numpy.var() in Python (original) (raw)

Last Updated : 3 Dec, 2018

numpy.var(arr, axis = None) : Compute the variance of the given data (array elements) along the specified axis(if any). Example :

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 = 7Step 2 : Summation of (x - x.mean())**2 = 178Step 3 : Finding Mean = 178 /20 = 8.9 This Result is Variance.

Parameters :

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.

Code #1:

Python3 1== `

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))

`

Output :

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

var of arr : 158.16

var of arr : 158.16

Code #2:

Python3 1== `

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))

`

Output :

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]