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

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

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

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

Hint

Use print(older_employees) to show the filtered array.

Practice

1. What is the main advantage of using numpy structured arrays in data science?

easy

A. They allow storing different data types in one array with named fields.

B. They only store integers efficiently.

C. They automatically visualize data.

D. They replace all pandas functionality.

Solution

1. Step 1: Understand structured arrays

   Structured arrays let you store mixed data types in one array with named fields, like columns in a table.

2. Step 2: Compare options

   Only They allow storing different data types in one array with named fields. correctly describes this main advantage. Others are incorrect or unrelated.

3. Final Answer:

   They allow storing different data types in one array with named fields. -> Option A

4. Quick Check:

   Structured arrays = mixed types + named fields [OK]

Hint: Remember: structured arrays hold mixed types with names [OK]

Common Mistakes:

• Thinking structured arrays only store one data type
• Confusing structured arrays with visualization tools
• Assuming structured arrays replace pandas completely

2. Which of the following is the correct way to define a structured array with fields 'name' (string) and 'age' (integer) in numpy?

easy

A. np.array([(b'Alice', 25), (b'Bob', 30)], dtype=[('name', 'f8'), ('age', 'S10')])

B. np.array([('Alice', 25), ('Bob', 30)], dtype=[('name', 'int'), ('age', 'float')])

C. np.array([(25, 'Alice'), (30, 'Bob')], dtype=[('age', 'i4'), ('name', 'S10')])

D. np.array([(b'Alice', 25), (b'Bob', 30)], dtype=[('name', 'S10'), ('age', 'i4')])

Solution

1. Step 1: Check field names and types

   The fields are 'name' as string (bytes) and 'age' as integer. 'S10' means string of max 10 bytes, 'i4' means 4-byte integer.

2. Step 2: Validate each option

   np.array([(b'Alice', 25), (b'Bob', 30)], dtype=[('name', 'S10'), ('age', 'i4')]) matches the correct dtype and data format. np.array([('Alice', 25), ('Bob', 30)], dtype=[('name', 'int'), ('age', 'float')]) uses wrong types. np.array([(25, 'Alice'), (30, 'Bob')], dtype=[('age', 'i4'), ('name', 'S10')]) swaps fields order and data. np.array([(b'Alice', 25), (b'Bob', 30)], dtype=[('name', 'f8'), ('age', 'S10')]) swaps types incorrectly.

3. Final Answer:

   np.array([(b'Alice', 25), (b'Bob', 30)], dtype=[('name', 'S10'), ('age', 'i4')]) -> Option D

4. Quick Check:

   Correct dtype and data order = np.array([(b'Alice', 25), (b'Bob', 30)], dtype=[('name', 'S10'), ('age', 'i4')]) [OK]

Hint: Match field names and types exactly in dtype [OK]

Common Mistakes:

• Using wrong data types in dtype
• Swapping field order and data
• Not using byte strings for fixed-length strings

3. Given the structured array:

data = np.array([(b'Alice', 25), (b'Bob', 30), (b'Carol', 22)], dtype=[('name', 'S10'), ('age', 'i4')])
sorted_data = np.sort(data, order='age')

What is the output of sorted_data['name']?

medium

A. [b'Carol' b'Alice' b'Bob']

B. [b'Alice' b'Bob' b'Carol']

C. [b'Bob' b'Carol' b'Alice']

D. [b'Carol' b'Bob' b'Alice']

Solution

1. Step 1: Understand sorting by 'age'

   The array is sorted by the 'age' field ascending: 22 (Carol), 25 (Alice), 30 (Bob).

2. Step 2: Extract 'name' field after sorting

   After sorting, the 'name' field order matches sorted ages: Carol, Alice, Bob.

3. Final Answer:

   [b'Carol' b'Alice' b'Bob'] -> Option A

4. Quick Check:

   Sort by age ascending = Carol, Alice, Bob [OK]

Hint: Sort by field then check that field's order [OK]

Common Mistakes:

• Assuming original order remains after sort
• Mixing up ascending vs descending order
• Confusing field names when accessing

4. Consider this code snippet:

data = np.array([(b'Alice', 25), (b'Bob', 30)], dtype=[('name', 'S10'), ('age', 'i4')])
filtered = data[data['age'] > 25]

What is the error in this code?

medium

A. ValueError because comparison with > is invalid on structured arrays.

B. No error; it correctly filters entries with age > 25.

C. TypeError because 'age' field is not accessible.

D. SyntaxError due to wrong indexing syntax.

Solution

1. Step 1: Check filtering syntax

   Filtering structured arrays by a field with a condition like data['age'] > 25 is valid and returns a boolean mask.

2. Step 2: Confirm no errors

   The code correctly filters rows where age is greater than 25, so no error occurs.

3. Final Answer:

   No error; it correctly filters entries with age > 25. -> Option B

4. Quick Check:

   Filtering with boolean mask on field works [OK]

Hint: Use boolean masks on fields to filter structured arrays [OK]

Common Mistakes:

• Thinking structured arrays can't be filtered by fields
• Confusing syntax for filtering
• Assuming comparison operators don't work on fields

5. You have a structured array of employees with fields 'name' (string), 'age' (int), and 'salary' (float). You want to find the average salary of employees older than 30. Which code snippet correctly does this?

hard

A. avg_salary = data[data['salary'] > 30]['age'].mean()

B. avg_salary = np.mean(data['salary'] > 30)

C. avg_salary = data['salary'][data['age'] > 30].mean()

D. avg_salary = data['salary'].mean(data['age'] > 30)

Solution

1. Step 1: Filter employees older than 30

   Use boolean mask data['age'] > 30 to select salaries of employees older than 30.

2. Step 2: Calculate mean salary of filtered data

   Apply .mean() on the filtered salary array to get average salary.

3. Final Answer:

   avg_salary = data['salary'][data['age'] > 30].mean() -> Option C

4. Quick Check:

   Filter by age, then mean salary = avg_salary = data['salary'][data['age'] > 30].mean() [OK]

Hint: Filter first, then compute mean on selected field [OK]

Common Mistakes:

• Using mean on boolean arrays instead of salaries
• Mixing up fields in filtering and aggregation
• Passing filter as argument to mean() incorrectly