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REGR: Fix interpolation on empty dataframe #35543

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Aug 17, 2020
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v1.1.1.rst
Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@ Fixed regressions

- Fixed regression where :func:`read_csv` would raise a ``ValueError`` when ``pandas.options.mode.use_inf_as_na`` was set to ``True`` (:issue:`35493`).
- Fixed regression in :class:`pandas.core.groupby.RollingGroupby` where column selection was ignored (:issue:`35486`)
- Fixed regression where :meth:`DataFrame.interpolate` would raise a ``TypeError`` when the :class:`DataFrame` was empty (:issue:`35598`).
- Fixed regression in :meth:`DataFrame.shift` with ``axis=1`` and heterogeneous dtypes (:issue:`35488`)
- Fixed regression in ``.groupby(..).rolling(..)`` where a segfault would occur with ``center=True`` and an odd number of values (:issue:`35552`)

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3 changes: 3 additions & 0 deletions pandas/core/generic.py
Original file line number Diff line number Diff line change
Expand Up @@ -6820,6 +6820,9 @@ def interpolate(

obj = self.T if should_transpose else self

if obj.empty:
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would prefer that this happens in the internal method itself (called on L6861)

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The error happens before this call (on line 6825)

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There were a few changes to interpolate in 1.1.0. will run git bisect to ascertain why this is now failing.

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There was a subtle change in #34752 where

if self.ndim == 2 and np.all(self.dtypes == np.dtype(object)):

was changed to

if obj.ndim == 2 and np.all(obj.dtypes == np.dtype(object)):

does reverting this change restore the old behaviour.

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the regression is from #33084, but since the error message is about DataFrame columns, I don't think the above should have been changed either. @jbrockmendel

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would prefer that this happens in the internal method itself (called on L6861)

As @sanderland notes, the check for all object dtype indeed happens here before calling into the internal method, so the check for empty thus also needs to happen here

However, @sanderland I think the df.empty check is not fully correct. Because this was also give True if you have columns but no rows. And for example an empty dataframe with a datetime64[ns] column will give a timedelta64[ns] column as result. That's something we should keep.

So I think we should explicitly check for no columns / no rows (alternatively could also add the check to the offending if obj.ndim == 2 and np.all(obj.dtypes == np.dtype(object)):, eg .. and obj.shape[0])

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@sanderland can you try @jorisvandenbossche suggestion here

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However, @sanderland I think the df.empty check is not fully correct. Because this was also give True if you have columns but no rows. And for example an empty dataframe with a datetime64[ns] column will give a timedelta64[ns] column as result. That's something we should keep.

I tried this in 1.0.x and it does not return a timedelta dtype, I dont see why interpolate would do this either. Could you suggest a test which fails with my approach but passes in 1.0.x?

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Hmm, you're fully correct. I might have been mixing up the review of two PRs, as I was also reviewing a regression for diff() and maybe was testing that method here as well .. ;) (since df.diff() for datetime64 gives timedelta64, also for empty dataframe)

Forget my comment!

return self

if method not in fillna_methods:
axis = self._info_axis_number

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7 changes: 7 additions & 0 deletions pandas/tests/frame/methods/test_interpolate.py
Original file line number Diff line number Diff line change
Expand Up @@ -34,6 +34,13 @@ def test_interp_basic(self):
expected.loc[5, "B"] = 9
tm.assert_frame_equal(result, expected)

def test_interp_empty(self):
# https://github.com/pandas-dev/pandas/issues/35598
df = DataFrame()
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result = df.interpolate()
expected = df
tm.assert_frame_equal(result, expected)

def test_interp_bad_method(self):
df = DataFrame(
{
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