numpy.unique(ar, return_index=False, return_inverse=False, return_counts=False, axis=None)
[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. Unless return_index : bool, optional If True, also return the indices of return_inverse : bool, optional If True, also return the indices of the unique array (for the specified axis, if provided) that can be used to reconstruct return_counts : bool, optional If True, also return the number of times each unique item appears in New in version 1.9.0. axis : int or None, optional The axis to operate on. If None, New in version 1.13.0. |
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Returns: |
unique : ndarray The sorted unique values. unique_indices : ndarray, optional The indices of the first occurrences of the unique values in the original array. Only provided if unique_inverse : ndarray, optional The indices to reconstruct the original array from the unique array. Only provided if unique_counts : ndarray, optional The number of times each of the unique values comes up in the original array. Only provided if |
See also
numpy.lib.arraysetops
When an axis is specified the subarrays indexed by the axis are sorted. This is done by making the specified axis the first dimension of the array and then flattening the subarrays in C order. The flattened subarrays are then viewed as a structured type with each element given a label, with the effect that we end up with a 1-D array of structured types that can be treated in the same way as any other 1-D array. The result is that the flattened subarrays are sorted in lexicographic order starting with the first element.
>>> 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 unique rows of a 2D array
>>> a = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 4]]) >>> np.unique(a, axis=0) array([[1, 0, 0], [2, 3, 4]])
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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https://docs.scipy.org/doc/numpy-1.14.2/reference/generated/numpy.unique.html