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uniform distribution python example

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Leo answered on March 18, 2021 Popularity 10/10 Helpfulness 5/10

answer uniform distribution python example

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uniform distribution python example

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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)

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Contributed on Mar 18 2021

Leo

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draw samples from a uniform distribution python

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s = np.random.uniform(-1,0,1000)

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Source: numpy.org

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Contributed on Sep 03 2022

amoo hammed

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python uniform distribution

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# 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)

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Contributed on Dec 13 2023

Innocent Iguana

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