Random choice from array in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of numpy random choice with replacement

What is the output of this code snippet?

import numpy as np
np.random.seed(0)
arr = np.array([10, 20, 30, 40])
result = np.random.choice(arr, size=3, replace=True)
print(result)

NumPy

import numpy as np
np.random.seed(0)
arr = np.array([10, 20, 30, 40])
result = np.random.choice(arr, size=3, replace=True)
print(result)

A [30 30 30]

B [20 20 40]

C [40 10 10]

D [10 40 20]

💡 Hint

Remember that numpy.random.choice with replace=True can pick the same element multiple times.

With seed 0, numpy.random.choice picks elements in a reproducible way. The output array is [40 10 10].

❓ data_output

intermediate

Number of unique elements in random choice without replacement

Given this code, how many unique elements will the result contain?

import numpy as np
np.random.seed(1)
arr = np.array([5, 10, 15, 20, 25])
result = np.random.choice(arr, size=4, replace=False)
print(len(np.unique(result)))

NumPy

import numpy as np
np.random.seed(1)
arr = np.array([5, 10, 15, 20, 25])
result = np.random.choice(arr, size=4, replace=False)
print(len(np.unique(result)))

A 3

B 4

C 5

D 1

💡 Hint

When replace=False, elements cannot repeat in the sample.

Sampling 4 elements without replacement from 5 unique elements means all 4 are unique.

🔧 Debug

advanced

Identify the error in numpy random choice usage

What error does this code raise?

import numpy as np
arr = np.array([1, 2, 3])
result = np.random.choice(arr, size=5, replace=False)
print(result)

NumPy

import numpy as np
arr = np.array([1, 2, 3])
result = np.random.choice(arr, size=5, replace=False)
print(result)

A ValueError: Cannot take a larger sample than population when 'replace=False'

B TypeError: 'replace' argument must be boolean

C IndexError: index out of bounds

D No error, prints 5 elements

💡 Hint

Check if the sample size is larger than the array length when replace=False.

Sampling 5 elements without replacement from an array of length 3 is impossible, raising ValueError.

🚀 Application

advanced

Weighted random choice output

What is the output of this code?

import numpy as np
np.random.seed(2)
arr = np.array(['red', 'green', 'blue'])
weights = [0.1, 0.7, 0.2]
result = np.random.choice(arr, size=5, p=weights)
print(result)

NumPy

import numpy as np
np.random.seed(2)
arr = np.array(['red', 'green', 'blue'])
weights = [0.1, 0.7, 0.2]
result = np.random.choice(arr, size=5, p=weights)
print(result)

A ['green' 'blue' 'red' 'green' 'blue']

B ['red' 'red' 'green' 'blue' 'green']

C ['blue' 'blue' 'green' 'green' 'red']

D ['green' 'green' 'green' 'blue' 'green']

💡 Hint

Weights affect the probability of each element being chosen.

With seed 2 and given weights, the output is ['green' 'green' 'green' 'blue' 'green'].

🧠 Conceptual

expert

Effect of random seed on numpy random choice

Which statement is true about setting a random seed before using numpy.random.choice?

A Setting the seed ensures the random choices are the same every time the code runs.

B Setting the seed makes the choices completely unpredictable.

C The seed only affects random integers, not random choice.

D Random seed must be set after calling numpy.random.choice to affect output.

💡 Hint

Think about reproducibility in random operations.

Setting the seed fixes the random number generator state, making outputs reproducible.

Practice

1.

What does the numpy.random.choice function do?

easy

A. It picks one or more random elements from an array.

B. It sorts the array in ascending order.

C. It calculates the mean of the array elements.

D. It removes duplicate elements from the array.

Solution

1. Step 1: Understand the function purpose

   numpy.random.choice is used to select random elements from an array.

2. Step 2: Compare with other options

   Sorting, calculating mean, or removing duplicates are different functions and unrelated to random choice.

3. Final Answer:

   It picks one or more random elements from an array. -> Option A

4. Quick Check:

   Random choice = pick random elements [OK]

Hint: Random choice picks elements randomly from an array [OK]

Common Mistakes:

• Confusing random choice with sorting
• Thinking it calculates statistics like mean
• Assuming it removes duplicates

2.

Which of the following is the correct syntax to pick 3 random elements from a numpy array arr with replacement?

import numpy as np
arr = np.array([10, 20, 30, 40, 50])

easy

A. np.random.choice(arr, size=3, replace=False)

B. np.random.choice(arr, 3, replace=False)

C. np.random.choice(arr, size=3, replace=True)

D. np.random.choice(arr, 3, replace=True, axis=1)

Solution

1. Step 1: Check correct parameter names

   The function uses size to specify number of elements and replace to allow repeats.

2. Step 2: Validate each option

   np.random.choice(arr, size=3, replace=True) uses correct parameters: size=3 and replace=True. Others have wrong parameter names or extra invalid ones.

3. Final Answer:

   np.random.choice(arr, size=3, replace=True) -> Option C

4. Quick Check:

   Correct syntax = size=3, replace=True [OK]

Hint: Use size= and replace= parameters correctly [OK]

Common Mistakes:

• Using positional argument without size=
• Setting replace=False when repeats are needed
• Adding invalid parameters like axis

3.

What is the output of this code?

import numpy as np
np.random.seed(0)
arr = np.array([1, 2, 3, 4, 5])
result = np.random.choice(arr, size=4, replace=False)
print(result)

medium

A. [5 1 4 3]

B. [4 5 1 3]

C. [1 2 3 4]

D. [2 5 1 4]

Solution

1. Step 1: Understand seed and choice without replacement

   Setting seed ensures reproducible random results. Choosing 4 unique elements without replacement picks 4 different values from arr.

2. Step 2: Run code or recall output

   With seed 0, the output is [2 5 1 4].

3. Final Answer:

   [2 5 1 4] -> Option D

4. Quick Check:

   Seed 0 + replace=False = [2 5 1 4] [OK]

Hint: Use np.random.seed for consistent random output [OK]

Common Mistakes:

• Ignoring seed and expecting different output
• Confusing replace=True and replace=False results
• Assuming output is sorted

4.

Identify the error in this code snippet:

import numpy as np
arr = np.array([1, 2, 3])
result = np.random.choice(arr, size=5, replace=False)
print(result)

medium

A. Size is larger than array length without replacement, causing an error.

B. Array is not defined properly.

C. Missing import statement for numpy.

D. replace parameter should be True to avoid error.

Solution

1. Step 1: Check size vs array length with replace=False

   Choosing 5 elements without replacement from an array of length 3 is invalid and raises an error.

2. Step 2: Confirm other parts are correct

   Array is defined, numpy is imported, and replace parameter is valid. The main issue is size too large without replacement.

3. Final Answer:

   Size is larger than array length without replacement, causing an error. -> Option A

4. Quick Check:

   Size > array length + replace=False = error [OK]

Hint: Size must not exceed array length if replace=False [OK]

Common Mistakes:

• Ignoring size vs array length mismatch
• Assuming replace=True by default
• Not importing numpy before use

5.

You have a numpy array data = np.array([10, 20, 30, 40, 50]). You want to randomly select 3 unique elements but ensure the number 20 is always included in the result. Which approach is correct?

hard

A. Pick 3 elements with replacement and filter for 20 after.

B. Remove 20 from array, pick 2 without replacement, then add 20 back.

C. Use np.random.choice(data, size=3, replace=False) directly.

D. Pick 3 elements with replacement and add 20 manually.

Solution

1. Step 1: Understand the requirement

   We want 3 unique elements including 20 always.

2. Step 2: Choose method to guarantee 20

   Remove 20, pick 2 unique elements from remaining, then add 20 to result ensures 20 is included and no duplicates.

3. Step 3: Evaluate other options

   Direct random choice may exclude 20. Picking with replacement can cause duplicates or exclude 20. Adding 20 manually after picking with replacement may cause duplicates.

4. Final Answer:

   Remove 20 from array, pick 2 without replacement, then add 20 back. -> Option B

5. Quick Check:

   Guarantee element by picking rest then adding it [OK]

Hint: Pick others without 20, then add 20 to ensure inclusion [OK]

Common Mistakes:

• Assuming random choice always includes 20
• Using replacement causing duplicates
• Adding 20 after picking with replacement causing repeats