根据熊猫df中其他列的值有条件地填充列

Question

这个问题类似于关于条件填充列的几个问题，但是我的df有点复杂。

我有一个包含浮点数和字符串的列的df 。 我试图有条件地填充包含基于字符串的浮点数的列。

根据以下df ：

如果Code的值以A开头，我想保持原样。

如果值Code以B开头，我想保留相同的初始值，并将nan's返回到以下各行，直到Code的下一个值。

如果Code的值以C开头，则我希望保持相同的第一个值，直到下一个浮点在['Numx','Numy]

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值为B时，我在想这样的事情：

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

所以我想要的输出将是：

   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

我相信需要：

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

要么：

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