As a toy example let's say that I have:
import numpy as np
np.array([['dog','sheep','sheep','dog','cat'],
['dog','dog','sheep','cat','cat']])
dict = {'dog':5,'cat':1,'sheep':3}
I want to find an efficient way to construct the desired array where I replaced the elements according to the dictionary.
(The real dictionary is the periodic table and the real array has thousands of elements)
You can vectorize the dict's get method.
>>> import numpy as np
>>> a = np.array([['dog','sheep','sheep','dog','cat'],
... ['dog','dog','sheep','cat','cat']])
>>> d = {'dog':5,'cat':1,'sheep':3}
>>>
>>> np.vectorize(d.get)(a)
array([[5, 3, 3, 5, 1],
[5, 5, 3, 1, 1]])
I have renamed dict to d because you should not shadow the builtin name dict with your own variables.
You mean something like this? This replaces the values before creating the numpy array:
import numpy as np
my_dict = {'dog': 5, 'cat': 1, 'sheep': 3}
np.array([
[my_dict[e] for e in ['dog','sheep','sheep','dog','cat']],
[my_dict[e] for e in ['dog','dog','sheep','cat','cat']],
])
Or do you need something more generalized?
