yeandy commented on a change in pull request #17026:
URL: https://github.com/apache/beam/pull/17026#discussion_r837530890
##########
File path: sdks/python/apache_beam/dataframe/frames_test.py
##########
@@ -1295,6 +1310,118 @@ def s_times_shuffled(times, s):
self._run_test(lambda s: s.pipe(s_times, 2), s)
self._run_test(lambda s: s.pipe((s_times_shuffled, 's'), 2), s)
+ def test_unstack_pandas_series_not_multiindex(self):
+ # Pandas should throw a ValueError if performing unstack
+ # on a Series without MultiIndex
+ s = pd.Series([1, 2, 3, 4], index=['one', 'two', 'three', 'four'])
+ with self.assertRaises((AttributeError, ValueError)):
+ self._run_test(lambda s: s.unstack(), s)
+
+ def test_unstack_non_categorical_index(self):
+ index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'), ('two',
'a'),
+ ('two', 'b')])
+ index = index.set_levels(
+ index.levels[0].astype(pd.CategoricalDtype(['one', 'two'])), level=0)
+ s = pd.Series(np.arange(1.0, 5.0), index=index)
+ with self.assertRaisesRegex(
+ frame_base.WontImplementError,
+ r"unstack\(\) is only supported on DataFrames if"):
+ self._run_test(lambda s: s.unstack(level=-1), s)
+
+ def _unstack_get_categorical_index(self):
+ index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'), ('two',
'a'),
+ ('two', 'b')])
+ index = index.set_levels(
+ index.levels[0].astype(pd.CategoricalDtype(['one', 'two'])), level=0)
+ index = index.set_levels(
+ index.levels[1].astype(pd.CategoricalDtype(['a', 'b'])), level=1)
+ return index
+
+ def test_unstack_pandas_example1(self):
+ index = self._unstack_get_categorical_index()
+ s = pd.Series(np.arange(1.0, 5.0), index=index)
+ self._run_test(lambda s: s.unstack(level=-1), s, check_dtypes=False)
+
+ def test_unstack_pandas_example2(self):
+ index = self._unstack_get_categorical_index()
+ s = pd.Series(np.arange(1.0, 5.0), index=index)
+ self._run_test(lambda s: s.unstack(level=0), s, check_dtypes=False)
+
+ def test_unstack_pandas_example3(self):
+ index = self._unstack_get_categorical_index()
+ s = pd.Series(np.arange(1.0, 5.0), index=index)
+ df = s.unstack(level=0)
+ if PD_VERSION < (1, 2):
+ with self.assertRaisesRegex(frame_base.WontImplementError,
+ r"pandas==1.1.5 has an indexing error bug"):
+ self._run_test(lambda df: df.unstack(), df, check_dtypes=False)
+ else:
+ self._run_test(lambda df: df.unstack(), df, check_dtypes=False)
+
+ @unittest.skipIf(
+ PD_VERSION < (1, 4),
+ "pandas=1.4 fixes error when concat() of boolean types results in
object")
+ def test_unstack_bool(self):
+ index = pd.MultiIndex.from_tuples([(True, 'a'), (True, 'b'), (False, 'a'),
+ (False, 'b')])
+ index = index.set_levels(index.levels[0].astype('boolean'), level=0)
+ index = index.set_levels(
+ index.levels[1].astype(pd.CategoricalDtype(['a', 'b'])), level=1)
+ s = pd.Series(np.arange(1.0, 5.0), index=index)
+ self._run_test(lambda s: s.unstack(level=0), s, check_dtypes=False)
+
+ def test_unstack_series_multiple_index_levels(self):
+ tuples = list(
+ zip(
+ *[
+ ["bar", "bar", "bar", "bar", "baz", "baz", "baz", "baz"],
+ ["one", "one", "two", "two", "one", "one", "two", "two"],
+ ["A", "B", "A", "B", "A", "B", "A", "B"],
+ ]))
+ index = pd.MultiIndex.from_tuples(
+ tuples, names=["first", "second", "third"])
+ index = index.set_levels(
+ index.levels[0].astype(pd.CategoricalDtype(['bar', 'baz'])), level=0)
+ index = index.set_levels(
+ index.levels[1].astype(pd.CategoricalDtype(['one', 'two'])), level=1)
+ index = index.set_levels(
+ index.levels[2].astype(pd.CategoricalDtype(['A', 'B'])), level=2)
+ df = pd.Series(np.random.randn(8), index=index)
+ self._run_test(
+ lambda df: df.unstack(level=['first', 'third']), df,
check_dtypes=False)
+ # self.assert_frame_data_equivalent(
+ # result, df.unstack(level=['first', 'third']))
+
+ def test_unstack_series_multiple_index_and_column_levels(self):
+ columns = pd.MultiIndex.from_tuples(
+ [
+ ("A", "cat", "long"),
+ ("B", "cat", "long"),
+ ("A", "dog", "short"),
+ ("B", "dog", "short"),
+ ],
+ names=["exp", "animal", "hair_length"],
+ )
+ index = pd.MultiIndex.from_product(
+ [['one', 'two'], ['a', 'b'], ['bar', 'baz']],
+ names=["first", "second", "third"])
+ index = index.set_levels(
+ index.levels[0].astype(pd.CategoricalDtype(['one', 'two'])), level=0)
+ index = index.set_levels(
+ index.levels[1].astype(pd.CategoricalDtype(['a', 'b'])), level=1)
+ index = index.set_levels(
+ index.levels[2].astype(pd.CategoricalDtype(['bar', 'baz'])), level=2)
+ df = pd.DataFrame(np.random.randn(8, 4), index=index, columns=columns)
+ df = df.stack(level=["animal", "hair_length"])
+ self._run_test(
+ lambda df: df.unstack(level=['second', 'third']),
+ df,
+ check_dtypes=False)
+ self._run_test(
+ lambda df: df.unstack(level=['second']), df, check_dtypes=False)
+ # self.assert_frame_data_equivalent(
+ # result, df.unstack(level=['second', 'third']))
Review comment:
Deleted
##########
File path: sdks/python/apache_beam/dataframe/frames_test.py
##########
@@ -1295,6 +1310,118 @@ def s_times_shuffled(times, s):
self._run_test(lambda s: s.pipe(s_times, 2), s)
self._run_test(lambda s: s.pipe((s_times_shuffled, 's'), 2), s)
+ def test_unstack_pandas_series_not_multiindex(self):
+ # Pandas should throw a ValueError if performing unstack
+ # on a Series without MultiIndex
+ s = pd.Series([1, 2, 3, 4], index=['one', 'two', 'three', 'four'])
+ with self.assertRaises((AttributeError, ValueError)):
+ self._run_test(lambda s: s.unstack(), s)
+
+ def test_unstack_non_categorical_index(self):
+ index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'), ('two',
'a'),
+ ('two', 'b')])
+ index = index.set_levels(
+ index.levels[0].astype(pd.CategoricalDtype(['one', 'two'])), level=0)
+ s = pd.Series(np.arange(1.0, 5.0), index=index)
+ with self.assertRaisesRegex(
+ frame_base.WontImplementError,
+ r"unstack\(\) is only supported on DataFrames if"):
+ self._run_test(lambda s: s.unstack(level=-1), s)
+
+ def _unstack_get_categorical_index(self):
+ index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'), ('two',
'a'),
+ ('two', 'b')])
+ index = index.set_levels(
+ index.levels[0].astype(pd.CategoricalDtype(['one', 'two'])), level=0)
+ index = index.set_levels(
+ index.levels[1].astype(pd.CategoricalDtype(['a', 'b'])), level=1)
+ return index
+
+ def test_unstack_pandas_example1(self):
+ index = self._unstack_get_categorical_index()
+ s = pd.Series(np.arange(1.0, 5.0), index=index)
+ self._run_test(lambda s: s.unstack(level=-1), s, check_dtypes=False)
+
+ def test_unstack_pandas_example2(self):
+ index = self._unstack_get_categorical_index()
+ s = pd.Series(np.arange(1.0, 5.0), index=index)
+ self._run_test(lambda s: s.unstack(level=0), s, check_dtypes=False)
+
+ def test_unstack_pandas_example3(self):
+ index = self._unstack_get_categorical_index()
+ s = pd.Series(np.arange(1.0, 5.0), index=index)
+ df = s.unstack(level=0)
+ if PD_VERSION < (1, 2):
+ with self.assertRaisesRegex(frame_base.WontImplementError,
+ r"pandas==1.1.5 has an indexing error bug"):
+ self._run_test(lambda df: df.unstack(), df, check_dtypes=False)
+ else:
+ self._run_test(lambda df: df.unstack(), df, check_dtypes=False)
+
+ @unittest.skipIf(
+ PD_VERSION < (1, 4),
+ "pandas=1.4 fixes error when concat() of boolean types results in
object")
+ def test_unstack_bool(self):
+ index = pd.MultiIndex.from_tuples([(True, 'a'), (True, 'b'), (False, 'a'),
+ (False, 'b')])
+ index = index.set_levels(index.levels[0].astype('boolean'), level=0)
+ index = index.set_levels(
+ index.levels[1].astype(pd.CategoricalDtype(['a', 'b'])), level=1)
+ s = pd.Series(np.arange(1.0, 5.0), index=index)
+ self._run_test(lambda s: s.unstack(level=0), s, check_dtypes=False)
+
+ def test_unstack_series_multiple_index_levels(self):
+ tuples = list(
+ zip(
+ *[
+ ["bar", "bar", "bar", "bar", "baz", "baz", "baz", "baz"],
+ ["one", "one", "two", "two", "one", "one", "two", "two"],
+ ["A", "B", "A", "B", "A", "B", "A", "B"],
+ ]))
+ index = pd.MultiIndex.from_tuples(
+ tuples, names=["first", "second", "third"])
+ index = index.set_levels(
+ index.levels[0].astype(pd.CategoricalDtype(['bar', 'baz'])), level=0)
+ index = index.set_levels(
+ index.levels[1].astype(pd.CategoricalDtype(['one', 'two'])), level=1)
+ index = index.set_levels(
+ index.levels[2].astype(pd.CategoricalDtype(['A', 'B'])), level=2)
+ df = pd.Series(np.random.randn(8), index=index)
+ self._run_test(
+ lambda df: df.unstack(level=['first', 'third']), df,
check_dtypes=False)
+ # self.assert_frame_data_equivalent(
+ # result, df.unstack(level=['first', 'third']))
Review comment:
Deleted
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