8000 BUG: Fix concat of frames with extension types (no reindexed columns) by jorisvandenbossche · Pull Request #34339 · pandas-dev/pandas · GitHub
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BUG: Fix concat of frames with extension types (no reindexed columns) #34339

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Merged
merged 12 commits into from
Jun 19, 2020
Merged

BUG: Fix concat of frames with extension types (no reindexed columns) #34339

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e130060
BUG: Fix concat of frames with extension types (no reindexed columns)
jorisvandenbossche May 23, 2020
ea33cfd
Merge remote-tracking branch 'upstream/master' into EA-concat-frame
jorisvandenbossche May 23, 2020
f83338a
clean-up
jorisvandenbossche May 23, 2020
5822694
fix concat of DatetimeBlock / DatetimeTZBlock to not take 'uniform' path
jorisvandenbossche May 23, 2020
3d32bc8
fix base extension tests
jorisvandenbossche May 23, 2020
951e356
Merge remote-tracking branch 'upstream/master' into EA-concat-frame
jorisvandenbossche Jun 6, 2020
d269229
fix test
jorisvandenbossche Jun 6, 2020
63a6570
add whatsnew
jorisvandenbossche Jun 6, 2020
e731802
Merge remote-tracking branch 'upstream/master' into EA-concat-frame
jorisvandenbossche Jun 15, 2020
d8faef5
update whatsnew
jorisvandenbossche Jun 15, 2020
2e1f1a6
Update doc/source/whatsnew/v1.1.0.rst
jorisvandenbossche Jun 18, 2020
94fe89d
Merge remote-tracking branch 'upstream/master' into EA-concat-frame
jorisvandenbossche Jun 18, 2020
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BUG: Fix concat of frames with extension types (no reindexed columns)
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@jorisvandenbossche
jorisvandenbossche committed May 23, 2020
commit e130060b381ee836f47b06d21bcc6101a86814fc
8 changes: 6 additions & 2 deletions pandas/core/arrays/integer.py
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Expand Up @@ -94,10 +94,14 @@ def construct_array_type(cls) -> Type["IntegerArray"]:

def _get_common_dtype(self, dtypes: List[DtypeObj]) -> Optional[DtypeObj]:
# for now only handle other integer types
if not all(isinstance(t, _IntegerDtype) for t in dtypes):
if not all(
isinstance(t, _IntegerDtype)
or (isinstance(t, np.dtype) and np.issubdtype(t, np.integer))
for t in dtypes
):
return None
np_dtype = np.find_common_type(
[t.numpy_dtype for t in dtypes], [] # type: ignore
[t.numpy_dtype if isinstance(t, BaseMaskedDtype) else t for t in dtypes], [] # type: ignore
)
if np.issubdtype(np_dtype, np.integer):
return _dtypes[str(np_dtype)]
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2 changes: 1 addition & 1 deletion pandas/core/dtypes/concat.py
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Expand Up @@ -138,7 +138,7 @@ def is_nonempty(x) -> bool:
single_dtype = len({x.dtype for x in to_concat}) == 1
any_ea = any(is_extension_array_dtype(x.dtype) for x in to_concat)

if any_ea and axis == 0:
if any_ea:
if not single_dtype:
target_dtype = find_common_type([x.dtype for x in to_concat])
to_concat = [_cast_to_common_type(arr, target_dtype) for arr in to_concat]
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9 changes: 9 additions & 0 deletions pandas/core/internals/concat.py
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Expand Up @@ -319,6 +319,15 @@ def _concatenate_join_units(join_units, concat_axis, copy):
concat_values = concat_values.copy()
else:
concat_values = concat_values.copy()
elif any(isinstance(t, ExtensionArray) for t in to_concat):
# concatting with at least one EA means we are concatting a single column
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I find it odd that the logic is now split here and concat_compat. I would strongly encourage to put this logic there.

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Having this here actually ensures a clear separation of concerns between dtypes/concat.py and internals/concat.py.

The concat_compat from dtypes/concat.py only needs to worry about concatting 1D arrays (for the case of ExtensionArrays, to be clear), and the code here in internals deals with ensuring the result is the proper dimension depending on how it is put in the BlockManager.

This keeps this BlockManager-related logic inside the internals, and ensures that concat_compat / dtypes/concat.py doesn't need to care about internals-specific details.

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my point is you should have a concat_ea which you call here (right near concat_compat), otherwise the logic is scatttered. the *dispatching) logic is fine, the problem is the operational logic does not belong here. (alternative is to pass anotherr arg to concat_compat to do what you are doing here).

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If there is a different concat_ea, then at each location where concat_compat is called, we would need to do a if any_ea: concat_ea(..); else: concat_compat(...)

What do you mean exactly with the "operational logic does not belong here". What part of the code below is the "operational" logic?

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every bit you added is operational logic.

pls do something like this

elif any(isinstance(t, ExtensionArray) for t in to_concat):
    values = concat_ea(...)

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this is moving L324-330. As I have said before you are mixing 2 types of logic here by adding this.

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To make sure I understand you correctly. You want to see the following function be put in dtypes/concat.py:

def concat_ea_frame(to_concat):
    to_concat = [t if isinstance(t, ExtensionArray) else t[0, :] for t in to_concat]
    concat_values = concat_compat(to_concat, axis=concat_axis)
    if not isinstance(concat_values, ExtensionArray):
        # if the result of concat is not an EA but an ndarray, reshape to
        # 2D to put it a non-EA Block
        concat_values = np.atleast_2d(concat_values)
    return concat_values

and called her. Is that correct?

I can do that if it ends this discussion, but just to note: this would create a function in dtypes/concat.py with logic tied to the internals (reshaping to conform to block's dimenions is something only needs to be known to the internals code) and that would only be used in the internals (here, in internals/concat.py)

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yes this is exactly inline with concat_dateime and concat_compat. I am surprised that you think this should be anywhere else.

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with logic tied to the internals (reshaping to conform to block's dimenions is something only needs to be known to the internals code) and that would only be used in the internals

i agree with Joris; internals-specific logic belongs in internals

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i guess

# the non-EA values are 2D arrays with shape (1, n)
to_concat = [t if isinstance(t, ExtensionArray) else t[0, :] for t in to_concat]
concat_values = concat_compat(to_concat, axis=concat_axis)
if not isinstance(concat_values, ExtensionArray):
# if the result of concat is not an EA but an ndarray, reshape to
# 2D to put it a non-EA Block
concat_values = np.atleast_2d(concat_values)
else:
concat_values = concat_compat(to_concat, axis=concat_axis)

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14 changes: 14 additions & 0 deletions pandas/tests/reshape/test_concat.py
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Expand Up @@ -2832,3 +2832,17 @@ def test_concat_preserves_subclass(obj):

result = concat([obj, obj])
assert isinstance(result, type(obj))


def test_concat_frame_axis0_extension_dtypes():
# preserve extension dtype (through common_dtype mechanism)
df1 = pd.DataFrame({"a": pd.array([1, 2, 3], dtype="Int64")})
df2 = pd.DataFrame({"a": np.array([4, 5, 6])})

result = pd.concat([df1, df2], ignore_index=True)
expected = pd.DataFrame({"a": [1, 2, 3, 4, 5, 6]}, dtype="Int64")
tm.assert_frame_equal(result, expected)

result = pd.concat([df2, df1], ignore_index=True)
expected = pd.DataFrame({"a": [4, 5, 6, 1, 2, 3]}, dtype="Int64")
tm.assert_frame_equal(result, expected)
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