[英]Apply function row wise on pandas data frame on columns with numerical values
I have the following data frame: 我有以下数据框:
import pandas as pd
df = pd.DataFrame({'AAA' : ['w','x','y','z'], 'BBB' : [10,20,30,40],'CCC' : [100,50,-30,-50]})
Which looks like this: 看起来像这样:
In [32]: df
Out[32]:
AAA BBB CCC
0 w 10 100
1 x 20 50
2 y 30 -30
3 z 40 -50
What I want to do is to perform function operation on every row for every column except those with non-numerical value (in this case AAA
). 我想要做的是对除了具有非数值的那些列(在这种情况下为
AAA
)之外的每一列执行函数操作。 In the real case the non-numerical case is always on first column, and the rest (could be greater than 2 columns) are always numerical. 在实际情况下,非数字情况总是在第一列,其余(可能大于2列)总是数字。
The final desired output is: 最终的期望输出是:
AAA BBB CCC Score
0 w 10 100 110
1 x 20 50 70
2 y 30 -30 0
3 z 40 -50 -10
I tried this but failed: 我尝试过但失败了:
import numpy as np
df["Score"] = df.apply(np.sum, axis=1)
What's the right way to do it? 什么是正确的方法呢?
Update2: UPDATE2:
This is the code that give SettingWithCopyWarning
. 这是给
SettingWithCopyWarning
的代码。 Please fresh start the ipython for testing. 请重新开始ipython测试。
import pandas as pd
import numpy as np
def cvscore(fclist):
sd = np.std(fclist)
mean = np.mean(fclist)
cv = sd/mean
return cv
def calc_cvscore_on_df(df):
df["CV"] = df.iloc[:,1:].apply(cvscore, axis=1)
return df
df3 = pd.DataFrame(np.random.randn(1000, 3), columns=['a', 'b', 'c'])
calc_cvscore_on_df(df3[["a","b"]])
To select everything but the first column, you could use df.iloc[:, 1:]
: 要选择除第一列之外的所有内容,可以使用
df.iloc[:, 1:]
:
In [371]: df['Score'] = df.iloc[:, 1:].sum(axis=1)
In [372]: df
Out[372]:
AAA BBB CCC Score
0 w 10 100 110
1 x 20 50 70
2 y 30 -30 0
3 z 40 -50 -10
To apply an arbitrary function, func
, to each row: 要对每一行应用任意函数
func
:
df.iloc[:, 1:].apply(func, axis=1)
For example, 例如,
import numpy as np
import pandas as pd
def cvscore(fclist):
sd = np.std(fclist)
mean = np.mean(fclist)
cv = sd/mean
return cv
df = pd.DataFrame({'AAA' : ['w','x','y','z'], 'BBB' : [10,20,30,40],
'CCC' : [100,50,-30,-50]})
df['Score'] = df.iloc[:, 1:].apply(cvscore, axis=1)
print(df)
yields 产量
AAA BBB CCC Score
0 w 10 100 1.211386
1 x 20 50 0.868377
2 y 30 -30 NaN
3 z 40 -50 -5.809058
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