I'm not certain of memory usage. But using 'make_dataclass' makes the
"noise" pretty much no worse than namedtuple.
Person = namedtuple("Person", "name age address")
Person = make_dataclass("Person", "name age address".split())
Unless you have millions of there's objects, memory probably isn't that
important. But I guess you might... and namedtuple did sell itself as "less
memory than small dictionaries"
On Sat, Jan 26, 2019, 1:26 PM Christopher Barker <[email protected] wrote:
> On Sat, Jan 26, 2019 at 10:13 AM David Mertz <[email protected]> wrote:
>
>> Indeed! I promise to use dataclass next time I find myself about to use
>> namedtuple. :-)
>>
>
> Indeed IIUC, namedtuple was purposely designed to be able to replace
> tuples as well as adding the named access.
>
> But that does indeed cause potential issues. However, dataclasses see kind
> of heavyweight to me -- am I imagining that, or could one make a
> named_not_tuple that was appreciably lighter weight? (in creation time,
> memory use, that sort of thing)
>
> -CHB
>
>
>
>
>
>
>
>>
>> I'm pretty sure that virtually all my uses will allow that.
>>
>> On Sat, Jan 26, 2019, 1:09 PM Eric V. Smith <[email protected] wrote:
>>
>>>
>>>
>>> On 1/26/2019 12:30 PM, David Mertz wrote:
>>> > On Sat, Jan 26, 2019 at 10:31 AM Steven D'Aprano <[email protected]
>>> > <mailto:[email protected]>> wrote:
>>> >
>>> > In what way is it worse, given that returning a namedtuple with
>>> named
>>> >
>>> > fields is backwards compatible with returning a regular tuple? We
>>> can
>>> > have our cake and eat it too.
>>> > Unless the caller does a type-check, there is no difference.
>>> Sequence
>>> > unpacking will still work, and namedtuples unlike regular tuples
>>> can
>>> > support optional attributes.
>>> >
>>> >
>>> > I suppose the one difference is where someone improperly relies on
>>> tuple
>>> > unpacking.
>>> >
>>> > Old version:
>>> >
>>> > def myfun():
>>> > # ...
>>> > return a, b, c
>>> >
>>> > # Call site
>>> > val1, val2, val3 = myfun()
>>> >
>>> >
>>> > New version:
>>> >
>>> > def myfun():
>>> > # ...
>>> > return a, b, c, d
>>> >
>>> >
>>> > Now the call site will get "ValueError: too many values to unpack".
>>> > Namedtuples don't solve this problem, of course. But they don't make
>>> > anything worse either.
>>> >
>>> > The better approach, of course, is to document the API as only using
>>> > attribute access, not positional. I reckon dataclasses from the start
>>> > could address that concern... but so can documentation alone. E.g.:
>>> >
>>> > Old version (improved):
>>> >
>>> > def myfun():
>>> >
>>> > mydata = namedtuple("mydata", "a b c")
>>> >
>>> > # ...
>>> > return mydata(a, b, c)
>>> >
>>> > # Call site
>>> > ret = myfun()
>>> >
>>> > val1, val2, val3 = ret.a, ret.b, ret.c
>>> >
>>> >
>>> > New version (improved)
>>> >
>>> > def myfun():
>>> >
>>> > mydata = namedtuple("mydata", "a b c d e")
>>> >
>>> > # ...
>>> > return mydata(a, b, c, d, e)
>>> >
>>> > Now the call site is completely happy with no changes (assuming it
>>> > doesn't need to care about what values 'ret.d' or 'ret.e' contain...
>>> but
>>> > presumably those extra values are optional in some way.
>>> >
>>> > Moreover, we are even perfectly fine if we had created
>>> > namedtuple("mydata", "e d c b a") for some reason, completely changing
>>> > the positions of all the named attributes in the improved namedtuple.
>>>
>>> Preventing this automatic unpacking (and preventing iteration in
>>> general) was one of the motivating factors for dataclasses:
>>> https://www.python.org/dev/peps/pep-0557/#id47
>>>
>>> Eric
>>>
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>
>
> --
> Christopher Barker, PhD
>
> Python Language Consulting
> - Teaching
> - Scientific Software Development
> - Desktop GUI and Web Development
> - wxPython, numpy, scipy, Cython
>
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