python - 如何使用 Pandas 仅用空字符串替换无？

Question

下面的代码生成一个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   |
+---+------+---------------------+------+------+-----+

我想用 ''(空字符串)替换里面的所有 None(python 中真正的 None，而不是 str)。

预期的 df 是

+---+---+---------------------+---+---+-----+
|   | 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   |
+---+---+---------------------+---+---+-----+

---

我做的是

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

但是我得到了

+---+---+---------------------+---+------+---+
|   | 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. 所有的日期都变成了大数字
2. 甚至 NaT 和 NaN 都被替换了，这是我不想要的。

我怎样才能正确有效地实现这一点？

Answer 1

这就足够了

df.fillna("",inplace=True)

df
Out[142]: 
   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   
3  C                       C     4

编辑 2021-07-26 根据@dWitty 的评论完成回复

如果你真的想保留 Nat 和 NaN 值而不是文本，你只需要为你的文本列填充 Na在您的示例中，这是 A、C、D

您只需为您的列发送替换值 的字典。每列的值可以不同。对于您的情况，您只需要构造字典

# default values to replace NA (None)
# values = {"A": "", "C": "", "D": ""}
values = (dict([[e,""] for e in ['A','C','D']]))
df.fillna(value=values, inplace=True)

df
Out[142]: 
   A                   B  C  D    E
0  A 2014-01-02 02:00:00     A  1.0
1  B 2014-01-02 03:00:00  B  B  2.0
2    2014-01-02 04:00:00  C  C  NaN
3  C                 NaT  C     4.0