Record arrays in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a record array from a list of tuples.

NumPy

import numpy as np

data = [(1, 'Alice', 3.5), (2, 'Bob', 4.0)]
dtype = [('id', 'i4'), ('name', 'U10'), ('score', 'f4')]
record_array = np.array(data, dtype=[1])

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

Arecord

Bdata

Clist

Ddtype

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the data list as dtype instead of the dtype variable.

Forgetting to specify dtype when creating the array.

We use the 'dtype' variable to specify the data types when creating the record array.

2fill in blank

medium

Complete the code to access the 'name' field from the record array.

NumPy

names = record_array[1]

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

A[0]

B.name

C['name']

D[['name']]

Attempts:

3 left

💡 Hint

Common Mistakes

Using double brackets which returns a subarray instead of a field.

Using string indexing which is invalid for record arrays.

You can access fields in a record array using dot notation with the field name.

3fill in blank

hard

Fix the error in the code to create a record array with named fields.

NumPy

import numpy as np

values = [(10, 'John', 2.5), (20, 'Jane', 3.0)]
dtype = [('age', 'i4'), ('name', 'U5'), ('height', 'f4')]
rec_arr = np.array(values, [1]=dtype)

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

Adtypes

Btype

Cdtype

Dformat

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'type' instead of 'dtype'.

Using 'dtypes' which is not recognized.

The correct keyword argument to specify data types is 'dtype'.

4fill in blank

hard

Fill both blanks to create a record array and access the 'score' field.

NumPy

import numpy as np

values = [(1, 'Ann', 4.2), (2, 'Ben', 3.8)]
dtype = [('id', 'i4'), ('name', 'U10'), ('score', 'f4')]
rec_arr = np.array([1], dtype=[2])
scores = rec_arr.score

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

Avalues

Bdtype

Cdata

Drecords

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping the data and dtype variables.

Using undefined variables for data or dtype.

We pass the data list 'values' and the data type 'dtype' to create the record array.

5fill in blank

hard

Fill all three blanks to create a record array, filter by score > 3.5, and get names.

NumPy

import numpy as np

values = [(1, 'Tom', 3.2), (2, 'Sue', 4.5), (3, 'Max', 3.8)]
dtype = [('id', 'i4'), ('name', 'U10'), ('score', 'f4')]
rec_arr = np.array([1], dtype=[2])
high_scores = rec_arr[rec_arr.score [3] 3.5]
names = high_scores.name

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

Avalues

Bdtype

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' for filtering.

Using wrong variable names for data or dtype.

We create the record array from 'values' and 'dtype', then filter scores greater than 3.5 using '>'.

Practice

1. What is the main advantage of using a record array in numpy?

easy

A. It speeds up numerical calculations on large arrays.

B. It automatically sorts data based on values.

C. It allows storing different data types in one array with named fields.

D. It compresses data to save memory.

Solution

1. Step 1: Understand record arrays

   Record arrays let you store mixed data types in one numpy array by using named fields.

2. Step 2: Compare options

   Only It allows storing different data types in one array with named fields. correctly describes this feature. Others describe unrelated features.

3. Final Answer:

   It allows storing different data types in one array with named fields. -> Option C

4. Quick Check:

   Record arrays = mixed types + named fields [OK]

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

Common Mistakes:

• Confusing record arrays with regular numeric arrays
• Thinking record arrays sort data automatically
• Assuming record arrays compress data

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

easy

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

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

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

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

Solution

1. Step 1: Check data and dtype matching

   np.rec.array([("Alice", 25), ("Bob", 30)], dtype=[('name', 'U10'), ('age', 'i4')]) matches tuples of (string, int) with dtype [('name', 'U10'), ('age', 'i4')].

2. Step 2: Validate other options

   np.array([("Alice", 25), ("Bob", 30)], dtype=[('name', 'i4'), ('age', 'U10')]) swaps types incorrectly; C has wrong input format; A swaps field order.

3. Final Answer:

   np.rec.array([("Alice", 25), ("Bob", 30)], dtype=[('name', 'U10'), ('age', 'i4')]) -> Option A

4. Quick Check:

   Data matches dtype order and types [OK]

Hint: Match tuple order with dtype fields exactly [OK]

Common Mistakes:

• Swapping field order between data and dtype
• Using wrong data types in dtype
• Passing flat list instead of list of tuples

3. What will be the output of the following code?

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

medium

A. TypeError

B. [3 7]

C. [1 3]

D. [3.5 7.5]

Solution

1. Step 1: Understand data and fields

   rec.x is integer array [1, 3], rec.y is float array [2.5, 4.5].

2. Step 2: Add integer and float arrays element-wise

   Adding [1, 3] + [2.5, 4.5] results in [3.5, 7.5] as floats.

3. Final Answer:

   [3.5 7.5] -> Option D

4. Quick Check:

   1+2.5=3.5 and 3+4.5=7.5 [OK]

Hint: Adding int and float fields results in float array [OK]

Common Mistakes:

• Expecting integer output instead of float
• Confusing field names or types
• Thinking addition causes error

4. Identify the error in this code snippet:

import numpy as np
rec = np.rec.array([(1, 'a'), (2, 'b')], dtype=[('num', 'i4'), ('char', 'U1')])
print(rec.num + rec.char)

medium

A. You cannot add integer and string fields directly.

B. The dtype specification is incorrect.

C. The data tuples have wrong length.

D. The record array must be created with np.array, not np.rec.array.

Solution

1. Step 1: Analyze the operation

   rec.num is integer array, rec.char is string array.

2. Step 2: Check addition of int and string

   Adding int + string causes a TypeError in numpy.

3. Final Answer:

   You cannot add integer and string fields directly. -> Option A

4. Quick Check:

   int + string = TypeError [OK]

Hint: Cannot add numbers and strings directly in numpy [OK]

Common Mistakes:

• Assuming dtype is wrong instead of operation
• Thinking np.rec.array is incorrect here
• Ignoring type mismatch in addition

5. You have a numpy record array rec with fields 'id' (int), 'score' (float), and 'passed' (bool). How do you create a new record array containing only records where passed is True and score is above 80?

hard

A. rec[rec.passed or rec.score > 80]

B. rec[(rec.passed) & (rec.score > 80)]

C. rec[rec.passed and rec.score > 80]

D. rec[(rec.passed) | (rec.score > 80)]

Solution

1. Step 1: Understand filtering syntax

   Use boolean indexing with & for element-wise AND, parentheses needed.

2. Step 2: Evaluate options

   rec[(rec.passed) & (rec.score > 80)] correctly uses (rec.passed) & (rec.score > 80). Options B and C use Python 'or'/'and' which don't work element-wise. rec[(rec.passed) | (rec.score > 80)] uses | (OR) instead of AND.

3. Final Answer:

   rec[(rec.passed) & (rec.score > 80)] -> Option B

4. Quick Check:

   Use & with parentheses for element-wise AND [OK]

Hint: Use & with parentheses for element-wise conditions [OK]

Common Mistakes:

• Using 'and' or 'or' instead of '&' or '|' for arrays
• Forgetting parentheses around conditions
• Using | instead of & for AND condition