Statistics Python Packages

scikit-learn

scikit-learn: machine learning in Python

statsmodels

Statsmodels: statistical modeling and econometrics in Python

boltons

🔩 Like builtins, but boltons. 250+ constructs, recipes, and snippets which extend (and rely on nothing but) the Python standard library. Nothing like Michael Bolton.

imbalanced-learn

A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

about-time

A cool helper for tracking time and throughput of code blocks, with beautiful human friendly renditions.

lifelines

Survival analysis in Python

statsforecast

Lightning ⚡️ fast forecasting with statistical and econometric models.

ydata-profiling

1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.

tensorflow-probability

Probabilistic reasoning and statistical analysis in TensorFlow

pytensor

PyTensor allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.

reliability

Reliability engineering toolkit for Python - https://reliability.readthedocs.io/en/latest/

pandas-profiling

1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.

pingouin

Statistical package in Python based on Pandas

mne

MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python

spreg

Spatial econometric regression in Python

tfp-nightly

Probabilistic reasoning and statistical analysis in TensorFlow

rpy2

Interface to use R from Python

hierarchicalforecast

Probabilistic Hierarchical forecasting 👑 with statistical and econometric methods.

ghlang

Generate pretty language-stats charts for your GitHub profile or local codebase.

lifetimes

Lifetime value in Python

pylibraft-cu12

RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.

libraft-cu12

RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.

lovely-numpy

NumPy arrays, ready for human consumption

causal-learn

Causal Discovery in Python. Learning causality from data.