Comments (1)
Having function factories for common operations could allow a speed up by using Numba under the hood. If DataFrame.aggregate
recognizes these special functions, it could make a single call instead of the current [function(x) for x in slices]
thus placing the loop over the groups in the Numba code.
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Related Issues (18)
- Switch Vector to use pandas.Series for proper NA support
- ListOfDicts.select should return in the requested order
- Round floats by significant digits, not decimals HOT 1
- UnicodeEncodeError with astype(bytes) in DataFrame string columns HOT 1
- Switch method chaining syntax to parantheses
- ListOfDicts.select returns regular dicts
- Print fails on None
- Check handling of object columns
- Represent strings as objects? HOT 1
- Use keyword-only arguments where appropriate
- Allow using strings with Numba aggregation functions HOT 1
- min and max should use missing_value, not np.nan
- Rename missing na?
- Allow using datetimes with Numba aggregation functions HOT 1
- Add grouped modify HOT 1
- Replace Pandas with Arrow
- Adapt to NumPy 2.0
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