NVIDIA/cudf

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# NVIDIA cuDF: A GPU-accelerated DataFrame library for tabular data processing NVIDIA cuDF (pronounced "KOO-dee-eff") is an [Apache 2.0 licensed](LICENSE), GPU-accelerated DataFrame library for tabular data processing. The cuDF library is one part of the [NVIDIA CUDA-X](https://developer.nvidia.com/cuda/cuda-x-libraries) suite of GPU Accelerated libraries. ## About cuDF is composed of multiple libraries including: * [libcudf](https://docs.nvidia.com/cudf/latest/libcudf/): A CUDA C++ library with [Apache Arrow](https://arrow.apache.org/) compliant data structures and fundamental algorithms for tabular data. * [pylibcudf](https://docs.nvidia.com/cudf/latest/pylibcudf/): A Python library providing [Cython](https://cython.org/) bindings for libcudf. * [cudf](https://docs.nvidia.com/cudf/latest/cudf/): A Python library providing - A DataFrame library mirroring the [pandas](https://pandas.pydata.org/) API - A zero-code change accelerator, [cudf.pandas](https://docs.nvidia.com/cudf/latest/cudf_pandas/), for existing pandas code. * [cudf-polars](https://docs.nvidia.com/cudf/latest/cudf_polars/): A Python library providing a GPU engine for [Polars](https://pola.rs/) * [dask-cudf](https://docs.nvidia.com/dask-cudf/latest/): A Python library providing a GPU backend for [Dask](https://www.dask.org/) DataFrames Notable projects that use cuDF include: * [NVIDIA cuDF plugin for Apache Spark](https://github.com/NVIDIA/cudf-spark): A GPU accelerator plugin for [Apache Spark](https://spark.apache.org/) * [Velox-cuDF](https://github.com/facebookincubator/velox/blob/main/velox/experimental/cudf/README.md): A [Velox](https://velox-lib.io/) extension module to execute Velox plans on the GPU * [Sirius](https://www.sirius-db.com/): A GPU-native SQL engine providing extensions for libraries like [DuckDB](https://duckdb.org/) * [TornadoVM](https://github.com/beehive-lab/TornadoVM): A GPU programming framework for Java that JIT-compiles Java to CUDA and runs [libcudf operations](https://github.com/beehive-lab/TornadoVM/tree/develop/tornado-native-libs/tornado-cudf) as tasks alongside those kernels; used as a GPU engine for [Apache Flink](https://flink.apache.org/) ## Installation ### System Requirements Operating System, GPU driver, and supported CUDA version information can be found at the [system requirements](https://docs.nvidia.com/datascience/install/#system-req) ### pip A stable release of each cudf library is available on PyPI. You will need to match the major version number of your installed CUDA version with a `-cu##` suffix when installing from PyPI. A development version of each library is available as a nightly release by including the `-i https://pypi.anaconda.org/rapidsai-wheels-nightly/simple/` index. ```bash # CUDA 13 pip install libcudf-cu13 pip install pylibcudf-cu13 pip install cudf-cu13 pip install cudf-polars-cu13 pip install dask-cudf-cu13 # CUDA 12 pip install libcudf-cu12 pip install pylibcudf-cu12 pip install cudf-cu12 pip install cudf-polars-cu12 pip install dask-cudf-cu12 ``` ### conda A stable release of each cudf library is available to be installed with the conda package manager by specifying the `-c rapidsai` channel. A development version of each library is available as a nightly release by specifying the `-c rapidsai-nightly` channel instead. ```bash conda install -c rapidsai libcudf conda install -c rapidsai pylibcudf conda install -c rapidsai cudf conda install -c rapidsai cudf-polars conda install -c rapidsai dask-cudf ``` ### source To install cuDF from source, please follow [the contribution guide](CONTRIBUTING.md#setting-up-your-build-environment) detailing how to setup the build environment. ## Examples The following examples showcase reading a parquet file, dropping missing rows with a null value, and performing a groupby aggregation on the data. ### cudf `import cudf` and the APIs are largely similar to pandas. ```python import cudf df = cudf.read_parquet("data.parquet") df.dropna().groupby(["A", "B"]).mean() ``` ### cudf.pandas With a Python file containing pandas code: ```python import pandas as pd df = pd.read_parquet("data.parquet") df.dropna().groupby(["A", "B"]).mean() ``` Use cudf.pandas by invoking `python` with `-m cudf.pandas` ```bash $ python -m cudf.pandas script.py ``` If running the pandas code in an interactive Jupyter environment, call `%load_ext cudf.pandas` before importing pandas. ```python In [1]: %load_ext cudf.pandas In [2]: import pandas as pd In [3]: df = pd.read_parquet("data.parquet") In [4]: df.dropna().groupby(["A", "B"]).mean() ``` ### cudf-polars Using Polars' [lazy API](https://docs.pola.rs/user-guide/lazy/), call `collect` with `engine="gpu"` to run the operation on the GPU ```python import polars as pl lf = pl.scan_parquet("data.parquet") lf.drop_nulls().group_by(["A", "B"]).mean().collect(engine="gpu") ``` ## Questions and Discussion For bug reports or feature requests, please [file an issue](https://github.com/NVIDIA/cudf/issues/new/choose) on the GitHub issue tracker. For questions or discussion about cuDF and GPU data processing, feel free to post in the [community Slack](https://rapids.ai/slack-invite) workspace. ## Contributing cuDF is open to contributions from the community! Please see our [guide for contributing to cuDF](CONTRIBUTING.md) for more information.

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2026-03-24

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