nfl-data-py

What it is and what it does

nfl_data_py is a wrapper library that fetches NFL statistics and metadata from multiple upstream sources and loads them into pandas DataFrames. It provides import functions for play-by-play data (the core use case), weekly and seasonal aggregates, rosters, draft picks, combine results, schedules, officials, and various other game-related datasets spanning back to 1999. The library depends on pandas for data manipulation, numpy for numerical operations, fastparquet for efficient file handling, and appdirs for cache management.

The package is designed for analysts and fantasy football enthusiasts who need structured access to historical NFL data without manually downloading or parsing files. It supports optional memory optimization through float downcasting and local caching to reduce repeated network requests. However, the repository is archived and abandoned, meaning no active maintenance or bug fixes are being applied—users relying on it should verify that the upstream data sources remain accessible and that the import functions continue to work with current data formats.

Use it for:

• Analyze historical play-by-play data to study offensive and defensive performance trends across seasons
• Build fantasy football models using weekly player statistics, combine metrics, and draft pick valuations
• Research receiver target share, air yards, and dominator ratings for talent evaluation
• Fetch NFL schedules, rosters, and injury reports for season-long tracking applications
• Retrieve QBR history and advanced stats from Pro Football Reference for quarterback analysis

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Imports NFL play-by-play, weekly, seasonal, roster, draft, combine, and other game data from nflfastR, nfldata, dynastyprocess, and Draft Scout into pandas DataFrames.

Yes, with conditions. The package is useful for NFL data analysis and fantasy football work, has low install friction, and carries a permissive MIT license. However, the abandoned repository status is a significant concern: there is no active maintenance, and you should verify that the upstream data sources remain accessible before committing to it in production. For one-off analysis or personal projects, it is likely stable enough; for ongoing systems, consider monitoring the upstream sources directly or seeking an actively maintained alternative.

Install

nfl-data-py on PyPI

pip install nfl-data-py

uv add nfl-data-py

poetry add nfl-data-py

Installing nfl-data-py

Before you install

Low install friction with four common dependencies (numpy, pandas, appdirs, fastparquet). However, the repository is archived and marked abandoned, with the last commit on 2025-09-25 and no active maintenance.

License in practice

MIT license permits commercial and private use with minimal restrictions, requiring only attribution and inclusion of the license text.

Quickstart

pip install nfl_data_py

import nfl_data_py as nfl

# Import play-by-play data for specified years
data = nfl.import_pbp_data(years=[1999])

# Import weekly stats
weekly = nfl.import_weekly_data(years=[1999])

Verify before relying

• Whether the upstream data sources (nflfastR, nfldata, dynastyprocess, Draft Scout) remain actively maintained and accessible
• Whether the package's data import functions continue to work reliably given the abandoned repository status
• Performance characteristics when importing large multi-year datasets with the downcast option

Package facts

Evidence: nfl_data_py-0.3.3-py3-none-any.whl

Tags

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