<code>numbers = [1, 2, 3, 4, 5]<br> squared_numbers = list(map(lambda x: x**2, numbers))<br> print(squared_numbers)</code>
Table of Contents
Table of Contents
Introduction
Python is a popular programming language used by developers all over the world. It has several built-in functions that help make coding easier and more efficient. Two of these functions are map() and apply(). Although they may seem similar, they have some key differences that can affect their performance. In this article, we will explore these differences and help you decide which one to use.What is map()?
The map() function is a built-in Python function that applies a given function to all elements of an iterable object, such as a list or a tuple. The result is a new iterable object containing the modified elements. The map() function is useful when you need to apply a function to every element of an iterable object.What is apply()?
The apply() function is a method of Pandas DataFrames and Series. It applies a function along an axis of the DataFrame or Series. The apply() function is useful when you need to apply a function to a specific column or row of a DataFrame or Series.Differences Between map() and apply()
Although both map() and apply() apply a given function to an iterable object, they have some key differences. One of the main differences is that map() is a built-in Python function, while apply() is a method of Pandas DataFrames and Series. Another difference is that map() applies a function to every element of an iterable object, while apply() applies a function along an axis of a DataFrame or Series.Performance Differences
When it comes to performance, map() is faster than apply() when working with simple functions. However, apply() is faster when working with more complex functions. This is because apply() takes advantage of the NumPy library, which is faster than Python's built-in map() function.When to Use map()
You should use map() when you need to apply a function to every element of an iterable object, such as a list or a tuple. Map() is useful when you need to modify every element of an iterable object in the same way.When to Use apply()
You should use apply() when you need to apply a function to a specific column or row of a Pandas DataFrame or Series. Apply() is useful when you need to modify a specific column or row of a DataFrame or Series.Examples
Let's look at some examples to help illustrate the differences between map() and apply().Example 1: Using map()
Suppose we have a list of numbers and we want to square each number. We can use the map() function to accomplish this.numbers = [1, 2, 3, 4, 5]
squared_numbers = list(map(lambda x: x**2, numbers))
print(squared_numbers)
Example 2: Using apply()
Suppose we have a DataFrame containing information about employees, including their salaries. We want to give each employee a 10% raise. We can use the apply() function to accomplish this.import pandas as pd
employees = pd.DataFrame({'name': ['Alice', 'Bob', 'Charlie'],
'salary': [50000, 60000, 70000]})
employees['salary'] = employees['salary'].apply(lambda x: x * 1.1)
print(employees)
