Generators

Concepts: pyGenerators

When working with large datasets, loading everything into memory at once is inefficient. Generators solve this by producing values on demand. Lazy Evaluation with Yield Memory-Efficient Iteration Generators are ideal for processing large files line by line, streaming data, or creating infinite sequences without exhausting memory. Generators shine in scenarios where loading all data at once would be wasteful or impossible. Here are the key properties that make them special. You can chain generators together to build data processing pipelines, passing each generator as an input to the next without loading intermediate results into memory. The itertools module in the standard library provides powerful tools for combining and manipulating generators without materializing data into memory.