ENH: gb.is_monotonic_increasing #17015

doc/source/api.rst

@@ -2239,6 +2239,8 @@ 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/v0.22.0.txt

@@ -138,6 +138,8 @@ Other Enhancements
 - :func:`Series` / :func:`DataFrame` tab completion also returns identifiers in the first level of a :func:`MultiIndex`. (:issue:`16326`)
 - :func:`read_excel()` has gained the ``nrows`` parameter (:issue:`16645`)
 - :func:``DataFrame.to_json`` and ``Series.to_json`` now accept an ``index`` argument which allows the user to exclude the index from the JSON output (:issue:`17394`)
+- :meth: groupby.is_monotonic_increasing and :meth: .is_monotonic_decreasing extend :meth: Series.is_monotonic_increasing to groups, returning whether each group is monotonically increasing or decreasing, respectively. (:issue:`17015`)
+-
 
 .. _whatsnew_0220.api_breaking:
 

pandas/core/groupby.py

@@ -1826,6 +1826,63 @@ def pipe(self, func, *args, **kwargs):
         """
         return _pipe(self, func, *args, **kwargs)
 
+    @Substitution(name='groupby')
+    @Appender(_doc_template)
+    def is_monotonic_increasing(self):
+        """
+        Returns whether each group is monotonically increasing.
+
+        Equivalent to ``.apply(lambda x: x.is_monotonic_increasing)``.
+
+        .. versionadded:: 0.22.0
+
+        Examples
+        --------
+        >>> source_dict = {
+        ...    'A': ['this', 'col', 'is', 'entirely', 'irrelevant', '.'],
+        ...    'B': ['cat_a', 'cat_a', 'cat_a', 'cat_b', 'cat_b', 'cat_b'],
+        ...    'C': [1, 2, 3, 2, 2, 0]}
+
+        >>> df = pd.DataFrame(source_dict)
+        ...    df.groupby(['B']).C.is_monotonic_increasing()
+        B
+        cat_a    True
+        cat_b    False
+        Name: C, dtype: bool
+
+        See Also
+        --------
+        pandas.Series.is_monotonic_increasing
+        pandas.Index.is_monotonic_increasing
+        """
+        return self.apply(lambda x: x.is_monotonic_increasing)
+
+    @Substitution(name='groupby')
+    @Appender(_doc_template)
+    def is_monotonic_decreasing(self):
+        """
+        Returns whether each group is monotonically decreasing.
+
+        Equivalent to ``.apply(lambda x: x.is_monotonic_decreasing)``.
+
+        .. versionadded:: 0.22.0
+
+        Examples
+        --------
+        >>> source_dict = {
+        ...    'A': ['this', 'col', 'is', 'entirely', 'irrelevant', '.'],
+        ...    'B': ['cat_a', 'cat_a', 'cat_a', 'cat_b', 'cat_b', 'cat_b'],
+        ...    'C': [1, 2, 3, 2, 2, 0]}
+
+        >>> df = pd.DataFrame(source_dict)
+        ...    df.groupby(['B']).C.is_monotonic_decreasing()
+        B
+        cat_a    False
+        cat_b    True
+        Name: C, dtype: bool
+        """
+        return self.apply(lambda x: x.is_monotonic_decreasing)
+
 
 GroupBy._add_numeric_operations()
 

pandas/tests/groupby/test_groupby.py

@@ -2475,7 +2475,7 @@ 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)
@@ -2600,6 +2600,65 @@ 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

@@ -184,7 +184,7 @@ def test_regression_whitelist_methods(
         axis, skipna, sort):
     # GH6944
     # GH 17537
-    # explicity test the whitelest methods
+    # explicitly test the whitelist methods
 
     if axis == 0:
         frame = raw_frame
@@ -249,7 +249,9 @@ 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',
+        'is_monotonic_increasing', 'is_monotonic_decreasing'
+    }
     assert results == expected