DOC: adjusted docstring examples in DataFrame.assign to illustrate python3.6+ feature.

Author: aeltanawy

pandas/core/frame.py

@@ -3251,32 +3251,33 @@ def assign(self, **kwargs):
  Examples
  --------
  >>> df = pd.DataFrame({'temp_c': (17.0, 25.0)},
- index=['portland', 'berkeley'])
+ index=['Portland', 'Berkeley'])
+ temp_c
+ Portland 17.0
+ Berkeley 25.0
 
  Where the value is a callable, evaluated on `df`:
-
  >>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
  temp_c temp_f
- portland 17.0 62.6
- berkeley 25.0 77.0
-
- Where the value already exists and is inserted:
+ Portland 17.0 62.6
+ Berkeley 25.0 77.0
 
+ Alternatively, the same behavior can be achieved by directly
+ referencing an existing Series or list-like:
  >>> newcol = df['temp_c'] * 9 / 5 + 32
  >>> df.assign(temp_f=newcol)
  temp_c temp_f
- portland 17.0 62.6
- berkeley 25.0 77.0
-
- Where the keyword arguments depend on each other
-
- >>> df = pd.DataFrame({'A': [1, 2, 3]})
-
- >>> df.assign(B=df.A, C=lambda x:x['A']+ x['B'])
- A B C
- 0 1 1 2
- 1 2 2 4
- 2 3 3 6
+ Portland 17.0 62.6
+ Berkeley 25.0 77.0
+
+ In Python 3.6+, you can create multiple columns within the same assign
+ where one of the columns depends on another one defined within the same
+ assign:
+ >>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,
+ temp_k=lambda x: (x['temp_f'] + 459.67) * 5 / 9)
+ temp_c temp_f temp_k
+ Portland 17.0 62.6 290.15
+ Berkeley 25.0 77.0 298.15
  """
  data = self.copy()