Poisson distribution in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the Poisson distribution from scipy.stats.

SciPy

from scipy.stats import [1]

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

Apoisson

Bbinom

Cnorm

Duniform

Attempts:

3 left

💡 Hint

Common Mistakes

Importing the normal distribution instead of Poisson.

Using a distribution for continuous data instead of discrete.

The Poisson distribution is available as poisson in scipy.stats.

2fill in blank

medium

Complete the code to calculate the probability of exactly 3 events when the average rate is 2.

SciPy

prob = poisson.pmf([1], mu=2)

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A2

B1

C0

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the mean value instead of the event count as the first argument.

Confusing pmf with cdf.

The pmf function calculates the probability of exactly k events. Here, k=3.

3fill in blank

hard

Fix the error in the code to calculate the cumulative probability of up to 4 events with mean 3.

SciPy

cum_prob = poisson.[1](4, mu=3)

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Apmf

Bcdf

Cpdf

Dsf

Attempts:

3 left

💡 Hint

Common Mistakes

Using pmf which gives probability of exactly k events.

Using sf which gives the survival function (1 - cdf).

The cdf function calculates the cumulative probability up to a given number of events.

4fill in blank

hard

Fill both blanks to create a dictionary of probabilities for 0 to 5 events with mean 2.

SciPy

prob_dict = {k: poisson.[1](k, mu=[2]) for k in range(6)}

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Apmf

Bcdf

C2

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using cdf instead of pmf for exact probabilities.

Using wrong mean value.

Use pmf to get exact probabilities and mean 2 as given.

5fill in blank

hard

Fill all three blanks to filter events with probability greater than 0.1 from a dictionary.

SciPy

filtered = {k: v for k, v in prob_dict.items() if v [1] [2]
threshold = [3]

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A>

B0.1

C<

D0.05

Attempts:

3 left

💡 Hint

Common Mistakes

Using less than instead of greater than.

Using wrong threshold value.

We filter probabilities greater than 0.1, so use > and 0.1 as threshold.