cross-posted from: https://programming.dev/post/8257343

Emily Riederer Writes:

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.

To preview these recommendations:

Set Up

• Installation: pyenv
• IDE: VS Code

Analysis

• Wrangling: polars
• Visualization: seaborn

Communication

• Tables: Great Tables
• Notebooks: Quarto

Miscellaneous

• Environment Management: pdm
• Code Quality: ruff

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