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diff --git a/doc/source/dsintro.rst b/doc/source/dsintro.rst
index 6334167b2c746..8f89cd6789f4f 100644
--- a/doc/source/dsintro.rst
+++ b/doc/source/dsintro.rst
@@ -943,7 +943,7 @@ Panel4D (Experimental)
 
 .. warning::
 
-   In 0.19.0 ``Panel4D` is deprecated and will be removed in a future version. The recommended way to represent these types of n-dimensional data are with the `xarray package <http://xarray.pydata.org/en/stable/>`__. Pandas provides a :meth:`~Panel4D.to_xarray` method to automate this conversion.
+   In 0.19.0 ``Panel4D`` is deprecated and will be removed in a future version. The recommended way to represent these types of n-dimensional data are with the `xarray package <http://xarray.pydata.org/en/stable/>`__. Pandas provides a :meth:`~Panel4D.to_xarray` method to automate this conversion.
 
 ``Panel4D`` is a 4-Dimensional named container very much like a ``Panel``, but
 having 4 named dimensions. It is intended as a test bed for more N-Dimensional named
@@ -1032,7 +1032,7 @@ PanelND (Experimental)
 
 .. warning::
 
-   In 0.19.0 ``PanelND` is deprecated and will be removed in a future version. The recommended way to represent these types of n-dimensional data are with the `xarray package <http://xarray.pydata.org/en/stable/>`__.
+   In 0.19.0 ``PanelND`` is deprecated and will be removed in a future version. The recommended way to represent these types of n-dimensional data are with the `xarray package <http://xarray.pydata.org/en/stable/>`__.
 
 PanelND is a module with a set of factory functions to enable a user to construct N-dimensional named
 containers like Panel4D, with a custom set of axis labels. Thus a domain-specific container can easily be
diff --git a/doc/source/io.rst b/doc/source/io.rst
index 7917e6b4cdfce..35d6639d21269 100644
--- a/doc/source/io.rst
+++ b/doc/source/io.rst
@@ -487,13 +487,13 @@ worth trying.
    you can end up with column(s) with mixed dtypes. For example,
 
    .. ipython:: python
-         :okwarning:
+        :okwarning:
 
-       df = pd.DataFrame({'col_1':range(500000) + ['a', 'b'] + range(500000)})
-       df.to_csv('foo')
-       mixed_df = pd.read_csv('foo')
-       mixed_df['col_1'].apply(type).value_counts()
-       mixed_df['col_1'].dtype
+        df = pd.DataFrame({'col_1':range(500000) + ['a', 'b'] + range(500000)})
+        df.to_csv('foo')
+        mixed_df = pd.read_csv('foo')
+        mixed_df['col_1'].apply(type).value_counts()
+        mixed_df['col_1'].dtype
 
    will result with `mixed_df` containing an ``int`` dtype for certain chunks
    of the column, and ``str`` for others due to the mixed dtypes from the
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt
index 4f81eafa3adaf..8a1a62deebf29 100644
--- a/doc/source/whatsnew/v0.19.0.txt
+++ b/doc/source/whatsnew/v0.19.0.txt
@@ -201,9 +201,6 @@ default of the index) in a DataFrame.
 
    from pandas.compat import StringIO
 
-.. _whatsnew_0190.enhancements.read_csv_dupe_col_names_support:
-
-
 :ref:`Duplicate column names <io.dupe_names>` are now supported in :func:`read_csv` whether
 they are in the file or passed in as the ``names`` parameter (:issue:`7160`, :issue:`9424`)
 
diff --git a/pandas/tseries/tdi.py b/pandas/tseries/tdi.py
index 921f60b23d187..271fee6341324 100644
--- a/pandas/tseries/tdi.py
+++ b/pandas/tseries/tdi.py
@@ -1010,14 +1010,14 @@ def timedelta_range(start=None, end=None, periods=None, freq='D',
         Make the interval closed with respect to the given frequency to
         the 'left', 'right', or both sides (None)
 
-    Notes
-    -----
-    2 of start, end, or periods must be specified
-
     Returns
     -------
     rng : TimedeltaIndex
 
+    Notes
+    -----
+    2 of start, end, or periods must be specified.
+
     To learn more about the frequency strings, please see `this link
     <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__.
     """