# numpy.place () in Python

NumPy | Python Methods and Functions

Parameters :

`  array:  [ndarray] Input array, we need to make changes into  mask:  [ array_like] Boolean that must have same size as that of the input array  value:  Values ​​to put into the array. Based on the mask condition it adds only N-elements to the array. If in case values ​​in val are smaller than the mask, same values ​​get repeated. `

Return:

` Array with change elements ie new elements being put `

 ` # Python program illustrating ` ` # numpy.place () method `   ` import ` ` numpy as geek `   ` array ` ` = ` ` geek. arange (` ` 12 ` `). reshape (` ` 3 ` `, ` ` 4 ` `) ` ` print ` ` (` `" Original array: "` `, array) ` `   # Introduce new elements `` a = geek.place (array, array & gt;  5 , [ 15 , 25 , 35 ]) print ( "Putting up elements to array:" , array)     array1 = geek.arange ( 6 ). reshape ( 2 , 3 ) print ( "Original array1:" , array)    # Introduce new elements a = geek.place (array1, array1 & gt; 2 , [ 44 , 55 ]) print ( "Putting new elements to array1:" , array1) `

Output:

` Original array: [[0 1 2 3] [4 5 6 7] [8 9 10 11]] Putting up elements to array: [[0 1 2 3] [4 5 15 25] [35 15 25 35]] Original array1: [[0 1 2 3] [4 5 15 25] [35 15 25 35]] Putting new elements to array1: [[0 1 2 ] [44 55 44]] `

Notes:
These codes will not work on online ID. Please run them on your systems to see how they work.
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