Integer random with integers() in NumPy - Step-by-Step Execution

Concept Flow - Integer random with integers()

Call np.random.Generator.integers(low, high, size)

↓

Generate random integers in range [low, high))

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Return array of random integers

↓

Use or display the random integers

The function generates random integers between low (inclusive) and high (exclusive), then returns them as an array.

Execution Sample

NumPy

import numpy as np
rng = np.random.default_rng()
rnd_ints = rng.integers(1, 5, size=4)
print(rnd_ints)

This code creates a random number generator and generates 4 random integers between 1 and 4.

Execution Table

Step	Action	Parameters	Generated Integers	Output
1	Create random generator	None	N/A	rng object created
2	Call integers()	low=1, high=5, size=4	[3, 1, 4, 2]	Array of 4 integers between 1 and 4
3	Print output	Array from step 2	[3 1 4 2]	[3 1 4 2] printed
4	End	N/A	N/A	Execution stops

💡 All requested integers generated and printed; program ends.

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
rng	None	Generator object	Generator object	Generator object	Generator object
rnd_ints	None	None	[3, 1, 4, 2]	[3, 1, 4, 2]	[3, 1, 4, 2]

Key Moments - 2 Insights

Why are the random integers always between 1 and 4, not including 5?

The integers() function generates numbers from low (inclusive) to high (exclusive). So 5 is not included, as shown in execution_table step 2.

What does the 'size' parameter control?

The 'size' parameter controls how many random integers are generated. In step 2, size=4 means 4 integers are returned.

Visual Quiz - 3 Questions

Look at the execution_table at step 2, what is the range of generated integers?

A From 1 to 4 inclusive

B From 0 to 4 inclusive

C From 1 to 5 inclusive

D From 1 to 3 inclusive

💡 Hint

Check the 'Parameters' and 'Generated Integers' columns in step 2.

According to variable_tracker, what is the value of rnd_ints after step 3?

A None

B [3, 1, 4, 2]

C Generator object

D An empty array

💡 Hint

Look at the 'rnd_ints' row under 'After Step 3' in variable_tracker.

If we change size=2 in the code, how would execution_table step 2 change?

A Generated Integers would have 4 numbers as before

B Generated Integers would be empty

C Generated Integers would have 2 numbers instead of 4

D The code would error out

💡 Hint

The 'size' parameter controls how many integers are generated, see key_moments about 'size'.

Concept Snapshot

np.random.Generator.integers(low, high, size)
- Generates random integers from low (inclusive) to high (exclusive)
- 'size' sets how many integers to generate
- Returns a NumPy array of random integers
- Use default_rng() to create a generator
- Example: rng.integers(1, 5, size=4) -> 4 ints from 1 to 4

Full Transcript

This visual execution shows how to generate random integers using numpy's integers() method. First, a random number generator object is created with default_rng(). Then integers() is called with parameters low=1, high=5, and size=4. This produces 4 random integers between 1 and 4 inclusive. The generated array is stored in rnd_ints and printed. The execution table traces each step, showing the parameters and output. The variable tracker shows how rng and rnd_ints change over time. Key moments clarify why the range excludes the high value and what the size parameter does. The quiz tests understanding of the range, variable values, and effect of changing size. The snapshot summarizes usage in a quick reference format.

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