diff --git a/doc/source/cookbook.rst b/doc/source/cookbook.rst
index 0c192a0aab24a..0f9726dc94816 100644
--- a/doc/source/cookbook.rst
+++ b/doc/source/cookbook.rst
@@ -1236,7 +1236,7 @@ the following Python code will read the binary file ``'binary.dat'`` into a
 pandas ``DataFrame``, where each element of the struct corresponds to a column
 in the frame:
 
-.. ipython:: python
+.. code-block:: python
 
    names = 'count', 'avg', 'scale'
 
diff --git a/doc/source/integer_na.rst b/doc/source/integer_na.rst
index befcf7016f155..eb0c5e3d05863 100644
--- a/doc/source/integer_na.rst
+++ b/doc/source/integer_na.rst
@@ -2,7 +2,7 @@
 
 {{ header }}
 
- .. _integer_na:
+.. _integer_na:
 
 **************************
 Nullable Integer Data Type
diff --git a/doc/source/io.rst b/doc/source/io.rst
index 3bbd4e8410fa5..967648f3a168a 100644
--- a/doc/source/io.rst
+++ b/doc/source/io.rst
@@ -4880,7 +4880,7 @@ below and the SQLAlchemy `documentation <https://docs.sqlalchemy.org/en/latest/c
 
 If you want to manage your own connections you can pass one of those instead:
 
-.. ipython:: python
+.. code-block:: python
 
    with engine.connect() as conn, conn.begin():
        data = pd.read_sql_table('data', conn)
diff --git a/doc/source/missing_data.rst b/doc/source/missing_data.rst
index a9234b83c78ab..a462f01dcd14f 100644
--- a/doc/source/missing_data.rst
+++ b/doc/source/missing_data.rst
@@ -759,12 +759,7 @@ the ``dtype="Int64"``.
 
 .. ipython:: python
 
-   s = pd.Series(np.random.randn(5), index=[0, 2, 4, 6, 7],
-                 dtype="Int64")
-   s > 0
-   (s > 0).dtype
-   crit = (s > 0).reindex(list(range(8)))
-   crit
-   crit.dtype
+   s = pd.Series([0, 1, np.nan, 3, 4], dtype="Int64")
+   s
 
 See :ref:`integer_na` for more.
diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst
index 59e8fa58fd9cf..84fca37318091 100644
--- a/doc/source/timeseries.rst
+++ b/doc/source/timeseries.rst
@@ -1874,7 +1874,7 @@ has multiplied span.
 
 .. ipython:: python
 
-   pd.PeriodIndex(start='2014-01', freq='3M', periods=4)
+   pd.period_range(start='2014-01', freq='3M', periods=4)
 
 If ``start`` or ``end`` are ``Period`` objects, they will be used as anchor
 endpoints for a ``PeriodIndex`` with frequency matching that of the
@@ -1882,8 +1882,8 @@ endpoints for a ``PeriodIndex`` with frequency matching that of the
 
 .. ipython:: python
 
-   pd.PeriodIndex(start=pd.Period('2017Q1', freq='Q'),
-                  end=pd.Period('2017Q2', freq='Q'), freq='M')
+   pd.period_range(start=pd.Period('2017Q1', freq='Q'),
+                   end=pd.Period('2017Q2', freq='Q'), freq='M')
 
 Just like ``DatetimeIndex``, a ``PeriodIndex`` can also be used to index pandas
 objects:
diff --git a/doc/source/tutorials.rst b/doc/source/tutorials.rst
index f4f33a921dcce..8e23c643280c1 100644
--- a/doc/source/tutorials.rst
+++ b/doc/source/tutorials.rst
@@ -33,7 +33,7 @@ repository <http://github.com/jvns/pandas-cookbook>`_.
 Learn Pandas by Hernan Rojas
 ----------------------------
 
-A set of lesson for new pandas users: `Learn pandas <https://bitbucket.org/hrojas/learn-pandas>`__.
+A set of lesson for new pandas users: https://bitbucket.org/hrojas/learn-pandas
 
 Practical data analysis with Python
 -----------------------------------
diff --git a/doc/source/whatsnew/v0.20.0.rst b/doc/source/whatsnew/v0.20.0.rst
index c78d5c1d178d2..c720e075012eb 100644
--- a/doc/source/whatsnew/v0.20.0.rst
+++ b/doc/source/whatsnew/v0.20.0.rst
@@ -189,14 +189,15 @@ URLs and paths are now inferred using their file extensions. Additionally,
 support for bz2 compression in the python 2 C-engine improved (:issue:`14874`).
 
 .. ipython:: python
-   :okwarning:
 
    url = ('https://github.com/{repo}/raw/{branch}/{path}'
           .format(repo='pandas-dev/pandas',
                   branch='master',
                   path='pandas/tests/io/parser/data/salaries.csv.bz2'))
-   df = pd.read_table(url, compression='infer')  # default, infer compression
-   df = pd.read_table(url, compression='bz2')  # explicitly specify compression
+   # default, infer compression
+   df = pd.read_csv(url, sep='\t', compression='infer')
+   # explicitly specify compression
+   df = pd.read_csv(url, sep='\t', compression='bz2')
    df.head(2)
 
 .. _whatsnew_0200.enhancements.pickle_compression: