np.random.default_rng() modern approach in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is np.random.default_rng() used for?

np.random.default_rng() creates a new random number generator object. It is the modern way to generate random numbers in NumPy, replacing older functions like np.random.rand().

beginner

How do you generate 5 random integers between 0 and 10 using default_rng()?

rng = np.random.default_rng()
random_ints = rng.integers(0, 10, size=5)

This creates 5 random integers from 0 up to (but not including) 10.

intermediate

Why is default_rng() preferred over older NumPy random functions?

It provides better randomness quality, is easier to control with a seed, and avoids global state issues. It also supports new features and is the recommended approach since NumPy 1.17.

beginner

How do you set a seed with default_rng() to get repeatable results?

rng = np.random.default_rng(42)

Using the same seed will produce the same sequence of random numbers every time.

beginner

What method would you use with default_rng() to generate random floats between 0 and 1?

Use rng.random(size) to generate random floats in the range [0, 1).

What does np.random.default_rng() return?

A A random integer

B A random float between 0 and 1

C A new random number generator object

D A global random seed

default_rng() returns a new random number generator object to generate random numbers.

How do you generate 10 random floats between 0 and 1 using default_rng()?

A rng.integers(0, 1, size=10)

B rng.uniform(0, 1)

C np.random.rand(10)

D rng.random(10)

rng.random(10) generates 10 random floats between 0 and 1.

Why should you use default_rng() instead of np.random.rand()?

A It is slower but more compatible

B It provides better randomness and control

C It uses global state

D It does not support seeding

default_rng() offers better randomness quality and easier control with seeds.

How do you make random results repeatable with default_rng()?

A Use <code>rng = np.random.default_rng(seed=123)</code>

B Use <code>rng = np.random.default_rng()</code> without arguments

C Call <code>rng.seed(123)</code> after creation

D Set a global seed with <code>np.random.seed(123)</code>

Passing a seed to default_rng() ensures repeatable random sequences.

Which method generates random integers with default_rng()?

A rng.integers()

B rng.random()

C rng.randint()

D rng.uniform()

rng.integers() generates random integers in a specified range.

Explain how to create a random number generator with a fixed seed using np.random.default_rng() and generate 3 random floats.

Think about how to pass a seed and call the method for floats.

Describe the advantages of using np.random.default_rng() over older NumPy random functions.

Focus on quality, control, and best practice.

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