300+ Numpy Interview Questions for Data Science

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Description: NumPy Interview Preparation CourseThis course is a focused collection of multiple-choice questions designed to prepare you for real-world NumPy interview scenarios. You'll cover core concepts like arrays, indexing, broadcasting, reshaping, vectorization, and performance optimization — all through question-and-explanation format.Unlike basic tutorials, this course helps you think like an interviewer, focusing on why certain answers are correct and how to avoid common pitfalls.NumPy Interview Topics for Data ScienceThis guide focuses exclusively on NumPy, covering fundamental to advanced concepts crucial for data science roles.I. NumPy Fundamentals1. Introduction to NumPy and ndarrayTopics:What is NumPy and why is it essential for data science? (Benefits over Python lists: speed, memory efficiency, mathematical operations, integration with other libraries)Understanding the ndarray object: homogeneous, fixed-size at creation, n-dimensionalKey attributes of ndarray: ndim, shape, size, dtype, itemsize, nbytesDifficulty Level: EasyMCQ Count: 152. Array CreationTopics:Creating arrays from Python lists/tuples using np array()Creating arrays with initial placeholders: np zeros(), np ones(), np full(), np empty()Creating sequences: np arange(), np linspace(), np logspace()Creating identity matrices: np eye()Understanding dtype and type casting (astype())Difficulty Level: EasyMCQ Count: 203. Array Indexing and SlicingTopics:Basic indexing (integer indexing for single elements, negative indexing)Slicing 1D, 2D, and multi-dimensional arrays ([start:st
Category: Development > Data Science > NumPy
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