How to Exclude Columns in Pandas?

Excluding columns in a Pandas DataFrame is a common operation when you want to work with only relevant data. In this article, we will discuss various methods to exclude columns from a DataFrame, including using .loc[], .drop(), and other techniques.

Exclude One Column using .loc[]

We can exclude a column by its location using the .loc [] function. The code below demonstrates how to exclude a specific column by comparing column names.

Output:

DataFrame after excluding 'cost' column using .loc[]

 food_id name city
0 1 idly delhi
1 2 dosa goa
2 3 poori hyd
3 4 chapathi chennai

If we need to exclude multiple columns, we can use .isin() function.

Output

 city cost
0 delhi 12
1 goa 34
2 hyd 21
3 chennai 23

Other methods to exclude columns in Pandas, apart from using DataFrame.loc[], are discussed below:

Excluding Columns Using .remove()

Although .remove() is not a direct Pandas method, we can use it with a list of column names before reassigning the DataFrame.

Output

 food_id name city
0 1 idly delhi
1 2 dosa goa
2 3 poori hyd
3 4 chapathi chennai

Excluding Columns by Name Using drop()

drop() method is one of the most common ways to exclude specific columns by name:

Output

 food_id name city
0 1 idly delhi
1 2 dosa goa
2 3 poori hyd
3 4 chapathi chennai

Exclude Columns by Data Type Using select_dtypes()

We can exclude columns based on their data type using the select_dtypes() method.

Output

 name city
0 idly delhi
1 dosa goa
2 poori hyd
3 chapathi chennai

Excluding Columns Dynamically Using List Comprehensions

If we need to exclude columns dynamically based on a condition, we can use list comprehensions. For example, to exclude columns whose names start with a specific letter:

Output

 food_id name
0 1 idly
1 2 dosa
2 3 poori
3 4 chapathi

These techniques make column exclusion in Pandas flexible and straightforward, helping us focus on the data we need.