[hgibbs]

I honestly can't think of a use case for excel that pandas doesn't handle better. The 1 million row limit is crazy limiting, it's just nicer to not have to worry about that kind of stuff.

[jimnotgym]

If the job is to model financial statements to produce a forecast model, to answer questions like

* what cash flows can we expect from this business

* what will the profit be in 12 months

* what will that do to my balance sheet

... then Excel is still the best tool. Financial statements are usually less than 100 rows each. All the modelling is on a tab, out front where you can print it and sense check it with a highlighter. People on hn often think towards the big data world ( and like the other poster I would exhaust sql before anything else for ad hoc analysis), but there is a huge amount of work done that is more like my use case than yours.

[fifilura]

Pandas just extends that 1M row limit to ~10M row limit (whatever fits in your RAM)

And whenever I work with pandas I run into pesky bugs with not handling integers properly.

My answer to that, for both pandas and excel work is to do as much of the work in SQL/views as possible.

[kzrdude]

I've been trying to use pd.NA to embrace the future, but it runs into a nightmare of other bugs - float vs Float64 mixing bugs and stuff like seaborn that doesn't handle Float64 anyway etc.

As an example of the sharp stuff that causes bugs.

[aulin]

could you elaborate more on the pandas bugs?

[kieferbc]

There isn’t a 1M row limit. There is an entire backend data model that can hook into any data source for the most part and can be written in a functional language.