numpy.ma.asarray¶

numpy.ma.asarray(a, dtype=None, order=None)[source]¶

Convert the input to a masked array of the given data-type.

No copy is performed if the input is already an ndarray. If a is a subclass of MaskedArray, a base class MaskedArray is returned.

Parameters:

Parameters:	a : array_like Input data, in any form that can be converted to a masked array. This includes lists, lists of tuples, tuples, tuples of tuples, tuples of lists, ndarrays and masked arrays. dtype : dtype, optional By default, the data-type is inferred from the input data. order : {‘C’, ‘F’}, optional Whether to use row-major (‘C’) or column-major (‘FORTRAN’) memory representation. Default is ‘C’.
Returns:	out : MaskedArray Masked array interpretation of a.

a : array_like

Input data, in any form that can be converted to a masked array. This includes lists, lists of tuples, tuples, tuples of tuples, tuples of lists, ndarrays and masked arrays.

dtype : dtype, optional

By default, the data-type is inferred from the input data.

order : {‘C’, ‘F’}, optional

Whether to use row-major (‘C’) or column-major (‘FORTRAN’) memory representation. Default is ‘C’.

Returns:

out : MaskedArray

Masked array interpretation of a.

See also

asanyarray: Similar to asarray, but conserves subclasses.

Examples

>>> x = np.arange(10.).reshape(2, 5)
>>> x
array([[ 0.,  1.,  2.,  3.,  4.],
       [ 5.,  6.,  7.,  8.,  9.]])
>>> np.ma.asarray(x)
masked_array(data =
 [[ 0.  1.  2.  3.  4.]
 [ 5.  6.  7.  8.  9.]],
             mask =
 False,
       fill_value = 1e+20)
>>> type(np.ma.asarray(x))
<class 'numpy.ma.core.MaskedArray'>