Defining structured dtypes in NumPy - Mini Project: Build & Apply

Defining structured dtypes in NumPy

📖 Scenario: You work in a small company that collects information about employees. You want to store each employee's name, age, and salary in a structured way using NumPy.

🎯 Goal: Create a NumPy structured array with a custom data type that holds employee name as a string, age as an integer, and salary as a float. Then, display the array.

📋 What You'll Learn

Create a structured dtype with fields: 'name' (string of length 10), 'age' (integer), and 'salary' (float).

Create a NumPy array called employees with exactly 3 entries using the structured dtype.

Fill the array with these exact employee data: ('Alice', 30, 70000.0), ('Bob', 25, 48000.5), ('Charlie', 35, 120000.0).

Print the employees array to show the stored data.

💡 Why This Matters

🌍 Real World

Structured dtypes help store complex data like employee records, sensor data, or any tabular data with mixed types efficiently in NumPy.

💼 Career

Knowing how to define and use structured dtypes is useful for data scientists and analysts working with heterogeneous datasets in Python.

1

Create the structured dtype

Create a NumPy structured dtype called employee_dtype with these fields: 'name' as a string of length 10, 'age' as an integer, and 'salary' as a float.

NumPy

import numpy as np
# Create the structured dtype employee_dtype here

Need a hint?

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

2

Create the employees array

Create a NumPy array called employees with 3 entries using the employee_dtype dtype. Fill it with these exact data: ('Alice', 30, 70000.0), ('Bob', 25, 48000.5), ('Charlie', 35, 120000.0).

NumPy

import numpy as np
employee_dtype = np.dtype([('name', 'U10'), ('age', 'i4'), ('salary', 'f8')])
# Create the employees array with the given data here

Need a hint?

Use np.array with a list of tuples and specify dtype=employee_dtype.

3

Access and print the employee names

Use a for loop with variable employee to iterate over employees. Inside the loop, print only the name field of each employee.

NumPy

import numpy as np
employee_dtype = np.dtype([('name', 'U10'), ('age', 'i4'), ('salary', 'f8')])
employees = np.array([('Alice', 30, 70000.0), ('Bob', 25, 48000.5), ('Charlie', 35, 120000.0)], dtype=employee_dtype)
# Use a for loop to print each employee's name

Need a hint?

Use for employee in employees: and inside print employee['name'].

4

Print the full employees array

Print the entire employees array to display all employee data.

NumPy

import numpy as np
employee_dtype = np.dtype([('name', 'U10'), ('age', 'i4'), ('salary', 'f8')])
employees = np.array([('Alice', 30, 70000.0), ('Bob', 25, 48000.5), ('Charlie', 35, 120000.0)], dtype=employee_dtype)
for employee in employees:
    print(employee['name'])
# Print the full employees array here

Need a hint?

Use print(employees) to show the full array.