Phase 2: NumPy for Data · ~25 minutes · Python · NumPy

Indexing Tricks

Two different ways to select the exact same values from an array can hand you back a view or a copy — and only one of those lets you accidentally edit the original.

Hiring signal: Checks np.shares_memory() before assuming a modification will or won't affect the original array, instead of guessing based on which syntax looks similar

What you will learn

Select array elements using slicing, boolean masks, and fancy (array/list) indexing
Explain the difference between a NumPy view and a NumPy copy
Verify whether an indexing operation returns a view or a copy using np.shares_memory()
Predict whether modifying an indexing result affects the original array

Introduction

Type: Learn Languages: Python, NumPy Prerequisites: Lesson 03 (Broadcasting) Time: ~25 minutes

Objective

Learning objectives

Select array elements using slicing, boolean masks, and fancy (array/list) indexing
Explain the difference between a NumPy view and a NumPy copy
Verify whether an indexing operation returns a view or a copy using np.shares_memory()
Predict whether modifying an indexing result affects the original array

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

Create a free account to unlock Phase 0 and Phase 1 of every course — no credit card.