Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-08-28 07:53 UTC

unionai-oss / pandera

A light-weight, flexible, and expressive statistical data testing library

PythonMIT★ 4,443 stars⑂ 435 forkssince Nov 2018View on GitHub ↗
KindCommand-line toolhow this is determined

unionai-oss/pandera holds a health index of 90 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (95/100) and lowest on Security (40/100). It was last updated 1 day ago. A single contributor accounts for most of its recent work.

90
overall / 100
Excellent

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

90
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 76 is calibrated to 90 on the published index scale (record calibration 2026-08-02).

Ownership

unionai-ossOrganization
90 followers82 public repossince Nov 2021

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIpanderapoints to another repo — not scored0.32.19,268,90712359 days agopandasvalidationdata-structures

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

94Exceptional · 21% of overall
How it's scored
36/36Push recency — last push 1 days ago
27/36Commit cadence — 39/52 weeks with commits
18/18Commit volume — 237 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year237
human_commit_share1
days_since_last_push1
active_weeks_last_year39

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 100 releases published
36/36Release recency — latest release 59 days ago
27/27Release cadence — a release every ~19.3 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count100
latest_release_tagv0.32.1
releases_from_tagsno
days_since_latest_release59
mean_days_between_releases19.3

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

87Excellent · 17% of overall
How it's scored
59.2/60Stars — 4,443 stars
22/25Forks — 435 forks
7.2/15Watchers — 21 watchers
Inputs used
forks435
stars4,443
watchers21
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

85Excellent
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges13
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesreadthedocs.org, shields.io
has_pull_request_templateno

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

57Moderate · 23% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
9.6/22.5Commit distribution — top contributor authored 57% of commits
13.5/13.5Contributor breadth — 99 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor1
contributors_sampled99
top_contributor_share0.573
How it's scored
27/42Issue resolution — 64% of issues closed
25.7/30PR acceptance — 945/1,104 decided PRs merged
9.1/13Newcomer PR acceptance — 7/10 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs945
open_issues393
closed_issues708
prs_merged_7d2
prs_decided_7d2
prs_merged_30d13
prs_decided_30d17
issue_closed_ratio0.643
closed_unmerged_prs159
first_time_authors_30d9
first_time_prs_merged_30d7
first_time_prs_decided_30d10
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
14.1/25Owner reach — 90 followers of unionai-oss
22.6/25Track record — 82 public repos, account ~4 yr old
Inputs used
followers90
owner_typeOrganization
is_verifiedno
owner_loginunionai-oss
public_repos82
account_age_days1,749

Engineering Quality

Are baseline engineering and documentation practices in place?

95Exceptional · 19% of overall
How it's scored
24/24CI workflows — 2 workflow(s)
24/24Tests present
16/16Linter config — .pylintrc, pyproject.toml ([tool.ruff])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://www.union.ai/pandera
10/10Repository description
10/10Topics — 18 topics
10/10Wiki
Inputs used
topicspandas, validation, schema, dataframes, testing, pandas-validation, pandas-dataframe, data-validation, data-cleaning, data-check, testing-tools, assertions, data-assertions, data-verification, dataframe-schema, hypothesis-testing, pandas-validator, data-processing
has_wikiyes
homepagehttps://www.union.ai/pandera
docs_sitehttps://www.union.ai/pandera
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

40Weak · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
25/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestspyproject.toml, requirements.txt, setup.py
has_codeql_workflowno
has_security_policyno
has_dependabot_configyes

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages9
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:pandera@0.32.1 runtime dependency closure — what installing the published package pulls in — 9 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

92Excellent · 4% of overall
How it's scored
45/45Agent instructions — AGENTS.md
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://www.union.ai/llms.txt)
40/40Legible commit history — 90 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://www.union.ai/llms.txt
legible_history_share0.9
agent_instruction_filesAGENTS.md
agent_instruction_max_bytes10,915
How it's scored
18/18One-command bootstrap — Makefile, docs/Makefile, noxfile.py
22/22Automated tests
11/11Lint / format config — .pylintrc, pyproject.toml ([tool.ruff])
11/11Static type checking — mypy.ini, pandera/py.typed
0/10Reproducible environment
10/10Demonstrated agent practice — 9 of the last 100 commits agent-authored or agent-credited
5/8Automated maintenance — dependency automation configured, none observed in the sampled commits
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileno
typed_languageno
bootstrap_filesMakefile, docs/Makefile, noxfile.py
has_devcontainerno
has_linter_configyes
typecheck_configsmypy.ini, pandera/py.typed
agent_commit_share0.09
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
27/45Type-checkable code — Python with type-check config (mypy.ini, pandera/py.typed)
54.2/55Manageable file sizes — 6/439 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes108,510
source_files_sampled439
oversized_source_files6
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20MCP server — not applicable to this kind of software
40/40Runnable examples — notebooks
Inputs used
example_dirsnotebooks
has_mcp_signalno
api_schema_files—
interfaces_expected_of—
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

4,443GitHub stars
99contributors
237commits, last 12 months
1days since last push
100releases
1bus factor
393open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi package 'pandera' points at a different repository (https://github.com/pandera-dev/pandera); excluded from ecosystem scoring
  • OpenSSF Scorecard did not return a usable result (killed by SIGKILL — most likely the container's memory limit); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 435 ⇿
0Stars
435Forks
100Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

010020030040050043362019-012022-112026-08
Major 0Minor 28Patch 48

Each point covers 7 days.

Direct dependencies 5
RegistryPackageVersion constraintManifest
PyPIpackaging>= 20.0pyproject.toml
PyPIpydantic—pyproject.toml
PyPItypeguard—pyproject.toml
PyPItyping_extensions—pyproject.toml
PyPItyping_inspect>= 0.6.0pyproject.toml
All dependencies 64

Full resolved dependency set from the GitHub dependency graph: 5 direct and 59 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIpackaging—direct
PyPIpydantic—direct
PyPItypeguard—direct
PyPItyping-extensions—direct
PyPItyping-inspect—direct
PyPIasv—indirect
PyPIblack—indirect
PyPIdask—indirect
PyPIdistributed—indirect
PyPIfastapi—indirect
PyPIfrictionless—indirect
PyPIfuro—indirect
PyPIgeopandas—indirect
PyPIgrpcio—indirect
PyPIhypothesis—indirect
PyPIibis-framework—indirect
PyPIisort—indirect
PyPIjoblib—indirect
PyPImodin—indirect
PyPImypy1.19.1indirect
PyPImyst-nb—indirect
PyPInarwhals—indirect
PyPInox—indirect
PyPInumpy—indirect
PyPIpandas—indirect
PyPIpandas-stubs—indirect
PyPIpip—indirect
PyPIpolars—indirect
PyPIprek—indirect
PyPIprotobuf—indirect
PyPIpyarrow—indirect
PyPIpyarrow-hotfix—indirect
PyPIpyspark—indirect
PyPIpytest—indirect
PyPIpytest-asyncio—indirect
PyPIpytest-cov—indirect
PyPIpytest-xdist—indirect
PyPIpython-multipart—indirect
PyPIpytz—indirect
PyPIpyyaml—indirect
PyPIray—indirect
PyPIrecommonmark—indirect
PyPIrich—indirect
PyPIscipy—indirect
PyPIscipy-stubs—indirect
PyPIsetuptools—indirect
PyPIshapely—indirect
PyPIsphinx—indirect
PyPIsphinx-autodoc-typehints—indirect
PyPIsphinx-copybutton—indirect
PyPIsphinx-design—indirect
PyPIsphinx-docsearch—indirect
PyPIsphinxcontrib-typer—indirect
PyPItwine—indirect
PyPItyper—indirect
PyPItypes-click—indirect
PyPItypes-pytz—indirect
PyPItypes-pyyaml—indirect
PyPItypes-requests—indirect
PyPItypes-setuptools—indirect
PyPIuv—indirect
PyPIuvicorn—indirect
PyPIxarray—indirect
PyPIxdoctest—indirect
Dependency advisories 0

Installing pypi:pandera@0.32.1 pulls in 9 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

No known advisories affect the assessed dependencies.

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.34.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics — PyPI.