Why structured arrays matter in NumPy - See It in Action

Why structured arrays matter

📖 Scenario: Imagine you work at a small library. You have a list of books, and each book has a title, an author, and a year it was published. You want to keep all this information together in one place so you can easily find and use it.

🎯 Goal: You will create a structured array using numpy to store information about books. Then, you will access and print the data to see why structured arrays are useful for keeping related data organized.

📋 What You'll Learn

Use numpy to create a structured array

Include fields: title (string), author (string), and year (integer)

Access and print the structured array data

💡 Why This Matters

🌍 Real World

Libraries, stores, and many businesses keep records with different types of information together, like names, dates, and numbers.

💼 Career

Data scientists and analysts often use structured arrays to organize and analyze complex data efficiently.

1

Create a structured array with book data

Import numpy as np. Create a structured array called books with three entries. Each entry should have these fields: title (string of max length 20), author (string of max length 20), and year (integer). Use these exact values: ('The Hobbit', 'Tolkien', 1937), ('1984', 'Orwell', 1949), and ('Python 101', 'Smith', 2020).

NumPy

# Import numpy and create the structured array 'books' with the given entries
# Your code here

Need a hint?

Use np.array with a dtype that defines the fields and their types.

2

Create a variable to hold the field names

Create a variable called fields that holds the list of field names in the books structured array. Use books.dtype.names to get the field names.

NumPy

import numpy as np

dtype = [('title', 'U20'), ('author', 'U20'), ('year', 'i4')]
books = np.array([
    ('The Hobbit', 'Tolkien', 1937),
    ('1984', 'Orwell', 1949),
    ('Python 101', 'Smith', 2020)
], dtype=dtype)

# Create 'fields' variable with the field names from 'books'
# Your code here

Need a hint?

Use books.dtype.names to get the tuple of field names.

3

Access the 'author' field from the structured array

Use the variable books to get all the authors. Create a variable called authors that contains the author field from books.

NumPy

import numpy as np

dtype = [('title', 'U20'), ('author', 'U20'), ('year', 'i4')]
books = np.array([
    ('The Hobbit', 'Tolkien', 1937),
    ('1984', 'Orwell', 1949),
    ('Python 101', 'Smith', 2020)
], dtype=dtype)

fields = books.dtype.names

# Create 'authors' variable with the 'author' field from 'books'
# Your code here

Need a hint?

Access a field in a structured array by using array['field_name'].

4

Print the structured array and the authors

Print the entire books structured array. Then print the authors variable.

NumPy

import numpy as np

dtype = [('title', 'U20'), ('author', 'U20'), ('year', 'i4')]
books = np.array([
    ('The Hobbit', 'Tolkien', 1937),
    ('1984', 'Orwell', 1949),
    ('Python 101', 'Smith', 2020)
], dtype=dtype)

fields = books.dtype.names

authors = books['author']

# Print the 'books' array and then print 'authors'
# Your code here

Need a hint?

Use two print statements: one for books and one for authors.