Normal distribution in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the normal distribution function from scipy.stats.

SciPy

from scipy.stats import [1]

# Now you can use norm to work with normal distribution

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

Agauss

Bnormal

Cnorm

Dstats

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'normal' instead of 'norm' which is not a valid import.

Trying to import 'gauss' which is not part of scipy.stats.

Importing 'stats' which is the module, not the distribution function.

The normal distribution function in scipy.stats is called norm. Importing it allows you to use its methods.

2fill in blank

medium

Complete the code to calculate the probability density function (PDF) of a normal distribution at x=0.

SciPy

from scipy.stats import norm

pdf_value = norm.pdf([1])
print(pdf_value)

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A0

B1

C-1

D0.5

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1 or -1 which are not the peak points of the standard normal distribution.

Using 0.5 which is arbitrary and not the peak.

The PDF of the standard normal distribution is highest at x=0, so we calculate it at 0.

3fill in blank

hard

Fix the error in the code to generate 5 random samples from a normal distribution with mean 10 and standard deviation 2.

SciPy

from scipy.stats import norm

samples = norm.rvs(loc=[1], scale=2, size=5)
print(samples)

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A2

B0

C5

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Setting loc to 2 which is the standard deviation, not the mean.

Setting loc to 0 or 5 which are incorrect for this distribution.

The loc parameter sets the mean of the normal distribution. Here, it should be 10.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps numbers 1 to 5 to their normal PDF values with mean 0 and std 1, but only include values where PDF is greater than 0.2.

SciPy

pdf_dict = {x: norm.pdf(x, loc=[1], scale=[2]) for x in range(1, 6) if norm.pdf(x, loc=0, scale=1) > 0.2}

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A0

B1

C2

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1 for mean or 0 for std which reverses the parameters.

Using 2 or 5 which are not standard parameters.

The mean (loc) is 0 and standard deviation (scale) is 1 for the standard normal distribution.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps numbers 1 to 5 to their cumulative distribution function (CDF) values for a normal distribution with mean 5 and std 2, but only include values where CDF is less than 0.8.

SciPy

cdf_dict = {x: norm.[1](x, loc=[2], scale=[3]) for x in range(1, 6) if norm.cdf(x, loc=5, scale=2) < 0.8}

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Acdf

B5

C2

Dpdf

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'pdf' instead of 'cdf' for cumulative probabilities.

Mixing up mean and std values.

The method to get cumulative probabilities is cdf. The mean is 5 and standard deviation is 2.