[isolatedsystem]

I've recently had to migrate over to Python from Matlab. Pandas has been doing my head in. The syntax is so unintuitive. In Matlab, everything begins with a `for` loop. Inelegant and slow, yes, but easy to reason about. Easy to see the scope and domain of the problem, to visualise the data wrangling.

Pandas insist you never use a for loop. So, I feel guilty if I ever need a throwaway variable on the way to creating a new column. Sometimes methods are attached to objects, other times they aren't. And if you need to use a function that isn't vectorised, you've got to do df.apply anyway. You have to remember to change the 'axis' too. Plotting is another thing that I can't get my head around. Am I supposed to use Pandas' helpers like df.plot() all the time? Or ditch it and use the low level matplotlib directly? What is idiomatic? I cannot find answers to much of it, even with ChatGPT. Worse, I can't seem to create a mental model of what Pandas expects me to do in a given situation.

Pandas has disabused me of the notion that Python syntax is self-explanatory and executable-pseudocode. I find it terrible to look at. Matlab was infinitely more enjoyable.

[radus]

Polars has a much more consistent API, give it a shot.

Regarding your plotting question: use seaborn when you can, but you’ll still need to know matplotlib.

[kelipso]

Yeah, pandas is truly awful. After working with things like R, ggplot, data.table, you soon realize pandas is the worst dataframe analysis and plotting library out there.

I pretty much consider anyone who likes it to have Stockholm syndrome.

[fifilura]

A lot of people appreciate the declarative approach.

A for loop is a lot about the "how" but apply, join etc are much closer to the "what".