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Vaex Large Dataset Analysis

Analyze larger-than-RAM tables with Vaex: lazy expressions, streamed stats, binned plots, and file conversion.

Source Download skill
Repository
K-Dense-AI/scientific-agent-skills
Stars
55,780
Licence
MIT
Files
8 files

What it does

This skill teaches an AI agent how to work with very large tabular datasets using the Vaex library, which processes data in chunks so it does not need to load everything into memory. Once installed, the agent follows a careful workflow: it checks schema and missing values, uses virtual columns and filters, computes batched statistics, plots aggregated grids instead of raw points, and converts files between HDF5, Arrow, CSV, and Parquet. It also warns where operations like sorting, joins, or model fitting can still need lots of RAM.

Great for

  • Analysts working with multi-gigabyte HDF5, Parquet, or CSV files on a single laptop or workstation
  • Scientists who need repeated histograms or summary statistics over large local tables
  • Developers preparing features for Vaex ML pipelines

Skip it if

  • Small datasets that pandas handles easily, where Vaex adds setup complexity for no gain
  • Distributed clusters or multi-machine big data jobs, since Vaex here targets a single machine
  • Loading untrusted state files, because the skill notes they can contain serialized executable objects

Try asking

  • “Compute the mean and count of energy for rows where x >= 3 across a 20 GB HDF5 file without loading it all into memory”
  • “Make a binned heatmap of two columns from a large Parquet dataset and show where data coverage is thin”
  • “Convert a large CSV to HDF5 in chunks and verify that row counts and schema match afterwards”
  • “Add a virtual feature column to a table and export it without fully materializing it first”
Step-by-step guide: how to use Vaex Large Dataset AnalysisInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 8 original files, the MIT licence and an attribution note.

    vaex.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip vaex.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip vaex.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Ask the agent to create a separate Python 3.12 virtual environment so Vaex versions stay pinned and isolated.
  • Give explicit column types and expected row counts up front, especially for CSV files with messy headers or late-appearing values.
  • Have the agent validate results on a small sampled subset against an independent calculation before trusting large-scale summaries.

What's inside

  • references/core_dataframes.md
  • references/data_processing.md
  • references/io_operations.md
  • references/machine_learning.md
  • references/performance.md
  • references/review.md
  • references/visualization.md
  • SKILL.md

Redistributed unmodified from K-Dense-AI/scientific-agent-skills under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.