Public record
Software health reportschema 0.31.0 · metrics 2.10.0 · 2026-08-13 03:40 UTC

raphaelvallat / pingouin

Statistical package in Python based on Pandas

PythonGPL-3.0★ 1,930 stars⑂ 167 forkssince Apr 2018View on GitHub ↗

raphaelvallat/pingouin holds a health index of 77 out of 100, placing it in the Good band. It scores highest on Engineering Quality (89/100) and lowest on Vitality (45/100). It was last updated 129 days ago. A single contributor accounts for most of its recent work.

77
overall / 100
Good

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.

77
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 66 is calibrated to 77 on the published index scale (record calibration 2026-08-02).

Ownership

Raphael VallatPersonal account
468 followers15 public repossince Feb 2017ŌURA

This repository is owned by a personal account. A single-owner project carries more continuity risk than an organization-backed one.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIpingouin0.6.1-43137 days ago

Metrics by category

Vitality

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

45Weak · 21% of overall
How it's scored
9.9/36Push recencylast push 129 days ago
4.2/36Commit cadence6/52 weeks with commits
12.1/18Commit volume21 commits in the last year
0/10OpenSSF Scorecard: Maintained0 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 0
Inputs used
commits_last_year21
human_commit_share1
days_since_last_push129
active_weeks_last_year6
How it's scored
27/27Ships releases42 releases published
27/36Release recencylatest release 137 days ago
12.6/27Release cadencea release every ~196.2 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count42
latest_release_tagv0.6.1
releases_from_tagsno
days_since_latest_release137
mean_days_between_releases196.2
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

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

82Excellent · 17% of overall
How it's scored
53.3/60Stars1,930 stars
18.5/25Forks167 forks
7.8/15Watchers26 watchers
Inputs used
forks167
stars1,930
watchers26
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

85Excellent
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (GPL-3.0)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges5
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesbadge.fury.io, codecov.io, github.com, shields.io
has_pull_request_templateno

Sustainability & Governance

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

67Good · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
3.2/22.5Commit distributiontop contributor authored 86% of commits
13.5/13.5Contributor breadth44 contributors
10/10OpenSSF Scorecard: Contributorsproject has 14 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled44
top_contributor_share0.857
How it's scored
41.1/42Issue resolution98% of issues closed
24.9/30PR acceptance127/153 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
4.5/15OpenSSF Scorecard: Code-ReviewFound 10/30 approved changesets -- score normalized to 3
Inputs used
merged_prs127
open_issues7
closed_issues318
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.978
closed_unmerged_prs26
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
10/30Ownership backingpersonal (user) account
0/20Verified domainnot applicable to user accounts
19.2/25Owner reach468 followers of raphaelvallat
20.8/25Track record15 public repos, account ~9 yr old
Inputs used
followers468
owner_typeUser
is_verified
owner_loginraphaelvallat
public_repos15
account_age_days3,469
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable1 package(s) on pypi
35/35Publish recencylatest publish 137 days ago
20/20Version history43 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagespingouin
ecosystemspypi
any_deprecatedno
min_days_since_publish137

Engineering Quality

Are baseline engineering and documentation practices in place?

89Excellent · 19% of overall
How it's scored
24/24CI workflows6 workflow(s)
24/24Tests present
16/16Linter config
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
14/20OpenSSF Scorecard: CI-Tests21 out of 30 merged PRs checked by a CI test -- score normalized to 7
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage sitehttps://pingouin-stats.org/
10/10Repository description
10/10Topics12 topics
0/10Wiki
Inputs used
topicspandas, statistics, anova, effect-size, multiple-comparisons, cohens-d, bayesian-statistics, ttest, correlations, circular-statistics, statistical-tests, statistical-methods
has_wikino
homepagehttps://pingouin-stats.org/
docs_sitehttps://pingouin-stats.org/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

52Moderate · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionno data
1.8/2.5CI-Tests21 out of 30 merged PRs checked by a CI test -- score normalized to 7
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
2.2/7.5Code-ReviewFound 10/30 approved changesets -- score normalized to 3
2.5/2.5Contributorsproject has 14 contributing companies or organizations
10/10Dangerous-Workflowno dangerous workflow patterns detected
0/7.5Dependency-Update-Toolno update tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
0/7.5Maintained0 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 0
0/5Packagingno data
0/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0/5SASTSAST tool is not run on all commits -- score normalized to 0
0/5Security-Policysecurity policy file not detected
0/7.5Signed-Releasesno data
0/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated15
scorecard_versionv5.5.0
checks_inconclusive3
scorecard_aggregate4
Excluded from scoring (no data or not applicable): Branch-Protection, Packaging, Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisoriesno direct dependency carries a known advisory
25/25Indirect dependencies free of known advisoriesno indirect dependency carries a known advisory
0/40No advisories left outstandingno advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages23
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:pingouin@0.6.1 runtime dependency closure — what installing the published package pulls in — 23 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.

55Moderate · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
38.9/40Legible commit history73 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.73
agent_instruction_files
agent_instruction_max_bytes
How it's scored
18/18One-command bootstrapdocs/Makefile
22/22Automated tests
11/11Lint / format config
0/11Static type checking
0/10Reproducible environment
0/10Demonstrated agent practiceno agent-authored commits among the last 100
0/8Automated maintenanceno automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_filesdocs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0
How it's scored
0/45Type-checkable codePython without a type-check config
52.4/55Manageable file sizes2/42 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes67,672
source_files_sampled42
oversized_source_files2
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)not applicable to this kind of software
0/20MCP servernot applicable to this kind of software
40/40Runnable examplesnotebooks
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

1,930GitHub stars
44contributors
21commits, last 12 months
129days since last push
42releases
1bus factor
7open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

More detail

Star and fork history 0 ★ / 167 ⇿
0Stars
167Forks
30Releases

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.

0408012016020016652018-112022-092026-07
Major 0Minor 4Patch 26

Each point covers 7 days.

OpenSSF Scorecard 4.0 / 10
4.0aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-08-13 03:39 UTC

10Binary-Artifactsno binaries found in the repo
n/aBranch-Protectioninternal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md
7CI-Tests21 out of 30 merged PRs checked by a CI test -- score normalized to 7
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
3Code-ReviewFound 10/30 approved changesets -- score normalized to 3
10Contributorsproject has 14 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
0Maintained0 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 0
n/aPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 9
RegistryPackageVersion constraintManifest
PyPImatplotlibpyproject.toml
PyPInumpy>=1.22.4pyproject.toml
PyPIpandas>=2.1.1pyproject.toml
PyPIpandas_flavorpyproject.toml
PyPIscikit-learn>=1.2.2pyproject.toml
PyPIscipy>=1.10.0pyproject.toml
PyPIseabornpyproject.toml
PyPIstatsmodels>=0.14.1pyproject.toml
PyPItabulatepyproject.toml
All dependencies 10

Full resolved dependency set from the GitHub dependency graph: 9 direct and 1 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPImatplotlibdirect
PyPInumpydirect
PyPIpandasdirect
PyPIpandas-flavordirect
PyPIscikit-learndirect
PyPIscipydirect
PyPIseaborndirect
PyPIstatsmodelsdirect
PyPItabulatedirect
PyPIpingouinindirect
Dependency advisories 0

Installing pypi:pingouin@0.6.1 pulls in 23 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 v0.31.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statisticsPyPI.