Record arrays in NumPy - Mini Project: Build & Apply

Working with Record Arrays in NumPy

📖 Scenario: Imagine you work in a small library. You want to keep track of books with their title, author, and year published. You will use a special kind of array called a record array to store this information neatly.

🎯 Goal: Create a NumPy record array to store book information, then select and display books published after a certain year.

📋 What You'll Learn

Create a NumPy record array with fields: 'title' (string), 'author' (string), and 'year' (integer).

Create a variable to hold the year threshold for filtering books.

Use a condition to select books published after the threshold year.

Print the filtered record array showing only those books.

💡 Why This Matters

🌍 Real World

Record arrays help store mixed data types in one array, useful for datasets like books, employees, or products.

💼 Career

Understanding record arrays is important for data scientists and analysts who work with structured data in NumPy.

1

Create a NumPy record array with book data

Create a NumPy record array called books with these exact entries: ('The Hobbit', 'J.R.R. Tolkien', 1937), ('1984', 'George Orwell', 1949), and ('To Kill a Mockingbird', 'Harper Lee', 1960). Use the data types 'U20' for title and author, and int for year.

NumPy

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

Hint

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

2

Set a year threshold for filtering books

Create a variable called year_threshold and set it to 1940.

NumPy

import numpy as np
books = np.array([
    ('The Hobbit', 'J.R.R. Tolkien', 1937),
    ('1984', 'George Orwell', 1949),
    ('To Kill a Mockingbird', 'Harper Lee', 1960)
], dtype=[('title', 'U20'), ('author', 'U20'), ('year', int)])

# Create the variable 'year_threshold' and set it to 1940
# Your code here

Hint

Just create a variable named year_threshold and assign the number 1940 to it.

3

Select books published after the threshold year

Create a variable called recent_books that selects from books only those entries where the year is greater than year_threshold.

NumPy

import numpy as np
books = np.array([
    ('The Hobbit', 'J.R.R. Tolkien', 1937),
    ('1984', 'George Orwell', 1949),
    ('To Kill a Mockingbird', 'Harper Lee', 1960)
], dtype=[('title', 'U20'), ('author', 'U20'), ('year', int)])
year_threshold = 1940

# Select books with year greater than year_threshold into 'recent_books'
# Your code here

Hint

Use boolean indexing with books['year'] > year_threshold to filter the record array.

4

Print the filtered record array

Write a print statement to display the recent_books record array.

NumPy

import numpy as np
books = np.array([
    ('The Hobbit', 'J.R.R. Tolkien', 1937),
    ('1984', 'George Orwell', 1949),
    ('To Kill a Mockingbird', 'Harper Lee', 1960)
], dtype=[('title', 'U20'), ('author', 'U20'), ('year', int)])
year_threshold = 1940
recent_books = books[books['year'] > year_threshold]

# Print the 'recent_books' record array
# Your code here

Hint

Use print(recent_books) to show the filtered books.

Practice

1. What is the main advantage of using a record array in numpy?

easy

A. It speeds up numerical calculations on large arrays.

B. It automatically sorts data based on values.

C. It allows storing different data types in one array with named fields.

D. It compresses data to save memory.

Solution

1. Step 1: Understand record arrays

   Record arrays let you store mixed data types in one numpy array by using named fields.

2. Step 2: Compare options

   Only It allows storing different data types in one array with named fields. correctly describes this feature. Others describe unrelated features.

3. Final Answer:

   It allows storing different data types in one array with named fields. -> Option C

4. Quick Check:

   Record arrays = mixed types + named fields [OK]

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

Common Mistakes:

• Confusing record arrays with regular numeric arrays
• Thinking record arrays sort data automatically
• Assuming record arrays compress data

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

easy

A. np.rec.array([("Alice", 25), ("Bob", 30)], dtype=[('name', 'U10'), ('age', 'i4')])

B. np.array([("Alice", 25), ("Bob", 30)], dtype=[('name', 'i4'), ('age', 'U10')])

C. np.rec.array(["Alice", 25, "Bob", 30], dtype=[('name', 'U10'), ('age', 'i4')])

D. np.rec.array([(25, "Alice"), (30, "Bob")], dtype=[('name', 'U10'), ('age', 'i4')])

Solution

1. Step 1: Check data and dtype matching

   np.rec.array([("Alice", 25), ("Bob", 30)], dtype=[('name', 'U10'), ('age', 'i4')]) matches tuples of (string, int) with dtype [('name', 'U10'), ('age', 'i4')].

2. Step 2: Validate other options

   np.array([("Alice", 25), ("Bob", 30)], dtype=[('name', 'i4'), ('age', 'U10')]) swaps types incorrectly; C has wrong input format; A swaps field order.

3. Final Answer:

   np.rec.array([("Alice", 25), ("Bob", 30)], dtype=[('name', 'U10'), ('age', 'i4')]) -> Option A

4. Quick Check:

   Data matches dtype order and types [OK]

Hint: Match tuple order with dtype fields exactly [OK]

Common Mistakes:

• Swapping field order between data and dtype
• Using wrong data types in dtype
• Passing flat list instead of list of tuples

3. What will be the output of the following code?

import numpy as np
rec = np.rec.array([(1, 2.5), (3, 4.5)], dtype=[('x', 'i4'), ('y', 'f4')])
print(rec.x + rec.y)

medium

A. TypeError

B. [3 7]

C. [1 3]

D. [3.5 7.5]

Solution

1. Step 1: Understand data and fields

   rec.x is integer array [1, 3], rec.y is float array [2.5, 4.5].

2. Step 2: Add integer and float arrays element-wise

   Adding [1, 3] + [2.5, 4.5] results in [3.5, 7.5] as floats.

3. Final Answer:

   [3.5 7.5] -> Option D

4. Quick Check:

   1+2.5=3.5 and 3+4.5=7.5 [OK]

Hint: Adding int and float fields results in float array [OK]

Common Mistakes:

• Expecting integer output instead of float
• Confusing field names or types
• Thinking addition causes error

4. Identify the error in this code snippet:

import numpy as np
rec = np.rec.array([(1, 'a'), (2, 'b')], dtype=[('num', 'i4'), ('char', 'U1')])
print(rec.num + rec.char)

medium

A. You cannot add integer and string fields directly.

B. The dtype specification is incorrect.

C. The data tuples have wrong length.

D. The record array must be created with np.array, not np.rec.array.

Solution

1. Step 1: Analyze the operation

   rec.num is integer array, rec.char is string array.

2. Step 2: Check addition of int and string

   Adding int + string causes a TypeError in numpy.

3. Final Answer:

   You cannot add integer and string fields directly. -> Option A

4. Quick Check:

   int + string = TypeError [OK]

Hint: Cannot add numbers and strings directly in numpy [OK]

Common Mistakes:

• Assuming dtype is wrong instead of operation
• Thinking np.rec.array is incorrect here
• Ignoring type mismatch in addition

5. You have a numpy record array rec with fields 'id' (int), 'score' (float), and 'passed' (bool). How do you create a new record array containing only records where passed is True and score is above 80?

hard

A. rec[rec.passed or rec.score > 80]

B. rec[(rec.passed) & (rec.score > 80)]

C. rec[rec.passed and rec.score > 80]

D. rec[(rec.passed) | (rec.score > 80)]

Solution

1. Step 1: Understand filtering syntax

   Use boolean indexing with & for element-wise AND, parentheses needed.

2. Step 2: Evaluate options

   rec[(rec.passed) & (rec.score > 80)] correctly uses (rec.passed) & (rec.score > 80). Options B and C use Python 'or'/'and' which don't work element-wise. rec[(rec.passed) | (rec.score > 80)] uses | (OR) instead of AND.

3. Final Answer:

   rec[(rec.passed) & (rec.score > 80)] -> Option B

4. Quick Check:

   Use & with parentheses for element-wise AND [OK]

Hint: Use & with parentheses for element-wise conditions [OK]

Common Mistakes:

• Using 'and' or 'or' instead of '&' or '|' for arrays
• Forgetting parentheses around conditions
• Using | instead of & for AND condition