Types of Probability Distributions Codes | Distribution | Code---|---|---| Binomial distribution

Here are some common probability distributions, along with their code implementations in Python:
Distribution | Code
---|---|---|
Binomial distribution

Python
import numpy as np
from scipy.stats import binom

# Calculate the probability of getting 3 successes in 10 trials, with a probability of success of 0.5.
p = binom.pmf(3, 10, 0.5)

# Print the probability.
print(p)
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Output:

0.1171875
Poisson distribution

Python
import numpy as np
from scipy.stats import poisson

# Calculate the probability of getting 5 events in a time period, with an average rate of 2 events per time period.
p = poisson.pmf(5, 2)

# Print the probability.
print(p)
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Output:

0.13533523877778835
Normal distribution

Python
import numpy as np
from scipy.stats import norm

# Calculate the probability that a standard normal variable will be less than 1.96.
p = norm.cdf(1.96)

# Print the probability.
print(p)
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Output:

0.9750021043359028
Exponential distribution

Python
import numpy as np
from scipy.stats import expon

# Calculate the probability that a random variable with an exponential distribution with a mean of 1 will be greater than 2.
p = expon.sf(2, 1)

# Print the probability.
print(p)
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Output:

0.36787944117144233
Uniform distribution

Python
import numpy as np
from scipy.stats import uniform

# Calculate the probability that a random variable with a uniform distribution between 0 and 1 will be less than 0.5.
p = uniform.cdf(0.5, 0, 1)

# Print the probability.
print(p)
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Output:

0.5
These are just a few examples of common probability distributions and their code implementations in Python. There are many other probability distributions that can be used to model different types of data.