numpy.array_equiv () in Python

Arrays | NumPy | Python Methods and Functions

Consistent form means that either they have the same form, or one input array can be passed to create the same form as another.

Parameters:

  arr1:  [array_like] Input array, we need to test.  arr2:  [array_like] Input array, we need to test.

Return:

 True, if both arrays are equivalent; otherwise False

Code: Explaining Operation

# Python program explaining
# array_equiv () function

import numpy as np

#input

arr1 = np.arange ( 4 )

arr2 = [ 7 , 4 , 6 , 7 ]

   print   (  "arr1:"  , arr1)  
   print   (  "arr2:"  , arr2) 
   
   print   (  "Result:"  , np.array_equiv (arr1, arr2))   
  
   arr1   =   np.arange (  4  )  
   arr2   =   np.arange (  4  ) 
   print   (  "arr1:"  , arr1) 
   print   ( " arr2: " , arr2)  
   
   print   (  "Result:"  , np.array_equiv (arr1, arr2)) 
   
   arr1   =   np.arange (  4  ) 
   arr2   =   np.arange (  5  ) 
   print   (  "arr1:"  , arr1) 
   print   (  "arr2:"  , arr2)

< code class = "undefined spaces">

print ( " Result: " , np.array_equiv (arr1, arr2))

a = np.array_equiv ([ 1 , 2 ], [[ 1 , 2 , 1 , 2 ], [ 1 , 2 , 1 , 2 ]])

b = np.array_equiv ([ 1 , 2 ], [[ 1 , 2 ], [ 1 , 2 ]])

   
   print   (  "a:"  , a) 
   print   (  "b:"  , b)

Output:

 arr1: [0 1 2 3] arr2: [7, 4, 6, 7] Result: False arr1: [0 1 2 3] arr2: [0 1 2 3] Result: Tru e arr1: [0 1 2 3] arr2: [0 1 2 3 4] Result: False a: False b: True

Links:
https://docs.scipy.org/doc/numpy-1.13.0 /reference/generated/numpy.array_equiv.html#numpy.array_equiv
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numpy.array_equiv () in Python

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