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.