Merge branch 'tuple-fix' of https://github.com/onesandzeroes/pandas into onesandzeroes-tuple-fix

Conflicts:
	doc/source/v0.14.1.txt

Author: jreback

doc/source/timeseries.rst

@@ -1244,7 +1244,7 @@ the quarter end:
 Time Zone Handling
 ------------------
 
-Pandas provides rich support for working with timestamps in different time zones using ``pytz`` and ``dateutil`` libraries. 
+Pandas provides rich support for working with timestamps in different time zones using ``pytz`` and ``dateutil`` libraries.
 ``dateutil`` support is new [in 0.14.1] and currently only supported for fixed offset and tzfile zones. The default library is ``pytz``.
 Support for ``dateutil`` is provided for compatibility with other applications e.g. if you use ``dateutil`` in other python packages.
 
@@ -1257,13 +1257,14 @@ By default, pandas objects are time zone unaware:
 
 To supply the time zone, you can use the ``tz`` keyword to ``date_range`` and
 other functions. Dateutil time zone strings are distinguished from ``pytz``
-time zones by starting with ``dateutil/``. In ``pytz`` you can find a list of
-common (and less common) time zones using ``from pytz import common_timezones, all_timezones``. 
-``dateutil`` uses the OS timezones so there isn't a fixed list available. For
+time zones by starting with ``dateutil/``.
+
+- In ``pytz`` you can find a list of common (and less common) time zones using ``from pytz import common_timezones, all_timezones``.
+- ``dateutil`` uses the OS timezones so there isn't a fixed list available. For
 common zones, the names are the same as ``pytz``.
 
 .. ipython:: python
-   
+
    # pytz
    rng_utc = date_range('3/6/2012 00:00', periods=10, freq='D', tz='UTC')
    rng_utc.tz
@@ -1314,7 +1315,7 @@ tz-aware data to another time zone:
 .. warning::
     Be very wary of conversions between libraries as ``pytz`` and ``dateutil``
     may have different definitions of the time zones. This is more of a problem for
-    unusual timezones than for 'standard' zones like ``US/Eastern``. 
+    unusual timezones than for 'standard' zones like ``US/Eastern``.
 
 Under the hood, all timestamps are stored in UTC. Scalar values from a
 ``DatetimeIndex`` with a time zone will have their fields (day, hour, minute)

doc/source/v0.14.1.txt

@@ -64,7 +64,9 @@ Enhancements
 
   .. ipython:: python
 
-   rng_utc_dateutil = date_range('3/6/2012 00:00', periods=10, freq='D',
+   rng_utc_dateutil = date_range('3/6/2012 00:00',
+                                 periods=10,
+                                 freq='D',
                                  tz='dateutil/UTC')
    rng_utc_dateutil.tz
 
@@ -114,3 +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 in ``.loc`` with a list of indexers on a single-multi index level (that is not nested) (:issue:`7349`)
+- 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

@@ -4873,6 +4873,25 @@ 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))