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value_counts python

Innocent Iguana answered on January 31, 2023 Popularity 10/10 Helpfulness 4/10

answer value_counts python

value_counts python

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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)

Popularity 10/10 Helpfulness 4/10 Language python

Source: Grepper

Tags: distribution python

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Contributed on Jan 31 2023

Innocent Iguana

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