Set row index in pandas based on whether another row's value has already been indexed or not-CodePudding

What I am trying to accomplish through Pandas is:

Let's say we have a Pandas DataFrame like this:

     transaction_code
1    4373-36
2    3626-68
3    3626-68
4    3281-23
5    4721-44
...
101  6273-56
102  2836-78
103  1657-28
104  3281-23
105  5323-64

I wanna create a new column called 'transaction_code_new_index' that will contain indexes just like the current existing one, buuuut whenever a transaction_code is duplicated (i.e. the code 6273-75 might exist 3 times in it), I want the index of those codes be the same for them (i.e. for every single transaction_code that matches 6273-75, their index must be the same)

Example:

     transaction_code transaction_code_new_index
1    4373-36          1
2    3626-68          2
3    3626-68          2 (because 3626-68 has already been indexed before)
4    3281-23          3
5    4721-44          4
...
101  6273-56          100
102  2836-78          101
103  1657-28          102
104  3281-23          3 (because 3281-23 has already been indexed before)
105  5323-64          103

Thanks.

CodePudding user response：

You can take the min index of every group. Using transform will assign the results back to the respective rows.

df['new_index'] = df.groupby('transaction_code')['transaction_code'].transform(lambda x: x.index.min())

Output

  transaction_code  new_index
1          4373-36          1
2          3626-68          2
3          3626-68          2
4          3281-23          4
5          4721-44          5