Either None or a readonly dictionary of metadata (mappingproxy).

The metadata field can be set using any dictionary at data-type creation. NumPy currently has no uniform approach to propagating metadata; although some array operations preserve it, there is no guarantee that others will.


Although used in certain projects, this feature was long undocumented and is not well supported. Some aspects of metadata propagation are expected to change in the future.


>>> dt = np.dtype(float, metadata={"key": "value"})
>>> dt.metadata["key"]
>>> arr = np.array([1, 2, 3], dtype=dt)
>>> arr.dtype.metadata
mappingproxy({'key': 'value'})

Adding arrays with identical datatypes currently preserves the metadata:

>>> (arr + arr).dtype.metadata
mappingproxy({'key': 'value'})

But if the arrays have different dtype metadata, the metadata may be dropped:

>>> dt2 = np.dtype(float, metadata={"key2": "value2"})
>>> arr2 = np.array([3, 2, 1], dtype=dt2)
>>> (arr + arr2).dtype.metadata is None
True  # The metadata field is cleared so None is returned