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FIX-#1467: Fix support for cummax and cummin across int and float #1800

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Merged
aregm merged 1 commit into from
Jul 24, 2020
Merged

FIX-#1467: Fix support for cummax and cummin across int and float #1800

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8 changes: 4 additions & 4 deletions modin/pandas/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -760,8 +760,8 @@ def cummax(self, axis=None, skipna=True, *args, **kwargs):
The cumulative maximum of the DataFrame.
"""
axis = self._get_axis_number(axis) if axis is not None else 0
if axis:
self._validate_dtypes()
if axis == 1:
self._validate_dtypes(numeric_only=True)
return self.__constructor__(
query_compiler=self._query_compiler.cummax(
axis=axis, skipna=skipna, **kwargs
Expand All @@ -779,8 +779,8 @@ def cummin(self, axis=None, skipna=True, *args, **kwargs):
The cumulative minimum of the DataFrame.
"""
axis = self._get_axis_number(axis) if axis is not None else 0
if axis:
self._validate_dtypes()
if axis == 1:
self._validate_dtypes(numeric_only=True)
return self.__constructor__(
query_compiler=self._query_compiler.cummin(
axis=axis, skipna=skipna, **kwargs
Expand Down
14 changes: 14 additions & 0 deletions modin/pandas/test/test_dataframe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3552,6 +3552,13 @@ def test_cummax(self, request, data, axis, skipna):
modin_result = modin_df.T.cummax(axis=axis, skipna=skipna)
df_equals(modin_result, pandas_result)

@pytest.mark.parametrize("axis", axis_values, ids=axis_keys)
def test_cummax_int_and_float(self, axis):
data = {"col1": list(range(1000)), "col2": [i * 0.1 for i in range(1000)]}
modin_df = pd.DataFrame(data)
pandas_df = pandas.DataFrame(data)
df_equals(modin_df.cummax(axis=axis), pandas_df.cummax(axis=axis))

@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
@pytest.mark.parametrize("axis", axis_values, ids=axis_keys)
@pytest.mark.parametrize(
Expand Down Expand Up @@ -3579,6 +3586,13 @@ def test_cummin(self, request, data, axis, skipna):
modin_result = modin_df.T.cummin(axis=axis, skipna=skipna)
df_equals(modin_result, pandas_result)

@pytest.mark.parametrize("axis", axis_values, ids=axis_keys)
def test_cummin_int_and_float(self, axis):
data = {"col1": list(range(1000)), "col2": [i * 0.1 for i in range(1000)]}
modin_df = pd.DataFrame(data)
pandas_df = pandas.DataFrame(data)
df_equals(modin_df.cummin(axis=axis), pandas_df.cummin(axis=axis))

@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
@pytest.mark.parametrize("axis", axis_values, ids=axis_keys)
@pytest.mark.parametrize(
Expand Down