Functions

ANOVA and T-test

anova([dv, between, data, detailed, …]) One-way and two-way ANOVA.
welch_anova([dv, between, data, export_filename]) One-way Welch ANOVA.
rm_anova([dv, within, subject, data, …]) One-way repeated measures ANOVA.
rm_anova2([dv, within, subject, data, …]) Two-way repeated measures ANOVA.
epsilon(data[, correction]) Epsilon adjustement factor for repeated measurements.
mixed_anova([dv, within, subject, between, …]) Mixed-design (split-plot) ANOVA.
ancova([dv, covar, between, data, …]) ANCOVA with one or more covariate(s).
ttest(x, y[, paired, tail, correction, r]) T-test.

Bayesian

bayesfactor_ttest(t, nx[, ny, paired, tail, r]) Bayes Factor of a T-test.
bayesfactor_pearson(r, n) Bayes Factor of a Pearson correlation.

Circular

circ_axial(alpha, n) Transforms n-axial data to a common scale.
circ_corrcc(x, y[, tail]) Correlation coefficient between two circular variables.
circ_corrcl(x, y[, tail]) Correlation coefficient between one circular and one linear variable random variables.
circ_mean(alpha[, w, axis]) Mean direction for circular data.
circ_r(alpha[, w, d, axis]) Mean resultant vector length for circular data.
circ_rayleigh(alpha[, w, d]) Rayleigh test for non-uniformity of circular data.
circ_vtest(alpha[, dir, w, d]) V test for non-uniformity of circular data with a specified mean direction.

Correlation and regression

corr(x, y[, tail, method]) (Robust) correlation between two variables.
pairwise_corr(data[, columns, tail, method, …]) Pairwise correlations between columns of a pandas dataframe.
partial_corr([data, x, y, covar, tail, method]) Partial correlation.
rm_corr([data, x, y, subject, tail]) Repeated measures correlation.
intraclass_corr([data, groups, raters, …]) Intra-class correlation coefficient.
linear_regression(X, y[, add_intercept, …]) (Multiple) Linear regression.
logistic_regression(X, y[, coef_only, …]) (Multiple) Binary logistic regression.
mediation_analysis([data, x, m, y, alpha, …]) Mediation analysis using a bias-correct non-parametric bootstrap method.

Distribution

anderson(*args[, dist]) Anderson-Darling test of distribution.
gzscore(x) Geometric standard (Z) score.
homoscedasticity(*args[, alpha]) Test equality of variance.
normality(*args[, alpha]) Shapiro-Wilk univariate normality test.
multivariate_normality(X[, alpha]) Henze-Zirkler multivariate normality test.
sphericity(data[, method, alpha]) Mauchly and JNS test for sphericity.

Effect sizes

compute_effsize(x, y[, paired, eftype]) Calculate effect size between two set of observations.
compute_effsize_from_t(tval[, nx, ny, N, eftype]) Compute effect size from a T-value.
convert_effsize(ef, input_type, output_type) Conversion between effect sizes.
compute_esci([stat, nx, ny, eftype, …]) Parametric confidence intervals around a Cohen d or a correlation coefficient.
compute_bootci(x[, y, func, method, paired, …]) Bootstrapped confidence intervals of univariate and bivariate functions.

Multiple comparisons and post-hoc tests

pairwise_ttests([dv, between, within, …]) Pairwise T-tests.
pairwise_tukey([dv, between, data, alpha, …]) Pairwise Tukey-HSD post-hoc test.
pairwise_gameshowell([dv, between, data, …]) Pairwise Games-Howell post-hoc test.
multicomp(pvals[, alpha, method]) P-values correction for multiple tests.
bonf(pvals[, alpha]) P-values correction with Bonferroni method.
holm(pvals[, alpha]) P-values correction with Holm method.
fdr(pvals[, alpha, method]) P-values correction with False Discovery Rate (FDR).

Non-parametric

cochran([dv, within, subject, data, …]) Cochran Q test.
friedman([dv, within, subject, data, …]) Friedman test for repeated measurements.
kruskal([dv, between, data, detailed, …]) Kruskal-Wallis H-test for independent samples.
mad(a[, normalize, axis]) Median Absolute Deviation along given axis of an array.
madmedianrule(a) Outlier detection based on the MAD-median rule.
mwu(x, y[, tail]) Mann-Whitney U Test (= Wilcoxon rank-sum test).
wilcoxon(x, y[, tail]) Wilcoxon signed-rank test.

Others

print_table(df[, floatfmt, tablefmt]) Pretty display of table.
pingouin.datasets.read_dataset(dname) Read example datasets.
pingouin.datasets.list_dataset() List available example datasets.

Plotting

plot_blandaltman(x, y[, agreement, …]) Generate a Bland-Altman plot to compare two sets of measurements.
plot_skipped_corr(x, y[, xlabel, ylabel, …]) Plot the bootstrapped 95% confidence intervals and distribution of a robust Skipped correlation.
qqplot(x[, dist, sparams, confidence, …]) Quantile-Quantile plot.

Power analysis

power_anova([eta, k, n, power, alpha]) Evaluate power, sample size, effect size or significance level of a one-way balanced ANOVA.
power_corr([r, n, power, alpha, tail]) Evaluate power, sample size, correlation coefficient or significance level of a correlation test.
power_ttest([d, n, power, alpha, contrast, tail]) Evaluate power, sample size, effect size or significance level of a one-sample T-test, a paired T-test or an independent two-samples T-test with equal sample sizes.
power_ttest2n(nx, ny[, d, power, alpha, tail]) Evaluate power, effect size or significance level of an independent two-samples T-test with unequal sample sizes.