What is multiprocessing in data science?
An example of an AI chatbot conversation about multiprocessing in data science.
What is multiprocessing in data science?
Multiprocessing in data science refers to the ability to run multiple processes simultaneously to speed up data analysis.
How does it improve performance compared to single-threading?
It allows for parallel execution of tasks, reducing the time needed for computations, especially with large datasets.
Can you give me an example of how to implement it in Python?
Sure! You can use the multiprocessing module in Python. For example, you can create a pool of workers to process data in parallel.
What are some common pitfalls to avoid when using multiprocessing?
Common pitfalls include issues with shared state, increased complexity, and overhead from process management.
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