Support merging DataFrames on a combo of columns and index levels (GH 14355) (pandas-dev#17484)

Author: jonmmease

doc/source/merging.rst

@@ -518,14 +518,16 @@ standard database join operations between DataFrame objects:
 
 - ``left``: A DataFrame object
 - ``right``: Another DataFrame object
-- ``on``: Columns (names) to join on. Must be found in both the left and
-  right DataFrame objects. If not passed and ``left_index`` and
+- ``on``: Column or index level names to join on. Must be found in both the left
+  and right DataFrame objects. If not passed and ``left_index`` and
   ``right_index`` are ``False``, the intersection of the columns in the
   DataFrames will be inferred to be the join keys
-- ``left_on``: Columns from the left DataFrame to use as keys. Can either be
-  column names or arrays with length equal to the length of the DataFrame
-- ``right_on``: Columns from the right DataFrame to use as keys. Can either be
-  column names or arrays with length equal to the length of the DataFrame
+- ``left_on``: Columns or index levels from the left DataFrame to use as
+  keys. Can either be column names, index level names, or arrays with length
+  equal to the length of the DataFrame
+- ``right_on``: Columns or index levels from the right DataFrame to use as
+  keys. Can either be column names, index level names, or arrays with length
+  equal to the length of the DataFrame
 - ``left_index``: If ``True``, use the index (row labels) from the left
   DataFrame as its join key(s). In the case of a DataFrame with a MultiIndex
   (hierarchical), the number of levels must match the number of join keys
@@ -563,6 +565,10 @@ standard database join operations between DataFrame objects:
 
   .. versionadded:: 0.21.0
 
+.. note::
+
+   Support for specifying index levels as the ``on``, ``left_on``, and
+   ``right_on`` parameters was added in version 0.22.0.
 
 The return type will be the same as ``left``. If ``left`` is a ``DataFrame``
 and ``right`` is a subclass of DataFrame, the return type will still be
@@ -1121,6 +1127,56 @@ This is not Implemented via ``join`` at-the-moment, however it can be done using
           labels=['left', 'right'], vertical=False);
    plt.close('all');
 
+.. _merging.merge_on_columns_and_levels:
+
+Merging on a combination of columns and index levels
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. versionadded:: 0.22
+
+Strings passed as the ``on``, ``left_on``, and ``right_on`` parameters
+may refer to either column names or index level names.  This enables merging
+``DataFrame`` instances on a combination of index levels and columns without
+resetting indexes.
+
+.. ipython:: python
+
+   left_index = pd.Index(['K0', 'K0', 'K1', 'K2'], name='key1')
+
+   left = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
+                        'B': ['B0', 'B1', 'B2', 'B3'],
+                        'key2': ['K0', 'K1', 'K0', 'K1']},
+                       index=left_index)
+
+   right_index = pd.Index(['K0', 'K1', 'K2', 'K2'], name='key1')
+
+   right = pd.DataFrame({'C': ['C0', 'C1', 'C2', 'C3'],
+                         'D': ['D0', 'D1', 'D2', 'D3'],
+                         'key2': ['K0', 'K0', 'K0', 'K1']},
+                        index=right_index)
+
+   result = left.merge(right, on=['key1', 'key2'])
+
+.. ipython:: python
+   :suppress:
+
+   @savefig merge_on_index_and_column.png
+   p.plot([left, right], result,
+          labels=['left', 'right'], vertical=False);
+   plt.close('all');
+
+.. note::
+
+   When DataFrames are merged on a string that matches an index level in both
+   frames, the index level is preserved as an index level in the resulting
+   DataFrame.
+
+.. note::
+
+   If a string matches both a column name and an index level name, then a
+   warning is issued and the column takes precedence. This will result in an
+   ambiguity error in a future version.
+
 Overlapping value columns
 ~~~~~~~~~~~~~~~~~~~~~~~~~
 

doc/source/whatsnew/v0.22.0.txt

@@ -32,6 +32,37 @@ The :func:`get_dummies` now accepts a ``dtype`` argument, which specifies a dtyp
    pd.get_dummies(df, columns=['c'], dtype=bool).dtypes
 
 
+.. _whatsnew_0220.enhancements.merge_on_columns_and_levels:
+
+Merging on a combination of columns and index levels
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Strings passed to :meth:`DataFrame.merge` as the ``on``, ``left_on``, and ``right_on``
+parameters may now refer to either column names or index level names.
+This enables merging ``DataFrame`` instances on a combination of index levels
+and columns without resetting indexes. See the :ref:`Merge on columns and
+levels <merging.merge_on_columns_and_levels>` documentation section.
+(:issue:`14355`)
+
+.. ipython:: python
+
+   left_index = pd.Index(['K0', 'K0', 'K1', 'K2'], name='key1')
+
+   left = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
+                        'B': ['B0', 'B1', 'B2', 'B3'],
+                        'key2': ['K0', 'K1', 'K0', 'K1']},
+                       index=left_index)
+
+   right_index = pd.Index(['K0', 'K1', 'K2', 'K2'], name='key1')
+
+   right = pd.DataFrame({'C': ['C0', 'C1', 'C2', 'C3'],
+                         'D': ['D0', 'D1', 'D2', 'D3'],
+                         'key2': ['K0', 'K0', 'K0', 'K1']},
+                        index=right_index)
+
+   left.merge(right, on=['key1', 'key2'])
+
+
 .. _whatsnew_0220.enhancements.other:
 
 Other Enhancements

pandas/core/frame.py

@@ -148,16 +148,17 @@
     * inner: use intersection of keys from both frames, similar to a SQL inner
       join; preserve the order of the left keys
 on : label or list
-    Field names to join on. Must be found in both DataFrames. If on is
-    None and not merging on indexes, then it merges on the intersection of
-    the columns by default.
+    Column or index level names to join on. These must be found in both
+    DataFrames. If `on` is None and not merging on indexes then this defaults
+    to the intersection of the columns in both DataFrames.
 left_on : label or list, or array-like
-    Field names to join on in left DataFrame. Can be a vector or list of
-    vectors of the length of the DataFrame to use a particular vector as
-    the join key instead of columns
+    Column or index level names to join on in the left DataFrame. Can also
+    be an array or list of arrays of the length of the left DataFrame.
+    These arrays are treated as if they are columns.
 right_on : label or list, or array-like
-    Field names to join on in right DataFrame or vector/list of vectors per
-    left_on docs
+    Column or index level names to join on in the right DataFrame. Can also
+    be an array or list of arrays of the length of the right DataFrame.
+    These arrays are treated as if they are columns.
 left_index : boolean, default False
     Use the index from the left DataFrame as the join key(s). If it is a
     MultiIndex, the number of keys in the other DataFrame (either the index
@@ -196,6 +197,11 @@
 
     .. versionadded:: 0.21.0
 
+Notes
+-----
+Support for specifying index levels as the `on`, `left_on`, and
+`right_on` parameters was added in version 0.22.0
+
 Examples
 --------
 
@@ -5214,12 +5220,12 @@ def join(self, other, on=None, how='left', lsuffix='', rsuffix='',
             Index should be similar to one of the columns in this one. If a
             Series is passed, its name attribute must be set, and that will be
             used as the column name in the resulting joined DataFrame
-        on : column name, tuple/list of column names, or array-like
-            Column(s) in the caller to join on the index in other,
-            otherwise joins index-on-index. If multiples
-            columns given, the passed DataFrame must have a MultiIndex. Can
-            pass an array as the join key if not already contained in the
-            calling DataFrame. Like an Excel VLOOKUP operation
+        on : name, tuple/list of names, or array-like
+            Column or index level name(s) in the caller to join on the index
+            in `other`, otherwise joins index-on-index. If multiple
+            values given, the `other` DataFrame must have a MultiIndex. Can
+            pass an array as the join key if it is not already contained in
+            the calling DataFrame. Like an Excel VLOOKUP operation
         how : {'left', 'right', 'outer', 'inner'}, default: 'left'
             How to handle the operation of the two objects.
 
@@ -5244,6 +5250,9 @@ def join(self, other, on=None, how='left', lsuffix='', rsuffix='',
         on, lsuffix, and rsuffix options are not supported when passing a list
         of DataFrame objects
 
+        Support for specifying index levels as the `on` parameter was added
+        in version 0.22.0
+
         Examples
         --------
         >>> caller = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],