Get unique values in a list of numpy arrays

As the error indicates, NumPy arrays aren't hashable. You can turn them to tuples, which are hashable and build a collections.Counter from the result:

from collections import Counter

Counter(map(tuple,mylist))
# Counter({(1, 2): 2, (3, 4): 1})

If you wanted a list of unique tuples, you could construct a set:

set(map(tuple,mylist))
# {(1, 2), (3, 4)}

In general, the best option is to use np.unique method with custom parameters

u, idx, counts = np.unique(X, axis=0, return_index=True, return_counts=True)

Then, according to documentation:

• u is an array of unique arrays
• idx is the indices of the X that give the unique values
• counts is the number of times each unique item appears in X

If you need a dictionary, you can't store hashable values in its keys, so you might like to store them as tuples like in @yatu's answer or like this:

dict(zip([tuple(n) for n in u], counts))

Pure numpy approach:

numpy.unique(mylist, axis=0)

which produces a 2d array with your unique arrays in rows:

numpy.array([
 [1 2],
 [3 4]])

Works if all your arrays have same length (like in your example). This solution can be useful depending on what you do earlier in your code: perhaps you would not need to get into plain Python at all, but stick to numpy instead, which should be faster.

Use the following:

import numpy as np
mylist = [np.array([1,2]),np.array([1,2]),np.array([3,4])]
np.unique(mylist, axis=0)

This gives out list of uniques arrays.

array([[1, 2],
       [3, 4]])

Source: https://numpy.org/devdocs/user/absolute_beginners.html#how-to-get-unique-items-and-counts