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Introduction to Data Science with Python

NumPy Arrays

Discover how to create and manipulate NumPy arrays, including one- and two-dimensional arrays, using Python lists and tuples. Learn various built-in methods like arange, linspace, zeros, ones, eye, and random generation functions essential for numerical computing and data analysis.

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NumPy

NumPy is a core library for scientific computing in Python, and understanding its APIs is important. It provides data structures and functions for numerical computing across mathematics, science, engineering, and data science. Key features include:

  • It’s a linear algebra library.
  • It’s powerful and incredibly fast.
  • It integrates C/C++ and Fortran code. In this lesson, we’ll cover the key concepts of NumPy, which we’ll frequently use in this course. Let’s start with NumPy arrays, along with some important built-in methods and attributes that are related to these arrays.

Note: For complete documentation of NumPy and to explore more on NumPy, visit its official website.

The primary data structure in NumPy is the homogeneous multidimensional array. It is used as a core data structure in many PyData ecosystem libraries. After installing NumPy, ...