/NumPy 1.17


numpy.ma.array(data, dtype=None, copy=False, order=None, mask=False, fill_value=None, keep_mask=True, hard_mask=False, shrink=True, subok=True, ndmin=0) [source]

An array class with possibly masked values.

Masked values of True exclude the corresponding element from any computation.


x = MaskedArray(data, mask=nomask, dtype=None, copy=False, subok=True,
                ndmin=0, fill_value=None, keep_mask=True, hard_mask=None,
                shrink=True, order=None)
data : array_like

Input data.

mask : sequence, optional

Mask. Must be convertible to an array of booleans with the same shape as data. True indicates a masked (i.e. invalid) data.

dtype : dtype, optional

Data type of the output. If dtype is None, the type of the data argument (data.dtype) is used. If dtype is not None and different from data.dtype, a copy is performed.

copy : bool, optional

Whether to copy the input data (True), or to use a reference instead. Default is False.

subok : bool, optional

Whether to return a subclass of MaskedArray if possible (True) or a plain MaskedArray. Default is True.

ndmin : int, optional

Minimum number of dimensions. Default is 0.

fill_value : scalar, optional

Value used to fill in the masked values when necessary. If None, a default based on the data-type is used.

keep_mask : bool, optional

Whether to combine mask with the mask of the input data, if any (True), or to use only mask for the output (False). Default is True.

hard_mask : bool, optional

Whether to use a hard mask or not. With a hard mask, masked values cannot be unmasked. Default is False.

shrink : bool, optional

Whether to force compression of an empty mask. Default is True.

order : {‘C’, ‘F’, ‘A’}, optional

Specify the order of the array. If order is ‘C’, then the array will be in C-contiguous order (last-index varies the fastest). If order is ‘F’, then the returned array will be in Fortran-contiguous order (first-index varies the fastest). If order is ‘A’ (default), then the returned array may be in any order (either C-, Fortran-contiguous, or even discontiguous), unless a copy is required, in which case it will be C-contiguous.

© 2005–2019 NumPy Developers
Licensed under the 3-clause BSD License.