Setting random seed for reproducibility in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of numpy random with fixed seed

What is the output of the following code snippet?

NumPy

import numpy as np
np.random.seed(42)
result = np.random.randint(0, 10, size=5)
print(result.tolist())

A ]6 ,4 ,7 ,3 ,6[

B [6, 3, 7, 4, 6]

C 6, 3, 7, 4, 6]

D [6, 3, 7, 4, 6

💡 Hint

Setting the seed fixes the random numbers generated by numpy.

Using np.random.seed(42) ensures the random numbers generated are always the same. The randint function with these parameters produces [6, 3, 7, 4, 6].

❓ data_output

intermediate

Number of unique values with and without seed

Consider the following code. How many unique values are in the array 'a' after running it?

NumPy

import numpy as np
np.random.seed(0)
a = np.random.choice([1, 2, 3, 4, 5], size=10, replace=True)
unique_count = len(np.unique(a))
print(unique_count)

A 2

B 4

C 5

D 3

💡 Hint

The seed fixes the random choices, so the unique count is consistent.

With seed 0, the random choice picks values so that all 5 unique numbers appear at least once in the array.

🔧 Debug

advanced

Identify the error in reproducibility code

What error will this code raise when run?

NumPy

import numpy as np
np.random.seed(123)
result = np.random.randint(0, 5, size='10')
print(result)

A No error, prints an array of 10 integers

B ValueError: size must be positive

C SyntaxError: invalid syntax

D TypeError: 'str' object cannot be interpreted as an integer

💡 Hint

Check the type of the 'size' argument in randint.

The size parameter must be an integer or tuple of integers, not a string. Passing '10' causes a TypeError.

🚀 Application

advanced

Reproducible random sampling from a DataFrame

You have a DataFrame with 100 rows. You want to randomly select 10 rows reproducibly. Which code snippet achieves this?

A df.sample(n=10, random_state=42)

B df.sample(n=10).set_seed(42)

C df.sample(n=10, seed=42)

D df.sample(n=10, random_seed=42)

💡 Hint

Check the correct parameter name for reproducible sampling in pandas.

The pandas sample method uses 'random_state' to set the seed for reproducibility.

🧠 Conceptual

expert

Effect of setting seed on multiple random generators

If you set np.random.seed(0) and then use both numpy and Python's built-in random module to generate random numbers, which statement is true?

A Setting np.random.seed(0) only affects numpy's random functions, not Python's random module.

B Setting np.random.seed(0) also fixes the output of Python's random module.

C Python's random module uses numpy's seed internally, so both are fixed.

D Neither numpy nor Python's random module are affected by np.random.seed(0).

💡 Hint

Consider if numpy and Python's random share the same seed state.

Numpy and Python's random module have separate random number generators and seeds. Setting numpy's seed does not affect Python's random module.

Practice

1. What is the main purpose of setting a random seed in numpy?

easy

A. To increase the range of random numbers

B. To speed up random number generation

C. To generate only positive random numbers

D. To make random number results repeatable

Solution

1. Step 1: Understand what a random seed does

   Setting a random seed fixes the starting point for random number generation.

2. Step 2: Effect of fixed seed on results

   This means the same sequence of random numbers is produced every time the code runs.

3. Final Answer:

   To make random number results repeatable -> Option D

4. Quick Check:

   Random seed = repeatable results [OK]

Hint: Seed fixes randomness to get same results every run [OK]

Common Mistakes:

• Thinking seed speeds up random generation
• Believing seed changes number range
• Assuming seed makes numbers positive only

2. Which of the following is the correct syntax to set a random seed to 42 in numpy?

easy

A. np.random.seed(42)

B. np.seed.random(42)

C. np.random.set_seed(42)

D. np.random.seed = 42

Solution

1. Step 1: Recall the correct function name and usage

   The correct function to set seed in numpy is np.random.seed().

2. Step 2: Check the argument and syntax

   The seed value is passed as an integer inside the parentheses, like np.random.seed(42).

3. Final Answer:

   np.random.seed(42) -> Option A

4. Quick Check:

   Correct function call = np.random.seed(42) [OK]

Hint: Use np.random.seed(number) exactly [OK]

Common Mistakes:

• Swapping function and module names
• Using incorrect function like set_seed
• Assigning seed instead of calling function

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

import numpy as np
np.random.seed(0)
print(np.random.randint(1, 10))
np.random.seed(0)
print(np.random.randint(1, 10))

medium

A. Different numbers both times

B. 5 followed by different number

C. 5 followed by 5

D. Error due to repeated seed setting

Solution

1. Step 1: Understand effect of setting seed before each random call

   Setting the seed to 0 resets the random number generator to the same start point.

2. Step 2: Predict output of np.random.randint(1, 10) after resetting seed

   Both calls produce the same random integer because the seed is reset before each call.

3. Final Answer:

   5 followed by 5 -> Option C

4. Quick Check:

   Same seed = same random number [OK]

Hint: Resetting seed repeats same random number [OK]

Common Mistakes:

• Assuming different numbers after resetting seed
• Thinking repeated seed causes error
• Ignoring seed reset effect

4. Identify the error in this code snippet that tries to set a random seed:

import numpy as np
np.random.seed = 123
print(np.random.rand())

medium

A. Missing import statement

B. Seed is assigned instead of called as a function

C. Seed value must be a float, not integer

D. np.random.rand() is incorrect function

Solution

1. Step 1: Check how seed is set

   The code assigns 123 to np.random.seed instead of calling it as a function.

2. Step 2: Understand consequence of assignment

   This overwrites the seed function with an integer, causing errors on later random calls.

3. Final Answer:

   Seed is assigned instead of called as a function -> Option B

4. Quick Check:

   Use np.random.seed(123), not assignment [OK]

Hint: Call seed() as function, don't assign it [OK]

Common Mistakes:

• Assigning seed instead of calling
• Using wrong random function
• Ignoring import statement

5. You want to generate a reproducible array of 3 random floats between 0 and 1 using numpy. Which code snippet achieves this correctly?

hard

A. np.random.seed(7)
arr = np.random.rand(3)
print(arr)

B. np.random.seed = 7
arr = np.random.rand(3)
print(arr)

C. np.random.set_seed(7)
arr = np.random.rand(3)
print(arr)

D. np.random.seed(7)
arr = np.random.randint(3)
print(arr)

Solution

1. Step 1: Set the random seed correctly

   Use np.random.seed(7) to fix the random sequence.

2. Step 2: Generate 3 random floats between 0 and 1

   np.random.rand(3) creates an array of 3 floats in [0,1).

3. Final Answer:

   np.random.seed(7) arr = np.random.rand(3) print(arr) -> Option A

4. Quick Check:

   Correct seed + rand(3) = reproducible floats [OK]

Hint: Seed with np.random.seed(), use np.random.rand(3) [OK]

Common Mistakes:

• Assigning seed instead of calling
• Using non-existent set_seed function
• Using randint instead of rand for floats