> The creator of duckdb argues that people using pandas are missing out of the 50 years of progress in database research, in the first 5 minutes of his talk here.
That's pandas. Polars builds on much of the same 50 years of progress in database research by offering a lazy DataFrame API which does query optimization, morsel-based columnar execution, predicate pushdown into file I/O, etc, etc.
Disclaimer: I work for Polars on said query execution.
The DataFrame interface itself is the problem. It's incredibly hard to read, write, debug, and test. Too much work has gone into reducing keystrokes rather than developing a better tool.
Not sure what you mean by this. The table concept is the same age as computers. Here is a table, do something with it -> this is the high level df api. All the functions make sense, what is hard to read, write or debug here?
I have used Polars to process 600M of xml files (with a bit of a hack) and the polars part of the code is readable with minimal comments.
Polars has a better api than pandas, at least the intent is easier to understand. (lazyness, yay)
The problem with the dataframe API is that whenever you want to change a small part of your logic, you usually have to rethink and rewrite the whole solution. It is too difficult to write reusable code. Too many functions that try to do too many things with a million kwargs that each have their own nuances. This is because these libraries tend to favor fewer keystrokes over composable design. So the easy stuff is easy and makes for pretty docs, but the hard stuff is obnoxious to reason through.
This article explains it pretty well: https://dynomight.net/numpy/
With all due respect, have you actually used the Polars expression API? We actually strive for composability of simple functions over dedicated methods with tons of options, where possible.
The original comment I responded to was confusing Pandas with Polars, and now your blog post refers to Numpy, but Polars takes a completely different approach to dataframes/data processing than either of these tools.
I have used numpy, but don't understand what it has to do with dataframe apis
Take two examples of dataframe apis, dplyr and ibis. Both can run on a range of SQL backends because dataframe apis are very similar to SQL DML apis.
Moreover, the SQL translation for tools for pivot_longer in R are a good illustration of complex dynamics dataframe apis can support, that you'd use something like dbt to implement in your SQL models. duckdb allows dynamic column selection in unpivot. But in some SQL dialects this is impossible. dataframe apis -> SQL tools (or dbt) enable them in these dialects.
Assuming you’re comparing polars/data frames to sql… SQL has literally the worst debugging experience imaginable.
Just wanted to say I'm a huge fan of your work. Been using Polars for my team's main project for years and it just keeps getting better.