Pandas Lesson 17 – | Dataplexa

GroupBy & Aggregations in Pandas

One of the most powerful features of Pandas is the ability to group data and perform calculations on those groups.

GroupBy allows you to summarize large datasets and answer real business questions such as:

Loading the Dataset

We continue using the same dataset used throughout this Pandas course.

import pandas as pd

df = pd.read_csv("dataplexa_pandas_sales.csv")
df["order_date"] = pd.to_datetime(df["order_date"])

groupby() splits the data into groups based on one or more columns, applies a function to each group, and then combines the results.

Think of it as:

Let’s calculate the total sales amount for each product.

df.groupby("product")["sales_amount"].sum()

This groups all rows by product name and sums the sales.

GroupBy results return the grouping column as an index. To convert it back into a regular column, use reset_index().

df.groupby("product")["sales_amount"].sum().reset_index()

You can calculate averages using mean().

Example: average sales amount per region.

df.groupby("region")["sales_amount"].mean()

To count how many records exist in each group, use count().

Example: number of orders per customer.

df.groupby("customer_id")["order_id"].count()

You can group data using more than one column.

Example: total sales by region and product.

df.groupby(["region", "product"])["sales_amount"].sum()

You can apply multiple aggregation functions using agg().

Example: total sales, average sales, and order count per product.

df.groupby("product").agg(
    total_sales=("sales_amount", "sum"),
    avg_sales=("sales_amount", "mean"),
    order_count=("order_id", "count")
)

GroupBy is very powerful when combined with date columns.

Example: total sales per month.

df.groupby(df["order_date"].dt.month)["sales_amount"].sum()

You can sort aggregated results to find top or lowest values.

df.groupby("product")["sales_amount"]
  .sum()
  .sort_values(ascending=False)

GroupBy is used in almost every real-world data analysis task.

It allows you to:

Using the dataset:

In the next lesson, you will learn how to create Pivot Tables in Pandas, which provide an Excel-style way to summarize grouped data.