Creating structured arrays in NumPy - Try It Yourself

Creating Structured Arrays with NumPy

📖 Scenario: You work in a small company that tracks employee information. You want to store each employee's name, age, and salary in a structured way so you can analyze the data easily.

🎯 Goal: Create a NumPy structured array to hold employee data with fields for name, age, and salary. Then display the array.

📋 What You'll Learn

Use NumPy to create a structured array

Define the data type with fields: 'name' as string, 'age' as integer, and 'salary' as float

Fill the array with given employee data

Print the structured array

💡 Why This Matters

🌍 Real World

Structured arrays help store complex data like employee records, sensor data, or survey results in an organized way.

💼 Career

Data scientists and analysts often use structured arrays to handle mixed data types efficiently for analysis and reporting.

1

Create the employee data list

Create a list called employee_data with these exact tuples: ("Alice", 30, 70000.0), ("Bob", 25, 48000.0), and ("Charlie", 35, 120000.0).

NumPy

# Create the list employee_data with the given tuples
# Your code here

Need a hint?

Use square brackets to create a list of tuples. Each tuple has name, age, and salary.

2

Define the structured array data type

Create a variable called dtype that defines a NumPy structured data type with fields: 'name' as 'U10' (string of max length 10), 'age' as int, and 'salary' as float.

NumPy

employee_data = [("Alice", 30, 70000.0), ("Bob", 25, 48000.0), ("Charlie", 35, 120000.0)]
# Define the dtype variable for the structured array
# Your code here

Need a hint?

Use a list of tuples to define the dtype. Each tuple has the field name and the data type.

3

Create the structured array

Create a NumPy structured array called employees using np.array() with employee_data and dtype.

NumPy

import numpy as np

employee_data = [("Alice", 30, 70000.0), ("Bob", 25, 48000.0), ("Charlie", 35, 120000.0)]
dtype = [('name', 'U10'), ('age', int), ('salary', float)]
# Create the structured array employees using np.array with employee_data and dtype
# Your code here

Need a hint?

Use np.array() with the data and the dtype to create the structured array.

4

Print the structured array

Print the variable employees to display the structured array.

NumPy

import numpy as np

employee_data = [("Alice", 30, 70000.0), ("Bob", 25, 48000.0), ("Charlie", 35, 120000.0)]
dtype = [('name', 'U10'), ('age', int), ('salary', float)]
employees = np.array(employee_data, dtype=dtype)
# Print the employees array
# Your code here

Need a hint?

Use print(employees) to show the structured array.