如何让熊猫数据框理解和评估一些表达式？

Question

假设我有一个像这个 -

df = pd.DataFrame({'a': [1,2,3], 'b':[10,20,30], 'c':[8,9,10]}) 
here columns are - 
c = ['a','b','c'] 
I can calculate different formulas like this - 
df['sum'] = df[c].sum(axis=1) 
df['avg'] = df[c].mean(axis=1) 

列现在我有其中的公式都写在下面formats-

1. SUM(df[c]) 
2. AVG(df[c]) + MAX(df[c]) 
3. AVG(SUM(df[c])) * 100 
etc.. 
Now is it possible to make above expressions being calculated by pandas without writing much logics in python code? 

Answer 1

import pandas as pd 
import numpy as np 
df = pd.DataFrame({'a': [1,2,3], 'b':[10,20,30], 'c':[8,9,10]}) 

def SUM(dfcols): 
    return np.sum(dfcols) 

SUM(df['a']) 

np.average(df['a']) 
np.amax(df['a']) 

有了这些，你可以做所有你需要的文件。