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BUG: fix DataFrame.apply returning wrong result when dealing with dtype (#28773) #30304

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BUG: fix DataFrame.apply returning wrong result when dealing with dtype (#28773) #30304

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d515b5b
BUG: fix DataFrame.apply returning wrong result when dealing with dty…
Dec 17, 2019
d045aed
Merge remote-tracking branch 'upstream/master' into apply_bug
Dec 17, 2019
85a2af7
Reformatting
Reksbril Dec 18, 2019
7b6e793
Resolve conflict with iterator variables
Reksbril Dec 22, 2019
dcaf64b
Change approach to #28773 fix
Jan 6, 2020
f47b420
Merge branch 'master' into apply_bug
Reksbril Jan 6, 2020
a0b89e9
Update apply.py
Reksbril Jan 6, 2020
9f24abb
Filter out mixed-dtypes from regular standard_apply procedure
Jan 14, 2020
6eb6d08
Merge branch 'master' into apply_bug
Reksbril Jan 14, 2020
b45b44d
Remove whitespace from test_apply.py
Reksbril Jan 14, 2020
3755138
Refactor
Jan 16, 2020
fdb2f43
Black pandas refactor
Jan 16, 2020
78f0154
Merge remote-tracking branch 'upstream/master' into apply_bug
Apr 17, 2020
1f81b77
apply fix
Apr 17, 2020
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v1.0.0.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -1161,6 +1161,7 @@ Other
- Bug in :meth:`DataFrame.append` that raised ``IndexError`` when appending with empty list (:issue:`28769`)
- Fix :class:`AbstractHolidayCalendar` to return correct results for
years after 2030 (now goes up to 2200) (:issue:`27790`)
- Bug in :meth:`DataFrame.apply` returning wrong result in some cases when dtype was involved in passed function (:issue:`28773`)
- Fixed :class:`~arrays.IntegerArray` returning ``inf`` rather than ``NaN`` for operations dividing by ``0`` (:issue:`27398`)
- Fixed ``pow`` operations for :class:`~arrays.IntegerArray` when the other value is ``0`` or ``1`` (:issue:`29997`)
- Bug in :meth:`Series.count` raises if use_inf_as_na is enabled (:issue:`29478`)
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18 changes: 15 additions & 3 deletions pandas/core/apply.py
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Expand Up @@ -274,13 +274,17 @@ def apply_standard(self):

# we cannot reduce using non-numpy dtypes,
# as demonstrated in gh-12244
if (
flag = (
self.result_type in ["reduce", None]
and not self.dtypes.apply(is_extension_array_dtype).any()
# Disallow complex_internals since libreduction shortcut
# cannot handle MultiIndex
and not isinstance(self.agg_axis, ABCMultiIndex)
):
and len(set(self.dtypes))
)
return_result = None
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why is this needed?

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I'm using it to determine if line 314 was run.


if flag:

values = self.values
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@Reksbril the underlying problem here is that this call to self.values is casting to object dtype which you want to avoid. if you add to the check in 277-283 and self.obj._is_homogeneous_type that should solve the whole thing

index = self.obj._get_axis(self.axis)
Expand Down Expand Up @@ -308,11 +312,19 @@ def apply_standard(self):
# reached via numexpr; fall back to python implementation
pass
else:
return self.obj._constructor_sliced(result, index=labels)
return_result = self.obj._constructor_sliced(result, index=labels)
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what are you trying to do here? we have a specific subclass for Series where this could live, but I am not clear what you are trying to catch

if self.axis != 0 and self.axis != "index":
return return_result

# compute the result using the series generator
results, res_index = self.apply_series_generator()

if flag and return_result is not None:
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what is this case tryng to catch?

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There are 2 cases

  1. can_reduce is False - the function doesn't change its behaviour, compared to previous version
  2. can_reduce is True - the return_result is calculated like before, but it's returned only if we don't use apply on columns or we don't have mixed dtypes. In other case I use apply_series_generator to obtain column-by-column result and after that I'm wrapping it using _constructor_sliced. It's done this way to preserve the "old" way - it was solution to problem with my function sometimes returning Series instead of DataFrame (or the other way). I assumed, that already existing way of getting result would be better, than thinking about some new solutions.

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@jreback

results = np.array(list(results.values()))
return self.obj._constructor_sliced(
results, index=res_index, dtype=return_result.dtype
)

# wrap results
return self.wrap_results(results, res_index)

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6 changes: 6 additions & 0 deletions pandas/tests/frame/test_apply.py
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Expand Up @@ -691,6 +691,12 @@ def test_apply_dup_names_multi_agg(self):

tm.assert_frame_equal(result, expected)

def test_apply_get_dtype(self):
# GH 28773
df = DataFrame({"col_1": [1, 2, 3], "col_2": ["hi", "there", "friend"]})
expected = Series(data=["int64", "object"], index=["col_1", "col_2"])
tm.assert_series_equal(df.apply(lambda x: x.dtype), expected)

def test_apply_nested_result_axis_1(self):
# GH 13820
def apply_list(row):
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