diff options
| -rw-r--r-- | numpy/ma/core.py | 76 |
1 files changed, 24 insertions, 52 deletions
diff --git a/numpy/ma/core.py b/numpy/ma/core.py index dc2346c61..f85f9145a 100644 --- a/numpy/ma/core.py +++ b/numpy/ma/core.py @@ -4887,13 +4887,13 @@ class MaskedArray(ndarray): def sort(self, axis= -1, kind='quicksort', order=None, endwith=True, fill_value=None): """ - Return a sorted copy of an array. + Sort the array, in-place Parameters ---------- a : array_like Array to be sorted. - axis : int or None, optional + axis : int, 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 @@ -4905,9 +4905,9 @@ class MaskedArray(ndarray): 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(). + fill_value : {var}, optional + Value used internally for the masked values. + If ``fill_value`` is not None, it supersedes ``endwith``. Returns ------- @@ -4923,56 +4923,28 @@ class MaskedArray(ndarray): 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. + See ``sort`` for notes on the different sorting algorithms. 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')]) - + >>> a = ma.array([1, 2, 5, 4, 3],mask=[0, 1, 0, 1, 0]) + >>> # Default + >>> a.sort() + >>> print a + [1 3 5 -- --] + + >>> a = ma.array([1, 2, 5, 4, 3],mask=[0, 1, 0, 1, 0]) + >>> # Put missing values in the front + >>> a.sort(endwith=False) + >>> print a + [-- -- 1 3 5] + + >>> a = ma.array([1, 2, 5, 4, 3],mask=[0, 1, 0, 1, 0]) + >>> # fill_value takes over endwith + >>> a.sort(endwith=False, fill_value=3) + >>> print a + [1 -- -- 3 5] + """ if self._mask is nomask: ndarray.sort(self, axis=axis, kind=kind, order=order) |