Cleanup

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

doc/source/basics.rst

@@ -313,11 +313,11 @@ To evaluate single-element pandas objects in a boolean context, use the method
 
    .. code-block:: python
 
-       >>> df and df2                     # noqa: E999
+       >>> df and df2
 
    These will both raise errors, as you are trying to compare multiple values.
 
-   .. code-block:: console
+   .. code-block:: python-traceback
 
        ValueError: The truth value of an array is ambiguous. Use a.empty, a.any() or a.all().
 

doc/source/contributing_docstring.rst

@@ -189,7 +189,7 @@ infinitive verb.
 
 .. code-block:: python
 
-    def astype(dtype):    # noqa: F811
+    def astype(dtype):
         """
         Casts Series type.
 

doc/source/enhancingperf.rst

@@ -363,8 +363,10 @@ take the plain Python code from above and annotate with the ``@jit`` decorator.
 Note that we directly pass NumPy arrays to the Numba function. ``compute_numba`` is just a wrapper that provides a
 nicer interface by passing/returning pandas objects.
 
->>> %timeit compute_numba(df)
-1000 loops, best of 3: 798 us per loop
+.. code-block:: ipython
+
+    In [4]: %timeit compute_numba(df)
+    1000 loops, best of 3: 798 us per loop
 
 In this example, using Numba was faster than Cython.
 

doc/source/gotchas.rst

@@ -316,7 +316,7 @@ 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:: console
+.. code-block:: python-traceback
 
     Traceback
         ...

doc/source/missing_data.rst

@@ -727,7 +727,7 @@ rules introduced in the table below.
     :header: "data type", "Cast to"
     :widths: 40, 40
 
-	integer, float
+    integer, float
     boolean, object
     float, no cast
     object, no cast