# numpy.ma.MaskedArray.view

method

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

  Return a view of the MaskedArray data.

  Parameters
    dtype data-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).

    type Python type, optional

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

    fill_value scalar, 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.

previous

numpy.ma.MaskedArray.__int__

next

numpy.ma.MaskedArray.astype