Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-17 06:50 UTC

modin-project / modin

Modin: Scale your Pandas workflows by changing a single line of code

PythonApache-2.0★ 10,390 stars⑂ 677 forkssince Jun 2018View on GitHub ↗

modin-project/modin holds a health index of 34 out of 100, placing it in the At Risk band. It scores highest on Community & Adoption (88/100) and lowest on Vitality (37/100). It was last updated 218 days ago. 3 contributors account for most of its recent work.

34
overall / 100
At Risk

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.

34
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 67 is calibrated to 78 on the published index scale (record calibration 2026-08-02). High-Risk Jurisdiction Policy applies a 50% multiplier to weighted overall health and gives it an At Risk ceiling of 34.

Ownership

modin-projectOrganization
104 followers9 public repossince Jun 2018

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPImodin0.37.11,150,231110349 days ago

Metrics by category

Vitality

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

37Weak · 21% of overall
How it's scored
3.6/36Push recency — last push 218 days ago
2.1/36Commit cadence — 3/52 weeks with commits
9.4/18Commit volume — 10 commits in the last year
0/10OpenSSF Scorecard: Maintained — 0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
Inputs used
commits_last_year10
human_commit_share1
days_since_last_push218
active_weeks_last_year3
How it's scored
27/27Ships releases — 100 releases published
16.2/36Release recency — latest release 349 days ago
19.8/27Release cadence — a release every ~51.5 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count100
latest_release_tag0.37.1
releases_from_tagsno
days_since_latest_release349
mean_days_between_releases51.5
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?

88Excellent · 17% of overall
How it's scored
60/60Stars — 10,390 stars
23.6/25Forks — 677 forks
11.3/15Watchers — 111 watchers
Inputs used
forks677
stars10,390
watchers111
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (Apache-2.0)
0/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges9
has_contributingno
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesbadge.fury.io, codecov.io, github.com, readthedocs.org, shields.io
has_pull_request_templateyes
How it's scored
80/80Monthly downloads — 1,150,231 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesmodin
dependents—
ecosystemspypi
total_downloads—
monthly_downloads1,150,231
unverified_packages_excluded—
Excluded from scoring (no data or not applicable): Registry dependents. Remaining weights renormalized.

Sustainability & Governance

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

79Good · 23% of overall
How it's scored
36/54Bus factor — 3 contributor(s) cover half of all commits
17.1/22.5Commit distribution — top contributor authored 24% of commits
13.5/13.5Contributor breadth — 99 contributors
10/10OpenSSF Scorecard: Contributors — project has 23 contributing companies or organizations
Inputs used
bus_factor3
contributors_sampled99
top_contributor_share0.238
How it's scored
35.4/42Issue resolution — 84% of issues closed
26.5/30PR acceptance — 2,922/3,309 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
15/15OpenSSF Scorecard: Code-Review — all changesets reviewed
Inputs used
merged_prs2,922
open_issues676
closed_issues3,661
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.844
closed_unmerged_prs387
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
30/30Ownership backing — organization-owned
0/20Verified domain
14.5/25Owner reach — 104 followers of modin-project
19.3/25Track record — 9 public repos, account ~8 yr old
Inputs used
followers104
owner_typeOrganization
is_verifiedno
owner_loginmodin-project
public_repos9
account_age_days3,009
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
26/35Publish recency — latest publish 349 days ago
20/20Version history — 110 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesmodin
ecosystemspypi
any_deprecatedno
min_days_since_publish349

Engineering Quality

Are baseline engineering and documentation practices in place?

88Excellent · 19% of overall
How it's scored
24/24CI workflows — 8 workflow(s)
24/24Tests present
16/16Linter config — setup.cfg ([flake8], [isort])
0/9.6Pre-commit hooks
0/6.4.editorconfig
16/20OpenSSF Scorecard: CI-Tests — 26 out of 30 merged PRs checked by a CI test -- score normalized to 8
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — http://modin.readthedocs.io
10/10Repository description
10/10Topics — 9 topics
10/10Wiki
Inputs used
topicsdataframe, pandas, distributed, datascience, modin, sql, python, analytics, data-science
has_wikiyes
homepagehttp://modin.readthedocs.io
docs_sitehttp://modin.readthedocs.io
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

42Weak · 16% of overall
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — no data
2/2.5CI-Tests — 26 out of 30 merged PRs checked by a CI test -- score normalized to 8
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
7.5/7.5Code-Review — all changesets reviewed
2.5/2.5Contributors — project has 23 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
0/7.5Maintained — 0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
5/5Packaging — packaging workflow detected
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
4.5/5SAST — SAST tool detected but not run on all commits
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — no data
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities — 27 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate5.4
high_risk_jurisdiction_cap34
high_risk_jurisdiction_multiplier50
security_posture_after_multiplier27
security_posture_before_jurisdiction54
Excluded from scoring (no data or not applicable): Branch-Protection, Signed-Releases. Remaining weights renormalized. High-Risk Jurisdiction Policy applies a 50% multiplier and gives Security posture an At risk ceiling of 34.

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_packages10
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:modin@0.37.1 runtime dependency closure — what installing the published package pulls in — 10 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.

67Good · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 100 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share1
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — setup.cfg ([flake8], [isort])
11/11Static type checking — mypy.ini
10/10Reproducible environment — Dockerfile
10/10Demonstrated agent practice — 6 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 — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configyes
typecheck_configsmypy.ini
agent_commit_share0.06
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
27/45Type-checkable code — Python with type-check config (mypy.ini)
52.8/55Manageable file sizes — 17/424 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes248,994
source_files_sampled424
oversized_source_files17
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 — examples, notebooks
Inputs used
example_dirsexamples, notebooks
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

10,390GitHub stars
99contributors
10commits, last 12 months
218days since last push
100releases
3bus factor
676open 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 ★ / 677 ⇿
0Stars
677Forks
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.

0125250375500625750671312018-062022-072026-09
Major 0Minor 36Patch 56

Each point covers 8 days.

OpenSSF Scorecard 5.4 / 10
5.4aggregate

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 · 2026-09-17 06:49 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
8CI-Tests26 out of 30 merged PRs checked by a CI test -- score normalized to 8
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
10Code-Reviewall changesets reviewed
10Contributorsproject has 23 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
0Maintained0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
10Packagingpackaging workflow detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
9SASTSAST tool detected but not run on all commits
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities27 existing vulnerabilities detected
All dependencies 65

Full resolved dependency set from the GitHub dependency graph: 0 direct and 65 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPI-—indirect
PyPIasv0.5.1indirect
PyPIblack—indirect
PyPIclick—indirect
PyPIcolorama—indirect
PyPIconnectorx—indirect
PyPIcoverage—indirect
PyPIdask—indirect
PyPIdataframe-api-compat—indirect
PyPIdistributed—indirect
PyPIfastparquet—indirect
PyPIflake8—indirect
PyPIflake8-no-implicit-concat—indirect
PyPIflake8-print—indirect
PyPIflask-cors—indirect
PyPIflatbuffers—indirect
PyPIfsspec—indirect
PyPIfuncsigs—indirect
PyPIfuzzydata—indirect
PyPIipywidgets—indirect
PyPIisort—indirect
PyPIjinja2—indirect
PyPIjupyterlab—indirect
PyPIlxml—indirect
PyPImatplotlib—indirect
PyPImock—indirect
PyPImodin—indirect
PyPImoto—indirect
PyPImypy—indirect
PyPInumexpr—indirect
PyPInumpy—indirect
PyPInumpydoc1.1.0indirect
PyPIopencv-python—indirect
PyPIopenpyxl—indirect
PyPIpackaging—indirect
PyPIpandas—indirect
PyPIpandas-gbq—indirect
PyPIpandas-stubs—indirect
PyPIpsutil—indirect
PyPIpsycopg2-binary—indirect
PyPIpyarrow—indirect
PyPIpydata-sphinx-theme—indirect
PyPIpygit2—indirect
PyPIpygithub—indirect
PyPIpymssql—indirect
PyPIpytest—indirect
PyPIpytest-benchmark—indirect
PyPIpytest-cov—indirect
PyPIpytest-xdist—indirect
PyPIpyyaml—indirect
PyPIray—indirect
PyPIray1.1.0indirect
PyPIrecommonmark—indirect
PyPIs3fs—indirect
PyPIscipy—indirect
PyPIsphinx—indirect
PyPIsphinx-click—indirect
PyPIsphinx-issues—indirect
PyPIsphinxcontrib-plantuml—indirect
PyPIsqlalchemy—indirect
PyPItables—indirect
PyPItqdm—indirect
PyPItyping-extensions—indirect
PyPIxarray—indirect
PyPIxlrd—indirect
Dependency advisories 0

Installing pypi:modin@0.37.1 pulls in 10 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.