Pingouin is an opensource statistical package written in Python 3 and based mostly on Pandas and NumPy. Some of its main features are listed below. For a full list of available functions, please refer to the API documentation.
 ANOVAs: Nways, repeated measures, mixed, ancova
 Pairwise posthocs tests (parametric and nonparametric) and pairwise correlations
 Robust, partial, distance and repeated measures correlations
 Linear/logistic regression and mediation analysis
 Bayes Factors
 Multivariate tests
 Reliability and consistency
 Effect sizes and power analysis
 Parametric/bootstrapped confidence intervals around an effect size or a correlation coefficient
 Circular statistics
 Chisquared tests
 Plotting: BlandAltman plot, QQ plot, paired plot, robust correlation...
Pingouin is designed for users who want simple yet exhaustive statistical functions.
For example, the ttest_ind
function of SciPy returns only the Tvalue and the pvalue. By contrast,
the ttest
function of Pingouin returns the Tvalue, the pvalue, the degrees of freedom, the effect size (Cohen's d), the 95% confidence intervals of the difference in means, the statistical power and the Bayes Factor (BF10) of the test.
Documentation
Chat
If you have questions, please ask them in the public Gitter chat
Installation
Dependencies
The main dependencies of Pingouin are :
 NumPy (>= 1.15)
 SciPy (>= 1.3.0)
 Pandas (>= 0.24)
 Pandasflavor (>= 0.1.2)
 Matplotlib (>= 3.0.2)
 Seaborn (>= 0.9.0)
In addition, some functions require :
 Statsmodels
 Scikitlearn
 Mpmath
Pingouin is a Python 3 package and is currently tested for Python 3.6 and 3.7. Pingouin does not work with Python 2.7.
User installation
Pingouin can be easily installed using pip
pip install pingouin
or conda
conda install c condaforge pingouin
New releases are frequent so always make sure that you have the latest version:
pip install upgrade pingouin
Quick start
Click on the link below and navigate to the notebooks/ folder to run a collection of interactive Jupyter notebooks showing the main functionalities of Pingouin. No need to install Pingouin beforehand, the notebooks run in a Binder environment.
10 minutes to Pingouin
1. Ttest
import numpy as np
import pingouin as pg
np.random.seed(123)
mean, cov, n = [4, 5], [(1, .6), (.6, 1)], 30
x, y = np.random.multivariate_normal(mean, cov, n).T
# Ttest
pg.ttest(x, y)
T  dof  tail  pval  CI95%  cohend  BF10  power 

3.401  58  twosided  0.001  [1.68 0.43]  0.878  26.155  0.917 
2. Pearson's correlation
pg.corr(x, y)
n  r  CI95%  r2  adj_r2  pval  BF10  power 

30  0.595  [0.3 0.79]  0.354  0.306  0.001  69.723  0.95 
3. Robust correlation
# Introduce an outlier
x[5] = 18
# Use the robust Shepherd's pi correlation
pg.corr(x, y, method="shepherd")
n  r  CI95%  r2  adj_r2  pval  power 

30  0.561  [0.25 0.77]  0.315  0.264  0.002  0.917 
4. Test the normality of the data
The pingouin.normality function works with lists, arrays, or pandas DataFrame in wide or longformat.
print(pg.normality(x)) # Univariate normality
print(pg.multivariate_normality(np.column_stack((x, y)))) # Multivariate normality
W  pval  normal 

0.615  0.000  False 
(False, 0.00018)
5. Oneway ANOVA using a pandas DataFrame
# Read an example dataset
df = pg.read_dataset('mixed_anova')
# Run the ANOVA
aov = pg.anova(data=df, dv='Scores', between='Group', detailed=True)
print(aov)
Source  SS  DF  MS  F  punc  np2 

Group  5.460  1  5.460  5.244  0.02320  0.029 
Within  185.343  178  1.041 



6. Repeated measures ANOVA
pg.rm_anova(data=df, dv='Scores', within='Time', subject='Subject', detailed=True)
Source  SS  DF  MS  F  punc  np2  eps 

Time  7.628  2  3.814  3.913  0.022629  0.062  0.999 
Error  115.027  118  0.975 




7. Posthoc tests corrected for multiplecomparisons
# FDRcorrected post hocs with Hedges'g effect size
posthoc = pg.pairwise_ttests(data=df, dv='Scores', within='Time', subject='Subject',
parametric=True, padjust='fdr_bh', effsize='hedges')
# Pretty printing of table
pg.print_table(posthoc, floatfmt='.3f')
Contrast  A  B  Paired  Parametric  T  dof  tail  punc  pcorr  padjust  BF10  CLES  hedges 

Time  August  January  True  True  1.740  59.000  twosided  0.087  0.131  fdr_bh  0.582  0.585  0.328 
Time  August  June  True  True  2.743  59.000  twosided  0.008  0.024  fdr_bh  4.232  0.644  0.485 
Time  January  June  True  True  1.024  59.000  twosided  0.310  0.310  fdr_bh  0.232  0.571  0.170 
8. Twoway mixed ANOVA
# Compute the twoway mixed ANOVA and export to a .csv file
aov = pg.mixed_anova(data=df, dv='Scores', between='Group', within='Time',
subject='Subject', correction=False,
export_filename='mixed_anova.csv')
pg.print_table(aov)
Source  SS  DF1  DF2  MS  F  punc  np2  eps 

Group  5.460  1  58  5.460  5.052  0.028  0.080 

Time  7.628  2  116  3.814  4.027  0.020  0.065  0.999 
Interaction  5.168  2  116  2.584  2.728  0.070  0.045 

9. Pairwise correlations between columns of a dataframe
import pandas as pd
np.random.seed(123)
z = np.random.normal(5, 1, 30)
data = pd.DataFrame({'X': x, 'Y': y, 'Z': z})
pg.pairwise_corr(data, columns=['X', 'Y', 'Z'])
X  Y  method  tail  n  r  CI95%  r2  adj_r2  z  punc  BF10  power 

X  Y  pearson  twosided  30  0.366  [0.01 0.64]  0.134  0.070  0.384  0.047  1.500  0.525 
X  Z  pearson  twosided  30  0.251  [0.12 0.56]  0.063  0.006  0.256  0.181  0.534  0.272 
Y  Z  pearson  twosided  30  0.020  [0.34 0.38]  0.000  0.074  0.020  0.916  0.228  0.051 
10. Convert between effect sizes
# Convert from Cohen's d to Hedges' g
pg.convert_effsize(0.4, 'cohen', 'hedges', nx=10, ny=12)
0.384
11. Multiple linear regression
pg.linear_regression(data[['X', 'Z']], data['Y'])
names  coef  se  T  pval  r2  adj_r2  CI[2.5%]  CI[97.5%] 

Intercept  4.650  0.841  5.530  0.000  0.139  0.076  2.925  6.376 
X  0.143  0.068  2.089  0.046  0.139  0.076  0.003  0.283 
Z  0.069  0.167  0.416  0.681  0.139  0.076  0.412  0.273 
12. Mediation analysis
pg.mediation_analysis(data=data, x='X', m='Z', y='Y', seed=42, n_boot=1000)
path  coef  se  pval  CI[2.5%]  CI[97.5%]  sig 

Z ~ X  0.103  0.075  0.181  0.051  0.256  No 
Y ~ Z  0.018  0.171  0.916  0.332  0.369  No 
Total  0.136  0.065  0.047  0.002  0.269  Yes 
Direct  0.143  0.068  0.046  0.003  0.283  Yes 
Indirect  0.007  0.025  0.898  0.070  0.029  No 
13. Contingency analysis
data = pg.read_dataset('chi2_independence')
expected, observed, stats = pg.chi2_independence(data, x='sex', y='target')
stats
test  lambda  chi2  dof  p  cramer  power 

pearson  1.000  22.717  1.000  0.000  0.274  0.997 
cressieread  0.667  22.931  1.000  0.000  0.275  0.998 
loglikelihood  0.000  23.557  1.000  0.000  0.279  0.998 
freemantukey  0.500  24.220  1.000  0.000  0.283  0.998 
modloglikelihood  1.000  25.071  1.000  0.000  0.288  0.999 
neyman  2.000  27.458  1.000  0.000  0.301  0.999 
Integration with Pandas
Several functions of Pingouin can be used directly as pandas DataFrame methods. Try for yourself with the code below:
import pingouin as pg
# Example 1  ANOVA
df = pg.read_dataset('mixed_anova')
df.anova(dv='Scores', between='Group', detailed=True)
# Example 2  Pairwise correlations
data = pg.read_dataset('mediation')
data.pairwise_corr(columns=['X', 'M', 'Y'], covar=['Mbin'])
# Example 3  Partial correlation matrix
data.pcorr()
The functions that are currently supported as pandas method are:
 pingouin.anova
 pingouin.ancova
 pingouin.rm_anova
 pingouin.mixed_anova
 pingouin.welch_anova
 pingouin.pairwise_ttests
 pingouin.pairwise_ttests
 pingouin.pairwise_corr
 pingouin.partial_corr
 pingouin.pcorr
 pingouin.rcorr
 pingouin.mediation_analysis
Development
Pingouin was created and is maintained by Raphael Vallat, mostly during his spare time. Contributions are more than welcome so feel free to contact me, open an issue or submit a pull request!
To see the code or report a bug, please visit the GitHub repository.
Note that this program is provided with NO WARRANTY OF ANY KIND. If you can, always double check the results with another statistical software.
Contributors
 Nicolas Legrand
 Richard Höchenberger
 Arthur Paulino
How to cite Pingouin?
If you want to cite Pingouin, please use the publication in JOSS:
Vallat, R. (2018). Pingouin: statistics in Python. Journal of Open Source Software, 3(31), 1026, https://doi.org/10.21105/joss.01026
@ARTICLE{Vallat2018,
title = "Pingouin: statistics in Python",
author = "Vallat, Raphael",
journal = "The Journal of Open Source Software",
volume = 3,
number = 31,
pages = "1026",
month = nov,
year = 2018
}
Acknowledgement
Several functions of Pingouin were inspired from R or Matlab toolboxes, including:
 effsize package (R)
 ezANOVA package (R)
 pwr package (R)
 circular statistics (Matlab) (Berens 2009)
 robust correlations (Matlab) (Pernet, Wilcox & Rousselet, 2012)
 repeatedmeasure correlation (R) (Bakdash & Marusich, 2017)
I am also grateful to Charles Zaiontz and his website www.realstatistics.com which has been useful to understand the practical implementation of several functions.