Various statistics on Python's standard library.

See stats.ipynb for charts that show the data in various ways.

You can also run an interactive Jupyter session using Binder:

stdlib.csv contains various details about the modules in the stdlib. The table is built using various JSON files found in this repository (discussed below). To tweak how various things are treated, you can edit the JSON files and run aggregate.py to update it accordingly.

The category_usage.csv counts the number of projects which use a specify module category. It also tallies all the commits the category is made up of.

The stats.ipynb is a Jupyter notebook which contains various charts that try to analyze the data from the CSV in various ways.

Public availability is (mostly) determined by documentation existing in Doc/library/.

private_modules.json maps public modules to any private modules they depend on. For modules that are "cheating" and using private modules directly instead of their equivalent public API, they not listed as a dependent (e.g. multiprocessing directly using _weakrefset instead of going through weakref).

Ignores Argument Clinic files and tests, but includes header files.

file_map.json maps module name to relative file paths in a git clone.

required.json lists the modules required to start Python (based on python -v -S -c pass).

usage.json lists the modules used by the 4000 most downloaded projects over the past year on PyPI.

The list of projects is listed in top-pypi-packages-365-days.json as fetched from Top PyPI Packages. The projects are downloaded by isidentical/syntax_test_suite.

categories.json groups modules by category accoring to the library index.

The __future__ module is specially treated and put in its own category.

commit_stats.json tracks the oldest, newest, and SHA hashes of all the commits made on a specific file.