![](/img/trans.png)
[英]How to apply a function to a column in Pandas depending on the value in another column?
[英]Apply function to column in Pandas depending on row value
假设我有以下数据框:
date,id,value
1/1/2017,5,300
1/1/2017,51,300
1/1/2017,54,300
1/2/2017,5,100
1/2/2017,51,100
1/2/2017,54,100
而且我有一个字典映射id
到这样的调整因子:
{5: 20, 51: 23.5, 54:10}
我想add
与id
对应的因数add
到数据框中的value
列,导致:
date,id,value,adjusted_value
1/1/2017,5,300,300+20=320
1/1/2017,51,310,310+23.5=333.5
1/1/2017,54,320,320+10=330
1/2/2017,5,110,110+20=130
1/2/2017,51,120,120+23.5=143.5
1/2/2017,54,130,130+10=140
有没有简单的方法可以做到这一点?
我想你正在寻找ngroup,cumcount和map即
x = df.groupby('date')
d = {5: 20, 51: 23.5, 54: 10}
df['new'] = (x.cumcount()+x.ngroup())*10 +df['id'].map(d)+df['value']
输出:
date id value new 0 1/1/2017 5 300 320.0 1 1/1/2017 51 300 333.5 2 1/1/2017 54 300 330.0 3 1/2/2017 5 100 130.0 4 1/2/2017 51 100 143.5 5 1/2/2017 54 100 140.0
说明
(x.cumcount()+x.ngroup()
0 0 1 1 2 2 3 1 4 2 5 3
(x.cumcount()+x.ngroup())*10 +df['value']
0 300 1 310 2 320 3 110 4 120 5 130 dtype: int64
一内胆:
df['adjusted_value'] = df.apply(lambda x: dictionary[x['id']] + x['value'] , axis=1)
更详细:
df['adjusted_value'] = [dictionary[i] for i in df['id']]
df['adjusted_value'] = df['adjusted_value'] + df['value']
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.