Datasets:

pushkar87dg
/

pypi-api-drift

license: cc-by-4.0
pretty_name: PyPI API Drift
language:
  - en
tags:
  - code
  - python
  - api-evolution
  - breaking-changes
  - benchmark
  - llm-evaluation
task_categories:
  - text-classification
size_categories:
  - 10M<n<100M
configs:
  - config_name: versionprobe
    data_files: benchmark/versionprobe.parquet
  - config_name: versionprobe_verified
    data_files: benchmark/versionprobe_verified.parquet
    default: true
  - config_name: versionprobe_lite
    data_files: benchmark/versionprobe_lite.parquet
  - config_name: changes
    data_files: data/changes.parquet
  - config_name: api_history
    data_files: data/api_history.parquet
  - config_name: releases
    data_files: data/releases.parquet
  - config_name: signatures
    data_files: data/signatures.parquet
  - config_name: module_coverage
    data_files: data/module_coverage.parquet

PyPI API Drift

Historical public-API snapshots for up to 80 recent final releases of each of the 1,872 most-downloaded Python libraries, drawn from 2021 to October 2026 (55,565 releases), and every change between consecutive releases (15,258,652 events, 210,535 distinct breaking changes, 181,023 high-confidence). It is a high-precision, low-recall record, not complete ground truth: names created at runtime are often missing. This is a research release; all validation was done by pipeline-isolated AI agents, not human annotators (a 200-item human labelling kit is in the repository).

How it was built: wheels are read with HTTP range requests (only the ZIP directory and changed .py/.pyi files, about 8.5% of the wheels' bytes, roughly 26 GB instead of 300 GB) and parsed with ast without importing or executing any package code. A name is labelled removed only when it is no longer reachable through any import path, or when a module __getattr__ explicitly rejects it; names merely dropped from __all__ are left_public_api. Filter changes on confidence == "high" and representative, and api_history on coverage_status == "static", for the strictest view.

Measured on samples the pipeline never saw	Result
Precision, high-confidence breaking changes, re-audited on v1.2	91.9% (95% CI 86.6–95.2%; n=160)
Precision, high-confidence breaking changes, first untuned audit	88.8% (95% CI 80.0–94.0%; n=80)
VersionProbe labels correct (fresh sample)	91.5% (95% CI 88.4–93.9%; n=400)
Recall within the dataset's static public-API definition	14/16
Recall over all runtime-visible changes	16/99 (runtime-generated APIs are invisible to static analysis)

VersionProbe (10,958 pairs from 583 libraries; versionprobe_lite: 983 pairs) asks a model whether a snippet is valid for an exact library version. Each pair is valid in one release and invalid in the next, and carries the release date so you can split results at a model's training cutoff. versionprobe_verified (492 pairs, 984 items, 375 libraries) contains only pairs whose labels were confirmed at runtime by AI agents isolated from the pipeline (no human adjudication; evidence in the repository); it is the default configuration and the one to rank models on.

Model, closed book, on `versionprobe_verified`	Item accuracy	Pair accuracy
`claude-opus-5-5`	58.4%	26.6%
`claude-fable-5-1`	56.5%	22.4%
`claude-sonnet-5-5`	54.3%	18.3%
`claude-haiku-5-5`	49.6%	9.2%

A constant answer scores 50% / 0%. Given API history retrieved from this dataset, claude-opus-5-5 scored 100.0%; that is an oracle condition showing the answer is in the record, not a general benefit.

In a preregistered code-repair experiment on 143 real breaking changes, repeated across 4 models from one model family, API history from this dataset raised API-compatible, intent-preserving migrations (under the evaluation protocol: runtime check of names and argument binding plus an AI judge of intent; not a behavioural test) to 68.5%–69.9% for every model, from 7.0%–56.6% unaided and 46.2%–62.2% with the new release's documentation.

module_coverage and the coverage_status column of api_history mark where static analysis cannot see the whole API (stub-only, dynamic or compiled modules), so absence of a name is not read as proof that it does not exist.

from datasets import load_dataset
probe = load_dataset("pushkar87dg/pypi-api-drift", "versionprobe_verified", split="train")
changes = load_dataset("pushkar87dg/pypi-api-drift", "changes", split="train")

Use breaking & ~parent_removed & representative & confidence == "high" for one row per real, high-confidence breaking change. Before use, check downloaded tables against DATA_MANIFEST.json (data version 2026.10-v1.3) with python verify_data.py DATA_DIR from the repository. The checker apidrift-check, the lookup tool apidrift-lookup, the MCP server apidrift-mcp, the evaluation script, the integrity checks and all audit material are in the GitHub repository: https://github.com/pushkardg/pypi-api-drift.

Author: Pushkar Devanahalli Gopalakrishna (Independent Researcher, USA). Citation: see CITATION.cff or the the paper, https://doi.org/10.5281/zenodo.23648339.