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
# Python Program illustrating# numpy.place() method import numpy as geek array = geek.arange(12).reshape(3, 4)print("Original array : \n", array) # Putting new elementsa = 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 elementsa = 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 IDE’s. So please, run them on your systems to explore the working.
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