Difference between loc() and iloc() in Pandas DataFrame

Pandas library of Python is very useful for the manipulation of mathematical data and is widely used in the field of machine learning. It comprises many methods for its proper functioning. loc() and iloc() are one of those methods. These are used in slicing data from the Pandas DataFrame. They help in the convenient selection of data from the DataFrame in Python. They are used in filtering the data according to some conditions. 

Difference between loc() and iloc() in Pandas DataFrame

Here, we will see the difference between loc() and iloc() Function in Pandas DataFrame. To see and compare the difference between these two, we will create a sample Dataframe that we will use in the whole paragraph. The working of both of these methods is explained in the sample dataset of cars.

Output

     Brand  Year  Kms Driven       City  Mileage
0   Maruti  2012       50000    Gurgaon       28
1  Hyundai  2014       30000      Delhi       27
2     Tata  2011       60000     Mumbai       25
3  Mahindra  2015       25000      Delhi       26
4   Maruti  2012       10000     Mumbai       28
5  Hyundai  2016       46000      Delhi       29
6  Renault  2014       31000     Mumbai       24
7     Tata  2018       15000    Chennai       21
8   Maruti  2019       12000  Ghaziabad       24

Python loc() function

The loc() function is label based data selecting method which means that we have to pass the name of the row or column which we want to select. This method includes the last element of the range passed in it, unlike iloc(). loc() can accept the boolean data unlike iloc(). Many operations can be performed using the loc() method like

Example 1: Selecting Data According to Some Conditions

In this example, the code uses the loc function to select and display rows from the DataFrame where the brand is ‘Maruti’ and the mileage is greater than 25, showing relevant information about Maruti cars with high mileage.

Output

    Brand  Year  Kms Driven       City  Mileage
0  Maruti  2012       50000    Gurgaon       28
4  Maruti  2012       10000     Mumbai       28

Example 2: Selecting a Range of Rows From the DataFrame

In this example, the code utilizes the loc function to extract and display rows with indices ranging from 2 to 5 (inclusive) from the DataFrame, providing information about a specific range of cars in the dataset.

Output

     Brand  Year  Kms Driven    City  Mileage
2     Tata  2011       60000  Mumbai       25
3  Mahindra  2015       25000   Delhi       26
4   Maruti  2012       10000  Mumbai       28
5  Hyundai  2016       46000   Delhi       29

Example 3: Updating the Value of Any Column

In this example, the code uses the loc function to update the ‘Mileage’ values to 22 for cars in the DataFrame where the manufacturing year is before 2015. The modified DataFrame is then displayed, reflecting the changes made to the Mileage column.

Output

     Brand  Year  Kms Driven       City  Mileage
0   Maruti  2012       50000    Gurgaon       22
1  Hyundai  2014       30000      Delhi       22
2     Tata  2011       60000     Mumbai       22
3  Mahindra  2015       25000      Delhi       26
4   Maruti  2012       10000     Mumbai       22
5  Hyundai  2016       46000      Delhi       29
6  Renault  2014       31000     Mumbai       22
7     Tata  2018       15000    Chennai       22
8   Maruti  2019       12000  Ghaziabad       22

Python iloc() function

The iloc() function is an indexed-based selecting method which means that we have to pass an integer index in the method to select a specific row/column. This method does not include the last element of the range passed in it unlike loc(). iloc() does not accept the boolean data unlike loc(). Operations performed using iloc() are:

Example 1: Selecting Rows Using Integer Indices

In this example, the code employs the iloc function to extract and display specific rows with indices 0, 2, 4, and 7 from the DataFrame, showcasing information about selected cars in the dataset.

Output

     Brand  Year  Kms Driven       City  Mileage
0   Maruti  2012       50000    Gurgaon       28
2     Tata  2011       60000     Mumbai       25
4   Maruti  2012       10000     Mumbai       28
7     Tata  2018       15000    Chennai       21

Example 2: Selecting a Range of Columns and Rows Simultaneously

In this example, the code utilizes the iloc function to extract and display a subset of the DataFrame, including rows 1 to 4 and columns 2 to 4. This provides information about a specific range of cars and their relevant attributes in the dataset.

Output

   Kms Driven    City  Mileage
1       30000   Delhi       27
2       60000  Mumbai       25
3       25000   Delhi       26
4       10000  Mumbai       28