A columnar storage format keeps values from the same table column together, making dense tabular data more efficient to store and access than with row-based storage. section 2.3.1
Polars uses columnar storage alongside high-performance execution, improved memory management, and large-dataset handling. Its DataFrame API resembles pandas, while eager and lazy modes support different execution patterns. section 2.3.1
Choosing a tabular-data engine
For massive or dense tabular datasets, evaluate Polars alongside pandas and Spark. Check whether required pandas functions are available before refactoring, then compare the options against dataset size and usage. section 2.3.1
