ENH: gb.is_monotonic_increasing pandas-dev#17015 minor fixes for @jreback

Author: No-Stream

doc/source/whatsnew/v0.21.1.txt

@@ -23,7 +23,6 @@ Other Enhancements
 
 - :meth:`Timestamp.timestamp` is now available in Python 2.7. (:issue:`17329`)
 - :class:`Grouper` and :class:`TimeGrouper` now have a friendly repr output (:issue:`18203`).
-- :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_0211.deprecations:

doc/source/whatsnew/v0.22.0.txt

@@ -45,6 +45,8 @@ Other Enhancements
 - Improved wording of ``ValueError`` raised in :func:`to_datetime` when ``unit=`` is passed with a non-convertible value (:issue:`14350`)
 - :func:`Series.fillna` now accepts a Series or a dict as a ``value`` for a categorical dtype (:issue:`17033`)
 - :func:`pandas.read_clipboard` updated to use qtpy, falling back to PyQt5 and then PyQt4, adding compatibility with Python3 and multiple python-qt bindings (:issue:`17722`)
+- :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

@@ -1834,6 +1834,8 @@ def is_monotonic_increasing(self):
 
         Equivalent to ``.apply(lambda x: x.is_monotonic_increasing)``.
 
+        .. versionadded:: 0.22.0
+
         Examples
         --------
         >>> source_dict = {
@@ -1848,6 +1850,10 @@ def is_monotonic_increasing(self):
         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)
 
@@ -1859,6 +1865,8 @@ def is_monotonic_decreasing(self):
 
         Equivalent to ``.apply(lambda x: x.is_monotonic_decreasing)``.
 
+        .. versionadded:: 0.22.0
+
         Examples
         --------
         >>> source_dict = {

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)
@@ -2601,13 +2601,16 @@ def test_cummin_cummax(self):
         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]),
@@ -2620,8 +2623,8 @@ def test_is_monotonic_increasing(self, in_vals, out_vals):
             'C': in_vals}
         df = pd.DataFrame(source_dict)
         result = df.groupby('B').C.is_monotonic_increasing()
-        expected = pd.Series(index=list('abcd'), data=out_vals, name='C')
-        expected.index.name = 'B'
+        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.
@@ -2634,9 +2637,11 @@ def test_is_monotonic_increasing(self, in_vals, out_vals):
         # 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]),
@@ -2650,8 +2655,8 @@ def test_is_monotonic_decreasing(self, in_vals, out_vals):
 
         df = pd.DataFrame(source_dict)
         result = df.groupby('B').C.is_monotonic_decreasing()
-        expected = pd.Series(index=list('abcd'), data=out_vals, name='C')
-        expected.index.name = 'B'
+        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):

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
@@ -250,7 +250,8 @@ def test_tab_completion(mframe):
         'take', 'tshift', 'pct_change', 'any', 'mad', 'corr', 'corrwith',
         'cov', 'dtypes', 'ndim', 'diff', 'idxmax', 'idxmin',
         'ffill', 'bfill', 'pad', 'backfill', 'rolling', 'expanding', 'pipe',
-        'is_monotonic_increasing', 'is_monotonic_decreasing'}
+        'is_monotonic_increasing', 'is_monotonic_decreasing'
+    }
     assert results == expected