numpy.argsort
- numpy. argsort ( a, axis=-1, kind='quicksort', order=None ) [source]
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Returns the indices that would sort an array.
Perform an indirect sort along the given axis using the algorithm specified by the kind keyword. It returns an array of indices of the same shape asa that index data along the given axis in sorted order.
Parameters: a : array_like
Array to sort.
axis : int or None, optional
Axis along which to sort. The default is -1 (the last axis). If None, the flattened array is used.
kind : {‘quicksort’, ‘mergesort’, ‘heapsort’}, optional
Sorting algorithm.
order : list, optional
When a is an array with fields defined, this argument specifies which fields to compare first, second, etc. Not all fields need be specified.
Returns: index_array : ndarray, int
Array of indices that sort a along the specified axis. In other words, a[index_array] yields a sorted a.
See also
- sort
- Describes sorting algorithms used.
- lexsort
- Indirect stable sort with multiple keys.
- ndarray.sort
- Inplace sort.
- argpartition
- Indirect partial sort.
Notes
See sort for notes on the different sorting algorithms.
As of NumPy 1.4.0 argsort works with real/complex arrays containing nan values. The enhanced sort order is documented in sort.
Examples
One dimensional array:
>>> x = np.array([3, 1, 2]) >>> np.argsort(x) array([1, 2, 0])Two-dimensional array:
>>> x = np.array([[0, 3], [2, 2]]) >>> x array([[0, 3], [2, 2]])>>> np.argsort(x, axis=0) array([[0, 1], [1, 0]])>>> np.argsort(x, axis=1) array([[0, 1], [0, 1]])Sorting with keys:
>>> x = np.array([(1, 0), (0, 1)], dtype=[('x', '<i4'), ('y', '<i4')]) >>> x array([(1, 0), (0, 1)], dtype=[('x', '<i4'), ('y', '<i4')])>>> np.argsort(x, order=('x','y')) array([1, 0])>>> np.argsort(x, order=('y','x')) array([0, 1])