Uniform random with random() in NumPy

Introduction

We use uniform random numbers to get values that are equally likely anywhere in a range. This helps when we want to simulate random events or test ideas with random data.

When simulating rolling a fair dice many times.

When picking random points inside a square or rectangle.

When testing how a program behaves with random inputs.

When creating random samples for experiments.

When generating random colors or positions in a game.

Syntax

NumPy

numpy.random.random(size=None)

size is optional and decides how many random numbers you get.

The numbers are between 0 (inclusive) and 1 (exclusive).

Examples

Get one random number between 0 and 1.

NumPy

import numpy as np
x = np.random.random()
print(x)

Get an array of 5 random numbers between 0 and 1.

NumPy

import numpy as np
arr = np.random.random(5)
print(arr)

Get a 2 by 3 matrix of random numbers between 0 and 1.

NumPy

import numpy as np
matrix = np.random.random((2,3))
print(matrix)

Sample Program

This program shows how to get one random number, an array of random numbers, and a matrix of random numbers using numpy.random.random().

NumPy

import numpy as np

# Generate one random number
one_num = np.random.random()
print(f"One random number: {one_num}")

# Generate 4 random numbers in an array
arr = np.random.random(4)
print("Array of 4 random numbers:", arr)

# Generate a 3x2 matrix of random numbers
matrix = np.random.random((3, 2))
print("3x2 matrix of random numbers:")
print(matrix)

Important Notes

Each time you run the code, the random numbers will change.

You can set a seed with np.random.seed(number) to get the same random numbers every time, which helps with testing.

Summary

Uniform random numbers are equally likely anywhere between 0 and 1.

Use numpy.random.random() to get these numbers.

You can get one number, an array, or a matrix by changing the size parameter.

Practice

1.

What does numpy.random.random() generate by default?

easy

A. A single random number between 0 and 1

B. A random integer between 0 and 1

C. A random number between -1 and 1

D. A list of random numbers

Solution

1. Step 1: Understand the function purpose

   numpy.random.random() generates random floats in the range [0.0, 1.0).

2. Step 2: Check default behavior

   Without any size argument, it returns a single float number between 0 and 1.

3. Final Answer:

   A single random number between 0 and 1 -> Option A

4. Quick Check:

   random() = single float [OK]

Hint: No size means one float between 0 and 1 [OK]

Common Mistakes:

• Thinking it returns integers
• Assuming range is -1 to 1
• Expecting an array without size

2.

Which of the following is the correct syntax to generate a 2x3 array of uniform random numbers using numpy.random.random()?

easy

A. numpy.random.random((2,3))

B. numpy.random.random[2,3]

C. numpy.random.random{2,3}

D. numpy.random.random(2,3)

Solution

1. Step 1: Recall correct function call

   The size parameter expects a tuple for shape, so use parentheses and commas inside.

2. Step 2: Check syntax options

   Only numpy.random.random((2,3)) correctly passes a tuple for size.

3. Final Answer:

   numpy.random.random((2,3)) -> Option A

4. Quick Check:

   Tuple size = (2,3) [OK]

Hint: Use double parentheses for size tuple [OK]

Common Mistakes:

• Using square brackets instead of parentheses
• Passing size as separate arguments
• Using curly braces instead of parentheses

3.

What is the output shape of the following code?

import numpy as np
arr = np.random.random(5)

medium

A. (1,5)

B. (5,1)

C. (5,)

D. 5

Solution

1. Step 1: Understand the size parameter

   Passing 5 as size creates a 1D array with 5 elements.

2. Step 2: Determine shape of the array

   A 1D array with 5 elements has shape (5,), not (1,5) or (5,1).

3. Final Answer:

   (5,) -> Option C

4. Quick Check:

   random(5) shape = (5,) [OK]

Hint: Single integer size makes 1D array shape (n,) [OK]

Common Mistakes:

• Confusing 1D shape with 2D shapes
• Expecting a scalar output
• Assuming shape is (1,5) or (5,1)

4.

Identify the error in this code snippet:

import numpy as np
arr = np.random.random[3,4]

medium

A. Size parameter must be a single integer

B. Missing import statement

C. random() does not accept size argument

D. Using square brackets instead of parentheses for function call

Solution

1. Step 1: Check function call syntax

   Functions in Python require parentheses, not square brackets.

2. Step 2: Identify the error

   Using square brackets [] causes a syntax error; correct is random((3,4)).

3. Final Answer:

   Using square brackets instead of parentheses for function call -> Option D

4. Quick Check:

   Function calls need parentheses [OK]

Hint: Function calls always use parentheses () [OK]

Common Mistakes:

• Using square brackets for function calls
• Confusing size argument type
• Assuming random() can't take size

5.

You want to create a 3x3 matrix of uniform random numbers but only want numbers greater than 0.5. Which code correctly achieves this?

hard

A. np.random.random((3,3))[np.random.random((3,3)) > 0.5]

B. np.random.random((3,3)) + 0.5

C. np.random.random((3,3)) * 0.5

D. np.random.random((3,3)) > 0.5

Solution

1. Step 1: Understand the goal

   Create a 3x3 matrix where all uniform random numbers are greater than 0.5.

2. Step 2: Analyze each option

   A indexes one random array with a mask from another, yielding a 1D array with values in [0,1) including some <0.5. B returns a 3x3 boolean array. C produces values in [0,0.5]. D shifts values to [0.5,1.5), ensuring all ≥0.5.

3. Final Answer:

   np.random.random((3,3)) + 0.5 -> Option B

4. Quick Check:

   +0.5 shifts to [0.5, 1.5) all >0.5 [OK]

Hint: Shift range by adding 0.5 to make all >0.5 [OK]

Common Mistakes:

• Boolean indexing with different array (A: values can be <0.5, shape changes)
• Comparison returns booleans (B)
• Scaling down makes values ≤0.5 (C)