How to (re-) learn Python 3 as an advanced R programmer

I've been writing R code several hours a day for the last 10 years and consider myself high intermediate/advanced. I learned python (python 2) years ago but I've used it so little over the last 5 years that my proficiency has atrophied to essentially zero.

What are some good resources for quickly building a good base proficiency with Python (python 3) for an advanced R programmer? I specifically want some focus on using pandas and want to learn in the context of using Jupyter, with some emphasis on using notebooks, its features, etc. I don't need or want fundamentals common to every language ("here's what a for loop does; here's how if/else works, etc"). I'm open to many formats - datacamp-style interactive courses, Coursera, or even a book.


I am coming to R from python. I would first take a look at Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney. Book is available both in Github and Amazon.


This is great, I'm in the same boat. My problem is that I am so comfortable with the RStudio ecosystem. What IDE are you using? (People suggested Rodeo, but I found it a but too buggy)

Posting a few of the responses I got on Twitter recommending resources:

Getting started with Python for R developers

:snake: Python Tricks: The Book –
Python Tricks: The Book – Real Python

Python for Data Science and Machine Learning Bootcamp | Udemy

Dive Into Python 3

jakevdp/WhirlwindTourOfPython: The Jupyter Notebooks behind my OReilly report, "A Whirlwind Tour of Python"

jakevdp/PythonDataScienceHandbook: Python Data Science Handbook: full text in Jupyter Notebooks

Python Data Science Handbook | Python Data Science Handbook


I came to R from Python and still try to keep up with some things from time to time (but I've been really bad about that!)

I find that Tom Augspurger's blog has a lot of great posts that likely really make sense to R programmers, e.g., he wrote something called stitch that is like knitr, he has a Modern Pandas series that I think is really good. Some cool things on dask as well. I also like that he's very friendly about R and generally interested in what happens in the data wrangling ecosystem. He also contributes to pandas.

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Python data science handbook is an excellent resource. In addition to the book, I think it helps to watch conference talks (pycon, pydata).