Python Performance Hacks - Part 1: Make Your Code Run Faster

Python Performance Hacks - Part 1: Make Your Code Run Faster

Master the craft of making Python code run faster comparable to code written in C/C++ and Rust programming languages

Buy Now

Python is one of the most popular programming languages in the world, celebrated for its simplicity and readability. However, Python’s ease of use comes at a cost—it’s not the fastest language. For developers working on performance-sensitive applications, the challenge is to write Python code that is both easy to read and fast to execute. This article is the first in a series on Python performance hacks, where we’ll explore techniques to make your Python code run faster without sacrificing its elegance.

1. Profile Your Code: Identify Bottlenecks

Before optimizing any code, it’s crucial to understand where the bottlenecks are. Guessing what’s slowing your program down is a recipe for wasted effort. Python provides several tools to profile code and pinpoint problem areas:

• cProfile: A built-in profiler that records function calls, their frequency, and execution time.
• line_profiler: Allows you to measure the time spent on each line of code.
• memory_profiler: Helps identify memory usage bottlenecks.

For example, to use cProfile, you can run:

bash
python -m cProfile -o output.prof your_script.py  

Then analyze the results using tools like pstats or graphical tools like SnakeViz.

2. Use Built-in Functions and Libraries

Python’s standard library and built-in functions are implemented in C, making them significantly faster than equivalent Python code. Whenever possible, leverage these functions instead of writing your own implementations.

For example, instead of using a custom loop to calculate the sum of a list:

python
# Slower  
def custom_sum(numbers):  
    total = 0  
    for number in numbers:  
        total += number  
    return total  

# Faster  
numbers = [1, 2, 3, 4, 5]  
result = sum(numbers)  

Built-in functions like sum, min, max, and sorted are highly optimized. Similarly, standard library modules like collections and itertools often outperform custom implementations.

3. Avoid Global Variables

Global variables can slow down your Python code due to the way Python manages variable scope. Accessing a global variable requires Python to look up its value in the global scope, which is slower than accessing local variables.

Instead of using global variables, pass variables explicitly to functions or encapsulate them within classes.

python
# Slower: Using a global variable  
x = 10  

def multiply_by_two():  
    global x  
    return x * 2  

# Faster: Using a local variable  
def multiply_by_two(x):  
    return x * 2  

4. Leverage List Comprehensions and Generator Expressions

List comprehensions and generator expressions are more efficient than traditional loops for creating or filtering lists. They’re not only faster but also more concise and readable.

python
# Slower: Traditional loop  
squares = []  
for i in range(10):  
    squares.append(i**2)  

# Faster: List comprehension  
squares = [i**2 for i in range(10)]  

For large datasets, consider using generator expressions to save memory:

python
# Generator expression  
squares = (i**2 for i in range(10))  

Generators produce items on the fly, which reduces memory usage and improves performance in cases where you don’t need to store all the results at once.

5. Optimize Loops

Loops are a common source of performance issues in Python. Here are a few tips to make them faster:

• Minimize computations inside loops: Move constant expressions outside the loop.
• Avoid function calls in loops: Inline simple functions if they’re called repeatedly.
• Use enumerate instead of range(len()): It’s more Pythonic and slightly faster.

Example:

python
# Slower  
for i in range(len(my_list)):  
    print(i, my_list[i])  

# Faster  
for i, value in enumerate(my_list):  
    print(i, value)  

6. Use NumPy for Numerical Operations

When working with large datasets or numerical computations, NumPy can dramatically improve performance. NumPy arrays are implemented in C and optimized for fast mathematical operations.

python
# Slower: Using lists  
numbers = [1, 2, 3, 4, 5]  
squared = [x**2 for x in numbers]  

# Faster: Using NumPy  
import numpy as np  

numbers = np.array([1, 2, 3, 4, 5])  
squared = numbers**2  

In addition to being faster, NumPy also provides a wealth of functions for complex mathematical operations.

7. Cache Results with functools.lru_cache

If your function performs expensive computations and gets called repeatedly with the same inputs, you can use functools.lru_cache to cache the results.

python
from functools import lru_cache  

@lru_cache(maxsize=100)  
def expensive_computation(x):  
    # Simulate a time-consuming operation  
    print(f"Computing {x}...")  
    return x**2  

# Cached results  
print(expensive_computation(10))  
print(expensive_computation(10))  # This call is cached  

By caching the results of previous computations, you can avoid redundant work and speed up your code.

8. Use Multiprocessing and Multithreading

Python’s Global Interpreter Lock (GIL) limits multithreading performance for CPU-bound tasks. However, you can use the multiprocessing module to parallelize these tasks across multiple processes.

python
from multiprocessing import Pool  

def square(x):  
    return x**2  

if __name__ == "__main__":  
    with Pool(4) as pool:  
        results = pool.map(square, range(10))  
    print(results)  

For I/O-bound tasks, such as reading files or making network requests, multithreading with concurrent.futures.ThreadPoolExecutor can yield performance improvements.

9. Compile Python Code with Cython

Cython allows you to compile Python code into C extensions, providing significant performance boosts for computationally intensive sections of your program.

To get started with Cython:

1. Write a .pyx file with your Python code.
2. Use Cython to compile it into a shared library.
3. Import and use the compiled module in your Python code.

Cython also allows you to add type annotations, which can further speed up your code.

10. Avoid Excessive Object Creation

Creating and destroying objects frequently can slow down your program, especially in performance-critical sections. Use mutable objects like lists or dictionaries to minimize object creation.

For example, instead of creating multiple strings in a loop, use str.join() or io.StringIO to efficiently concatenate strings:

python
# Slower  
result = ""  
for word in words:  
    result += word  

# Faster  
result = "".join(words)  

11. Use Async Programming for I/O-bound Tasks

For programs that perform many I/O-bound tasks, such as web scraping or database queries, asynchronous programming can dramatically improve performance. Python’s asyncio module provides tools for writing asynchronous code:

python
import asyncio  

async def fetch_data(url):  
    print(f"Fetching {url}")  
    await asyncio.sleep(1)  # Simulate network delay  
    return f"Data from {url}"  

async def main():  
    urls = ["http://example.com/1", "http://example.com/2"]  
    results = await asyncio.gather(*(fetch_data(url) for url in urls))  
    print(results)  

asyncio.run(main())  

Asynchronous programming allows you to overlap I/O operations, reducing idle time and improving throughput.

Conclusion

Optimizing Python code doesn’t mean sacrificing readability or maintainability. By using the tips outlined in this article—profiling your code, leveraging built-in functions, optimizing loops, and employing libraries like NumPy—you can make your Python programs significantly faster while keeping them elegant.

Stay tuned for Part 2 of this series, where we’ll dive deeper into advanced techniques like Just-in-Time (JIT) compilation with PyPy, advanced data structures, and more. With these performance hacks, you’ll be well on your way to writing Python code that’s not just clean but also blazing fast.