Python statistical ecosystem is comprised of multiple packages. However, it still has numerous gaps and is surpassed by R packages and capabilities.
SciPy (version 1.2.0) offers Student, Wilcoxon,
and Mann-Whitney tests which are not adapted to multiple pairwise comparisons.
Statsmodels (version 0.9.0) features
TukeyHSD test which needs some extra actions to be fluently integrated into a
data analysis pipeline. Statsmodels also has good helper methods:
allpairtest (adapts an external function such as
to multiple pairwise comparisons) and
multipletests (adjusts p values to
minimize type I and II errors). PMCMRplus
is a very good R package which has no rivals in Python as it offers more than 40
various tests (including post hoc tests) for factorial and block design data.
PMCMRplus was an inspiration and a reference for scikit-posthocs.
scikit-posthocs attempts to improve Python statistical capabilities by offering a lot of parametric and nonparametric post hoc tests along with outliers detection and basic plotting methods.
Durbin test (for balanced incomplete block design).
Parametric pairwise multiple comparisons tests:
Student T test.
Tamhane T2 test.
Non-parametric tests for factorial design:
Dwass, Steel, Critchlow, and Fligner test.
Nashimoto and Wright (NPM) test.
van Waerden test.
Non-parametric tests for block design:
Durbin and Conover test.
Hayter (OSRT) test.
Outliers detection tests:
Simple test based on interquartile range (IQR).
Generalized Extreme Studentized Deviate test (ESD test).
Plotting functionality (e.g. significance plots).
All post hoc tests are capable of p value adjustments for multiple pairwise comparisons.