Programming Essentials Python - Overview of Pandas Libraries - Performing Grouped Aggregations

Performing Grouped Aggregations

In this article, we will explore how to perform grouped or by key aggregations using Pandas to analyze and aggregate data efficiently.

Explanation for the video

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Key Concepts Explanation

Grouped Aggregations

Grouped aggregations involve grouping data based on a specific key and applying aggregate functions to the grouped data. Here are the key steps to perform grouped aggregations using Pandas:

  • Ensure data is loaded into a Data Frame.
  • Identify the key for grouping the data.
  • Use the groupby function to group the data based on the key.
  • Apply aggregate functions to obtain aggregated results.
  • Multiple aggregate functions can be applied to the grouped data.
  • Aliases for aggregated fields can be provided using the rename function.

Hands-On Tasks

  1. Task 1: Get order_item_count and order_revenue for each order_id.
  2. Task 2: Get order count by month using orders data for specific order_status.
  3. Task 3: Calculate order_revenue and order_quantity for each order_id by aggregating order_item_subtotal and order_item_quantity.


In this article, we have learned how to efficiently perform grouped aggregations using Pandas. By following the steps and tasks outlined, readers can gain a better understanding of data analysis and aggregation techniques. Remember to practice these concepts and engage with the community for further learning and growth.

Thank you for reading and happy coding!

Watch the video tutorial here