# dask.array.ma.masked_array

dask.array.ma.masked_array(data, mask=np.False_, fill_value=None, **kwargs)

  An array class with possibly masked values.

  This docstring was copied from numpy.ma.masked_array.

  Some inconsistencies with the Dask version may exist.

  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:
    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 (Not supported in Dask)

      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 (Not supported in Dask)

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

    subok bool, optional (Not supported in Dask)

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

    ndmin int, optional (Not supported in Dask)

      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 (Not supported in Dask)

      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 (Not supported in Dask)

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

    shrink bool, optional (Not supported in Dask)

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

    order{‘C’, ‘F’, ‘A’}, optional (Not supported in Dask)

      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

      >>> import numpy as np

  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