This is a good point to plug my talks on 'Pandas from the Inside' (A), 'Big Pandas' (B) and 'Pandas 2.0' (C) that I presented at various PyData conferences over the last 18 months:
- PyData London 2016 - (A), 60 mins, videos online
- PyData Washington DC 2016 - (A), 90 mins, videos online
- PyData Amsterdam 2017 - (A) and (B), 3 hours
- PyData Berlin 2017 - (A) and (B), compressed 90 mins, video probably online
Pandas isn't perfect, but for small- to medium-size datasets, I haven't seen much that matches its performance, and I haven't seen anything that matches its combination of performance and ease of use.
As a person who deeply enjoys developing with python, I'd have to reluctantly say R's tidyverse is a delight to use and often faster than pandas in my experience.
- PyData London 2016 - (A), 60 mins, videos online
- PyData Washington DC 2016 - (A), 90 mins, videos online
- PyData Amsterdam 2017 - (A) and (B), 3 hours
- PyData Berlin 2017 - (A) and (B), compressed 90 mins, video probably online
- PyCon UK 2017 - (A), (B) and (C), 2 hours
PDF slides for (A) are here: https://github.com/stevesimmons/pydata-ams2017-pandas-and-da...
Others are in my other repos on github: https://github.com/stevesimmons