Python libraries for doing statistics¶

see draft here: https://docs.google.com/document/d/1a-Ohl3hs7w8AiOr85PxW0DZP1ofFK02sk55IEw0zQPE/edit?tab=t.0

Notebook setup¶

In [1]:

# Install stats library
%pip install --quiet ministats 

# Install stats library %pip install --quiet ministats

[notice] A new release of pip is available: 24.3.1 -> 25.0.1
[notice] To update, run: pip install --upgrade pip
Note: you may need to restart the kernel to use updated packages.

In [2]:

# Figures setup
import matplotlib.pyplot as plt
import seaborn as sns

plt.clf()  # needed otherwise `sns.set_theme` doesn't work
sns.set_theme(
    style="whitegrid",
    rc={'figure.figsize': (7, 2)},
)
# High-resolution figures please
%config InlineBackend.figure_format = 'retina'

def savefig(fig, filename):
    fig.tight_layout()
    fig.savefig(filename, dpi=300, bbox_inches="tight", pad_inches=0)

# Figures setup import matplotlib.pyplot as plt import seaborn as sns plt.clf() # needed otherwise `sns.set_theme` doesn't work sns.set_theme( style="whitegrid", rc={'figure.figsize': (7, 2)}, ) # High-resolution figures please %config InlineBackend.figure_format = 'retina' def savefig(fig, filename): fig.tight_layout() fig.savefig(filename, dpi=300, bbox_inches="tight", pad_inches=0)

<Figure size 640x480 with 0 Axes>

Introduction¶

Data management with Pandas¶

Numerical computing with NumPy¶

Scientific computing with SciPy¶

Data visualization with Seaborn¶

Frequentist statistics with Statsmodels¶

Honorable mention: pingouin¶

Bayesian statistics with Bambi (based on PyMC)¶

Conclusion¶

Links¶

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