[fnord123]

I've wrapped datetime for company work (pre pandas, pre datetime64) to make sure it follows the rules of the data analysis platform we developed (adding functions for moving to next month of year based on various financial calendar rules for example). I wish I hadn't done it and had just wrapped a boost_datetime since the performance of datetime is slow when you have a large timeseries of them. The performance is especially unacceptable if you also have timezones attached to your datetimes.

Now we have pandas, yay. But I don't see why one would use arrow. If you're patient enough, could you explain why you would use it? The website doesn't seem to be very convincing.

[rspeer]

Not everything is a DataFrame. You would use Arrow because you want a nice API for dates and times in use cases that have nothing to do with Pandas.