BUG: DataFrame.diff(axis=1) with mixed (or EA) dtypes #32995
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I don't think we can just raise NotImplemented here. I suspect a mix of floats and bool will be somewhat common.
Can we instead skip doing things blockwise for axis=1 and do it columnwise instead? Something like
In [39]: df = pd.DataFrame({'a': [1,1,2,2],'b': [1, 2, 3, 4.], 'c': [True, False, False, True]})
In [40]: import toolz
In [41]: pd.concat([a - b for a, b in toolz.sliding_window(2, (df.iloc[:, i] for i in range(len(df.columns))))], axis=1)
Out[41]:
0 1
0 0.0 0.0
1 -1.0 2.0
2 -1.0 3.0
3 -2.0 3.0(without using toolz, and including the all-NA columns and fixing the column names). We could perhaps only do that when nblocks > 1, to preserve the performance in the homogenous case.
That would also allow us to avoid consolidating, which would be nice. |
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updated to operate column-wise. DatetimeTZBlock.diff could have its NotImplementedError case removed in a follow-up |
| @@ -6667,6 +6667,30 @@ def diff(self, periods: int = 1, axis: Axis = 0) -> "DataFrame": | |||
| 5 NaN NaN NaN | |||
| """ | |||
| bm_axis = self._get_block_manager_axis(axis) | |||
| self._consolidate_inplace() | |||
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this is not pretty. why are we not simply transposing and calling .diff()?
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why are we not simply transposing and calling .diff()?
I'd be fine with that, but it is potentially costly
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I personally think the column-wise approach could be fine
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this should be co-located with BlockManager.diff. but to be honest I think transposing is just fine here. The block type is already inferred and handled. This is just adding a lot of complexity.
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if you really want to do this column based approach them move this with the other .diff methods (I actually prefer just a transpose here, its totally fine for now)
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Do we have anything approaching consensus here? I'm OK with either column-wise or transpose |
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I'm pretty strongly in favor of columnwise when there's more than 1 block. While I don't think sparse support should ever be a primary motivator, the difference in performance of a columnwise diff vs. a In [6]: a = pd.arrays.SparseArray([1] + [0] * 1000)
In [7]: df = pd.DataFrame({"A": a, "B": a})will be huge. |
| @@ -6667,6 +6667,30 @@ def diff(self, periods: int = 1, axis: Axis = 0) -> "DataFrame": | |||
| 5 NaN NaN NaN | |||
| """ | |||
| bm_axis = self._get_block_manager_axis(axis) | |||
| self._consolidate_inplace() | |||
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if you really want to do this column based approach them move this with the other .diff methods (I actually prefer just a transpose here, its totally fine for now)
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@jreback is column-wise vs transpose a deal breaker? if not, i think this is ready |
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if you really want to do columnwise can you move impl to another location as indicated the code is pretty complex and should be separated from inline in the function |
jreback
added
the
Numeric Operations
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thanks, IIRC there might be some issues this closes if you'd have a look |
black pandasgit diff upstream/master -u -- "*.py" | flake8 --diff