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References: NumPy and Scientific Computing

  1. NumPy — Wikipedia https://en.wikipedia.org/wiki/NumPy Covers the history, design, and capabilities of the NumPy library, including its n-dimensional array object and mathematical functions used throughout scientific Python programming.

  2. Array data structure — Wikipedia https://en.wikipedia.org/wiki/Array_(data_structure) Explains what arrays are, how they store elements in contiguous memory, and why they enable fast mathematical operations — foundational knowledge for understanding NumPy arrays.

  3. Scientific computing — Wikipedia https://en.wikipedia.org/wiki/Computational_science Introduces the field of computational science, showing why tools like NumPy exist and how Python has become a leading language for scientific data analysis and simulation.

  4. Python Crash Course by Eric Matthes (No Starch Press) Provides a beginner-friendly introduction to Python lists and data structures that serves as direct preparation for understanding NumPy arrays and their slice notation.

  5. Think Python by Allen B. Downey (O'Reilly, free online at greenteapress.com/wp/think-python-2e/) Emphasizes mathematical thinking and algorithmic problem-solving, giving students the conceptual grounding they need to apply NumPy functions to real scientific problems.

  6. NumPy Quickstart Tutorial — Official NumPy Documentation https://numpy.org/doc/stable/user/quickstart.html The official beginner guide to NumPy covering array creation, indexing, slicing, reshaping, and broadcasting — directly maps to every major topic in this chapter.

  7. Python Lists and Arrays — W3Schools https://www.w3schools.com/python/python_lists.asp Explains Python lists with interactive examples; understanding built-in lists is essential context before learning how NumPy arrays extend and improve on them.

  8. NumPy Arrays in Python — GeeksforGeeks https://www.geeksforgeeks.org/python-numpy/ A comprehensive reference with code examples covering NumPy array creation, operations, slicing, and mathematical functions — useful for looking up specific syntax while working through exercises.

  9. NumPy Tutorial for Beginners — Real Python https://realpython.com/numpy-tutorial/ A step-by-step tutorial explaining NumPy arrays, broadcasting, and vectorized operations with clear examples designed for Python learners who are new to scientific computing.

  10. NumPy Arrays — Programiz https://www.programiz.com/python-programming/numpy/array Beginner-friendly explanations of how to create and manipulate NumPy arrays, with runnable examples and simple descriptions well suited for students ages 10–13 exploring scientific Python.