Uniform random with random() in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of numpy.random.random() shape

What is the shape of the array produced by numpy.random.random((3, 4))?

NumPy

import numpy as np
arr = np.random.random((3, 4))
print(arr.shape)

A (12,)

B (4, 3)

C (3, 4)

D (3,)

💡 Hint

The argument to random() defines the shape of the output array.

The function numpy.random.random() takes a shape tuple and returns an array of that shape filled with random floats between 0 and 1.

❓ data_output

intermediate

Range of values from numpy.random.random()

What is the range of values produced by numpy.random.random(5)?

NumPy

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

A Values between 0 and 1, including 0 but excluding 1

B Values between 0 and 1, including both 0 and 1

C Values between -1 and 1

D Values between 1 and 10

💡 Hint

random() generates floats in the half-open interval [0.0, 1.0).

numpy.random.random() generates floats from 0 (inclusive) up to but not including 1.

❓ visualization

advanced

Histogram of uniform random numbers

Which option shows the correct histogram plot code for 1000 samples from numpy.random.random()?

NumPy

import numpy as np
import matplotlib.pyplot as plt
samples = np.random.random(1000)

A plt.plot(samples); plt.show()

B plt.hist(samples, bins=10, range=(0,1)); plt.show()

C plt.scatter(range(1000), samples); plt.show()

D plt.bar(range(10), samples[:10]); plt.show()

💡 Hint

Histograms show frequency distribution of values.

Using plt.hist with bins and range shows the distribution of uniform random samples between 0 and 1.

🧠 Conceptual

advanced

Effect of seed on numpy.random.random()

What happens if you set the seed using np.random.seed(42) before calling np.random.random(3) twice?

A The second call produces an error

B Both calls produce different arrays of 3 random numbers

C The seed only affects integers, not floats

D Both calls produce the same array of 3 random numbers

💡 Hint

Seed fixes the random number generator state.

Setting the seed makes the random numbers reproducible, so repeated calls produce the same output.

🔧 Debug

expert

Identify error in numpy.random.random() usage

What error does this code raise?

import numpy as np
arr = np.random.random(3,4)
print(arr)

A TypeError: random() takes from 0 to 1 positional arguments but 2 were given

B ValueError: shape must be a tuple of integers

C SyntaxError: invalid syntax

D No error, prints a 3x4 array

💡 Hint

random() expects a single argument for shape, which should be a tuple.

random() expects one argument for shape, passing two separate arguments causes TypeError.

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)