this code, you will immediately see the resultsctab=Crosstab(data,row='genre',column='age_ord')# however, if you want to somehow use separate statistics, you can call them this wayprint(ctab.chi_square,ctab.pvalue,ctab.n_cells)Modulecomparison_of_central_tendencyThis module contains both parametric and non-parametric methods for comparison of central tendency statistics. These methods do not require to usefitfunction, i.e. you only need to call the necessary class:fromrandan.comparison_of_central_tendencyimportANOVA# with this code, you will immediately see the resultsanv=ANOVA(data,dependent_variables='kinopoisk_rate',independent_variable='genre')# however, if you want to somehow use separate statistics, you can call them this wayprint(anv.F,anv.pvalue,anv.SSt)ModuleclusteringThis module includes two main clustering methods: k-means and hierarchical (agglomerative) clustering.Clustering methods belong to unsupervised learning, which means you should use thefitfunction after calling the appropriate class, and then, if necessary, thetransformfunction to acquire cluster membership (and / or distances to each center in case of k-means).fromrandan.clusteringimportKMeans# with this code, you will immediately see the results, including visualization of clusterskm=KMeans(2).fit(data,['year','time','kinopoisk_rate_count'])# this is how you can predict the cluster membership,# and the distances from each observation to each cluster's centerclusters=km.transform(distance_to_centers=True)If you experience troubles with visualization and see captions like <Figure size 800x500 with 1 Axes> instead of plots, just re-run the code that produces them.Moduledimension_reductionThis module unites methods for factorization of nominal and scale variables: correspondence analysis (classCA) and principal component analysis (classPCA).Factorization methods belong to unsupervised learning, which means you should use thefitfunction after calling the appropriate class, and then, if necessary, thetransformfunction to acquire so-called factor scores.fromrandan.dimension_reductionimportPCAvars_=['trstprl','trstlgl','trstplc','ppltrst','pplfair','pplhlp']# with this code, you will immediately see the resultspca=PCA(n_components=2,rotation='varimax').fit(data,variables=vars_)# this is how you can predict the factor scoresf_scores=pca.transform()ModuleregressionThis module consists of two classical regression models: linear regression and binary logistic regression. This group of methods belongs to supervised learning, which means you should use thefitfunction after calling the appropriate class, and then, if necessary, thepredictfunction to acquire predictions.fromrandan.regressionimportLinearRegression# with this code, you will immediately see the resultsformula='kinopoisk_rate = time + year + genre + genre*type'regr=LinearRegression().fit(data,formula=formula,categorical_variables=['genre','type'],collinearity_statistics=True)# this is how you can predict values of the dependent variable for the given data...predictions=regr.predict()# ... save various types of residuals ...residuals=regr.save_residuals(unstardandized=False,studentized=True)# ... and even save values of independent variables# if you didn't create them manually (e.g. dummies and interactions) ...indep_vars=regr.save_independent_variables()ModuletreeThis module includes various methods of building decision trees. If you have a categorical dependent variable, please use those methods that containClassifierpart in their names. Otherwise, if you have a scale dependent variable, please use the methods that containRegressorpart in their names.This group of methods belongs to supervised learning, which means you should use thefitfunction after calling the appropriate class, and then, if necessary, thepredictfunction to acquire predictions.fromrandan.treeimportCHAIDRegressor# with this code, you will immediately see the results, including the plot of your treechaid=CHAIDRegressor().fit(data,dependent_variable='kinopoisk_rate',independent_variables=['genre','age_ord','year','time','type','kinopoisk_rate_count'],scale_variables=['year','time','kinopoisk_rate_count'],ordinal_variables=['age_ord'])# this is how you can predict values of the dependent variable, the node membership,# and the description of the node in terms of interactions for the given datapredictions=chaid.predict(node=True,interaction=True)