You'll get a lot of responses saying Polars is better than Pandas. I argue those people are missing the point and don't understand Pandas' real strength or why people choose Pandas today.

Pandas was never meant to be a technologist's tool. It was meant to be a researcher's tool and was unfortunately coopted to be a technical solution as well. It has not well escaped it's roots.

Pandas is fantastic for doing iterative and interactive research on semi-structured data. It has a lot of QoL facilities and utility functions for seamlessly dealing with exploratory timeseries analytics for in-core data. Data that fits into memory.

For example, I can take two time series and calculate their product:

ts3 = ts1 * ts2

This one line does a huge amount of heavily lifting by automatically aligning the timestamps and columns between the two inputs so that I'm not accidentally multiplying two entries that have the same ordinal but not the same timestamp or column label.

Can I do the same with Polars? Yes, but it comes with exponentially more cognitive overhead. And this is just one example.

Pandas is ultimately a flawed product as it's origin's go back more than a decade where R's dataframe was cutting edge. A lot of innovation happened since then and the API and internals of Pandas mean that certain choices that were made early on are nontrivial to change.

This doesn't change the fact that Pandas is still immensely useful. Eventually perhaps Polars will come close to it, but so far the focus wasn't on interactive use ergonomics unfortunately.

As it stands, I use pandas for research and polars for production systems.