`DataFrame.dictmap` - apply multiple column transforms simultaneously via a dict and `DataFrame.map`
Adds a `dictmap` instance method to `DataFrame`, that allows callers to apply multiple column transforms simultaneously via a dict of column names and transform functions.

The specified columns are keyed in the dict passed to the method, and the values are the associated transforms. 

The transforms are applied via `DataFrame.map`, while the columns are concatenated along the columns axis.

The row index is unchanged, and the output has the same shape as before.

Example below:
```
>>> d = pd.DataFrame([[1,4,7],
...                   [2,5,8],
...                   [3,6,9]],
...                   columns=['x', 'y', 'z']
>>> df
   x  y  z
0  1  4  7
1  2  5  8
2  3  6  9
>>> df.dictmap({'x': lambda x: x ** 2,
                'z': lambda z: 1 - z/2})
   x  y    z
0  1  4 -2.5
1  4  5 -3.0
2  9  6 -3.5
```

-1 this is duplicative of assign

please open a feature request issue for discussion before a PR > please open a feature request issue for discussion before a PR

OK, this isn't quite clear in your contributions guide.