# Statistical function in SCIPY

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All of the statistics functions are located in the sub-package
scipy.stats and a fairly complete listing of these functions can be obtained using info(stats) function

## Normal Continuous Random Variable

from scipy.stats import norm
import numpy as np
print(norm.cdf(np.array([1,-1., 0, 1, 3, 4, -2, 6])))

CDF means the Cumulative Distribution Function.
In probability theory and statistics, the cumulative distribution function of a real-valued random variable, or just distribution function of, evaluated at, is the probability that will take a value less than or equal to.
To find the median of distribution, we can use the Percent Point Function (PPF), which is the inverse of the CDF. Let us understand by using the following example.
from scipy.stats import norm
import numpy as np
print(norm.ppf(0.8))
To generate a sequence of random variates, we should use the size keyword argument, which is shown in the following example.
from scipy.stats import norm
print(norm.rvs(size = 10))
Uniform Distribution
A uniform distribution can be generated using the uniform function. Let us consider the following example.
from scipy.stats import uniform
print(uniform.cdf([0, 1, 2, 3, 4, 5], loc = 1, scale = 4))
Descriptive Statistics
The basic stats such as Min, Max, Mean, and Variance takes the NumPy array as input and returns the respective results. A few basic statistical functions were available in the scipy.stats package
from scipy import stats
import numpy as np
x = np.array([1,2,3,4,5,6,7,8,9])
print(x.max(),x.min(),x.mean(),x.var())

from scipy import stats
import numpy as np
x = np.array([1,2,3,4,5,6,7,8,9])
stats.describe(x)

Mean Value Calculation:-

from scipy.stats.mstats import gmean
arr1 = gmean([1, 3, 27])
print("Geometric Mean is :", arr1)

Standard Error

from scipy import stats
import numpy as np

# array elements ranging from 0 to 19
x = np.arange(20)

print("Trimmed Standard error :", stats.tsem(x))

print("\nTrimmed Standard error by setting limit : ",
stats.tsem(x, (2, 10)))

Calculate Mean Value:-
import scipy

arr1 = scipy.mean([1, 3, 27])

print("Arithmetic Mean is :", arr1)

Trimmed Maximum Value:-

from scipy import stats
import numpy as np

# array elements ranging from 0 to 19
x = [1, 3, 27, 56, 2, 4, 13, 3, 6]

print("Trimmed Maximum :", stats.tmax(x))

print("\nTrimmed Maximum by setting limit : ",
stats.tmax(x, (10)))