numpy.var() in Python

x = 1 1 1 1 1 Standard Deviation = 0 . Variance = 0 y = 9, 2, 5, 4, 12, 7, 8, 11, 9, 3, 7, 4, 12, 5, 4, 10, 9, 6, 9, 4 Step 1 : Mean of distribution 4 = 7 Step 2 : Summation of (x - x.mean())**2 = 178 Step 3 : Finding Mean = 178 /20 = 8.9 This Result is Variance.

arr : [array_like] input array. axis : [int or tuples of int] axis along which we want to calculate the variance. Otherwise, it will consider arr to be flattened (works on all the axis). axis = 0 means variance along the column and axis = 1 means variance along the row. out : [ndarray, optional] Different array in which we want to place the result. The array must have the same dimensions as expected output. dtype : [data-type, optional] Type we desire while computing variance. Results : Variance of the array (a scalar value if axis is none) or array with variance values along specified axis.

# Python Program illustrating 
# numpy.var() method 
import numpy as np 
    
# 1D array 
arr = [20, 2, 7, 1, 34] 

print("arr : ", arr) 
print("var of arr : ", np.var(arr)) 

print("\nvar of arr : ", np.var(arr, dtype = np.float32)) 
print("\nvar of arr : ", np.var(arr, dtype = np.float64)) 

arr :  [20, 2, 7, 1, 34]
var of arr :  158.16

var of arr :  158.16

var of arr :  158.16

# Python Program illustrating 
# numpy.var() method 
import numpy as np 
    
# 2D array 
arr = [[2, 2, 2, 2, 2], 
    [15, 6, 27, 8, 2], 
    [23, 2, 54, 1, 2, ], 
    [11, 44, 34, 7, 2]] 

    
# var of the flattened array 
print("\nvar of arr, axis = None : ", np.var(arr)) 
    
# var along the axis = 0 
print("\nvar of arr, axis = 0 : ", np.var(arr, axis = 0)) 

# var along the axis = 1 
print("\nvar of arr, axis = 1 : ", np.var(arr, axis = 1)) 

var of arr, axis = None :  236.14000000000004

var of arr, axis = 0 :  [ 57.1875 312.75   345.6875   9.25     0.    ]

var of arr, axis = 1 :  [  0.    77.04 421.84 269.04]