diff --git a/doc/source/indexing.rst b/doc/source/indexing.rst
index 49289862a3acd..add1a4e587240 100644
--- a/doc/source/indexing.rst
+++ b/doc/source/indexing.rst
@@ -134,9 +134,10 @@ indexing functionality:
 .. ipython:: python
 
    dates = pd.date_range('1/1/2000', periods=8)
-   df = pd.DataFrame(np.random.randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D'])
+   df = pd.DataFrame(np.random.randn(8, 4),
+                     index=dates, columns=['A', 'B', 'C', 'D'])
    df
-   panel = pd.Panel({'one' : df, 'two' : df - df.mean()})
+   panel = pd.Panel({'one': df, 'two': df - df.mean()})
    panel
 
 .. note::
@@ -174,14 +175,14 @@ columns.
    .. ipython:: python
 
       df[['A', 'B']]
-      df.loc[:,['B', 'A']] = df[['A', 'B']]
+      df.loc[:, ['B', 'A']] = df[['A', 'B']]
       df[['A', 'B']]
 
    The correct way to swap column values is by using raw values:
 
    .. ipython:: python
 
-      df.loc[:,['B', 'A']] = df[['A', 'B']].to_numpy()
+      df.loc[:, ['B', 'A']] = df[['A', 'B']].to_numpy()
       df[['A', 'B']]
 
 
@@ -199,7 +200,7 @@ as an attribute:
 
 .. ipython:: python
 
-   sa = pd.Series([1,2,3],index=list('abc'))
+   sa = pd.Series([1, 2, 3], index=list('abc'))
    dfa = df.copy()
 
 .. ipython:: python
@@ -239,7 +240,7 @@ You can also assign a ``dict`` to a row of a ``DataFrame``:
 .. ipython:: python
 
    x = pd.DataFrame({'x': [1, 2, 3], 'y': [3, 4, 5]})
-   x.iloc[1] = dict(x=9, y=99)
+   x.iloc[1] = {'x': 9, 'y': 99}
    x
 
 You can use attribute access to modify an existing element of a Series or column of a DataFrame, but be careful;
@@ -248,10 +249,10 @@ new column. In 0.21.0 and later, this will raise a ``UserWarning``:
 
 .. code-block:: ipython
 
-    In[1]: df = pd.DataFrame({'one': [1., 2., 3.]})
-    In[2]: df.two = [4, 5, 6]
+    In [1]: df = pd.DataFrame({'one': [1., 2., 3.]})
+    In [2]: df.two = [4, 5, 6]
     UserWarning: Pandas doesn't allow Series to be assigned into nonexistent columns - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute_access
-    In[3]: df
+    In [3]: df
     Out[3]:
        one
     0  1.0
@@ -308,7 +309,9 @@ Selection By Label
 
   .. ipython:: python
 
-     dfl = pd.DataFrame(np.random.randn(5,4), columns=list('ABCD'), index=pd.date_range('20130101',periods=5))
+     dfl = pd.DataFrame(np.random.randn(5, 4),
+                        columns=list('ABCD'),
+                        index=pd.date_range('20130101', periods=5))
      dfl
 
   .. code-block:: ipython
@@ -345,7 +348,7 @@ The ``.loc`` attribute is the primary access method. The following are valid inp
 
 .. ipython:: python
 
-   s1 = pd.Series(np.random.randn(6),index=list('abcdef'))
+   s1 = pd.Series(np.random.randn(6), index=list('abcdef'))
    s1
    s1.loc['c':]
    s1.loc['b']
@@ -361,7 +364,7 @@ With a DataFrame:
 
 .. ipython:: python
 
-   df1 = pd.DataFrame(np.random.randn(6,4),
+   df1 = pd.DataFrame(np.random.randn(6, 4),
                       index=list('abcdef'),
                       columns=list('ABCD'))
    df1
@@ -404,7 +407,7 @@ are returned:
 
 .. ipython:: python
 
-   s = pd.Series(list('abcde'), index=[0,3,2,5,4])
+   s = pd.Series(list('abcde'), index=[0, 3, 2, 5, 4])
    s.loc[3:5]
 
 If at least one of the two is absent, but the index is sorted, and can be
@@ -444,7 +447,7 @@ The ``.iloc`` attribute is the primary access method. The following are valid in
 
 .. ipython:: python
 
-   s1 = pd.Series(np.random.randn(5), index=list(range(0,10,2)))
+   s1 = pd.Series(np.random.randn(5), index=list(range(0, 10, 2)))
    s1
    s1.iloc[:3]
    s1.iloc[3]
@@ -460,9 +463,9 @@ With a DataFrame:
 
 .. ipython:: python
 
-   df1 = pd.DataFrame(np.random.randn(6,4),
-                      index=list(range(0,12,2)),
-                      columns=list(range(0,8,2)))
+   df1 = pd.DataFrame(np.random.randn(6, 4),
+                      index=list(range(0, 12, 2)),
+                      columns=list(range(0, 8, 2)))
    df1
 
 Select via integer slicing:
@@ -516,7 +519,7 @@ an empty axis (e.g. an empty DataFrame being returned).
 
 .. ipython:: python
 
-   dfl = pd.DataFrame(np.random.randn(5,2), columns=list('AB'))
+   dfl = pd.DataFrame(np.random.randn(5, 2), columns=list('AB'))
    dfl
    dfl.iloc[:, 2:3]
    dfl.iloc[:, 1:3]
@@ -818,7 +821,7 @@ In the ``Series`` case this is effectively an appending operation.
 
 .. ipython:: python
 
-   se = pd.Series([1,2,3])
+   se = pd.Series([1, 2, 3])
    se
    se[5] = 5.
    se
@@ -827,10 +830,10 @@ A ``DataFrame`` can be enlarged on either axis via ``.loc``.
 
 .. ipython:: python
 
-   dfi = pd.DataFrame(np.arange(6).reshape(3,2),
-                   columns=['A','B'])
+   dfi = pd.DataFrame(np.arange(6).reshape(3, 2),
+                      columns=['A', 'B'])
    dfi
-   dfi.loc[:,'C'] = dfi.loc[:,'A']
+   dfi.loc[:, 'C'] = dfi.loc[:, 'A']
    dfi
 
 This is like an ``append`` operation on the ``DataFrame``.
@@ -870,7 +873,7 @@ You can also set using these same indexers.
 
 .. ipython:: python
 
-   df.at[dates[-1]+1, 0] = 7
+   df.at[dates[-1] + 1, 0] = 7
    df
 
 Boolean indexing
@@ -908,9 +911,9 @@ more complex criteria:
 
 .. ipython:: python
 
-   df2 = pd.DataFrame({'a' : ['one', 'one', 'two', 'three', 'two', 'one', 'six'],
-                       'b' : ['x', 'y', 'y', 'x', 'y', 'x', 'x'],
-                       'c' : np.random.randn(7)})
+   df2 = pd.DataFrame({'a': ['one', 'one', 'two', 'three', 'two', 'one', 'six'],
+                       'b': ['x', 'y', 'y', 'x', 'y', 'x', 'x'],
+                       'c': np.random.randn(7)})
 
    # only want 'two' or 'three'
    criterion = df2['a'].map(lambda x: x.startswith('t'))
@@ -928,7 +931,7 @@ and :ref:`Advanced Indexing <advanced>` you may select along more than one axis
 
 .. ipython:: python
 
-   df2.loc[criterion & (df2['b'] == 'x'),'b':'c']
+   df2.loc[criterion & (df2['b'] == 'x'), 'b':'c']
 
 .. _indexing.basics.indexing_isin:
 
@@ -1032,7 +1035,8 @@ The code below is equivalent to ``df.where(df < 0)``.
    :suppress:
 
    dates = pd.date_range('1/1/2000', periods=8)
-   df = pd.DataFrame(np.random.randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D'])
+   df = pd.DataFrame(np.random.randn(8, 4),
+                     index=dates, columns=['A', 'B', 'C', 'D'])
 
 .. ipython:: python
 
@@ -1065,7 +1069,7 @@ without creating a copy:
 .. ipython:: python
 
    df_orig = df.copy()
-   df_orig.where(df > 0, -df, inplace=True);
+   df_orig.where(df > 0, -df, inplace=True)
    df_orig
 
 .. note::
@@ -1086,7 +1090,7 @@ partial setting via ``.loc`` (but on the contents rather than the axis labels).
 .. ipython:: python
 
    df2 = df.copy()
-   df2[ df2[1:4] > 0] = 3
+   df2[df2[1:4] > 0] = 3
    df2
 
 Where can also accept ``axis`` and ``level`` parameters to align the input when
@@ -1095,14 +1099,14 @@ performing the ``where``.
 .. ipython:: python
 
    df2 = df.copy()
-   df2.where(df2>0,df2['A'],axis='index')
+   df2.where(df2 > 0, df2['A'], axis='index')
 
 This is equivalent to (but faster than) the following.
 
 .. ipython:: python
 
    df2 = df.copy()
-   df.apply(lambda x, y: x.where(x>0,y), y=df['A'])
+   df.apply(lambda x, y: x.where(x > 0, y), y=df['A'])
 
 .. versionadded:: 0.18.1
 
@@ -1163,25 +1167,12 @@ with the name ``a``.
 If instead you don't want to or cannot name your index, you can use the name
 ``index`` in your query expression:
 
-.. ipython:: python
-   :suppress:
-
-   old_index = index
-   del index
-
 .. ipython:: python
 
    df = pd.DataFrame(np.random.randint(n, size=(n, 2)), columns=list('bc'))
    df
    df.query('index < b < c')
 
-.. ipython:: python
-   :suppress:
-
-   index = old_index
-   del old_index
-
-
 .. note::
 
    If the name of your index overlaps with a column name, the column name is
@@ -1191,7 +1182,7 @@ If instead you don't want to or cannot name your index, you can use the name
 
       df = pd.DataFrame({'a': np.random.randint(5, size=5)})
       df.index.name = 'a'
-      df.query('a > 2') # uses the column 'a', not the index
+      df.query('a > 2')  # uses the column 'a', not the index
 
    You can still use the index in a query expression by using the special
    identifier 'index':
@@ -1293,15 +1284,6 @@ The ``in`` and ``not in`` operators
 ``not in`` comparison operators, providing a succinct syntax for calling the
 ``isin`` method of a ``Series`` or ``DataFrame``.
 
-.. ipython:: python
-   :suppress:
-
-   try:
-       old_d = d
-       del d
-   except NameError:
-       pass
-
 .. ipython:: python
 
    # get all rows where columns "a" and "b" have overlapping values
@@ -1325,7 +1307,8 @@ You can combine this with other expressions for very succinct queries:
 
 .. ipython:: python
 
-   # rows where cols a and b have overlapping values and col c's values are less than col d's
+   # rows where cols a and b have overlapping values
+   # and col c's values are less than col d's
    df.query('a in b and c < d')
 
    # pure Python
@@ -1401,15 +1384,6 @@ Of course, expressions can be arbitrarily complex too:
 
    shorter == longer
 
-.. ipython:: python
-   :suppress:
-
-   try:
-       d = old_d
-       del old_d
-   except NameError:
-       pass
-
 
 Performance of :meth:`~pandas.DataFrame.query`
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -1433,7 +1407,8 @@ floating point values generated using ``numpy.random.randn()``.
 .. ipython:: python
    :suppress:
 
-   df = pd.DataFrame(np.random.randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D'])
+   df = pd.DataFrame(np.random.randn(8, 4),
+                     index=dates, columns=['A', 'B', 'C', 'D'])
    df2 = df.copy()
 
 
@@ -1500,8 +1475,8 @@ default value.
 
 .. ipython:: python
 
-   s = pd.Series([1,2,3], index=['a','b','c'])
-   s.get('a')               # equivalent to s['a']
+   s = pd.Series([1, 2, 3], index=['a', 'b', 'c'])
+   s.get('a')  # equivalent to s['a']
    s.get('x', default=-1)
 
 The :meth:`~pandas.DataFrame.lookup` Method
@@ -1513,8 +1488,8 @@ NumPy array.  For instance:
 
 .. ipython:: python
 
-  dflookup = pd.DataFrame(np.random.rand(20,4), columns = ['A','B','C','D'])
-  dflookup.lookup(list(range(0,10,2)), ['B','C','A','B','D'])
+  dflookup = pd.DataFrame(np.random.rand(20, 4), columns = ['A', 'B', 'C', 'D'])
+  dflookup.lookup(list(range(0, 10, 2)), ['B', 'C', 'A', 'B', 'D'])
 
 .. _indexing.class:
 
@@ -1641,7 +1616,9 @@ Missing values
    idx1
    idx1.fillna(2)
 
-   idx2 = pd.DatetimeIndex([pd.Timestamp('2011-01-01'), pd.NaT, pd.Timestamp('2011-01-03')])
+   idx2 = pd.DatetimeIndex([pd.Timestamp('2011-01-01'),
+                            pd.NaT,
+                            pd.Timestamp('2011-01-03')])
    idx2
    idx2.fillna(pd.Timestamp('2011-01-02'))
 
@@ -1664,10 +1641,10 @@ To create a new, re-indexed DataFrame:
 .. ipython:: python
    :suppress:
 
-   data = pd.DataFrame({'a' : ['bar', 'bar', 'foo', 'foo'],
-                        'b' : ['one', 'two', 'one', 'two'],
-                        'c' : ['z', 'y', 'x', 'w'],
-                        'd' : [1., 2., 3, 4]})
+   data = pd.DataFrame({'a': ['bar', 'bar', 'foo', 'foo'],
+                        'b': ['one', 'two', 'one', 'two'],
+                        'c': ['z', 'y', 'x', 'w'],
+                        'd': [1., 2., 3, 4]})
 
 .. ipython:: python
 
@@ -1746,8 +1723,8 @@ When setting values in a pandas object, care must be taken to avoid what is call
                         list('efgh'),
                         list('ijkl'),
                         list('mnop')],
-                       columns=pd.MultiIndex.from_product([['one','two'],
-                                                           ['first','second']]))
+                       columns=pd.MultiIndex.from_product([['one', 'two'],
+                                                           ['first', 'second']]))
    dfmi
 
 Compare these two access methods:
@@ -1758,7 +1735,7 @@ Compare these two access methods:
 
 .. ipython:: python
 
-   dfmi.loc[:,('one','second')]
+   dfmi.loc[:, ('one', 'second')]
 
 These both yield the same results, so which should you use? It is instructive to understand the order
 of operations on these and why method 2 (``.loc``) is much preferred over method 1 (chained ``[]``).
@@ -1783,6 +1760,11 @@ But it turns out that assigning to the product of chained indexing has
 inherently unpredictable results. To see this, think about how the Python
 interpreter executes this code:
 
+.. ipython:: python
+    :suppress:
+
+    value = None
+
 .. code-block:: python
 
    dfmi.loc[:, ('one', 'second')] = value
@@ -1820,7 +1802,8 @@ that you've done this:
    def do_something(df):
        foo = df[['bar', 'baz']]  # Is foo a view? A copy? Nobody knows!
        # ... many lines here ...
-       foo['quux'] = value  # We don't know whether this will modify df or not!
+       # We don't know whether this will modify df or not!
+       foo['quux'] = value
        return foo
 
 Yikes!
@@ -1850,9 +1833,9 @@ chained indexing expression, you can set the :ref:`option <options>`
 .. ipython:: python
    :okwarning:
 
-   dfb = pd.DataFrame({'a' : ['one', 'one', 'two',
-                              'three', 'two', 'one', 'six'],
-                       'c' : np.arange(7)})
+   dfb = pd.DataFrame({'a': ['one', 'one', 'two',
+                             'three', 'two', 'one', 'six'],
+                       'c': np.arange(7)})
 
    # This will show the SettingWithCopyWarning
    # but the frame values will be set
@@ -1880,8 +1863,8 @@ This is the correct access method:
 
 .. ipython:: python
 
-   dfc = pd.DataFrame({'A':['aaa','bbb','ccc'],'B':[1,2,3]})
-   dfc.loc[0,'A'] = 11
+   dfc = pd.DataFrame({'A': ['aaa', 'bbb', 'ccc'], 'B': [1, 2, 3]})
+   dfc.loc[0, 'A'] = 11
    dfc
 
 This *can* work at times, but it is not guaranteed to, and therefore should be avoided:
diff --git a/doc/source/missing_data.rst b/doc/source/missing_data.rst
index ebe577feb706c..6a089decde3f5 100644
--- a/doc/source/missing_data.rst
+++ b/doc/source/missing_data.rst
@@ -81,7 +81,7 @@ Series and DataFrame objects:
 
    .. ipython:: python
 
-      None == None
+      None == None                                                 # noqa: E711
       np.nan == np.nan
 
    So as compared to above, a scalar equality comparison versus a ``None/np.nan`` doesn't provide useful information.
@@ -102,7 +102,7 @@ pandas objects provide compatibility between ``NaT`` and ``NaN``.
    df2 = df.copy()
    df2['timestamp'] = pd.Timestamp('20120101')
    df2
-   df2.loc[['a','c','h'],['one','timestamp']] = np.nan
+   df2.loc[['a', 'c', 'h'], ['one', 'timestamp']] = np.nan
    df2
    df2.get_dtype_counts()
 
@@ -187,7 +187,7 @@ The sum of an empty or all-NA Series or column of a DataFrame is 0.
 .. ipython:: python
 
    pd.Series([np.nan]).sum()
-   
+
    pd.Series([]).sum()
 
 The product of an empty or all-NA Series or column of a DataFrame is 1.
@@ -195,7 +195,7 @@ The product of an empty or all-NA Series or column of a DataFrame is 1.
 .. ipython:: python
 
    pd.Series([np.nan]).prod()
-   
+
    pd.Series([]).prod()
 
 
@@ -287,10 +287,10 @@ use case of this is to fill a DataFrame with the mean of that column.
 
 .. ipython:: python
 
-        dff = pd.DataFrame(np.random.randn(10,3), columns=list('ABC'))
-        dff.iloc[3:5,0] = np.nan
-        dff.iloc[4:6,1] = np.nan
-        dff.iloc[5:8,2] = np.nan
+        dff = pd.DataFrame(np.random.randn(10, 3), columns=list('ABC'))
+        dff.iloc[3:5, 0] = np.nan
+        dff.iloc[4:6, 1] = np.nan
+        dff.iloc[5:8, 2] = np.nan
         dff
 
         dff.fillna(dff.mean())
@@ -473,7 +473,8 @@ filled since the last valid observation:
 
 .. ipython:: python
 
-   ser = pd.Series([np.nan, np.nan, 5, np.nan, np.nan, np.nan, 13, np.nan, np.nan])
+   ser = pd.Series([np.nan, np.nan, 5, np.nan, np.nan,
+                    np.nan, 13, np.nan, np.nan])
 
    # fill all consecutive values in a forward direction
    ser.interpolate()
diff --git a/doc/source/options.rst b/doc/source/options.rst
index 31359c337fdb8..e91be3e6ae730 100644
--- a/doc/source/options.rst
+++ b/doc/source/options.rst
@@ -38,9 +38,9 @@ and so passing in a substring will work - as long as it is unambiguous:
 .. ipython:: python
 
    pd.get_option("display.max_rows")
-   pd.set_option("display.max_rows",101)
+   pd.set_option("display.max_rows", 101)
    pd.get_option("display.max_rows")
-   pd.set_option("max_r",102)
+   pd.set_option("max_r", 102)
    pd.get_option("display.max_rows")
 
 
@@ -93,7 +93,7 @@ All options also have a default value, and you can use ``reset_option`` to do ju
 .. ipython:: python
 
    pd.get_option("display.max_rows")
-   pd.set_option("display.max_rows",999)
+   pd.set_option("display.max_rows", 999)
    pd.get_option("display.max_rows")
    pd.reset_option("display.max_rows")
    pd.get_option("display.max_rows")
@@ -113,9 +113,9 @@ are restored automatically when you exit the `with` block:
 
 .. ipython:: python
 
-   with pd.option_context("display.max_rows",10,"display.max_columns", 5):
-        print(pd.get_option("display.max_rows"))
-        print(pd.get_option("display.max_columns"))
+   with pd.option_context("display.max_rows", 10, "display.max_columns", 5):
+       print(pd.get_option("display.max_rows"))
+       print(pd.get_option("display.max_columns"))
    print(pd.get_option("display.max_rows"))
    print(pd.get_option("display.max_columns"))
 
@@ -150,7 +150,7 @@ lines are replaced by an ellipsis.
 
 .. ipython:: python
 
-   df = pd.DataFrame(np.random.randn(7,2))
+   df = pd.DataFrame(np.random.randn(7, 2))
    pd.set_option('max_rows', 7)
    df
    pd.set_option('max_rows', 5)
@@ -162,7 +162,7 @@ dataframes to stretch across pages, wrapped over the full column vs row-wise.
 
 .. ipython:: python
 
-   df = pd.DataFrame(np.random.randn(5,10))
+   df = pd.DataFrame(np.random.randn(5, 10))
    pd.set_option('expand_frame_repr', True)
    df
    pd.set_option('expand_frame_repr', False)
@@ -174,7 +174,7 @@ dataframes to stretch across pages, wrapped over the full column vs row-wise.
 
 .. ipython:: python
 
-   df = pd.DataFrame(np.random.randn(10,10))
+   df = pd.DataFrame(np.random.randn(10, 10))
    pd.set_option('max_rows', 5)
    pd.set_option('large_repr', 'truncate')
    df
@@ -190,7 +190,7 @@ of this length or longer will be truncated with an ellipsis.
 
    df = pd.DataFrame(np.array([['foo', 'bar', 'bim', 'uncomfortably long string'],
                                ['horse', 'cow', 'banana', 'apple']]))
-   pd.set_option('max_colwidth',40)
+   pd.set_option('max_colwidth', 40)
    df
    pd.set_option('max_colwidth', 6)
    df
@@ -201,7 +201,7 @@ will be given.
 
 .. ipython:: python
 
-   df = pd.DataFrame(np.random.randn(10,10))
+   df = pd.DataFrame(np.random.randn(10, 10))
    pd.set_option('max_info_columns', 11)
    df.info()
    pd.set_option('max_info_columns', 5)
@@ -215,7 +215,7 @@ can specify the option ``df.info(null_counts=True)`` to override on showing a pa
 
 .. ipython:: python
 
-   df = pd.DataFrame(np.random.choice([0,1,np.nan], size=(10,10)))
+   df = pd.DataFrame(np.random.choice([0, 1, np.nan], size=(10, 10)))
    df
    pd.set_option('max_info_rows', 11)
    df.info()
@@ -228,10 +228,10 @@ This is only a suggestion.
 
 .. ipython:: python
 
-   df = pd.DataFrame(np.random.randn(5,5))
-   pd.set_option('precision',7)
+   df = pd.DataFrame(np.random.randn(5, 5))
+   pd.set_option('precision', 7)
    df
-   pd.set_option('precision',4)
+   pd.set_option('precision', 4)
    df
 
 ``display.chop_threshold`` sets at what level pandas rounds to zero when
@@ -240,7 +240,7 @@ precision at which the number is stored.
 
 .. ipython:: python
 
-   df = pd.DataFrame(np.random.randn(6,6))
+   df = pd.DataFrame(np.random.randn(6, 6))
    pd.set_option('chop_threshold', 0)
    df
    pd.set_option('chop_threshold', .5)
@@ -252,7 +252,9 @@ The options are 'right', and 'left'.
 
 .. ipython:: python
 
-   df = pd.DataFrame(np.array([np.random.randn(6), np.random.randint(1,9,6)*.1, np.zeros(6)]).T,
+   df = pd.DataFrame(np.array([np.random.randn(6),
+                               np.random.randint(1, 9, 6) * .1,
+                               np.zeros(6)]).T,
                      columns=['A', 'B', 'C'], dtype='float')
    pd.set_option('colheader_justify', 'right')
    df
@@ -454,14 +456,14 @@ For instance:
 
    pd.set_eng_float_format(accuracy=3, use_eng_prefix=True)
    s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])
-   s/1.e3
-   s/1.e6
+   s / 1.e3
+   s / 1.e6
 
 .. ipython:: python
    :suppress:
    :okwarning:
 
-   pd.reset_option('^display\.')
+   pd.reset_option("^display")
 
 To round floats on a case-by-case basis, you can also use :meth:`~pandas.Series.round` and :meth:`~pandas.DataFrame.round`.
 
@@ -483,7 +485,7 @@ If a DataFrame or Series contains these characters, the default output mode may
 .. ipython:: python
 
    df = pd.DataFrame({u'国籍': ['UK', u'日本'], u'名前': ['Alice', u'しのぶ']})
-   df;
+   df
 
 .. image:: _static/option_unicode01.png
 
@@ -494,7 +496,7 @@ times than the standard ``len`` function.
 .. ipython:: python
 
    pd.set_option('display.unicode.east_asian_width', True)
-   df;
+   df
 
 .. image:: _static/option_unicode02.png
 
@@ -506,7 +508,7 @@ By default, an "Ambiguous" character's width, such as "¡" (inverted exclamation
 .. ipython:: python
 
    df = pd.DataFrame({'a': ['xxx', u'¡¡'], 'b': ['yyy', u'¡¡']})
-   df;
+   df
 
 .. image:: _static/option_unicode03.png
 
@@ -518,7 +520,7 @@ However, setting this option incorrectly for your terminal will cause these char
 .. ipython:: python
 
    pd.set_option('display.unicode.ambiguous_as_wide', True)
-   df;
+   df
 
 .. image:: _static/option_unicode04.png
 
diff --git a/doc/source/release.rst b/doc/source/release.rst
index 67a30984ff0a7..abbba9d6ff8ec 100644
--- a/doc/source/release.rst
+++ b/doc/source/release.rst
@@ -2,11 +2,6 @@
 
 {{ header }}
 
-.. ipython:: python
-   :suppress:
-
-   import pandas.util.testing as tm
-
 *************
 Release Notes
 *************
@@ -2851,7 +2846,7 @@ API Changes
    In [5]: arr / arr2
    Out[5]: array([0, 0, 1, 4])
 
-   In [6]: pd.Series(arr) / pd.Series(arr2) # no future import required
+   In [6]: pd.Series(arr) / pd.Series(arr2)  # no future import required
    Out[6]:
    0    0.200000
    1    0.666667
@@ -3662,12 +3657,12 @@ Improvements to existing features
 
   .. ipython:: python
 
-     p = pd.Panel(np.random.randn(3,4,4),items=['ItemA','ItemB','ItemC'],
-                  major_axis=pd.date_range('20010102',periods=4),
-                  minor_axis=['A','B','C','D'])
+     p = pd.Panel(np.random.randn(3, 4, 4), items=['ItemA', 'ItemB', 'ItemC'],
+                  major_axis=pd.date_range('20010102', periods=4),
+                  minor_axis=['A', 'B', 'C', 'D'])
      p
      p.reindex(items=['ItemA']).squeeze()
-     p.reindex(items=['ItemA'],minor=['B']).squeeze()
+     p.reindex(items=['ItemA'], minor=['B']).squeeze()
 
 - Improvement to Yahoo API access in ``pd.io.data.Options`` (:issue:`2758`)
 - added option `display.max_seq_items` to control the number of elements printed per sequence pprinting it. (:issue:`2979`)
@@ -3681,10 +3676,10 @@ Improvements to existing features
   .. ipython:: python
 
       idx = pd.date_range("2001-10-1", periods=5, freq='M')
-      ts = pd.Series(np.random.rand(len(idx)),index=idx)
+      ts = pd.Series(np.random.rand(len(idx)), index=idx)
       ts['2001']
 
-      df = pd.DataFrame(dict(A = ts))
+      df = pd.DataFrame({'A': ts})
       df['2001']
 
 - added option `display.mpl_style` providing a sleeker visual style for plots. Based on https://gist.github.com/huyng/816622 (:issue:`3075`).
diff --git a/setup.cfg b/setup.cfg
index 30b4d13bd0a66..fd258e7334ff0 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -77,10 +77,6 @@ exclude =
     doc/source/contributing_docstring.rst
     doc/source/enhancingperf.rst
     doc/source/groupby.rst
-    doc/source/indexing.rst
-    doc/source/missing_data.rst
-    doc/source/options.rst
-    doc/source/release.rst
 
 
 [yapf]