base on cuDF - GPU DataFrame Library # <div align="left"><img src="img/rapids_logo.png" width="90px"/>&nbsp;cuDF - A GPU-accelerated DataFrame library for tabular data processing</div> 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 [RAPIDS](https://rapids.ai/) GPU Accelerated Data Science suite of libraries. ## About cuDF is composed of multiple libraries including: * [libcudf](https://docs.rapids.ai/api/cudf/stable/libcudf_docs/): A CUDA C++ library with [Apache Arrow](https://arrow.apache.org/) compliant data structures and fundamental algorithms for tabular data. * [pylibcudf](https://docs.rapids.ai/api/cudf/stable/pylibcudf/): A Python library providing [Cython](https://cython.org/) bindings for libcudf. * [cudf](https://docs.rapids.ai/api/cudf/stable/user_guide/): A Python library providing - A DataFrame library mirroring the [pandas](https://pandas.pydata.org/) API - A zero-code change accelerator, [cudf.pandas](https://docs.rapids.ai/api/cudf/stable/cudf_pandas/), for existing pandas code. * [cudf-polars](https://docs.rapids.ai/api/cudf/stable/cudf_polars/): A Python library providing a GPU engine for [Polars](https://pola.rs/) * [dask-cudf](https://docs.rapids.ai/api/dask-cudf/stable/): A Python library providing a GPU backend for [Dask](https://www.dask.org/) DataFrames Notable projects that use cuDF include: * [Spark RAPIDS](https://github.com/NVIDIA/spark-rapids): 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 [RAPIDS Installation Guide](https://docs.rapids.ai/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 = cudf.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 = cudf.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/rapidsai/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 [RAPIDS 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. ", 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"