# 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/)
## 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.