如何更新pandas数据框中另一列中特定值的行中的列值？

Question

I have a dataframe with 3 columns id , type , type_value .

They're already populated with values. type column contains repeating values type1 , type2 , type3 , type4 .

type_value contains a Counter object which has frequencies of occurrences in it.

All I need is to perform some operations on the Counter dictionary and update the type_value column when the type is matched with type1 and leave the other rows untouched.

Input:

id  type    type_value
1   type1   Counter object that needs to be updated
2   type2   some random value
3   type3   some random value
4   type4   some random value
5   type1   Counter object that needs to be updated
6   type2   some random value
7   type3   some random value
8   type4   some random value
9   type1   Counter object that needs to be updated
10  type2   some random value
11  type3   some random value
12  type4   some random value
13  type1   Counter object that needs to be updated
14  type2   some random value
15  type3   some random value
16  type4   some random value

Output:

id  type    type_value
1   type1   Do an operation on this and update with new values
2   type2   some random value
3   type3   some random value
4   type4   some random value
5   type1   Do an operation on this and update with new values
6   type2   some random value
7   type3   some random value
8   type4   some random value
9   type1   Do an operation on this and update with new values
10  type2   some random value
11  type3   some random value
12  type4   some random value
13  type1   Do an operation on this and update with new values
14  type2   some random value
15  type3   some random value
16  type4   some random value

Answer 1

Fitler rows by condition and apply your custom function:

def func(x):
    #your operation
    return x

mask = df['type'] == 'type1'
df.loc[mask, 'type_value'] = df.loc[mask, 'type_value'].apply(func)