GroupBy and Aggregation Functions

One of the most powerful capabilities in Pandas is grouping data and performing calculations on each subset.

This technique helps uncover patterns across categories — for example, sales by region, average scores per class, or revenue by product.

The groupby() method splits your data into groups based on the values in one or more columns.

Once grouped, you can apply aggregation functions such as:

• sum(): total value per group
• mean(): average value per group
• count(): number of rows per group
• max(): highest value per group
• min(): lowest value per group

Imagine you have a dataset of sales transactions from different cities. You might want to:

• Calculate total sales for each city
• Find the average transaction amount per store
• Count how many transactions happened in each region

Pandas makes this easy. For example, to calculate total sales per city, you can write:

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import pandas as pd

df = pd.DataFrame({
    "City": ["New York", "New York", "Los Angeles", "Los Angeles", "Chicago", "Chicago"],
    "Sales": [100000, 150000, 200000, 250000, 300000, 350000]
})

df.groupby("City")["Sales"].sum()

# Output:
# City
# New York    250000
# Los Angeles    450000 
# Chicago    650000

Here's a simple pattern:

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df.groupby("ColumnName")["TargetColumn"].agg("aggregation_function")

You can also use .agg() to apply multiple aggregation functions at once for richer summaries.

For example, to calculate the sum, mean, and count of the sales for each category, you can write:

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df = pd.DataFrame({
    "Category": ["A", "A", "B", "B", "C", "C"],
    "Amount": [100, 200, 300, 400, 500, 600]
})

df.groupby("Category")["Amount"].agg(["sum", "mean", "count"])

# Output:
#    sum  mean  count
# Category
# A    300  150      2
# B    700  350      2
# C   1100  550      2

The output shows that:

• sum = total of all values in the group
• mean = average value
• count = number of rows in the group

The categories (A, B, C) appear as index labels for clarity.