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BUG: DataFrame with Int64 columns casts to float64 with .max()/.min() #34210

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BUG: DataFrame with Int64 columns casts to float64 with .max()/.min() #34210

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9728d81
BUG: DataFrame with Int64 columns casts to float64 with .max()/.min()
simonjayhawkins May 16, 2020
7765bac
temp fix
simonjayhawkins May 17, 2020
c58e85d
Merge remote-tracking branch 'upstream/master' into dataframe-minmax-…
simonjayhawkins May 18, 2020
af366b8
Merge remote-tracking branch 'upstream/master' into dataframe-minmax-…
simonjayhawkins May 18, 2020
4a14032
changes condition
simonjayhawkins May 18, 2020
e4e6c8e
make results consistent for numeric_only parameter and numeric_only d…
simonjayhawkins May 18, 2020
9b5f41c
black fixup
simonjayhawkins May 18, 2020
e704f51
comments
simonjayhawkins May 18, 2020
60ab03a
Merge remote-tracking branch 'upstream/master' into dataframe-minmax-…
simonjayhawkins Jun 30, 2020
7b18d89
change is_numeric to be False on empty DataFrame
simonjayhawkins Jun 30, 2020
e850108
reorder condition
simonjayhawkins Jun 30, 2020
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v1.1.0.rst
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Expand Up @@ -865,6 +865,7 @@ ExtensionArray
- Fixed bug where :meth:`StringArray.memory_usage` was not implemented (:issue:`33963`)
- Fixed bug where :meth:`DataFrameGroupBy` would ignore the ``min_count`` argument for aggregations on nullable boolean dtypes (:issue:`34051`)
- Fixed bug that `DataFrame(columns=.., dtype='string')` would fail (:issue:`27953`, :issue:`33623`)
- Fixed bug where :meth:`DataFrame.min` and :meth:`DataFrame.max` with Int64 columns casts to float64 (:issue:`32651`)

Other
^^^^^
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2 changes: 1 addition & 1 deletion pandas/core/arrays/sparse/array.py
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Expand Up @@ -1220,7 +1220,7 @@ def any(self, axis=0, *args, **kwargs):

return values.any().item()

def sum(self, axis=0, *args, **kwargs):
def sum(self, axis=0, min_count=0, *args, **kwargs):
"""
Sum of non-NA/null values

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4 changes: 3 additions & 1 deletion pandas/core/frame.py
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Expand Up @@ -8419,7 +8419,9 @@ def _get_data(axis_matters):
raise NotImplementedError(msg)
return data

if numeric_only is not None and axis in [0, 1]:
if (
self._is_homogeneous_type and self._mgr.any_extension_types and axis == 0
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do we really need all of these conditions?

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no I don't think so, as this is hiding some bugs (or inconsistencies)

>>> import pandas as pd
>>>
>>> pd.__version__
'1.1.0.dev0+1600.gdede2c7a1e'
>>>
>>> df = pd.DataFrame({"A": pd.Series([0, 1], dtype="category")})
>>> df
   A
0  0
1  1
>>>
>>> df.all()
A    False
dtype: bool
>>>
>>> df.A.all()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Users\simon\pandas\pandas\core\generic.py", line 11394, in logical_func
    return self._reduce(
  File "C:\Users\simon\pandas\pandas\core\series.py", line 4028, in _reduce
    return delegate._reduce(name, skipna=skipna, **kwds)
  File "C:\Users\simon\pandas\pandas\core\arrays\categorical.py", line 2076, in _reduce
    raise TypeError(f"Categorical cannot perform the operation {name}")
TypeError: Categorical cannot perform the operation all
>>>

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will need to fix #21020 first

) or (numeric_only is not None and axis in [0, 1]):
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the axis in [0, 1] is superfluous

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test_any_all_np_func passes axis=None for reduction along both axes.

df = self
if numeric_only is True:
df = _get_data(axis_matters=True)
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13 changes: 13 additions & 0 deletions pandas/tests/frame/test_analytics.py
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Expand Up @@ -907,6 +907,19 @@ def test_mean_extensionarray_numeric_only_true(self):
expected = pd.DataFrame(arr).mean()
tm.assert_series_equal(result, expected)

@pytest.mark.parametrize("numeric_only", [True, False, None])
@pytest.mark.parametrize("method", ["min", "max"])
def test_minmax_extensionarray(self, method, numeric_only):
# https://github.com/pandas-dev/pandas/issues/32651
int64_info = np.iinfo("int64")
ser = Series([int64_info.max, None, int64_info.min], dtype=pd.Int64Dtype())
df = DataFrame({"Int64": ser})
result = getattr(df, method)(numeric_only=numeric_only)
expected = Series(
[getattr(int64_info, method)], index=pd.Index(["Int64"], dtype="object")
)
tm.assert_series_equal(result, expected)

def test_stats_mixed_type(self, float_string_frame):
# don't blow up
float_string_frame.std(1)
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