numpy.ma.masked_array.filled#

method

ma.masked_array.filled(fill_value=None)[source]#

Return a copy of self, with masked values filled with a given value. However, if there are no masked values to fill, self will be returned instead as an ndarray.

Parameters:

fill_valuearray_like, optional: The value to use for invalid entries. Can be scalar or non-scalar. If non-scalar, the resulting ndarray must be broadcastable over input array. Default is None, in which case, the fill_value attribute of the array is used instead.

Returns:

filled_arrayndarray: A copy of self with invalid entries replaced by fill_value (be it the function argument or the attribute of self), or self itself as an ndarray if there are no invalid entries to be replaced.

Notes

The result is not a MaskedArray!

Examples

>>> import numpy as np
>>> x = np.ma.array([1,2,3,4,5], mask=[0,0,1,0,1], fill_value=-999)
>>> x.filled()
array([   1,    2, -999,    4, -999])
>>> x.filled(fill_value=1000)
array([   1,    2, 1000,    4, 1000])
>>> type(x.filled())
<class 'numpy.ndarray'>

Subclassing is preserved. This means that if, e.g., the data part of the masked array is a recarray, filled returns a recarray:

>>> x = np.array([(-1, 2), (-3, 4)], dtype='i8,i8').view(np.recarray)
>>> m = np.ma.array(x, mask=[(True, False), (False, True)])
>>> m.filled()
rec.array([(999999,      2), (    -3, 999999)],
          dtype=[('f0', '<i8'), ('f1', '<i8')])