I stumbled upon this post asking how to create a new numpy matrix based upon a simple logical expression
For instance
>>> import numpy as np
>>> a = np.random.randint(0, 5, size=(5, 4))
>>> a
array([[4, 2, 1, 1],
[3, 0, 1, 2],
[2, 0, 1, 1],
[4, 0, 2, 3],
[0, 0, 0, 2]])
>>> b = a < 3
>>> c = b.astype(int)
>>> c
array([[0, 1, 1, 1],
[0, 1, 1, 1],
[1, 1, 1, 1],
[0, 1, 1, 0],
[1, 1, 1, 1]])
But I would like to use a more complex logical function, for example I would like to create a categorical, one-hot encoded variable, like so
X_Slow = (Y < 500).astype(int)
X_Medium = (Y >= 500 and Y < 1000).astype(int)
X_Fast = (Y >= 1000).astype(int)
But obviously the above manipulation is incorrect syntactically. I have tried
X_Medium = (Y[np.logical_and(Y >= 500,Y < 1000)]).astype(int)
Though this only returns an array with the number of elements that match the given criteria, I would like both "1s and 0s" in the column.
How can I do this in numpy?
