Full Title: NumPy Documentation
Author/Publisher: NumPy Developers / NumPy Project
Link: https://numpy.org/doc/stable/
The official NumPy documentation is the authoritative reference for all NumPy functions, the ndarray object, array creation routines, mathematical and statistical functions, linear algebra, sorting, searching, and I/O utilities. It includes detailed API references, usage examples, and conceptual guides directly aligned with every topic in this course material.
Full Title: NumPy User Guide
Author/Publisher: NumPy Developers / NumPy Project
Link: https://numpy.org/doc/stable/user/index.html
This section of the official NumPy site provides narrative, beginner-friendly explanations of core concepts including what NumPy is, the ndarray object, key array attributes, and how NumPy arrays compare to Python lists. It is especially useful for the introductory and array-creation sections of this material.
Full Title: Python Data Science Handbook: Essential Tools for Working with Data
Author/Publisher: Jake VanderPlas — O'Reilly Media, 2016 (ISBN: 978-1491912058)
Link: https://jakevdp.github.io/PythonDataScienceHandbook/
Chapter 2 of this freely available textbook provides a thorough, example-driven introduction to NumPy, covering ndarray attributes, array creation (including arange, linspace, and random generation), mathematical and statistical functions, linear algebra, sorting, and data I/O. It is one of the most widely recommended resources for learners at all levels.
Full Title: Array Creation Routines — NumPy Reference
Author/Publisher: NumPy Developers / NumPy Project
Link: https://numpy.org/doc/stable/reference/routines.array-creation.html
This reference page catalogues every NumPy array-creation function, including np.array(), np.arange(), np.linspace(), np.zeros(), np.ones(), np.eye(), np.diag(), and all random generation functions. It directly supports the Creating NumPy Arrays section of the course.
Full Title: Mathematical Functions — NumPy Reference
Author/Publisher: NumPy Developers / NumPy Project
Link: https://numpy.org/doc/stable/reference/routines.math.html
This page provides the complete reference for NumPy's mathematical and arithmetic functions, including trigonometric, exponential, logarithmic, and rounding operations. It is an essential companion to the NumPy Functions: Mathematical and Arithmetic Functions topic in this course.
Full Title: Linear Algebra (numpy.linalg) — NumPy Reference
Author/Publisher: NumPy Developers / NumPy Project
Link: https://numpy.org/doc/stable/reference/routines.linalg.html
This reference documents all functions in the numpy.linalg module, covering matrix multiplication, determinants, inverses, eigenvalues, singular value decomposition, and solving linear systems. It directly supports the Linear Algebra Functions section of this course material.
Full Title: Introduction to Computation and Programming Using Python: With Application to Understanding Data
Author/Publisher: John V. Guttag — MIT Press, 3rd Edition, 2021 (ISBN: 978-0262542364)
Link: https://mitpress.mit.edu/9780262542364/
This textbook provides rigorous coverage of Python programming foundations and introduces NumPy arrays, statistical computations, and data manipulation in the context of scientific computing. It is useful for learners who want to understand the conceptual differences between Python lists and NumPy arrays alongside practical programming skills.
Full Title: Statistics and Sorting/Searching Routines — NumPy Reference
Author/Publisher: NumPy Developers / NumPy Project
Link: https://numpy.org/doc/stable/reference/routines.statistics.html
This section of the NumPy reference manual covers statistical functions such as np.mean(), np.median(), np.std(), np.var(), and percentile calculations, as well as sorting and searching functions like np.sort(), np.argsort(), and np.where(). It directly supports both the Statistical Functions and Sorting and Searching Functions topics.
Full Title: Numerical Python: Scientific Computing and Data Science Applications with NumPy, SciPy, and Matplotlib
Author/Publisher: Robert Johansson — Apress, 2nd Edition, 2019 (ISBN: 978-1484242452)
Link: https://www.apress.com/gp/book/9781484242452
This textbook offers in-depth, practical coverage of NumPy for scientific computing, including array creation, manipulation, mathematical and statistical functions, linear algebra, and data I/O. It is particularly valuable for learners who want to move beyond introductory use and apply NumPy to real computational problems.
Full Title: Input/Output and Array Manipulation Routines — NumPy Reference
Author/Publisher: NumPy Developers / NumPy Project
Link: https://numpy.org/doc/stable/reference/routines.io.html
This reference page covers NumPy's data loading and saving functions (np.load(), np.save(), np.loadtxt(), np.genfromtxt()) as well as array manipulation routines including reshaping, stacking, splitting, and transposing arrays. It supports both the Input/Output and Data Loading and Array Manipulation Functions sections of this course.