ENH: groupby().is_monotonic_increasing #17015 (#17453)

Author: No-Stream

doc/source/api.rst

@@ -2240,6 +2240,8 @@ 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.
 

doc/source/whatsnew/v0.23.0.txt

@@ -323,6 +323,7 @@ Other Enhancements
 
 - ``IntervalIndex.astype`` now supports conversions between subtypes when passed an ``IntervalDtype`` (:issue:`19197`)
 - :class:`IntervalIndex` and its associated constructor methods (``from_arrays``, ``from_breaks``, ``from_tuples``) have gained a ``dtype`` parameter (:issue:`19262`)
+- Added :func:`SeriesGroupBy.is_monotonic_increasing` and :func:`SeriesGroupBy.is_monotonic_decreasing` (:issue:`17015`)
 
 .. _whatsnew_0230.api_breaking:
 

pandas/core/groupby.py

@@ -336,7 +336,9 @@
 ]) | _plotting_methods
 
 _series_apply_whitelist = ((_common_apply_whitelist |
-                            {'nlargest', 'nsmallest'}) -
+                            {'nlargest', 'nsmallest',
+                             'is_monotonic_increasing',
+                             'is_monotonic_decreasing'}) -
                            {'boxplot'}) | frozenset(['dtype', 'unique'])
 
 _dataframe_apply_whitelist = ((_common_apply_whitelist |

pandas/tests/groupby/test_groupby.py

@@ -2639,7 +2639,7 @@ def test_group_shift_with_null_key(self):
         # Generate a moderately large dataframe with occasional missing
         # values in column `B`, and then group by [`A`, `B`]. This should
         # force `-1` in `labels` array of `g.grouper.group_info` exactly
-        # at those places, where the group-by key is partilly missing.
+        # at those places, where the group-by key is partially missing.
         df = DataFrame([(i % 12, i % 3 if i % 3 else np.nan, i)
                         for i in range(n_rows)], dtype=float,
                        columns=["A", "B", "Z"], index=None)
@@ -2764,6 +2764,65 @@ def test_cummin_cummax(self):
         expected = pd.Series([1, 2, 1], name='b')
         tm.assert_series_equal(result, expected)
 
+    @pytest.mark.parametrize('in_vals, out_vals', [
+
+        # Basics: strictly increasing (T), strictly decreasing (F),
+        # abs val increasing (F), non-strictly increasing (T)
+        ([1, 2, 5, 3, 2, 0, 4, 5, -6, 1, 1],
+         [True, False, False, True]),
+
+        # Test with inf vals
+        ([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': in_vals}
+        df = pd.DataFrame(source_dict)
+        result = df.groupby('B').C.is_monotonic_increasing
+        index = Index(list('abcd'), name='B')
+        expected = pd.Series(index=index, data=out_vals, name='C')
+        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)
+
+    @pytest.mark.parametrize('in_vals, out_vals', [
+        # Basics: strictly decreasing (T), strictly increasing (F),
+        # abs val decreasing (F), non-strictly increasing (T)
+        ([10, 9, 7, 3, 4, 5, -3, 2, 0, 1, 1],
+         [True, False, False, True]),
+
+        # Test with inf vals
+        ([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': in_vals}
+
+        df = pd.DataFrame(source_dict)
+        result = df.groupby('B').C.is_monotonic_decreasing
+        index = Index(list('abcd'), name='B')
+        expected = pd.Series(index=index, data=out_vals, name='C')
+        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

pandas/tests/groupby/test_whitelist.py

@@ -88,6 +88,8 @@
     'unique',
     'nlargest',
     'nsmallest',
+    'is_monotonic_increasing',
+    'is_monotonic_decreasing',
 ])
 
 
@@ -184,7 +186,7 @@ def test_regression_whitelist_methods(
         axis, skipna, sort):
     # GH6944
     # GH 17537
-    # explicitly test the whitelest methods
+    # explicitly test the whitelist methods
 
     if axis == 0:
         frame = raw_frame
@@ -249,7 +251,8 @@ def test_tab_completion(mframe):
         'cumsum', 'cumcount', 'ngroup', 'all', 'shift', 'skew',
         'take', 'tshift', 'pct_change', 'any', 'mad', 'corr', 'corrwith',
         'cov', 'dtypes', 'ndim', 'diff', 'idxmax', 'idxmin',
-        'ffill', 'bfill', 'pad', 'backfill', 'rolling', 'expanding', 'pipe'}
+        'ffill', 'bfill', 'pad', 'backfill', 'rolling', 'expanding', 'pipe',
+    }
     assert results == expected