What are some ways to optimize Python code for speed?

In the first example, we are going to optimize Python code by using list comprehension instead of traditional for loops. List comprehension is a more concise and faster way to create lists in Python. Here is the code before optimization: 
result = []
for i in range(1, 11):
    result.append(i * i)

And here is the optimized code using list comprehension: 
result = [i * i for i in range(1, 11)]

By using list comprehension, we are able to achieve the same result in a single line of code, making it more readable and efficient. This can lead to significant speed improvements, especially when dealing with large datasets. In the second example, we are going to optimize Python code by leveraging the built-in functions and libraries. Python provides many built-in functions and libraries that are optimized for speed and efficiency. Here is the code before optimization, where we are summing all numbers in a list: 
numbers = [1, 2, 3, 4, 5]
total = 0
for num in numbers:
    total += num

And here is the optimized code using the built-in sum() function: 
numbers = [1, 2, 3, 4, 5]
total = sum(numbers)

By using the sum() function, we are able to achieve the same result in a single line of code, which is not only more readable but also more efficient. The built-in functions and libraries in Python are highly optimized, making them a great choice for speeding up your code.

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