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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.
Returns: 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.
See also
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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])
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numpy.unique()
2017-01-10 18:19:08
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