alterlab-vaex
De la autor
Out-of-core tabular analytics with Vaex — memory-mapped HDF5/Arrow/Parquet via vaex.open, lazy virtual columns, delayed single-pass aggregations on billion-row tables, binned histograms/heatmaps, and vaex.ml transformers on one machine. Vaex is in minimal-maintenance mode (vaex-core 4.19 from Sep 2025 supports only Python 3.9–3.12 and pandas < 3), so use it to run, fix, or migrate existing Vaex code; for new larger-than-RAM work prefer Polars lazy/streaming queries (alterlab-polars) or Dask (alterlab-dask). Use when a project already depends on Vaex, when opening memory-mapped HDF5/Arrow files, batching aggregations with delay=True, or porting a Vaex pipeline to Polars. Part of the AlterLab Academic Skills suite.
Instalare
npx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-vaex -a claude-code
Se instalează în .claude/skills/alterlab-vaex/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
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Skill-uri similare
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- vaexK-Dense-AI/scientific-agent-skillsProcesses large tabular scientific datasets with Vaex expressions, filtered views, streamed statistics, binned visualizations, and file conversion. Use for larger-than-RAM HDF5, Arrow, CSV, or Parquet analysis, virtual feature engineering, or Vaex ML preprocessing; distinguishes these operations from estimators and conversions that materialize data.Date★ 54.5K
- vaex-dataframesjaechang-hits/SciAgent-SkillsOut-of-core DataFrame for billion-row data via lazy evaluation and memory-mapped files. Use when data exceeds RAM (10 GB–TB) for fast aggregation, filtering, virtual columns, and visualization without loading. Supports HDF5, Arrow, Parquet, CSV with cloud (S3, GCS, Azure). Built-in ML transformers (scaling, PCA, K-means). In-memory: polars; distributed: dask.DevOps★ 383
- alterlab-daskAlterLab-IEU/AlterLab-Academic-SkillsScales pandas/NumPy workflows beyond memory with Dask distributed computing — parallel DataFrames, arrays, delayed task graphs, and cluster execution. Use when existing pandas/NumPy code must run on larger-than-RAM data or across clusters, for parallel file processing, distributed ML, or integration with existing pandas code. For out-of-core analytics on a single machine prefer vaex; for in-memory speed prefer polars. Part of the AlterLab Academic Skills suite.Date★ 78
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- polars-dataframesjaechang-hits/SciAgent-SkillsFast in-memory DataFrame with lazy evaluation, parallel execution, Arrow backend. Use for tabular data in RAM (1–100 GB) when pandas is too slow. Expression API: select, filter, group_by, joins, pivots, window. Lazy mode enables predicate/projection pushdown. Reads CSV, Parquet, JSON, Excel, DBs, cloud. Larger-than-RAM: Dask; GPU: cuDF.Date★ 383
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