Setting random seed for reproducibility in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to set the random seed to 42 using numpy.

NumPy

import numpy as np
np.random.[1](42)

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

Arand

Brandom

Cshuffle

Dseed

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.random.rand(42) instead of np.random.seed(42).

Forgetting to import numpy as np.

The function np.random.seed() sets the random seed for reproducibility.

2fill in blank

medium

Complete the code to generate a random integer between 0 and 9 after setting the seed.

NumPy

import numpy as np
np.random.seed(123)
num = np.random.[1](10)
print(num)

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

Arandint

Brandom

Crand

Dchoice

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.random.rand(10) which generates floats, not integers.

Using np.random.random(10) which is incorrect syntax.

np.random.randint(10) generates a random integer from 0 to 9.

3fill in blank

hard

Fix the error in the code to set the seed correctly before generating random numbers.

NumPy

import numpy as np
np.random.[1](7)
print(np.random.rand())

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

Aseed

Brandom

Cseed_value

Dseed()

Attempts:

3 left

💡 Hint

Common Mistakes

Assigning a value to np.random.seed instead of calling it as a function.

Using np.random.seed() without an argument.

You must call np.random.seed(7) as a function, not assign a value to np.random.seed.

4fill in blank

hard

Fill both blanks to create a reproducible array of 5 random floats between 0 and 1.

NumPy

import numpy as np
np.random.[1](0)
arr = np.random.[2](5)
print(arr)

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

Aseed

Brand

Crandint

Drandom

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.random.randint(5) which generates integers, not floats.

Not setting the seed before generating random numbers.

np.random.seed(0) sets the seed, np.random.rand(5) generates 5 random floats.

5fill in blank

hard

Fill all three blanks to set the seed, generate 3 random integers from 0 to 4, and print them.

NumPy

import numpy as np
np.random.[1](10)
numbers = np.random.[2](0, 5, [3])
print(numbers)

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

Aseed

Brandint

C3

Drand

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.random.rand instead of randint for integers.

Forgetting to specify the size parameter.

np.random.seed(10) sets the seed, np.random.randint(0, 5, 3) generates 3 integers from 0 to 4.

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