Defining structured dtypes in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of structured dtype array creation

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

NumPy

import numpy as np

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

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

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

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

D [('Alice', 25.0) ('Bob', 30.0)]

💡 Hint

Check the dtype definitions and how numpy stores integers and strings.

The dtype specifies 'U10' for strings and 'i4' for 4-byte integers. The array stores names as strings and ages as integers, so the output shows tuples with string and integer values.

❓ data_output

intermediate

Number of fields in a structured dtype

Given this structured dtype, how many fields does it contain?

NumPy

import numpy as np

dtype = [('id', 'i4'), ('score', 'f8'), ('passed', 'b1')]
print(len(dtype))

A 4

B 2

C 1

D 3

💡 Hint

Count each tuple inside the dtype list.

The dtype list has three tuples, each defining one field: 'id', 'score', and 'passed'. So the length is 3.

🔧 Debug

advanced

Identify the error in structured dtype definition

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

NumPy

import numpy as np

dtype = [('name', 'U10'), ('age')]
arr = np.array([('Alice', 25)], dtype=dtype)

A ValueError: field definition must be a tuple

B No error, array created successfully

C TypeError: data type not understood

D SyntaxError: invalid syntax

💡 Hint

Check the dtype list elements format.

Each field in dtype must be a tuple with field name and type. ('age') is a string, not a tuple, causing TypeError: data type not understood.

🚀 Application

advanced

Accessing fields in a structured array

Given this structured array, what is the output of accessing the 'score' field?

NumPy

import numpy as np

dtype = [('name', 'U10'), ('score', 'f4')]
data = np.array([('John', 88.5), ('Jane', 92.0)], dtype=dtype)
print(data['score'])

A [88.5 92. ]

B ['88.5' '92.0']

C [('John', 88.5) ('Jane', 92.0)]

D [88 92]

💡 Hint

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

The 'score' field is float32, so accessing it returns a numpy array of floats: [88.5 92.0].

🧠 Conceptual

expert

Memory layout of structured dtypes

Which statement correctly describes the memory layout of a numpy structured array with fields of different types?

A Fields are stored contiguously in memory in the order defined, with padding added to align data types.

B Fields are stored randomly in memory without any order or alignment.

C All fields are stored as separate arrays internally, not contiguous.

D Fields are stored as strings regardless of their defined type.

💡 Hint

Think about how numpy aligns data for performance.

Numpy stores structured arrays with fields in order, adding padding if needed to align data types for efficient access.