Exclude particular column in Dataframe in Python

By Rani Bane

In this article, we will study how to exclude a particular column in Dataframe using Python.

Let us create DataFrame. For this, we first need to import Pandas. Pandas is an open source Python library. It allows us to create and manipulate data. Look at the following code:

import pandas as pd

details_of_employee = {"Name":["Ruchita","Avni","Deepak","Vish"],
                      "Age" :[23,45,21,39],
                      "Designation":["C.A","PHP Developer","Android Developer","Data Scientist"],
                      "Salary":[34000,45000,56000,89000],
                      "Experience":[2,3,6,7]}

df = pd.DataFrame(details_of_employee)

print(df)

OUTPUT

Name

Age

Designation

Salary

Experience

0

Ruchita

23

C.A

34000

2

1

Avni

45

PHP Developer

45000

3

2

Deepak

21

Android Developer

56000

6

3

Vish

39

Data Scientist

89000

7

We will perform all the operations on this DataFrame.

Exclude particular column from a DataFrame in Python

Let us now look at ways to exclude particluar column of pandas dataframe using Python.

(i) dataframe.columns.difference()

The dataframe.columns.difference() provides the difference of the values which we pass as arguments. It excludes particular column from the existing dataframe and creates new dataframe. Look at the following code:

new_df = df[df.columns.difference(['Experience'])]
print(new_df)

OUTPUT

Age

Designation

Name

Salary

0

23

C.A

Ruchita

34000

1

45

PHP Developer

Avni

45000

2

21

Android Developer

Deepak

56000

3

39

Data Scientist

Vish

89000

In this case, we have passed the column “Experience” as an argument. Hence, a new dataframe is created by excluding “Experience” column.

(ii) dataframe.columns != ‘column_name’

The dataframe.columns != ‘column_name’ excludes the column which is passed to “column_name”. This can be achieved using dataframe.loc. This function access group of rows and columns respectively. Look at the following code:

new_df = df.loc[:, df.columns != 'Age']
print(new_df)

OUTPUT

Name

Designation

Salary

Experience

0

Ruchita

C.A

34000

2

1

Avni

PHP Developer

45000

3

2

Deepak

Android Developer

56000

6

3

Vish

Data Scientist

89000

7

DataFrame.loc takes rows and column respectively. In this case, “:” indicates all rows and df.columns != ‘Age’ indicates all columns except “Age”. Hence, a new dataframe is created by excluding “Age” column.

(iii) ~dataframe.columns.isin([‘column_name’])

The dataframe.columns.isin() selects the columns which are passed into the function as an argument. Therefore, ~dataframe.columns.isin() will exclude the column which is passed as an argument and it will select rest of the columns. This can be achieved using dataframe.loc. Look at the following code:

new_df = df.loc[:, ~df.columns.isin(['Salary'])]
print(new_df)

OUTPUT

Name

Age

Designation

Experience

0

Ruchita

23

C.A

2

1

Avni

45

PHP Developer

3

2

Deepak

21

Android Developer

6

3

Vish

39

Data Scientist

7

DataFrame.loc takes rows and column respectively. In this case, “:” indicates all rows and ~df.columns.isin([‘Salary’]) indicates all columns except “Salary”. Hence, a new dataframe is created by excluding “Salary” column.

In this way, we can exclude particular column from DataFrame using Python.

Thank You.

You may also read: How to convert DataFrame into List using Python?