Performance Optimization

Profiling, memory optimization, caching, and performance patterns.

Overview

Master Python performance techniques.

Profiling

import cProfile
import time

def slow_function():
    total = 0
    for i in range(1000000):
        total += i
    return total

# Profile execution
cProfile.run('slow_function()')

# Manual profiling
start = time.perf_counter()
slow_function()
end = time.perf_counter()
print(f"Time: {end - start:.4f}s")

Memory Optimization

import sys

# Check object size
my_list = [1, 2, 3, 4, 5]
print(sys.getsizeof(my_list))  # 104 bytes

# Use generators for large datasets
def large_dataset():
    for i in range(1000000):
        yield i

# Memory efficient
total = sum(large_dataset())

# Slots for classes
class Point:
    __slots__ = ['x', 'y']
    
    def __init__(self, x, y):
        self.x = x
        self.y = y

Caching

from functools import lru_cache
import time

@lru_cache(maxsize=128)
def fibonacci(n):
    if n < 2:
        return n
    return fibonacci(n-1) + fibonacci(n-2)

start = time.time()
fibonacci(100)
print(f"Time: {time.time() - start:.4f}s")

Practice

Optimize a slow data processing pipeline.