cuDF - GPU DataFrames

📢 cuDF can now be used as a no-code-change accelerator for pandas! To learn more, see here!

cuDF (pronounced "KOO-dee-eff") is a GPU DataFrame library
for loading, joining, aggregating, filtering, and otherwise
manipulating data. cuDF leverages
libcudf, a
blazing-fast C++/CUDA dataframe library and the Apache
Arrow columnar format to provide a
GPU-accelerated pandas API.

You can import cudf directly and use it like pandas:

import cudf

tips_df = cudf.read_csv("https://github.com/plotly/datasets/raw/master/tips.csv")
tips_df["tip_percentage"] = tips_df["tip"] / tips_df["total_bill"] * 100

# display average tip by dining party size
print(tips_df.groupby("size").tip_percentage.mean())

Or, you can use cuDF as a no-code-change accelerator for pandas, using
cudf.pandas.
cudf.pandas supports 100% of the pandas API, utilizing cuDF for
supported operations and falling back to pandas when needed:

%load_ext cudf.pandas  # pandas operations now use the GPU!

import pandas as pd

tips_df = pd.read_csv("https://github.com/plotly/datasets/raw/master/tips.csv")
tips_df["tip_percentage"] = tips_df["tip"] / tips_df["total_bill"] * 100

# display average tip by dining party size
print(tips_df.groupby("size").tip_percentage.mean())

Resources

Try cudf.pandas now: Explore cudf.pandas on a free GPU enabled instance on Google Colab!
Install: Instructions for installing cuDF and other RAPIDS libraries.
cudf (Python) documentation
libcudf (C++/CUDA) documentation
RAPIDS Community: Get help, contribute, and collaborate.

See the RAPIDS install page for
the most up-to-date information and commands for installing cuDF
and other RAPIDS packages.

Installation

CUDA/GPU requirements

CUDA 11.2+
NVIDIA driver 450.80.02+
Volta architecture or better (Compute Capability >=7.0)

Pip

cuDF can be installed via pip from the NVIDIA Python Package Index.
Be sure to select the appropriate cuDF package depending
on the major version of CUDA available in your environment:

For CUDA 11.x:

pip install --extra-index-url=https://pypi.nvidia.com cudf-cu11

For CUDA 12.x:

pip install --extra-index-url=https://pypi.nvidia.com cudf-cu12

Conda

cuDF can be installed with conda (via miniforge) from the rapidsai channel:

conda install -c rapidsai -c conda-forge -c nvidia \
    cudf=25.06 python=3.13 cuda-version=12.8

We also provide nightly Conda packages built from the HEAD
of our latest development branch.

Note: cuDF is supported only on Linux, and with Python versions 3.10 and later.

See the RAPIDS installation guide for more OS and version info.

Build/Install from Source

See build instructions.

Contributing

Please see our guide for contributing to cuDF.

Dependencies of libcudf-cu12 (3).
Dependency	Optional
libkvikio-cu12	false
librmm-cu12	false
rapids-logger	false

Package profile

libcudf-cu12

Releases

Release table

PyPI Downloads

Dependencies

Details

Project URLs

Dependencies

Package description