numpy.ma.MaskedArray.view

method

ma.MaskedArray.view(dtype=None, type=None, fill_value=None)

Return a view of the MaskedArray data.

Parameters

dtypedata-type or ndarray sub-class, optional

Data-type descriptor of the returned view, e.g., float32 or int16. The default, None, results in the view having the same data-type as a. As with ndarray.view, dtype can also be specified as an ndarray sub-class, which then specifies the type of the returned object (this is equivalent to setting the type parameter).

typePython type, optional

Type of the returned view, either ndarray or a subclass. The default None results in type preservation.

fill_valuescalar, optional

The value to use for invalid entries (None by default). If None, then this argument is inferred from the passed dtype, or in its absence the original array, as discussed in the notes below.

See also

numpy.ndarray.view

Equivalent method on ndarray object.

Notes

a.view() is used two different ways:

a.view(some_dtype) or a.view(dtype=some_dtype) constructs a view of the array’s memory with a different data-type. This can cause a reinterpretation of the bytes of memory.

a.view(ndarray_subclass) or a.view(type=ndarray_subclass) just returns an instance of ndarray_subclass that looks at the same array (same shape, dtype, etc.) This does not cause a reinterpretation of the memory.

If fill_value is not specified, but dtype is specified (and is not an ndarray sub-class), the fill_value of the MaskedArray will be reset. If neither fill_value nor dtype are specified (or if dtype is an ndarray sub-class), then the fill value is preserved. Finally, if fill_value is specified, but dtype is not, the fill value is set to the specified value.

For a.view(some_dtype), if some_dtype has a different number of bytes per entry than the previous dtype (for example, converting a regular array to a structured array), then the behavior of the view cannot be predicted just from the superficial appearance of a (shown by print(a)). It also depends on exactly how a is stored in memory. Therefore if a is C-ordered versus fortran-ordered, versus defined as a slice or transpose, etc., the view may give different results.

numpy.ma.MaskedArray.__int__ numpy.ma.MaskedArray.astype