Switching languages is about switching mindsets - not just syntax. New developments in python data science toolings, like polars and seaborn’s object interface, can capture the ‘feel’ that converts from R/tidyverse love while opening the door to truly pythonic workflows
Switching languages is about switching mindsets - not just syntax. New developments in python data science toolings, like polars and seaborn’s object interface, can capture the ‘feel’ that converts from R/tidyverse love while opening the door to truly pythonic workflows
Just to be clear:
This is not a post about why python is better than R so R users should switch all their work to python
This is not a post about why R is better than python so R semantics and conventions should be forced into python
This is not a post about why python users are better than R users so R users need coddling
This is not a post about why R users are better than python users and have superior tastes for their toolkit
This is not a post about why these python tools are the only good tools and others are bad tools
The Stack
WIth that preamble out of the way, below are a few recommendations for the most ergonomic tools for getting set up, conducting core data analysis, and communication results.
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Rgonomics reminds me of Rogernomics, which has bad connotations (Wikipedia article)
[Due to Rogernomics], over 15 years, New Zealand's economy and social capital faced serious problems: the proliferation of food banks increased dramatically to an estimated 365 in 1994; the number of New Zealanders estimated to be living in poverty grew by at least 35% between 1989 and 1992 while child poverty doubled from 14% in 1982 to 29% in 1994.
Edit: Downvote if you love child poverty, I guess. ¯\_(ツ)_/¯