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https://github.com/rapidsai/cudf
cuDF - GPU DataFrame Library
https://github.com/rapidsai/cudf
arrow cpp cuda cudf dask data-analysis data-science dataframe gpu pandas pydata python rapids
Last synced: 3 days ago
JSON representation
cuDF - GPU DataFrame Library
- Host: GitHub
- URL: https://github.com/rapidsai/cudf
- Owner: rapidsai
- License: apache-2.0
- Created: 2017-05-07T03:43:37.000Z (over 7 years ago)
- Default Branch: branch-24.12
- Last Pushed: 2024-10-29T09:41:21.000Z (about 1 month ago)
- Last Synced: 2024-10-29T09:54:17.955Z (about 1 month ago)
- Topics: arrow, cpp, cuda, cudf, dask, data-analysis, data-science, dataframe, gpu, pandas, pydata, python, rapids
- Language: C++
- Homepage: https://docs.rapids.ai/api/cudf/stable/
- Size: 146 MB
- Stars: 8,397
- Watchers: 153
- Forks: 898
- Open Issues: 1,077
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
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README
#
cuDF - GPU DataFrames
## 📢 cuDF can now be used as a no-code-change accelerator for pandas! To learn more, see [here](https://rapids.ai/cudf-pandas/)!
cuDF (pronounced "KOO-dee-eff") is a GPU DataFrame library
for loading, joining, aggregating, filtering, and otherwise
manipulating data. cuDF leverages
[libcudf](https://docs.rapids.ai/api/libcudf/stable/), a
blazing-fast C++/CUDA dataframe library and the [Apache
Arrow](https://arrow.apache.org/) columnar format to provide a
GPU-accelerated pandas API.
You can import `cudf` directly and use it like `pandas`:
```python
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`](https://docs.rapids.ai/api/cudf/stable/cudf_pandas).
`cudf.pandas` supports 100% of the pandas API, utilizing cuDF for
supported operations and falling back to pandas when needed:
```python
%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](https://nvda.ws/rapids-cudf): Explore `cudf.pandas` on a free GPU enabled instance on Google Colab!
- [Install](https://docs.rapids.ai/install): Instructions for installing cuDF and other [RAPIDS](https://rapids.ai) libraries.
- [cudf (Python) documentation](https://docs.rapids.ai/api/cudf/stable/)
- [libcudf (C++/CUDA) documentation](https://docs.rapids.ai/api/libcudf/stable/)
- [RAPIDS Community](https://rapids.ai/learn-more/#get-involved): Get help, contribute, and collaborate.
See the [RAPIDS install page](https://docs.rapids.ai/install) 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:
```bash
pip install --extra-index-url=https://pypi.nvidia.com cudf-cu11
```
For CUDA 12.x:
```bash
pip install --extra-index-url=https://pypi.nvidia.com cudf-cu12
```
### Conda
cuDF can be installed with conda (via [miniforge](https://github.com/conda-forge/miniforge)) from the `rapidsai` channel:
```bash
conda install -c rapidsai -c conda-forge -c nvidia \
cudf=24.12 python=3.12 cuda-version=12.5
```
We also provide [nightly Conda packages](https://anaconda.org/rapidsai-nightly) 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](https://docs.rapids.ai/install) for more OS and version info.
## Build/Install from Source
See build [instructions](CONTRIBUTING.md#setting-up-your-build-environment).
## Contributing
Please see our [guide for contributing to cuDF](CONTRIBUTING.md).