DOC: Added examples to pandas.concat documentation

pandas/tools/merge.py

@@ -705,7 +705,7 @@ def _get_join_info(self):
                 _left_join_on_index(left_ax, right_ax, self.left_join_keys,
                                     sort=self.sort)
 
-        elif self.left_index and self.how == 'right':
+        elif self.tleft_index and self.how == 'right':
             join_index, right_indexer, left_indexer = \
                 _left_join_on_index(right_ax, left_ax, self.right_join_keys,
                                     sort=self.sort)
@@ -1398,9 +1398,11 @@ def concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
            copy=True):
     """
     Concatenate pandas objects along a particular axis with optional set logic
-    along the other axes. Can also add a layer of hierarchical indexing on the
-    concatenation axis, which may be useful if the labels are the same (or
-    overlapping) on the passed axis number
+    along the other axes.
+
+    Can also add a layer of hierarchical indexing on the concatenation axis,
+    which may be useful if the labels are the same (or overlapping) on
+    the passed axis number
 
     Parameters
     ----------
@@ -1436,13 +1438,90 @@ def concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
     copy : boolean, default True
         If False, do not copy data unnecessarily
 
-    Notes
-    -----
-    The keys, levels, and names arguments are all optional
-
     Returns
     -------
     concatenated : type of objects
+
+    Notes
+    -----
+    The keys, levels, and names arguments are all optional.
+
+    Examples
+    --------
+    Combine two ``Series``.
+
+    >>> s1 = pd.Series(['a', 'b'])
+    >>> s2 = pd.Series(['c', 'd'])
+    >>> pd.concat([s1, s2])
+    0    a
+    1    b
+    0    c
+    1    d
+    dtype: object
+
+    Clear the existing index and reset it in the result
+    by setting the ``ignore_index`` option to ``True``.
+
+    >>> s1 = pd.Series(['a', 'b'])
+    >>> s2 = pd.Series(['c', 'd'])
+    >>> pd.concat([s1, s2], ignore_index=True)
+    0    a
+    1    b
+    2    c
+    3    d
+    dtype: object
+
+    Add a ``hierarchical index`` at the outermost level of
+    the data.
+
+    >>> s1 = pd.Series(['a', 'b', 'c'])
+    >>> s2 = pd.Series(['c', 'd', 'e'])
+    >>> c = pd.concat([s1, s2], keys=["s1", 's2',])
+    >>> c
+    s1  0    a
+        1    b
+        2    c
+    s2  0    c
+        1    d
+        2    e
+    dtype: object
+    >>> c.ix['s1']
+    0    a
+    1    b
+    2    c
+    dtype: object
+    >>> c.ix['s2']
+    0    c
+    1    d
+    2    e
+    dtype: object
+    >>> c.ix['s1'].ix[0]
+    'a'
+
+    Combine two ``DataFrame`` objects with identical columns.
+
+    >>> df1 = pd.DataFrame(
+    ...     [['a', 1], ['b', 2]],
+    ...     columns=['letter', 'number']
+    ... )
+    >>> df1
+      letter  number
+    0      a       1
+    1      b       2
+    >>> df2 = pd.DataFrame(
+    ...     [['c', 3], ['d', 4]],
+    ...     columns=['letter', 'number']
+    ... )
+    >>> df2
+      letter  number
+    0      c       3
+    1      d       4
+    >>> pd.concat([df1, df2])
+      letter  number
+    0      a       1
+    1      b       2
+    0      c       3
+    1      d       4
     """
     op = _Concatenator(objs, axis=axis, join_axes=join_axes,
                        ignore_index=ignore_index, join=join,