numpy.nanvar(arr, axis = None) : Compute the variance of the given data (array elements) along the specified axis(if any), while ignoring NaN values.
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; while ignoring NaN values.
Code #1:
# Python Program illustrating # numpy.nanvar() method import numpy as np # 1D array arr = [20, 2, np.nan, 1, 34] print("arr : ", arr) print("\nnanvar of arr : ", np.nanvar(arr)) print("var of arr : ", np.var(arr)) print("\nnanvar of arr : ", np.nanvar(arr, dtype = np.float32)) print("var of arr : ", np.var(arr, dtype = np.float32)) |
Output :
arr : [20, 2, nan, 1, 34] nanvar of arr : 187.1875 var of arr : nan nanvar of arr : 187.1875 var of arr : nan
Code #2:
# Python Program illustrating # numpy.nanvar() method import numpy as np # 2D array arr = [[2, 2, 2, 2, 2], [15, 6, np.nan, 8, 2], [23, 2, 54, 1, 2, ], [np.nan, 44, 34, 7, 2]] # nanvar of the flattened array print("\nnanvar of arr, axis = None : ", np.nanvar(arr)) print("\nvar of arr, axis = None : ", np.var(arr)) # nanvar along the axis = 0 print("\nnanvar of arr, axis = 0 : \n", np.nanvar(arr, axis = 0)) print("\nvar of arr, axis = 0 : ", np.var(arr, axis = 0)) # nanvar along the axis = 1 print("\nnanvar of arr, axis = 1 : ", np.nanvar(arr, axis = 1)) |
Output :
nanvar of arr, axis = None : 249.88888888888889 var of arr, axis = None : nan nanvar of arr, axis = 0 : [ 74.88888889 312.75 458.66666667 9.25 0. ] var of arr, axis = 0 : [ nan 312.75 nan 9.25 0. ] nanvar of arr, axis = 1 : [ 0. 22.1875 421.84 313.1875]
