ENH/BUG: allow single versus multi-index joining on inferred level (GH3662)

pandas/core/index.py

@@ -1265,8 +1265,48 @@ def join(self, other, how='left', level=None, return_indexers=False):
         -------
         join_index, (left_indexer, right_indexer)
         """
-        if (level is not None and (isinstance(self, MultiIndex) or
-                                   isinstance(other, MultiIndex))):
+        self_is_mi = isinstance(self, MultiIndex)
+        other_is_mi = isinstance(other, MultiIndex)
+
+        # try to figure out the join level
+        # GH3662
+        if (level is None and (self_is_mi or other_is_mi)):
+
+            # have the same levels/names so a simple join
+            if self.names == other.names:
+                pass
+
+            else:
+
+                # figure out join names
+                self_names = [ n for n in self.names if n is not None ]
+                other_names = [ n for n in other.names if n is not None ]
+                overlap = list(set(self_names) & set(other_names))
+
+                # need at least 1 in common
+                if not len(overlap):
+                    raise ValueError("cannot join with no level specified and no overlapping names")
+
+                if self_is_mi and other_is_mi:
+                    raise ValueError("cannot join between multiple multi-indexes")
+
+                # make the indices into mi's that match
+                if self_is_mi:
+                    level = self.names.index(overlap[0])
+                    result = other._join_level(self, level, how=how,
+                                               return_indexers=return_indexers)
+
+                    # reversed the results (as we reversed the inputs)
+                    if isinstance(result, tuple):
+                        return result[0], result[2], result[1]
+
+                else:
+                    level = other.names.index(overlap[0])
+                    return self._join_level(other, level, how=how,
+                                            return_indexers=return_indexers)
+
+        # join on the level
+        if (level is not None and (self_is_mi or other_is_mi)):
             return self._join_level(other, level, how=how,
                                     return_indexers=return_indexers)
 

pandas/tools/tests/test_merge.py

@@ -8,7 +8,7 @@
 import numpy as np
 import random
 
-from pandas.compat import range, lrange, lzip, zip
+from pandas.compat import range, lrange, lzip, zip, StringIO
 from pandas import compat, _np_version_under1p7
 from pandas.tseries.index import DatetimeIndex
 from pandas.tools.merge import merge, concat, ordered_merge, MergeError
@@ -1025,6 +1025,65 @@ def test_int64_overflow_issues(self):
         result = merge(df1, df2, how='outer')
         self.assertTrue(len(result) == 2000)
 
+    def test_join_multi_levels(self):
+
+        # GH 3662
+        # merge multi-levels
+        from pandas import read_table
+        household = read_table(
+            StringIO(
+"""household_id,male,wealth
+1,0,196087.3
+2,1,316478.7
+3,0,294750
+"""
+                ),
+            sep=',', index_col='household_id'
+            )
+
+        portfolio = read_table(
+            StringIO(
+""""household_id","asset_id","name","share"
+"1","nl0000301109","ABN Amro","1.0"
+"2","nl0000289783","Robeco","0.4"
+"2","gb00b03mlx29","Royal Dutch Shell","0.6"
+"3","gb00b03mlx29","Royal Dutch Shell","0.15"
+"3","lu0197800237","AAB Eastern Europe Equity Fund","0.6"
+"3","nl0000289965","Postbank BioTech Fonds","0.25"
+"4",,,"1.0"
+"""
+                ),
+            sep=',', index_col=['household_id', 'asset_id']
+            )
+
+        result = household.join(portfolio, how='inner')
+        expected = DataFrame(dict(male = [0,1,1,0,0,0],
+                                  wealth = [ 196087.3, 316478.7, 316478.7, 294750.0, 294750.0, 294750.0 ],
+                                  name = ['ABN Amro','Robeco','Royal Dutch Shell','Royal Dutch Shell','AAB Eastern Europe Equity Fund','Postbank BioTech Fonds'],
+                                  share = [1.00,0.40,0.60,0.15,0.60,0.25],
+                                  household_id = [1,2,2,3,3,3],
+                                  asset_id = ['nl0000301109','nl0000289783','gb00b03mlx29','gb00b03mlx29','lu0197800237','nl0000289965']),
+                             ).set_index(['household_id','asset_id']).reindex(columns=['male','wealth','name','share'])
+        assert_frame_equal(result,expected)
+
+        result = household.join(portfolio, how='outer')
+        expected = concat([expected,DataFrame(dict(share = [1.00]),
+                                              index=MultiIndex.from_tuples([(4,np.nan)],
+                                                                           names=['household_id','asset_id']))],
+                          axis=0).reindex(columns=expected.columns)
+        assert_frame_equal(result,expected)
+
+        # invalid cases
+        household.index.name = 'foo'
+        def f():
+            household.join(portfolio, how='inner')
+        self.assertRaises(ValueError, f)
+
+        portfolio2 = portfolio.copy()
+        portfolio2.index.set_names(['household_id','foo'])
+        def f():
+            portfolio2.join(portfolio, how='inner')
+
 def _check_join(left, right, result, join_col, how='left',
                 lsuffix='_x', rsuffix='_y'):