Fähigkeiten polars
⚡

polars

Sicher ⚙️ Externe Befehle🔑 Umgebungsvariablen⚡ Enthält Skripte🌐 Netzwerkzugriff

Arbeiten Sie effizient mit Polars DataFrames

Auch verfügbar von: davila7

Verarbeiten Sie große Datensätze im Speicher mit Polars, der leistungsstarken DataFrame-Bibliothek. Funktionen umfassen verzögerte Auswertung, parallele Ausführung und Apache Arrow-Backend für 10x schnellere Operationen als pandas.

Unterstützt: Claude Codex Code(CC)
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Teste es

Verwendung von "polars". Laden Sie eine CSV-Datei und filtern Sie Zeilen, in denen das Alter größer als 25 ist

Erwartetes Ergebnis:

  • DataFrame mit Spalten erstellt: name, age, city
  • 2 Zeilen gefiltert, wo age > 25
  • Ausgewählte Spalten: name, age

Verwendung von "polars". Gruppieren Sie Verkaufsdaten nach Produktkategorie und berechnen Sie Gesamt- und Durchschnittsumsatz

Erwartetes Ergebnis:

  • Nach product_category gruppiert
  • Summe und Mittelwert von sales_amount berechnet
  • Ergebnis enthält: category, total_sales, avg_sales

Verwendung von "polars". Lesen Sie eine Parquet-Datei mit verzögerter Auswertung und sammeln Sie nur benötigte Spalten

Erwartetes Ergebnis:

  • scan_parquet für lazy Loading verwendet
  • Nur erforderliche Spalten frühzeitig ausgewählt
  • Mit Predicate-Pushdown-Optimierung gesammelt

Sicherheitsaudit

Sicher
v4 • 1/17/2026

This skill contains ONLY markdown documentation files with Python code examples. All 690 static findings are FALSE POSITIVES. The analyzer misidentified markdown code blocks, Python syntax, and Polars library methods as security threats. No executable code, shell commands, credential access, or network operations exist.

8
Gescannte Dateien
7,074
Analysierte Zeilen
4
befunde
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Gesamtzahl Audits

Risikofaktoren

⚙️ Externe Befehle (647)
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🔑 Umgebungsvariablen (9)
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⚡ Enthält Skripte (1)
references/operations.md:532
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Was du bauen kannst

ETL-Pipelines erstellen

Erstellen Sie effiziente Datenpipelines mit verzögerter Auswertung für Speicheroptimierung und parallele Ausführung.

Daten transformieren und aggregieren

Filtern, gruppieren und aggregieren Sie große Datensätze mit ausdrucksbasierter Syntax und Fensterfunktionen.

pandas durch schnellere Alternative ersetzen

Migrieren Sie bestehenden pandas-Code zu Polars für erhebliche Leistungsverbesserungen bei mittelgroßen Datensätzen.

Probiere diese Prompts

Daten laden und erkunden 
Laden Sie eine CSV-Datei mit Polars und zeigen Sie die ersten Zeilen, Spaltentypen und grundlegende Statistiken.
Filtern und auswählen 
Filtern Sie Zeilen, in denen eine Spalte eine Bedingung erfüllt, und wählen Sie bestimmte Spalten mit Polars-Ausdrücken aus.
Gruppieren und aggregieren 
Gruppieren Sie Daten nach einer oder mehreren Spalten und berechnen Sie Aggregation wie Mittelwert, Summe und Anzahl.
Lazy-Optimierung 
Konvertieren Sie diese DataFrame-Operation zur Verwendung von verzögerter Auswertung und erklären Sie die Leistungsvorteile.

Bewährte Verfahren

  • Verwenden Sie scan_csv oder scan_parquet mit verzögerter Auswertung für große Datensätze, um Abfrageoptimierung zu ermöglichen
  • Filtern und wählen Sie Spalten frühzeitig in Ihrer Pipeline aus, um Speichernutzung zu reduzieren und die Leistung zu verbessern
  • Bevorzugen Sie native Polars-Ausdrücke gegenüber Python-Funktionen, um parallele Ausführung zu ermöglichen

Vermeiden

  • Vermeiden Sie die Verwendung von read_csv auf großen Dateien, wenn verzögerte Auswertung ausreichen würde
  • Wenden Sie Python-Funktionen nicht in heißen Pfaden an, wenn Polars-Ausdrücke dieselbe Aufgabe erfüllen können
  • Vermeiden Sie das Laden ganzer Datensätze in den Speicher, wenn Streaming mit collect(streaming=True) funktionieren würde

Häufig gestellte Fragen

Wie unterscheidet sich Polars von pandas?
Polars hat keinen Index, verwendet strikte Typisierung, bietet verzögerte Auswertung und parallelisiert Operationen standardmäßig. Es ist schneller für mittelgroße Datensätze.
Wann sollte ich verzögerte Auswertung verwenden?
Verwenden Sie verzögerte Auswertung für große Datensätze, komplexe Pipelines oder wenn Leistung wichtig ist. Sie ermöglicht Abfrageoptimierung vor der Ausführung.
Welche Datengrößen funktionieren am besten mit Polars?
Polars eignet sich hervorragend für Datensätze von 1MB bis 100GB, die in den RAM passen. Verwenden Sie dask oder vaex für größere Daten.
Kann ich einfach von pandas migrieren?
Ja, die Migration ist straightforward mit ähnlichen Konzepten, aber unterschiedlicher Syntax. Die meisten Operationen lassen sich direkt auf Polars-Äquivalente abbilden.
Unterstützt Polars Cloud-Speicher?
Ja, Polars liest und schreibt auf S3, Azure Blob Storage und Google Cloud Storage mit entsprechend konfigurierten Anmeldeinformationen.
Welche Dateiformate unterstützt Polars?
Polars unterstützt CSV, Parquet, JSON, Excel, IPC/Arrow und Datenbanken über Konnektoren. Parquet wird für Leistung empfohlen.

Entwicklerdetails

Lizenz

https://github.com/pola-rs/polars/blob/main/LICENSE

Repository

https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/polars

Ref

main

Dateistruktur

📁 references/

📄 best_practices.md

📄 core_concepts.md

📄 io_guide.md

📄 operations.md

📄 pandas_migration.md

📄 transformations.md

📄 SKILL.md