Creating structured arrays in NumPy - Practice Exercises

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of a structured array creation

What is the output of this code that creates a structured array with fields 'name' and 'age'?

NumPy

import numpy as np

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

A ])03 ,'boB'( )52 ,'ecilA'([

B [('Alice', '25') ('Bob', '30')]

C [('Alice', 25) ('Bob', 30)]

D [('Alice', 25) ('Bob', 30) ('Charlie', 35)]

💡 Hint

Look at the dtype and how the values are stored as integers or strings.

The structured array stores 'name' as a Unicode string of max length 10 and 'age' as a 4-byte integer. The output shows the array with tuples of (name, age).

❓ data_output

intermediate

Number of elements in a structured array

How many elements are in this structured array?

NumPy

import numpy as np

arr = np.array([(1, 2.5), (3, 4.5), (5, 6.5)], dtype=[('x', 'i4'), ('y', 'f4')])
print(len(arr))

A 1

B 2

C 6

D 3

💡 Hint

Count the number of tuples in the array creation.

The array has three tuples, so its length is 3.

🔧 Debug

advanced

Identify the error in structured array creation

What error does this code raise when trying to create a structured array?

NumPy

import numpy as np

arr = np.array([(1, 2.5), (3, '4.5')], dtype=[('x', 'i4'), ('y', 'f4')])

A ValueError: could not convert string to float: '4.5'

B TypeError: data type mismatch

C SyntaxError: invalid syntax

D No error, array created successfully

💡 Hint

Check the data types and the values provided for each field.

The second tuple has '4.5' as a string, but the dtype expects a float. This causes a ValueError during conversion.

🚀 Application

advanced

Accessing fields in a structured array

Given this structured array, what is the output of printing arr['age']?

NumPy

import numpy as np

arr = np.array([('John', 28), ('Jane', 32)], dtype=[('name', 'U10'), ('age', 'i4')])
print(arr['age'])

A [('John', 28) ('Jane', 32)]

B [28 32]

C ['John' 'Jane']

D [28, 32]

💡 Hint

Accessing a field returns an array of that field's values.

arr['age'] returns a numpy array of the ages as integers, shown as [28 32].

🧠 Conceptual

expert

Memory layout of structured arrays

Which statement about the memory layout of numpy structured arrays is TRUE?

A Structured arrays store data as a single contiguous block with fields laid out in order, enabling efficient memory use.

B Each field in a structured array is stored in a separate numpy array internally.

C Structured arrays cannot store mixed data types in the same array.

D Structured arrays store all fields contiguously in memory, allowing fast access to any field.

💡 Hint

Think about how numpy stores data for performance and memory efficiency.

Numpy structured arrays store all fields in a single contiguous block of memory with fields laid out sequentially. This allows efficient memory use and access.