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'].

Practice

1. What is the main advantage of using numpy structured arrays?

easy

A. They automatically visualize data.

B. They only store integers efficiently.

C. They replace Python lists completely.

D. They allow storing different data types in one array with named fields.

Solution

1. Step 1: Understand structured arrays

   Structured arrays let you store multiple data types together, like numbers and text, in one array with named fields.

2. Step 2: Compare options

   Only They allow storing different data types in one array with named fields. correctly describes this main advantage. Others are incorrect or unrelated.

3. Final Answer:

   They allow storing different data types in one array with named fields. -> Option D

4. Quick Check:

   Structured arrays = multiple types + named fields [OK]

Hint: Remember: structured arrays hold mixed data types by field names [OK]

Common Mistakes:

• Thinking structured arrays only hold one data type
• Confusing structured arrays with visualization tools
• Assuming structured arrays replace all Python lists

2. Which of the following is the correct way to define a structured array with fields 'name' (string) and 'age' (integer)?

easy

A. np.array([('Alice', 25), ('Bob', 30)], dtype=[('name', 'int'), ('age', 'float')])

B. np.array([(b'Alice', 25), (b'Bob', 30)], dtype=[('name', 'S10'), ('age', 'i4')])

C. np.array([('Alice', 25), ('Bob', 30)], dtype=[('age', 'i4'), ('name', 'S10')])

D. np.array(['Alice', 25, 'Bob', 30], dtype=[('name', 'S10'), ('age', 'i4')])

Solution

1. Step 1: Check data and dtype match

   np.array([(b'Alice', 25), (b'Bob', 30)], dtype=[('name', 'S10'), ('age', 'i4')]) correctly uses byte strings for names and integer type for age, matching the dtype fields.

2. Step 2: Identify errors in other options

   B uses wrong types ('int' for name, 'float' for age); C passes string first ('Alice') to 'age' ('i4'), causing type mismatch; D passes a flat list instead of tuples.

3. Final Answer:

   np.array([(b'Alice', 25), (b'Bob', 30)], dtype=[('name', 'S10'), ('age', 'i4')]) -> Option B

4. Quick Check:

   Correct dtype and data tuple format = np.array([(b'Alice', 25), (b'Bob', 30)], dtype=[('name', 'S10'), ('age', 'i4')]) [OK]

Hint: Match data tuples exactly to dtype field order and types [OK]

Common Mistakes:

• Using wrong data types for fields
• Passing flat lists instead of tuples
• Mixing field order between data and dtype

3. What will be the output of this code?

import numpy as np
arr = np.array([(1, 2.5), (3, 4.5)], dtype=[('x', 'i4'), ('y', 'f4')])
print(arr['y'])

medium

A. [2.5 4.5]

B. [1 3]

C. [2 4]

D. Error: No field named 'y'

Solution

1. Step 1: Understand structured array fields

   The array has fields 'x' (integers) and 'y' (floats). Accessing arr['y'] returns all values in 'y' field.

2. Step 2: Check printed output

   Values in 'y' are 2.5 and 4.5, so output is array([2.5, 4.5]).

3. Final Answer:

   [2.5 4.5] -> Option A

4. Quick Check:

   arr['y'] = [2.5 4.5] [OK]

Hint: Access fields by name to get that column's values [OK]

Common Mistakes:

• Confusing field names and indexes
• Expecting error when field exists
• Misreading float values as integers

4. Identify the error in this code snippet:

import numpy as np
arr = np.array([(1, 'Alice'), (2, 'Bob')], dtype=[('id', 'i4'), ('name', 'S10')])
print(arr['age'])

medium

A. Tuple data format is wrong.

B. Data types in dtype are incorrect.

C. Field 'age' does not exist in the structured array.

D. Array creation syntax is invalid.

Solution

1. Step 1: Check dtype fields

   The structured array has fields 'id' and 'name', but no 'age' field.

2. Step 2: Analyze the print statement

   Trying to print arr['age'] causes an error because 'age' is not defined in dtype.

3. Final Answer:

   Field 'age' does not exist in the structured array. -> Option C

4. Quick Check:

   Accessing undefined field = error [OK]

Hint: Check field names carefully before accessing [OK]

Common Mistakes:

• Assuming all fields exist by default
• Ignoring dtype field names
• Confusing data values with field names

5. You have a structured array with fields 'name' (string), 'age' (int), and 'score' (float). How can you sort this array first by 'age' ascending, then by 'score' descending?

hard

A. Use arr['score'] = -arr['score']arr.sort(order=['age', 'score'])

B. Use np.sort(arr, order=['age', 'score']) with a custom comparator for descending score.

C. Use arr.sort(order=['age']) then arr['score'] = -arr['score'] before sorting again.

D. Use arr.sort(order=['age']) then arr[arr['age'] == age_value].sort(order='score') for each age.

Solution

1. Step 1: Understand sorting by multiple fields

   NumPy structured arrays can be sorted by multiple fields using sort(order=[...]), but only ascending.

2. Step 2: Handle descending order

   To sort 'score' descending, negate it first (arr['score'] = -arr['score']), then arr.sort(order=['age', 'score']). This sorts age ascending, then negated score ascending (original score descending).

3. Final Answer:

   Use arr['score'] = -arr['score']
arr.sort(order=['age', 'score']) -> Option A

4. Quick Check:

   Negate score + sort(['age', 'score']) = age asc + score desc [OK]

Hint: Sort ascending then reverse for descending fields [OK]

Common Mistakes:

• Expecting sort(order=...) to handle descending directly
• Trying to negate fields without sorting again
• Sorting subsets separately without combining results