Create dataframe with Repeated rows based on column value

Question

I am trying to expand out a dataset that has two columns and expand it out in python.

Basket        | Times 
______________|_______
Bread         | 5     
Orange, Bread | 3

I would like, based on the number in the Times column that many rows. So for the example above

Newcolumn  
_______ 
Bread1
Bread2
Bread3
Bread4
Bread5   
Orange, Bread1
Orange, Bread2
Orange, Bread3

Ynjxsjmh · Accepted Answer · 2022-05-09 16:35:18Z

1

You can try apply on rows to generate desired list and explode the column

df['Newcolumn'] = df.apply(lambda row: [f"{row['Basket']}_{i+1}" for i in range(row['Times'])], axis=1)
df = df.explode('Newcolumn', ignore_index=True)

print(df)

          Basket  Times        Newcolumn
0          Bread      5          Bread_1
1          Bread      5          Bread_2
2          Bread      5          Bread_3
3          Bread      5          Bread_4
4          Bread      5          Bread_5
5  Orange, Bread      3  Orange, Bread_1
6  Orange, Bread      3  Orange, Bread_2
7  Orange, Bread      3  Orange, Bread_3

answered May 9, 2022 at 16:35

Ynjxsjmh

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not_speshal · Accepted Answer · 2022-05-09 16:23:59Z

1

Use np.repeat to repeat each value the required number of times. Then groupby and cumcount to add the required suffixes:

import numpy as np
srs = np.repeat(df["Basket"],df["Times"])

output = (srs+srs.groupby(level=0).cumcount().add(1).astype(str)).reset_index(drop=True)

>>> output
0            Bread1
1            Bread2
2            Bread3
3            Bread4
4            Bread5
5    Orange, Bread1
6    Orange, Bread2
7    Orange, Bread3
dtype: object

answered May 9, 2022 at 16:23

not_speshal

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