reading-notes

Python list comprehension

is a concise way to create a new list by applying an expression to each element of an existing list or iterable. The basic syntax of list comprehension is as follows:

new_list = [expression for item in iterable if condition]

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Here’s a breakdown of the components:

• expression: The expression that will be applied to each item in the iterable.
• item: The variable representing the current item being processed in the iterable.
• iterable: The existing list or iterable from which elements are being extracted.
• condition (optional): An optional condition that filters the elements from the iterable based on a Boolean expression. Only the elements satisfying the condition will be included in the new list.

List comprehension

is often more concise and readable compared to using a traditional for loop to create a list. It combines the iteration and transformation steps into a single line, reducing the code required and making it easier to understand.

Here’s an example that demonstrates how to square each element in a given list of integers using list comprehension:

numbers = [1, 2, 3, 4, 5]
squared_numbers = [x ** 2 for x in numbers]
print(squared_numbers) Output:


[1, 4, 9, 16, 25]

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In the example above, the expression x ** 2 squares each element x in the numbers list, and the resulting squared numbers are stored in the squared_numbers list using list comprehension.

Now, let’s move on to the concept of decorators in Python.

In Python, decorators are:

a way to modify or enhance the behavior of a function or class without changing its source code. Decorators are implemented as functions that take another function as an argument and return a modified version of that function. They allow you to wrap additional functionality around an existing function or class dynamically.

Decorators are commonly used for tasks such as logging, authentication, caching, input validation, and more. They provide a clean and reusable way to add extra functionality to functions or classes without modifying their original implementation.

Here’s an example of a simple decorator function that measures the execution time of a function:

import time

def measure_time(func):
  def wrapper(*args, **kwargs):
      start_time = time.time()
      result = func(*args, **kwargs)
      end_time = time.time()
      execution_time = end_time - start_time
      print(f"Execution time: {execution_time} seconds")
      return result
  return wrapper

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In the example above, the measure_time function is a decorator that takes a function func as an argument. It defines an inner function wrapper that measures the execution time of func by calculating the time difference between the start and end time. It then prints the execution time and returns the result.

To apply the measure_time decorator to a specific function, you can prefix the function definition with the @measure_time syntax, like this:

@measure_time
def my_function():
   # Code for the function
pass

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Now, whenever my_function is called, it will be wrapped with the functionality provided by the measure_time decorator. The execution time will be printed, and the original function’s behavior will be preserved.

It’s important to note that decorators can also take arguments, allowing you to customize their behavior further. However, that goes beyond the scope of this explanation.