Why random generation matters in NumPy - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to generate 5 random numbers between 0 and 1 using NumPy.

NumPy

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

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

Arand

Bchoice

Crandint

Drandom

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.random.randint which generates integers, not floats.

Using np.random.choice without specifying a list.

Using np.random.random which is a valid function but not the best here.

The np.random.rand function generates random floats between 0 and 1. Here, it creates 5 such numbers.

2fill in blank

medium

Complete the code to generate 5 random integers between 1 and 10 (inclusive) using NumPy.

NumPy

import numpy as np
random_ints = np.random.[1](1, 11, 5)
print(random_ints)

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

Arandom

Brand

Crandint

Dchoice

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.random.rand which generates floats, not integers.

Using np.random.random which generates floats.

Using np.random.choice without a list.

The np.random.randint function generates random integers. The arguments specify the start (inclusive), end (exclusive), and number of values.

3fill in blank

hard

Fix the error in the code to generate 3 random numbers from a normal distribution with mean 0 and standard deviation 1.

NumPy

import numpy as np
samples = np.random.normal([1], 1, 3)
print(samples)

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

A3

B1

C-1

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Setting mean to 1 instead of 0.

Confusing mean with standard deviation.

Using sample size as mean.

The mean of the normal distribution should be 0 to get standard normal samples.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their lengths as values, but only include words longer than 3 letters.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
lengths = {word: [1] for word in words if [2]
print(lengths)

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Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Comparing the word string directly to 3.

Not filtering words by length.

We use len(word) to get the length and filter words with length greater than 3.

5fill in blank

hard

Fill both blanks to create a dictionary with uppercase words as keys and their lengths as values, including only words longer than 3 letters.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
lengths = {word.upper(): {BLANK_2}} for word in words if {{BLANK_2}}
print(lengths)

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

Aword.upper()

Blen(word)

Clen(word) > 3

D{

Attempts:

3 left

💡 Hint

Common Mistakes

Not starting the dictionary with a curly brace.

Using the word itself as key instead of uppercase.

Not filtering words by length.

The dictionary starts with {. Keys are uppercase words using word.upper(), values are lengths, and filter checks length > 3.

Practice

1. Why is random number generation important in data science?

easy

A. It helps create unpredictable data for testing and simulations.

B. It always produces the same output for every run.

C. It removes the need for data cleaning.

D. It guarantees perfect model accuracy.

Solution

1. Step 1: Understand the role of randomness

   Random generation creates data that is not predictable, which is useful for testing and simulations.

2. Step 2: Evaluate the options

   Only 'It helps create unpredictable data for testing and simulations.' correctly states the importance of random generation. Options A, B, and D are incorrect because random generation does not remove the need for data cleaning, does not always produce the same output, and does not guarantee perfect accuracy.

3. Final Answer:

   It helps create unpredictable data for testing and simulations. -> Option A

4. Quick Check:

   Random generation importance = unpredictable data [OK]

Hint: Random means unpredictable data for testing [OK]

Common Mistakes:

• Thinking random data is always the same
• Assuming random data fixes all errors
• Believing random data guarantees perfect results

2. Which of the following is the correct way to generate 5 random numbers between 0 and 1 using NumPy?

easy

A. np.random.randn(5)

B. np.random.rand(5)

C. np.random.randint(0, 1, 5)

D. np.random.choice(5)

Solution

1. Step 1: Review NumPy random functions

   np.random.rand(5) generates 5 random floats between 0 and 1.

2. Step 2: Check other options

   np.random.randn(5) generates samples from the standard normal distribution (not uniform [0,1)); np.random.randint(0, 1, 5) returns zeros only; np.random.choice(5) picks one number from 0 to 4.

3. Final Answer:

   np.random.rand(5) -> Option B

4. Quick Check:

   Correct syntax for 5 random floats = np.random.rand(5) [OK]

Hint: Use np.random.rand(n) for n floats 0 to 1 [OK]

Common Mistakes:

• Using randint with 0 and 1 returns only zeros
• Using choice without specifying size returns one value
• Confusing randn with rand

3. What will be the output of the following code?

import numpy as np
np.random.seed(0)
print(np.random.rand(3))

medium

A. [0.5488135 0.71518937 0.60276338]

B. [0.5488135 0.60276338 0.71518937]

C. [0.37454012 0.95071431 0.73199394]

D. [0.71518937 0.60276338 0.5488135 ]

Solution

1. Step 1: Understand seed effect

   Setting np.random.seed(0) fixes the random numbers to a known sequence.

2. Step 2: Check known output for seed 0

   For seed 0, np.random.rand(3) produces [0.5488135 0.71518937 0.60276338]. The other options show different sequences or orders.

3. Final Answer:

   [0.37454012 0.95071431 0.73199394] -> Option C

4. Quick Check:

   Seed 0 fixed output = [0.37454012 0.95071431 0.73199394] [OK]

Hint: Seed fixes output; np.random.rand(3) gives 3 floats [OK]

Common Mistakes:

• Ignoring seed leads to different outputs
• Mixing order of numbers in output
• Confusing randint with rand

4. The following code is intended to generate 4 random integers between 1 and 10, but it raises an error. What is the problem?

import numpy as np
np.random.randint(1, 10, size=4, seed=42)

medium

A. The 'seed' argument is not valid in randint function.

B. The 'size' argument should be a tuple, not an integer.

C. The range 1 to 10 is invalid for randint.

D. The function randint does not exist in numpy.

Solution

1. Step 1: Check randint parameters

   NumPy's randint does not accept a seed parameter directly.

2. Step 2: Understand how to set seed

   Seed must be set using np.random.seed(42) before calling randint.

3. Final Answer:

   The 'seed' argument is not valid in randint function. -> Option A

4. Quick Check:

   Seed set separately, not in randint [OK]

Hint: Set seed with np.random.seed(), not in randint [OK]

Common Mistakes:

• Passing seed inside randint
• Using wrong size type
• Thinking randint is missing

5. You want to simulate rolling a fair six-sided die 1000 times using NumPy. Which code snippet correctly generates this data?

hard

A. np.random.rand(1000) * 6 + 1

B. np.random.randint(0, 6, size=1000)

C. np.random.choice(6, size=1000)

D. np.random.randint(1, 7, size=1000)

Solution

1. Step 1: Understand die roll range

   A fair six-sided die has values 1 through 6 inclusive.

2. Step 2: Check code options

   np.random.randint(1, 7, size=1000) uses randint(1,7) which includes 1 and excludes 7, so values 1 to 6 are generated correctly. np.random.rand(1000) * 6 + 1 generates floats, not integers. np.random.choice(6, size=1000) picks from default [0,1,2,3,4,5]. np.random.randint(0, 6, size=1000) generates 0 to 5, which is incorrect.

3. Final Answer:

   np.random.randint(1, 7, size=1000) -> Option D

4. Quick Check:

   Correct die roll simulation = randint(1,7) [OK]

Hint: Use randint(1,7) for integers 1 to 6 [OK]

Common Mistakes:

• Using randint(0,6) gives 0 to 5
• Using rand() gives floats, not integers
• Forgetting to set size for multiple rolls