add cuDF

Readme.md

@@ -237,7 +237,7 @@ Note: the one marked as `Live Trading` has reasonable live trading support for a
 - [Spark](https://github.com/apache/spark) ![GitHub last commit (branch)](https://img.shields.io/github/last-commit/apache/spark/master) | `Scala` | - Apache Spark - A unified analytics engine for large-scale data processing
 - [Hamilton](https://github.com/dagworks-inc/hamilton) ![GitHub last commit (branch)](https://img.shields.io/github/last-commit/dagworks-inc/hamilton/main)| `Python` | - A scalable general purpose micro-framework for defining dataflows. You can use it to build dataframes, numpy matrices, python objects, ML models, etc. Embed Hamilton anywhere python runs, e.g. spark, airflow, jupyter, fastapi, python scripts, etc.
 - [Incremental (JaneStreet)](https://github.com/janestreet/incremental) ![GitHub last commit (branch)](https://img.shields.io/github/last-commit/janestreet/incremental/master) | `Ocaml` | - Incremental is a library that gives you a way of building complex computations that can update efficiently in response to their inputs changing, inspired by the work of Umut Acar et. al. on self-adjusting computations. Incremental can be useful in a number of applications
-- [Joblib](https://github.com/joblib/joblib)  ![GitHub last commit (branch)](https://img.shields.io/github/last-commit/joblib/joblib/master) | `Python` | - running Python functions as pipeline jobs
+- [Joblib](https://github.com/joblib/joblib)  ![GitHub last commit (branch)](https://img.shields.io/github/last-commit/joblib/joblib/main) | `Python` | - running Python functions as pipeline jobs
 - [Tributary](https://github.com/timkpaine/tributary) | `Python` | - Streaming reactive and dataflow graphs in Python
 - [GraphKit(No activity)](https://github.com/yahoo/graphkit) | `Python` | - A lightweight Python module for creating and running ordered graphs of computations.
 - [Man MDF (No activity)](https://github.com/man-group/mdf) | `Python` | - Data-flow programming toolkit for Python
@@ -252,7 +252,8 @@ Note: the one marked as `Live Trading` has reasonable live trading support for a
 - [pybind11](https://github.com/pybind/pybind11) ![GitHub last commit (branch)](https://img.shields.io/github/last-commit/pybind/pybind11/master) - Seamless operability between C++11 and Python
 - [pyo3](https://github.com/PyO3/pyo3)  ![GitHub last commit (branch)](https://img.shields.io/github/last-commit/numba/numba/main) - Rust bindings for the Python interpreter
 - [CuPy](https://github.com/cupy/cupy/) ![GitHub last commit (branch)](https://img.shields.io/github/last-commit/cupy/cupy/main) | `Python`, `C++`, `Cython`, `Cuda` | - CuPy is an open-source array library for GPU-accelerated computing with Python. 100x Boost for some operations
-- [codon](https://github.com/exaloop/codon) ![GitHub last commit (branch)](https://img.shields.io/github/last-commit/exaloop/codon/main) | `C++` | - A high-performance, zero-overhead, extensible Python compiler using LLVM
+- [CuDF](https://github.com/rapidsai/cudf) ![GitHub last commit (branch)](https://img.shields.io/github/last-commit/rapidsai/cudf/main) | `Python` | - cuDF - GPU DataFrame Library. No-code-change accelerator for pandas.
+- [codon](https://github.com/exaloop/codon) ![GitHub last commit (branch)](https://img.shields.io/github/last-commit/exaloop/codon/develop) | `C++` | - A high-performance, zero-overhead, extensible Python compiler using LLVM
 - [Bottleneck](https://github.com/pydata/bottleneck) ![GitHub last commit (branch)](https://img.shields.io/github/last-commit/pydata/bottleneck/master) | `Python`, `C` | - Fast NumPy array functions written in C
 - [NumExpr](https://github.com/pydata/numexpr) ![GitHub last commit (branch)](https://img.shields.io/github/last-commit/pydata/numexpr/master) | `Python`, `C++` | - Fast numerical array expression evaluator for Python, NumPy, PyTables, pandas, bcolz and more
 - [pandarallel](https://github.com/nalepae/pandarallel) ![GitHub last commit (branch)](https://img.shields.io/github/last-commit/nalepae/pandarallel/master)  | `Python` | - A simple and efficient tool to parallelize Pandas operations on all available CPUs