Practical uses of structured arrays in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of accessing fields in a structured array

What is the output of this code snippet that creates a structured array and accesses one of its fields?

NumPy

import numpy as np

arr = np.array([(1, 2.5, 'a'), (2, 3.5, 'b')], dtype=[('x', 'i4'), ('y', 'f4'), ('z', 'U1')])
print(arr['y'])

A [1 2]

B [2.5 3.5 4.5]

C ['a' 'b']

D [2.5 3.5]

💡 Hint

Look at the dtype and which field is accessed by arr['y'].

The structured array has fields 'x', 'y', and 'z'. Accessing arr['y'] returns the array of values in the 'y' field, which are floats 2.5 and 3.5.

❓ data_output

intermediate

Number of records after filtering a structured array

Given this structured array, how many records remain after filtering for 'age' > 30?

NumPy

import numpy as np

people = np.array([(25, 'Alice'), (35, 'Bob'), (40, 'Charlie')], dtype=[('age', 'i4'), ('name', 'U10')])
adults = people[people['age'] > 30]
print(len(adults))

A 0

B 1

C 2

D 3

💡 Hint

Count how many ages are greater than 30.

Only Bob (35) and Charlie (40) have age > 30, so 2 records remain.

🔧 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), (2, 'abc')], dtype=[('a', 'i4'), ('b', 'f4')])

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

B TypeError: data type mismatch

C SyntaxError: invalid syntax

D No error, array created successfully

💡 Hint

Check the data types of the tuples and dtype fields.

The second tuple has a string 'abc' where a float is expected, causing a ValueError during conversion.

🚀 Application

advanced

Using structured arrays to sort by multiple fields

Which option correctly sorts this structured array first by 'grade' ascending, then by 'age' descending?

NumPy

import numpy as np

students = np.array([(90, 20), (90, 22), (85, 21)], dtype=[('grade', 'i4'), ('age', 'i4')])

A students[np.lexsort((-students['age'], students['grade']))]

B np.sort(students, order=['grade', 'age'])

C students[np.lexsort((students['age'], students['grade']))]

D np.sort(students, order=['grade', 'age'])[::-1]

💡 Hint

Use lexsort with negative age for descending order.

np.lexsort sorts by keys from last to first. Using -students['age'] sorts age descending, and students['grade'] ascending.

🧠 Conceptual

expert

Why use structured arrays over regular arrays?

Which is the main advantage of using structured arrays in numpy compared to regular numpy arrays?

A They compress data to use less memory than regular arrays.

B They allow storing heterogeneous data types in one array with named fields.

C They automatically parallelize computations for faster processing.

D They support dynamic resizing like Python lists.

💡 Hint

Think about data types and field names.

Structured arrays let you store different data types in one array with named fields, unlike regular arrays which are homogeneous.