GroupBy, Agreggate Strings and return unique values

Question

How to add a new column of aggregated data

I want to create 03 new columns in a dataframe

Column 01: unique_list

Create a new column in the dataframe of unique values of cfop_code for each key

Column 02: unique_count

A column that check the number of unique values that shows in unique_list

Column 03: not_unique_count

A column that check the number of not unique values that shows in unique_list

example_df

	key	product	cfop_code
0	12345678901234567890	product a	2551
1	12345678901234567890	product b	2551
2	12345678901234567890	product c	3551
3	12345678901234567895	product a	2551
4	12345678901234567897	product b	2551
5	12345678901234567897	product c	2407

Expected Result

	key	product	cfop_code	unique_list	unique_count	not_unique_count
0	12345678901234567890	product a	2551	2251, 3551	2	3
1	12345678901234567890	product b	2551	2251, 3551	2	3
2	12345678901234567890	product c	3551	2251, 3551	2	3
3	12345678901234567895	product a	2551	2251	1	1
4	12345678901234567897	product b	2551	2407, 2551	2	2
5	12345678901234567897	product c	2407	2407, 2551	2	2

---

What i had tried

Create a list of unique values

df.groupby('key')["cfop"].unique()

key
12345678901234567890    [2551, 3551]
12345678901234567895          [2551]
12345678901234567897    [2551, 2407]
Name: cfop, dtype: object

Getting the count not unique values

df.groupby("key").agg(**{"unique_values": pd.NamedAgg(column='cfop', aggfunc="size")}).reset_index()

key unique_values
0   12345678901234567890    3
1   12345678901234567895    1
2   12345678901234567897    2

Getting the count unique values into data frame

df.groupby("key").agg(**{"unique_values": pd.NamedAgg(column='cfop', aggfunc="nunique")}).reset_index()

key unique_values
0   12345678901234567890    2
1   12345678901234567895    1
2   12345678901234567897    2

But FAIL adding a new column

df['unique_list'] = df.groupby('key')["cfop"].unique()

df['unique_count'] = df.groupby("key").agg(**{"unique_values": pd.NamedAgg(column='cfop', aggfunc="nunique")}).reset_index()
df['not_unique_count'] =df.groupby("key").agg(**{"unique_values": pd.NamedAgg(column='cfop', aggfunc="size")}).reset_index()

Answer 1

Try:

tmp = (
    df.groupby("key")["cfop_code"]
    .agg(
        unique_list = lambda s: sorted(s.unique()), 
        unique_count = "nunique", 
        not_unique_count = "size")
    .reset_index()
)
res = df.merge(tmp, on="key")

print(res)
                    key    product  cfop_code   unique_list  unique_count  not_unique_count
0  12345678901234567890  product a       2551  [2551, 3551]             2                 3
1  12345678901234567890  product b       2551  [2551, 3551]             2                 3
2  12345678901234567890  product c       3551  [2551, 3551]             2                 3
3  12345678901234567895  product a       2551        [2551]             1                 1
4  12345678901234567897  product b       2551  [2407, 2551]             2                 2
5  12345678901234567897  product c       2407  [2407, 2551]             2                 2

The problem with your attempt is that:

df.groupby("key").agg(**{"unique_values": pd.NamedAgg(column='cfop_code', aggfunc="nunique")}).reset_index()

returns a DataFrame.You try to assign this whole DataFrame to a new column which fails.