Compétences vaex
📊
vaex
Sûr ⚙️
Commandes externes🌐
Accès réseau📁
Accès au système de fichiers🔑
Variables d’environnement
Processar conjuntos de dados com bilhões de linhas de forma eficiente
Également disponible depuis: K-Dense-AI
Trabalhar com grandes conjuntos de dados que excedem a RAM causa erros de memória e desempenho lento. Vaex usa avaliação preguiçosa e mapeamento de memória para processar bilhões de linhas instantaneamente sem carregar dados na memória.
Prend en charge: Claude Codex Code(CC)
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Activez et commencez à utiliser
Tester
Utilisation de "vaex". Carregar meu arquivo de dados de vendas de 10GB e mostrar a distribuição de receita por região
Résultat attendu:
- Formato do conjunto de dados: 150.000.000 linhas × 25 colunas
- Uso de memória: 0 bytes (HDF5 mapeado em memória)
- Receita por região:
- • Norte: $12,5B (média: $245)
- • Sul: $8,3B (média: $198)
Utilisation de "vaex". Criar uma coluna virtual para IMC a partir das colunas de altura e peso
Résultat attendu:
- Coluna virtual criada: df['bmi']
- Sobrecarga de memória: 0 bytes
- Fórmula: df.weight_kg / (df.height_m ^ 2)
- Pronto para agregações e filtragem.
Utilisation de "vaex". Mostrar os 10 principais clientes por valor total de compra
Résultat attendu:
- Análise de clientes:
- • Principal cliente: $1,2M total
- • Top 10 clientes: $8,5M combinado
- • Tempo de processamento: 0,3 segundos (avaliação preguiçosa)
Audit de sécurité
Sûrv5 • 1/17/2026
This is a pure documentation skill containing only reference guides and Python code examples for the Vaex library. All 501 static findings are false positives triggered by documentation patterns. The analyzer misinterprets markdown code examples, placeholder credential documentation, and legitimate feature descriptions as security issues. No executable code, network operations, or credential exposure exists.
8
Fichiers analysés
3,938
Lignes analysées
4
résultats
5
Total des audits
Facteurs de risque
⚙️ Commandes externes (444)
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🌐 Accès réseau (2)
📁 Accès au système de fichiers (16)
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🔑 Variables d’environnement (1)
Audité par: claude Voir l’historique des audits →
Score de qualité
45
Architecture
100
Maintenabilité
85
Contenu
20
Communauté
100
Sécurité
91
Conformité aux spécifications
Ce que vous pouvez construire
Analisar conjuntos de dados massivos
Explorar e analisar conjuntos de dados com bilhões de linhas sem erros de memória ou amostragem.
Treinar modelos em big data
Construir e implantar pipelines de ML em conjuntos de dados grandes demais para ferramentas tradicionais.
Processar dados de séries temporais
Trabalhar com grandes dados de séries temporais financeiras para análise de risco e previsão.
Essayez ces prompts
Carregar conjunto de dados grande
Load a large HDF5/Parquet file with Vaex and show basic statistics and column info.
Filtrar e agregar
Filter the dataset by condition and compute groupby aggregations efficiently.
Criar visualizações
Create a heatmap or histogram visualization of the large dataset.
Construir pipeline de ML
Preprocess features using Vaex ML transformers and train an XGBoost model.
Bonnes pratiques
- Converter arquivos CSV para formato HDF5 ou Arrow para carregamento instantâneo
- Usar colunas virtuais em vez de materializar dados para economizar memória
- Agrupar múltiplas operações com delay=True para computação de passagem única
- Aproveitar seleções em vez de criar novos DataFrames para filtragem
Éviter
- Usar .to_pandas_df() em grandes conjuntos de dados anula o propósito do Vaex
- Converter dados para arrays NumPy com .values quando não necessário
- Exportar para CSV repetidamente em vez de usar HDF5/Arrow
- Materializar colunas virtuais sem um bom motivo
Foire aux questions
Como o Vaex lida com conjuntos de dados maiores que a RAM?
Vaex mapeia arquivos em memória, mantendo dados no disco e lendo apenas as porções acessadas na memória.
Quais formatos de arquivo funcionam melhor com Vaex?
HDF5 e Apache Arrow fornecem carregamento instantâneo. CSV é lento para arquivos grandes.
Posso usar Vaex com código pandas?
Vaex tem API similar ao pandas, mas algumas operações diferem. Compatibilidade total com pandas não é garantida.
Meus dados estão seguros ao usar Vaex?
Vaex nunca modifica arquivos de origem. Todas as transformações criam colunas virtuais ou novas exportações.
Por que minhas operações estão executando lentamente?
Verifique se você está usando formato HDF5/Arrow, não CSV. Use delay=True para múltiplas agregações.
Como o Vaex se compara ao Dask ou Polars?
Vaex se destaca em conjuntos de dados com bilhões de linhas com memória mínima. Dask lida com computação distribuída, Polars é mais rápido para dados em memória.