Integer random with integers() in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of numpy integers() with size parameter

What is the output of the following code snippet?

NumPy

import numpy as np
np.random.default_rng(seed=42).integers(low=5, high=10, size=3)

A [7 8 5]

B [6 5 5]

C [7 5 6]

D [7 5 5]

💡 Hint

Remember that integers() generates random integers in [low, high) range.

The integers() method with seed=42 produces the array [7 5 6] for the given parameters.

❓ data_output

intermediate

Shape and values from integers() with 2D size

What is the shape and one possible output of this code?

NumPy

import numpy as np
rng = np.random.default_rng(1)
rng.integers(0, 3, size=(2, 2))

A

Shape: (2, 2), Output: [[0 2]
 [1 0]]

B

Shape: (2, 2), Output: [[1 0]
 [0 2]]

C

Shape: (2, 2), Output: [[1 2]
 [0 1]]

D

Shape: (2, 2), Output: [[2 2]
 [1 0]]

💡 Hint

Check the shape parameter and the random seed effect.

With seed=1, integers(0,3,size=(2,2)) produces array([[0, 2], [1, 0]]).

🔧 Debug

advanced

Identify the error in this integers() call

What error does this code raise?

NumPy

import numpy as np
rng = np.random.default_rng()
rng.integers(low=10, high=5)

A No error, returns an integer

B TypeError: integers() missing required argument 'size'

C SyntaxError: invalid syntax

D ValueError: high must be greater than low

💡 Hint

Check the relationship between low and high parameters.

The high parameter must be greater than low; otherwise, ValueError is raised.

🧠 Conceptual

advanced

Range of values generated by integers()

Which statement about numpy's integers() output range is correct?

A integers(low, high) generates values in [low, high] inclusive

B integers(low, high) generates values in [low, high) exclusive of high

C integers(low, high) generates values in (low, high) exclusive of both ends

D integers(low, high) generates values in (low, high] inclusive of high only

💡 Hint

Think about typical Python range behavior.

numpy's integers() generates integers from low (inclusive) up to but not including high.

🚀 Application

expert

Generate a 1D array of 5 unique random integers between 0 and 9

Which code snippet correctly generates 5 unique random integers between 0 and 9 using numpy's integers()?

A

rng = np.random.default_rng()
rng.choice(10, size=5, replace=False)

B

rng = np.random.default_rng()
rng.integers(0, 10, size=5)

C

rng = np.random.default_rng()
rng.integers(0, 10, size=5, replace=False)

D

rng = np.random.default_rng()
rng.integers(0, 10, size=5, endpoint=True)

💡 Hint

integers() does not support replace=False, but choice() does.

To get unique integers, use choice() with replace=False. integers() does not have replace parameter.

Practice

1.

What does the numpy.random.integers(low, high) function do?

easy

A. Generates random numbers only equal to low or high.

B. Generates random whole numbers from low (inclusive) to high (inclusive).

C. Generates a sequence of numbers from low to high.

D. Generates random decimal numbers between low and high.

Solution

1. Step 1: Understand the function purpose

   numpy.random.integers(low, high) generates random integers in a range.

2. Step 2: Check the range behavior

   The function generates from low inclusive to high inclusive (default endpoint=True).

3. Final Answer:

   Generates random whole numbers from low (inclusive) to high (inclusive). -> Option B

4. Quick Check:

   integers() returns [low, high] random integers [OK]

Hint: integers() generates [low, high] by default [OK]

Common Mistakes:

• Thinking it generates decimal numbers
• Thinking high is exclusive
• Confusing with sequence generation

2.

Which of the following is the correct syntax to generate 5 random integers between 1 and 10 using numpy.random.integers()?

import numpy as np
# Your code here

easy

A. np.random.integers(1, 10, size=5)

B. np.random.integers(1, 10, count=5)

C. np.random.integers(low=1, high=10, count=5)

D. np.random.integers(1, 10, length=5)

Solution

1. Step 1: Check the function parameters

   integers(low, high, size=number) generates an array of random integers.

2. Step 2: Identify correct keyword for number of values

   The correct keyword is size, so size=5 generates 5 numbers.

3. Final Answer:

   np.random.integers(1, 10, size=5) -> Option A

4. Quick Check:

   Use size= for array length [OK]

Hint: Use size= to specify number of random integers [OK]

Common Mistakes:

• Using positional third argument without size keyword
• Using wrong keyword like count or length
• Missing import statement

3.

What is the output of the following code?

import numpy as np
np.random.seed(0)
arr = np.random.integers(1, 5, size=4)
print(arr)

medium

A. [5 4 3 5]

B. [1 2 3 4]

C. [3 3 3 3]

D. [4 4 1 3]

Solution

1. Step 1: Set random seed for reproducibility

   Using np.random.seed(0) fixes the random numbers generated.

2. Step 2: Generate 4 random integers between 1 and 5 inclusive

   np.random.integers(1, 5, size=4) produces the array [4 4 1 3] with this seed.

3. Final Answer:

   [4 4 1 3] -> Option D

4. Quick Check:

   Seed 0 + integers(1,5,4) = [4 4 1 3] [OK]

Hint: Use seed to get repeatable random integers [OK]

Common Mistakes:

• Ignoring seed and expecting different output
• Confusing inclusive range with exclusive
• Miscounting size parameter

4.

Find the error in this code snippet and choose the correct fix:

import numpy as np
arr = np.random.integers(0, 10, 5)
print(arr)

medium

A. No error; code runs correctly as is.

B. Change third argument to size=5 to specify array size.

C. Add parentheses around arguments: integers((0,10),5).

D. Replace integers with randint to fix syntax.

Solution

1. Step 1: Check function signature

   integers(low, high, size=...) accepts size as positional or keyword argument.

2. Step 2: Identify argument usage

   Passing 5 as positional third argument works correctly.

3. Final Answer:

   No error; code runs correctly as is. -> Option A

4. Quick Check:

   Positional size argument works [OK]

Hint: size can be positional or keyword in integers() [OK]

Common Mistakes:

• Confusing integers() with randint()
• Using wrong keyword like count or length
• Assuming positional size causes error

5.

You want to generate a 2x3 array of random integers from 10 to 20 using numpy.random.integers() and then filter to keep only even numbers. Which code correctly does this?

hard

A. arr = np.random.integers(5, 10, size=(2,3)) * 2

B. arr = np.random.integers(10, 20, size=(2,3)); arr = arr * 2

C. arr = np.random.integers(10, 20, size=(2,3)); arr = arr[arr % 2 == 0]

D. arr = np.random.integers(10, 20, size=(2,3)); arr = arr[arr % 2 != 0]

Solution

1. Step 1: Generate 2x3 array of integers 10 to 20

   Use np.random.integers(10, 20, size=(2,3)) to get random integers in the range.

2. Step 2: Filter only even numbers

   Use boolean indexing arr[arr % 2 == 0] to keep even numbers only.

3. Final Answer:

   arr = np.random.integers(10, 20, size=(2,3)); arr = arr[arr % 2 == 0] -> Option C

4. Quick Check:

   Filter with modulo 2 equals zero for evens [OK]

Hint: Filter with arr % 2 == 0 to keep even numbers [OK]

Common Mistakes:

• Multiplying random numbers instead of filtering
• Filtering odd numbers by mistake
• Using wrong range for even numbers