Exclude particular column in Dataframe in Python
By Rani BaneIn 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?
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