Groupby tuples

xref pandas-dev#17996

Author: Pietro Battiston <me@pietrobattiston.it>

Closes pandas-dev#18249 from toobaz/groupby_tuples and squashes the following commits:

dafc838 [Pietro Battiston] DOC: Clarification of groupby(by=) argument
e0bdfa7 [Pietro Battiston] TST: Test for tuples in columns, fixes to previous tests
74f91e0 [Pietro Battiston] TST: Fix tests which used tuples to pass multiple keys
201a4fe [Pietro Battiston] BUG: Never interpret a tuple as a list of keys

doc/source/whatsnew/v0.22.0.txt

@@ -101,7 +101,7 @@ Indexing
 ^^^^^^^^
 
 - Bug in :func:`Series.truncate` which raises ``TypeError`` with a monotonic ``PeriodIndex`` (:issue:`17717`)
-- Bug in :func:`DataFrame.groupby` where key as tuple in a ``MultiIndex`` were interpreted as a list of keys (:issue:`17979`)
+- Bug in :func:`DataFrame.groupby` where tuples were interpreted as lists of keys rather than as keys (:issue:`17979`, :issue:`18249`)
 -
 -
 

pandas/core/generic.py

@@ -5092,14 +5092,15 @@ def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True,
 
         Parameters
         ----------
-        by : mapping, function, str, or iterable
+        by : mapping, function, label, or list of labels
             Used to determine the groups for the groupby.
             If ``by`` is a function, it's called on each value of the object's
             index. If a dict or Series is passed, the Series or dict VALUES
             will be used to determine the groups (the Series' values are first
             aligned; see ``.align()`` method). If an ndarray is passed, the
-            values are used as-is determine the groups. A str or list of strs
-            may be passed to group by the columns in ``self``
+            values are used as-is determine the groups. A label or list of
+            labels may be passed to group by the columns in ``self``. Notice
+            that a tuple is interpreted a (single) key.
         axis : int, default 0
         level : int, level name, or sequence of such, default None
             If the axis is a MultiIndex (hierarchical), group by a particular

pandas/core/groupby.py

@@ -2756,7 +2756,6 @@ def _get_grouper(obj, key=None, axis=0, level=None, sort=True,
 
     """
     group_axis = obj._get_axis(axis)
-    is_axis_multiindex = isinstance(obj._info_axis, MultiIndex)
 
     # validate that the passed single level is compatible with the passed
     # axis of the object
@@ -2817,9 +2816,8 @@ def _get_grouper(obj, key=None, axis=0, level=None, sort=True,
     elif isinstance(key, BaseGrouper):
         return key, [], obj
 
-    # when MultiIndex, allow tuple to be a key
-    if not isinstance(key, (tuple, list)) or \
-            (isinstance(key, tuple) and is_axis_multiindex):
+    # Everything which is not a list is a key (including tuples):
+    if not isinstance(key, list):
         keys = [key]
         match_axis_length = False
     else:

pandas/tests/groupby/test_groupby.py

@@ -264,7 +264,7 @@ def test_len(self):
         df = pd.DataFrame(dict(a=[np.nan] * 3, b=[1, 2, 3]))
         assert len(df.groupby(('a'))) == 0
         assert len(df.groupby(('b'))) == 3
-        assert len(df.groupby(('a', 'b'))) == 3
+        assert len(df.groupby(['a', 'b'])) == 3
 
     def test_basic_regression(self):
         # regression

pandas/tests/groupby/test_grouping.py

@@ -366,13 +366,18 @@ def test_groupby_multiindex_tuple(self):
         result = df.groupby(('b', 1)).groups
         tm.assert_dict_equal(expected, result)
 
-        df2 = pd.DataFrame([[1, 2, 3, 4], [3, 4, 5, 6], [1, 4, 2, 3]],
+        df2 = pd.DataFrame(df.values,
                            columns=pd.MultiIndex.from_arrays(
                                [['a', 'b', 'b', 'c'],
                                 ['d', 'd', 'e', 'e']]))
-        df2.groupby([('b', 'd')]).groups
-        expected = df.groupby([('b', 'd')]).groups
-        result = df.groupby(('b', 'd')).groups
+        expected = df2.groupby([('b', 'd')]).groups
+        result = df.groupby(('b', 1)).groups
+        tm.assert_dict_equal(expected, result)
+
+        df3 = pd.DataFrame(df.values,
+                           columns=[('a', 'd'), ('b', 'd'), ('b', 'e'), 'c'])
+        expected = df3.groupby([('b', 'd')]).groups
+        result = df.groupby(('b', 1)).groups
         tm.assert_dict_equal(expected, result)
 
     @pytest.mark.parametrize('sort', [True, False])

pandas/tests/groupby/test_nth.py

@@ -202,7 +202,7 @@ def test_nth(self):
                                        freq='B')
         df = DataFrame(1, index=business_dates, columns=['a', 'b'])
         # get the first, fourth and last two business days for each month
-        key = (df.index.year, df.index.month)
+        key = [df.index.year, df.index.month]
         result = df.groupby(key, as_index=False).nth([0, 3, -2, -1])
         expected_dates = pd.to_datetime(
             ['2014/4/1', '2014/4/4', '2014/4/29', '2014/4/30', '2014/5/1',

pandas/tests/groupby/test_value_counts.py

@@ -43,7 +43,7 @@ def seed_df(seed_nans, n, m):
 
         df = seed_df(seed_nans, n, m)
         bins = None, np.arange(0, max(5, df['3rd'].max()) + 1, 2)
-        keys = '1st', '2nd', ('1st', '2nd')
+        keys = '1st', '2nd', ['1st', '2nd']
         for k, b in product(keys, bins):
             binned.append((df, k, b, n, m))
             ids.append("{}-{}-{}".format(k, n, m))