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

Uniform random numbers with numpy.random.random()

📖 Scenario: Imagine you are helping a game developer create random positions for objects in a 2D game world. The positions should be random numbers between 0 and 1, representing coordinates inside the game screen.

🎯 Goal: You will create a list of random numbers using numpy.random.random() to simulate these random positions.

📋 What You'll Learn

Use numpy to generate random numbers

Create an array of 5 random numbers between 0 and 1

Store the random numbers in a variable called positions

Print the positions array

💡 Why This Matters

🌍 Real World

Random numbers are used in games, simulations, and data sampling to create unpredictable results.

💼 Career

Data scientists often generate random samples to test models or simulate data.

1

Import numpy and create an empty array

Import the numpy library as np. Then create an empty variable called positions and set it to None.

NumPy

import numpy as np
positions = None
# Your code here

Hint

Use import numpy as np to import numpy. Initialize positions with None for now.

2

Generate 5 uniform random numbers

Use np.random.random() with the argument 5 to generate an array of 5 random numbers between 0 and 1. Assign this array to the variable positions.

NumPy

import numpy as np
positions = None
# Generate 5 random numbers between 0 and 1 and assign to positions

Hint

Call np.random.random(5) to get 5 random floats between 0 and 1.

3

Check the type and shape of positions

Use type(positions) and positions.shape to check the type and shape of the positions array. Store the type in a variable called pos_type and the shape in a variable called pos_shape.

NumPy

import numpy as np
positions = np.random.random(5)
# Store the type of positions in pos_type
# Store the shape of positions in pos_shape

Hint

Use type(positions) and positions.shape to get the type and shape.

4

Print the positions array

Print the positions array to see the 5 random numbers.

NumPy

import numpy as np
positions = np.random.random(5)
pos_type = type(positions)
pos_shape = positions.shape
# Print the positions array

Hint

Use print(positions) to display the array.

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)