Description
Both Python and R boast large data science communities. Each have developed a fantastic collection of packages from reading/writing data to plotting and visualization. Unfortunately, some tools are only available in one language or the other, but not both. Python and R provide relatively simple mechanisms for interacting with C, C++, and Fortran. There are many tools that take advantage of this interoperability. While not a simple matter, developing data science tools in these low level languages and providing Python and R wrappers allows code reuse between languages, speed benefits notwithstanding. In this talk we will discuss strategies and lessons learned from porting existing packages to Python from R and writing cross language tools from scratch.