Repeat rows in a pandas DataFrame based on column value

Question

I have the following df:

code . role    . persons
123 .  Janitor . 3
123 .  Analyst . 2
321 .  Vallet  . 2
321 .  Auditor . 5

The first line means that I have 3 persons with the role Janitors. My problem is that I would need to have one line for each person. My df should look like this:

df:

code . role    . persons
123 .  Janitor . 3
123 .  Janitor . 3
123 .  Janitor . 3
123 .  Analyst . 2
123 .  Analyst . 2
321 .  Vallet  . 2
321 .  Vallet  . 2
321 .  Auditor . 5
321 .  Auditor . 5
321 .  Auditor . 5
321 .  Auditor . 5
321 .  Auditor . 5

How could I do that using pandas?

Answer 1

reindex+ repeat

df.reindex(df.index.repeat(df.persons))
Out[951]: 
   code  .     role ..1  persons
0   123  .  Janitor   .        3
0   123  .  Janitor   .        3
0   123  .  Janitor   .        3
1   123  .  Analyst   .        2
1   123  .  Analyst   .        2
2   321  .   Vallet   .        2
2   321  .   Vallet   .        2
3   321  .  Auditor   .        5
3   321  .  Auditor   .        5
3   321  .  Auditor   .        5
3   321  .  Auditor   .        5
3   321  .  Auditor   .        5

PS: you can add.reset_index(drop=True) to get the new index

Answer 2

Wen's solution is really nice and intuitive. Here's an alternative, calling repeat on df.values.

df

   code     role  persons
0   123  Janitor        3
1   123  Analyst        2
2   321   Vallet        2
3   321  Auditor        5


pd.DataFrame(df.values.repeat(df.persons, axis=0), columns=df.columns)

   code     role persons
0   123  Janitor       3
1   123  Janitor       3
2   123  Janitor       3
3   123  Analyst       2
4   123  Analyst       2
5   321   Vallet       2
6   321   Vallet       2
7   321  Auditor       5
8   321  Auditor       5
9   321  Auditor       5
10  321  Auditor       5
11  321  Auditor       5

Answer 3

Not enough reputation to comment, but building on @cs95's answer and @lmiguelvargasf's comment, one can preserve dtypes with:

pd.DataFrame(
    df.values.repeat(df.persons, axis=0),
    columns=df.columns,
).astype(df.dtypes)