I'm about publish Effective Pandas 2 (waiting for pandas 2.2 release) and an getting reviewed for my next book, Effective Polars.
Happy to assist and answer any questions I can.
Happy to assist and answer any questions I can.
Regarding polars, would you have time to answer these questions?
1) How are polars supported by popular data science packages, e.g., for plotting?
2) I know it is a bit silly: is there a way to get around typing `pl.col`, etc. all the time?
3) Besides `tidypolars`, are there any reasonable packages that add support for dplyr-style pipes or operation chaining?
1. `from polars import col` which at least shortens each use by a few characters 2. For columns that I use very frequently, define a "constant" for them e.g. ID=col("id").
This lets you do df.groupby(ID) instead of df.groupby(pl.col("ID")). Another advantage of defining these column "constants" is that it makes it much easier to refactor to rename all usages of the column (without needing to check whether each string "id" is being used as a column name vs. something else)
2 - If you just want the columns, there is no need to make an expression; just pass the string. There are shortcuts like pl.sum(col) instead of pl.col(col).sum(). I guess you could shortcut, c = pl.col, if you really hate typing it...
3 - Polars supports chaining and encourages it out of the box. Anything specific you are looking for?