Another way to get data out would be to use the new | operator (i.e. x = {} | y essentially copies dictionary x to y) or the update method or ** unpacking operator (e.g. x = {**y}). But maybe those come under the umbrella of iterating as you mentioned.
d.setdefault(k, computevalue())
d = defaultdict(computevalue)
But I have never benchmarked!
I'd say "or" rather than "and": defaultdict has higher overhead to initialise the default (especially if you don't need a function call in the setdefault call) but because it uses a fallback of dict lookup it's essentially free if you get a hit. As a result, either a very high redundancy with a cheap default or a low amount of redundancy with a costly default will have the defaultdict edge out.
For the most extreme case of the former,
d = {}
for i in range(N):
d.setdefault(0, [])
d = defaultdict(list)
for i in range(N):
d[0]
UserDict will route '.get', '.setdefault', and even iteration via '.items()' through the '__getitem__' method.
edited to remove "(maybe all?) edge cases". As soon as I posted, I thought of several less common/obvious edge cases.
That is, it isn't just knowing whether or not the data is ever used. It is useful to know if it was used in this specific run. And often times, seeing what parts of the data was not used is a good clue as to what went wrong. At the least, you can use it to rule out what code was not hit.
This can be used to evaluate the migration quality and spot what can be improved.
https://github.com/xwiki-contrib/confluence/blob/7a95bf96787...
I am currently teaching (typed) Python to a team of Windows sysadmins and it's been incredibly difficult to explain when to use a dataclass, a NamedTuple, a Pydantic model, or a dictionary.
I mean I agree w.r.t. the blurriness in general but this PEP is not going to change anything about that, in neither direction.
I get the rationale for "anonymous strict" return types, but then I think a better way would be to think up some way to accomplish that for dataclasses.
Let me make this more concrete: Those sysadmins frequently need to process and pass around complex (as in heavily nested) structured data. The data often comes in the form of singleton objects, i.e. they are built in single place, then used in another place and then thrown away (or merged into some other structure). In other words, any class hierarchy you build represents boilerplate code you'll only ever use once and which will be annoying to maintain as you refactor your code. Do you pick dataclasses or TypedDicts (or something else) for your map data structures?
In TypeScript you would just use `const data = <heavily nested object> as const` and be done with it.
However: the dict in this case would also include dataclasses, and I’d be interested in finding what exact attributes within those dataclasses were accessed, and also be able to mark all attributes in those dataclasses as accessed if the parent dataclasses is accessed, and with those dataclasses, being config objects, being able to do the same to its own children, so that the topmost dictionary has a tree of all accessed keys.
I couldn’t figure out how to do that, but welcome to ideas.
> The need for this class has been partially supplanted by the ability to subclass directly from dict; however, this class can be easier to work with because the underlying dictionary is accessible as an attribute.
Sounds like (unless you need the dict as a separate data member) this class is a historical artefact. Unless there's some other issue you know of not mentioned in the documentation?
[1] https://typing.python.org/en/latest/spec/overload.html
Even then, to be honest I'm a bit sceptical. Can you point at a link in the official documentation that says overriding methods of dictionaries may not work? I would have thought the link to UserDict would have mentioned that if true. What do you mean they are "runtime dependent"?
UserDict isn't just some historical artifact of a bygone era like some of the posters below are miscorrecing me on.
It doesn't support what you said in your previous comment. It seemed to suggest that any calls to your overridden methods might magically use the base class instead, even when directly called from application code. But the book says something very different:
> The code of the built-ins (written in C) usually does not call methods overridden by user-defined classes.
But that is nothing to do with them being built-in classes or even being written in C. Any class, even written in pure Python, is not guaranteed to implement any of its methods in terms of other public methods, even when it's possible to do that. It just depends on how it's implemented.
Indeed that's true even for UserDict. It's no longer implemented in pure Python, but if you look at the 2.7 version [1], which was, you can see some methods implemented in terms of others (e.g. get() uses `key not in self` and `self[key]`) and others that aren't (e.g. keys() just uses self.dict.keys() rather than being implemented in terms of self.items()).
There was even a breaking change in Python 3.12 to which other methods UerDict.__getitem__() uses in its implementation [2]. I can sort of see some utility to UserDict but it seems at least as unpredictable as deriving from built in dict so it doesn't really buy you much. Either way, the only really safe way to use them is to override all methods that you want to behave differently.
[1] https://github.com/enthought/Python-2.7.3/blob/master/Lib/Us...
The UserDict class is mostly defunct and is only still in the standard library because there were a few existing uses that were hard to replace (such as avoiding base class conflicts in multiple inheritance).
self.accessed_keys = set()
@property
def accessed_keys(self):
return self._accessed_keysTherefore, I really wanted to know that I was actually pulling in all of the data I needed, so I tracked what was seen vs not seen, and compared against what was attempted to see.
In the end it was basically a wrapper around the JSON object itself, that allowed lookup of data via a string in "dot notation" (so you could do "keyA.key2" to get the same thing you would have directly in JSON. Then, it would either return a simple value (if there was one), or another instance of the wrapper if the result was itself an object (or an array or wrapped objects). All instances would share the "seen" list.
It's unfortunately locked behind NDA/copyright stuff, but the implementation was only 67 lines.
A queue-based traversal has flatter memory utilization for deeply nested dicts than a recursive traversal in Python without TCO.
Given a visitor pattern traversal, a visit() function can receive the node path as a list of path components, and update a Counter() with a (full,path,tuple) or "delimiter\.escaped.path" key.
Python collections.UserDict implements the methods necessary to proxy the dict Mapping/MutableMapping interface to self.data. For dicts with many keys, it would probably be faster to hook methods that mutate the UserDict.data dict like __setitem__, get, setdefault, update() and maybe __init__() in order to track which keys have changed instead of copying keys() into a set to do an unordered difference with a list.
React requires setState() for all mutations this.state because there's no way to hook dunder methods in JS: setState() updates this.state and then notifies listeners or calls a list of functions to run when anything in this.state or when a value associated with certain keys or nested keys in this.state changes.
FWIU ipyflow exposes the subscriber refcount/reflist but RxPy specifically does not: ipyflow/core/test/test_refcount.py: https://github.com/ipyflow/ipyflow/blob/master/core/test/tes...
Anyways,
For test assertions, unittest.mock MagicMock can track call_count and call_args_list on methods that mutate a dict like __getitem__ and get(). There's also mock_calls, which keeps an ordered list of the args passed: https://docs.python.org/3/library/unittest.mock.html
Rust front: Here's a faster ls called ls-rs with different defaults, you should use this!
Go front: Here's reverse proxy #145728283 it is an open source project that has slightly different parameters than all the others.
Python hobo front: Uhh guys here's a dict that kinda might remember what you've accessed if you used it in a particular way.