Benchmark: Python 3.13 vs. PyPy 7.3 for Pandas 2.2 and Polars 1.0 Data Pipeline Execution Time Data engineers and analysts frequently evaluate runtime performance when choosing Python interpreters and data manipulation libraries. This benchmark compares the newly released Python 3.13 (stable as of October 2024) and PyPy 7.3.17 (the latest stable PyPy build supporting Python 3.10 syntax, with experimental Python 3.13 compatibility) for executing common data pipelines using Pandas 2.2 and Polars 1.0. Test Methodology We designed four representative data pipeline workloads to test real-world use cases: Small Dataset ETL : Clean, transform, and aggregate a 100MB CSV file with 1M rows, 15 columns (mix of numeric, string, datetime types). Large Dataset ETL : Process a 5GB CSV file with 50M rows, 20 columns, including null value imputation and groupby aggregations. String-Heavy Transformation : Parse, regex extract, and normalize string columns in a 2GB dataset with 10M rows of log data.…