Fixing PEP8 issues

Signed-off-by: Fabian Haase <haase.fabian@gmail.com>

Author: FHaase

doc/source/advanced.rst

@@ -318,13 +318,13 @@ As usual, **both sides** of the slicers are included as this is label indexing.
 
    .. code-block:: python
 
-      df.loc[(slice('A1','A3'),.....), :]
+      df.loc[(slice('A1', 'A3'), ...), :]             # noqa: E999
 
    You should **not** do this:
 
    .. code-block:: python
 
-      df.loc[(slice('A1','A3'),.....)]
+      df.loc[(slice('A1', 'A3'), ...)]                # noqa: E999
 
 .. ipython:: python
 
@@ -740,15 +740,13 @@ values **not** in the categories, similarly to how you can reindex **any** panda
 
    .. code-block:: python
 
-      In [9]: df3 = pd.DataFrame({'A' : np.arange(6),
-                                  'B' : pd.Series(list('aabbca')).astype('category')})
+      >>> df3 = pd.DataFrame({'A': np.arange(6),
+      ...                     'B': pd.Series(list('aabbca')).astype('category')})
+      >>> df3 = df3.set_index('B')
+      >>> df3.index
+      CategoricalIndex([u'a', u'a', u'b', u'b', u'c', u'a'], categories=[u'a', u'b', u'c'], ordered=False, name=u'B', dtype='category')
 
-      In [11]: df3 = df3.set_index('B')
-
-      In [11]: df3.index
-      Out[11]: CategoricalIndex([u'a', u'a', u'b', u'b', u'c', u'a'], categories=[u'a', u'b', u'c'], ordered=False, name=u'B', dtype='category')
-
-      In [12]: pd.concat([df2, df3]
+      >>> pd.concat([df2, df3])
       TypeError: categories must match existing categories when appending
 
 .. _indexing.rangeindex:
@@ -1033,11 +1031,11 @@ On the other hand, if the index is not monotonic, then both slice bounds must be
 .. code-block:: python
 
     # 0 is not in the index
-    In [9]: df.loc[0:4, :]
+    >>> df.loc[0:4, :]
     KeyError: 0
 
     # 3 is not a unique label
-    In [11]: df.loc[2:3, :]
+    >>> df.loc[2:3, :]
     KeyError: 'Cannot get right slice bound for non-unique label: 3'
 
 ``Index.is_monotonic_increasing`` and ``Index.is_monotonic_decreasing`` only check that

doc/source/basics.rst

@@ -302,23 +302,17 @@ To evaluate single-element pandas objects in a boolean context, use the method
 
 .. warning::
 
-   You might be tempted to do the following:
+   Using a DataFrame as a condition will raise errors,
+   as you are trying to compare multiple values:
 
    .. code-block:: python
 
        >>> if df:
-            ...
-
-   Or
-
-   .. code-block:: python
-
-       >>> df and df2
-
-   These will both raise errors, as you are trying to compare multiple values.
-
-   .. code-block:: python
+       ...     do_something()
+       ValueError: The truth value of an array is ambiguous. Use a.empty, a.any() or a.all().
 
+       >>> if df and df2:
+       ...     do_something()
        ValueError: The truth value of an array is ambiguous. Use a.empty, a.any() or a.all().
 
 See :ref:`gotchas<gotchas.truth>` for a more detailed discussion.
@@ -732,9 +726,8 @@ with the equivalent
 .. code-block:: python
 
    >>> (df.pipe(h)
-          .pipe(g, arg1=1)
-          .pipe(f, arg2=2, arg3=3)
-       )
+   ...    .pipe(g, arg1=1)
+   ...    .pipe(f, arg2=2, arg3=3))
 
 Pandas encourages the second style, which is known as method chaining.
 ``pipe`` makes it easy to use your own or another library's functions

doc/source/dsintro.rst

@@ -566,13 +566,12 @@ To write code compatible with all versions of Python, split the assignment in tw
    .. code-block:: python
 
       >>> dependent = pd.DataFrame({"A": [1, 1, 1]})
-      >>> dependent.assign(A=lambda x: x["A"] + 1,
-                           B=lambda x: x["A"] + 2)
+      >>> dependent.assign(A=lambda x: x["A"] + 1, B=lambda x: x["A"] + 2)
 
    For Python 3.5 and earlier the expression creating ``B`` refers to the
    "old" value of ``A``, ``[1, 1, 1]``. The output is then
 
-   .. code-block:: python
+   .. code-block:: console
 
          A  B
       0  2  3
@@ -582,7 +581,7 @@ To write code compatible with all versions of Python, split the assignment in tw
    For Python 3.6 and later, the expression creating ``A`` refers to the
    "new" value of ``A``, ``[2, 2, 2]``, which results in
 
-   .. code-block:: python
+   .. code-block:: console
 
          A  B
       0  2  4

doc/source/reshaping.rst

@@ -45,7 +45,11 @@ For the curious here is how the above ``DataFrame`` was created:
 
 .. code-block:: python
 
-   import pandas.util.testing as tm; tm.N = 3
+   import pandas.util.testing as tm
+
+   tm.N = 3
+
+
    def unpivot(frame):
        N, K = frame.shape
        data = {'value': frame.values.ravel('F'),