Added to docs

Author: Nick Eubank

doc/source/merging.rst

@@ -356,7 +356,7 @@ standard database join operations between DataFrame objects:
 
     merge(left, right, how='left', on=None, left_on=None, right_on=None,
           left_index=False, right_index=False, sort=True,
-          suffixes=('_x', '_y'), copy=True)
+          suffixes=('_x', '_y'), copy=True, indicator=False)
 
 Here's a description of what each argument is for:
 
@@ -387,6 +387,12 @@ Here's a description of what each argument is for:
     cases but may improve performance / memory usage. The cases where copying
     can be avoided are somewhat pathological but this option is provided
     nonetheless.
+  - ``indicator``: Add a column to the output DataFrame called ``_merge``
+    with information on the source of each row. ``_merge`` is Categorical-type 
+    and takes on a value of ``left_only`` for observations whose merge key 
+    only appears in ``'left'`` DataFrame, ``right_only`` for observations whose 
+    merge key only appears in ``'right'`` DataFrame, and ``both`` if the 
+    observation's merge key is found in both. 
 
 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

doc/source/whatsnew/v0.16.1.txt

@@ -71,6 +71,15 @@ Enhancements
 
 - ``DataFrame`` and ``Series`` now have ``_constructor_expanddim`` property as overridable constructor for one higher dimensionality data. This should be used only when it is really needed, see :ref:`here <ref-subclassing-pandas>`
 
+- ``merge`` now accepts the argument ``indicator``. If ``True``, adds a column to output DataFrame called ``_merge`` with information on the source of each row. ``_merge`` is Categorical-type and takes on a value of ``left_only`` for observations whose merge key only appears in ``'left'`` DataFrame, ``right_only`` for observations whose merge key only appears in ``'right'`` DataFrame, and ``both`` if the observation's merge key is found in both. (:issue:`7412` and :issue:`8790`.)
+
+  .. ipython:: python
+
+    df1 = pd.DataFrame({'col1':[0,1], 'col_left':['a','b']})
+    df2 = pd.DataFrame({'col1':[1,2,2],'col_right':[2,2,2]})
+    pd.merge(df1, df2, on='col1', how='outer', indicator=True)
+
+
 .. _whatsnew_0161.enhancements.categoricalindex:
 
 CategoricalIndex