Accessing fields by name in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

NumPy

ages = data['[1]']

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

Aages

Bage

Cdata

Dname

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong field name.

Trying to access fields with dot notation instead of brackets.

To access a field by name in a structured NumPy array, use the field name as a string inside square brackets.

2fill in blank

medium

Complete the code to get the 'score' field from the structured array.

NumPy

scores = data['[1]']

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

Ascore

Bscores

Cvalue

DScore

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect capitalization.

Using plural form when the field is singular.

The field name is case-sensitive and must match exactly as defined in the structured array.

3fill in blank

hard

Fix the error in accessing the 'height' field from the structured array.

NumPy

heights = data[1]'height']

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

A[

B.

C('

D{

Attempts:

3 left

💡 Hint

Common Mistakes

Using dot notation instead of brackets.

Missing the opening bracket.

Fields in structured arrays are accessed using square brackets, not dot notation.

4fill in blank

hard

Fill both blanks to create a dictionary of names and ages from the structured array.

NumPy

result = {data['[1]'][i]: data['[2]'][i] for i in range(len(data))}

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

Aname

Bage

Cscore

Dheight

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping keys and values.

Using incorrect field names.

To create a dictionary mapping names to ages, use the 'name' field as keys and 'age' field as values.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase names and their scores if the score is above 80.

NumPy

result = {data['[1]'][i].upper(): data['[2]'][i] for i in range(len(data)) if data['[3]'][i] > 80}

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

Aname

Bscore

Dage

Attempts:

3 left

💡 Hint

Common Mistakes

Using different fields for keys and values.

Not filtering scores correctly.

This dictionary comprehension uses the 'name' field converted to uppercase as keys, and the 'score' field as values, filtering only scores above 80.

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