roger-mike commented on a change in pull request #15827:
URL: https://github.com/apache/beam/pull/15827#discussion_r756250175
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File path: sdks/python/apache_beam/dataframe/frames_test.py
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@@ -1100,6 +1100,82 @@ def test_dt_tz_localize_nonexistent(self):
'Europe/Warsaw', ambiguous='NaT', nonexistent=pd.Timedelta('1H')),
s)
+ def test_idxmin(self):
+ df = pd.DataFrame({
+ 'consumption': [10.51, 103.11, 55.48],
+ 'co2_emissions': [37.2, 19.66, 1712]
+ },
+ index=['Pork', 'Wheat Products', 'Beef'])
+
+ df2 = df.copy()
+ df2.loc['Pork', 'co2_emissions'] = None
+ df2.loc['Wheat Products', 'consumption'] = None
+ df2.loc['Beef', 'co2_emissions'] = None
+
+ df3 = pd.DataFrame({
+ 'consumption': [1.1, 2.2, 3.3], 'co2_emissions': [3.3, 2.2, 1.1]
+ },
+ index=[0, 1, 2])
+
+ s = pd.Series(data=[4, 3, None, 1], index=['A', 'B', 'C', 'D'])
+ s2 = pd.Series(data=[1, 2, 3], index=[1, 2, 3])
+
+ self._run_test(lambda df: df.idxmin(), df)
+ self._run_test(lambda df: df.idxmin(skipna=False), df)
+ self._run_test(lambda df: df.idxmin(axis=1), df)
+ self._run_test(lambda df: df.idxmin(axis=1, skipna=False), df)
+
+ self._run_test(lambda df2: df2.idxmin(), df2)
+ self._run_test(lambda df2: df2.idxmin(axis=1), df2)
+
+ # These tests are failing
Review comment:
Not sure how to handle the `skipna=False` case when there are NaNs in
the DataFrame or Series. The expected type is different from the proxy's type.
I don't know how it can be inferred. @TheNeuralBit Do you have any examples of
how this can be done?
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