Normal distribution with normal() in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to generate 5 random numbers from a normal distribution with mean 0 and standard deviation 1.

NumPy

import numpy as np
samples = np.random.normal(loc=0, scale=[1], size=5)
print(samples)

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

A10

B0

C1

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Using 0 for scale which results in all zeros.

Confusing mean (loc) with standard deviation (scale).

The scale parameter sets the standard deviation. For a standard normal distribution, it is 1.

2fill in blank

medium

Complete the code to generate 10 random numbers from a normal distribution with mean 5 and standard deviation 2.

NumPy

import numpy as np
samples = np.random.normal(loc=[1], scale=2, size=10)
print(samples)

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

A5

B0

C2

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Using scale instead of loc for the mean.

Setting mean to 0 instead of 5.

The loc parameter sets the mean. Here, the mean is 5.

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(loc=0, scale=1, size=[1])
print(samples)

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

A3

B1

C0

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Passing size as a string instead of an integer.

Using scale 0 which results in no variation.

The size parameter must be an integer, not a string. Use 3.

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', 'bat', 'carrot', 'dog', 'elephant']
lengths = {word: [1] for word in words if len(word) [2] 4}
print(lengths)

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

Alen(word)

Bword

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

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

Using '<' instead of '>' in the condition.

We want the length of each word as the value, so use len(word). To include words longer than 4 characters, use the condition len(word) > 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 = ['sun', 'moon', 'star', 'sky', 'planet']
result = { [1]: [2] for word in words if len(word) [3] 3 }
print(result)

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

Aword.upper()

Blen(word)

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the original word as key instead of uppercase.

Using '<' instead of '>' in the condition.

Using word instead of length for values.

The keys should be uppercase words using word.upper(). The values are the lengths using len(word). The condition includes words longer than 3 characters using >.

Practice

1. What does the loc parameter control in the numpy.random.normal() function?

easy

A. The spread (standard deviation) of the normal distribution

B. The center (mean) of the normal distribution

C. The number of random values generated

D. The shape of the distribution curve

Solution

1. Step 1: Understand the parameters of normal()

   The normal() function has parameters loc and scale. loc sets the mean (center) of the distribution.

2. Step 2: Identify the role of loc

   The mean is the center point where most values cluster in a bell curve.

3. Final Answer:

   The center (mean) of the normal distribution -> Option B

4. Quick Check:

   loc = center [OK]

Hint: Remember: loc = center, scale = spread [OK]

Common Mistakes:

• Confusing loc with scale
• Thinking loc controls number of samples
• Assuming loc changes distribution shape

2. Which of the following is the correct syntax to generate 5 random numbers from a normal distribution with mean 10 and standard deviation 2 using numpy?

easy

A. numpy.random.normal(size=5, mean=10, std=2)

B. numpy.normal(10, 2, 5)

C. numpy.random.normal(5, loc=10, scale=2)

D. numpy.random.normal(loc=10, scale=2, size=5)

Solution

1. Step 1: Recall the correct function and parameters

   The function is numpy.random.normal() with parameters loc for mean, scale for std dev, and size for number of samples.

2. Step 2: Match parameters to correct syntax

   numpy.random.normal(loc=10, scale=2, size=5) correctly uses loc=10, scale=2, and size=5.

3. Final Answer:

   numpy.random.normal(loc=10, scale=2, size=5) -> Option D

4. Quick Check:

   Correct parameter names and order [OK]

Hint: Use loc=mean, scale=std, size=number [OK]

Common Mistakes:

• Using wrong parameter names like mean or std
• Mixing order without keywords
• Calling numpy.normal instead of numpy.random.normal

3. What is the output shape of the following code?

import numpy as np
arr = np.random.normal(loc=0, scale=1, size=(3,4))
print(arr.shape)

medium

A. (12,)

B. (4, 3)

C. (3, 4)

D. (3,)

Solution

1. Step 1: Understand the size parameter

   The size argument is set to (3,4), which means generate a 2D array with 3 rows and 4 columns.

2. Step 2: Check the shape of the generated array

   Printing arr.shape returns the shape tuple, which matches the size argument.

3. Final Answer:

   (3, 4) -> Option C

4. Quick Check:

   size=(3,4) means shape=(3,4) [OK]

Hint: size tuple = output shape [OK]

Common Mistakes:

• Confusing rows and columns order
• Expecting flattened array shape
• Ignoring tuple format for size

4. Identify the error in this code snippet:

import numpy as np
samples = np.random.normal(mean=0, std=1, size=10)
print(samples)

medium

A. Incorrect parameter names: should use loc and scale instead of mean and std

B. Missing import statement for numpy

C. size parameter must be a tuple, not an integer

D. The print statement syntax is wrong

Solution

1. Step 1: Check parameter names for normal()

   The function np.random.normal() expects loc for mean and scale for standard deviation, not mean or std.

2. Step 2: Verify other parts of the code

   Import is correct, size can be integer, and print syntax is valid.

3. Final Answer:

   Incorrect parameter names: should use loc and scale instead of mean and std -> Option A

4. Quick Check:

   Use loc and scale for mean and std [OK]

Hint: Use loc=mean, scale=std; mean/std are invalid [OK]

Common Mistakes:

• Using mean or std instead of loc and scale
• Thinking size must be tuple always
• Assuming print syntax error

5. You want to simulate daily temperatures for a week that average 20°C with a standard deviation of 3°C. Which code correctly generates this data and calculates the average temperature?

hard

A. temps = np.random.normal(loc=20, scale=3, size=7)
avg_temp = temps.mean()
print(round(avg_temp, 2))

B. temps = np.random.normal(mean=20, std=3, size=7)
avg_temp = temps.sum()
print(avg_temp)

C. temps = np.random.normal(loc=3, scale=20, size=7)
avg_temp = temps.mean()
print(avg_temp)

D. temps = np.random.normal(loc=20, scale=3, size=7)
avg_temp = temps.median()
print(avg_temp)

Solution

1. Step 1: Generate temperatures with correct parameters

   Use loc=20 for mean temperature and scale=3 for standard deviation, with size=7 for a week.

2. Step 2: Calculate the average temperature correctly

   Use temps.mean() to get the average. Round for neat output.

3. Final Answer:

   temps = np.random.normal(loc=20, scale=3, size=7) avg_temp = temps.mean() print(round(avg_temp, 2)) -> Option A

4. Quick Check:

   loc=mean, scale=std, mean() for average [OK]

Hint: Use loc=mean, scale=std, mean() to average [OK]

Common Mistakes:

• Swapping loc and scale values
• Using mean or std instead of loc and scale
• Using sum() instead of mean() for average
• Using median() instead of mean()