Practical uses of structured arrays in NumPy - Mini Project: Build & Apply

Practical uses of structured arrays

📖 Scenario: You work in a small company that tracks employee information. You want to store each employee's name, age, and salary in a way that keeps all data together and easy to access.

🎯 Goal: Create a structured array to hold employee data, filter employees by age, and display the filtered results.

📋 What You'll Learn

Create a structured numpy array with fields: 'name' (string), 'age' (integer), and 'salary' (float).

Create a variable to hold the age threshold for filtering employees.

Use boolean indexing to select employees older than the age threshold.

Print the filtered structured array.

💡 Why This Matters

🌍 Real World

Companies often store employee records with multiple data types like names, ages, and salaries. Structured arrays help keep this data organized and easy to analyze.

💼 Career

Data analysts and scientists use structured arrays to handle mixed data types efficiently, enabling quick filtering and analysis in real-world datasets.

1

Create a structured array with employee data

Create a numpy structured array called employees with these exact entries: ('Alice', 28, 70000.0), ('Bob', 35, 85000.0), ('Charlie', 22, 48000.0). Use the data types: 'U10' for name, int for age, and float for salary.

NumPy

import numpy as np
# Create the structured array 'employees' with the given data and data types
# Your code here

Need a hint?

Use np.array with a list of tuples and specify dtype as a list of tuples with field names and types.

2

Set the age threshold for filtering

Create a variable called age_limit and set it to 25 to use as the age threshold for filtering employees.

NumPy

import numpy as np
employees = np.array([
    ('Alice', 28, 70000.0),
    ('Bob', 35, 85000.0),
    ('Charlie', 22, 48000.0)
], dtype=[('name', 'U10'), ('age', int), ('salary', float)])
# Create the variable 'age_limit' and set it to 25
# Your code here

Need a hint?

Just assign the number 25 to a variable named age_limit.

3

Filter employees older than the age limit

Create a variable called older_employees that selects all entries from employees where the age field is greater than age_limit.

NumPy

import numpy as np
employees = np.array([
    ('Alice', 28, 70000.0),
    ('Bob', 35, 85000.0),
    ('Charlie', 22, 48000.0)
], dtype=[('name', 'U10'), ('age', int), ('salary', float)])
age_limit = 25
# Select employees older than age_limit into 'older_employees'
# Your code here

Need a hint?

Use boolean indexing with employees['age'] > age_limit inside the brackets.

4

Print the filtered employees

Write a print statement to display the older_employees structured array.

NumPy

import numpy as np
employees = np.array([
    ('Alice', 28, 70000.0),
    ('Bob', 35, 85000.0),
    ('Charlie', 22, 48000.0)
], dtype=[('name', 'U10'), ('age', int), ('salary', float)])
age_limit = 25
older_employees = employees[employees['age'] > age_limit]
# Print the 'older_employees' array
# Your code here

Need a hint?

Use print(older_employees) to show the filtered array.