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SubQuery is an abstract base class (per the abc module) with one or more abstract methods that you did not override. By adding ABC to the list of base classes, you defined ValueSum itself to be an abstract base class. That means you aren't forced to override the methods, but it also means you cannot instantiate ValueSum itself.
PyCharm is warning you ahead of time that you need to implement the abstract methods inherited from SubQuery; if you don't, you would get an error from Python when you actually tried to instantiate ValueSum.
As to what inheriting from ABC does, the answer is... not much. It's a convenience for setting the metaclass. The following are equivalent:
class Foo(metaclass=abc.ABCMeta):
...
and
class Foo(abc.ABC):
...
The metaclass modifies __new__ so that every attempt to create an instance of your class checks that the class has implemented all methods decorated with @abstractmethod in a parent class.
The 'Abstract Base classes" or abc.ABC is a helper class
https://docs.python.org/3/library/abc.html
Here's a snippet of why they exist:
The collections module has some concrete classes that derive from ABCs; these can, of course, be further derived. In addition, the
collections.abcsubmodule has some ABCs that can be used to test whether a class or instance provides a particular interface, for example, if it is hashable or if it is a mapping.
A good example here: https://pymotw.com/2/abc/ | https://pymotw.com/3/abc/
From pymotw:
Forgetting to set the metaclass properly means the concrete implementations do not have their APIs enforced. To make it easier to set up the abstract class properly, a base class is provided that sets the metaclass automatically.
abc_abc_base.py
import abc
class PluginBase(abc.ABC):
@abc.abstractmethod
def load(self, input):
"""Retrieve data from the input source
and return an object.
"""
@abc.abstractmethod
def save(self, output, data):
"""Save the data object to the output."""
class SubclassImplementation(PluginBase):
def load(self, input):
return input.read()
def save(self, output, data):
return output.write(data)
if __name__ == '__main__':
print('Subclass:', issubclass(SubclassImplementation,
PluginBase))
print('Instance:', isinstance(SubclassImplementation(),
PluginBase))
@Oddthinking's answer is not wrong, but I think it misses the real, practical reason Python has ABCs in a world of duck-typing.
Abstract methods are neat, but in my opinion they don't really fill any use-cases not already covered by duck typing. Abstract base classes' real power lies in the way they allow you to customise the behaviour of isinstance and issubclass. (__subclasshook__ is basically a friendlier API on top of Python's __instancecheck__ and __subclasscheck__ hooks.) Adapting built-in constructs to work on custom types is very much part of Python's philosophy.
Python's source code is exemplary. Here is how collections.Container is defined in the standard library (at time of writing):
class Container(metaclass=ABCMeta):
__slots__ = ()
@abstractmethod
def __contains__(self, x):
return False
@classmethod
def __subclasshook__(cls, C):
if cls is Container:
if any("__contains__" in B.__dict__ for B in C.__mro__):
return True
return NotImplemented
This definition of __subclasshook__ says that any class with a __contains__ attribute is considered to be a subclass of Container, even if it doesn't subclass it directly. So I can write this:
class ContainAllTheThings(object):
def __contains__(self, item):
return True
>>> issubclass(ContainAllTheThings, collections.Container)
True
>>> isinstance(ContainAllTheThings(), collections.Container)
True
In other words, if you implement the right interface, you're a subclass! ABCs provide a formal way to define interfaces in Python, while staying true to the spirit of duck-typing. Besides, this works in a way that honours the Open-Closed Principle.
Python's object model looks superficially similar to that of a more "traditional" OO system (by which I mean Java*) - we got yer classes, yer objects, yer methods - but when you scratch the surface you'll find something far richer and more flexible. Likewise, Python's notion of abstract base classes may be recognisable to a Java developer, but in practice they are intended for a very different purpose.
I sometimes find myself writing polymorphic functions that can act on a single item or a collection of items, and I find isinstance(x, collections.Iterable) to be much more readable than hasattr(x, '__iter__') or an equivalent try...except block. (If you didn't know Python, which of those three would make the intention of the code clearest?)
That said, I find that I rarely need to write my own ABC and I typically discover the need for one through refactoring. If I see a polymorphic function making a lot of attribute checks, or lots of functions making the same attribute checks, that smell suggests the existence of an ABC waiting to be extracted.
*without getting into the debate over whether Java is a "traditional" OO system...
Addendum: Even though an abstract base class can override the behaviour of isinstance and issubclass, it still doesn't enter the MRO of the virtual subclass. This is a potential pitfall for clients: not every object for which isinstance(x, MyABC) == True has the methods defined on MyABC.
class MyABC(metaclass=abc.ABCMeta):
def abc_method(self):
pass
@classmethod
def __subclasshook__(cls, C):
return True
class C(object):
pass
# typical client code
c = C()
if isinstance(c, MyABC): # will be true
c.abc_method() # raises AttributeError
Unfortunately this one of those "just don't do that" traps (of which Python has relatively few!): avoid defining ABCs with both a __subclasshook__ and non-abstract methods. Moreover, you should make your definition of __subclasshook__ consistent with the set of abstract methods your ABC defines.
Short version
ABCs offer a higher level of semantic contract between clients and the implemented classes.
Long version
There is a contract between a class and its callers. The class promises to do certain things and have certain properties.
There are different levels to the contract.
At a very low level, the contract might include the name of a method or its number of parameters.
In a staticly-typed language, that contract would actually be enforced by the compiler. In Python, you can use EAFP or type introspection to confirm that the unknown object meets this expected contract.
But there are also higher-level, semantic promises in the contract.
For example, if there is a __str__() method, it is expected to return a string representation of the object. It could delete all contents of the object, commit the transaction and spit a blank page out of the printer... but there is a common understanding of what it should do, described in the Python manual.
That's a special case, where the semantic contract is described in the manual. What should the print() method do? Should it write the object to a printer or a line to the screen, or something else? It depends - you need to read the comments to understand the full contract here. A piece of client code that simply checks that the print() method exists has confirmed part of the contract - that a method call can be made, but not that there is agreement on the higher level semantics of the call.
Defining an Abstract Base Class (ABC) is a way of producing a contract between the class implementers and the callers. It isn't just a list of method names, but a shared understanding of what those methods should do. If you inherit from this ABC, you are promising to follow all the rules described in the comments, including the semantics of the print() method.
Python's duck-typing has many advantages in flexibility over static-typing, but it doesn't solve all the problems. ABCs offer an intermediate solution between the free-form of Python and the bondage-and-discipline of a staticly-typed language.