Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-19 02:54 UTC

business-science / pytimetk

Time series easier, faster, more fun. Pytimetk.

PythonMIT★ 966 stars⑂ 85 forkssince Apr 2021View on GitHub ↗
KindLibraryhow this is determined
#pandas#polars#time#time-series#timeseries#timeseries-analysis

business-science/pytimetk holds a health index of 77 out of 100, placing it in the Good band. It scores highest on Vitality (77/100) and lowest on Security (42/100). It was last updated 2 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

Business ScienceOrganization
1,290 followers46 public repossince Mar 2017

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIpytimetk2.5.314,231282 days agotime-seriesforecastingmachine-learningdata-science

Metrics by category

Vitality

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

77Good · 21% of overall

Development activity

69Good
How it's scored
36/36Push recency — last push 2 days ago
4.8/36Commit cadence — 7/52 weeks with commits
18/18Commit volume — 109 commits in the last year
10/10OpenSSF Scorecard: Maintained — 19 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year109
human_commit_share1
days_since_last_push2
active_weeks_last_year7

Release discipline

90Excellent
How it's scored
27/27Ships releases — 27 releases published
36/36Release recency — latest release 2 days ago
27/27Release cadence — a release every ~40.6 days
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Inputs used
releases_count27
latest_release_tagv2.5.3
releases_from_tagsno
days_since_latest_release2
mean_days_between_releases40.6

Community & Adoption

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

70Good · 17% of overall

Popularity & adoption

71Good
How it's scored
48.4/60Stars — 966 stars
16/25Forks — 85 forks
6.8/15Watchers — 18 watchers
Inputs used
forks85
stars966
watchers18
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

70Good
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges6
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateno

Ecosystem adoption (downloads)

69Good
How it's scored
55.4/80Monthly downloads — 14,231 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagespytimetk
dependents—
ecosystemspypi
total_downloads—
monthly_downloads14,231
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?

66Good · 23% of overall

Maintainer resilience (bus factor)

33At Risk
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
4.2/22.5Commit distribution — top contributor authored 81% of commits
9.5/13.5Contributor breadth — 7 contributors
10/10OpenSSF Scorecard: Contributors — project has 4 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled7
top_contributor_share0.813

Issue & PR responsiveness

67Good
How it's scored
41.7/42Issue resolution — 99% of issues closed
25.7/30PR acceptance — 126/147 decided PRs merged
0/13Newcomer PR acceptance — 0/1 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Review — Found 0/30 approved changesets -- score normalized to 0
Inputs used
merged_prs126
open_issues1
closed_issues173
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d1
issue_closed_ratio0.994
closed_unmerged_prs21
first_time_authors_30d1
first_time_prs_merged_30d0
first_time_prs_decided_30d1

Ownership & stewardship

77Good
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
22.4/25Owner reach — 1,290 followers of business-science
24.2/25Track record — 46 public repos, account ~9 yr old
Inputs used
followers1,290
owner_typeOrganization
is_verifiedno
owner_loginbusiness-science
public_repos46
account_age_days3,501

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 2 days ago
20/20Version history — 28 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagespytimetk
ecosystemspypi
any_deprecatedno
min_days_since_publish2

Engineering Quality

Are baseline engineering and documentation practices in place?

76Good · 19% of overall

Engineering practices

60Moderate
How it's scored
24/24CI workflows — 3 workflow(s)
24/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configno
has_precommit_configno
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: CI-Tests. Remaining weights renormalized.

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://business-science.github.io/pytimetk/
10/10Repository description
10/10Topics — 6 topics
10/10Wiki
Inputs used
topicspandas, polars, time, time-series, timeseries, timeseries-analysis
has_wikiyes
homepagehttps://business-science.github.io/pytimetk/
docs_sitehttps://business-science.github.io/pytimetk/
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

Security posture

28At Risk
How it's scored
4.5/7.5Binary-Artifacts — binaries present in source code
0/7.5Branch-Protection — branch protection not enabled on development/release branches
0/2.5CI-Tests — no data
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0/7.5Code-Review — Found 0/30 approved changesets -- score normalized to 0
2.5/2.5Contributors — project has 4 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
0/7.5Dependency-Update-Tool — no update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 19 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
0/5Packaging — no data
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
0/5SAST — no SAST tool detected
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — Project has not signed or included provenance with any releases.
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities — 59 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate2.8
Excluded from scoring (no data or not applicable): CI-Tests, Packaging. Remaining weights renormalized.

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_packages59
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:pytimetk@2.5.3 runtime dependency closure — what installing the published package pulls in — 59 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.

43Weak · 4% of overall

Agent context & guidance

24At Risk
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
24/40Legible commit history — 45 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.45
agent_instruction_files—
agent_instruction_max_bytes—

Verify loop (build / test / typecheck)

32At Risk
How it's scored
0/18One-command bootstrap
22/22Automated tests
0/11Lint / format config
0/11Static type checking
10/10Reproducible environment — lockfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfilespoetry.lock
has_dockerfileno
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configno
typecheck_configs—
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0

Code legibility for models

54Moderate
How it's scored
0/45Type-checkable code — Python without a type-check config
54.4/55Manageable file sizes — 2/174 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes78,129
source_files_sampled174
oversized_source_files2

Machine-readable interfaces

100Exceptional
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

966GitHub stars
7contributors
109commits, last 12 months
2days since last push
27releases
1bus factor
1open 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 ★ / 85 ⇿
0Stars
85Forks
26Releases

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.

0204060801008552021-082024-032026-10
Major 2Minor 11Patch 13

Each point covers 5 days.

OpenSSF Scorecard 2.8 / 10
2.8aggregate

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 · 2026-10-19 02:54 UTC

6Binary-Artifactsbinaries present in source code
0Branch-Protectionbranch protection not enabled on development/release branches
n/aCI-Testsno pull request found
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 0/30 approved changesets -- score normalized to 0
10Contributorsproject has 4 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained19 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
n/aPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTno SAST tool detected
0Security-Policysecurity policy file not detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities59 existing vulnerabilities detected
Direct dependencies 20
RegistryPackageVersion constraintManifest
PyPIpandas—pyproject.toml
PyPIpandas-flavor>=0.7.0pyproject.toml
PyPImatplotlib>=3.8.0pyproject.toml
PyPIplotnine>=0.12.3pyproject.toml
PyPIplotly>=5.17.0pyproject.toml
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PyPItsfeatures>=0.4.5pyproject.toml
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PyPIpolars>=1.2.0pyproject.toml
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PyPIhmmlearn>=0.3.0pyproject.toml
PyPIcudf-cu12>=24.10.0pyproject.toml
PyPIray>=2.10.0pyproject.toml
All dependencies 138

Full resolved dependency set from the GitHub dependency graph: 20 direct and 118 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
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Dependency advisories 0

Installing pypi:pytimetk@2.5.3 pulls in 59 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

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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.

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