Python-在字典中使用numpy数组作为键的替代方法

Question

I'm pretty new to Python numpy. I was attempted to use numpy array as the key in dictionary in one of my functions and then been told by Python interpreter that numpy array is not hashable. I've just found out that one way to work this issue around is to use repr() function to convert numpy array to a string but it seems very expensive. Is there any better way to achieve same effect?

Update: I could create a new class to contain the numpy array, which seems to be right way to achieve what I want. Just wondering if there is any better method?

update 2: Using a class to contain data in the array and then override __hash__ function is acceptable, however, I'd prefer the solution provided by @hpaulj. Convert the array/list to a tuple fits my need in a better way as it does not require an additional class.

Answer 1

After done some researches and reading through all comments. I think I've known the answer to my own question so I'd just write them down.

1. Write a class to contain the data in the array and then override __hash__ function to amend the way how it is hashed as mentioned by ZdaR
2. Convert this array to a tuple , which makes the list hashable instantaneously.Thanks to hpaulj

I'd prefer method No.2 because it fits my need better, as well as simpler. However, using a class might bring some additional benefits so it could also be useful.

Answer 2

If you want to quickly store a numpy.ndarray as a key in a dictionary, a fast option is to use ndarray.tobytes () which will return a raw python bytes string which is immutable

my_array = numpy.arange(4).reshape((2,2))
my_dict = {}
my_dict[my_array.tobytes()] = None

Answer 3

I just ran into that issue and there's a very simple solution to it using list comprehension:

import numpy as np

dict = {'key1':1, 'key2':2}
my_array = np.array(['key1', 'key2'])

result = np.array( [dict[element] for element in my_array] )
print(result)

The result should be:

[1 2]

I don't know how efficient this is but seems like a very practical and straight-forward solution, no conversions or new classes needed :)