Chapter 9

NumPy Arrays

Arrays versus lists, creating arrays, shape, dtype, axes, indexing, slicing, views, copies, reductions, and array errors.

Subject: Python
Lessons: 7 lessons
Practice: 12 checks
Review: 3 pages

What this chapter does

NumPy arrays are the working data structure for numerical Python. This chapter makes shape, dtype, and axis inspection ordinary before the reader moves into heavier array computation.

Lessons

Read these in order.

The chapter opening gives the main idea. Move through these lessons next; each page reuses ideas from the pages before it.

01
Arrays Versus Lists
Lists as flexible containers, arrays as shaped numerical containers, and why addition differs.
02
Creating Arrays
Creating vectors, matrices, zeros, ones, ranges, and small inspectable arrays.
03
Shape, Dtype, and Axis
The three facts to inspect before array computation: shape, dtype, and axis meaning.
04
Indexing and Slicing Arrays
Selecting scalar values, rows, columns, and slices from one- and two-dimensional arrays.
05
Views and Copies
When slices may share data with the original array and when to copy before mutation.
06
Reductions
Summarizing arrays with sums, means, minima, maxima, and axis-specific reductions.
07
Array Errors
Shape mismatches, dtype surprises, index errors, and the debugging checklist.

•
Conclusion
How shape and dtype inspection prepare readers for array computation.
•
Review
A compact review of arrays, shape, dtype, axes, indexing, views, copies, reductions, and errors.
•
Exercises
Chapter-level practice for creating, inspecting, slicing, copying, and reducing arrays.

You are ready when

Explain how arrays differ from lists.
Create small vectors and matrices.
Inspect shape, dtype, and axis meaning.
Select rows, columns, and slices.
Distinguish views from copies before mutation.
Compute row and column reductions.
Debug common array shape and dtype errors.

Where this leads

Array Computation
Plotting and Inspection
Small Numerical Experiments