As noted elsewhere, the DataFrame
class has a custom __deepcopy__
method which does not necessarily copy arbitrary attributes assigned to an instance, as with a normal object.
Interestingly, there is an internal _metadata
attribute that seems intended to be able to list additional attributes of an NDFrame
that should be kept when copying/serializing it. This is discussed some here: https://github.com/pandas-dev/pandas/issues/9317
Unfortunately this is still considered an undocumented internal detail, so it probably shouldn't be used. From looking at the code you can in principle do:
mydf = pd.DataFrame(...)
mydf.name = 'foo'
mydf._metadata += ['name']
and when you copy it it should take the name with it.
You could subclass DataFrame
to make this the default:
import functools
class NamedDataFrame(pd.DataFrame):
_metadata = pd.DataFrame._metadata + ['name']
def __init__(self, name, *args, **kwargs):
self.name = name
super().__init__(*args, **kwargs)
@property
def _constructor(self):
return functools.partial(self.__class__, self.name)
You could also do this without relying on this internal _metadata
attribute if you provide your own wrapper to the existing copy
method, and possibly also __getstate__
and __setstate__
.
Update: It seems actually use of the _metadata
attribute for extending Pandas classes is now documented. So the above example should more or less work. These docs are more for development of Pandas itself so it might still be a bit volatile. But this is how Pandas itself extends subclasses of NDFrame
.
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