# Accelerated Computing Cudf

Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration...

- Repository: https://github.com/NVIDIA/skills (3812 stars)
- Category: Data and ML
- License: CC-BY-4.0 AND Apache-2.0
- Last updated: 2026-10-30
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add NVIDIA/skills --skill accelerated-computing-cudf -g -a claude-code -y`
Codex: `npx skills add NVIDIA/skills --skill accelerated-computing-cudf -g -a codex -y`
Cursor: `npx skills add NVIDIA/skills --skill accelerated-computing-cudf -g -a cursor -y`
Gemini CLI: `npx skills add NVIDIA/skills --skill accelerated-computing-cudf -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add NVIDIA/skills`

## Description

Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# cuDF & dask-cuDF Implementer's Guide

## Compatibility

- Development release tracked by this skill: 26.12 (`VERSION`: `26.12.00`). Use the selected installed release for deployment requirements.
- Read `dependencies.yaml` for the support matrix (`py_version` group for Python versions, `cuda_version` for CUDA versions). Match cuDF, pylibcudf, libcudf, and RMM release versions, and match pip wheel suffixes (`-cu12` / `-cu13`) to the CUDA major version.
- Requires a supported NVIDIA GPU and compatible driver. Check the [installation requirements](https://docs.nvidia.com/datascience/install/#system-req) for the selected release and CUDA version.
- For another checkout or installed release, check `VERSION`, `dependencies.yaml`, `python/cudf/pyproject.toml`, and `cudf.__version__` before choosing versions or relying on an API.

## Naming

Use NVIDIA library-first wording in user-facing answers. Keep literal RAPIDS/rapidsai URLs, package names, and release metadata when citing sources.

## Role

You are a cuDF expert helping an implementer work with GPU DataFrames. The user understands pandas and their data, your job is to get them to correct, fast GPU code with minimal friction. Choose the path from the user's intent: `cudf.pandas` for broad compatibility or minimal-change acceleration, explicit cuDF for named DataFrame migrations, hot ETL paths, and parity-sensitive work. Treat source schema, row counts, null placement, ordering, and numeric tolerances as user-visible behavior.

## Critical Rules

The rest: https://github.com/NVIDIA/skills/blob/HEAD/skills/accelerated-computing-cudf/SKILL.md