Efficient and scalable Iceberg I/O for Python data workloads is essential, yet achieving scalability often involves trade-offs between Pythonic experience and performance. PyIceberg and Daft struggle with scalability and performance issues, whereas Spark offers scalability but lacks native Python ergonomics. The Bodo DataFrame library, however, provides a compelling solution by acting as a drop-in replacement for Pandas and enabling scalability across multiple cores and nodes using high-performance computing techniques without requiring changes in syntax or JVM dependencies. In a benchmark evaluating the performance of copying a large Iceberg table stored in Amazon S3 using Bodo, Spark, PyIceberg, and Daft, Bodo outperformed Spark by up to three times, completing the task in under 12 minutes on a four-node cluster. PyIceberg and Daft were unable to complete the benchmark due to lack of multi-node support and memory limitations, respectively. Bodo's success is attributed to its MPI-based parallelism, streaming execution, and efficient Iceberg I/O implementation, offering a Python-native syntax that simplifies scaling Python workloads from laptops to clusters.