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I think another area pandas has done a lot of work on is with datetimes. Numpy's datetime objects are pretty deficient when you need to perform computations / d
by _coveredInBees 7y ago
I think another area pandas has done a lot of work on is with datetimes. Numpy's datetime objects are pretty deficient when you need to perform computations / data wrangling with them and utilizing python's native datetime objects would slow things down a decent bit. So they have done a lot of work to create their own datetime implementation that helps a lot when dealing with tabular data and performing date/time based arithmetic/manipulations.
- spectramax 7y agoDatetimes are still a mess. Now we have 3 datetime objects and converting between them is not obvious or trivial: https://i.stack.imgur.com/uiXQd.png https://i.stack.imgur.com/uiXQd.png
- _coveredInBees 7y agoYeah sure, but that isn't their fault really. I can see a role for python's datetime being separate from numpy/pandas, but I do think a consolidated datetime object would be better to have rather than the numpy and pandas versions that are similar but not the same.
- spectramax 7y agoDefinitely agree, pandas/numpy can have internal representations of efficient datetimes but the user should not have to deal with these conversions.
- logicchains 7y agoI use Julia and this is one area where Pandas still kicks the Julia ecosystem's proverbial ass: awesome support for working with nanosecond precision epoch timestamps.