[Python] How to replace None only with empty string using pandas?

the code below generates a df:

import pandas as pd
from datetime import datetime as dt
import numpy as np

dates = [dt(2014, 1, 2, 2), dt(2014, 1, 2, 3), dt(2014, 1, 2, 4), None]
strings1 = ['A', 'B',None, 'C']
strings2 = [None, 'B','C', 'C']
strings3 = ['A', 'B','C', None]
vals = [1.,2.,np.nan, 4.]
df = pd.DataFrame(dict(zip(['A','B','C','D','E'],
[strings1, dates, strings2, strings3, vals])))



+---+------+---------------------+------+------+-----+
| | A | B | C | D | E |
+---+------+---------------------+------+------+-----+
| 0 | A | 2014-01-02 02:00:00 | None | A | 1 |
| 1 | B | 2014-01-02 03:00:00 | B | B | 2 |
| 2 | None | 2014-01-02 04:00:00 | C | C | NaN |
| 3 | C | NaT | C | None | 4 |
+---+------+---------------------+------+------+-----+


I would like to replace all None (real None in python, not str) inside with ''(empty string).

The expected df is

+---+---+---------------------+---+---+-----+
| | A | B | C | D | E |
+---+---+---------------------+---+---+-----+
| 0 | A | 2014-01-02 02:00:00 | | A | 1 |
| 1 | B | 2014-01-02 03:00:00 | B | B | 2 |
| 2 | | 2014-01-02 04:00:00 | C | C | NaN |
| 3 | C | NaT | C | | 4 |
+---+---+---------------------+---+---+-----+


what I did is

df = df.replace([None], [''], regex=True)

But I got

+---+---+---------------------+---+------+---+
| | A | B | C | D | E |
+---+---+---------------------+---+------+---+
| 0 | A | 1388628000000000000 | | A | 1 |
| 1 | B | 1388631600000000000 | B | B | 2 |
| 2 | | 1388635200000000000 | C | C | |
| 3 | C | | C | | 4 |
+---+---+---------------------+---+------+---+

1. all the dates becomes big numbers
2. Even NaT and NaN are replaced, which I don't want.

How can I achieve that correctly and efficently?

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