Discover how to run parallel execution of pandas operations on multiple CPUs using pandarallel.

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As the amount of data being processed continues to grow, traditional data manipulation and analysis methods are challenged to keep up with the increasing computational demands. While pandas has become the de facto standard for data manipulation in Python, its operations can become time-consuming when applied to large datasets.

To address this issue, we can leverage the open-source pandarallel library. The pandarallel library is a useful extension to pandas that enables the parallel execution of various operations using multiple CPUs. This results in significant speed-ups because it’s designed to distribute processing across all available cores.

A major drawback of pandas is that it uses only one core by default. Therefore, it’s easy to see how the pandarallel can provide performance improvements by tapping into multiple cores. The downside to pandarallel is that it needs twice the memory that standard pandas operations normally require.

Note: Parallelization comes with an inherent cost due to the instantiating of new processes and the sending of data via shared memory. Therefore, parallelization is efficient only if the amount of computation is high enough. For small datasets, the use of parallelization may not deliver performance benefits.

In this lesson, we’ll work with an online retail transaction dataset, as shown below:

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