Handling Larger-than-Memory Datasets with Polars LazyFrame

Pandas is the de-facto standard when working with tabular datasets in Python, but it encounters significant challenges when handling large datasets. The core issue stems from Pandas’ architecture, which relies entirely on in-memory processing. This design requires loading the entire dataset into memory, resulting in a hard limit on the size of datasets that Pandas can process. When data volumes…