Once you can create NumPy arrays, the next essential skill is getting data back out of them. Indexing and slicing let you pull a single element, a row, a column, a sub-block, or a scattered set of values from an array. In engineering work this is how you isolate a sensor channel from a data matrix, trim a transient from the start of a time series, or grab every tenth sample from a long measurement record.
This topic moves from simple to advanced. Basic integer indexing uses positions (remember: counting starts at 0, and negative indices count from the end). Slicing uses the start:stop:step pattern and extends to multiple dimensions, such as A[1:4, 0:2]. Boolean (mask) indexing builds an array of True/False values from a condition and uses it to filter data, which is how you answer questions like "which readings exceeded the limit?" Fancy indexing passes an array or list of integers to select elements in any order, with repeats allowed. Finally, np.where gives you conditional selection: either the positions where a condition holds, or an element-wise choice between two values.
As you watch, pay close attention to three things: whether the result is a view into the original array or a copy (slices are views, fancy and Boolean indexing produce copies), how the shape of the result follows from the shape of the index, and how multi-dimensional indexing separates row and column specifications with a comma. Try every example in your own notebook and deliberately change the indices to see what breaks and why.
A focused tutorial on NumPy array indexing from the WsCube Tech NumPy Mastery series, covering how to access individual elements by position in one-dimensional and multi-dimensional arrays. Expect coverage of zero-based indexing, negative indexing, and the row/column comma notation used for 2D arrays, demonstrated live in Python. The pacing is beginner-friendly with worked examples rather than theory.
It is the most direct match for the Basic Integer Indexing subtopic and establishes the position-based notation that all the later techniques (slicing, masking, fancy indexing) build on.
From the same NumPy course series, this video reviews array creation and then works through slicing operations and array attributes. Look for the start:stop:step syntax, omitting values to take everything from or to an endpoint, step sizes for skipping elements, and slicing along two dimensions at once. The attributes portion (shape, size, dtype, ndim) helps you predict and verify the shape of each slice you extract.
It covers the Slicing Arrays subtopic in depth while reinforcing array creation from earlier in the module, and the attribute discussion gives you a practical way to check that a slice returned what you expected.
A short, targeted quick-tip video on fancy indexing, the technique of passing a list or array of integer indices to select multiple arbitrary elements at once. In three minutes it demonstrates how fancy indexing differs from a simple slice, including selecting elements out of order or repeating selections. It is the natural follow-on once basic indexing and slicing feel comfortable.
It addresses the Fancy (Integer Array) Indexing subtopic specifically and concisely, which is the step most students find unintuitive after mastering ordinary slices.
Together these videos target MO3 directly: the first builds integer indexing, the second builds slicing and the shape awareness needed to confirm your selections, and the third introduces fancy integer-array indexing. The creation and attribute material in the second video also reinforces MO1. Boolean masking and np.where are listed as subtopics but are only lightly touched by these videos, so plan to supplement them with the course notes and practice problems, where you will combine conditions with comparison operators and use np.where to filter or recode engineering datasets before computing statistics in later topics.
These videos were chosen from YouTube on October 2, 2026. Each has captions; the player turns them on. Videos on YouTube can be changed or removed — if one no longer plays, tell your instructor. If a player does not appear, use its Watch on YouTube link.