Tools 1.1Lesson outlineLesson 1 of 7 in Module 1
Why NumPy
Say what an array holds that a list does not, and why that makes it faster and smaller.
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Lesson outline: Why NumPy
In this lesson7 parts
- 01Summing a list with a Python loop, and timing it
- 02Why NumPy, and array oriented computing
- 03References, objects and the packed buffer
- 04Creating an array with np.array, and summing it
- 05How the speed difference scales with array size
- 06Fixed width types, and object dtype
- 07Recap and your challenge
Key terms: Why NumPy
The words this lesson introduces, each in one line. The module’s glossary collects them all, and the AI glossary has every term across the courses.
- Array
- Values in a typed buffer, interpreted through a shape
- Buffer
- Contiguous storage for the array's elements
- Object dtype
- References to Python objects instead of fixed-width numeric values
Quiz: Why NumPy
Your first pick on each question is the one that counts. Getting one wrong here is how the lesson sticks.