Learn how to create lazy properties in Python using @property and functools.lru_cache for efficient computation.

Lazy Properties with @property and functools.lru_cache

Lazy properties delay computation until the value is actually needed. This is useful for expensive calculations or data loading. In Python, you can achieve this by combining @property with functools.lru_cache.

Why Lazy Properties?

Expensive computations can slow down your application if done upfront. Lazy properties calculate values only on first access, then cache them for subsequent calls.

Python Implementation

Use @property to make a method act like an attribute and @lru_cache to memoize it:

import functools

class DataProcessor:
    def __init__(self, value):
        self.value = value

    @property
    @functools.lru_cache(maxsize=None)
    def expensive_computation(self):
        print(\\"Computing...\\")
        return sum(i * i for i in range(self.value))

# Test Lazy Property
processor = DataProcessor(10000)
print(processor.expensive_computation)  # First call computes
print(processor.expensive_computation)  # Second call uses cache

The first access prints \\"Computing...\\" and calculates. The second access is instant, thanks to caching.

Key Takeaways

Lazy properties optimize performance for costly calculations. Combining @property and @lru_cache is an elegant and Pythonic solution.

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