The article starts well, on trying to condense pandas' gaziliion of inconsistent and continuously-deprecated functions with tens of keyword arguments into a small, condensed set of composable operations - but it lost me then.

The more interesting nugget for me is about this project they mention: https://modin.readthedocs.io/en/latest/index.html called Modin, which apparently went to the effort of analysing common pandas uses and compressed the API into a mere handful of operations. Which sounds great!

Sadly for me the purpose seems to have been rather to then recreate the full pandas API, only running much faster, backed by things like Ray and Dask. So it's the same API, just much faster.

To me it's a shame. Pandas is clearly quite ergonomic for various exploratory interactive analyses, but the API is, imo, awful. The speed is usually not a concern for me - slow operations often seem to be avoidable, and my data tends to fit in (a lot of) RAM.

I can't see that their more condensed API is public facing and usable.

Pandas and so on exist for the same reason Django's ORM and SqlAlchemy do: people do not want to string interpolate to talk to their database. SQL is great for DBA's, and absolutely sucks for programmers. Microsoft was really onto something with LINQ, in my opinion.

I felt like one or two decades ago, all the rage was about rewriting programs into just two primitives: map and reduce.

For example filter can be expressed as:

  is_even = lambda x: x % 2 == 0
  mapped = map(lambda x: [x] if is_even(x) else [], data)
  filtered = reduce(lambda x, y: x + y, mapped, [])

But then the world moved on from it because it was too rigid

When I started reading about pandas complexity and the smaller set of operations needed, couldn't help but think of R's data.table simplicity.

Granted, it's got more than 15 functions, but its simplicity seems to me very similar to what the author presented in the end.

>a dataframe is a tuple (A, R, C, D): an array of data A, row labels R, column labels C, and a vector of column domains D.

What is 'a vector of column domains D'? A description of how the data A maps to columns?

Hmm. Folks trying to discover the elegant core of data frame manipulation by studying... pandas usage patterns. When R's dplyr solved this over a decade ago, mostly by respecting SQL and following its lead.

The pandas API feels like someone desperately needed a wheel and had never heard of a wheel, so they made a heptagon, and now millions of people are riding on heptagon wheels. Because it's locked in now, everyone uses heptagon wheels, what can you do? And now a category theorist comes along, studies the heptagon, and says hey look, you could get by on a hexagon. Maybe even a square or a triangle. That would be simpler!

No. Stop. Data frames are not fundamentally different from database tables [1]. There's no reason to invent a completely new API for them. You'll get within 10% of optimal just by porting SQL to your language. Which dplyr does, and then closes most of the remaining optimality gap by going beyond SQL's limitations.

You found a small core of operations that generates everything? Great. Also, did you know Brainfuck is Turing-complete? Nobody cares. Not all "complete" systems are created equal. A great DSL is not just about getting down to a small number of operations. It's about getting down to meaningful operations that are grammatically composable. The relational algebra that inspired SQL already nailed this. Build on SQL. Don't make up your own thing.

Like, what is "drop duplicates"? What are duplicates? Why would anyone need to drop them? That's a pandas-brained operation. You want the distinct keys defined by a select set of key columns, like SQL and dplyr provide.

Who needs a separate select and rename? Select is already using names, so why not do your name management there? One flexible select function can do it all. Again, like both SQL and dplyr.

Who needs a separate difference operation? There's already a type of join, the anti-join, that gets that done more concisely and flexibly, and without adding a new primitive, just a variation on the concept of a join. Again, like both SQL and dplyr.

Props to pandas for helping so many people who have no choice but to do tabular data analysis in Python, but the pandas API is not the right foundation for anything, not even a better version of pandas.

[1] No, row labels and transposition are not a good enough reason to regard them as different. They are both just structures that support pivoting, which is vastly more useful, and again, implemented by both R and many popular dialects of SQL.

Dups of a few days ago:

- https://news.ycombinator.com/item?id=47567087

there is also ZIO Prelude and ZIO schema...

It's very insightful how they explain the difference between dataframes and SQL tables / standard relational structures!

I like this sort of study- but it really misses the point to not give more credit for some of the observations and designs to Codd and others.

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I guess this article is an interesting exercise from a pure maths point of view. But, as someone developing a drag and drop data wrangling tool the important thing is creating a set of composable operations/primitive that are meaningful and useful to your end user. We have ended up 73 distinct transforms in Easy Data Transform. Sure they overlap to an extent, but feel they are at the right semantic level for our users, who are not category theorists.