numpy.ma.array

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)

An array class with possibly masked values.

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

Construction:

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)

Parameters

dataarray_like

Input data.

masksequence, optional

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

dtypedtype, 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.

copybool, optional

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

subokbool, optional

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

ndminint, optional

Minimum number of dimensions. Default is 0.

fill_valuescalar, optional

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

keep_maskbool, 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_maskbool, optional

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

shrinkbool, 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.

Examples

The mask can be initialized with an array of boolean values with the same shape as data.

>>> data = np.arange(6).reshape((2, 3))
>>> np.ma.MaskedArray(data, mask=[[False, True, False],
...                               [False, False, True]])
masked_array(
  data=[[0, --, 2],
        [3, 4, --]],
  mask=[[False,  True, False],
        [False, False,  True]],
  fill_value=999999)

Alternatively, the mask can be initialized to homogeneous boolean array with the same shape as data by passing in a scalar boolean value:

>>> np.ma.MaskedArray(data, mask=False)
masked_array(
  data=[[0, 1, 2],
        [3, 4, 5]],
  mask=[[False, False, False],
        [False, False, False]],
  fill_value=999999)

>>> np.ma.MaskedArray(data, mask=True)
masked_array(
  data=[[--, --, --],
        [--, --, --]],
  mask=[[ True,  True,  True],
        [ True,  True,  True]],
  fill_value=999999,
  dtype=int64)

Note

The recommended practice for initializing mask with a scalar boolean value is to use True/False rather than np.True_/np.False_. The reason is nomask is represented internally as np.False_.

>>> np.False_ is np.ma.nomask
True