Let's say I have a list of Numpy arrays with varying shapes and need to replace all values of 255 with 1.
A = np.array([[0,255], [0,0]])
B = np.array([[0, 255,255], [255,0,0]])
list_of_array = [A, B] # list could have many more arrays
Methods like np.place() and X[X == 255] = 1 do not work on lists.
If you have to have a list of arrays, and want to modify the values in those arrays rather than creating new ones, then you can do it by iterating over the list.
import numpy as np
A = np.array([[0,255], [0,0]])
B = np.array([[0, 255,255], [255,0,0]])
list_of_array = [A, B] # list could have many more arrays
for array in list_of_array:
array[array == 255] = 1
You can use np.where in a list comprehension to create a new list of modified arrays:
updated_arrays = [np.where(a == 255, 1, a) for a in list_of_array]
np.where will allocate new arrays that have 255 replaced with 1, rather than replacing the values in the existing arrays.