Unique

The goal

numpy.unique

numpy.unique(ar, return_index=False, return_inverse=False, return_counts=False, axis=None, *, equal_nan=True)

Find the unique elements of an array.

Returns the sorted unique elements of an array. There are three optional outputs in addition to the unique elements:

the indices of the input array that give the unique values

the indices of the unique array that reconstruct the input array

the number of times each unique value comes up in the input array

unique can be used on multi-dimensional arrays. However, the results are strange since empty places need to be filled for shaping the results into one common matrix.

import numpy as np


a = np.arange(10, 21)
print(a)  # -> [10 11 12 13 14 15 16 17 18 19 20]
idx = np.r_[0:5, 3:8]
print(idx)  # -> [0 1 2 3 4 3 4 5 6 7]
print(a[idx])  # -> [10 11 12 13 14 13 14 15 16 17]

print(np.unique(idx))  # -> [0 1 2 3 4 5 6 7]
print(np.unique(a[idx]))  # -> [10 11 12 13 14 15 16 17]

There are more return arguments available

import numpy as np


a = np.r_[0:5, 3:8]
print(a)  # -> [0 1 2 3 4 3 4 5 6 7]

values, unique_index = np.unique(a, return_index=True)
_, unique_inverse = np.unique(a, return_inverse=True)
_, unique_counts = np.unique(a, return_counts=True)

print(values)  # -> [0 1 2 3 4 5 6 7]
print(unique_index)  # -> [0 1 2 3 4 7 8 9]
print(unique_inverse)  # -> [0 1 2 3 4 3 4 5 6 7]
print(unique_counts)  # -> [1 1 1 2 2 1 1 1]

The source code is Open Source and can be found on GitHub.