An interactive HTML lesson on NumPy for engineering that guides learners through ndarray basics, array creation, vectorized operations, broadcasting, indexing, linear algebra, and practical engineering applications with explanations and embedded code examples.
Topics
This module covers 9 topics. Open a topic to see its subtopics.
Module Content
15 items.
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A derived version of Assignment 5 that directs students to use NumPy library functions and the Newton-Raphson iterative method specifically to numerically solve for the depth of flow in an open channel, submitted as a Colab notebook.
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The original Assignment 5 that instructs students to apply Manning's Equation and an iterative numerical technique to calculate the depth of flow in an open channel, submitted as a Colab notebook.
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A reading that introduces NumPy's ndarray data structure, explaining how it differs from native Python data structures and why it is foundational for numerical computing in engineering; learners use it to build the conceptual and practical groundwork needed before applying NumPy to engineering calculations.
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This HTML reference page links learners to the official NumPy documentation covering array indexing, slicing, fancy indexing, reshaping, transposing, and array manipulation, serving as a supplementary reading resource that students and instructors can consult to deepen their understanding of these NumPy techniques in an engineering computation context.
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This HTML page is a reference resource linking learners to the official NumPy documentation along with curated readings on NumPy fundamentals—including the ndarray structure, array creation methods, and built-in NumPy functions—which students and instructors can use as a supplementary reference while working through engineering numerical computation topics in the module.
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A reading that surveys NumPy's built-in function families — element-wise math and arithmetic, trigonometric and exponential functions, statistical routines like mean, median, standard deviation, min/max and axis-wise sums — which learners use as a reference when choosing the right ready-made function for an engineering calculation.
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An introductory lesson explaining what NumPy is and why the ndarray — a contiguous, fixed-type N-dimensional array — makes whole-array operations far faster than plain Python, giving learners the conceptual foundation before they start writing array code.
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A lesson on getting data into arrays, covering conversion from Python lists and tuples plus generator functions for time vectors, evenly spaced sample grids, zeros and ones matrices, identity matrices, and random values; learners follow it to create the starting arrays for any computation.
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A lesson on vectorized arithmetic and universal functions, showing how expressions like a + b, a * 2, and np.sin(a) apply element by element in compiled code, so learners can replace slow Python for-loops with concise, fast array expressions.
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A lesson explaining NumPy's broadcasting rules for combining arrays of different but compatible shapes by stretching length-1 or missing dimensions, helping learners write algebra-like code without manually tiling or copying data.
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A lesson on retrieving data from arrays through basic indexing, slicing, and fancy (array-based) indexing to extract single elements, rows, columns, sub-blocks, or scattered values — demonstrated with tasks like isolating a sensor channel or trimming a transient from a time series.
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A lesson on restructuring arrays with reshape, transpose, and joining or splitting operations, used when learners must convert a 1-D sensor stream into a table, orient a matrix for multiplication, or combine and separate results from multiple trials.
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A lesson on the numpy.linalg module's matrix and vector operations — solving systems, matrix products, and least-squares fitting — framed around engineering applications such as circuit analysis, static equilibrium, finite element models, vibration, and control systems.
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A combined module page that compiles all of the unit's readings on NumPy — functions, the ndarray, array creation, element-wise operations, broadcasting, indexing, reshaping, and linear algebra — into a single document for continuous reading or review.