Unlock the power of DataFrame.apply() method in Pandas (4 examples)

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


When working with data in Python, Pandas is arguably the most widely used library due to its power, flexibility, and expressive syntax. One of the most versatile and powerful methods in Pandas is apply(), which allows you to apply a function along an axis of the DataFrame. This tutorial will take you through the use of the apply() method, from basic examples to more advanced use cases, to help you unlock its full potential in your data analysis workflow.

Introduction to the apply() Method

The apply() method in Pandas can be used to apply a function along any axis of the DataFrame to transform data efficiently. It can work with user-defined functions, lambda functions, or any other callable that takes a series or data frame as input and returns a list-like or a DataFrame.

Here’s a simple example to illustrate how apply() works:

import pandas as pd
import numpy as np

# Create a simple DataFrame
df = pd.DataFrame({
    'A': range(1, 6),
    'B': np.random.randn(5)

# Define a function to multiply by 2
def double(x):
    return x * 2

# Apply the function to each element of column 'A'
df['A'] = df['A'].apply(double)

This code multiplies each element in column ‘A’ by 2 using the apply() function and a user-defined function named double.


    A         B
0   2 -0.031861
1   4 -0.706053
2   6  0.478431
3   8 -1.608530
4  10 -0.589225

Working with Lambda Functions

Lambda functions are often used with apply() for quick and concise data manipulation. Here’s how to use a lambda function to achieve the same result:

df['A'] = df['A'].apply(lambda x: x * 2)

This is more succinct and convenient for simple operations that do not require the overhead of defining a separate function.

Applying Functions Row-wise

Using the axis parameter, you can apply a function to each row instead of each column. This is particularly useful for operations that need to consider multiple columns.

import pandas as pd
import numpy as np

# Create a simple DataFrame
df = pd.DataFrame({"A": range(1, 6), "B": np.random.randn(5)})

def sum_row(row):
    return row["A"] + row["B"]

df["A+B"] = df.apply(sum_row, axis=1)


   A         B       A+B
0  1 -0.419789  0.580211
1  2  0.098816  2.098816
2  3  0.011634  3.011634
3  4 -0.542527  3.457473
4  5 -1.868813  3.131187

This stitches together the values of columns ‘A’ and ‘B’ for each row and creates a new column ‘A+B’ with the sum.

Using apply() with GroupBy

The apply() method can also be seamlessly integrated with GroupBy objects to perform grouped operations. This is particularly useful for data aggregation and transformation operations within groups.

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'Key': ['A', 'B', 'A', 'B'],
    'Data': [1, 2, 3, 4]

grouped = df.groupby('Key')
result = grouped['Data'].apply(lambda x: x.max() - x.min())


A    2
B    2
Name: Data, dtype: int64

This example demonstrates using apply() to calculate the range (max – min) of the ‘Data’ column within each group defined by ‘Key’.

Advanced Use Cases

As you become more comfortable with apply(), you can start to explore more advanced use cases. For example, using apply() to transform DataFrame columns based on conditional logic, apply complex aggregations, or even integrate external APIs for data enrichment.

Consider this example where we use apply() to conditionally update values in our DataFrame:

import pandas as pd
import numpy as np

df = pd.DataFrame({"A": range(1, 6), "B": np.random.randn(5)})

def update_value(x):
    if x["A"] % 2 == 0:
        return x["A"] * 3
        return x["A"] * 2

df["A"] = df.apply(update_value, axis=1)


      A         B
0   2.0  1.488545
1   6.0  1.250215
2   6.0  1.737063
3  12.0  0.226782
4  10.0 -1.361206

This function multiplies the value in column ‘A’ by 3 if it is even and by 2 if it is odd, demonstrating how apply() can be used for more complex data transformations.


The apply() method is a versatile and powerful tool in the Pandas library that can greatly enhance your data analysis tasks. From simple row-wise or column-wise function applications to more complex grouped or conditional transformations, apply() allows for concise and readable code. Mastering apply() will surely add robustness to your data manipulation toolkit.