@@ -41,8 +41,7 @@ The pandas I/O API is a set of top level ``reader`` functions accessed like
4141
4242.. note ::
4343   For examples that use the ``StringIO `` class, make sure you import it
44-    according to your Python version, i.e. ``from StringIO import StringIO `` for
45-    Python 2 and ``from io import StringIO `` for Python 3.
44+    with ``from io import StringIO `` for Python 3.
4645
4746.. _io.read_csv_table :
4847
@@ -912,16 +911,6 @@ data columns:
912911   significantly faster, ~20x has been observed.
913912
914913
915- .. note ::
916- 
917-    When passing a dict as the `parse_dates ` argument, the order of
918-    the columns prepended is not guaranteed, because `dict ` objects do not impose
919-    an ordering on their keys. On Python 2.7+ you may use `collections.OrderedDict `
920-    instead of a regular `dict ` if this matters to you. Because of this, when using a
921-    dict for 'parse_dates' in conjunction with the `index_col ` argument, it's best to
922-    specify `index_col ` as a column label rather then as an index on the resulting frame.
923- 
924- 
925914Date parsing functions
926915++++++++++++++++++++++ 
927916
@@ -2453,7 +2442,7 @@ Specify a number of rows to skip:
24532442
24542443   dfs =  pd.read_html(url, skiprows = 0 ) 
24552444
2456- xrange `` (Python 2 only)  works
2445+ range ``  works
24572446as well):
24582447
24592448.. code-block :: python 
@@ -3124,11 +3113,7 @@ Pandas supports writing Excel files to buffer-like objects such as ``StringIO``
31243113
31253114.. code-block :: python 
31263115
3127-    #  Safe import for either Python 2.x or 3.x 
3128-    try : 
3129-        from  io import  BytesIO 
3130-    except  ImportError : 
3131-        from  cStringIO import  StringIO as  BytesIO 
3116+    from  io import  BytesIO 
31323117
31333118   bio =  BytesIO() 
31343119
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