This chapter emphasizes the significance of selecting appropriate data structures for improving performance through JIT compilation in data science. It covers the benefits of NumPy arrays over Python lists, the introduction of typed lists and dictionaries, and the trade-offs of data access in Python versus Numba.
Python performance has come a long way in recent times. And it's often the data scientists, with their computational algorithms and large quantities of data, who care the most about this form of performance. It's great to have Stan Seibert back on the show to talk about Python's performance for data scientists. We cover a wide range of tools and techniques that will be valuable for many Python developers and data scientists.
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