numpy.var() in Python
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 = 0y = 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.
Parameters :
arr : [array_like] input array.
axis : [int or tuples of int] axis along which we want to calculate the variance. Otherwise, it will considerarrto 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:
# 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:
# 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]
