I have a dataframe in python pandas with several columns taken from a CSV file.
我在python pandas中有一个数据框，其中有几列来自CSV文件。
For instance, data =:
例如，data =：
Day P1S1 P1S2 P1S3 P2S1 P2S2 P2S3
1 1 2 2 3 1 2
2 2 2 3 5 4 2
And what I need is to get the sum of all columns which name starts with P1... something like P1* with a wildcard.
我需要的是得到名称以P1开头的所有列的总和......类似P1 *的通配符。
Something like the following which gives an error:
像下面这样的错误：
P1Sum = data["P1*"]
P1Sum =数据[“P1 *”]
Is there any why to do this with pandas?
有没有为什么要用熊猫做这个？
2 个解决方案
#1
45
I found the answer.
我找到了答案。
Using the data, dataframe from the question:
使用数据，来自问题的数据框：
from pandas import *
P1Channels = data.filter(regex="P1")
P1Sum = P1Channels.sum(axis=1)
#2
0
Thanks for the tip jbssm, for anyone else looking for a sum total, I ended up adding .sum() at the end, so:
感谢提示jbssm，对于其他任何寻找总和的人，我最后在最后添加了.sum（），所以：
P1Sum= P1Channels.sum(axis=1).sum()
#1
45
I found the answer.
我找到了答案。
Using the data, dataframe from the question:
使用数据，来自问题的数据框：
from pandas import *
P1Channels = data.filter(regex="P1")
P1Sum = P1Channels.sum(axis=1)
#2
0
Thanks for the tip jbssm, for anyone else looking for a sum total, I ended up adding .sum() at the end, so:
感谢提示jbssm，对于其他任何寻找总和的人，我最后在最后添加了.sum（），所以：
P1Sum= P1Channels.sum(axis=1).sum()