Uniform random with random() in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to generate a single random number between 0 and 1 using numpy.

NumPy

import numpy as np
random_number = np.random.[1]()

Drag options to blanks, or click blank then click option'

Arandom

Brandint

Cchoice

Drandn

Attempts:

3 left

💡 Hint

Common Mistakes

Using randint which returns integers.

Using randn which returns numbers from a normal distribution.

The np.random.random() function generates a random float between 0 and 1.

2fill in blank

medium

Complete the code to generate an array of 5 random floats between 0 and 1.

NumPy

import numpy as np
random_array = np.random.[1](5)

Drag options to blanks, or click blank then click option'

Arandom

Bchoice

Crandint

Drandn

Attempts:

3 left

💡 Hint

Common Mistakes

Using randint which returns integers.

Using randn which returns normally distributed numbers.

np.random.random(5) creates an array of 5 random floats between 0 and 1.

3fill in blank

hard

Fix the error in the code to generate a 2x3 array of random floats between 0 and 1.

NumPy

import numpy as np
random_matrix = np.random.random([1])

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A[2,3]

B(2,3)

C{2,3}

D2,3

Attempts:

3 left

💡 Hint

Common Mistakes

Passing shape as separate arguments instead of a tuple.

Using square brackets instead of parentheses.

The shape argument must be passed as a tuple inside parentheses: random(2, 3) is incorrect, it should be random((2, 3)).

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their random scores between 0 and 1 as values.

NumPy

import numpy as np
words = ['apple', 'banana', 'cherry']
scores = {word: np.random.[1]() for word in [2]

Drag options to blanks, or click blank then click option'

Arandom

Brandint

Cwords

Drange(3)

Attempts:

3 left

💡 Hint

Common Mistakes

Using randint which generates integers.

Iterating over range(3) instead of the words list.

Use np.random.random() to generate random floats and iterate over the list words to create the dictionary.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase words as keys and random scores greater than 0.5 as values.

NumPy

import numpy as np
words = ['dog', 'cat', 'bird']
result = {word.[1](): np.random.random() for word in words if np.random.random() [2] 0.5 and word [3] 'cat'}

Drag options to blanks, or click blank then click option'

Aupper

B>

C!=

Dlower

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'lower' instead of 'upper'.

Using '==' instead of '!=' to exclude 'cat'.

Use word.upper() to make keys uppercase, select random values greater than 0.5, and exclude the word 'cat' using !=.

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)