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
CodePudding user response:
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
CodePudding user response:
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