The interface includes the following abstract methods that must be implemented by subclasses:
type
name
construct_array_type
The following attributes and methods influence the behavior of the dtype in pandas operations
_is_numeric
_is_boolean
_get_common_dtype
The :None:None:`na_value` class attribute can be used to set the default NA value for this type. numpy.nan
is used by default.
ExtensionDtypes are required to be hashable. The base class provides a default implementation, which relies on the _metadata
class attribute. _metadata
should be a tuple containing the strings that define your data type. For example, with PeriodDtype
that's the freq
attribute.
If you have a parametrized dtype you should set the ``_metadata`` class property.
Ideally, the attributes in _metadata
will match the parameters to your ExtensionDtype.__init__
(if any). If any of the attributes in _metadata
don't implement the standard __eq__
or __hash__
, the default implementations here will not work.
For interaction with Apache Arrow (pyarrow), a __from_arrow__
method can be implemented: this method receives a pyarrow Array or ChunkedArray as only argument and is expected to return the appropriate pandas ExtensionArray for this dtype and the passed values:
class ExtensionDtype:
def __from_arrow__(
self, array: Union[pyarrow.Array, pyarrow.ChunkedArray]
) -> ExtensionArray:
...
This class does not inherit from 'abc.ABCMeta' for performance reasons. Methods and properties required by the interface raise pandas.errors.AbstractMethodError
and no register
method is provided for registering virtual subclasses.
A custom data type, to be paired with an ExtensionArray.
extensions.ExtensionArray
Abstract base class for custom 1-D array types.
extensions.register_extension_dtype
Register an ExtensionType with pandas as class decorator.
The following pages refer to to this document either explicitly or contain code examples using this.
pandas.core.dtypes.base.register_extension_dtype
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