Pandas has a rather unnatural API. For instance you have to name the DataFrame anytime you want to pull a column out of it, so filtering is e.g., `my_df[(my_df[

Pandas has a rather unnatural API. For instance you have to name the DataFrame anytime you want to pull a column out of it, so filtering is e.g., `my_df[(my_df['col1'] == value) & (my_df['col2'] + my_df['col3'] > 42)]`. The indexing is also kind of a mess — there are like seven different styles. Row-wise mapping is a huge pain. And of course there is no optimization; it's all computed eagerly so you are responsible for your own optimizations. Polars, on the other hand, lets you refer to columns like `col('col1')`, which starts to add up if your DataFrame has a long name. It has no row indexing; it's all done by filtering, which is conceptually very simple. Row-wise mapping is trivial. And there is an optimizer that runs before execution. But more than that, polars has a very fluent API, whereas pandas relies heavily on statements that can't be chained; it really breaks the flow.