Integer random with integers() in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does numpy.random.Generator.integers() do?

It generates random integers from a specified range, similar to picking random numbers from a hat between a low and high value.

beginner

What parameters are needed for integers() to generate random numbers?

You need to provide low (start of range), high (end of range, exclusive), and optionally size (how many numbers to generate).

intermediate

How is integers() different from randint() in NumPy?

integers() is part of the new random Generator API, offering better performance and features, while randint() is from the older RandomState API.

beginner

What happens if you set size=None in integers()?

It returns a single random integer instead of an array of integers.

intermediate

Why is it better to use numpy.random.default_rng().integers() over older random functions?

Because it uses a newer, more reliable random number generator that is faster and has better statistical properties.

Which parameter in integers() defines the upper limit (exclusive) of the random numbers?

A low

B high

C size

D endpoint

The high parameter sets the exclusive upper bound for the random integers.

What does size=5 do in integers(low=0, high=10, size=5)?

A Generates integers from 0 to 5

B Generates integers up to 10

C Generates 5 random integers

D Generates a single integer

The size parameter controls how many random integers are generated.

What is the default behavior if size is not specified in integers()?

A Returns an empty array

B Returns an array of size 1

C Returns an error

D Returns a single integer

If size is None, integers() returns a single integer.

Which of these is the correct way to create a random integer generator in NumPy?

A numpy.random.default_rng().integers()

B numpy.random.random()

C numpy.random.randint()

D numpy.random.integers()

default_rng() creates a new Generator object, which then can use integers().

If you want random integers between 1 and 10 inclusive, which integers() call is correct?

A integers(1, 11)

B integers(1, 10)

C integers(0, 10)

D integers(0, 11)

The high parameter is exclusive, so to include 10, set high=11.

Explain how to generate 3 random integers between 5 and 15 using NumPy's integers().

Remember the upper bound is exclusive, so add 1 to include 15.

Describe the advantages of using integers() from the new Generator API over older random integer functions.

Think about reliability and speed improvements.

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