# numpy.nanvar () in Python

` numpy.nanvar (arr, axis = None) `: compute the variance of data (array elements) along the specified axis (if any), ignoring NaN values.

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 = 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 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; 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 ( "nanvar of arr:" , np.nanvar (arr))    print ( "var of arr:" , np.var (arr))     print ( "nanvar 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 ],  < / p> ```` [ 15 , 6 , np.nan, 8 , 2 ],  [ 23 , 2 , 54 , 1 , 2 , ],  [np.nan, 44 , 34 , 7 , 2 ]]       # flattened array nanvar print ( "nanvar of arr, axis = None : " , np.nanvar (arr))     print ( "var of arr, axis = None:" , np.var (arr))      # nanvar along axis = 0 print ( "nanvar of arr, axis = 0:" , np.nanvar (arr, axis = 0 ))     print ( "var of arr, axis = 0 : " , np.var (arr, axis = 0 ))    # nanwar along axis = 1 print ( " nanvar 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] `