Limited Rows Selection with given Column in Pandas - Python

Selecting specific rows using numeric positions allows you to retrieve one or more rows based on their 0-based index. In this article, we demonstrate two methods to do this in Pandas: iloc[] and iat[], with concise examples for each.

Using loc[]

The loc[] method selects rows and columns based on labels (row index and column names).

Example 1: In this example, we select rows 1 to 3 and only the columns Name and Department.

 Name Department
1 Riya IT
2 Karan Finance
3 Neha Marketing

Explanation: Here df.loc[1:3, ['Name','Department']] fetches rows 1 to 3 and only the selected columns.

Example 2: This program retrieves the row at index 2 with all its column values.

Output

Name Karan
Age 30
Department Finance
Salary 55000
Name: 2, dtype: object

Explanation: The : selects all columns of row index 2.

Using iloc[]

The iloc[] method selects rows and columns based on integer positions.

Example 1: In this example, we select the first three rows and columns at positions 0 and 2.

Output

Name Department
0 Amit HR
1 Riya IT
2 Karan Finance

Explanation: The code picks rows 0-2 and only the first (Name) and third (Department) columns.

Example 2: This code selects rows at positions 1 and 4 with columns at positions 1 and 3.

Output

Age Salary
1 24 60000
4 29 70000

Explanation: iloc[[1,4], [1,3]] fetches row 1 and 4 and columns Age and Salary.