我有以下数据样本。我想
np.NaN with 999
,np.NaN
。我的尝试都没有奏效,我不确定原因。
import pandas
from pandas import DataFrame
import numpy as np
df = DataFrame({'A' : ['foo', 'foo', 'foo', 'foo',
'bar', 'bar', 'bar', 'bar'],
'B' : ['one', 'one', 'two', 'three',
'two', 'two', 'one', 'three'],
'C' : [1, np.NaN, 1, 2, np.NaN, 1, 1, 2], 'D' : [2, '', 1, 1, '', 2, 2, 1]})
print df
df.C.fillna(999)
df.D.replace('', np.NaN)
print df
Output:
A B C D
0 foo one 1 2
1 foo one NaN
2 foo two 1 1
3 foo three 2 1
4 bar two NaN
5 bar two 1 2
6 bar one 1 2
7 bar three 2 1
A B C D
0 foo one 1 2
1 foo one NaN
2 foo two 1 1
3 foo three 2 1
4 bar two NaN
5 bar two 1 2
6 bar one 1 2
7 bar three 2 1
答案 0 :(得分:3)
这些操作返回数据的副本(大多数pandas操作行为相同),除非您明确说明(默认为inplace=False
),否则它们不会就位,请参阅{{ 3}}和fillna
:
df.C.fillna(999, inplace=True)
df.D.replace('', np.NaN, inplace=True)
或转回:
df['C'] = df.C.fillna(999)
df['D'] = df.D.replace('', np.NaN)
此外,我强烈建议您使用下标运算符[]
访问列,而不是使用点运算符.
作为属性来避免模糊行为
In [60]:
df = pd.DataFrame({'A' : ['foo', 'foo', 'foo', 'foo',
'bar', 'bar', 'bar', 'bar'],
'B' : ['one', 'one', 'two', 'three',
'two', 'two', 'one', 'three'],
'C' : [1, np.NaN, 1, 2, np.NaN, 1, 1, 2], 'D' : [2, '', 1, 1, '', 2, 2, 1]})
df.C.fillna(999, inplace =True)
df.D.replace('', np.NaN, inplace=True)
df
Out[60]:
A B C D
0 foo one 1 2
1 foo one 999 NaN
2 foo two 1 1
3 foo three 2 1
4 bar two 999 NaN
5 bar two 1 2
6 bar one 1 2
7 bar three 2 1