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Grouping and Aggregating with Pandas

Grouping and Aggregating with Pandas

Last Updated : 26 Jul, 2025

When working with large datasets it's used to group and summarize the data to make analysis easier. Pandas a popular Python library provides powerful tools for this. In this article you'll learn how to use Pandas' groupby() and aggregation functions step by step with clear explanations and practical examples. 

Aggregation in Pandas

Aggregation means applying a mathematical function to summarize data. It can be used to get a summary of columns in our dataset like getting sum, minimum, maximum etc. from a particular column of our dataset. The function used for aggregation is agg() the parameter is the function we want to perform. Some functions used in the aggregation are:

Function Description sum() Compute sum of column values min() Compute min of column values max() Compute max of column values mean() Compute mean of column size() Compute column sizes describe() Generates descriptive statistics first() Compute first of group values last() Compute last of group values count() Compute count of column values std() Standard deviation of column var() Compute variance of column sem() Standard error of the mean of column Creating a Sample Dataset

Let's create a small dataset of student marks in Maths, English, Science and History.

Python 
import pandas as pd

df = pd.DataFrame([[9, 4, 8, 9],
                   [8, 10, 7, 6],
                   [7, 6, 8, 5]],
                  columns=['Maths',  'English', 
                           'Science', 'History'])

print(df)

Output:

Now that we have a dataset let’s perform aggregation.

1. Summing Up All Values (sum())

The sum() function adds up all values in each column.

Python

Output:

2. Getting a Summary (describe())

Instead of calculating sum, mean, min and max separately we can use describe() which provides all important statistics in one go.

Python

Output:

3. Applying Multiple Aggregations at Once (agg())

The .agg() function lets you apply multiple aggregation functions at the same time.

Python 
df.agg(['sum', 'min', 'max'])

Output:

Grouping in Pandas

Grouping in Pandas means organizing your data into groups based on some columns. Once grouped you can perform actions like finding the total, average, count or even pick the first row from each group. This method follows a split-apply-combine process:

Let’s understand grouping in Pandas using a small bakery order dataset as an example.

Python 
import pandas as pd

data = {
    'Item': ['Cake', 'Cake', 'Bread', 'Pastry', 'Cake'],
    'Flavor': ['Chocolate', 'Vanilla', 'Whole Wheat', 'Strawberry', 'Chocolate'],
    'Price': [250, 220, 80, 120, 250]
}

df = pd.DataFrame(data)
print(df)

Output:

Bakery dataset 1. Grouping Data by One Column Using groupby()

Let’s say we want to group the orders based on the Item column.

Python 
grouped = df.groupby('Item')
print(grouped)

Output:

<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7867484be150>

This doesn't show the result directly it just creates a grouped object. To actually see the data we need to apply a method like .sum(), .mean() or first(). Let’s find the total price of each item sold:

Python 
print(df.groupby('Item')['Price'].sum())

Output:

Grouping data by one columns

The above output shows the total earnings from each item.

2. Grouping by Multiple Columns

Now let’s group by Item and Flavor to see how each flavored item sold.

Python 
print(df.groupby(['Item', 'Flavor'])['Price'].sum())

Output:

Grouping by multiple columns

The above output show Chocolate Cakes earned ₹500 and Vanilla Cake earned ₹220 and more.


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