np.random.default_rng() modern approach in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a new random number generator using the modern numpy approach.

NumPy

rng = np.random.[1]()

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

Adefault_rng

Brandint

Crandom

Dseed

Attempts:

3 left

💡 Hint

Common Mistakes

Using older functions like np.random.seed() instead of default_rng()

Trying to call np.random.random() to create a generator

The default_rng() function creates a new random number generator instance in numpy's modern API.

2fill in blank

medium

Complete the code to generate 5 random integers between 1 and 10 using the modern numpy random generator.

NumPy

rng = np.random.default_rng()
random_numbers = rng.integers([1], 11, size=5)

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

A5

B1

C0

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Setting low to 0 which includes 0 in output

Setting high to 10 which excludes 10

The integers(low, high) method generates random integers from low (inclusive) to high (exclusive). To get numbers from 1 to 10, low must be 1 and high 11.

3fill in blank

hard

Fix the error in the code to generate 3 random floats between 0 and 1 using the modern numpy random generator.

NumPy

rng = np.random.default_rng()
random_floats = rng.[1](3)

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

Arandom

Brandint

Cintegers

Dchoice

Attempts:

3 left

💡 Hint

Common Mistakes

Using integers() which returns integers

Using randint() which is not a method of the generator instance

The random() method generates random floats in [0, 1). The integers() method generates integers, so it is incorrect here.

4fill in blank

hard

Fill both blanks to create a random number generator with a fixed seed and generate 4 random integers from 0 to 9.

NumPy

rng = np.random.[1](12345)
numbers = rng.[2](0, 10, size=4)

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

Adefault_rng

Bintegers

Crandom

Dseed

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.random.seed() which does not return a generator

Using random() instead of integers() for integers

Use default_rng(seed) to create a reproducible generator. Then use integers() to generate random integers.

5fill in blank

hard

Fill all three blanks to create a dictionary of word lengths for words longer than 3 letters using a comprehension and the modern numpy random generator to shuffle the list.

NumPy

words = ['apple', 'bat', 'carrot', 'dog', 'elephant']
rng = np.random.[1]()
rng.shuffle(words)
lengths = {word: [2] for word in words if len(word) [3] 3}

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

Adefault_rng

Blen(word)

C>

Drandom

Attempts:

3 left

💡 Hint

Common Mistakes

Using random() instead of default_rng()

Using < instead of > in the condition

Using word instead of len(word) for the dictionary values

Create the generator with default_rng(). Use len(word) to get word length. Filter words with length greater than 3 using >.

Practice

1. What does np.random.default_rng() do in NumPy?

easy

A. Creates a modern random number generator instance

B. Generates a fixed list of numbers

C. Imports the NumPy library

D. Sorts an array in ascending order

Solution

1. Step 1: Understand the function purpose

   np.random.default_rng() creates a new random number generator object using the modern Generator API.

2. Step 2: Compare with other options

   It does not generate fixed lists, import libraries, or sort arrays.

3. Final Answer:

   Creates a modern random number generator instance -> Option A

4. Quick Check:

   default_rng() = modern RNG instance [OK]

Hint: Remember default_rng() always creates a new RNG object [OK]

Common Mistakes:

• Confusing it with random number generation output
• Thinking it imports NumPy
• Mixing it up with sorting functions

2. Which of the following is the correct way to create a random number generator with a seed of 42 using np.random.default_rng()?

easy

A. rng = np.random.default_rng(42, seed=42)

B. rng = np.random.default_rng(42)

C. rng = np.random.default_rng().seed(42)

D. rng = np.random.default_rng().random(42)

Solution

1. Step 1: Check the correct syntax for seeding

   The seed is passed as an argument directly to default_rng(), so np.random.default_rng(42) is correct.

2. Step 2: Evaluate other options

   rng = np.random.default_rng(42, seed=42) is invalid because it passes seed both positionally and as keyword, causing TypeError: multiple values for 'seed'. rng = np.random.default_rng().seed(42) tries to call seed() method which does not exist on the Generator. rng = np.random.default_rng().random(42) calls random(42) which generates numbers, not seeds.

3. Final Answer:

   rng = np.random.default_rng(42) -> Option B

4. Quick Check:

   Seed passed as argument = rng = np.random.default_rng(42) [OK]

Hint: Pass seed directly inside default_rng() parentheses [OK]

Common Mistakes:

• Passing seed both positionally and as keyword argument
• Calling seed() method on the generator
• Confusing random() method with seeding

3. What is the output of this code?

import numpy as np
rng = np.random.default_rng(123)
print(rng.integers(1, 10, size=3))

medium

A. [3 3 7]

B. [3 1 7]

C. [2 3 7]

D. [3 3 6]

Solution

1. Step 1: Understand the code

   The code creates a random number generator with seed 123, then generates 3 random integers between 1 (inclusive) and 10 (exclusive).

2. Step 2: Run the code or recall output

   Running this code produces the array [3 3 7].

3. Final Answer:

   [3 3 7] -> Option A

4. Quick Check:

   Seed 123 + integers(1,10,3) = [3 3 7] [OK]

Hint: Seed fixes output; integers(1,10,3) gives same 3 numbers [OK]

Common Mistakes:

• Assuming inclusive upper bound 10
• Confusing seed effect on output
• Mixing output with floats instead of integers

4. Identify the error in this code snippet:

import numpy as np
rng = np.random.default_rng()
random_numbers = rng.random(5, seed=10)
print(random_numbers)

medium

A. random() should be called without parentheses

B. default_rng() requires a seed argument

C. random() does not accept a seed argument

D. rng.random() returns integers, not floats

Solution

1. Step 1: Check method signature of random()

   The random() method of the Generator does not accept a seed parameter; seeding is done when creating the generator.

2. Step 2: Identify the error

   Passing seed=10 to random() causes a TypeError.

3. Final Answer:

   random() does not accept a seed argument -> Option C

4. Quick Check:

   Seed only in default_rng(), not in random() [OK]

Hint: Seed only when creating RNG, not in random() calls [OK]

Common Mistakes:

• Trying to seed random() method
• Thinking random() returns integers
• Believing default_rng() needs seed always

5. You want to generate a reproducible shuffled version of the list [10, 20, 30, 40, 50] using np.random.default_rng(). Which code correctly achieves this?

hard

A. rng = np.random.default_rng(7)
arr = [10, 20, 30, 40, 50]
rng.shuffle(arr)
print(arr)

B. rng = np.random.default_rng()
arr = [10, 20, 30, 40, 50]
rng.shuffle(arr)
print(arr)

C. rng = np.random.default_rng(7)
arr = [10, 20, 30, 40, 50]
shuffled = np.random.shuffle(arr)
print(shuffled)

D. rng = np.random.default_rng(7)
arr = [10, 20, 30, 40, 50]
shuffled = rng.permutation(arr)
print(shuffled)

Solution

1. Step 1: Understand reproducible shuffling

   To get reproducible shuffling, seed the generator and use its methods. rng.shuffle() shuffles in-place and returns None, while rng.permutation() returns a shuffled copy.

2. Step 2: Analyze options

   rng = np.random.default_rng(7) arr = [10, 20, 30, 40, 50] rng.shuffle(arr) print(arr) fails because rng.shuffle() requires a NumPy ndarray but arr is a list (TypeError). rng = np.random.default_rng() arr = [10, 20, 30, 40, 50] rng.shuffle(arr) print(arr) has no seed, so not reproducible (also fails on list). rng = np.random.default_rng(7) arr = [10, 20, 30, 40, 50] shuffled = np.random.shuffle(arr) print(shuffled) incorrectly uses np.random.shuffle() which ignores rng, requires ndarray (fails on list), and returns None. rng = np.random.default_rng(7) arr = [10, 20, 30, 40, 50] shuffled = rng.permutation(arr) print(shuffled) seeds and uses permutation() to get a reproducible shuffled copy.

3. Final Answer:

   rng = np.random.default_rng(7) arr = [10, 20, 30, 40, 50] shuffled = rng.permutation(arr) print(shuffled) -> Option D

4. Quick Check:

   Seed + permutation() = reproducible shuffled copy [OK]

Hint: Use rng.permutation(arr) with seed for reproducible shuffle [OK]

Common Mistakes:

• Using np.random.shuffle() ignoring seed
• Expecting shuffle() to return a new list
• Not seeding the generator for reproducibility