Here’s a tutorial on how to speed up your Python code using some optimization techniques that are both effective and easy to implement.
To set the stage why you might want to optimize your Python code in the first place. Maybe you have a slow script or program, maybe it’s eating up too much memory, or maybe you just want to impress your friends with your coding skills (let’s be real here).
Regardless of your reasons for wanting faster and more efficient Python code, there are some simple techniques that can help you achieve those goals. Here are a few:
1. Use list comprehensions instead of loops whenever possible. List comprehensions are often much faster than traditional for-loops because they’re optimized by the interpreter. For example, let’s say we have a list of numbers and we want to find all the even ones. Instead of using a loop:
# Create an empty list to store even numbers
even_numbers = []
# Loop through the list of numbers
for num in numbers:
# Check if the number is even by using the modulo operator
if num % 2 == 0:
# If the number is even, append it to the even_numbers list
even_numbers.append(num)
# The corrected script uses a for-loop to iterate through the list of numbers and checks if each number is even before appending it to the even_numbers list. This can be improved by using a list comprehension, which is more efficient and concise.
# Create a list comprehension to find all even numbers in the numbers list
even_numbers = [num for num in numbers if num % 2 == 0]
# The list comprehension creates a new list by iterating through the numbers list and only adding numbers that are even to the new list. This eliminates the need for a separate for-loop and if statement, making the code more efficient.
We can use list comprehension instead:
# This script uses list comprehension to create a new list of even numbers from a given list of numbers.
# First, we define a new list called "even_numbers" and use list comprehension to iterate through the list "numbers".
even_numbers = [num for num in numbers
# Next, we use the "if" statement to check if the current number being iterated through is divisible by 2 with no remainder, indicating it is an even number.
if num % 2 == 0]
# Finally, the even number is added to the new list "even_numbers" and the process continues until all numbers in the original list have been checked.
# The result is a new list containing only even numbers from the original list.
# Define a list of numbers
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
# Use list comprehension to create a new list of even numbers from the original list
even_numbers = [num for num in numbers if num % 2 == 0]
# Print the new list of even numbers
print(even_numbers)
# Output: [2, 4, 6, 8, 10]
This is not only faster, but it’s also more concise and easier to read.
2. Use the built-in functions whenever possible. Python has a ton of useful built-in functions that can save you time and effort when writing code. For example, instead of using a loop to find the maximum value in a list:
# This script uses built-in functions to find the maximum value in a list of numbers.
# First, we define a list of numbers to work with.
numbers = [5, 2, 8, 3, 10]
# We use the built-in function max() to find the maximum value in the list.
max_value = max(numbers)
# We print the result to the console.
print(max_value)
# Output: 10
# The max() function takes in a list as its argument and returns the maximum value in that list.
# We can also use the built-in function sorted() to sort the list in ascending order.
sorted_numbers = sorted(numbers)
# We print the sorted list to the console.
print(sorted_numbers)
# Output: [2, 3, 5, 8, 10]
# The sorted() function takes in a list as its argument and returns a new list with the elements sorted in ascending order.
# We can also use the built-in function sum() to find the sum of all the numbers in the list.
sum_of_numbers = sum(numbers)
# We print the result to the console.
print(sum_of_numbers)
# Output: 28
# The sum() function takes in a list as its argument and returns the sum of all the elements in that list.
We can use the built-in `max()` function instead:
# Using the built-in `max()` function to find the maximum value in a list of numbers
# `max()` takes in a list of numbers as its argument and returns the maximum value
# `max_value` is a variable that will store the maximum value returned by `max()`
max_value = max(numbers)
This not only saves us time and effort, but it’s also more concise and easier to read.
3. Use generators whenever possible. Generators are a powerful tool in Python that can help you write faster code by generating values on the fly instead of storing them all in memory at once. For example, let’s say we have a large list of numbers and we want to find all the even ones using a generator:
# This function takes in a list of numbers and returns a generator object that yields only the even numbers in the list.
def get_even_numbers(numbers):
for num in numbers:
if num % 2 == 0: # Checks if the number is even by using the modulo operator to check if there is a remainder when divided by 2.
yield num # Yields the even number, allowing it to be generated on the fly instead of storing it in memory.
# Creates a new list called "evens" by using a list comprehension to iterate through the generator object returned by the get_even_numbers function.
evens = [num for num in get_even_numbers(large_list)]
This is not only faster than using a list comprehension, but it’s also more memory-efficient because we don’t have to store all the even numbers at once. Instead, they are generated on the fly as needed.
4. Use the `timeit` module for measuring performance. The `timeit` module is a built-in Python tool that can help you measure how long it takes your code to run. For example:
# Import the timeit module to measure performance
import timeit
# Define a function to find the maximum value in a list of numbers
def get_max(numbers):
# Initialize the maximum value to None
max_value = None
# Loop through each number in the list
for num in numbers:
# Check if the current number is greater than the current maximum value
if max_value is None or num > max_value:
# If so, update the maximum value
max_value = num
# Return the maximum value
return max_value
# Set up the timeit module to use the get_max function
setup = 'from __main__ import get_max'
# Create a list of numbers to test the function with
nums = [1, 2, 3, 4, 5]
# Print the time it takes for the get_max function to run with the given list of numbers
print(timeit.timeit("get_max(nums)", setup=setup))
# Output: 0.08774999999999999
# The timeit module measures the time it takes for the code to run and returns the result in seconds.
# The get_max function loops through the list of numbers and compares each number to the current maximum value.
# If the current number is greater than the current maximum value, it is updated as the new maximum value.
# The setup variable sets up the timeit module to use the get_max function.
# The nums variable is a list of numbers to test the get_max function with.
# The print statement prints the time it takes for the get_max function to run with the given list of numbers.
This will measure how long it takes to run the `get_max()` function on a list of numbers using the `timeit` module. You can use this technique to compare different optimization techniques and see which ones are fastest.
5. Use the `functools` module for decorators. The `functools` module is another built-in Python tool that provides some useful functions, including decorators. Decorators are a powerful way to add functionality to your code without changing its structure or behavior. For example:
# Import the functools module to use decorators
import functools
# Define the memoize function, which takes in a function as an argument
def memoize(func):
# Create a dictionary to store the results of the function calls
cache = {}
# Use the functools.wraps decorator to preserve the original function's metadata
@functools.wraps(func)
# Define the wrapper function, which takes in any number of arguments and keyword arguments
def wrapper(*args, **kwargs):
# Check if the arguments are already in the cache
if args not in cache:
# If not, call the original function with the given arguments and store the result in the cache
result = func(*args, **kwargs)
cache[args] = result
# Return the result from the cache
return cache[args]
# Return the wrapper function
return wrapper
# Use the memoize decorator on the get_max function
@memoize
# Define the get_max function, which takes in a list of numbers as an argument
def get_max(numbers):
# ... (code for finding the maximum number in the list)
# Return the maximum number
return max(numbers)
# Now, every time the get_max function is called with the same list of numbers, it will return the result from the cache instead of recalculating it. This improves the performance of the function.
In this example, we’re using the `memoize()` decorator to add caching functionality to our `get_max()` function. This can help us avoid redundant calculations and improve performance in certain cases.
These are just a few of the many optimization techniques available for Python code. By following these simple tips, you should be able to write faster and more efficient code that will save you time, money, and effort.