10 Proven Techniques to Supercharge Your Python Code by 500%

1. Use Built-In Libraries and Functions

Python’s built-in functions and libraries are implemented in C, making them faster than custom Python code.

Examples:

Use sum() instead of manual loops for summing a list.
Use map() and filter() instead of list comprehensions where applicable.

2. Optimize Data Structures

Choose efficient data structures for your use case:

Use sets for membership checks.
Use dictionaries for key-value lookups.
Use NumPy arrays instead of lists for numerical data.

3. Use NumPy or Pandas for Data Processing

NumPy and Pandas leverage optimized C and Fortran routines.

import numpy as np
arr = np.array([1, 2, 3])
result = arr * 5

4. Leverage Multithreading or Multiprocessing

Use the concurrent.futures or multiprocessing modules to parallelize tasks.

from concurrent.futures import ThreadPoolExecutor

def task(n):
    return n * 2

with ThreadPoolExecutor() as executor:
    results = list(executor.map(task, range(1000)))

5. Use Cython or PyPy

Cython: Converts Python code into C for faster execution.