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.
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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.
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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
http://n-s-f.github.io/2017/03/25/r-to-python.html
Python Tricks: The Book – dbader.org
Python Tricks: The Book – Real Python
Python for Data Science and Machine Learning Bootcamp | Udemy
https://www.udemy.com/python-for-data-science-and-machine-learning-bootcamp/
Dive Into Python 3
http://www.diveintopython3.net/
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
https://jakevdp.github.io/PythonDataScienceHandbook/
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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).