How To Group Rows In CSV File Python

When dealing with a large amount of data, it’s often useful to group rows based on certain shared values. By grouping data rows, you can simplify the presentation of the dataset and enable quick and efficient data analysis. In this tutorial, we’ll explore how to group rows in a CSV file using Python.

For this purpose, we’ll use the powerful Pandas library that offers excellent built-in functions to handle data stored in CSV files. If you haven’t installed the Pandas library yet, you can install it using the following command:

Now, let’s proceed with the steps to group rows in a CSV file using Python.

Step 1: Import the Required Libraries

First, we need to import the necessary libraries for this tutorial. We’ll import Pandas and give it a nickname pd.

Step 2: Load the CSV File to a DataFrame

Let’s assume you have the following CSV file (sample.csv) that you want to group by the column “Category”:


To load the CSV file, use the read_csv() function provided by the Pandas library:

Step 3: Group Rows Based on a Column

Now, we’re ready to group the rows of the DataFrame based on the “Category” column using the .group by() method. You can replace “Category” with any other column name you intend to use for grouping:

Step 4: Display the Grouped Data

Utilize the get_group() method to retrieve the rows for a particular group. For example, to get all the rows for the “Fruit” category:

Similarly, we can display the group for the “Vegetable” category:

Putting everything together in a single Python script:


Fruit Group:
   ID    Name Category  Price
0   1   Apple    Fruit    1.2
1   2  Banana    Fruit    0.5
3   4    Date    Fruit    1.8
5   6     Fig    Fruit    2.1

Vegetable Group:
   ID      Name   Category  Price
2   3    Carrot  Vegetable    0.7
4   5  Edamame  Vegetable    1.3


In this tutorial, we’ve demonstrated how to group rows in a CSV file using Python and the Pandas library. With this knowledge, you can efficiently manipulate and analyze large datasets by grouping them based on certain values. Remember to adapt the column used for grouping according to your specific use case.