PyIceberg is a Python library designed to manage large tabular datasets using the Iceberg open table format, offering robust metadata and schema evolution features. A challenge faced by users is efficiently processing data from these large tables, as the typical method of using table.scan(...).to_pandas() is limited by single-core processing and memory constraints, potentially leading to an OutOfMemoryError. PyIceberg 0.10 introduces an integration with Bodo's high-performance distributed DataFrame engine through the Table.to_bodo() method. This integration allows for automatic multi-core and multi-node parallelism, optimized query plans, and streaming execution for datasets larger than cluster memory, all while maintaining compatibility with the Pandas API. The Bodo engine leverages lazy query construction, plan optimization, automatic filter pushdown, and parallel compute to minimize memory usage and enhance performance, allowing users to process large datasets efficiently without rewriting existing Pandas code.