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bundles / numpy 2.5.0.dev0+git20251130.2de293a / numpy / ma / MaskedArray

class

numpy.ma:MaskedArray

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

Signature

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

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

Referenced by

This package

Other packages