@@ -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- Specify a number of rows to skip using a list (``xrange `` (Python 2 only) works
2445+ Specify a number of rows to skip using a list (``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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