Fixing PEP8 issues within code-blocks

Author: FHaase

doc/source/10min.rst

@@ -45,7 +45,7 @@ a default integer index:
 
 .. ipython:: python
 
-   s = pd.Series([1,3,5,np.nan,6,8])
+   s = pd.Series([1, 3, 5, np.nan, 6, 8])
    s
 
 Creating a :class:`DataFrame` by passing a NumPy array, with a datetime index
@@ -62,12 +62,12 @@ Creating a ``DataFrame`` by passing a dict of objects that can be converted to s
 
 .. ipython:: python
 
-   df2 = pd.DataFrame({ 'A' : 1.,
-                        'B' : pd.Timestamp('20130102'),
-                        'C' : pd.Series(1,index=list(range(4)),dtype='float32'),
-                        'D' : np.array([3] * 4,dtype='int32'),
-                        'E' : pd.Categorical(["test","train","test","train"]),
-                        'F' : 'foo' })
+   df2 = pd.DataFrame({'A': 1.,
+                       'B': pd.Timestamp('20130102'),
+                       'C': pd.Series(1, index=list(range(4)),dtype='float32'),
+                       'D': np.array([3] * 4, dtype='int32'),
+                       'E': pd.Categorical(["test", "train", "test", "train"]),
+                       'F': 'foo'})
    df2
 
 The columns of the resulting ``DataFrame`` have different 
@@ -283,9 +283,9 @@ Using the :func:`~Series.isin` method for filtering:
 .. ipython:: python
 
    df2 = df.copy()
-   df2['E'] = ['one', 'one','two','three','four','three']
+   df2['E'] = ['one', 'one', 'two', 'three', 'four', 'three']
    df2
-   df2[df2['E'].isin(['two','four'])]
+   df2[df2['E'].isin(['two', 'four'])]
 
 Setting
 ~~~~~~~
@@ -295,7 +295,7 @@ by the indexes.
 
 .. ipython:: python
 
-   s1 = pd.Series([1,2,3,4,5,6], index=pd.date_range('20130102', periods=6))
+   s1 = pd.Series([1, 2, 3, 4, 5, 6], index=pd.date_range('20130102', periods=6))
    s1
    df['F'] = s1
 
@@ -394,7 +394,7 @@ In addition, pandas automatically broadcasts along the specified dimension.
 
 .. ipython:: python
 
-   s = pd.Series([1,3,5,np.nan,6,8], index=dates).shift(2)
+   s = pd.Series([1, 3, 5, np.nan, 6, 8], index=dates).shift(2)
    s
    df.sub(s, axis='index')
 
@@ -492,7 +492,7 @@ section.
 
 .. ipython:: python
 
-   df = pd.DataFrame(np.random.randn(8, 4), columns=['A','B','C','D'])
+   df = pd.DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D'])
    df
    s = df.iloc[3]
    df.append(s, ignore_index=True)
@@ -512,12 +512,12 @@ See the :ref:`Grouping section <groupby>`.
 
 .. ipython:: python
 
-   df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
-                             'foo', 'bar', 'foo', 'foo'],
-                      'B' : ['one', 'one', 'two', 'three',
-                             'two', 'two', 'one', 'three'],
-                      'C' : np.random.randn(8),
-                      'D' : np.random.randn(8)})
+   df = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar',
+                            'foo', 'bar', 'foo', 'foo'],
+                      'B': ['one', 'one', 'two', 'three',
+                            'two', 'two', 'one', 'three'],
+                      'C': np.random.randn(8),
+                      'D': np.random.randn(8)})
    df
 
 Grouping and then applying the :meth:`~DataFrame.sum` function to the resulting 
@@ -532,7 +532,7 @@ apply the ``sum`` function.
 
 .. ipython:: python
 
-   df.groupby(['A','B']).sum()
+   df.groupby(['A', 'B']).sum()
 
 Reshaping
 ---------
@@ -578,11 +578,11 @@ See the section on :ref:`Pivot Tables <reshaping.pivot>`.
 
 .. ipython:: python
 
-   df = pd.DataFrame({'A' : ['one', 'one', 'two', 'three'] * 3,
-                      'B' : ['A', 'B', 'C'] * 4,
-                      'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,
-                      'D' : np.random.randn(12),
-                      'E' : np.random.randn(12)})
+   df = pd.DataFrame({'A': ['one', 'one', 'two', 'three'] * 3,
+                      'B': ['A', 'B', 'C'] * 4,
+                      'C': ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,
+                      'D': np.random.randn(12),
+                      'E': np.random.randn(12)})
    df
 
 We can produce pivot tables from this data very easily:
@@ -653,7 +653,7 @@ pandas can include categorical data in a ``DataFrame``. For full docs, see the
 
 .. ipython:: python
 
-    df = pd.DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})
+    df = pd.DataFrame({"id":[1, 2, 3, 4, 5, 6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})
 
 Convert the raw grades to a categorical data type.
 
@@ -753,13 +753,13 @@ Writing to a HDF5 Store.
 
 .. ipython:: python
 
-   df.to_hdf('foo.h5','df')
+   df.to_hdf('foo.h5', 'df')
 
 Reading from a HDF5 Store.
 
 .. ipython:: python
 
-   pd.read_hdf('foo.h5','df')
+   pd.read_hdf('foo.h5', 'df')
 
 .. ipython:: python
    :suppress:
@@ -796,7 +796,7 @@ If you are attempting to perform an operation you might see an exception like:
 .. code-block:: python
 
     >>> if pd.Series([False, True, False]):
-        print("I was true")
+    ...     print("I was true")
     Traceback
         ...
     ValueError: The truth value of an array is ambiguous. Use a.empty, a.any() or a.all().

doc/source/comparison_with_sas.rst

@@ -298,8 +298,8 @@ see the :ref:`timeseries documentation<timeseries>` for more details.
 .. ipython:: python
    :suppress:
 
-   tips = tips.drop(['date1','date2','date1_year',
-      'date2_month','date1_next','months_between'], axis=1)
+   tips = tips.drop(['date1', 'date2', 'date1_year', 'date2_month',
+                     'date1_next', 'months_between'], axis=1)
 
 Selection of Columns
 ~~~~~~~~~~~~~~~~~~~~
@@ -744,12 +744,9 @@ XPORT is a relatively limited format and the parsing of it is not as
 optimized as some of the other pandas readers. An alternative way
 to interop data between SAS and pandas is to serialize to csv.
 
-.. code-block:: python
-
-   # version 0.17, 10M rows
-
-   In [8]: %time df = pd.read_sas('big.xpt')
-   Wall time: 14.6 s
+>>> # version 0.17, 10M rows
+>>> %time df = pd.read_sas('big.xpt')
+Wall time: 14.6 s
 
-   In [9]: %time df = pd.read_csv('big.csv')
-   Wall time: 4.86 s
+>>> %time df = pd.read_csv('big.csv')
+Wall time: 4.86 s

doc/source/contributing_docstring.rst

@@ -189,31 +189,34 @@ infinitive verb.
 
 .. code-block:: python
 
-    def astype(dtype):
+    def astype(dtype):    # noqa: F811
         """
         Casts Series type.
 
         Verb in third-person of the present simple, should be infinitive.
         """
         pass
 
-    def astype(dtype):
+
+    def astype(dtype):    # noqa: F811
         """
         Method to cast Series type.
 
         Does not start with verb.
         """
         pass
 
-    def astype(dtype):
+
+    def astype(dtype):    # noqa: F811
         """
         Cast Series type
 
         Missing dot at the end.
         """
         pass
 
-    def astype(dtype):
+
+    def astype(dtype):    # noqa: F811
         """
         Cast Series type from its current type to the new type defined in
         the parameter dtype.
@@ -624,6 +627,7 @@ A simple example could be:
 .. code-block:: python
 
     class Series:
+
         def head(self, n=5):
             """
             Return the first elements of the Series.
@@ -684,9 +688,8 @@ shown:
     import numpy as np
     import pandas as pd
 
-
 Any other module used in the examples must be explicitly imported, one per line (as
-recommended in `PEP-8 <https://www.python.org/dev/peps/pep-0008/#imports>`_)
+recommended in :pep:`8#imports`)
 and avoiding aliases. Avoid excessive imports, but if needed, imports from
 the standard library go first, followed by third-party libraries (like
 matplotlib).
@@ -720,6 +723,7 @@ positional arguments ``head(3)``.
 .. code-block:: python
 
     class Series:
+
         def mean(self):
             """
             Compute the mean of the input.
@@ -946,12 +950,14 @@ substitute the children's class names in this docstring.
            """Apply my function to %(klass)s."""
            ...
 
+
    class ChildA(Parent):
        @Substitution(klass="ChildA")
        @Appender(Parent.my_function.__doc__)
        def my_function(self):
            ...
 
+
    class ChildB(Parent):
        @Substitution(klass="ChildB")
        @Appender(Parent.my_function.__doc__)

doc/source/gotchas.rst

@@ -96,40 +96,32 @@ something to a ``bool``. This happens in an ``if``-statement or when using the
 boolean operations: ``and``, ``or``, and ``not``. It is not clear what the result
 of the following code should be:
 
-.. code-block:: python
-
-    >>> if pd.Series([False, True, False]):
-         ...
+>>> if pd.Series([False, True, False]):
+...     print("I was true")
 
 Should it be ``True`` because it's not zero-length, or ``False`` because there 
 are ``False`` values? It is unclear, so instead, pandas raises a ``ValueError``:
 
-.. code-block:: python
-
-    >>> if pd.Series([False, True, False]):
-        print("I was true")
-    Traceback
-        ...
-    ValueError: The truth value of an array is ambiguous. Use a.empty, a.any() or a.all().
+>>> if pd.Series([False, True, False]):
+...     print("I was true")
+Traceback
+    ...
+ValueError: The truth value of an array is ambiguous. Use a.empty, a.any() or a.all().
 
 You need to explicitly choose what you want to do with the ``DataFrame``, e.g.
 use :meth:`~DataFrame.any`, :meth:`~DataFrame.all` or :meth:`~DataFrame.empty`.
 Alternatively, you might want to compare if the pandas object is ``None``:
 
-.. code-block:: python
-
-    >>> if pd.Series([False, True, False]) is not None:
-           print("I was not None")
-    >>> I was not None
+>>> if pd.Series([False, True, False]) is not None:
+...    print("I was not None")
+I was not None
 
 
 Below is how to check if any of the values are ``True``:
 
-.. code-block:: python
-
-    >>> if pd.Series([False, True, False]).any():
-           print("I am any")
-    >>> I am any
+>>> if pd.Series([False, True, False]).any():
+...     print("I am any")
+I am any
 
 To evaluate single-element pandas objects in a boolean context, use the method 
 :meth:`~DataFrame.bool`:
@@ -316,7 +308,7 @@ Occasionally you may have to deal with data that were created on a machine with
 a different byte order than the one on which you are running Python. A common 
 symptom of this issue is an error like:
 
-.. code-block:: python
+.. code-block:: console
 
     Traceback
         ...

doc/source/groupby.rst

@@ -79,12 +79,12 @@ pandas objects can be split on any of their axes. The abstract definition of
 grouping is to provide a mapping of labels to group names. To create a GroupBy
 object (more on what the GroupBy object is later), you may do the following:
 
-.. code-block:: ipython
+.. code-block:: python
 
-   # default is axis=0
-   >>> grouped = obj.groupby(key)
-   >>> grouped = obj.groupby(key, axis=1)
-   >>> grouped = obj.groupby([key1, key2])
+    # default is axis=0
+    >>> grouped = obj.groupby(key)
+    >>> grouped = obj.groupby(key, axis=1)
+    >>> grouped = obj.groupby([key1, key2])
 
 The mapping can be specified many different ways:
 
@@ -1272,7 +1272,7 @@ arbitrary function, for example:
 
 .. code-block:: python
 
-   (df.groupby(['Store', 'Product']).pipe(report_func)
+   df.groupby(['Store', 'Product']).pipe(report_func)
 
 where ``report_func`` takes a GroupBy object and creates a report
 from that.

doc/source/timeseries.rst

@@ -898,7 +898,7 @@ custom date increment logic, such as adding business days:
 .. code-block:: python
 
     class BDay(DateOffset):
-    """DateOffset increments between business days"""
+        """DateOffset increments between business days"""
         def apply(self, other):
             ...
 

setup.cfg

@@ -32,11 +32,11 @@ exclude =
     .tox
 
 [flake8-rst]
-bootstrap =
-    import pandas as pd
-    import numpy as np
 ignore =
     F821,  # undefined name
+    F401,  # imported but unused
+    W391,  # blank line at end of file [Seems to be a bug (v0.4.1)]
+
 
 [yapf]
 based_on_style = pep8