程式碼是：n_digits = len(np.unique(digits.target)) 我想檢視unique意義，

執行程式碼：

import numpy as np

print(help(np.unique))

得到： Help on function unique in module numpy.lib.arraysetops:

unique(ar, return_index=False, return_inverse=False, return_counts=False, axis=None)     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 `axis` is specified, this will be flattened if it         is not already 1-D.     return_index : bool, optional         If True, also return the indices of `ar` (along the specified axis,         if provided, or in the flattened array) that result in the unique array.     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 `ar`.     return_counts : bool, optional         If True, also return the number of times each unique item appears         in `ar`.              .. versionadded:: 1.9.0          axis : int or None, optional         The axis to operate on. If None, `ar` will be flattened. If an integer,         the subarrays indexed by the given axis will be flattened and treated         as the elements of a 1-D array with the dimension of the given axis,         see the notes for more details.  Object arrays or structured arrays         that contain objects are not supported if the `axis` kwarg is used. The         default is None.              .. versionadded:: 1.13.0          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 `return_index` is True.     unique_inverse : ndarray, optional         The indices to reconstruct the 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.         .. versionadded:: 1.9.0          See Also     --------     numpy.lib.arraysetops : Module with a number of other functions for                             performing set operations on arrays.          Notes     -----     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.          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 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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