improves whatsnew on ipython block and explanation of dependent kwargs

Author: datajanko

doc/source/whatsnew/v0.23.0.txt

@@ -181,15 +181,15 @@ Please note that the string `index` is not supported with the round trip format,
 ``.assign()`` accepts dependent arguments
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-The :func:`DataFrame.assign()` now accepts dependent kwargs. (:issue:`14207`)
+The :func:`DataFrame.assign()` now accepts dependent kwargs for python version later than 3.6 (see also `PEP 468
+<https://www.python.org/dev/peps/pep-0468/>`_). Now the keyword-value pairs passed to `.assign()` may depend on their predecessors if the values are callables. (:issue:`14207`)
 
-Specifically, defining a new column inside ``.assign()`` may be referenced in the same assign statement if a callable is used. For example
+.. ipython:: python     
 
-.. 	ipython:: python     
+    df = pd.DataFrame({'A': [1, 2, 3]})
+    df
+	df.assign(B=df.A, C=lambda x:x['A']+ x['B'])
 
-    In [3]: df = pd.DataFrame({'A': [1, 2, 3]})
-
-    In [4]: df.assign(B=df.A, C=lambda x:x['A']+ x['B'])
 .. warning::
 
 This may subtly change the behavior of your code when you're
@@ -213,7 +213,7 @@ New Behaviour:
 
 .. ipython:: python
 
-    In [5]: df.assign(A=df.A+1, C= lambda df: df.A* -1)
+    df.assign(A=df.A+1, C= lambda df: df.A* -1)
 
 .. _whatsnew_0230.enhancements.other: