BUG: Series.map fails when mapping a dict with tuple keys

Explicitly use keys as index to prevent conversion to MultiIndex

Add test for mapping with tuple-keyed dict

Specify expected output for the test and use assert_series_equal

Add note to v0.14.1 release notes

Author: onesandzeroes

doc/source/v0.14.1.txt

@@ -116,3 +116,4 @@ Bug Fixes
 - Bug in ``CustomBusinessDay.apply`` raiases ``NameError`` when ``np.datetime64`` object is passed (:issue:`7196`)
 - Bug in ``MultiIndex.append``, ``concat`` and ``pivot_table`` don't preserve timezone (:issue:`6606`)
 - Bug all ``StringMethods`` now work on empty Series (:issue:`7242`)
+- Bug in ``Series.map`` when mapping a dict with tuple keys of different lengths (:issue:`7333`)

pandas/core/series.py

@@ -1940,7 +1940,7 @@ def map_f(values, f):
 
         if isinstance(arg, (dict, Series)):
             if isinstance(arg, dict):
-                arg = self._constructor(arg)
+                arg = self._constructor(arg, index=arg.keys())
 
             indexer = arg.index.get_indexer(values)
             new_values = com.take_1d(arg.values, indexer)

pandas/tests/test_series.py

@@ -4860,6 +4860,25 @@ def test_map_na_exclusion(self):
         exp = s * 2
         assert_series_equal(result, exp)
 
+    def test_map_dict_with_tuple_keys(self):
+        '''
+        Due to new MultiIndex-ing behaviour in v0.14.0,
+        dicts with tuple keys passed to map were being
+        converted to a multi-index, preventing tuple values
+        from being mapped properly.
+        '''
+        df = pd.DataFrame({'a': [(1,), (2,), (3, 4), (5, 6)]})
+        label_mappings = {
+            (1,): 'A',
+            (2,): 'B',
+            (3, 4): 'A',
+            (5, 6): 'B'
+        }
+        df['labels'] = df['a'].map(label_mappings)
+        df['expected_labels'] = pd.Series(['A', 'B', 'A', 'B'], index=df.index)
+        # All labels should be filled now
+        tm.assert_series_equal(df['labels'], df['expected_labels'])
+
     def test_apply(self):
         assert_series_equal(self.ts.apply(np.sqrt), np.sqrt(self.ts))