Why structured arrays matter in NumPy - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a structured array with fields 'name' and 'age'.

NumPy

import numpy as np

person = np.array([('Alice', 25), ('Bob', 30)], dtype=[1])

Drag options to blanks, or click blank then click option'

A[('name', 'U10'), ('age', 'i4')]

B[('name', 'S10'), ('age', 'f4')]

C[('name', 'U5'), ('age', 'i2')]

D[('name', 'U20'), ('age', 'f8')]

Attempts:

3 left

💡 Hint

Common Mistakes

Using string types that are not Unicode (like 'S10') for names.

Using float type for age instead of integer.

The dtype must define the fields with correct types: 'name' as Unicode string of length 10 and 'age' as 4-byte integer.

2fill in blank

medium

Complete the code to access the 'age' field from the structured array.

NumPy

ages = person[1]

Drag options to blanks, or click blank then click option'

A[1]

B['age']

C.age

D['name']

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to access fields with dot notation which does not work for numpy structured arrays.

Using the wrong field name.

To get the 'age' field from a structured array, use the field name as a key: person['age'].

3fill in blank

hard

Fix the error in the code to create a structured array with fields 'city' and 'population'.

NumPy

cities = np.array([('Paris', 2148327), ('Berlin', 3769495)], dtype=[1])

Drag options to blanks, or click blank then click option'

A[('city', 'U10'), ('population', 'f4')]

B[('city', 'S10'), ('population', 'i8')]

C[('city', 'U10'), ('population', 'i4')]

D[('city', 'i4'), ('population', 'i4')]

Attempts:

3 left

💡 Hint

Common Mistakes

Using integer dtype for city names.

Using float dtype for population when integers are better.

The 'city' field should be a Unicode string type to hold city names, and 'population' an integer type.

4fill in blank

hard

Fill both blanks to create a structured array and access the 'score' field.

NumPy

data = np.array([(1, 95.5), (2, 88.0)], dtype=[1])
scores = data[2]

Drag options to blanks, or click blank then click option'

A[('id', 'i4'), ('score', 'f4')]

B['score']

C['id']

D[('score', 'i4'), ('id', 'f4')]

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up field names in dtype or access.

Using wrong data types for fields.

The dtype defines 'id' as integer and 'score' as float. To get scores, access data['score'].

5fill in blank

hard

Fill all three blanks to create a structured array, filter by age, and get names.

NumPy

people = np.array([
    ('Anna', 28),
    ('Tom', 22),
    ('Mike', 35)
], dtype=[1])
adults = people[people[2] 25]
names = adults[3]

Drag options to blanks, or click blank then click option'

A[('name', 'U10'), ('age', 'i4')]

B['age']

C['name']

D[('age', 'i4'), ('name', 'U10')]

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong field names in dtype or filtering.

Trying to filter with dot notation instead of bracket notation.

The dtype defines 'name' and 'age'. Filter adults by age > 25 using people['age'] > 25. Then get their names with adults['name'].