Question

此问题类似于有关条件填充列的几个问题，但我的df稍微复杂一些。

我有一个df，其中的列包含浮点数和字符串。我试图根据字符串有条件地填充包含浮点数的列。

基于以下df：

如果Code中的值以A开头，我希望保留这些值。

如果值Code以B开头，我希望保持相同的初始值并将nan's返回到以下行，直到Code中的下一个值。< / p>

如果Code中的值以C开头，我希望保持相同的第一个值，直到['Numx','Numy]

中的下一个浮点数为止

import pandas as pd
import numpy as np


d = ({                          
      'Code' :['A1','A1','','B1','B1','A2','A2','','B2','B2','','A3','A3','A3','','B1','','B4','B4','A2','A2','A1','A1','','B4','B4','C1','C1','','','D1','','B2'],
      'Numx' : [30.2,30.5,30.6,35.6,40.2,45.5,46.1,48.1,48.5,42.2,'',30.5,30.6,35.6,40.2,45.5,'',48.1,48.5,42.2, 40.1,48.5,42.2,'',48.5,42.2,43.1,44.1,'','','','',45.1],
      'Numy' : [1.9,2.3,2.5,2.2,2.5,3.1,3.4,3.6,3.7,5.4,'',2.3,2.5,2.2,2.5,3.1,'',3.6,3.7,5.4,6.5,8.5,2.2,'',8.5,2.2,2.3,2.5,'','','','',3.2]
      })

df = pd.DataFrame（data = d）

输出：

   Code  Numx Numy
0    A1  30.2  1.9
1    A1  30.5  2.3
2        30.6  2.5
3    B1  35.6  2.2
4    B1  40.2  2.5
5    A2  45.5  3.1
6    A2  46.1  3.4
7        48.1  3.6
8    B2  48.5  3.7
9    B2  42.2  5.4
10        nan  nan       
11   A3  30.5  2.3
12   A3  30.6  2.5
13   A3  35.6  2.2
14       40.2  2.5
15   B1  45.5  3.1
16        nan  nan     
17   B4  48.1  3.6
18   B4  48.5  3.7
19   A2  42.2  5.4
20   A2  40.1  6.5
21   A1  48.5  8.5
22   A1  42.2  2.2
23        nan  nan      
24   B4  48.5  8.5
25   B4  42.2  2.2
26   C1  43.1  2.3
27   C1  44.1  2.5
28        nan  nan      
29        nan  nan   
30   D1   nan  nan      
31        nan  nan        
32   B2  45.1  3.2

当Code中的值为B时，我正在考虑这样的事情：

df['Numx'] = np.where(df['Code'] == 'B-'.ffill()) 
df['Numy'] = np.where(df['Code'] == 'B-'.ffill())

所以我想要的输出是：

   Code  Numx Numy
0    A1  30.2  1.9
1    A1  30.5  2.3
2        30.6  2.5
3    B1  35.6  2.2
4    B1   nan  nan
5    A2  45.5  3.1
6    A2  46.1  3.4
7        48.1  3.6
8    B2  48.5  3.7
9    B2   nan  nan
10        nan  nan        
11   A3  30.5  2.3
12   A3  30.6  2.5
13   A3  35.6  2.2
14       40.2  2.5
15   B1  45.5  3.1
16        nan  nan         
17   B4  48.1  3.6
18   B4   nan  nan
19   A2  42.2  5.4
20   A2  40.1  6.5
21   A1  48.5  8.5
22   A1  42.2  2.2
23        nan  nan      
24   B4  48.5  8.5
25   B4   nan  nan
26   C1  43.1  2.3
27   C1  43.1  2.3
28       43.1  2.3   
29       43.1  2.3   
30   D1  43.1  2.3   
31       43.1  2.3         
32   B2  45.1  3.2

Answer 1

我认为需要：

df['Code_new'] = df['Code'].where(df['Code'].isin(['AA','BB'])).ffill()

df[['Numx','Numy']] = df[['Numx','Numy']].mask(df['Code_new'].duplicated())
mask = df['Code_new'] == 'BB'
df.loc[mask, ['Numx','Numy']] = df.loc[mask, ['Numx','Numy']].ffill()
print (df)
  Code  Numx Numy Code_new
0   AA  30.2  1.9       AA
1        NaN  NaN       AA
2        NaN  NaN       AA
3   BB  35.6  2.2       BB
4       35.6  2.2       BB
5       35.6  2.2       BB
6       35.6  2.2       BB
7   CC  35.6  2.2       BB
8       35.6  2.2       BB
9   DD  35.6  2.2       BB

或者：

df = df.replace('nan', np.nan)

df['Code_new'] = df['Code'].where(df['Code'].isin(['AA','BB'])).ffill()

m1 = df['Code_new'].duplicated() & (df['Code_new'] == 'AA')
df[['Numx','Numy']] = df[['Numx','Numy']].mask(m1)
m2 = df['Code_new'] == 'BB'
df.loc[m2, ['Numx','Numy']] = df.loc[m2, ['Numx','Numy']].ffill()
print (df)
  Code  Numx  Numy Code_new
0   AA  30.2   1.9       AA
1        NaN   NaN       AA
2        NaN   NaN       AA
3   BB  35.6   2.2       BB
4       40.2   2.5       BB
5       45.5   3.1       BB
6       45.5   3.1       BB
7   CC  45.5   3.1       BB
8       45.5   3.1       BB
9   DD  42.2   5.4       BB

在pandas df

1 个答案: