pylibcudf-cu13 25.10.0

pylibcudf - Python bindings for libcudf

 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 12.0+ with a compatible NVIDIA driver
• 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:

# CUDA 13
pip install cudf-cu13

# CUDA 12
pip install cudf-cu12

Conda

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

# CUDA 13
conda install -c rapidsai -c conda-forge cudf=25.10 cuda-version=13.0

# CUDA 12
conda install -c rapidsai -c conda-forge cudf=25.10 cuda-version=12.9

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.