{"repo":"oneoffcoder/py-pair","free":true,"listed":false,"github":"https://github.com/oneoffcoder/py-pair","clone":"git clone https://github.com/oneoffcoder/py-pair.git","description":"Pairwise association measures of statistical variable types","language":"Python","stars":21,"topics":["association","correlation","symmetric","asymmetric","causation","contingency-table","confusion-matrix","concordance","ranking","biserial"],"license":"Apache-2.0","category":"data-pipelines","readme_excerpt":"PyPair PyPair computes pairwise association measures between variables (binary, categorical, ordinal, and continuous), with local NumPy-first implementations and Spark dataframe support. Modern toolchain (Python 3.13 + uv) Build wheel/sdist: Publish a release: Put your PyPI credentials file at ./.pypirc in the repository root. The repo already ignores .pypirc . Use make publish-testpypi if you want to upload to TestPyPI instead. Profiling Run the built-in profiling workload: This writes cProfile stats to .profiles/pypair.prof , prints the hottest frames, and can also emit internal timings for the decorated contingency measures. It also writes a memory report with tracemalloc allocation hotspots and process peak RSS to .profiles/pypair.memory.txt . Override the default workload or scale with PROFILE FLAGS when needed: Quick usage Input types Local pairwise APIs accept 1D array-like inputs: - numpy.ndarray - pandas.Series - Python lists / tuples - Other iterables that can be consumed once Use categorical/object-like inputs for categorical metrics and numeric inputs for continuous or concordance metrics. For best runtime and lowest allocation overhead, prefer already-materialized numpy.ndarray or pandas.Series inputs with the right dtype instead of generators or mixed-object containers. The shared type aliases live in pypair.typing , for example ArrayLike1D and NumericArrayLike1D . DataFrame integration - Pandas : use pypair.util.corr(df, func) to build pairwise association matr","default_branch":null,"files":null,"tree":[],"storefront":"/r/oneoffcoder","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/oneoffcoder/py-pair/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}