61 lines
1.7 KiB
Markdown
61 lines
1.7 KiB
Markdown
# Unique
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{:.no_toc}
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<nav markdown="1" class="toc-class">
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* TOC
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{:toc}
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</nav>
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## The goal
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Questions to [David Rotermund](mailto:davrot@uni-bremen.de)
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## [numpy.unique](https://numpy.org/doc/stable/reference/generated/numpy.unique.html)
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```python
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numpy.unique(ar, return_index=False, return_inverse=False, return_counts=False, axis=None, *, equal_nan=True)
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```
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> Find the unique elements of an array.
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>
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> Returns the sorted unique elements of an array. There are three optional outputs in addition to the unique elements:
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> * the indices of the input array that give the unique values
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> * the indices of the unique array that reconstruct the input array
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> * the number of times each unique value comes up in the input array
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**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.**
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```python
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import numpy as np
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a = np.arange(10, 21)
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print(a) # -> [10 11 12 13 14 15 16 17 18 19 20]
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idx = np.r_[0:5, 3:8]
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print(idx) # -> [0 1 2 3 4 3 4 5 6 7]
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print(a[idx]) # -> [10 11 12 13 14 13 14 15 16 17]
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print(np.unique(idx)) # -> [0 1 2 3 4 5 6 7]
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print(np.unique(a[idx])) # -> [10 11 12 13 14 15 16 17]
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```
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## There are more return arguments available
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```python
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import numpy as np
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a = np.r_[0:5, 3:8]
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print(a) # -> [0 1 2 3 4 3 4 5 6 7]
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values, unique_index = np.unique(a, return_index=True)
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_, unique_inverse = np.unique(a, return_inverse=True)
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_, unique_counts = np.unique(a, return_counts=True)
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print(values) # -> [0 1 2 3 4 5 6 7]
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print(unique_index) # -> [0 1 2 3 4 7 8 9]
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print(unique_inverse) # -> [0 1 2 3 4 3 4 5 6 7]
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print(unique_counts) # -> [1 1 1 2 2 1 1 1]
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```
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