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?

Asked By: aabujamra

||

Source

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

Answered By: BENY

Answer 2

Wen’s solution is really nice and intuitive, however it will fail for duplicate rows by throwing ValueError: cannot reindex from a duplicate axis.

Here’s an alternative which avoids this by 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

Answered By: cs95

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)

Answered By: SultanOrazbayev

Answer 4

You can apply the Series method repeat:

df = pd.DataFrame({'col1': [2, 3],
                   'col2': ['a', 'b'],
                   'col3': [20, 30]})

df.apply(lambda x: x.repeat(df['col1']))
# df.apply(pd.Series.repeat, repeats=df['col1'])

or the numpy function repeat:

df.apply(np.repeat, repeats=df['col1'])

Output:

   col1 col2  col3
0     2    a    20
0     2    a    20
1     3    b    30
1     3    b    30
1     3    b    30

Answered By: Mykola Zotko

Repeat rows in a pandas DataFrame based on column value

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