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 wonβt run on online-ID. Please run them on your systems to explore the working.
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