正则表达式：将“空白”字符和 - 字符更改为 null

Question

import pandas as pd
import numpy as np

df = pd.DataFrame([
    [-0.532681, 'foo sai', 0],
    [1.490752, 'bar', 1],
    [-1.387326, 'foo-', '-'],
    [0.814772, 'baz', ' - '],     
    [-0.222552, ' -', '   -'],
    [-1.176781,  'qux', '- '],         
], columns='A B C'.split())

print(df)
print('-------------------------------')

print(df.replace(r'[^\w][\s]', np.nan, regex=True))

我如何用正则表达式替换任何whitespace 字符和-？
使用我的代码，返回：

          A        B    C
0 -0.532681  foo sai    0
1  1.490752      bar    1
2 -1.387326     foo-    -
3  0.814772      baz  NaN
4 -0.222552        -  NaN
5 -1.176781      qux  NaN

but return that i expect is this:<br>
              A        B    C
    0 -0.532681  foo sai    0
    1  1.490752      bar    1
    2 -1.387326     foo-  Nan
    3  0.814772      baz  NaN
    4 -0.222552      Nan  NaN
    5 -1.176781      qux  NaN

Answer 1

你可以使用

df.replace(r'^[\s-]+$', np.nan, regex=True)

输出：

          A        B    C
0 -0.532681  foo sai  0.0
1  1.490752      bar  1.0
2 -1.387326     foo-  NaN
3  0.814772      baz  NaN
4 -0.222552      NaN  NaN
5 -1.176781      qux  NaN

^[\s-]+$ 模式匹配

• ^ - 字符串开头
• [\s-]+ - 一个或多个空格或- 字符
• $ - 字符串结束。