Think Python Is Slow? Try These Hacks for 3x Faster Scripts Today

1. Use Built-In Functions Over Loops

Python’s built-in functions like map(), filter(), and list comprehensions are optimized in C, making them much faster than manual loops.

# Slow
result = []
for i in range(1, 1000000):
    result.append(i * 2)
# Fast
result = [i * 2 for i in range(1, 1000000)]

2. Avoid Global Variables

Accessing global variables slows down performance. Use function parameters and local variables instead.

# Slow
x = 100
def calculate():
    global x
    return x * 2

# Fast
def calculate(x):
    return x * 2

3. Leverage Numpy for Numerical Tasks

Replace native Python lists with NumPy arrays for faster operations on large datasets.

# Slow
data = [i * 2 for i in range(1000000)]

# Fast
import numpy as np
data = np.arange(1000000) * 2