python - 每隔一行用特殊条件用 0 替换空值

Question

这是数据集的以下子集:

A  B    C         D         R        sentence              ADR1         ADR2     
112 135 21  EffexorXR.21    1    lack of good feeling.     good        feeling
113 135 21  EffexorXR.21    1                               1
115 136 21  EffexorXR.21    2    Feel disconnected        disconnected   feel    
116 136 21  EffexorXR.21    2                                             0
118 142 22  EffexorXR.22    1    Weight gain               gain         
119 142 22  EffexorXR.22    1                                1             

在 ADR1 和 ADR2 列中，对于每个单词，行中应该有 1 或 0。如果缺少该值，我需要将其替换为“0”。这是输出:

A  B    C         D         R        sentence              ADR1         ADR2     
112 135 21  EffexorXR.21    1    lack of good feeling.     good        feeling
113 135 21  EffexorXR.21    1                               1             0
115 136 21  EffexorXR.21    2    Feel disconnected        disconnected   feel    
116 136 21  EffexorXR.21    2                                 0            0
118 142 22  EffexorXR.22    1    Weight gain               gain         
119 142 22  EffexorXR.22    1                                1    

我试过了

df[ADR1].fillna(0, inplace=True) and df[ADR2].fillna(0, inplace=True)

但是这段代码产生了下面的 df，这是不需要的

 A  B    C         D         R        sentence              ADR1         ADR2     
112 135 21  EffexorXR.21    1    lack of good feeling.     good        feeling
    113 135 21  EffexorXR.21    1                               1        0
    115 136 21  EffexorXR.21    2    Feel disconnected        disconnected   feel                                                                 0
    116 136 21  EffexorXR.21    2                                             0
    118 142 22  EffexorXR.22    1    Weight gain               gain           0
    119 142 22  EffexorXR.22    1                                1            0 

Answer 1

您可以使用 reshape允许一次处理每隔一行的数据。像这样的东西:

代码:

for col in ['ADR1', 'ADR2']:
    data = np.reshape(df[col].values, (-1, 2))
    need_fill = np.logical_and(data[:, 0] != '', data[:, 1] == '')
    data[np.where(need_fill),1] = 0

测试代码:

import pandas as pd
from io import StringIO
import numpy as np

df = pd.read_fwf(StringIO(u"""
    A   B   C   D             R  sentence              ADR1         ADR2     
    112 135 21  EffexorXR.21  1  lack of good feeling  good         feeling
    113 135 21  EffexorXR.21  1                        1
    115 136 21  EffexorXR.21  2  Feel disconnected     disconnected feel    
    116 136 21  EffexorXR.21  2                                     0
    118 142 22  EffexorXR.22  1  Weight gain           gain         
    119 142 22  EffexorXR.22  1                        1"""),
                 header=1).fillna('')

print(df)
for col in ['ADR1', 'ADR2']:
    data = np.reshape(df[col].values, (-1, 2))
    need_fill = np.logical_and(data[:, 0] != '', data[:, 1] == '')
    data[np.where(need_fill),1] = 0
print(df)

结果:

     A    B   C             D  R              sentence          ADR1     ADR2
0  112  135  21  EffexorXR.21  1  lack of good feeling          good  feeling
1  113  135  21  EffexorXR.21  1                                   1         
2  115  136  21  EffexorXR.21  2     Feel disconnected  disconnected     feel
3  116  136  21  EffexorXR.21  2                                            0
4  118  142  22  EffexorXR.22  1           Weight gain          gain         
5  119  142  22  EffexorXR.22  1                                   1         

     A    B   C             D  R              sentence          ADR1     ADR2
0  112  135  21  EffexorXR.21  1  lack of good feeling          good  feeling
1  113  135  21  EffexorXR.21  1                                   1        0
2  115  136  21  EffexorXR.21  2     Feel disconnected  disconnected     feel
3  116  136  21  EffexorXR.21  2                                   0        0
4  118  142  22  EffexorXR.22  1           Weight gain          gain         
5  119  142  22  EffexorXR.22  1                                   1