alt Hacker NewsPython Polars Cheatsheet (based on our O'Reilly book)
Comments
Despite writing most of my procedural code in Python, I've always preferred doing my data analysis in R. For all of R's warts, the ergonomics of the dplyr + ggplot + the rest of the tidyverse are very tough to beat. My few attempts to use Pandas and matplotlib/seaborne have always proved frustrating. Based on this cheatsheet though, it seems like Polars addresses some of the friction of Pandas. Looking forward to trying it!
Pandas and Polars are both a place where I _really_ would love to have some sort of macro subsystem for Python. `pl.col(...)` is a neat trick for slicing, the pandas `df[df["foo"] == "bar"]` thing has always felt a bit of a mouthful (especially if you deign to use a longer name for your dataframe).
I appreciate Polars offering some alternative APIs for poking around in data, though. I feel like at some point someone will land on a _very_ nice to use API
I get that the data science world has moved on to python, but I always felt that R's data.table had the slickest dataframe developer experience. I have toyed with Polars for a few hours, maybe I should give it a better chance.
I've moved from python/polars/pandas to DuckDB and have not looked back
I'm sure Polars is great, but I can't get over needing 10 characters of ceremony every time I want to refer to a column in a data frame.
pl.col("...")
Why do Python users use acronyms instead of verbose variable names?
We spent the last few weeks compressing our book, Python Polars: The Definitive Guide (nearly 500 pages), down to a two-page cheatsheet. It's a highly lossy compression, but hopefully a useful one! Besides the PDF, there's also an accessible HTML version.
We're curious to hear what you think. Let us know if we missed any of your favorite Polars operations, or if you have any feedback on how we organized it.