MaskedArray.squeeze(axis=None) [source] Remove single-dimensional entries from the shape of a. Refer to numpy.squeeze for full documentation. See also numpy.squeeze equivalent function

MaskedArray.std(axis=None, dtype=None, out=None, ddof=0) [source] Compute the standard deviation along the specified axis. Returns the standard deviation, a measure of the spread of a distribution, of the array elements. The standard deviation is computed for the flattened array by default, otherwise over the specified axis. Parameters: a : array_like Calculate the standard deviation of these values. axis : None or int or tuple of ints, optional Axis or axes along which the standard dev

MaskedArray.sort(axis=-1, kind='quicksort', order=None, endwith=True, fill_value=None) [source] Sort the array, in-place Parameters: a : array_like Array to be sorted. 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 Sorting algorithm. Default is ?quicksort?. order : list, optional When a is a structured array, this argument specif

MaskedArray.soften_mask() [source] Force the mask to soft. Whether the mask of a masked array is hard or soft is determined by its hardmask property. soften_mask sets hardmask to False. See also hardmask

MaskedArray.size Number of elements in the array. Equivalent to np.prod(a.shape), i.e., the product of the array?s dimensions. Examples >>> x = np.zeros((3, 5, 2), dtype=np.complex128) >>> x.size 30 >>> np.prod(x.shape) 30

MaskedArray.shrink_mask() [source] Reduce a mask to nomask when possible. Parameters: None Returns: None Examples >>> x = np.ma.array([[1,2 ], [3, 4]], mask=[0]*4) >>> x.mask array([[False, False], [False, False]], dtype=bool) >>> x.shrink_mask() >>> x.mask False

MaskedArray.set_fill_value(value=None) [source] Set the filling value of the masked array. Parameters: value : scalar, optional The new filling value. Default is None, in which case a default based on the data type is used. See also ma.set_fill_value Equivalent function. Examples >>> x = np.ma.array([0, 1.], fill_value=-np.inf) >>> x.fill_value -inf >>> x.set_fill_value(np.pi) >>> x.fill_value 3.1415926535897931 Reset to default: >>> x.s

MaskedArray.shape Tuple of array dimensions. Notes May be used to ?reshape? the array, as long as this would not require a change in the total number of elements Examples >>> x = np.array([1, 2, 3, 4]) >>> x.shape (4,) >>> y = np.zeros((2, 3, 4)) >>> y.shape (2, 3, 4) >>> y.shape = (3, 8) >>> y array([[ 0., 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0., 0.]

MaskedArray.resize(newshape, refcheck=True, order=False) [source] Warning This method does nothing, except raise a ValueError exception. A masked array does not own its data and therefore cannot safely be resized in place. Use the numpy.ma.resize function instead. This method is difficult to implement safely and may be deprecated in future releases of NumPy.

MaskedArray.searchsorted(v, side='left', sorter=None) Find indices where elements of v should be inserted in a to maintain order. For full documentation, see numpy.searchsorted See also numpy.searchsorted equivalent function