Creating structured arrays in NumPy - Interactive 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

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

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

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

B[('name', 'U10'), ('age', 'f8')]

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

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

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect data types like float for age.

Not defining dtype as a list of tuples.

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 = data[1]
print(ages)

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A['name']

B['age']

C.age

D[1]

Attempts:

3 left

💡 Hint

Common Mistakes

Using dot notation which does not work for numpy structured arrays.

Using incorrect field names.

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 create a structured array with fields 'x' and 'y' as floats.

NumPy

points = np.array([(1.0, 2.0), (3.0, 4.0)], dtype=[1])
print(points)

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

A[('x', 'f8'), ('y', 'f8')]

B[('x', 'i4'), ('y', 'i4')]

C[('x', 'U5'), ('y', 'U5')]

D[('x', 'f4'), ('y', 'f4')]

Attempts:

3 left

💡 Hint

Common Mistakes

Using integer types for float data.

Using string types for numeric data.

Using 'f8' means 8-byte float (double precision), which matches the float values given.

4fill in blank

hard

Fill both blanks to create a structured array with fields 'id' as integer and 'score' as float.

NumPy

results = np.array([(1, 95.5), (2, 88.0)], dtype=[[1], [2]])
print(results)

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

A('id', 'i4')

B('score', 'f4')

C('score', 'f8')

D('id', 'i8')

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up the order of fields.

Using incompatible data types for the values.

The dtype list must contain tuples defining each field name and its data type.

5fill in blank

hard

Fill all three blanks to create a structured array with fields 'name' (string), 'age' (int), and 'height' (float).

NumPy

people = np.array([
    ('John', 28, 5.9),
    ('Jane', 32, 5.5)
], dtype=[[1], [2], [3]])
print(people)

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

A('name', 'U10')

B('age', 'i4')

C('height', 'f4')

D('weight', 'f4')

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong field names or data types.

Including extra fields not in the data.

The dtype list defines each field with its name and data type matching the data.