In this file, data wrangling operations are implemented twice: in Python and R cells, adjacent to each other. This article is accompanied by an R Markdown file, which you can find on github. So, with a little extra effort, you can master data wrangling in both languages, and become a “super R-Pythonista”!īe a supe R-Pythonista (instead of just a Pythonista!) A Bilingual R Markdown file After you read the article, it will become obvious that Python and R, are quite similar in many of their expressions, at least in the area of data wrangling. In this article, we will discuss data wrangling in Python and R, using the pandas and tidyverse libraries respectively, and tips to speed up your code.

Knowing basic functionality, such as data wrangling, in both languages, expands your programming horizons, allows you to work with people of either language and create bilingual notebooks, leveraging the best of each language. These days, there are libraries, such as reticulate and PypeR, that allow you to incorporate Python code in R Markdown and R code in a Jupyter notebook respectively. Both languages offer great functionality from data exploration to modeling and have very loyal fans. A couple of years back, you would write your data analysis program, exclusively in one of these two languages: Python or R.

Python in rstudio