Pandas: Get Nth row or every Nth row in a DataFrame

# Table of Contents

# Get the Nth row in a Pandas DataFrame

Use the DataFrame.iloc integer-based indexer to get the Nth row in a Pandas DataFrame.

You can specify the index in two sets of square brackets to get a DataFrame result or one set to get the output in a Series.

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan', 'Ethan'],
    'experience': [1, 3, 5, 7, 9],
    'salary': [175.1, 180.2, 190.3, 205.4, 210.5],
})

print(df)

print('-' * 50)

first_row = df.iloc[[0]]
print(first_row)

print('-' * 50)

second_row = df.iloc[[1]]
print(second_row)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
    name  experience  salary
0  Alice           1   175.1
1  Bobby           3   180.2
2   Carl           5   190.3
3    Dan           7   205.4
4  Ethan           9   210.5
--------------------------------------------------
    name  experience  salary
0  Alice           1   175.1
--------------------------------------------------
    name  experience  salary
1  Bobby           3   180.2

get nth row in pandas dataframe

Note that indexes are zero-based, so use 0 to select the first row and 1 to select the second row.

When you use two sets of square brackets, you get the corresponding row in a DataFrame.

main.py

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first_row = df.iloc[[0]]

#     name  experience  salary
# 0  Alice           1   175.1
print(first_row)

If you want to get the result in a Series, use one set of square brackets.

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan', 'Ethan'],
    'experience': [1, 3, 5, 7, 9],
    'salary': [175.1, 180.2, 190.3, 205.4, 210.5],
})

print(df)

print('-' * 50)

first_row = df.iloc[0]
print(first_row)

print('-' * 50)

second_row = df.iloc[1]
print(second_row)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
    name  experience  salary
0  Alice           1   175.1
1  Bobby           3   180.2
2   Carl           5   190.3
3    Dan           7   205.4
4  Ethan           9   210.5
--------------------------------------------------
name          Alice
experience        1
salary        175.1
Name: 0, dtype: object
--------------------------------------------------
name          Bobby
experience        3
salary        180.2
Name: 1, dtype: object

get nth row in dataframe as series

# Getting the Nth row of a DataFrame using `DataFrame.take()`

You can also use the DataFrame.take() method to get the Nth row of a DataFrame.

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan', 'Ethan'],
    'experience': [1, 3, 5, 7, 9],
    'salary': [175.1, 180.2, 190.3, 205.4, 210.5],
})

print(df)

print('-' * 50)

first_row = df.take([0])
print(first_row)

print('-' * 50)

second_row = df.take([1])
print(second_row)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
    name  experience  salary
0  Alice           1   175.1
1  Bobby           3   180.2
2   Carl           5   190.3
3    Dan           7   205.4
4  Ethan           9   210.5
--------------------------------------------------
    name  experience  salary
0  Alice           1   175.1
--------------------------------------------------
    name  experience  salary
1  Bobby           3   180.2

get nth row of dataframe using take

The DataFrame.take() method returns the elements in the specified indices along an axis.

Notice that we call the take() method with a list containing an index and not with the index directly.

main.py

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first_row = df.take([0])
print(first_row)

# Getting every Nth row in a Pandas DataFrame

Use the DataFrame.iloc integer-based indexer to get every Nth row in a Pandas DataFrame.

The expression with return a new DataFrame that only contains every Nth row.

The following code sample selects every 2nd row of the DataFrame.

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan', 'Ethan'],
    'experience': [1, 3, 5, 7, 9],
    'salary': [175.1, 180.2, 190.3, 205.4, 210.5],
})

print(df)

print('-' * 50)

every_second_row = df.iloc[::2, :]
print(every_second_row)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
    name  experience  salary
0  Alice           1   175.1
1  Bobby           3   180.2
2   Carl           5   190.3
3    Dan           7   205.4
4  Ethan           9   210.5
--------------------------------------------------
    name  experience  salary
0  Alice           1   175.1
2   Carl           5   190.3
4  Ethan           9   210.5

get every nth row in pandas dataframe

We used the DataFrame.iloc integer-based indexer to select every second row of the DataFrame.

main.py

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# 👇️ selects every second row
every_second_row = df.iloc[::2, :]

The same approach can be used if you want to select every third row.

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan', 'Ethan'],
    'experience': [1, 3, 5, 7, 9],
    'salary': [175.1, 180.2, 190.3, 205.4, 210.5],
})

print(df)

print('-' * 50)

every_third_row = df.iloc[::3, :]
print(every_third_row)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
    name  experience  salary
0  Alice           1   175.1
1  Bobby           3   180.2
2   Carl           5   190.3
3    Dan           7   205.4
4  Ethan           9   210.5
--------------------------------------------------
    name  experience  salary
0  Alice           1   175.1
3    Dan           7   205.4

select every third row of dataframe

You can also omit the column part in the expression (after the comma, between the square brackets).

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan', 'Ethan'],
    'experience': [1, 3, 5, 7, 9],
    'salary': [175.1, 180.2, 190.3, 205.4, 210.5],
})

print(df)

print('-' * 50)

# 👇️ omitting column part
every_second_row = df.iloc[::2]
print(every_second_row)

omitting column part in expression

# Select every Nth row of DataFrame, starting at row X

You can use the same approach to select every Nth row of a DataFrame, starting at row X.

The following code sample selects every second row, starting at row index 1.

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan', 'Ethan'],
    'experience': [1, 3, 5, 7, 9],
    'salary': [175.1, 180.2, 190.3, 205.4, 210.5],
})

print(df)

print('-' * 50)

every_second_row = df.iloc[1::2, :]
print(every_second_row)

The code for this article is available on GitHub

Running the code sample produces the following result.

shell

Copied!
    name  experience  salary
0  Alice           1   175.1
1  Bobby           3   180.2
2   Carl           5   190.3
3    Dan           7   205.4
4  Ethan           9   210.5
--------------------------------------------------
    name  experience  salary
1  Bobby           3   180.2
3    Dan           7   205.4

select every nth row in dataframe starting at specific row

Similarly, the following code sample selects every 3rd row, starting at row index 1.

main.py

Copied!
import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan', 'Ethan'],
    'experience': [1, 3, 5, 7, 9],
    'salary': [175.1, 180.2, 190.3, 205.4, 210.5],
})

print(df)

print('-' * 50)

every_second_row = df.iloc[1::3, :]
print(every_second_row)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
    name  experience  salary
0  Alice           1   175.1
1  Bobby           3   180.2
2   Carl           5   190.3
3    Dan           7   205.4
4  Ethan           9   210.5
--------------------------------------------------
    name  experience  salary
1  Bobby           3   180.2
4  Ethan           9   210.5

# Get every Nth row in DataFrame using modulo operator

You can also use the modulo (%) operator to select every Nth row in a DataFrame.

main.py

Copied!
import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan', 'Ethan'],
    'experience': [1, 3, 5, 7, 9],
    'salary': [175.1, 180.2, 190.3, 205.4, 210.5],
})

print(df)

print('-' * 50)

every_second_row = df[df.index % 2 == 0]
print(every_second_row)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
    name  experience  salary
0  Alice           1   175.1
1  Bobby           3   180.2
2   Carl           5   190.3
3    Dan           7   205.4
4  Ethan           9   210.5
--------------------------------------------------
    name  experience  salary
0  Alice           1   175.1
2   Carl           5   190.3
4  Ethan           9   210.5

get every nth row in dataframe using modulo operator

We check if the remainder of dividing each index by 2 is equal to 0.

If the condition is met, the row gets included in the resulting DataFrame.

If your index doesn't start at 0, you might have to call reset_index().

main.py

Copied!
import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan', 'Ethan'],
    'experience': [1, 3, 5, 7, 9],
    'salary': [175.1, 180.2, 190.3, 205.4, 210.5],
})

print(df)

print('-' * 50)

every_second_row = df[df.reset_index().index % 2 == 0]
print(every_second_row)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
    name  experience  salary
0  Alice           1   175.1
1  Bobby           3   180.2
2   Carl           5   190.3
3    Dan           7   205.4
4  Ethan           9   210.5
--------------------------------------------------
    name  experience  salary
0  Alice           1   175.1
2   Carl           5   190.3
4  Ethan           9   210.5

# Exclude every Nth row from a Pandas DataFrame

You can use the same approach to exclude every Nth row in a DataFrame.

main.py

Copied!
import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan', 'Ethan'],
    'experience': [1, 3, 5, 7, 9],
    'salary': [175.1, 180.2, 190.3, 205.4, 210.5],
})

print(df)

print('-' * 50)

exclude_every_2nd = df[df.index % 2 != 0]
print(exclude_every_2nd)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
    name  experience  salary
0  Alice           1   175.1
1  Bobby           3   180.2
2   Carl           5   190.3
3    Dan           7   205.4
4  Ethan           9   210.5
--------------------------------------------------
    name  experience  salary
1  Bobby           3   180.2
3    Dan           7   205.4

exclude every nth row from pandas dataframe

We check if dividing each index by 2 does not have a remainder of 0.

If the condition is met, the rows get included in the result.

In practice, the code sample excludes every second row from the DataFrame, starting at index 0.

I've also written an article on how to select the first N or the last N columns of a DataFrame.

# Additional Resources

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Pandas: Get Nth row or every Nth row in a DataFrame

# Table of Contents

# Get the Nth row in a Pandas DataFrame

# Getting the Nth row of a DataFrame using DataFrame.take()

# Getting every Nth row in a Pandas DataFrame

# Select every Nth row of DataFrame, starting at row X

# Get every Nth row in DataFrame using modulo operator

# Exclude every Nth row from a Pandas DataFrame

# Additional Resources

Borislav Hadzhiev

# Getting the Nth row of a DataFrame using `DataFrame.take()`