# probability of happenning 7 or less in range 0 to 12 in a continuous uniform distribution 

from scipy.stats import uniform

uniform.cdf(7, 0, 12)

​

# probability of 7 or fewer success in 10 trials with 50% success rate in discrete distribution

from scipy.stats import binom

binom.cdf(7, 10, 0.5)

# probability of exactly 7 success in 10 trials with 50% success rate

binom.pmf(7, 10, 0.5)

​

# probability of people shorter than 154 with mean height 161 and std of 7

from scipy.stats import norm

norm.cdf(154, 161, 7)

# Percentile (quantile) at which 90% of the distribution is below

norm.ppf(0.9, 161, 7)

#Percentile (quantile) at which 90% of the distribution is above

norm.ppf((1-0.9), 161, 7)

​

# probability of happenning exactly 5 when average value lambda is 8

from scipy.stats import poisson

poisson.pmf(5, 8)

# probability of happenning 5 or less when average value lambda is 8

poisson.cdf(5, 8)

​

# probability of waiting less than 1 minute when average value lambda is 0.5 (0.5 event per unit time)

from scipy.stats import expon

scale = 1/λ = 1/0.5 = 2

expon.cdf(1, scale=2)

​

# Probability of having a t-value less than 2.0 with 5 degrees of freedom

from scipy.stats import t

t.cdf(2.0, df=5)

# Percentile (quantile) at which 90% of the distribution is below

t.ppf(0.9, df=5)

​

​

# Probability of a log-normal variable being less than 2.0 with mean 1.5 and standard deviation 0.8

from scipy.stats import lognorm

lognorm.cdf(2.0, s=0.8, scale=1.5)

# Percentile (quantile) at which 75% of the distribution is below

lognorm.ppf(0.75, s=0.8, scale=1.5)

​

from random import uniform

array = []

​

for i in range(10):

    array.append(uniform(0.0, 100.0))

​

print(array)

[40.59813585027852, 68.7703401239061, 26.913790531262215, 2.439230591613828, 76.58211953479649, 42.42738253824879, 78.99082452505225, 42.01341952516737, 30.749439064487294, 79.55554892599972]

# random floating point values with uniform distribution

# (every number has the same chance)