Fixing PEP8 issues

cookbook.rst:971 -> python of .. code-block:: deleted to disable
                    flake8-rst checks. %timeit is invalid Syntax,
                    # noqa: E999 does not have an effect.

Signed-off-by: Fabian Haase <[email protected]>

Author: FHaase

doc/source/cookbook.rst

@@ -968,37 +968,34 @@ Parsing date components in multi-columns
 
 Parsing date components in multi-columns is faster with a format
 
-.. code-block:: python
-
-    In [30]: i = pd.date_range('20000101',periods=10000)
-
-    In [31]: df = pd.DataFrame(dict(year = i.year, month = i.month, day = i.day))
+.. code-block::
 
-    In [32]: df.head()
-    Out[32]:
+    >>> i = pd.date_range('20000101', periods=10000)
+    >>> df = pd.DataFrame({year: i.year, month: i.month, day: i.day})
+    >>> df.head()
        day  month  year
     0    1      1  2000
     1    2      1  2000
     2    3      1  2000
     3    4      1  2000
     4    5      1  2000
 
-    In [33]: %timeit pd.to_datetime(df.year*10000+df.month*100+df.day,format='%Y%m%d')
+    >>> %timeit pd.to_datetime(df.year * 10000 + df.month * 100 + df.day,
+    ...                        format='%Y%m%d')
     100 loops, best of 3: 7.08 ms per loop
 
     # simulate combinging into a string, then parsing
-    In [34]: ds = df.apply(lambda x: "%04d%02d%02d" % (x['year'],x['month'],x['day']),axis=1)
-
-    In [35]: ds.head()
-    Out[35]:
+    >>> ds = df.apply(lambda x: "%04d%02d%02d" %
+    ...               (x['year'], x['month'], x['day']), axis=1)
+    >>> ds.head()
     0    20000101
     1    20000102
     2    20000103
     3    20000104
     4    20000105
     dtype: object
 
-    In [36]: %timeit pd.to_datetime(ds)
+    >>> %timeit pd.to_datetime(ds)
     1 loops, best of 3: 488 ms per loop
 
 Skip row between header and data
@@ -1255,6 +1252,7 @@ The `method` argument within `DataFrame.corr` can accept a callable in addition
     ...
     ...     return cov_ab / std_a / std_b
     ...
+    ...
     >>> df = pd.DataFrame(np.random.normal(size=(100, 3)))
     ...
     >>> df.corr(method=distcorr)

doc/source/extending.rst

@@ -157,6 +157,7 @@ your ``MyExtensionArray`` class, as follows:
     class MyExtensionArray(ExtensionArray, ExtensionScalarOpsMixin):
         pass
 
+
     MyExtensionArray._add_arithmetic_ops()
     MyExtensionArray._add_comparison_ops()
 
@@ -189,6 +190,7 @@ To use a test, subclass it:
 
    from pandas.tests.extension import base
 
+
    class TestConstructors(base.BaseConstructorsTests):
        pass
 
@@ -261,6 +263,7 @@ Below example shows how to define ``SubclassedSeries`` and ``SubclassedDataFrame
        def _constructor_expanddim(self):
            return SubclassedDataFrame
 
+
    class SubclassedDataFrame(DataFrame):
 
        @property
@@ -281,7 +284,7 @@ Below example shows how to define ``SubclassedSeries`` and ``SubclassedDataFrame
    >>> type(to_framed)
    <class '__main__.SubclassedDataFrame'>
 
-   >>> df = SubclassedDataFrame({'A', [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})
+   >>> df = SubclassedDataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})
    >>> df
       A  B  C
    0  1  4  7
@@ -297,6 +300,7 @@ Below example shows how to define ``SubclassedSeries`` and ``SubclassedDataFrame
    0  1  4
    1  2  5
    2  3  6
+
    >>> type(sliced1)
    <class '__main__.SubclassedDataFrame'>
 
@@ -306,6 +310,7 @@ Below example shows how to define ``SubclassedSeries`` and ``SubclassedDataFrame
    1    2
    2    3
    Name: A, dtype: int64
+
    >>> type(sliced2)
    <class '__main__.SubclassedSeries'>
 

doc/source/indexing.rst

@@ -537,10 +537,10 @@ A list of indexers where any element is out of bounds will raise an
 
 .. code-block:: python
 
-   dfl.iloc[[4, 5, 6]]
+   >>> dfl.iloc[[4, 5, 6]]
    IndexError: positional indexers are out-of-bounds
 
-   dfl.iloc[:, 4]
+   >>> dfl.iloc[:, 4]
    IndexError: single positional indexer is out-of-bounds
 
 .. _indexing.callable:
@@ -1794,7 +1794,7 @@ interpreter executes this code:
 
 .. code-block:: python
 
-   dfmi.loc[:,('one','second')] = value
+   dfmi.loc[:, ('one', 'second')] = value
    # becomes
    dfmi.loc.__setitem__((slice(None), ('one', 'second')), value)
 
@@ -1827,10 +1827,10 @@ that you've done this:
 .. code-block:: python
 
    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!
-      return foo
+       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!
+       return foo
 
 Yikes!
 

doc/source/missing_data.rst

@@ -696,9 +696,8 @@ You can also operate on the DataFrame in place:
 
    .. code-block:: python
 
-      s = pd.Series([True, False, True])
-      s.replace({'a string': 'new value', True: False})  # raises
-
+      >>> s = pd.Series([True, False, True])
+      >>> s.replace({'a string': 'new value', True: False})  # raises
       TypeError: Cannot compare types 'ndarray(dtype=bool)' and 'str'
 
    will raise a ``TypeError`` because one of the ``dict`` keys is not of the