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Benchmarking High-Performance Pandas Alternatives For Data Analytics
What if there are better alternatives to Pandas — here they are!
Pandas is an extraordinarily powerful tool in Python’s data science ecosystem, offering several data manipulation and cleaning capabilities. However, while great for medium-sized datasets, it can face performance issues when dealing with large datasets, prompting the need for high-performance alternatives.
This comprehensive article introduces some of these alternatives and compares them through benchmarking in terms of data loading time, execution time, memory usage, scalability, and ease of use.
Understanding the Benchmarks
Benchmarks are a point of reference against which software or hardware may be compared for performance evaluation.
This reference is relevant in software performance optimization as it allows us to measure the efficiency of different methods, algorithms, or tools.
In this context, the key metrics for benchmarking data manipulation libraries include execution time, memory usage, scalability, and ease of use.
