Pandas Is Dead. Machine Learning Teams Are Using These Tools Instead.

Author(s): Julia

Originally published on Towards AI.

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Python’s Pandas library has been a long-standing favorite among data analysts due to its powerful DataFrame structure and intuitive API. However, for handling extensive datasets, Pandas is not always the most efficient option, as it is limited by its single-core processing design. When dealing with large datasets on a single machine, exploring faster, more scalable alternatives can be advantageous. In this article we will cover four high-performance Pandas alternatives: Polars, DuckDB, Vaex, and Modin. Each of these libraries has unique features that make them suitable for handling large datasets on single machines with faster processing.

Pandas is an incredibly versatile tool for data manipulation, but it was designed to operate on a single CPU core. This single-threaded approach often leads to slower performance when working with large datasets, as Pandas cannot leverage multiple cores for parallel processing. The result? Lengthy data processing times, especially for operations like filtering, joining, and aggregation, which are common in analytics workflows. For cases where the dataset size remains manageable on a single machine but requires faster processing, switching to an alternative library can make a noticeable difference.

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Published via Towards AI