如何将函数应用于Pandas数据框的两列 [英] How to apply a function to two columns of Pandas dataframe

问题描述

假设我有一个df，其中的列为'ID', 'col_1', 'col_2'.我定义了一个函数:

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.

现在，我想将f应用于df的两个列'col_1', 'col_2'，以元素方式计算新列'col_3'，有点像:

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)  
# Pandas gives : TypeError: ('<lambda>() takes exactly 2 arguments (1 given)'

怎么办?

** 添加详细示例如下 ***

** Add detail sample as below ***

import pandas as pd

df = pd.DataFrame({'ID':['1','2','3'], 'col_1': [0,2,3], 'col_2':[1,4,5]})
mylist = ['a','b','c','d','e','f']

def get_sublist(sta,end):
    return mylist[sta:end+1]

#df['col_3'] = df[['col_1','col_2']].apply(get_sublist,axis=1)
# expect above to output df as below 

  ID  col_1  col_2            col_3
0  1      0      1       ['a', 'b']
1  2      2      4  ['c', 'd', 'e']
2  3      3      5  ['d', 'e', 'f']

推荐答案

下面是在数据帧上使用apply的示例，我正在使用axis = 1进行调用.

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

请注意，不同之处在于，不是尝试将两个值传递给函数f，而是重写该函数以接受pandas Series对象，然后对Series进行索引以获取所需的值.

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

根据您的用例，有时创建熊猫group对象，然后在组上使用apply有时会很有帮助.

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

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