From 444b889a05a8697133f01bcbd7ada20424127bdd Mon Sep 17 00:00:00 2001 From: Bradley Dice Date: Wed, 24 Mar 2021 06:09:30 -0500 Subject: [PATCH] Add Resources to README. (#7697) Resolves #7217 by adding a section of commonly needed resource links at the top of the README. In #7217, I also proposed adding relevant badges (e.g. for build status, download links, citation information, etc.). I would be happy to add that to this PR if that is of interest. I'm opening the PR without badges for now, because I think the "Resources" section is valuable by itself, for readers who want quick access to common destinations. Authors: - Bradley Dice (@bdice) Approvers: - Mark Harris (@harrism) URL: https://github.com/rapidsai/cudf/pull/7697 --- README.md | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/README.md b/README.md index c0fa500ad77..687d25c200b 100644 --- a/README.md +++ b/README.md @@ -4,6 +4,17 @@ **NOTE:** For the latest stable [README.md](https://github.com/rapidsai/cudf/blob/main/README.md) ensure you are on the `main` branch. +## Resources + +- [cuDF Reference Documentation](https://docs.rapids.ai/api/cudf/stable/): Python API reference, tutorials, and topic guides. +- [libcudf Reference Documentation](https://docs.rapids.ai/api/libcudf/stable/): C/C++ CUDA library API reference. +- [Getting Started](https://rapids.ai/start.html): Instructions for installing cuDF. +- [RAPIDS Community](https://rapids.ai/community.html): Get help, contribute, and collaborate. +- [GitHub repository](https://github.com/rapidsai/cudf): Download the cuDF source code. +- [Issue tracker](https://github.com/rapidsai/cudf/issues): Report issues or request features. + +## Overview + Built based on the [Apache Arrow](http://arrow.apache.org/) columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data. cuDF provides a pandas-like API that will be familiar to data engineers & data scientists, so they can use it to easily accelerate their workflows without going into the details of CUDA programming.