PYTHON

numpy standard deviation

import numpy as np
data = [68,86,36,57,24,46,32,53] #define some data
data_std = np.std(data) #outputs 19.00493356999703

numpy calculate standard deviation

import numpy
numbers = [1,5,6,7,9,11,13]
standard = numpy.std(numbers) #Calculates standard deviation
print(standard)

numpy standard deviation

aux = np.array( [[0, 0, 0], [1, 2, 3]] )
np.std( aux, axis=0 )

standard deviation in python numpy

a = [1,2,3,4,5]
numpy.std(a) # will give the standard deviation of a

numpy standard deviation

import numpy as np


speed = [10, 20, 30, 40]

# mean of an array - sum(speed) / len(speed)
x = np.mean(speed)
print(x)
# output 25.0

# return the median number - If there are two numbers in the middle, divide the sum of those numbers by two.
x = np.median(speed)
print(x)
# output 25.0

# return standard deviation - the lower the number return the closer the data is related
x = np.std(speed)
print(x)
# output 11.180339887498949

# return Variance of array - show how spread out the data is. The smaller the number the closer the data is related
x = np.var(speed)
print(x)
# output 125.0

# returns percentile of an array.
x = np.percentile(speed, 20)
print(f"20 percent of speed is {x} or lower")
# output 20 percent of speed is 16.0 or lower

x = np.percentile(speed, 90)
print(f"90 percent of speed is {x} or lower")
# output 90 percent of speed is 37.0 or lower

# We specify that the mean value is 5.0, and the standard deviation is .2.
# the lower the scale the closer the random numbers are to the loc number
# returns size of 100 floats in array
# normal distribution
x = np.random.normal(loc=5.0, scale=.2, size=100)
print(x)

# create array
arr = np.array([10, 20, 20, 30, 30, 20])
print("Original array:")
print(arr)

print("Mode: Most frequent value in the above array:")
print(np.bincount(arr).argmax())
# output
# Most frequent value in the above array:
# 20
# returns the least common multiple
x = np.lcm(3, 4)
print(x)
# output 12


# returns the lowest common multiple of items in array
arr = np.array([3, 6, 9])
x = np.lcm.reduce(arr)
print(x)
# 18

# returns the greatest common multiple of 2 numbers
x = np.gcd(3, 4)
print(x)
# output 1

# return the highest common multiple of items in array
arr = np.array([20, 8, 32, 36, 16])
x = np.gcd.reduce(arr)
print(x)
# output 4

standard deviation in python without numpy

import math

xs = [0.5,0.7,0.3,0.2]     # values (must be floats!)
mean = sum(xs) / len(xs)   # mean
var  = sum(pow(x-mean,2) for x in xs) / len(xs)  # variance
std  = math.sqrt(var)  # standard deviation