How to repeat Rows N times in a Pandas DataFrame

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# How to repeat Rows N times in a Pandas DataFrame

Use the DataFrame.index.repeat() method to repeat the rows in a Pandas DataFrame N times.

The repeat() method will repeat each index in the DataFrame the specified number of times.

main.py

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import pandas as pd

df = pd.DataFrame({
    'first_name': ['Alice', 'Bobby', 'Carl'],
    'salary': [175.1, 180.2, 190.3],
})

df2 = df.loc[df.index.repeat(2)]

print(df2)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

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  first_name  salary
0      Alice   175.1
0      Alice   175.1
1      Bobby   180.2
1      Bobby   180.2
2       Carl   190.3
2       Carl   190.3

repeat rows n times in pandas dataframe

The index.repeat() method repeats the elements of an index.

main.py

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import pandas as pd

df = pd.DataFrame({
    'first_name': ['Alice', 'Bobby', 'Carl'],
    'salary': [175.1, 180.2, 190.3],
})

# 👇️ Index([0, 0, 1, 1, 2, 2], dtype='int64')
print(df.index.repeat(2))

The code for this article is available on GitHub

We then used the DataFrame.loc indexer to access the group of rows and columns by the indices.

main.py

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df2 = df.loc[df.index.repeat(2)]

#   first_name  salary
# 0      Alice   175.1
# 0      Alice   175.1
# 1      Bobby   180.2
# 1      Bobby   180.2
# 2       Carl   190.3
# 2       Carl   190.3
print(df2)

Notice that there are also repeat indices in the output.

If you want to reset the indices, use the DataFrame.reset_index() method.

main.py

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import pandas as pd

df = pd.DataFrame({
    'first_name': ['Alice', 'Bobby', 'Carl'],
    'salary': [175.1, 180.2, 190.3],
})

df2 = df.loc[df.index.repeat(2)].reset_index(drop=True)

print(df2)

Running the code sample produces the following output.

shell

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  first_name  salary
0      Alice   175.1
1      Alice   175.1
2      Bobby   180.2
3      Bobby   180.2
4       Carl   190.3
5       Carl   190.3

repeat rows n times in pandas dataframe and reset index

The DataFrame.reset_index method resets the index of the DataFrame, so the default index is used.

# Repeating each row N times in a DataFrame based on another column

The same approach can be used to repeat each row N times based on another column.

main.py

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import pandas as pd

df = pd.DataFrame({
    'first_name': ['Alice', 'Bobby', 'Carl'],
    'times': [1, 2, 3],
})

df2 = df.loc[df.index.repeat(df.times)].reset_index(drop=True)

#   first_name  times
# 0      Alice      1
# 1      Bobby      2
# 2      Bobby      2
# 3       Carl      3
# 4       Carl      3
# 5       Carl      3
print(df2)

repeat rows n times based on another column

The code for this article is available on GitHub

We used the times column to repeat each row N times.

The first row is not repeated, the second row is repeated once and the third row is repeated twice in the example.

The code sample also uses the reset_index() method to reset the index, however, this is optional.

# Repeat Rows N times in a Pandas DataFrame using `np.repeat()`

You can also use the numpy.repeat() method to repeat the rows of a DataFrame N times.

main.py

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import pandas as pd
import numpy as np

df = pd.DataFrame({
    'first_name': ['Alice', 'Bobby', 'Carl'],
    'salary': [175.1, 180.2, 190.3],
})

df2 = pd.DataFrame(np.repeat(df.values, 2, axis=0))

df2.columns = df.columns

print(df2)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

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  first_name salary
0      Alice  175.1
1      Alice  175.1
2      Bobby  180.2
3      Bobby  180.2
4       Carl  190.3
5       Carl  190.3

repeat rows n times using numpy repeat

Make sure you have the numpy module installed to be able to run the code sample.

shell

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pip install pandas numpy

# or with pip3
pip3 install pandas numpy

The numpy.repeat() method takes an input array, the number of repetitions and the axis as parameters and returns an array with the repeated values.

main.py

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import pandas as pd
import numpy as np

df = pd.DataFrame({
    'first_name': ['Alice', 'Bobby', 'Carl'],
    'salary': [175.1, 180.2, 190.3],
})

# [['Alice' 175.1]
#  ['Alice' 175.1]
#  ['Bobby' 180.2]
#  ['Bobby' 180.2]
#  ['Carl' 190.3]
#  ['Carl' 190.3]]
print(np.repeat(df.values, 2, axis=0))

We have to pass the array to the pandas.DataFrame() constructor and set the columns of the new DataFrame to the columns of the existing DataFrame.

main.py

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df2 = pd.DataFrame(np.repeat(df.values, 2, axis=0))

df2.columns = df.columns

You can also set the columns when initializing the new DataFrame.

main.py

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import pandas as pd
import numpy as np

df = pd.DataFrame({
    'first_name': ['Alice', 'Bobby', 'Carl'],
    'salary': [175.1, 180.2, 190.3],
})


df2 = pd.DataFrame(np.repeat(df.values, 2, axis=0), columns=df.columns)

#   first_name salary
# 0      Alice  175.1
# 1      Alice  175.1
# 2      Bobby  180.2
# 3      Bobby  180.2
# 4       Carl  190.3
# 5       Carl  190.3
print(df2)

The code for this article is available on GitHub

The code sample uses the column argument of the pandas.DataFrame() class to achieve the same result.

You can also use the loc indexer as we did in the first subheading.

main.py

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import pandas as pd
import numpy as np

df = pd.DataFrame({
    'first_name': ['Alice', 'Bobby', 'Carl'],
    'salary': [175.1, 180.2, 190.3],
})


df2 = df.loc[np.repeat(df.index, 2)].reset_index(drop=True)

#   first_name  salary
# 0      Alice   175.1
# 1      Alice   175.1
# 2      Bobby   180.2
# 3      Bobby   180.2
# 4       Carl   190.3
# 5       Carl   190.3
print(df2)

However, when using the df.loc indexer, we have to manually reset the indices of the DataFrame by calling reset_index().

# Repeat Rows N times in a Pandas DataFrame using `pd.concat()`

You can also use the pandas.concat method to repeat the rows in a DataFrame N times.

main.py

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import pandas as pd

df = pd.DataFrame({
    'first_name': ['Alice', 'Bobby', 'Carl'],
    'salary': [175.1, 180.2, 190.3],
})


df2 = pd.concat([df] * 2).sort_index().reset_index(drop=True)

#   first_name  salary
# 0      Alice   175.1
# 1      Alice   175.1
# 2      Bobby   180.2
# 3      Bobby   180.2
# 4       Carl   190.3
# 5       Carl   190.3
print(df2)

repeat dataframe rows n times using concat

The code for this article is available on GitHub

The pandas.concat() method concatenates Pandas objects along a particular axis.

main.py

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import pandas as pd

df = pd.DataFrame({
    'first_name': ['Alice', 'Bobby', 'Carl'],
    'salary': [175.1, 180.2, 190.3],
})

#   first_name  salary
# 0      Alice   175.1
# 1      Bobby   180.2
# 2       Carl   190.3
# 0      Alice   175.1
# 1      Bobby   180.2
# 2       Carl   190.3
print(pd.concat([df] * 2))

We then have to:

Use the sort_index method to sort the indices.
Use the reset_index() method to reset the indices of the DataFrame.

main.py

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import pandas as pd

df = pd.DataFrame({
    'first_name': ['Alice', 'Bobby', 'Carl'],
    'salary': [175.1, 180.2, 190.3],
})


df2 = pd.concat([df] * 2).sort_index().reset_index(drop=True)

#   first_name  salary
# 0      Alice   175.1
# 1      Alice   175.1
# 2      Bobby   180.2
# 3      Bobby   180.2
# 4       Carl   190.3
# 5       Carl   190.3
print(df2)

repeat dataframe rows n times using concat

The code for this article is available on GitHub

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How to repeat Rows N times in a Pandas DataFrame

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