Integer random with integers() in NumPy - Mini Project: Build & Apply

Integer Random Numbers with numpy.integers()

📖 Scenario: You are working on a simple data science task where you need to generate random integer data for analysis. This is common when simulating data or testing algorithms.

🎯 Goal: Learn how to use numpy.random.Generator.integers() to create random integers within a specific range.

📋 What You'll Learn

Create a numpy random Generator instance

Generate random integers between 10 and 20

Generate exactly 5 random integers

Print the generated random integers

💡 Why This Matters

🌍 Real World

Generating random integers is useful in simulations, testing algorithms, and creating sample datasets.

💼 Career

Data scientists often need to create random data for experiments and model testing.

1

Create a numpy random Generator instance

Import numpy as np and create a random number generator instance called rng using np.random.default_rng().

NumPy

import numpy as np
# Create a random number generator instance called rng
# Your code here

Hint

Use np.random.default_rng() to create the generator.

2

Set the range for random integers

Create two variables called low and high and set them to 10 and 20 respectively. These will define the range for the random integers.

NumPy

import numpy as np
rng = np.random.default_rng()
# Set low to 10 and high to 20
# Your code here

Hint

Assign 10 to low and 20 to high.

3

Generate 5 random integers between low and high

Use the integers() method of rng to generate 5 random integers between low (inclusive) and high (exclusive). Store the result in a variable called random_numbers.

NumPy

import numpy as np
rng = np.random.default_rng()
low = 10
high = 20
# Generate 5 random integers between low and high and store in random_numbers
# Your code here

Hint

Use rng.integers(low, high, size=5) to generate the numbers.

4

Print the generated random integers

Print the variable random_numbers to display the generated random integers.

NumPy

import numpy as np
rng = np.random.default_rng()
low = 10
high = 20
random_numbers = rng.integers(low, high, size=5)
# Print the random_numbers array
# Your code here

Hint

Use print(random_numbers) to show the array.

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