Pandas: Select first N or last N columns of DataFrame

# Table of Contents

# Pandas: Select first N columns of DataFrame

Use the DataFrame.iloc integer-based indexer to select the first N columns of a DataFrame in Pandas.

You can specify the n value after the comma, in the expression.

main.py

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import pandas as pd


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

print(df)

print('-' * 50)

first_2_columns = df.iloc[:, :2]
print(first_2_columns)

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           1   180.2
2   Carl           5   190.3
3    Dan           7   205.4
4  Ethan           7   210.5
--------------------------------------------------
    name  experience
0  Alice           1
1  Bobby           1
2   Carl           5
3    Dan           7
4  Ethan           7

select first n columns of dataframe

The DataFrame.loc indexer is used for selection by position (index).

We specified the n value after the comma in the expression.

main.py

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import pandas as pd


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

print(df)

print('-' * 50)

n = 2

first_2_columns = df.iloc[:, :n]

#     name  experience
# 0  Alice           1
# 1  Bobby           1
# 2   Carl           5
# 3    Dan           7
# 4  Ethan           7
print(first_2_columns)

extract n value in variable

The code for this article is available on GitHub

If you have to do this often, define a reusable function.

main.py

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import pandas as pd


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


def select_first_n_rows(data_frame, n):
    return data_frame.iloc[:, :n]


print(select_first_n_rows(df, 2))

print('-' * 50)

print(select_first_n_rows(df, 1))

Here is the output of running the code sample.

shell

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    name  experience
0  Alice           1
1  Bobby           1
2   Carl           5
3    Dan           7
4  Ethan           7
--------------------------------------------------
    name
0  Alice
1  Bobby
2   Carl
3    Dan
4  Ethan

define reusable function to select first n columns

# Pandas: Select last N columns of DataFrame

You can also use the DataFrame.iloc position-based indexer to select the last N columns of a DataFrame.

main.py

Copied!
import pandas as pd


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


last_2_columns = df.iloc[:, -2:]

#    experience  salary
# 0           1   175.1
# 1           1   180.2
# 2           5   190.3
# 3           7   205.4
# 4           7   210.5
print(last_2_columns)

select last n columns of dataframe

The code for this article is available on GitHub

The code sample selects the last 2 columns of the DataFrame.

Notice that we used -n between the square brackets.

If you have to do this often, define a reusable function.

main.py

Copied!
import pandas as pd


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


def last_n_columns(data_frame, n):
    return data_frame.iloc[:, -n:]


print(last_n_columns(df, 2))

print('-' * 50)

print(last_n_columns(df, 1))

Running the code sample produces the following output.

shell

Copied!
   experience  salary
0           1   175.1
1           1   180.2
2           5   190.3
3           7   205.4
4           7   210.5
--------------------------------------------------
   salary
0   175.1
1   180.2
2   190.3
3   205.4
4   210.5

select last n columns of dataframe with reusable function

The function takes the DataFrame and n as parameters and returns the last n columns of the DataFrame.

# Exclude the last N columns from a `DataFrame`

A similar approach can be used to exclude the last N columns from a DataFrame.

main.py

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import pandas as pd


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


print(df)

print('-' * 50)

exclude_last_2_columns = df.iloc[:, :-2]
print(exclude_last_2_columns)

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           1   180.2
2   Carl           5   190.3
3    Dan           7   205.4
4  Ethan           7   210.5
--------------------------------------------------
    name
0  Alice
1  Bobby
2   Carl
3    Dan
4  Ethan

exclude last n columns from dataframe

We excluded the last 2 columns from the DataFrame.

If you have to do this often, define a reusable function.

main.py

Copied!
import pandas as pd


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


def exclude_last_n_columns(data_frame, n):
    return data_frame.iloc[:, :-n]


print(exclude_last_n_columns(df, 2))

print('-' * 50)

print(exclude_last_n_columns(df, 1))

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
    name
0  Alice
1  Bobby
2   Carl
3    Dan
4  Ethan
--------------------------------------------------
    name  experience
0  Alice           1
1  Bobby           1
2   Carl           5
3    Dan           7
4  Ethan           7

exclude last n columns from data frame using reusable function

# Select the Last N columns of a `DataFrame` using `DataFrame.columns`

You can also use slicing with the DataFrame.columns attribute to select the last N columns of a DataFrame.

main.py

Copied!
import pandas as pd


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

print(df)

print('-' * 50)

last_2_columns = df[df.columns[-2:]]

print(last_2_columns)

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           1   180.2
2   Carl           5   190.3
3    Dan           7   205.4
4  Ethan           7   210.5
--------------------------------------------------
   experience  salary
0           1   175.1
1           1   180.2
2           5   190.3
3           7   205.4
4           7   210.5

extract last n columns of dataframe using columns attribute

The code sample selects the last 2 columns of the DataFrame using the DataFrame.columns attribute.

The attribute returns an index that contains the column labels of the DataFrame.

main.py

Copied!
import pandas as pd


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

# Index(['name', 'experience', 'salary'], dtype='object')
print(df.columns)

The code for this article is available on GitHub

I've also written an article on how to get the Nth row or every Nth row in a DataFrame.

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Pandas: Select first N or last N columns of DataFrame

# Table of Contents

# Pandas: Select first N columns of DataFrame

# Pandas: Select last N columns of DataFrame

# Exclude the last N columns from a DataFrame

# Select the Last N columns of a DataFrame using DataFrame.columns

# Additional Resources

Borislav Hadzhiev

# Exclude the last N columns from a `DataFrame`

# Select the Last N columns of a `DataFrame` using `DataFrame.columns`