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.

Install: pip install cython

def add(int a, int b):
    return a + b
        

PyPy: A JIT (Just-In-Time) compiler for Python that can significantly speed up runtime.

6. Avoid Redundant Calculations

Use memoization or caching to store results of expensive operations.

Example: functools.lru_cache for caching function calls.

In?[?]:from functools import lru_cache @lru_cache def fib(n): if n < 2: return n return fib(n-1) + fib(n-2)

7. Profile and Identify Bottlenecks

Use cProfile or timeit to identify slow parts of the code.

import cProfile
cProfile.run('your_function()')
        

8. Optimize Loops

Minimize computations inside loops.

Combine loops or use list comprehensions where appropriate.

9. Use Libraries for Heavy Computations

Libraries like TensorFlow or PyTorch are optimized for tasks involving large-scale matrix computations.

10. Move Critical Code to C/C++

Use libraries like ctypes or cffi to integrate C/C++ code with Python.

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