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.

Practice

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

easy

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

B. They only store integers efficiently.

C. They automatically visualize data.

D. They replace all pandas functionality.

Solution

1. Step 1: Understand structured arrays

   Structured arrays let you store mixed data types in one array with named fields, like columns in a table.

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 A

4. Quick Check:

   Structured arrays = mixed types + named fields [OK]

Hint: Remember: structured arrays hold mixed types with names [OK]

Common Mistakes:

• Thinking structured arrays only store one data type
• Confusing structured arrays with visualization tools
• Assuming structured arrays replace pandas completely

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

easy

A. np.array([(b'Alice', 25), (b'Bob', 30)], dtype=[('name', 'f8'), ('age', 'S10')])

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

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

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

Solution

1. Step 1: Check field names and types

   The fields are 'name' as string (bytes) and 'age' as integer. 'S10' means string of max 10 bytes, 'i4' means 4-byte integer.

2. Step 2: Validate each option

   np.array([(b'Alice', 25), (b'Bob', 30)], dtype=[('name', 'S10'), ('age', 'i4')]) matches the correct dtype and data format. np.array([('Alice', 25), ('Bob', 30)], dtype=[('name', 'int'), ('age', 'float')]) uses wrong types. np.array([(25, 'Alice'), (30, 'Bob')], dtype=[('age', 'i4'), ('name', 'S10')]) swaps fields order and data. np.array([(b'Alice', 25), (b'Bob', 30)], dtype=[('name', 'f8'), ('age', 'S10')]) swaps types incorrectly.

3. Final Answer:

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

4. Quick Check:

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

Hint: Match field names and types exactly in dtype [OK]

Common Mistakes:

• Using wrong data types in dtype
• Swapping field order and data
• Not using byte strings for fixed-length strings

3. Given the structured array:

data = np.array([(b'Alice', 25), (b'Bob', 30), (b'Carol', 22)], dtype=[('name', 'S10'), ('age', 'i4')])
sorted_data = np.sort(data, order='age')

What is the output of sorted_data['name']?

medium

A. [b'Carol' b'Alice' b'Bob']

B. [b'Alice' b'Bob' b'Carol']

C. [b'Bob' b'Carol' b'Alice']

D. [b'Carol' b'Bob' b'Alice']

Solution

1. Step 1: Understand sorting by 'age'

   The array is sorted by the 'age' field ascending: 22 (Carol), 25 (Alice), 30 (Bob).

2. Step 2: Extract 'name' field after sorting

   After sorting, the 'name' field order matches sorted ages: Carol, Alice, Bob.

3. Final Answer:

   [b'Carol' b'Alice' b'Bob'] -> Option A

4. Quick Check:

   Sort by age ascending = Carol, Alice, Bob [OK]

Hint: Sort by field then check that field's order [OK]

Common Mistakes:

• Assuming original order remains after sort
• Mixing up ascending vs descending order
• Confusing field names when accessing

4. Consider this code snippet:

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

What is the error in this code?

medium

A. ValueError because comparison with > is invalid on structured arrays.

B. No error; it correctly filters entries with age > 25.

C. TypeError because 'age' field is not accessible.

D. SyntaxError due to wrong indexing syntax.

Solution

1. Step 1: Check filtering syntax

   Filtering structured arrays by a field with a condition like data['age'] > 25 is valid and returns a boolean mask.

2. Step 2: Confirm no errors

   The code correctly filters rows where age is greater than 25, so no error occurs.

3. Final Answer:

   No error; it correctly filters entries with age > 25. -> Option B

4. Quick Check:

   Filtering with boolean mask on field works [OK]

Hint: Use boolean masks on fields to filter structured arrays [OK]

Common Mistakes:

• Thinking structured arrays can't be filtered by fields
• Confusing syntax for filtering
• Assuming comparison operators don't work on fields

5. You have a structured array of employees with fields 'name' (string), 'age' (int), and 'salary' (float). You want to find the average salary of employees older than 30. Which code snippet correctly does this?

hard

A. avg_salary = data[data['salary'] > 30]['age'].mean()

B. avg_salary = np.mean(data['salary'] > 30)

C. avg_salary = data['salary'][data['age'] > 30].mean()

D. avg_salary = data['salary'].mean(data['age'] > 30)

Solution

1. Step 1: Filter employees older than 30

   Use boolean mask data['age'] > 30 to select salaries of employees older than 30.

2. Step 2: Calculate mean salary of filtered data

   Apply .mean() on the filtered salary array to get average salary.

3. Final Answer:

   avg_salary = data['salary'][data['age'] > 30].mean() -> Option C

4. Quick Check:

   Filter by age, then mean salary = avg_salary = data['salary'][data['age'] > 30].mean() [OK]

Hint: Filter first, then compute mean on selected field [OK]

Common Mistakes:

• Using mean on boolean arrays instead of salaries
• Mixing up fields in filtering and aggregation
• Passing filter as argument to mean() incorrectly