Generating random samples in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to generate 5 random numbers from a uniform distribution between 0 and 1.

NumPy

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

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

Achoice

Brand

Cnormal

Drandint

Attempts:

3 left

💡 Hint

Common Mistakes

Using randint which generates integers, not floats.

Using normal which generates samples from a normal distribution.

The np.random.rand function generates random samples from a uniform distribution over [0, 1).

2fill in blank

medium

Complete the code to generate 10 random integers between 1 and 100 (inclusive).

NumPy

import numpy as np
ints = np.random.[1](1, 101, 10)

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

Arandint

Brand

Cchoice

Drandom_sample

Attempts:

3 left

💡 Hint

Common Mistakes

Using rand which generates floats, not integers.

Using choice without specifying the range.

The np.random.randint function generates random integers from low (inclusive) to high (exclusive). Here, 101 is used to include 100.

3fill in blank

hard

Fix the error in the code to generate 7 random samples from a normal distribution with mean 0 and standard deviation 1.

NumPy

import numpy as np
samples = np.random.normal([1], 1, 7)

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

A1

B7

C0

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing mean and standard deviation values.

Passing size as the mean.

The mean of the normal distribution should be 0, not 1 or 7.

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 4 characters.

NumPy

words = ['apple', 'dog', 'banana', 'cat', 'grape']
lengths = {word: [1] for word in words if [2]

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

Alen(word)

Blen(word) > 4

Cword

Dword > 4

Attempts:

3 left

💡 Hint

Common Mistakes

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

Using the word string in the condition instead of its length.

The dictionary comprehension uses len(word) for values and filters words with length greater than 4.

5fill in blank

hard

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

NumPy

words = ['tree', 'sun', 'flower', 'sky']
result = { [1]: [2] for w in words if [3] }

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

Aw.upper()

Blen(w)

Clen(w) > 3

Dw.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase keys instead of uppercase.

Not filtering words by length correctly.

The dictionary comprehension uses uppercase words as keys, their lengths as values, and filters words longer than 3 characters.

Practice

1. What does the numpy.random.choice function do?

easy

A. It calculates the mean of an array.

B. It selects random elements from a given array or list.

C. It sorts an array in ascending order.

D. It reshapes an array into a new shape.

Solution

1. Step 1: Understand the function purpose

   numpy.random.choice is designed to pick random elements from a given array or list.

2. Step 2: Compare with other options

   Sorting, calculating mean, and reshaping are different numpy functions, not related to random sampling.

3. Final Answer:

   It selects random elements from a given array or list. -> Option B

4. Quick Check:

   Random sampling = selecting elements randomly [OK]

Hint: Remember: choice means picking randomly from data [OK]

Common Mistakes:

• Confusing choice with sorting or reshaping functions
• Thinking it calculates statistics like mean
• Assuming it modifies array shape

2. Which of the following is the correct syntax to randomly select 3 elements from array arr without replacement using numpy?

easy

A. numpy.random.choice(arr, 3, replace=True)

B. numpy.choice(arr, size=3, replace=False)

C. numpy.random.choice(arr, size=3, replace=False)

D. numpy.random.choice(arr, size=3, replace=True)

Solution

1. Step 1: Identify correct function and parameters

   The function is numpy.random.choice. To select 3 elements without replacement, use size=3 and replace=False.

2. Step 2: Check each option

   numpy.random.choice(arr, size=3, replace=False) uses correct function and parameters. numpy.random.choice(arr, 3, replace=True) uses replacement True (wrong). numpy.choice(arr, size=3, replace=False) uses wrong function name. numpy.random.choice(arr, size=3, replace=True) uses replacement True (wrong).

3. Final Answer:

   numpy.random.choice(arr, size=3, replace=False) -> Option C

4. Quick Check:

   Correct syntax = choice + size + replace=False [OK]

Hint: Use replace=False to avoid repeated picks [OK]

Common Mistakes:

• Using replace=True when no repeats wanted
• Misspelling function name as numpy.choice
• Passing size as positional without keyword

3. What is the output of this code?

import numpy as np
np.random.seed(0)
arr = np.array([10, 20, 30, 40])
sample = np.random.choice(arr, size=2, replace=False)
sample_sorted = np.sort(sample)
sample_sorted.tolist()

medium

A. [10, 40]

B. [10, 20]

C. [20, 40]

D. [30, 40]

Solution

1. Step 1: Understand random seed and choice

   Setting seed to 0 fixes randomness. Using choice with size=2 and replace=False picks 2 unique elements from [10,20,30,40].

2. Step 2: Determine chosen elements and sort

   With seed 0, the chosen elements are [10, 40]. Sorting gives [10, 40].

3. Final Answer:

   [10, 40] -> Option A

4. Quick Check:

   Seed 0 + choice + sort = [10, 40] [OK]

Hint: Seed fixes output; sort to order chosen elements [OK]

Common Mistakes:

• Ignoring seed and expecting different output
• Not sorting before converting to list
• Assuming replacement allows duplicates

4. The following code throws an error. What is the cause?

import numpy as np
arr = np.array([1, 2, 3])
sample = np.random.choice(arr, size=5, replace=False)

medium

A. Incorrect function name used.

B. Array contains integers instead of floats.

C. Missing import statement for numpy.

D. Size is larger than array length without replacement.

Solution

1. Step 1: Analyze parameters and array size

   The array has 3 elements, but size=5 is requested without replacement.

2. Step 2: Understand replacement=False effect

   Without replacement, you cannot pick more elements than exist. This causes a ValueError.

3. Final Answer:

   Size is larger than array length without replacement. -> Option D

4. Quick Check:

   Sampling more than available without replace=False causes error [OK]

Hint: Check if sample size > array length when replace=False [OK]

Common Mistakes:

• Assuming replacement=True by default
• Ignoring array length vs sample size
• Thinking data type causes error

5. You want to simulate rolling a weighted 6-sided die 10 times using numpy, where side 6 is twice as likely as others. Which code correctly generates this sample?

hard

A. np.random.choice([1,2,3,4,5,6], size=10, replace=True, p=[1/7,1/7,1/7,1/7,1/7,2/7])

B. np.random.choice([1,2,3,4,5,6], size=10, replace=False, p=[1/6]*6)

C. np.random.choice([1,2,3,4,5,6], size=10, replace=True)

D. np.random.choice([1,2,3,4,5,6], size=10, replace=True, p=[1/6,1/6,1/6,1/6,1/6,1/6])

Solution

1. Step 1: Understand weighted probabilities

   Side 6 should be twice as likely, so probabilities sum to 1 with side 6 having weight 2/7 and others 1/7 each.

2. Step 2: Check sampling parameters

   Sampling 10 times with replacement is needed to allow repeats. np.random.choice([1,2,3,4,5,6], size=10, replace=True, p=[1/7,1/7,1/7,1/7,1/7,2/7]) uses correct probabilities and replace=True.

3. Step 3: Verify other options

   The code with replace=False, p=[1/6]*6 incorrectly prevents repeats needed for multiple rolls. The codes with uniform probabilities (explicit [1/6,1/6,1/6,1/6,1/6,1/6] or none specified) do not weight side 6 twice as likely.

4. Final Answer:

   np.random.choice([1,2,3,4,5,6], size=10, replace=True, p=[1/7,1/7,1/7,1/7,1/7,2/7]) -> Option A

5. Quick Check:

   Weighted probabilities + replace=True for repeated rolls [OK]

Hint: Use p= with weights summing to 1 and replace=True [OK]

Common Mistakes:

• Using replace=False for multiple rolls
• Not setting probabilities for weighted sides
• Using equal probabilities when weights differ