Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Lære Lazy Evaluation in Python: Optimizing Memory and Performance | Mastering Iterators and Generators in Python
Python Advanced Concepts

bookLazy 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.

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

Var alt klart?

Hvordan kan vi forbedre det?

Tak for dine kommentarer!

Sektion 6. Kapitel 3

Spørg AI

expand

Spørg AI

ChatGPT

Spørg om hvad som helst eller prøv et af de foreslåede spørgsmål for at starte vores chat

Awesome!

Completion rate improved to 3.13

bookLazy Evaluation in Python: Optimizing Memory and Performance

Stryg for at vise menuen

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.

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

Var alt klart?

Hvordan kan vi forbedre det?

Tak for dine kommentarer!

Sektion 6. Kapitel 3
some-alt