Apply function to pandas DataFrame that can return multiple rows

Question

I am trying to transform DataFrame, such that some of the rows will be replicated a given number of times. For example:

df = pd.DataFrame({\'class\': [\'A\',         


        

Answer 1

There is even a simpler and significantly more efficient solution. I had to make similar modification for a table of about 3.5M rows, and the previous suggested solutions were extremely slow.

A better way is to use numpy's repeat procedure for generating a new index in which each row index is repeated multiple times according to its given count, and use iloc to select rows of the original table according to this index:

import pandas as pd
import numpy as np

df = pd.DataFrame({'class': ['A', 'B', 'C'], 'count': [1, 0, 2]})
spread_ixs = np.repeat(range(len(df)), df['count'])
spread_ixs 

array([0, 2, 2])

df.iloc[spread_ixs, :].drop(columns='count').reset_index(drop=True)

  class
0     A
1     C
2     C