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numpy.place () in Python

|

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"  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" 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]] 

Links:
https://docs.scipy.org /doc/numpy-dev/reference/generated/numpy.place.html#numpy.place

Notes:
These codes will not work on online ID. Please run them on your systems to see how they work. 
,
This article is provided by Mohit Gupta_OMG

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