parametrized tests for gb.is_monotonic_increasing/decreasing

Author: No-Stream

doc/source/api.rst

@@ -2052,6 +2052,8 @@ The following methods are available only for ``SeriesGroupBy`` objects.
    SeriesGroupBy.nunique
    SeriesGroupBy.unique
    SeriesGroupBy.value_counts
+   SeriesGroupBy.is_monotonic_increasing
+   SeriesGroupBy.is_monotonic_decreasing
 
 The following methods are available only for ``DataFrameGroupBy`` objects.
 

pandas/tests/groupby/test_groupby.py

@@ -3701,128 +3701,68 @@ def test_cummin_cummax(self):
         expected = pd.Series([1, 2, 1], name='b')
         tm.assert_series_equal(result, expected)
 
-    def test_is_increasing_is_decreasing(self):
-        # GH 17015
-
+    @pytest.mark.parametrize('in_vals, out_vals', [
         # Basics: strictly increasing (T), strictly decreasing (F),
         # abs val increasing (F), non-strictly increasing (T)
-        source_dict = {
-            'A': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11'],
-            'B': ['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'c', 'd', 'd'],
-            'C': [1, 2, 5, 3, 2, 0, 4, 5, -6, 1, 1]}
-        df = pd.DataFrame(source_dict)
-        result = df.groupby(['B']).C.is_monotonic_increasing()
-        expected = pd.Series(index=['a', 'b', 'c', 'd'],
-                             data=[True, False, False, True],
-                             name='C')
-        expected.index.name = 'B'
-        tm.assert_series_equal(result, expected)
-        # Also check result equal to manually taking x.is_monotonic_increasing.
-        expected = df.groupby('B').C.apply(lambda x: x.is_monotonic_increasing)
-        tm.assert_series_equal(result, expected)
-
+        ([1, 2, 5, 3, 2, 0, 4, 5, -6, 1, 1],
+         [True, False, False, True]),
         # Test with inf vals
-        source_dict = {
-            'A': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11'],
-            'B': ['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'c', 'd', 'd'],
-            'C': [1, 2.1, np.inf, 3, 2, np.inf, -np.inf, 5, 11, 1, -np.inf]}
-        expected.index.name = 'B'
-        df = pd.DataFrame(source_dict)
-        result = df.groupby(['B']).C.is_monotonic_increasing()
-        expected = pd.Series(index=['a', 'b', 'c', 'd'],
-                             data=[True, False, True, False],
-                             name='C')
-        expected.index.name = 'B'
-        tm.assert_series_equal(result, expected)
-        # Also check result equal to manually taking x.is_monotonic_increasing.
-        expected = df.groupby('B').C.apply(lambda x: x.is_monotonic_increasing)
-        tm.assert_series_equal(result, expected)
-
+        ([1, 2.1, np.inf, 3, 2, np.inf, -np.inf, 5, 11, 1, -np.inf],
+         [True, False, True, False]),
         # Test with nan vals; should always be False
+        ([1, 2, np.nan, 3, 2, np.nan, np.nan, 5, -np.inf, 1, np.nan],
+         [False, False, False, False]),
+    ])
+    def test_is_monotonic_increasing(self, in_vals, out_vals):
+        # GH 17015
         source_dict = {
             'A': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11'],
             'B': ['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'c', 'd', 'd'],
-            'C': [1, 2, np.nan, 3, 2, np.nan, np.nan, 5, -np.inf, 1, np.nan]}
+            'C': in_vals}
         df = pd.DataFrame(source_dict)
         result = df.groupby(['B']).C.is_monotonic_increasing()
         expected = pd.Series(index=['a', 'b', 'c', 'd'],
-                             data=[False, False, False, False],
+                             data=out_vals,
                              name='C')
         expected.index.name = 'B'
         tm.assert_series_equal(result, expected)
-        # Also check result equal to manually taking x.is_monotonic_increasing.
-        expected = df.groupby('B').C.apply(lambda x: x.is_monotonic_increasing)
-        tm.assert_series_equal(result, expected)
 
-        # Test with single member groups; should be True except for np.nan
-        source_dict = {
-            'A': ['1', '2', '3', '4'],
-            'B': ['a', 'b', 'c', 'd'],
-            'C': [1, 2, np.nan, np.inf]}
-        df = pd.DataFrame(source_dict)
-        result = df.groupby(['B']).C.is_monotonic_increasing()
-        expected = pd.Series(index=['a', 'b', 'c', 'd'],
-                             data=[True, True, False, True],
-                             name='C')
-        expected.index.name = 'B'
-        expected.index.name = 'B'
-        tm.assert_series_equal(result, expected)
         # Also check result equal to manually taking x.is_monotonic_increasing.
-        expected = df.groupby('B').C.apply(lambda x: x.is_monotonic_increasing)
+        expected = (
+            df.groupby(['B']).C.apply(lambda x: x.is_monotonic_increasing))
         tm.assert_series_equal(result, expected)
 
-        # As above, for .is_monotonic_decreasing()
+    @pytest.mark.parametrize('in_vals, out_vals', [
         # Basics: strictly decreasing (T), strictly increasing (F),
         # abs val decreasing (F), non-strictly increasing (T)
-        source_dict = {
-            'A': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11'],
-            'B': ['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'c', 'd', 'd'],
-            'C': [10, 9, 7, 3, 4, 5, -3, 2, 0, 1, 1]}
-        df = pd.DataFrame(source_dict)
-        result = df.groupby(['B']).C.is_monotonic_decreasing()
-        expected = pd.Series(index=['a', 'b', 'c', 'd'],
-                             data=[True, False, False, True],
-                             name='C')
-        expected.index.name = 'B'
-        tm.assert_series_equal(result, expected)
-        # Also check result equal to manually taking x.is_monotonic_decreasing.
-        expected = df.groupby('B').C.apply(lambda x: x.is_monotonic_decreasing)
-        tm.assert_series_equal(result, expected)
-
+        ([10, 9, 7, 3, 4, 5, -3, 2, 0, 1, 1],
+         [True, False, False, True]),
         # Test with inf vals
-        source_dict = {
-            'A': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11'],
-            'B': ['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'c', 'd', 'd'],
-            'C': [np.inf, 1, -np.inf, np.inf, 2, -3, -np.inf, 5, -3, -np.inf,
-                  -np.inf]}
-        df = pd.DataFrame(source_dict)
-        result = df.groupby(['B']).C.is_monotonic_decreasing()
-        expected = pd.Series(index=['a', 'b', 'c', 'd'],
-                             data=[True, True, False, True],
-                             name='C')
-        expected.index.name = 'B'
-        tm.assert_series_equal(result, expected)
-        # Also check result equal to manually taking x.is_monotonic_decreasing.
-        expected = df.groupby('B').C.apply(lambda x: x.is_monotonic_decreasing)
-        tm.assert_series_equal(result, expected)
-
+        ([np.inf, 1, -np.inf, np.inf, 2, -3, -np.inf, 5, -3, -np.inf, -np.inf],
+         [True, True, False, True]),
         # Test with nan vals; should always be False
+        ([1, 2, np.nan, 3, 2, np.nan, np.nan, 5, -np.inf, 1, np.nan],
+         [False, False, False, False]),
+    ])
+    def test_is_monotonic_decreasing(self, in_vals, out_vals):
+        # GH 17015
         source_dict = {
             'A': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11'],
             'B': ['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'c', 'd', 'd'],
-            'C': [1, 2, np.nan, 3, 2, np.nan, np.nan, 5, -np.inf, 1, np.nan]}
+            'C': in_vals}
+
         df = pd.DataFrame(source_dict)
-        result = df.groupby(['B']).C.is_monotonic_decreasing()
+        result = df.groupby('B').C.is_monotonic_decreasing()
         expected = pd.Series(index=['a', 'b', 'c', 'd'],
-                             data=[False, False, False, False],
+                             data=out_vals,
                              name='C')
         expected.index.name = 'B'
         tm.assert_series_equal(result, expected)
+
         # Also check result equal to manually taking x.is_monotonic_decreasing.
         expected = df.groupby('B').C.apply(lambda x: x.is_monotonic_decreasing)
         tm.assert_series_equal(result, expected)
 
-
     def test_apply_numeric_coercion_when_datetime(self):
         # In the past, group-by/apply operations have been over-eager
         # in converting dtypes to numeric, in the presence of datetime