Document

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: 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.

An array class with possibly masked values.

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]])

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)

np.ma.MaskedArray(data, mask=True)

.. 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 :attr:`nomask` is represented internally as ``np.False_``.

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

See :

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

GitHub : /numpy/ma/core.py#2697
type: <class 'type'>
Commit: