np.random.default_rng() modern approach in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of random integers with default_rng

What is the output of this code snippet using np.random.default_rng()?

NumPy

import numpy as np
rng = np.random.default_rng(42)
result = rng.integers(1, 5, size=4)
print(result.tolist())

A [1, 3, 2, 2]

B [3, 1, 2, 4]

C [4, 1, 1, 4]

D [2, 4, 3, 1]

💡 Hint

Remember that integers(low, high) generates numbers from low (inclusive) to high (exclusive).

The default_rng with seed 42 produces a fixed sequence. The call rng.integers(1, 5, size=4) generates four integers between 1 and 4 inclusive. The exact output list is [4, 1, 1, 4].

❓ data_output

intermediate

Shape of array from random normal distribution

What is the shape of the array produced by this code?

NumPy

import numpy as np
rng = np.random.default_rng()
arr = rng.normal(loc=0, scale=1, size=(3, 2))
print(arr.shape)

A (3, 2)

B (2, 3)

C (6,)

D (3,)

💡 Hint

The size parameter defines the shape of the output array.

The rng.normal function generates samples from a normal distribution. The size=(3, 2) means the output array has 3 rows and 2 columns, so shape is (3, 2).

🔧 Debug

advanced

Identify the error in random choice usage

What error does this code raise?

NumPy

import numpy as np
rng = np.random.default_rng()
result = rng.choice([10, 20, 30], size=3, replace=False, p=[0.2, 0.5])

A ValueError: probabilities do not sum to 1

B TypeError: choice() got an unexpected keyword argument 'p'

C No error, runs successfully

D ValueError: probabilities must be same length as array

💡 Hint

Check the length of the probability array compared to the choices array.

The p parameter must have the same length as the array to choose from. Here, the array has length 3 but p has length 2, causing a ValueError.

🧠 Conceptual

advanced

Understanding seed behavior in default_rng

Which statement about np.random.default_rng(seed) is true?

A Using the same seed always produces the same random sequence.

B The seed must be a float between 0 and 1.

C default_rng() ignores the seed parameter completely.

D The seed controls the shape of the output array.

💡 Hint

Think about reproducibility in random number generation.

The seed initializes the random number generator. Using the same seed ensures the same sequence of random numbers, which helps with reproducibility.

🚀 Application

expert

Generate reproducible shuffled DataFrame rows

You have a pandas DataFrame df. Which code snippet correctly shuffles its rows reproducibly using np.random.default_rng()?

NumPy

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': range(5), 'B': list('abcde')})

A

rng = np.random.default_rng(42)
shuffled_df = df.sample(frac=1, random_state=rng.bit_generator)

B

rng = np.random.default_rng(42)
shuffled_df = df.iloc[rng.permutation(len(df))].reset_index(drop=True)

C

rng = np.random.default_rng(42)
shuffled_df = df.sample(frac=1, random_state=42)

D

rng = np.random.default_rng(42)
shuffled_df = df.sample(frac=1, random_state=rng)

💡 Hint

Pandas sample expects an int or np.random.RandomState for random_state, but default_rng returns a Generator.

Pandas sample does not accept np.random.Generator directly as random_state. Using rng.permutation to shuffle indices and then reindexing is the correct reproducible approach.

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