​Python太慢？那是你没用对方法​！

Summary of Techniques for Improving Memory Efficiency of Python Classes

This article discusses ways to optimize memory usage in Python classes for better performance in both data-intensive projects and object-oriented programming. Three key techniques are introduced to improve memory efficiency in Python classes.

1. Using __slots__

Defining __slots__ in a class can limit the attributes to a fixed set, eliminating the need for a dynamic dictionary for each instance and thereby saving memory. The example provided demonstrates how __slots__ can be used in an Ant class, resulting in significant memory savings when instantiating many instances. The Colony class is used to illustrate creating a large number of Ant instances, showing the practical memory benefits of using __slots__.

2. Using Lazy Initialization

Lazy Initialization delays object creation until necessary, optimizing resource usage. In Python, the cached_property decorator can be used to create properties that are computed only once and then cached. An example with a DataLoader class demonstrates how data can be lazily loaded upon first access, which is beneficial when dealing with large datasets. The DataProcessor class is used to illustrate how data can be processed on demand, saving memory and enhancing performance.

3. Using Generators

Generators in Python are efficient for handling large datasets because they generate values on the fly rather than storing them all at once in memory. The ChunkProcessor class example showcases how generators can be used to load, process, and save data in chunks, conserving memory. It is noted that using pandas' built-in chunksize parameter with pd.read_csv() can further simplify the code. Considerations for parallel processing of generators are also mentioned, indicating that it may require more advanced techniques beyond the scope of this article.

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