improves assign section in dsintro

- reworks warning
- uses :func ...
- improved wording
- removed comment in code sample due to redundancy

Remains open:

frame.py probably is responsible vor travis not passing: doc test that requires python 3.6

Author: datajanko

doc/source/dsintro.rst

@@ -507,57 +507,18 @@ of one argument to be called on the ``DataFrame``. A *copy* of the original
 DataFrame is returned, with the new values inserted.
 
 .. warning::
-  Starting from Python 3.6 ``**kwargs`` is an ordered dictionary and ``assign`` 
-  respects the order of the keyword arguments. It is allowed to write
+  Starting from Python 3.6 ``**kwargs`` is an ordered dictionary and :func`DataFrame.assign`
+  respects the order of the keyword arguments. You may now use assign in the following way:
 
-  .. ipython::
-      :verbatim:
+  .. ipython:: python
 
-      In [1]: # Allowed for Python 3.6 and later
-               df.assign(C = lambda x: x['A'] + x['B'],
-                         D = lambda x: x['A'] + x['C'])
+      In [1]: df.assign(C = lambda x: x['A'] + x['B'],
+                        D = lambda x: x['A'] + x['C'])
 
   This may subtly change the behavior of your code when you're
   using ``.assign()`` to update an existing column. Prior to Python 3.6,
   callables referring to other variables being updated would get the "old" values
 
-  Previous Behaviour:
-
-  .. code-block:: ipython
-
-      In [2]: df = pd.DataFrame({"A": [1, 2, 3]})
-
-      In [3]: df.assign(A=lambda df: df.A + 1, C=lambda df: df.A * -1)
-      Out[3]:
-         A  C
-      0  2 -1
-      1  3 -2
-      2  4 -3
-
-  New Behaviour:
-
-  .. ipython:: python
-
-      df.assign(A=df.A+1, C= lambda df: df.A* -1)
-
-  For Python 3.5 and earlier the function signature of ``assign`` is ``**kwargs``, 
-  a dictionary, the order of the new columns in the resulting DataFrame cannot be guaranteed
-  to match the order you pass in. To make things predictable, items are inserted
-  alphabetically (by key) at the end of the DataFrame.
-
-  All expressions are computed first, and then assigned. So you can't refer
-  to another column being assigned in the same call to ``assign``. For example:
-
-   .. ipython::
-       :verbatim:
-
-       In [1]: # Don't do this, bad reference to `C`
-               df.assign(C = lambda x: x['A'] + x['B'],
-                         D = lambda x: x['A'] + x['C'])
-       In [2]: # Instead, break it into two assigns
-               (df.assign(C = lambda x: x['A'] + x['B'])
-                  .assign(D = lambda x: x['A'] + x['C']))
-
 Indexing / Selection
 ~~~~~~~~~~~~~~~~~~~~
 The basics of indexing are as follows: