Statistical package in Python based on Pandas
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Pingouin is an open-source statistical package written in Python 3 and based mostly on Pandas and NumPy.

  1. ANOVAs: one- and two-ways, repeated measures, mixed, ancova
  2. Post-hocs tests and pairwise comparisons
  3. Robust correlations
  4. Partial correlation, repeated measures correlation and intraclass correlation
  5. Linear/logistic regression and mediation analysis
  6. Bayesian T-test and Pearson correlation
  7. Tests for sphericity, normality and homoscedasticity
  8. Effect sizes and power analysis
  9. Parametric/bootstrapped confidence intervals around an effect size or a correlation coefficient
  10. Circular statistics
  11. Plotting: Bland-Altman plot, Q-Q plot, etc...

Pingouin is designed for users who want simple yet exhaustive statistical functions.

For example, the ttest_ind function of SciPy returns only the T-value and the p-value. By contrast, the ttest function of Pingouin returns the T-value, p-value, degrees of freedom, effect size (Cohen's d), statistical power and Bayes Factor (BF10) of the test.

Documentation

Chat

If you have questions, please ask them in the public Gitter chat

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Installation

Dependencies

The main dependencies of Pingouin are :

  • NumPy (>= 1.15)
  • SciPy (>= 1.1.0)
  • Pandas (>= 0.23)
  • Matplotlib (>= 3.0.2)
  • Seaborn (>= 0.9.0)

In addition, some functions require :

  • Statsmodels
  • Scikit-learn

Pingouin is a Python 3 package. While most of the functions should work with Python 2.7, we strongly recommend using Python >= 3.6.

User installation

Pingouin can be easily installed using pip

pip install pingouin

or conda

conda install -c conda-forge pingouin

New releases are frequent so always make sure that you have the latest version:

pip install --upgrade pingouin

Quick start

Try before you buy! Click on the link below and navigate to the notebooks folder to load a collection of interactive Jupyter notebooks demonstrating the main functionalities of Pingouin. No need to install Pingouin beforehand as the notebooks run in a Binder environment.

10 minutes to Pingouin

1. T-test

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

# T-test
pg.ttest(x, y)
Output
T p-val dof tail cohen-d power BF10
-3.401 0.001 58 two-sided 0.878 0.917 26.155

2. Pearson's correlation

pg.corr(x, y)
Output
n r CI95% r2 adj_r2 p-val BF10 power
30 0.595 [0.3 0.79] 0.354 0.306 0.001 54.222 0.95

3. Robust correlation

# Introduce an outlier
x[5] = 18
# Use the robust Shepherd's pi correlation
pg.corr(x, y, method="shepherd")
Output
n r CI95% r2 adj_r2 p-val power
30 0.561 [0.25 0.77] 0.315 0.264 0.002 0.917

4. Test the normality of the data

# Return a boolean (true if normal) and the associated p-value
print(pg.normality(x, y))                                 # Univariate normality
print(pg.multivariate_normality(np.column_stack((x, y)))) # Multivariate normality
(array([False,  True]), array([0., 0.552]))
(False, 0.00018)

5. One-way ANOVA using a pandas DataFrame

# Read an example dataset
from pingouin.datasets import read_dataset
df = read_dataset('mixed_anova')

# Run the ANOVA
aov = pg.anova(data=df, dv='Scores', between='Group', detailed=True)
print(aov)
Output
Source SS DF MS F p-unc 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)
Output
Source SS DF MS F p-unc np2 eps
Time 7.628 2 3.814 3.913 0.022629 0.062 0.999
Error 115.027 118 0.975

7. Post-hoc tests corrected for multiple-comparisons

# FDR-corrected post hocs with Hedges'g effect size
posthoc = pg.pairwise_ttests(data=df, dv='Scores', within='Time', subject='Subject',
                             padjust='fdr_bh', effsize='hedges')

# Pretty printing of table
pg.print_table(posthoc, floatfmt='.3f')
Output
Contrast A B Paired T tail p-unc p-corr p-adjust BF10 efsize eftype
Time August January True -1.740 two-sided 0.087 0.131 fdr_bh 0.582 -0.328 hedges
Time August June True -2.743 two-sided 0.008 0.024 fdr_bh 4.232 -0.485 hedges
Time January June True -1.024 two-sided 0.310 0.310 fdr_bh 0.232 -0.170 hedges

8. Two-way mixed ANOVA

# Compute the two-way 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)
Output
Source SS DF1 DF2 MS F p-unc 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

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'])
Output
X Y method tail n r CI95% r2 adj_r2 z p-unc BF10 power
X Y pearson two-sided 30 0.366 [0.01 0.64] 0.134 0.070 0.384 0.047 1.006 0.525
X Z pearson two-sided 30 0.251 [-0.12 0.56] 0.063 -0.006 0.256 0.181 0.344 0.272
Y Z pearson two-sided 30 0.020 [-0.34 0.38] 0.000 -0.074 0.020 0.916 0.142 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'])
Linear regression summary
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', n_boot=500)
Mediation summary
Path Beta CI[2.5%] CI[97.5%] Sig
X -> M 0.103 -0.051 0.256 No
M -> Y 0.018 -0.332 0.369 No
X -> Y 0.136 0.002 0.269 Yes
Direct 0.143 0.003 0.283 Yes
Indirect -0.007 -0.050 0.027 No

Development

Pingouin was created and is maintained by Raphael Vallat. 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

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:

I am also grateful to Charles Zaiontz and his website www.real-statistics.com which has been useful to understand the practical implementation of several functions.