Full Title: NumPy Documentation — Indexing, Slicing, and Reshaping
Publisher: NumPy Developers / NumPy Project
Link: https://numpy.org/doc/stable/user/basics.indexing.html
The official NumPy documentation provides authoritative, comprehensive coverage of all indexing strategies — including basic integer indexing, slicing, Boolean masking, and fancy (integer array) indexing — directly aligned with MO3. It also includes detailed reference pages for reshaping, transposing, stacking, and splitting, making it the primary go-to reference for every topic in this module.
Full Title: Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter, 3rd Edition
Author: Wes McKinney
Citation: McKinney, W. (2022). Python for Data Analysis (3rd ed.). O'Reilly Media. ISBN: 978-1-098-10403-0
Chapter 4 of this widely adopted textbook provides an accessible, example-driven treatment of NumPy array indexing and manipulation — including slicing, Boolean masking, fancy indexing, np.where, reshaping, and transposing. Its applied examples involving real datasets make it especially effective for helping students reach the Apply level demanded by MO3.
Full Title: NumPy Reference — Array Manipulation Routines
Publisher: NumPy Developers / NumPy Project
Link: https://numpy.org/doc/stable/reference/routines.array-manipulation.html
This official reference page catalogs every array manipulation function in NumPy, including reshape, ravel, flatten, transpose, stack, hstack, vstack, split, expand_dims, and squeeze. It serves as an essential quick-reference for the reshaping and manipulation topics in this module.
Full Title: NumPy Illustrated: The Visual Guide to NumPy
Author: Lev Maximov
Link: https://betterprogramming.pub/numpy-illustrated-the-visual-guide-to-numpy-3b1d4976de1d
This highly regarded visual guide uses diagrams to illustrate how indexing, slicing, reshaping, stacking, and transposing transform array structure, making abstract operations concrete. It is particularly valuable for students who benefit from visual representations when learning to apply array selection and manipulation techniques at the Apply level of MO3.
Full Title: From Python to NumPy
Author: Nicolas P. Rougier
Link: https://www.labri.fr/perso/nrougier/from-python-to-numpy/
This freely available, open-access book offers deep explanations of NumPy's indexing mechanisms — including views vs. copies, fancy indexing, and Boolean masking — alongside array manipulation strategies. Its emphasis on understanding why these operations work as they do supports the applied problem-solving demanded by MO3.
Full Title: SciPy Lecture Notes — 1.3 NumPy: Creating and Manipulating Numerical Data
Author: SciPy Lecture Notes Contributors (Varoquaux, Gouillart, Vahtras, et al.)
Link: https://scipy-lectures.org/intro/numpy/index.html
These peer-reviewed, community-maintained lecture notes cover NumPy indexing (basic, Boolean, fancy), reshaping, transposing, and array manipulation with clear examples and exercises. They are especially well-suited for engineering students because examples are framed around numerical and scientific data workflows, directly supporting MO3.
Full Title: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition — Appendix: NumPy
Author: Aurélien Géron
Citation: Géron, A. (2022). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (3rd ed.). O'Reilly Media. ISBN: 978-1-098-12597-4
The NumPy appendix of this widely used applied machine learning textbook provides concise, practical coverage of indexing (including np.where), Boolean masking, fancy indexing, and array reshaping in the context of preparing real engineering and data science datasets. This contextual framing helps students see the applied purpose of every operation targeted by MO3.