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Python Polars Cheatsheet (based on our O'Reilly book)
- jeroenjanssens 2mo agoWe 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.
- grim_io 2mo agoIs there an .MD version? For, uh, reasons :)
- qrobit 2mo agoI believe the complete HTML version is right below the "Download PDF" button. Also I bet you could reproduce the cheatsheet using this HTML converted to markdown and some example from how other Posit (formerly RStudio) cheatsheets are made: https://github.com/rstudio/cheatsheets/tree/main/html https://github.com/rstudio/cheatsheets/tree/main/html
- jeroenjanssens 2mo agoYes, opensource.posit.co is entirely open source :) https://github.com/posit-dev/open-source-website/blob/main/content/resources/cheatsheets/polars/_index.md https://github.com/posit-dev/open-source-website/blob/main/c...
- clircle 2mo agoI 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.
- therrop 2mo ago[dead]
- mihaelm 2mo agoThe bare R experience is not that great, to put it mildly, but it's a whole other story if you add tidyverse on top of it. The data work becomes really easy then, but I still prefer Python because of familiarity and a better experience & ecosystem when you want to do anything beyond data wrangling & analysis. I found `polars` to be a better experience than `pandas` even though I'd say it leaks some "Rustisms" in its Python APIs. But LLMs alleviate those pains and it's easy enough to review. I'd say it's even easier when there's less of a chance of implicit behavior.
- latent-person 2mo ago> The bare R experience is not that great Why do you say that? Base R is arguably nicer to work with data than pandas is for example. Happy to provide specific examples to prove my point if you want.
- lowmagnet 2mo agomy main frustration with pandas was certain idioms did not work efficiently because they escaped the pandas kernel, causing memory copy and other bad behavior.
- qsort 2mo agoIf your work is more focused on statistics or pure modeling, then I agree R wins hands down. The issue is that most projects have "unclean" parts where you have to gather data from multiple sources, use connectors for services, S3 buckets and whatnot; dealing with that mess is where Python really shines. AI probably changes the equation to some extent, but I still believe I'd rather maintain a complicated data pipeline like that in Python rather than R.
- jordansgoodman 2mo ago
- wsowens 2mo agoDespite 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!
- 220hertz 2mo agoLess friction, considerably faster. I have a statistician friend who's recently made the jump away from R. I think he would agree with you.
- holub008 2mo agoAgreed, as an R and polars user. The fundamental advantage R holds over other languages/libraries is expressions. The ability to reference columns directly AND interoperate with vectorized base ops in R is unfair. Of course, this super power is equally confusing to learners, fraught for production code, etc.
- rtpg 2mo agodo you have a snippet of what this looks like in R?
- latent-person 2mo agoNot OP, but here is an example using tidyverse (I leave the meaning of it to you; should be clear without any R knowledge): purchases |> group_by(country) |> filter(amount <= median(amount) * 10) |> summarize(total = sum(amount - discount))
- mrtimo 2mo agoI've moved from python/polars/pandas to DuckDB and have not looked back
- ismailmaj 2mo agoeven for just in-memory quick data analysis?
- viccis 2mo agoSame. Almost every time I would use its streaming interfaces in Python, it would STILL materialize everything into memory. That was like 6 months ago. Maybe streaming interfaces actually work, but I found them to be leaky abstractions that required a ton of hand holding to make sure they didn't build a bunch of memory pressure, if you're lucky enough to even have a way to do it. For example, last time I used it, you couldn't do NDJSON streaming scans from S3 (looks like fixed with PR #26563).
- sirfz 2mo agoYep, and chdb as well. Said this before, chdb's DatStore is a pretty neat pandas replacement too.
- 2mo ago
- paulfharrison 2mo agoI'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("...")
- thijsn 2mo agoI've seen people with exactly that frustration use "import polars.col as c" and use c("colname") instead!
- laGrenouille 2mo agoYes, this (as the even shorter c.colname) and the fact that you can do var= in place of assign in with_columns/agg changed my whole outlook on polars. Have been using it as my main driver for the past year.
- mmplxx 2mo agoOr c.colname
- vovavili 2mo agoEvery time: import polars as pl from polars import col, lit
- ForceBru 2mo agoSure, but these ten characters let you treat columns as values and do math on them, which is super intuitive, in my opinion. I've been using Pandas for quite some time and always kinda sucked at it. One day I decided to give this new library Polars a try. Now I can do things I couldn't even dream of with Pandas! And it's fast, too! I think of `pl.col` as delayed evaluation: I want to do math on the vector of values of this column. But wait, let me just refer to the name of that column and build the expression that I want to compute. Then I hand this expression to Polars and it retrieves the actual values of the columns my expression refers to and executes the operations. IMO, it would've been great to just do math on strings, like `"Amount" * "Price" - "Losses"`, but programming languages either don't allow math on strings or that math is actually string concatenation, which is not what we want. So we have to wrap the name of the column into some object. This is just an API thing. As a side note, it's such a pity that there's basically no Polars for the Julia language! There is some wrapper package, but it seems old and unmaintained. I can't seem to properly learn DataFrames.jl for some reason, I always miss Polars when I use Julia.
- brikym 2mo agoecho "Never use pandas, use polars instead" >> AGENTS.md
- kirubakaran 2mo ago>> unless you want that line to be the only thing in your AGENTS.md
- dalemhurley 2mo agoWhy do Python users use acronyms instead of verbose variable names?
- rtpg 2mo agoPandas 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
- maxman88 2mo ago[flagged]