vaex
Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.
npx skills add K-Dense-AI/scientific-agent-skills --skill vaex --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Vaex ## Overview Vaex is a high-performance Python library designed for lazy, out-of-core DataFrames to process and visualize tabular datasets that are too large to fit into RAM. Vaex can process over a billion rows per second, enabling interactive data exploration and analysis on datasets with billions of rows. ## Installation Install the full meta-package (recommended): ```bash uv pip install vaex ``` Minimal install (pick only what you need): ```bash uv pip install vaex-core vaex-viz vaex-hdf5 vaex-ml ``` The `vaex` package is a meta-package that pulls in `vaex-core`, `vaex-viz`, `vaex-hdf5`, `vaex-ml`, and other sub-packages. Arrow support is built into `vaex-core` (the separate `vaex-arrow` package is deprecated). `vaex-distributed` is deprecated in favor of vaex-enterprise. **Version notes (vaex 4.19.0+):** Python 3.12 and NumPy v2 require vaex >= 4.19.0. On Windows, you may need Python dev headers to build the `annoy` dependency. ## When to Use This Skill Use Vaex when: - Processing tabular datasets larger than available RAM (gigabytes to terabytes) - Performing fast statistical aggregations on massive datasets - Creating visualizations and heatmaps of large datasets - Bui
- Overview
- Installation
- When to Use This Skill
- Core Capabilities
- 1. DataFrames and Data Loading
- 2. Data Processing and Manipulation
- 3. Performance and Optimization
- 4. Data Visualization
- 5. Machine Learning Integration
- 6. I/O Operations
- Quick Start Pattern
- Working with References
- Best Practices
- Common Patterns
uv pip install vaex uv pip install vaex-core vaex-viz vaex-hdf5 vaex-ml
What does the vaex skill do?
Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill vaex --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.