python dataframe 针对多列执行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 ?

译文：怎么同时对列 col_1 和 col_2　执行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.

译文:利用apply函数，在apply函数参数处指定自定义函数．自定义函数同时对多列进行计算，返回计算结果即可，详见代码．

来源：stackoverflow

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python dataframe 针对多列执行map操作

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