base on cuDF - GPU DataFrame Library # <div align="left"><img src="img/rapids_logo.png" width="90px"/>&nbsp;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=25.02 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). ", Assign "at most 3 tags" to the expected json: {"id":"4752","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"