How to apply multiple custom functions on multiple columns in grouped DataFrame in pandas?

Question

I have a pandas DataFrame which is grouped by p_id. The goal is to get a DataFrame with data shown under 'Output I'm looking for'. I've tried a few things, but I am struggling applying two custom aggregated functions:

apply(list) for x_id
'||'.join for x_name.

How can I solve this problem?

Input

| p_id | x_id | x_name |
|------|------|--------|
| 1    | 4    | Text   |
| 2    | 4    | Text   |
| 2    | 5    | Text2  |
| 2    | 6    | Text3  |
| 3    | 4    | Text   |
| 3    | 7    | Text4  |

Output I'm looking for

| p_id | x_ids   | x_names            |
|------|---------|--------------------|
| 1    | [4]     | Text               |
| 2    | [4,5,6] | Text||Text2||Text3 |
| 3    | [4,7]   | Text||Text4        |

Quang Hoang · Accepted Answer · 2021-02-09 21:52:36Z

1

You can certainly do:

df.groupby('pid').agg({'x_id':list, 'x_name':'||'.join})

Or a little more advanced with named agg:

df.groupby('pid').agg(x_ids=('x_id',list),
                      x_names=('x_name', '||'.join))

answered Feb 9, 2021 at 21:52

Quang Hoang

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1 Comment

konichiwa Over a year ago

Perfect! I didn't know you could use custom functions with agg! :)

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