Pandas: Using DataFrame.aggregate() method (5 examples)

Updated: February 19, 2024 By: Guest Contributor Post a comment


When analyzing data with Python, Pandas is one of the go-to libraries thanks to its powerful and easy-to-use data structures. One of the key functionalities provided by Pandas is the .aggregate() method (or its alias .agg()), which allows for applying one or more operations to DataFrame columns. In this tutorial, we’ll explore the flexibility of DataFrame.aggregate() through five practical examples, increasing in complexity and utility. Understanding this method can significantly streamline your data analysis processes.

Preparing a Sample DataFrame

Before diving into the examples, ensure that you have Pandas installed. You can install it via pip if needed:

pip install pandas

If you’re following along, you might also want to have some sample data. We will use a simple DataFrame throughout this tutorial:

import pandas as pd

# Sample DataFrame
data = {
    'A': [1, 2, 3, 4],
    'B': [5, 6, 7, 8],
    'C': [9, 10, 11, 12]
df = pd.DataFrame(data)

Example 1: Basic Aggregation

Let’s start with the basics. The aggregate() method allows you to perform operations such as 'sum', 'mean', and 'std' (standard deviation) on your DataFrame columns. For starters, let’s calculate the sum of each column:

result = df.aggregate(['sum'])

This results in:

     A   B   C
sum 10  26  42

It’s worth noting that you can achieve the same result by calling df.sum(), but aggregate() becomes really powerful as we move to more complex examples.

Example 2: Applying Multiple Functions

The aggregate() method shines when you need to apply multiple functions to your DataFrame columns. Suppose we want to calculate both the sum and the mean for each column:

result = df.aggregate(['sum', 'mean'])

The output shows each calculation beneath its corresponding column:

         A    B    C
sum   10.0 26.0 42.0
mean   2.5  6.5 10.5

Example 3: Column-specific Aggregations

With aggregate(), you can also perform different aggregations for each column. For instance, let’s calculate the sum for column ‘A’, the mean for ‘B’, and the standard deviation for ‘C’:

result = df.aggregate({'A': 'sum', 'B': 'mean', 'C': 'std'})

This operation reflects the flexibility of aggregate(), allowing for tailored computations across the DataFrame:

A     10.000000
B      6.500000
C      1.290994

Example 4: Custom Functions

Beyond predefined functions, aggregate() enables the use of custom functions. To illustrate, we will calculate the range (the difference between the max and min) for each column:

def data_range(x):
    return x.max() - x.min()

result = df.aggregate(data_range)

The custom function data_range is applied to each column, providing the range:

A    3
B    3
C    3

This example underscores the method’s potential for incorporating more complex, user-defined operations within your data processing workflows.

Example 5: Aggregating with GroupBy

Often, data isn’t uniform across a DataFrame, and you might want to apply aggregation functions to distinct groups. You can combine aggregate() with .groupby() for such cases. Assume we add a ‘Category’ column to our DataFrame to demonstrate grouping:

df['Category'] = ['X', 'Y', 'X', 'Y']

# Group by 'Category' and aggregate
grouped_result = df.groupby('Category').aggregate(['sum', 'mean'])

The aggregation is now applied separately to each group, offering insights specific to ‘X’ and ‘Y’ categories:

                A          B          C
          sum mean  sum mean  sum mean
X          4   2    14  7    24  12
Y          6   3    12  6    18  9


The aggregate() method is a pivotal tool in the Pandas library, offering the flexibility to perform both simple and complex data aggregations efficiently. Through the presented examples, we’ve seen how this method can handle everything from basic statistical operations to custom-defined functions and group-specific aggregations. Harnessing the power of DataFrame.aggregate() will undoubtedly streamline and enhance your data analysis projects.