The article discusses the limitations of using Pandas for large-scale data processing and introduces alternatives like Polars, DuckDB, and PySpark based on performance, memory usage, and ease of use. It highlights that while Pandas excels in quick exploratory analysis on small datasets, other tools offer significant advantages for larger files or distributed environments.
For decision-making, the article provides a framework: use Pandas for small data exploration, Polars for fast processing of medium-sized datasets on single machines, DuckDB for querying large files without loading them into memory, and PySpark for handling massive datasets across clusters. Each tool is evaluated based on speed, scalability, and specific use cases. A performance benchmark script comparing Pandas, Polars, and DuckDB further illustrates the practical benefits of these alternatives in real-world scenarios.
Read the full article at Towards AI - Medium
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