Efficient way to use apply a function on Pandas rows

Question

I am looking for an efficient way to apply a function on each row of a dataframe to perform some operation and repeat the row by a number defined in other column. Currently, I am doing it by iterate on each row, but it takes too long on a large dataframe.

Sample code is as below:

`import pandas as pd
def my_func(row):
    row = row.to_frame().T
    repeated_row = row.loc[row.index.repeat(row['col2'])]
    return repeated_row
df = pd.DataFrame(data = {'col1':list('abc'),
                          'col2': [2,2,3]})
df_comb = pd.DataFrame()
for i, row in df.iterrows():
    df_rep = my_func(row)
    df_comb = pd.concat([df_comb, df_rep], axis=0)`

However, I want a solution that's not using the for loop as above and I couldn't find an answer for this historically. I imagine there will be an equivalent way to use "apply" function to this df, such as:

df_comp = pd.concat([df.apply(lambda row: my_func(row)), axis=1], axis=0)

But at the moment this syntax does not work properly.

Much appreciated if you could point out the correct solution.