Accessing fields by name in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Accessing a single field by name in a structured NumPy array

What is the output of this code snippet?

NumPy

import numpy as np

arr = np.array([(1, 2.0), (3, 4.0)], dtype=[('x', 'i4'), ('y', 'f4')])
result = arr['y']
print(result)

A [1 3]

B [2. 4.]

C [(1, 2.) (3, 4.)]

D [2 4]

💡 Hint

Accessing a field by name returns an array of that field's values.

The code creates a structured array with fields 'x' and 'y'. Accessing arr['y'] returns the values of the 'y' field as a float array.

❓ data_output

intermediate

Shape of field extracted from structured array

Given this structured array, what is the shape of the extracted field 'b'?

NumPy

import numpy as np

arr = np.array([(1, (2, 3)), (4, (5, 6))], dtype=[('a', 'i4'), ('b', 'i4', (2,))])
field_b = arr['b']
print(field_b.shape)

A (2, 2)

B (2,)

C (2, 1)

D (1, 2)

💡 Hint

The field 'b' is a 2-element integer array per record, and there are 2 records.

Each record has a field 'b' which is an array of shape (2,). Since there are 2 records, the extracted field has shape (2, 2).

🔧 Debug

advanced

Identify the error when accessing a non-existent field

What error does this code raise?

NumPy

import numpy as np

arr = np.array([(1, 2)], dtype=[('x', 'i4'), ('y', 'i4')])
print(arr['z'])

A IndexError: index out of range

B TypeError: unhashable type: 'slice'

C AttributeError: 'numpy.ndarray' object has no attribute 'z'

D KeyError: 'z'

💡 Hint

Accessing a field by a name not in the dtype raises a KeyError.

The structured array does not have a field named 'z', so accessing arr['z'] raises a KeyError.

🚀 Application

advanced

Extracting multiple fields and combining them

Given this structured array, which option correctly creates a new array combining fields 'name' and 'age' as a list of tuples?

NumPy

import numpy as np

arr = np.array([('Alice', 25), ('Bob', 30)], dtype=[('name', 'U10'), ('age', 'i4')])

A np.array((arr['name'], arr['age']))

B arr[['name', 'age']].tolist()

C list(zip(arr['name'], arr['age']))

D [arr['name'], arr['age']]

💡 Hint

Use zip to pair elements from two arrays into tuples.

Using zip on arr['name'] and arr['age'] pairs each name with its age, then list converts it to a list of tuples.

🧠 Conceptual

expert

Understanding memory layout when accessing fields by name

When you access a field by name in a structured NumPy array, what best describes the memory layout of the returned array?

A It is a view of the original array sharing the same memory.

B It is a new array with data copied and reshaped.

C It is a deep copy with independent memory.

D It is a list of Python objects extracted from the array.

💡 Hint

Accessing a field returns a view, not a copy, unless explicitly copied.

Accessing a field by name returns a view into the original array's memory, so changes to the field affect the original array.

Practice

1. What is the correct way to access the field named 'age' from a NumPy structured array data?

easy

A. data['age']

B. data.age()

C. data[age]

D. data.get('age')

Solution

1. Step 1: Understand structured array field access

   In NumPy, fields in structured arrays are accessed using square brackets with the field name as a string.

2. Step 2: Identify correct syntax for field access

   The syntax data['age'] correctly accesses the 'age' field. Other options use incorrect methods or syntax.

3. Final Answer:

   data['age'] -> Option A

4. Quick Check:

   Field access uses square brackets with field name [OK]

Hint: Use square brackets with field name as string [OK]

Common Mistakes:

• Using unquoted field name like data[age]
• Calling field as a method like data.age()
• Using data.get() which is not valid for structured arrays

2. Which of the following is the correct syntax to create a NumPy structured array with fields 'name' (string) and 'score' (integer)?

easy

A. np.array([('Alice', 90), ('Bob', 85)], dtype=[('name', 'U10'), ('score', 'i4')])

B. np.array([('Alice', 90), ('Bob', 85)], dtype={name: 'U10', score: 'i4'})

C. np.array([('Alice', 90), ('Bob', 85)], dtype=[{name: 'U10'}, {score: 'i4'}])

D. np.array([('Alice', 90), ('Bob', 85)], dtype=('name', 'U10', 'score', 'i4'))

Solution

1. Step 1: Understand dtype format for structured arrays

   The dtype should be a list of tuples, each tuple with field name and data type.

2. Step 2: Match correct dtype syntax

   np.array([('Alice', 90), ('Bob', 85)], dtype=[('name', 'U10'), ('score', 'i4')]) uses the correct list of tuples format: [('name', 'U10'), ('score', 'i4')]. Other options use incorrect dtype formats.

3. Final Answer:

   np.array([('Alice', 90), ('Bob', 85)], dtype=[('name', 'U10'), ('score', 'i4')]) -> Option A

4. Quick Check:

   dtype as list of (name, type) tuples [OK]

Hint: Use list of (field, type) tuples for dtype [OK]

Common Mistakes:

• Using dict instead of list of tuples for dtype
• Passing dtype as a flat tuple instead of list
• Incorrect nested dict inside dtype list

3. Given the structured array arr = np.array([(1, 2.5), (3, 4.5)], dtype=[('x', 'i4'), ('y', 'f4')]), what is the output of arr['y']?

medium

A. [1. 3.]

B. [2.5 4.5]

C. [(1, 2.5) (3, 4.5)]

D. Error: field 'y' not found

Solution

1. Step 1: Understand the structured array fields

   The array has two fields: 'x' (integers) and 'y' (floats). The values for 'y' are 2.5 and 4.5.

2. Step 2: Access the 'y' field values

   Using arr['y'] returns an array of the 'y' values: [2.5, 4.5].

3. Final Answer:

   [2.5 4.5] -> Option B

4. Quick Check:

   arr['y'] returns float values [OK]

Hint: Access field returns array of that field's values [OK]

Common Mistakes:

• Confusing field 'x' values with 'y'
• Expecting full tuples instead of single field array
• Assuming error due to wrong field name

4. What is wrong with this code snippet?

arr = np.array([(1, 2), (3, 4)], dtype=[('id', 'i4'), ('b', 'i4')])
print(arr.a)

medium

A. It raises a TypeError because dtype is incorrect.

B. It raises a SyntaxError due to missing quotes around field names.

C. It prints the array correctly without errors.

D. It raises an AttributeError because fields are accessed with brackets, not dot notation.

Solution

1. Step 1: Check field access method

   NumPy structured array fields must be accessed using square brackets with the field name as a string, not dot notation.

2. Step 2: Identify error from dot notation

   Using arr.a causes AttributeError because 'a' is not an attribute but a field name.

3. Final Answer:

   It raises an AttributeError because fields are accessed with brackets, not dot notation. -> Option D

4. Quick Check:

   Use arr['a'], not arr.a [OK]

Hint: Use brackets, not dot, to access fields [OK]

Common Mistakes:

• Using dot notation to access fields
• Assuming dtype syntax error
• Expecting code to print without error

5. You have a structured array data with fields 'name' (string), 'age' (int), and 'score' (float). How do you create a new array containing only the 'name' and 'score' fields?

hard

A. data['name']['score']

B. data[['name'], ['score']]

C. data[['name', 'score']]

D. data.get(['name', 'score'])

Solution

1. Step 1: Understand field selection syntax

   To select multiple fields, use a list of field names inside double square brackets: data[['field1', 'field2']].

2. Step 2: Apply correct syntax to select 'name' and 'score'

   Using data[['name', 'score']] returns a new structured array with only those fields.

3. Final Answer:

   data[['name', 'score']] -> Option C

4. Quick Check:

   Use double brackets with list of fields [OK]

Hint: Use double brackets with list of fields to select multiple [OK]

Common Mistakes:

• Chaining field accesses like data['name']['score']
• Passing separate lists for each field
• Using .get() method which does not exist