Öffentliches RegisterMittel
Software-GesundheitsberichtSchema 0.26.0 · Metriken 1.13.0 · 2026-07-21 23:20 UTC
tensorflow / fairness-indicators
Tensorflow's Fairness Evaluation and Visualization Toolkit
tensorflow/fairness-indicators erreicht einen Gesundheitsindex von 69 von 100 und liegt damit im Bereich Mittel. Am stärksten schneidet es bei Engineering Quality (88/100) ab, am schwächsten bei AI Readiness (29/100). Zuletzt vor 11 Tagen aktualisiert. 3 Mitwirkende tragen den Großteil der jüngsten Arbeit.
69
gesamt / 100Software-Gesundheitsindex
Metriken werden auf einer Skala von 1–100 in gewichtete Kategorien gruppiert. Der Gesamtwert beginnt als ihr Mittel; sobald öffentliche Evidenz die Richtlinie für Hochrisikojurisdiktionen auslöst, wird die Bewertung angepasst und erhält die Obergrenze 49 (Gefährdet). AI Readiness liegt außerhalb.
69
Exzellent85-100Vorbildlich; erfüllt im Wesentlichen alle geprüften Kriterien
Gut70-84Gesund; geringfügige Lücken
Mittel50-69Akzeptabel mit deutlichen Lücken; Überprüfung empfohlen
Gefährdet30-49Erhebliche Schwächen; eine Übernahme erfordert Vorsicht
Kritisch1-29Schwerwiegende Probleme (aufgegeben, nur ein Maintainer, keine Hygiene)
Bewertungsprofil
Jede Achse ist eine Kategorie. Die Form zählt mehr als der Durchschnitt — ein gesundes Projekt füllt die gesamte Fläche, während ein Profil aus Spitzen und Kratern bedeutet, dass Stärke in einer Dimension Risiken in einer anderen verdeckt.
Eigentümerschaft
tensorflowOrganisation
21.576 Follower107 öffentliche Reposseit Nov. 2015
Dieses Repository wird von einer Organisation getragen — geteilte, rechenschaftspflichtige Trägerschaft, die jeden einzelnen Maintainer überdauern kann.
Paket-Ökosysteme
| Registry | Paket | Version | Downloads / Monat | Versionen | Zuletzt veröffentlicht | Tags |
|---|---|---|---|---|---|---|
| PyPI | fairness_indicators | 0.52.0 | - | 34 | vor 11 Tagen | tensorflowmodelanalysisfairnessindicatorstensorboardmachinelearning |
| PyPI | tensorboard_plugin_fairness_indicators | 0.52.0 | 241 | 30 | vor 11 Tagen | tensorflowmodelanalysisfairnessindicatorstensorboardmachinelearning |
Metriken nach Kategorie
Vitalität
Lebt das Projekt — wird Code geschrieben und werden Releases ausgeliefert?
58
Entwicklungsaktivität
41Gefährdet
Wie die Bewertung erfolgt
| 28.8/36 | Push-Aktualität — letzter Push vor 11 Tagen |
| 2.1/36 | Commit-Rhythmus — 3/52 Wochen mit Commits |
| 9.7/18 | Commit-Volumen — 11 Commits im letzten Jahr |
| 0/10 | OpenSSF Scorecard: Maintained — 1 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0 |
Verwendete Eingangsdaten
| commits_last_year | 11 |
| human_commit_share | 1 |
| days_since_last_push | 11 |
| active_weeks_last_year | 3 |
Release-Disziplin
84Gut
Wie die Bewertung erfolgt
| 27/27 | Liefert Releases aus — 27 Releases veröffentlicht |
| 36/36 | Release-Aktualität — letztes Release vor 11 Tagen |
| 12.6/27 | Release-Rhythmus — ein Release etwa alle 167,2 Tage |
| 0/10 | OpenSSF Scorecard: Signed-Releases — keine Daten |
Verwendete Eingangsdaten
| releases_count | 27 |
| latest_release_tag | v0.52.0 |
| releases_from_tags | nein |
| days_since_latest_release | 11 |
| mean_days_between_releases | 167,2 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): OpenSSF Scorecard: Signed-Releases. Die verbleibenden Gewichte wurden renormalisiert.
Community & Verbreitung
Hat das Projekt Nutzer, Downloads, Aufmerksamkeit und ein einladendes Umfeld für Beitragende?
60
Popularität & Verbreitung
65Mittel
Wie die Bewertung erfolgt
| 41.4/60 | Stars — 358 Stars |
| 16.2/25 | Forks — 89 Forks |
| 7.1/15 | Watcher — 20 Watcher |
Verwendete Eingangsdaten
| forks | 89 |
| stars | 358 |
| watchers | 20 |
| growth_state | organic |
| growth_factor_pct | 100 |
Community-Gesundheit
70Gut
Wie die Bewertung erfolgt
| 22.5/22.5 | README |
| 22.5/22.5 | Lizenz — anerkannte Lizenz (Apache-2.0) |
| 18/18 | CONTRIBUTING-Leitfaden |
| 0/13.5 | Verhaltenskodex |
| 0/7.2 | Issue-Vorlage |
| 0/6.3 | PR-Vorlage |
Verwendete Eingangsdaten
| has_readme | ja |
| has_license | ja |
| has_contributing | ja |
| has_issue_template | nein |
| has_code_of_conduct | nein |
| has_pull_request_template | nein |
Ökosystem-Verbreitung (Downloads)
40Gefährdet
Wie die Bewertung erfolgt
| 31.8/80 | Downloads pro Monat — 241 Downloads/Monat über pypi |
| 0/20 | Abhängige in der Registry — von diesem Ökosystem nicht ausgewiesen |
Verwendete Eingangsdaten
| packages | fairness_indicators, tensorboard_plugin_fairness_indicators |
| dependents | — |
| ecosystems | pypi |
| total_downloads | — |
| monthly_downloads | 241 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Abhängige in der Registry. Die verbleibenden Gewichte wurden renormalisiert.
Nachhaltigkeit & Governance
Überdauert das Projekt die Menschen, die es tragen — Bus-Faktor, Reaktionsfähigkeit, Trägerschaft und Paketpflege?
80
Wie die Bewertung erfolgt
| 36/54 | Bus-Faktor — 3 Beitragende decken die Hälfte aller Commits ab |
| 17.7/22.5 | Commit-Verteilung — wichtigste beitragende Person verfasste 21 % der Commits |
| 13.5/13.5 | Breite der Beitragenden — 31 Beitragende |
| 10/10 | OpenSSF Scorecard: Contributors — project has 7 contributing companies or organizations |
Verwendete Eingangsdaten
| bus_factor | 3 |
| contributors_sampled | 31 |
| top_contributor_share | 0,213 |
Wie die Bewertung erfolgt
| 27.5/46.8 | Issue-Lösungsquote — 59 % der Issues geschlossen |
| 29.9/38.3 | PR-Annahme — 274/350 entschiedene PRs gemergt |
| 12/15 | OpenSSF Scorecard: Code-Review — Found 5/6 approved changesets -- score normalized to 8 |
Verwendete Eingangsdaten
| merged_prs | 274 |
| open_issues | 7 |
| closed_issues | 10 |
| issue_closed_ratio | 0,588 |
| closed_unmerged_prs | 76 |
Wie die Bewertung erfolgt
| 30/30 | Organisatorische Trägerschaft — im Besitz einer Organisation |
| 0/20 | Verifizierte Domain |
| 25/25 | Reichweite des Inhabers — 21.576 Follower von tensorflow |
| 25/25 | Kontohistorie — 107 öffentliche Repos, Kontoalter ca. 10 Jahre |
Verwendete Eingangsdaten
| followers | 21.576 |
| owner_type | Organization |
| is_verified | — |
| owner_login | tensorflow |
| public_repos | 107 |
| account_age_days | 3.912 |
Paketpflege
100Exzellent
Wie die Bewertung erfolgt
| 25/25 | Veröffentlicht & auflösbar — 2 Paket(e) auf pypi |
| 35/35 | Veröffentlichungsaktualität — letzte Veröffentlichung vor 11 Tagen |
| 20/20 | Versionshistorie — 34 veröffentlichte Versionen |
| 20/20 | Nicht veraltet — aktiv, nicht veraltet oder zurückgezogen |
Verwendete Eingangsdaten
| packages | fairness_indicators, tensorboard_plugin_fairness_indicators |
| ecosystems | pypi |
| any_deprecated | nein |
| min_days_since_publish | 11 |
Engineering-Qualität
Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?
88
Engineering-Praktiken
90Exzellent
Wie die Bewertung erfolgt
| 24/24 | CI-Workflows — 4 Workflow(s) |
| 24/24 | Tests vorhanden |
| 16/16 | Linter-Konfiguration |
| 9.6/9.6 | Pre-Commit-Hooks |
| 0/6.4 | .editorconfig |
| 16/20 | OpenSSF Scorecard: CI-Tests — 4 out of 5 merged PRs checked by a CI test -- score normalized to 8 |
Verwendete Eingangsdaten
| has_ci | ja |
| has_tests | ja |
| has_editorconfig | nein |
| has_linter_config | ja |
| has_precommit_config | ja |
Dokumentation
85Exzellent
Wie die Bewertung erfolgt
| 30/30 | README |
| 25/25 | Dokumentationsverzeichnis |
| 0/15 | Dokumentations-/Homepage-Site |
| 10/10 | Repository-Beschreibung |
| 10/10 | Topics — 3 Topics |
| 10/10 | Wiki |
Verwendete Eingangsdaten
| topics | tensorflow, fairness-ml, fairness-indicators |
| has_wiki | ja |
| homepage | — |
| has_readme | ja |
| has_docs_dir | ja |
| has_description | ja |
Sicherheit
Sind die sichtbaren Sicherheits- und Lieferkettenpraktiken belastbar, ohne ungeklärte Exposition gegenüber Hochrisikojurisdiktionen?
52
Sicherheitslage
44Gefährdet
Wie die Bewertung erfolgt
| 7.5/7.5 | Binary-Artifacts — no binaries found in the repo |
| 0/7.5 | Branch-Protection — branch protection not enabled on development/release branches |
| 2/2.5 | CI-Tests — 4 out of 5 merged PRs checked by a CI test -- score normalized to 8 |
| 0/2.5 | CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected |
| 6/7.5 | Code-Review — Found 5/6 approved changesets -- score normalized to 8 |
| 2.5/2.5 | Contributors — project has 7 contributing companies or organizations |
| 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 | Lizenz — license file detected |
| 0/7.5 | Maintained — 1 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0 |
| 5/5 | Packaging — packaging workflow detected |
| 0/5 | Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0 |
| 0/5 | SAST — SAST tool is not run on all commits -- score normalized to 0 |
| 0/5 | Security-Policy — security policy file not detected |
| 0/7.5 | Signed-Releases — keine Daten |
| 0/7.5 | Token-Permissions — detected GitHub workflow tokens with excessive permissions |
| 7.5/7.5 | Vulnerabilities — 0 existing vulnerabilities detected |
Verwendete Eingangsdaten
| source | openssf_scorecard |
| checks_evaluated | 17 |
| scorecard_version | v5.5.0 |
| checks_inconclusive | 1 |
| scorecard_aggregate | 4,4 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): signed_releases. Die verbleibenden Gewichte wurden renormalisiert.
Wie die Bewertung erfolgt
| 35/35 | Direkte Abhängigkeiten ohne bekannte Advisories — keine direkte Abhängigkeit trägt ein bekanntes Advisory |
| 8.4/25 | Indirekte Abhängigkeiten ohne bekannte Advisories — 2 betroffen: cryptography 47.0.0 (high 7.5), httplib2 0.31.2 (high 7.5) |
| 40/40 | Keine offenen Advisories — kein Advisory ist länger als 90 Tage öffentlich |
Verwendete Eingangsdaten
| source | osv |
| advisories | 2 |
| affected_packages | 2 |
| assessed_packages | 162 |
| unassessed_packages | 0 |
| affected_by_severity | high 2 |
| direct_affected_packages | 0 |
Abgeglichen wurde die Laufzeit-Abhängigkeitshülle von pypi:fairness_indicators@0.52.0 — das, was die Installation des veröffentlichten Pakets nach sich zieht — mit 162 Paketen. Erreichbarkeit wird nicht analysiert.
AI Readiness
Wie gut ist das Repository dafür ausgestattet, mit KI-Coding-Agenten entwickelt und gepflegt zu werden? Ein unabhängiges, experimentelles Badge — Gewicht 0,0, es wird eigenständig ausgewiesen und verändert den Gesamt-Gesundheitswert nicht.
29
AI-Agentenkontext & Leitlinien
4Kritisch
Wie die Bewertung erfolgt
| 0/45 | Agentenanweisungen — keine CLAUDE.md / AGENTS.md / Editor-Regeln |
| 0/15 | Maschinenlesbare Doku (llms.txt) |
| 4.3/40 | Lesbare Commit-Historie — 8 von 100 menschlichen Commits benennen ihre Absicht (strukturierter Betreff oder erläuternder Text) |
Verwendete Eingangsdaten
| has_llms_txt | nein |
| legible_history_share | 0,08 |
| agent_instruction_files | — |
| agent_instruction_max_bytes | — |
AI-Verify-Loop (Build / Test / Typprüfung)
33Gefährdet
Wie die Bewertung erfolgt
| 0/18 | Bootstrap mit einem Befehl |
| 22/22 | Automatisierte Tests |
| 11/11 | Lint-/Format-Konfiguration |
| 0/11 | Statische Typprüfung |
| 0/10 | Reproduzierbare Umgebung |
| 0/10 | Belegte Agentenpraxis — keine von Agenten verfassten Commits unter den letzten 100 |
| 0/8 | Automatisierte Wartung — keine automatisierten Abhängigkeits-Updates beobachtet |
| 0/10 | OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0 |
Verwendete Eingangsdaten
| has_nix | nein |
| has_tests | ja |
| lockfiles | — |
| has_dockerfile | nein |
| typed_language | nein |
| bootstrap_files | — |
| has_devcontainer | nein |
| has_linter_config | ja |
| typecheck_configs | — |
| agent_commit_share | 0 |
| toolchain_manifests | — |
| dependency_bot_commit_share | 0 |
AI-Code-Lesbarkeit für Modelle
55Mittel
Wie die Bewertung erfolgt
| 0/45 | Typprüfbarer Code — Python ohne Typprüfungs-Konfiguration |
| 55/55 | Handhabbare Dateigrößen — 0/25 Quelldateien über 60 KB |
Verwendete Eingangsdaten
| primary_language | Python |
| largest_source_bytes | 11.671 |
| source_files_sampled | 25 |
| oversized_source_files | 0 |
Maschinenlesbare AI-Schnittstellen
40Gefährdet
Wie die Bewertung erfolgt
| 0/40 | API-Schema (OpenAPI/GraphQL/proto) |
| 0/20 | MCP-Server |
| 40/40 | Lauffähige Beispiele — notebooks |
Verwendete Eingangsdaten
| example_dirs | notebooks |
| has_mcp_signal | nein |
| api_schema_files | — |
Eckdaten
358GitHub-Sterne
31Mitwirkende
11Commits, letzte 12 Monate
11Tage seit letztem Push
27Releases
3Bus-Faktor
7offene Issues
PyPIPaket-Ökosysteme
Weitere Details
Stern- und Fork-Verlauf 358 ★ / 89 ⇿
358Sterne
89Forks
26Releases
Wann jeder Stern und Fork hinzugefügt wurde, von GitHub erfasst und nach Tagen gruppiert. Das kumulierte Wachstum steht direkt über den täglichen Zugängen, aus denen es besteht, sodass beide gegeneinander lesbar sind: stetiger organischer Zuwachs sieht ganz anders aus als ein abrupter, kurzlebiger Ausschlag. Wo dieser Unterschied messbar ist, wird er als Wachstumsauthentizität ausgewiesen.
Major 0Minor 23Patch 3
Jeder Punkt umfasst 7 Tage.
OpenSSF Scorecard 4.4 / 10
4.4Gesamtwert
Unabhängige, werkzeugneutrale Sicherheitsbewertung durch das quelloffene OpenSSF Scorecard. Jede Prüfung honoriert eine Sicherheits-Praxis, nicht das Werkzeug eines bestimmten Anbieters. Prüfungen, die Scorecard nicht ermitteln konnte, sind mit k. A. markiert und vom Sicherheitswert ausgeschlossen (nie als null gezählt).
| 10 | Binary-Artifacts | no binaries found in the repo |
| 0 | Branch-Protection | branch protection not enabled on development/release branches |
| 8 | CI-Tests | 4 out of 5 merged PRs checked by a CI test -- score normalized to 8 |
| 0 | CII-Best-Practices | no effort to earn an OpenSSF best practices badge detected |
| 8 | Code-Review | Found 5/6 approved changesets -- score normalized to 8 |
| 10 | Contributors | project has 7 contributing companies or organizations |
| 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 | 1 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0 |
| 10 | Packaging | packaging workflow detected |
| 0 | Pinned-Dependencies | dependency not pinned by hash detected -- score normalized to 0 |
| 0 | SAST | SAST tool is not run on all commits -- score normalized to 0 |
| 0 | Security-Policy | security policy file not detected |
| k. A. | Signed-Releases | no releases found |
| 0 | Token-Permissions | detected GitHub workflow tokens with excessive permissions |
| 10 | Vulnerabilities | 0 existing vulnerabilities detected |
Alle Abhängigkeiten 14
Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 0 direkte und 14 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.
| Registry | Paket | Version | Beziehung |
|---|---|---|---|
| PyPI | mkdocs | — | indirekt |
| PyPI | mkdocs-jupyter | — | indirekt |
| PyPI | mkdocs-material | — | indirekt |
| PyPI | numpy | — | indirekt |
| PyPI | protobuf | — | indirekt |
| PyPI | pytest | — | indirekt |
| PyPI | tensorboard | — | indirekt |
| PyPI | tensorflow | — | indirekt |
| PyPI | tensorflow-data-validation | — | indirekt |
| PyPI | tensorflow-hub | — | indirekt |
| PyPI | tensorflow-model-analysis | — | indirekt |
| PyPI | tf-keras | — | indirekt |
| PyPI | werkzeug | — | indirekt |
| PyPI | witwidget | — | indirekt |
Abhängigkeits-Advisories 2
Die Installation von pypi:fairness_indicators@0.52.0 zieht 162 Pakete nach sich, direkt und transitiv: 2 tragen bekannte Advisories, davon 0 direkte Abhängigkeiten.
| Paket | Version | Beziehung | Schweregrad | Advisories | Behoben in |
|---|---|---|---|---|---|
| cryptography | 47.0.0 | indirekt | hoch | 1 | 48.0.1 |
| httplib2 | 0.31.2 | indirekt | hoch | 1 | 0.32.0 |
Ein Advisory bedeutet, dass die im Abhängigkeitsgraphen erfasste Version in den betroffenen Bereich eines Advisories fällt. Erreichbarkeit wird nicht analysiert, und der Graph enthält Entwicklungs- und Test-Pins — ein Fund kann das Werkzeug betreffen und nicht die ausgelieferte Software.
JSON-Rohbericht maschinenlesbar
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"kind": "minor",
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"tag": "v0.38.0",
"kind": "minor",
"published_at": "2022-03-07T20:04:18Z"
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"tag": "v0.28.0",
"kind": "minor",
"published_at": "2021-03-09T20:20:07Z"
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{
"tag": "v0.27.0",
"kind": "minor",
"published_at": "2021-01-28T22:02:14Z"
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{
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{
"key": "community_health",
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"note": null,
"notes": [],
"value": 70,
"inputs": {
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"has_license": true,
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"components": [
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"status": "met",
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{
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{
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{
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}
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{
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{
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"inputs": {
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"components": [
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{
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{
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{
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{
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{
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{
"key": "vulnerabilities",
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{
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"components": [
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],
"max_points": 35
},
{
"key": "indirect_dependencies_free_of_known_advisories",
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"status": "partial",
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{
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"description": "How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score."
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}Bewertungen sind Signale, keine Garantien. Sie spiegeln öffentlich sichtbare Praxis auf GitHub wider — kein Code-Audit und keine Sicherheitsgarantie.
Fehlende Daten werden ausgeschlossen und die Gewichte neu normiert, nie als null bewertet. Die Methodik ist versioniert und offen: Metriken v1.13.0, Schema v0.26.0 — vollständige Methodik · Metriken-Wiki.
Wie ein einzelnes Ergebnis im Gesamtregister steht: aggregierte Statistiken — PyPI.