Repeat or replicate the rows of dataframe in pandas python (create duplicate rows)

Repeat or replicate the rows of dataframe in pandas python (create duplicate rows) can be done in a roundabout way by using concat() function. Let’s see how to

  •  Repeat or replicate the dataframe in pandas python.
  • Repeat or replicate the dataframe in pandas along with index.

With examples

First let’s create a dataframe


import pandas as pd
import numpy as np

#Create a DataFrame
df1 = {
    'State':['Arizona AZ','Georgia GG','Newyork NY','Indiana IN','Florida FL'],
   'Score':[62,47,55,74,31]}

df1 = pd.DataFrame(df1,columns=['State','Score'])
print(df1)

df1 will be

Repeat or replicate the dataframe in pandas python 1

 

Repeat or replicate the rows of dataframe in pandas python:

Repeat the dataframe 3 times with concat function.  Ignore_index=True does not repeat the index. So new index will be created for the repeated columns

''' Repeat without index '''
df_repeated = pd.concat([df1]*3, ignore_index=True)
print(df_repeated)

So the resultant dataframe will be

Repeat or replicate the dataframe in pandas python 2

 

Repeat or replicate the dataframe in pandas with index:

Concat function repeats the dataframe in pandas with index. So index will also be repeated

''' Repeat with index'''
df_repeated_with_index = pd.concat([df1]*2)
print(df_repeated_with_index)

So the resultant dataframe will be

Repeat or replicate the dataframe in pandas python 3

 

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