This is documentation for an old release of NumPy (version 1.3.). Read this page in the documentation of the latest stable release (version > 1.17).
numpy.ma.array¶
- numpy.ma.array(data, dtype=None, copy=False, order=False, mask=False, fill_value=None, keep_mask=True, hard_mask=False, shrink=True, subok=True, ndmin=0)¶
Arrays with possibly masked values. Masked values of True exclude the corresponding element from any computation.
- Construction:
- x = MaskedArray(data, mask=nomask, dtype=None, copy=True, fill_value=None, keep_mask=True, hard_mask=False, shrink=True)
Parameters: data : {var}
Input data.
mask : {nomask, sequence}, optional
Mask. Must be convertible to an array of booleans with the same shape as data: True indicates a masked (eg., 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 : {False, True}, optional
Whether to copy the input data (True), or to use a reference instead. Note: data are NOT copied by default.
subok : {True, False}, optional
Whether to return a subclass of MaskedArray (if possible) or a plain MaskedArray.
ndmin : {0, int}, optional
Minimum number of dimensions
fill_value : {var}, optional
Value used to fill in the masked values when necessary. If None, a default based on the datatype is used.
keep_mask : {True, boolean}, optional
Whether to combine mask with the mask of the input data, if any (True), or to use only mask for the output (False).
hard_mask : {False, boolean}, optional
Whether to use a hard mask or not. With a hard mask, masked values cannot be unmasked.
shrink : {True, boolean}, optional
Whether to force compression of an empty mask.
