Software health reportschema 0.31.0 · metrics 2.10.0 · 2026-08-13 05:46 UTC

predict-idlab / plotly-resampler

Visualize large time series data with plotly.py

PythonMIT★ 1,205 stars⑂ 75 forkssince Nov 2021View on GitHub ↗

#visualization #time-series #plotly #data-science #python #plotly-dash #data-visualization #data-analysis

predict-idlab/plotly-resampler holds a health index of 57 out of 100, placing it in the Moderate band. It scores highest on Community & Adoption (71/100) and lowest on Vitality (32/100). It was last updated 249 days ago. A single contributor accounts for most of its recent work.

overall / 100

Moderate

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.

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)

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 55 is calibrated to 57 on the published index scale (record calibration 2026-08-02).

Ownership

PreDiCT.IDLabOrganization

111 followers74 public repossince Jun 2021

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

Package ecosystems

Registry	Package	Version	Downloads / mo	Versions	Last publish	Tags
PyPI	plotly-resampler	0.11.0	-	64	348 days ago	time-series visualization resampling plotly plotly-dash

Metrics by category

Vitality

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

32At Risk · 21% of overall

Development activity

12Critical

How it's scored

3.6/36	Push recency — last push 249 days ago
0.7/36	Commit cadence — 1/52 weeks with commits
8.1/18	Commit volume — 7 commits in the last year
0/10	OpenSSF Scorecard: Maintained — 0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0

Inputs used

commits_last_year	7
human_commit_share	1
days_since_last_push	249
active_weeks_last_year	1

Release discipline

62Moderate

How it's scored

27/27	Ships releases — 11 releases published
16.2/36	Release recency — latest release 348 days ago
12.6/27	Release cadence — a release every ~129.9 days
0/10	OpenSSF Scorecard: Signed-Releases — no data

Inputs used

releases_count	11
latest_release_tag	v0.11.0
releases_from_tags	no
days_since_latest_release	348
mean_days_between_releases	129.9

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?

71Good · 17% of overall

Popularity & adoption

72Good

How it's scored

50/60	Stars — 1,205 stars
15.6/25	Forks — 75 forks
6.5/15	Watchers — 16 watchers

Inputs used

forks	75
stars	1,205
watchers	16
growth_state	unverified
growth_factor_pct	100
growth_unverified_reason	no_history

Community health

70Good

How it's scored

22.5/22.5	README
22.5/22.5	License — recognized license (MIT)
18/18	CONTRIBUTING guide
0/13.5	Code of conduct
0/7.2	Issue template
0/6.3	PR template

Inputs used

has_readme	yes
has_license	yes
readme_badges	8
has_contributing	yes
has_issue_template	no
has_code_of_conduct	no
readme_badge_services	github.com, shields.io
has_pull_request_template	no

Sustainability & Governance

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

67Good · 23% of overall

Maintainer resilience (bus factor)

34At Risk

How it's scored

9/54	Bus factor — 1 contributor(s) cover half of all commits
8.4/22.5	Commit distribution — top contributor authored 63% of commits
13.5/13.5	Contributor breadth — 13 contributors
3/10	OpenSSF Scorecard: Contributors — project has 1 contributing companies or organizations -- score normalized to 3

Inputs used

bus_factor	1
contributors_sampled	13
top_contributor_share	0.628

Issue & PR responsiveness

71Good

How it's scored

28.3/42	Issue resolution — 67% of issues closed
26.2/30	PR acceptance — 119/136 decided PRs merged
0/13	Newcomer PR acceptance — no first-time contributor's PR decided in 30d
7.5/15	OpenSSF Scorecard: Code-Review — Found 9/18 approved changesets -- score normalized to 5

Inputs used

merged_prs	119
open_issues	63
closed_issues	130
prs_merged_7d	0
prs_decided_7d	0
prs_merged_30d	0
prs_decided_30d	0
issue_closed_ratio	0.674
closed_unmerged_prs	17
first_time_authors_30d	0
first_time_prs_merged_30d	0
first_time_prs_decided_30d	0

Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

85Excellent

How it's scored

30/30	Ownership backing — organization-owned
0/20	Verified domain — verified-domain status not read for this organization
14.7/25	Owner reach — 111 followers of predict-idlab
23.3/25	Track record — 74 public repos, account ~5 yr old

Inputs used

followers	111
owner_type	Organization
is_verified	—
owner_login	predict-idlab
public_repos	74
account_age_days	1,888

Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

91Excellent

How it's scored

25/25	Published & resolvable — 1 package(s) on pypi
26/35	Publish recency — latest publish 348 days ago
20/20	Version history — 64 published versions
20/20	Not deprecated — active, not deprecated or yanked

Inputs used

packages	plotly-resampler
ecosystems	pypi
any_deprecated	no
min_days_since_publish	348

Engineering Quality

Are baseline engineering and documentation practices in place?

60Moderate · 19% of overall

Engineering practices

50Moderate

How it's scored

24/24	CI workflows — 4 workflow(s)
24/24	Tests present
0/16	Linter config
0/9.6	Pre-commit hooks
0/6.4	.editorconfig
2/20	OpenSSF Scorecard: CI-Tests — 1 out of 9 merged PRs checked by a CI test -- score normalized to 1

Inputs used

has_ci	yes
has_tests	yes
has_editorconfig	no
has_linter_config	no
has_precommit_config	no

Documentation

75Good

How it's scored

30/30	README
0/25	Documentation directory
15/15	Documentation / homepage site — https://predict-idlab.github.io/plotly-resampler/latest
10/10	Repository description
10/10	Topics — 8 topics
10/10	Wiki

Inputs used

topics	visualization, time-series, plotly, data-science, python, plotly-dash, data-visualization, data-analysis
has_wiki	yes
homepage	https://predict-idlab.github.io/plotly-resampler/latest
docs_site	https://predict-idlab.github.io/plotly-resampler/latest
has_readme	yes
has_docs_dir	no
has_description	yes

Security

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

46Weak · 16% of overall

Security posture

33At Risk

How it's scored

7.5/7.5	Binary-Artifacts — no binaries found in the repo
0/7.5	Branch-Protection — no data
0.2/2.5	CI-Tests — 1 out of 9 merged PRs checked by a CI test -- score normalized to 1
0/2.5	CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
3.8/7.5	Code-Review — Found 9/18 approved changesets -- score normalized to 5
0.8/2.5	Contributors — project has 1 contributing companies or organizations -- score normalized to 3
10/10	Dangerous-Workflow — no dangerous workflow patterns detected
0/7.5	Dependency-Update-Tool — no update tool detected
0/5	Fuzzing — project is not fuzzed
2.5/2.5	License — license file detected
0/7.5	Maintained — 0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
0/5	Packaging — no data
0/5	Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
3.5/5	SAST — SAST tool detected but not run on all commits
0/5	Security-Policy — security policy file not detected
0/7.5	Signed-Releases — no data
0/7.5	Token-Permissions — detected GitHub workflow tokens with excessive permissions
0/7.5	Vulnerabilities — 99 existing vulnerabilities detected

Inputs used

source	openssf_scorecard
checks_evaluated	15
scorecard_version	v5.5.0
checks_inconclusive	3
scorecard_aggregate	3.3

Excluded from scoring (no data or not applicable): Branch-Protection, Packaging, Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional

How it's scored

35/35	Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25	Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40	No advisories left outstanding — no advisory carries a publication date

Inputs used

source	osv
advisories	0
affected_packages	0
assessed_packages	34
unassessed_packages	0
affected_by_severity	none
direct_affected_packages	0

Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:plotly-resampler@0.11.0 runtime dependency closure — what installing the published package pulls in — 34 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.

50Moderate · 4% of overall

Agent context & guidance

24At Risk

How it's scored

0/45	Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15	Machine-readable docs (llms.txt)
24/40	Legible commit history — 45 of 100 human commits state their intent (structured subject or explanatory body)

Inputs used

has_llms_txt	no
llms_txt_url	—
legible_history_share	0.45
agent_instruction_files	—
agent_instruction_max_bytes	—

Verify loop (build / test / typecheck)

50Moderate

How it's scored

18/18	One-command bootstrap — Makefile
22/22	Automated tests
0/11	Lint / format config
0/11	Static type checking
10/10	Reproducible environment — lockfile
0/10	Demonstrated agent practice — no agent-authored commits among the last 100
0/8	Automated maintenance — no automated dependency updates observed
0/10	OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0

Inputs used

has_nix	no
has_tests	yes
lockfiles	poetry.lock
has_dockerfile	no
typed_language	no
bootstrap_files	Makefile
has_devcontainer	no
has_linter_config	no
typecheck_configs	—
agent_commit_share	0
toolchain_manifests	—
dependency_bot_commit_share	0

Code legibility for models

51Moderate

How it's scored

0/45	Type-checkable code — Python without a type-check config
51.4/55	Manageable file sizes — 3/46 source files over 60KB

Inputs used

primary_language	Python
largest_source_bytes	73,804
source_files_sampled	46
oversized_source_files	3

Machine-readable interfaces

100Exceptional

How it's scored

0/40	API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20	MCP server — not applicable to this kind of software
40/40	Runnable examples — examples, notebooks

Inputs used

example_dirs	examples, notebooks
has_mcp_signal	no
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

1,205GitHub stars

13contributors

7commits, last 12 months

249days since last push

11releases

1bus factor

63open 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 ★ / 75 ⇿

0Stars

75Forks

11Releases

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.

Major 0Minor 6Patch 3

Each point covers 5 days.

OpenSSF Scorecard 3.3 / 10

3.3aggregate

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 05:46 UTC

10	Binary-Artifacts	no binaries found in the repo
n/a	Branch-Protection	internal 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
1	CI-Tests	1 out of 9 merged PRs checked by a CI test -- score normalized to 1
0	CII-Best-Practices	no effort to earn an OpenSSF best practices badge detected
5	Code-Review	Found 9/18 approved changesets -- score normalized to 5
3	Contributors	project has 1 contributing companies or organizations -- score normalized to 3
10	Dangerous-Workflow	no dangerous workflow patterns detected
0	Dependency-Update-Tool	no update tool detected
0	Fuzzing	project is not fuzzed
10	License	license file detected
0	Maintained	0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
n/a	Packaging	packaging workflow not detected
0	Pinned-Dependencies	dependency not pinned by hash detected -- score normalized to 0
7	SAST	SAST tool detected but not run on all commits
0	Security-Policy	security policy file not detected
n/a	Signed-Releases	no releases found
0	Token-Permissions	detected GitHub workflow tokens with excessive permissions
0	Vulnerabilities	99 existing vulnerabilities detected

Direct dependencies 8

Registry	Package	Version constraint	Manifest
PyPI	plotly	`>=5.5.0,<7.0.0`	pyproject.toml
PyPI	dash	`>=2.11.0`	pyproject.toml
PyPI	pandas	`—`	pyproject.toml
PyPI	numpy	`—`	pyproject.toml
PyPI	orjson	`^3.10.0`	pyproject.toml
PyPI	Flask-Cors	`^4.0.2`	pyproject.toml
PyPI	kaleido	`0.2.1`	pyproject.toml
PyPI	tsdownsample	`>=0.1.3`	pyproject.toml

All dependencies 204

Full resolved dependency set from the GitHub dependency graph: 12 direct and 192 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

Registry	Package	Version	Relation
PyPI	dash	`2.18.2`	direct
PyPI	flask-cors	`—`	direct
PyPI	flask-cors	`4.0.2`	direct
PyPI	kaleido	`—`	direct
PyPI	kaleido	`0.2.1`	direct
PyPI	numpy	`1.24.4`	direct
PyPI	numpy	`2.2.3`	direct
PyPI	orjson	`3.10.15`	direct
PyPI	pandas	`2.0.3`	direct
PyPI	pandas	`2.2.3`	direct
PyPI	plotly	`6.0.0`	direct
PyPI	tsdownsample	`0.1.4.1`	direct
PyPI	anyio	`4.5.2`	indirect
PyPI	anywidget	`0.9.13`	indirect
PyPI	appnope	`0.1.4`	indirect
PyPI	argon2-cffi	`23.1.0`	indirect
PyPI	argon2-cffi-bindings	`21.2.0`	indirect
PyPI	arrow	`1.3.0`	indirect
PyPI	asttokens	`3.0.0`	indirect
PyPI	astunparse	`1.6.3`	indirect
PyPI	async-lru	`2.0.4`	indirect
PyPI	attrs	`25.1.0`	indirect
PyPI	babel	`2.17.0`	indirect
PyPI	backcall	`0.2.0`	indirect
PyPI	backrefs	`5.7.post1`	indirect
PyPI	beautifulsoup4	`4.13.3`	indirect
PyPI	black	`24.8.0`	indirect
PyPI	bleach	`6.1.0`	indirect
PyPI	blinker	`1.7.0`	indirect
PyPI	brotli	`1.1.0`	indirect
PyPI	certifi	`2025.1.31`	indirect
PyPI	cffi	`1.17.1`	indirect
PyPI	charset-normalizer	`3.4.1`	indirect
PyPI	click	`8.1.8`	indirect
PyPI	colorama	`0.4.6`	indirect
PyPI	comm	`0.2.2`	indirect
PyPI	coverage	`7.6.1`	indirect
PyPI	cryptography	`43.0.3`	indirect
PyPI	dash-bootstrap-components	`—`	indirect
PyPI	dash-core-components	`2.0.0`	indirect
PyPI	dash-extensions	`1.0.20`	indirect
PyPI	dash-html-components	`2.0.0`	indirect
PyPI	dash-table	`5.0.0`	indirect
PyPI	debugpy	`1.8.12`	indirect
PyPI	decorator	`5.1.1`	indirect
PyPI	defusedxml	`0.7.1`	indirect
PyPI	exceptiongroup	`1.2.2`	indirect
PyPI	executing	`2.2.0`	indirect
PyPI	fastjsonschema	`2.21.1`	indirect
PyPI	flask	`3.0.3`	indirect
PyPI	fqdn	`1.5.1`	indirect
PyPI	ghp-import	`2.1.0`	indirect
PyPI	griffe	`1.4.0`	indirect
PyPI	h11	`0.14.0`	indirect
PyPI	h2	`4.1.0`	indirect
PyPI	hpack	`4.0.0`	indirect
PyPI	httpcore	`1.0.7`	indirect
PyPI	httpx	`0.28.1`	indirect
PyPI	hyperframe	`6.0.1`	indirect
PyPI	idna	`3.10`	indirect
PyPI	importlib-metadata	`8.5.0`	indirect
PyPI	importlib-resources	`6.4.5`	indirect
PyPI	iniconfig	`2.0.0`	indirect
PyPI	ipykernel	`6.29.5`	indirect
PyPI	ipython	`8.12.3`	indirect
PyPI	ipython-genutils	`0.2.0`	indirect
PyPI	ipywidgets	`—`	indirect
PyPI	ipywidgets	`7.8.5`	indirect
PyPI	isoduration	`20.11.0`	indirect
PyPI	itsdangerous	`2.2.0`	indirect
PyPI	jedi	`0.19.2`	indirect
PyPI	jinja2	`3.1.5`	indirect
PyPI	json5	`0.10.0`	indirect
PyPI	jsonpointer	`3.0.0`	indirect
PyPI	jsonschema	`4.23.0`	indirect
PyPI	jsonschema-specifications	`2023.12.1`	indirect
PyPI	jupyter-client	`8.6.3`	indirect
PyPI	jupyter-core	`5.7.2`	indirect
PyPI	jupyter-events	`0.10.0`	indirect
PyPI	jupyter-lsp	`2.2.5`	indirect
PyPI	jupyter-server	`2.14.2`	indirect
PyPI	jupyter-server-terminals	`0.5.3`	indirect
PyPI	jupyterlab	`4.3.5`	indirect
PyPI	jupyterlab-pygments	`0.3.0`	indirect
PyPI	jupyterlab-server	`2.27.3`	indirect
PyPI	jupyterlab-widgets	`1.1.11`	indirect
PyPI	kaitaistruct	`0.10`	indirect
PyPI	line-profiler	`—`	indirect
PyPI	line-profiler	`4.2.0`	indirect
PyPI	markdown	`3.7`	indirect
PyPI	markupsafe	`2.1.5`	indirect
PyPI	matplotlib-inline	`0.1.7`	indirect
PyPI	memory-profiler	`—`	indirect
PyPI	memory-profiler	`0.60.0`	indirect
PyPI	mergedeep	`1.3.4`	indirect
PyPI	mike	`1.1.2`	indirect
PyPI	mistune	`3.1.2`	indirect
PyPI	mkdocs	`1.6.1`	indirect
PyPI	mkdocs-autorefs	`1.2.0`	indirect
PyPI	mkdocs-gen-files	`0.5.0`	indirect
PyPI	mkdocs-get-deps	`0.2.0`	indirect
PyPI	mkdocs-literate-nav	`0.6.1`	indirect
PyPI	mkdocs-material	`9.6.18`	indirect
PyPI	mkdocs-material-extensions	`1.3.1`	indirect
PyPI	mkdocs-section-index	`0.3.9`	indirect
PyPI	mkdocstrings	`0.25.2`	indirect
PyPI	mkdocstrings-python	`1.10.9`	indirect
PyPI	mypy-extensions	`1.0.0`	indirect
PyPI	narwhals	`1.27.1`	indirect
PyPI	nbclient	`0.10.1`	indirect
PyPI	nbconvert	`7.16.6`	indirect
PyPI	nbformat	`5.10.4`	indirect
PyPI	nest-asyncio	`1.6.0`	indirect
PyPI	notebook	`7.3.2`	indirect
PyPI	notebook-shim	`0.2.4`	indirect
PyPI	outcome	`1.3.0.post0`	indirect
PyPI	overrides	`7.7.0`	indirect
PyPI	packaging	`24.2`	indirect
PyPI	paginate	`0.5.7`	indirect
PyPI	pandocfilters	`1.5.1`	indirect
PyPI	parso	`0.8.4`	indirect
PyPI	pathspec	`0.12.1`	indirect
PyPI	pexpect	`4.9.0`	indirect
PyPI	pickleshare	`0.7.5`	indirect
PyPI	pkgutil-resolve-name	`1.3.10`	indirect
PyPI	platformdirs	`4.3.6`	indirect
PyPI	pluggy	`1.5.0`	indirect
PyPI	prometheus-client	`0.21.1`	indirect
PyPI	prompt-toolkit	`3.0.50`	indirect
PyPI	psutil	`7.0.0`	indirect
PyPI	psygnal	`0.11.1`	indirect
PyPI	ptyprocess	`0.7.0`	indirect
PyPI	pure-eval	`0.2.3`	indirect
PyPI	py	`1.11.0`	indirect
PyPI	pyarrow	`—`	indirect
PyPI	pyarrow	`17.0.0`	indirect
PyPI	pyarrow	`19.0.1`	indirect
PyPI	pyasn1	`0.6.1`	indirect
PyPI	pycparser	`2.22`	indirect
PyPI	pydivert	`2.1.0`	indirect
PyPI	pyfunctional	`—`	indirect
PyPI	pygments	`2.19.1`	indirect
PyPI	pymdown-extensions	`10.14.3`	indirect
PyPI	pyopenssl	`25.0.0`	indirect
PyPI	pyparsing	`3.1.4`	indirect
PyPI	pysocks	`1.7.1`	indirect
PyPI	pytest	`7.4.4`	indirect
PyPI	pytest-base-url	`2.0.0`	indirect
PyPI	pytest-cov	`3.0.0`	indirect
PyPI	pytest-html	`3.2.0`	indirect
PyPI	pytest-lazy-fixture	`0.6.3`	indirect
PyPI	pytest-metadata	`2.0.4`	indirect
PyPI	pytest-selenium	`2.0.1`	indirect
PyPI	pytest-variables	`2.0.0`	indirect
PyPI	python-dateutil	`2.9.0.post0`	indirect
PyPI	python-json-logger	`3.2.1`	indirect
PyPI	pytz	`2025.1`	indirect
PyPI	pywin32	`308`	indirect
PyPI	pywinpty	`2.0.14`	indirect
PyPI	pyyaml	`6.0.2`	indirect
PyPI	pyyaml-env-tag	`0.1`	indirect
PyPI	pyzmq	`26.2.1`	indirect
PyPI	referencing	`0.35.1`	indirect
PyPI	requests	`2.32.3`	indirect
PyPI	retrying	`1.3.4`	indirect
PyPI	rfc3339-validator	`0.1.4`	indirect
PyPI	rfc3986-validator	`0.1.1`	indirect
PyPI	rpds-py	`0.20.1`	indirect
PyPI	ruff	`0.12.9`	indirect
PyPI	selenium	`4.2.0`	indirect
PyPI	selenium-wire	`5.1.0`	indirect
PyPI	send2trash	`1.8.3`	indirect
PyPI	setuptools	`75.3.0`	indirect
PyPI	six	`1.17.0`	indirect
PyPI	sniffio	`1.3.1`	indirect
PyPI	sortedcontainers	`2.4.0`	indirect
PyPI	soupsieve	`2.6`	indirect
PyPI	stack-data	`0.6.3`	indirect
PyPI	tenacity	`6.3.1`	indirect
PyPI	terminado	`0.18.1`	indirect
PyPI	tinycss2	`1.2.1`	indirect
PyPI	tomli	`2.2.1`	indirect
PyPI	tornado	`6.4.2`	indirect
PyPI	traitlets	`5.14.3`	indirect
PyPI	trio	`0.27.0`	indirect
PyPI	trio-websocket	`0.12.1`	indirect
PyPI	types-python-dateutil	`2.9.0.20241206`	indirect
PyPI	typing-extensions	`4.12.2`	indirect
PyPI	tzdata	`2025.1`	indirect
PyPI	uri-template	`1.3.0`	indirect
PyPI	urllib3	`1.26.20`	indirect
PyPI	urllib3-secure-extra	`0.1.0`	indirect
PyPI	verspec	`0.1.0`	indirect
PyPI	watchdog	`4.0.2`	indirect
PyPI	wcwidth	`0.2.13`	indirect
PyPI	webcolors	`24.8.0`	indirect
PyPI	webencodings	`0.5.1`	indirect
PyPI	websocket-client	`1.8.0`	indirect
PyPI	werkzeug	`3.0.6`	indirect
PyPI	wheel	`0.45.1`	indirect
PyPI	widgetsnbextension	`3.6.10`	indirect
PyPI	wsproto	`1.2.0`	indirect
PyPI	zipp	`3.20.2`	indirect
PyPI	zstandard	`0.23.0`	indirect

Dependency advisories 0

Installing pypi:plotly-resampler@0.11.0 pulls in 34 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.

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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 statistics — PyPI.

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