python dataframe 針對多列執行map操作

Suppose I have a df which has columns of 'ID', 'col_1', 'col_2'. And I define a function :

　f = lambda x, y : my_function_expression.

Now I want to apply the f to df's two columns 'col_1', 'col_2' to element-wise calculate a new column 'col_3' , somewhat like :　　

df['col_3'] = df[['col_1','col_2']].apply(f)

How to do ?

譯文：怎么同時對列 col_1 和 col_2　執行map操作，生成新的一列？

答：

Here's an example using apply on the dataframe, which I am calling with axis = 1.

Note the difference is that instead of trying to pass two values to the function f, rewrite the function to accept a pandas Series object, and then index the Series to get the values needed.

In [49]: df
Out[49]: 
          0         1
0  1.000000  0.000000
1 -0.494375  0.570994
2  1.000000  0.000000
3  1.876360 -0.229738
4  1.000000  0.000000

In [50]: def f(x):    
   ....:  return x[0] + x[1]  
   ....:  

In [51]: df.apply(f, axis=1) #passes a Series object, row-wise
Out[51]: 
0    1.000000
1    0.076619
2    1.000000
3    1.646622
4    1.000000

Depending on your use case, it is sometimes helpful to create a pandas group object, and then use apply on the group.

譯文:利用apply函數，在apply函數參數處指定自定義函數．自定義函數同時對多列進行計算，返回計算結果即可，詳見代碼．

來源：stackoverflow