这个问题类似于关于有条件地填充列的几个问题,但是我df
有点复杂。
我有一个df
包含浮点数和字符串的with列。我试图有条件地填充包含基于字符串的浮点数的列。
基于df
以下内容:
如果中的值以Code
开头A
,则我希望保持原样。
如果该值Code
以开头B
,则我希望保留相同的初始值,然后返回nan's
到以下各行,直到中的下一个值Code
。
如果中的值以Code
开头C
,我要保持相同的第一个值,直到下一个浮动['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
当值Code
是时,我在想这样的事情B
:
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
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
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