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Python Performance Tips: Make Your Code 10x Faster

#performance #programming #python #tutorial

Python Performance Tips: Make Your Code 10x Faster

Speed matters. Here are the most impactful Python optimizations.

1. Use Built-in Functions 

# Slow
total = 0
for n in numbers:
    total += n

# Fast - use built-ins
total = sum(numbers)
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2. List Comprehensions Over Loops 

# Slow
squares = []
for x in range(1000):
    squares.append(x ** 2)

# Fast
squares = [x ** 2 for x in range(1000)]

# Fastest for large data - generator
squares_gen = (x ** 2 for x in range(1000000))
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3. Use Sets for Membership Testing 

# Slow - O(n) for list
items_list = [1, 2, 3, ...]
if 999 in items_list:  # searches entire list

# Fast - O(1) for set
items_set = {1, 2, 3, ...}
if 999 in items_set:  # instant lookup
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4. String Concatenation 

# Slow - creates new string each iteration
result = ""
for s in strings:
    result += s

# Fast - join is O(n)
result = "".join(strings)
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5. Cache with lru_cache 

from functools import lru_cache

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

print(fibonacci(100))  # Instant after first call
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6. Use numpy for Numerical Operations 

import numpy as np

# Python list - slow
result = [x * 2 for x in range(1000000)]

# numpy - 100x faster
arr = np.arange(1000000)
result = arr * 2
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7. Avoid Global Variables 

# Slow - global lookup each iteration
count = 0
def slow():
    global count
    for i in range(1000000):
        count += i

# Fast - local variable
def fast():
    count = 0
    for i in range(1000000):
        count += i
    return count
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8. Profile Before Optimizing 

import cProfile
cProfile.run('your_function()', sort='cumulative')
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