| Name | cudf-cu11 JSON |
| Version |
24.12.0
JSON |
| download |
| home_page | None |
| Summary | cuDF - GPU Dataframe |
| upload_time | 2024-12-13 03:49:14 |
| maintainer | None |
| docs_url | None |
| author | NVIDIA Corporation |
| requires_python | >=3.10 |
| license | Apache 2.0 |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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No Travis.
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# <div align="left"><img src="img/rapids_logo.png" width="90px"/> cuDF - GPU DataFrames</div>
## 📢 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).
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"description": "# <div align=\"left\"><img src=\"img/rapids_logo.png\" width=\"90px\"/> cuDF - GPU DataFrames</div>\n\n## \ud83d\udce2 cuDF can now be used as a no-code-change accelerator for pandas! To learn more, see [here](https://rapids.ai/cudf-pandas/)!\n\ncuDF (pronounced \"KOO-dee-eff\") is a GPU DataFrame library\nfor loading, joining, aggregating, filtering, and otherwise\nmanipulating data. cuDF leverages\n[libcudf](https://docs.rapids.ai/api/libcudf/stable/), a\nblazing-fast C++/CUDA dataframe library and the [Apache\nArrow](https://arrow.apache.org/) columnar format to provide a\nGPU-accelerated pandas API.\n\nYou can import `cudf` directly and use it like `pandas`:\n\n```python\nimport cudf\n\ntips_df = cudf.read_csv(\"https://github.com/plotly/datasets/raw/master/tips.csv\")\ntips_df[\"tip_percentage\"] = tips_df[\"tip\"] / tips_df[\"total_bill\"] * 100\n\n# display average tip by dining party size\nprint(tips_df.groupby(\"size\").tip_percentage.mean())\n```\n\nOr, you can use cuDF as a no-code-change accelerator for pandas, using\n[`cudf.pandas`](https://docs.rapids.ai/api/cudf/stable/cudf_pandas).\n`cudf.pandas` supports 100% of the pandas API, utilizing cuDF for\nsupported operations and falling back to pandas when needed:\n\n```python\n%load_ext cudf.pandas # pandas operations now use the GPU!\n\nimport pandas as pd\n\ntips_df = pd.read_csv(\"https://github.com/plotly/datasets/raw/master/tips.csv\")\ntips_df[\"tip_percentage\"] = tips_df[\"tip\"] / tips_df[\"total_bill\"] * 100\n\n# display average tip by dining party size\nprint(tips_df.groupby(\"size\").tip_percentage.mean())\n```\n\n## Resources\n\n- [Try cudf.pandas now](https://nvda.ws/rapids-cudf): Explore `cudf.pandas` on a free GPU enabled instance on Google Colab!\n- [Install](https://docs.rapids.ai/install): Instructions for installing cuDF and other [RAPIDS](https://rapids.ai) libraries.\n- [cudf (Python) documentation](https://docs.rapids.ai/api/cudf/stable/)\n- [libcudf (C++/CUDA) documentation](https://docs.rapids.ai/api/libcudf/stable/)\n- [RAPIDS Community](https://rapids.ai/learn-more/#get-involved): Get help, contribute, and collaborate.\n\nSee the [RAPIDS install page](https://docs.rapids.ai/install) for\nthe most up-to-date information and commands for installing cuDF\nand other RAPIDS packages.\n\n## Installation\n\n### CUDA/GPU requirements\n\n* CUDA 11.2+\n* NVIDIA driver 450.80.02+\n* Volta architecture or better (Compute Capability >=7.0)\n\n### Pip\n\ncuDF can be installed via `pip` from the NVIDIA Python Package Index.\nBe sure to select the appropriate cuDF package depending\non the major version of CUDA available in your environment:\n\nFor CUDA 11.x:\n\n```bash\npip install --extra-index-url=https://pypi.nvidia.com cudf-cu11\n```\n\nFor CUDA 12.x:\n\n```bash\npip install --extra-index-url=https://pypi.nvidia.com cudf-cu12\n```\n\n### Conda\n\ncuDF can be installed with conda (via [miniforge](https://github.com/conda-forge/miniforge)) from the `rapidsai` channel:\n\n```bash\nconda install -c rapidsai -c conda-forge -c nvidia \\\n cudf=24.12 python=3.12 cuda-version=12.5\n```\n\nWe also provide [nightly Conda packages](https://anaconda.org/rapidsai-nightly) built from the HEAD\nof our latest development branch.\n\nNote: cuDF is supported only on Linux, and with Python versions 3.10 and later.\n\nSee the [RAPIDS installation guide](https://docs.rapids.ai/install) for more OS and version info.\n\n## Build/Install from Source\nSee build [instructions](CONTRIBUTING.md#setting-up-your-build-environment).\n\n## Contributing\n\nPlease see our [guide for contributing to cuDF](CONTRIBUTING.md).\n",
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