Pingouin

Warnings

• breaking The `plot_shift` function was removed in Pingouin 0.6.0. Any code relying on this function will break.
  Fix: Remove calls to `plot_shift` and use alternative plotting libraries like Matplotlib or Seaborn for similar visualizations, or revert to an older version if absolutely necessary.
• breaking The minimum required SciPy version for `compute_bootci` was bumped to 1.10.0 in Pingouin 0.6.0. Ensure your SciPy installation meets this requirement to avoid `ImportError` or unexpected behavior.
  Fix: Upgrade SciPy to at least version 1.10.0: `pip install --upgrade scipy`.
• deprecated The `pingouin.gzscore()` function is deprecated and will be removed in a future release. It is recommended to use `scipy.stats.gzscore()` instead for robust z-score calculation.
  Fix: Replace `pg.gzscore()` with `scipy.stats.gzscore()`.
• gotcha Pingouin functions, especially those involving paired measurements (e.g., paired T-test, correlation, repeated measures ANOVA), automatically perform listwise deletion of missing values. This means entire rows with any missing data are removed, which can be drastic for datasets with many missing values.
  Fix: Be aware of missing data handling. Consider imputing missing values using Pandas or using statistical models that natively support missing values (e.g., linear mixed-effect models), though the latter are not implemented in Pingouin.
• gotcha The `pingouin.rm_anova` function had an issue in earlier versions (pre-0.6.0, specifically around March 2022 releases) where eta-squared (n2) effect size was incorrectly calculated and was identical to partial eta-squared. Users should double-check any effect sizes previously obtained with `rm_anova` from affected versions.
  Fix: Upgrade to the latest version of Pingouin (0.6.0+) and re-run analyses if concerned about the accuracy of eta-squared values from older versions.
• gotcha The `mediation_analysis` function currently only supports continuous outcome variables and does not work with binary or ordinal outcomes. Additionally, the p-value for the indirect effect should be interpreted with caution as it's computed using a bootstrap distribution and not strictly conditioned on a true null hypothesis.
  Fix: Ensure the outcome variable is continuous. For binary/ordinal outcomes or more advanced mediation models, consider alternative R packages like `lavaan` or `mediation`, or the PROCESS macro for SPSS.

Install

• pip install pingouin pip
• conda install -c conda-forge pingouin Conda (Conda-Forge)

Imports

• pingouin

  import pingouin as pg

  Standard import for accessing all Pingouin functions.
• ttest

  from pingouin import ttest

  Import specific functions directly to avoid namespace pollution if only a few functions are needed.

Quickstart

This quickstart demonstrates performing an independent samples t-test and a one-way ANOVA using Pingouin. It highlights the library's ability to take raw numerical arrays or Pandas DataFrames and return rich statistical output in a DataFrame format, including T-values, p-values, degrees of freedom, effect sizes (e.g., Cohen's d), and power.

import pingouin as pg
import numpy as np
import pandas as pd

# Simulate two independent groups of data
np.random.seed(123)
data_group1 = np.random.normal(loc=10, scale=2, size=30)
data_group2 = np.random.normal(loc=12, scale=2.5, size=30)

# Perform an independent samples t-test
result = pg.ttest(data_group1, data_group2, correction='auto')

print(result)

# Example with a DataFrame for ANOVA
df_anova = pd.DataFrame({
    'dv': [10, 12, 11, 13, 15, 14, 16, 18, 17, 19, 20, 22],
    'group': ['A']*4 + ['B']*4 + ['C']*4
})
aov_result = pg.anova(data=df_anova, dv='dv', between='group')
print("\nANOVA Result:")
print(aov_result)