numpy.union1d

numpy.in1d

# numpy.unique¶

numpy.unique(ar, return_index=False, return_inverse=False, return_counts=False)[source]

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

Parameters: ar : array_like Input array. This will be flattened if it is not already 1-D. return_index : bool, optional If True, also return the indices of ar that result in the unique array. return_inverse : bool, optional If True, also return the indices of the unique array that can be used to reconstruct ar. return_counts : bool, optional If True, also return the number of times each unique value comes up in ar. New in version 1.9.0. unique : ndarray The sorted unique values. unique_indices : ndarray, optional The indices of the first occurrences of the unique values in the (flattened) original array. Only provided if return_index is True. unique_inverse : ndarray, optional The indices to reconstruct the (flattened) original array from the unique array. Only provided if return_inverse is True. unique_counts : ndarray, optional The number of times each of the unique values comes up in the original array. Only provided if return_counts is True. New in version 1.9.0.

numpy.lib.arraysetops
Module with a number of other functions for performing set operations on arrays.

Examples

```>>> np.unique([1, 1, 2, 2, 3, 3])
array([1, 2, 3])
>>> a = np.array([[1, 1], [2, 3]])
>>> np.unique(a)
array([1, 2, 3])
```

Return the indices of the original array that give the unique values:

```>>> a = np.array(['a', 'b', 'b', 'c', 'a'])
>>> u, indices = np.unique(a, return_index=True)
>>> u
array(['a', 'b', 'c'],
dtype='|S1')
>>> indices
array([0, 1, 3])
>>> a[indices]
array(['a', 'b', 'c'],
dtype='|S1')
```

Reconstruct the input array from the unique values:

```>>> a = np.array([1, 2, 6, 4, 2, 3, 2])
>>> u, indices = np.unique(a, return_inverse=True)
>>> u
array([1, 2, 3, 4, 6])
>>> indices
array([0, 1, 4, 3, 1, 2, 1])
>>> u[indices]
array([1, 2, 6, 4, 2, 3, 2])
```