In a world that has become increasingly data-centric, efficiently working with numerical data has become more important than ever. With the growth of fields like machine learning, deep learning, and artificial intelligence, the demand for users with experience performing numerical computations quickly and accurately has never been higher. The foundational Python library that allows users to perform efficient numerical data processing is NumPy (Numerical Python). NumPy provides powerful utilities for working with multi-dimensional arrays and a rich suite of mathematical operations, all of which perform faster and use less memory than standard Python lists.
NumPy is the foundational library of many other prominent data libraries/codebases/scientific libraries. Pandas’ implementation relies on using NumPy arrays to manipulate data sets. SciPy builds while using NumPy as a core for scientific and technical computing. Scikit-learn uses NumPy arrays for training models and evaluating them. As such, learning about the NumPy library is a critical first step in working effectively with data.
The NumPy Cheat Sheet presented here by the United States Data Science Institute (USDSI®) brings together essential commands and operations in a simplified and easy-to-reference format. USDSI®’s globally recognized, vendor-neutral data science certifications are developed to help learners build strong analytical foundations rooted in practical numerical computing and real-world data handling.
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