dataset = np.array([49, 84, 57, 79, 90, 43, 87])
np.percentile(dataset, 25, axis=1) # default is axis=1
np.percentile(dataset, 50)
np.percentile(dataset, 75)
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