`numpy.ma.core:array`

source: build-install/usr/lib/python3.14/site-packages/numpy/ma/core.py :6843

Signature

   def     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     )

Summary

An array class with possibly masked values.

Extended Summary

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.

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]])

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

Aliases

numpy.ma.array