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

Recall & Review

beginner

What does numpy.random.random() do?

It generates a random float number between 0.0 (inclusive) and 1.0 (exclusive), following a uniform distribution.

beginner

How to generate 5 random numbers between 0 and 1 using numpy?

Use numpy.random.random(5) to get an array of 5 random floats between 0 and 1.

intermediate

How can you get random numbers between 10 and 20 using numpy.random.random()?

Multiply the output by 10 and add 10: 10 + 10 * numpy.random.random().

intermediate

What is the difference between numpy.random.random() and numpy.random.uniform()?

random() generates numbers between 0 and 1 only, while uniform(low, high) lets you specify any range.

beginner

Why is uniform random useful in data science?

It helps create random samples, simulate data, and test models with evenly spread values.

What range of values does numpy.random.random() generate?

A 0.0 (inclusive) to 1.0 (exclusive)

B 0 to 1 inclusive

C -1 to 1

D Any float number

numpy.random.random() generates floats from 0.0 up to but not including 1.0.

How do you generate an array of 3 random numbers between 0 and 1?

A numpy.random.uniform(0,3)

B numpy.random.random() * 3

C numpy.random.random(3)

D numpy.random.randint(0,3)

Passing 3 as an argument returns an array of 3 random floats between 0 and 1.

How to get a random number between 5 and 15 using numpy.random.random()?

A numpy.random.uniform(5, 15)

B 5 + 10 * numpy.random.random()

C numpy.random.random() * 15

D numpy.random.random() + 5

Multiply by the range (10) and add the minimum (5) to scale the output.

Which function allows specifying any range for uniform random numbers?

A numpy.random.normal()

B numpy.random.random()

C numpy.random.randint()

D numpy.random.uniform()

numpy.random.uniform(low, high) lets you set the range explicitly.

Why use uniform random numbers in simulations?

A To get evenly spread random values

B To get only integers

C To get normal distribution

D To get fixed values

Uniform random numbers spread evenly across the range, useful for fair sampling.

Explain how to generate random numbers between any two values using numpy.random.random().

Think about multiplying and shifting the 0 to 1 output.

Describe the difference between numpy.random.random() and numpy.random.uniform().

One is fixed range, the other is flexible.

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)