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Python Advanced Concepts

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Lazy Evaluation in Python: Optimizing Memory and Performance

In this chapter, we introduce the concept of lazy evaluation, a technique where data is produced only when needed rather than being computed and stored upfront. Lazy evaluation is a key feature of iterators and is particularly useful for working with large datasets or infinite sequences.

Key Benefits:

  • Memory Efficiency: only one element is generated at a time;
  • Performance Optimization: computation occurs only when needed;
  • Support for Infinite Sequences: you can work with sequences of arbitrary size without running out of memory.

Let's create an infinite dice roller that generates random rolls on demand. This ensures we never need to store all the rolls in memory, no matter how many rolls we perform.

import random

# Infinite dice roller
class InfiniteDiceRoller:
def __iter__(self):
return self

def __next__(self):
return random.randint(1, 6)

# Using the infinite dice roller
dice_roller = InfiniteDiceRoller()
for i, roll in enumerate(dice_roller):
if i >= 10: # Stop after 10 rolls
break
print(f"Roll {i + 1}: {roll}")

              
12345678910111213141516

            
import random

# Infinite dice roller
class InfiniteDiceRoller:
    def __iter__(self):
        return self

    def __next__(self):
        return random.randint(1, 6)

# Using the infinite dice roller
dice_roller = InfiniteDiceRoller()
for i, roll in enumerate(dice_roller):
    if i >= 10:  # Stop after 10 rolls
        break
    print(f"Roll {i + 1}: {roll}")
copy

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Section 6. Chapter 3
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