Sample df:
In [2004]: df
Out[2004]:
index table_name column_name data_type default max_length
0 0 f_person active integer NaN NaN
1 0 f_person actv integer NaN NaN
2 5 f_person ssn varchar NaN 256.0
3 5 f_person ssn varchar NaN 99.0
4 6 f_person pl varchar 10.0 256.0
5 6 f_person pl bigint NaN 256.0
6 8 f_person prefix varchar NaN 256.0
7 8 f_person prefix integer NaN 256.0
For the same index, I want to add a new column schema and populate different values for each row. Number of rows per group will be always <= 2.
Expected Output:
In [2006]: df
Out[2006]:
index table_name column_name data_type default max_length schema
0 0 f_person active integer NaN NaN s1
1 0 f_person actv integer NaN NaN s2
2 5 f_person ssn varchar NaN 256.0 s1
3 5 f_person ssn varchar NaN 99.0 s2
4 6 f_person pl varchar 10.0 256.0 s1
5 6 f_person pl bigint NaN 256.0 s2
6 8 f_person prefix varchar NaN 256.0 s1
7 8 f_person prefix integer NaN 256.0 s2
I solved it using a for loop, but there must be a better way. Can someone please suggest a more pandaic way?
>Solution :
Assuming you want to populate from a list a defined values:
values = ['s1', 's2']
d = dict(enumerate(values))
df['schema'] = df.groupby('index').cumcount().map(d)
Otherwise, this is already covered in previous questions
output:
index table_name column_name data_type default max_length schema
0 0 f_person active integer NaN NaN s1
1 0 f_person actv integer NaN NaN s2
2 5 f_person ssn varchar NaN 256.0 s1
3 5 f_person ssn varchar NaN 99.0 s2
4 6 f_person pl varchar 10.0 256.0 s1
5 6 f_person pl bigint NaN 256.0 s2
6 8 f_person prefix varchar NaN 256.0 s1
7 8 f_person prefix integer NaN 256.0 s2