scikits.timeseries.TimeSeries.sort

TimeSeries.sort(axis=-1, kind='quicksort', order=None, endwith=True, fill_value=None)

Return a sorted copy of an array.

Parameters:

a : array_like

Array to be sorted.

axis : int or None, optional

Axis along which to sort. If None, the array is flattened before sorting. The default is -1, which sorts along the last axis.

kind : {‘quicksort’, ‘mergesort’, ‘heapsort’}, optional

Sorting algorithm. Default is ‘quicksort’.

order : list, optional

When a is a structured array, this argument specifies which fields to compare first, second, and so on. This list does not need to include all of the fields.

endwith : {True, False}, optional

Whether missing values (if any) should be forced in the upper indices (at the end of the array) (True) or lower indices (at the beginning).

fill_value : {var}

Value used to fill in the masked values. If None, use the the output of minimum_fill_value().

Returns:

sorted_array : ndarray

Array of the same type and shape as a.

See also

ndarray.sort

Method to sort an array in-place.

argsort

Indirect sort.

lexsort

Indirect stable sort on multiple keys.

searchsorted

Find elements in a sorted array.

Notes

The various sorting algorithms are characterized by their average speed, worst case performance, work space size, and whether they are stable. A stable sort keeps items with the same key in the same relative order. The three available algorithms have the following properties:

kind	speed	worst case	work space	stable
‘quicksort’	1	O(n^2)	0	no
‘mergesort’	2	O(n*log(n))	~n/2	yes
‘heapsort’	3	O(n*log(n))	0	no

All the sort algorithms make temporary copies of the data when sorting along any but the last axis. Consequently, sorting along the last axis is faster and uses less space than sorting along any other axis.

Examples

>>> a = np.array([[1,4],[3,1]])
>>> np.sort(a)                # sort along the last axis
array([[1, 4],
       [1, 3]])
>>> np.sort(a, axis=None)     # sort the flattened array
array([1, 1, 3, 4])
>>> np.sort(a, axis=0)        # sort along the first axis
array([[1, 1],
       [3, 4]])

Use the order keyword to specify a field to use when sorting a structured array:

>>> dtype = [('name', 'S10'), ('height', float), ('age', int)]
>>> values = [('Arthur', 1.8, 41), ('Lancelot', 1.9, 38),
...           ('Galahad', 1.7, 38)]
>>> a = np.array(values, dtype=dtype)       # create a structured array
>>> np.sort(a, order='height')                        # doctest: +SKIP
array([('Galahad', 1.7, 38), ('Arthur', 1.8, 41),
       ('Lancelot', 1.8999999999999999, 38)],
      dtype=[('name', '|S10'), ('height', '<f8'), ('age', '<i4')])

Sort by age, then height if ages are equal:

>>> np.sort(a, order=['age', 'height'])               # doctest: +SKIP
array([('Galahad', 1.7, 38), ('Lancelot', 1.8999999999999999, 38),
       ('Arthur', 1.8, 41)],
      dtype=[('name', '|S10'), ('height', '<f8'), ('age', '<i4')])