Python
126 posts
git-pandas Caching: Faster Analysis
Boost git repository analysis speed! Learn how git-pandas now uses caching to dramatically improve performance for repeated queries on large codebases.
Category Encoders v1.2.4 Release
Category Encoders v1.2.4 is out! Includes pandas categorical type support, improved missing value handling, better error messages, BaseN fixes, and docs.
BaseN Encoding Grid Search in Category Encoders
Explore category_encoders' BaseN encoder for representing categorical data. Learn how to use scikit-learn's grid search to find the optimal encoding base.
Category Encoders accepted into scikit-learn-contrib
Category Encoders, a Python library for encoding categorical variables, has been accepted into the scikit-learn-contrib ecosystem. A project milestone!
Category Encoders now on conda-forge
The category_encoders Python package is now available on conda-forge, making installation easier for Conda users. Learn about the package and feedstock.
Introducing unified glob-syntax in git-pandas
Explore the unified glob syntax (`include_globs`, `ignore_globs`) for git-pandas v2.0, offering flexible file pattern specification and usability.
Parallelizing cumulative blame in git-pandas with joblib
Boost git-pandas cumulative blame analysis performance with joblib. Parallel processing via multithreading speeds up this costly operation.
When do I work on what?
Use git-pandas to analyze and visualize work patterns across open source vs. closed source projects. Compare commit times with punchcard plots. Learn the code.
Estimating the time spent on a project with git-pandas
Learn how to estimate project development time using commit history with git-pandas. Compares to git_time_extractor, git-hours, and glass.
Automating documentation workflow with sphinx and github pages
Explore a comprehensive guide on automating the deployment of Sphinx documentation to GitHub Pages, streamlining your workflow with efficient practices.