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numpy.place() in Python
  • Last Updated : 24 Nov, 2020

The numpy.place() method makes changes in the array according the parameters – conditions and value(uses first N-values to put into array as per the mask being set by the user). It works opposite to numpy.extract().
Syntax:

numpy.place(array, mask, vals) 

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 i.e. new elements being put




# Python Program illustrating
# numpy.place() method
   
import numpy as geek
   
array = geek.arange(12).reshape(3, 4)
print("Original array : \n", array)
   
# Putting new elements
a = geek.place(array, array > 5, [15, 25, 35])
print("\nPutting up elements to array: \n", array)
  
  
array1 = geek.arange(6).reshape(2, 3)
print("\n\nOriginal array1 : \n", array)
  
# Putting new elements
a = geek.place(array1, array1>2, [44, 55])
print("\nPutting new elements to array1 : \n", 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]]

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



Note :
These codes won’t run on online-ID. Please run them on your systems to explore the working.
.
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