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numpy.random.rand() in Python
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numpy.var() in Python

Last Updated : 03 Dec, 2018
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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 = 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.

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]
 

                                                                                      

                                                                                
                                          
                                                                                  
                                                                  
                              
                                
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