Practical uses of structured arrays in NumPy - Interactive Code Practice

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

people = 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', 'U20'), ('age', 'f8')]

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

Attempts:

3 left

💡 Hint

Common Mistakes

Using a wrong dtype format like a list of strings instead of tuples.

Choosing float type for age instead of integer.

The dtype defines the structure with a string field 'name' of length 10 and an integer field 'age'.

2fill in blank

medium

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

NumPy

ages = people[1]

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

A['age']

B.age

C[1]

D['name']

Attempts:

3 left

💡 Hint

Common Mistakes

Using dot notation which raises an error.

Accessing by index which returns a row, not a field.

Accessing a field in a structured array is done by indexing with the field name as a string.

3fill in blank

hard

Fix the error in the code to filter people older than 27.

NumPy

older = people[people['age'] [1] 27]

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

A<=

B<

C==

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using less than or equal operator which filters the wrong group.

Using equality operator which filters only age 27.

To filter people older than 27, use the greater than operator >.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension mapping names to ages for people younger than 30.

NumPy

young_dict = {person[1]: person[2] for person in people if person['age'] < 30}

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

A['name']

B['age']

C.name

D.age

Attempts:

3 left

💡 Hint

Common Mistakes

Using dot notation which causes an error.

Mixing up 'name' and 'age' fields.

Use indexing with field names to access 'name' and 'age' fields in each person.

5fill in blank

hard

Fill all three blanks to create a new structured array with uppercase names and ages increased by 1.

NumPy

new_people = np.array([(person[1].upper(), person[2] + [3]) for person in people], dtype=people.dtype)

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

A['name']

B['age']

C1

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Using dot notation for fields.

Adding 2 instead of 1 to age.

Forgetting to use upper() on the name.

Access 'name' and 'age' fields with square brackets, convert name to uppercase, and add 1 to age.