  # 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 `