Duplicate row based on value in different column

Question

I have a dataframe of transactions. Each row represents a transaction of two item (think of it like a transaction of 2 event tickets or something). I want to duplicate each row based on the quantity sold.

Here's example code:

# dictionary of transactions  d = {     '1': ['20',  'NYC', '2'],     '2': ['30',  'NYC', '2'],     '3': ['5',   'NYC', '2'],     '4': ['300', 'LA',  '2'],     '5': ['30',  'LA',  '2'],     '6': ['100', 'LA',  '2'] }  columns=['Price', 'City', 'Quantity']  # create dataframe and rename columns  df = pd.DataFrame.from_dict(     data=d, orient='index' ) df.columns = columns 

This produces a dataframe that looks like this

Price   City    Quantity 20       NYC         2 30       NYC         2 5        NYC         2 300      LA          2 30       LA          2 100      LA          2 

So in the case above, each row will transform into two duplicate rows. If the 'quantity' column was 3, then that row would transform into three duplicate rows.

Answer 1

Answer by using repeat

df.loc[df.index.repeat(df.Quantity)] Out[448]:    Price City Quantity 1    20  NYC        2 1    20  NYC        2 2    30  NYC        2 2    30  NYC        2 3     5  NYC        2 3     5  NYC        2 4   300   LA        2 4   300   LA        2 5    30   LA        2 5    30   LA        2 6   100   LA        2 6   100   LA        2