Understanding the significance of the “@” symbol in Python is crucial for writing code that’s both tidy and efficient, especially when you’re dealing with decorators or performing matrix multiplication operations in your programs. In this article, we’ll delve into the role of the “@” symbol in Python and examine how it comes into play in different situations.
What Is the @ Symbol in Python?
In Python, the “@” symbol is primarily associated with decorators. Decorators are a powerful and flexible way to modify or extend the behavior of functions or methods without changing their code. They are often used for tasks such as logging, authentication, and performance monitoring. The “@” symbol is used to apply a decorator to a function or method.
Some more decorators are:
- @property decorator is a built-in decorator in Python that is helpful in defining the properties effortlessly without manually calling the inbuilt function property().
- When a function decorated with @staticmethod is called, we don’t pass an instance of the class to it as it is normally done with methods.
Example 1: Apply the Decorator to a Function
We define a decorator function called log_function_call. This decorator logs information about the function calls it decorates, such as the function name, arguments, and keyword arguments.
- Here, We apply the @log_function_call decorator to the add function. This means that when we call add(5, 3), it is actually executing the wrapper function defined within log_function_call, which adds logging behavior around the original add function.
- When we call add(5, 3), it prints information about the function call, performs the addition operation, logs the result, and returns it. The result variable will store the value 8, which is the result of 5 + 3.
Python3
# Define a decorator functiondef log_function_call(func): def wrapper(*args, **kwargs): print(f"Calling function {func.__name__} with arguments {args} and keyword arguments {kwargs}") result = func(*args, **kwargs) print(f"Function {func.__name__} returned {result}") return result return wrapper# Apply the decorator to a function@log_function_calldef add(a, b): return a + b# Call the decorated functionresult = add(5, 3) |
Output:
Calling function add with arguments (5, 3) and keyword arguments {}
Function add returned 8
Example 2: Matrix Multiplication using the “@” operator
This Python code uses the NumPy library to perform matrix multiplication. The two matrices, matrix_a and matrix_b, are defined as NumPy array. The “@” operator, introduced for matrix multiplication in Python, that utilize to calculate the matrix product of matrix_a and matrix_b. Finally, the resulting matrix, which represents the outcome of the multiplication, is computed and printed to the console. NumPy simplifies matrix operations in Python, making it an efficient choice for mathematical and numerical computations involving matrices and arrays.
Python3
import numpy as np# Define two matrices as NumPy arraysmatrix_a = np.array([[1, 2], [3, 4]])matrix_b = np.array([[5, 6], [7, 8]])# Perform matrix multiplication using the "@" operatorresult = matrix_a @ matrix_b# Print the resultprint(result) |
Output:
[[19 22]
[43 50]]
