I've been going through all the Stackoverflow answers on dynamic property setting, but for whatever reason I can't seem to get this to work.
I have a class, Evolution_Base, that in its init creates an instance of Value_Differences. Value_Differences should be dynamically creating properties, based on the list I pass, that returns the function value from _get_df_change:
from pandas import DataFrame
from dataclasses import dataclass
import pandas as pd
class Evolution_Base():
def __init__(self, res_date_0 : DataFrame , res_date_1 : DataFrame):
#dataclass
class Results_Data():
res_date_0_df : DataFrame
res_date_1_df : DataFrame
self.res = Results_Data(res_date_0_df= res_date_0,
res_date_1_df= res_date_1)
property_list = ['abc', 'xyz']
self.difference = Value_Differences(parent = self, property_list=property_list)
# Shared Functions
def _get_df_change(self, df_name, operator = '-'):
df_0 = getattr(self.res.res_date_0_df, df_name.lower())
df_1 = getattr(self.res.res_date_1_df, df_name.lower())
return self._df_change(df_1, df_0, operator=operator)
def _df_change(self, df_1 : pd.DataFrame, df_0 : pd.DataFrame, operator = '-') -> pd.DataFrame:
"""
Returns df_1 <operator | default = -> df_0
"""
# is_numeric mask
m_1 = df_1.select_dtypes('number')
m_0 = df_0.select_dtypes('number')
def label_me(x):
x.columns = ['t_1', 't_0']
return x
if operator == '-':
return label_me(df_1[m_1] - df_0[m_0])
elif operator == '+':
return label_me(df_1[m_1] + df_0[m_0])
class Value_Differences():
def __init__(self, parent : Evolution_Base, property_list = []):
self._parent = parent
for name in property_list:
def func(self, prop_name):
return self._parent._get_df_change(name)
# I've tried the following...
setattr(self, name, property(fget = lambda cls_self: func(cls_self, name)))
setattr(self, name, property(func(self, name)))
setattr(self, name, property(func))
Its driving me nuts... Any help appreciated!
My desired outcome is for:
evolution = Evolution_Base(df_1, df_2)
evolution.difference.abc == evolution._df_change('abc')
evolution.difference.xyz == evolution._df_change('xyz')
EDIT: The simple question is really, how do I setattr for a property function?
As asked
how do I setattr for a property function?
To be usable as a property, the accessor function needs to be wrapped as a property and then assigned as an attribute of the class, not the instance.
That function, meanwhile, needs to have a single unbound parameter - which will be an instance of the class, but is not necessarily the current self. Its logic needs to use the current value of name, but late binding will be an issue because of the desire to create lambdas in a loop.
A clear and simple way to work around this is to define a helper function accepting the Value_Differences instance and the name to use, and then bind the name value eagerly.
Naively:
from functools import partial
def _get_from_parent(name, instance):
return instance._parent._get_df_change(name)
class Value_Differences:
def __init__(self, parent: Evolution_Base, property_list = []):
self._parent = parent
for name in property_list:
setattr(Value_Differences, name, property(
fget = partial(_get_from_parent, name)
))
However, this of course has the issue that every instance of Value_Differences will set properties on the class, thus modifying what properties are available for each other instance. Further, in the case where there are many instances that should have the same properties, the setup work will be repeated at each instance creation.
The apparent goal
It seems that what is really sought, is the ability to create classes dynamically, such that a list of property names is provided and a corresponding class pops into existence, with code filled in for the properties implementing a certain logic.
There are multiple approaches to this.
Factory A: Adding properties to an instantiated template
Just like how functions can be nested within each other and the inner function will be an object that can be modified and returned (as is common when creating a decorator), a class body can appear within a function and a new class object (with the same name) is created every time the function runs. (The code in the OP already does this, for the Results_Data dataclass.)
def example():
class Template:
pass
return Template
>>> TemplateA, TemplateB = example(), example()
>>> TemplateA is TemplateB
False
>>> isinstance(TemplateA(), TemplateB)
False
>>> isinstance(TemplateB(), TemplateA)
False
So, a "factory" for value-difference classes could look like
from functools import partial
def _make_value_comparer(property_names, access_func):
class ValueDifferences:
def __init__(self, parent):
self._parent = parent
for name in property_names:
setattr(Value_Differences, name, property(
fget = partial(access_func, name)
))
return ValueDifferences
Notice that instead of hard-coding a helper, this factory expects to be provided with a function that implements the access logic. That function takes two parameters: a property name, and the ValueDifferences instance. (They're in that order because it's more convenient for functools.partial usage.)
Factory B: Using the type constructor directly
The built-in type in Python has two entirely separate functions.
With one argument, it discloses the type of an object.
With three arguments, it creates a new type. The class syntax is in fact syntactic sugar for a call to this builtin. The arguments are:
a string name (will be set as the __name__ attribute)
a list of classes to use as superclasses (will be set as __bases__)
a dict mapping attribute names to their values (including methods and properties - will become the __dict__, roughly)
In this style, the same factory could look something like:
from functools import partial
def _make_value_comparer(property_names, access_func):
methods = {
name: property(fget = partial(access_func, name)
for name in property_names
}
methods['__init__'] = lambda self, parent: setattr(self, '_parent', parent)
return type('ValueDifferences', [], methods)
Using the factory
In either of the above cases, EvolutionBase would be modified in the same way.
Presumably, every EvolutionBase should use the same ValueDifferences class (i.e., the one that specifically defines abc and xyz properties), so the EvolutionBase class can cache that class as a class attribute, and use it later:
class Evolution_Base():
def _get_from_parent(name, mvd):
# mvd._parent will be an instance of Evolution_Base.
return mvd._parent._get_df_change(name)
_MyValueDifferences = _make_value_comparer(['abc', 'xyz'], _get_from_parent)
def __init__(self, res_date_0 : DataFrame , res_date_1 : DataFrame):
#dataclass
class Results_Data():
res_date_0_df : DataFrame
res_date_1_df : DataFrame
self.res = Results_Data(res_date_0_df= res_date_0,
res_date_1_df= res_date_1)
self.difference = _MyValueDifferences(parent = self)
Notice that the cached _MyValueDifferences class no longer requires a list of property names to be constructed. That's because it was already provided when the class was created. The actual thing that varies per instance of _MyValueDifferences, is the parent, so that's all that gets passed.
Simpler approaches
It seems that the goal is to have a class whose instances are tightly associated with instances of Evolution_Base, providing properties specifically named abc and xyz that are computed using the Evolution_Base's data.
That could just be hard-coded as a nested class:
class Evolution_Base:
class EBValueDifferences:
def __init__(self, parent):
self._parent = parent
#property
def abc(self):
return self._parent._get_df_change('abc')
#property
def xyz(self):
return self._parent._get_df_change('xyz')
def __init__(self, res_date_0 : DataFrame , res_date_1 : DataFrame):
#dataclass
class Results_Data():
res_date_0_df : DataFrame
res_date_1_df : DataFrame
self.res = Results_Data(res_date_0_df = res_date_0,
res_date_1_df = res_date_1)
self.difference = EBValueDifferences(self)
# _get_df_change etc. as before
Even simpler, provide corresponding properties directly on Evolution_Base:
class Evolution_Base:
#property
def abc_difference(self):
return self._get_df_change('abc')
#property
def xyz_difference(self):
return self._get_df_change('xyz')
def __init__(self, res_date_0 : DataFrame , res_date_1 : DataFrame):
#dataclass
class Results_Data():
res_date_0_df : DataFrame
res_date_1_df : DataFrame
self.res = Results_Data(res_date_0_df = res_date_0,
res_date_1_df = res_date_1)
# _get_df_change etc. as before
# client code now calls my_evolution_base.abc_difference
# instead of my_evolution_base.difference.abc
If there are a lot of such properties, they could be attached using a much simpler dynamic approach (that would still be reusable for other classes that define a _get_df_change):
def add_df_change_property(name, cls):
setattr(
cls, f'{name}_difference',
property(fget = lambda instance: instance._get_df_change(name))
)
which can also be adapted for use as a decorator:
from functools import partial
def exposes_df_change(name):
return partial(add_df_change_property, name)
#exposes_df_change('abc')
#exposes_df_change('def')
class Evolution_Base:
# `self.difference` can be removed, no other changes needed
This is quite the rabbit hole. Impossible is a big call, but I will say this: they don't intend you to do this. The 'Pythonic' way of achieving your example use case is the __getattr__ method. You could also override the __dir__ method to insert your custom attributes for discoverability.
This is the code for that:
class Value_Differences():
def __init__(self, parent : Evolution_Base, property_list = []):
self._parent = parent
self._property_list = property_list
def __dir__(self):
return sorted(set(
dir(super(Value_Differences, self)) + \
list(self.__dict__.keys()) + self._property_list))
def __getattr__(self, __name: str):
if __name in self._property_list:
return self._parent._get_df_change(__name)
But that wasn't the question, and respect for a really, really interesting question. This is one of those things that you look at and say 'hmm, should be possible' and can get almost to a solution. I initially thought what you asked for was technically possible, just very hacky to achieve. But it turns out that it would be very, very weird hackery if it was possible.
Two small foundational things to start with:
Remind ourselves of the hierarchy of Python objects that the runtime is working with when defining and instantiating classes:
The metaclass (defaulting to type), which is used to build classes. I'm going to refer to this as the Metaclass Type Object (MTO).
The class definition, which is used to build objects. I'm going to refer to this as the Class Type Object (CTO).
And the class instance or object, which I'll refer to as the Class Instance Object (CIO).
MTOs are subclasses of type. CTOs are subclasses of object. CIOs are instances of CTOs, but instantiated by MTOs.
Python runs code inside class definitions as if it was running a function:
class Class1:
print("1")
def __init__(self, v1):
print("4")
print("2")
print("3")
c1 = Class1("x")
print("5")
gives 1, 2, 3, 4, 5
Put these two things together with:
class Class1:
def attr1_get(self):
return 'attr1 value'
attr1 = property(attr1_get)
we are defining a function attr1_get as part of the class definition. We are then running an inline piece of code that creates an object of type property. Note that this is just the name of the object's type - it isn't a property as you would describe it. Just an object with some attributes, being references to various functions. We then assign that object to an attribute in the class we are defining.
In the terms I used above, once that code is run we have a CTO instantiated as an object in memory that contains an attribute attr1 of type property (an object subclass, containing a bunch of attributes itself - one of which is a reference to the function attr1_get).
That can be used to instantiate an object, the CIO.
This is where the MTO comes in. You instantiate the property object while defining the CTO so that when the runtime applies the MTO to create the CIO from the CTO, an attribute on the CIO will be formed with a custom getter function for that attribute rather than the 'standard' getter function the runtime would use. The property object means something to the type object when it is building a new object.
So when we run:
c1 = Class1()
we don't get a CIO c1 with an attribute attr1 that is an object of type property. The metaclass of type type formed a set of references against the attribute's internal state to all the functions we stored in the property object. Note that this is happening inside the runtime, and you can't call this directly from your code - you just tell the type metaclass to do it by using the property wrapper object.
So if you directly assign a property() result to an attribute of a CIO, you have a Pythonic object assigned that references some functions, but the internal state for the runtime to use to reference the getter, setter, etc. is not set up. The getter of an attribute that contains a property object is the standard getter and so returns the object instance, and not the result of the functions it wraps,
This next bit of code demonstrates how this flows:
print("Let's begin")
class MetaClass1(type):
print("Starting to define MetaClass1")
def __new__(cls, name, bases, dct):
x = super().__new__(cls, name, bases, dct)
print("Metaclass1 __new__({})".format(str(cls)))
return x
print("__new__ of MetaClass1 is defined")
def __init__(cls, name, bases, dct):
print("Metaclass1 __init__({})".format(str(cls)))
print("__init__ of MetaClass1 is defined")
print("Metaclass is defined")
class Class1(object,metaclass=MetaClass1):
print("Starting to define Class1")
def __new__(cls, *args, **kwargs):
print("Class1 __new__({})".format(str(cls)))
return super(Class1, cls).__new__(cls, *args, **kwargs)
print("__new__ of Class1 is defined")
def __init__(self):
print("Class1 __init__({})".format(str(self)))
print("__init__ of Class1 is defined")
def g1(self):
return 'attr1 value'
print("g1 of Class1 is defined")
attr1 = property(g1)
print("Class1.attr1 = ", attr1)
print("attr1 of Class1 is defined")
def addProperty(self, name, getter):
setattr(self, name, property(getter))
print("self.", name, " = ", getattr(self, name))
print("addProperty of Class1 is defined")
print("Class is defined")
c1 = Class1()
print("Instance is created")
print(c1.attr1)
def g2(cls):
return 'attr2 value'
c1.addProperty('attr2', g2)
print(c1.attr2)
I have put all those print statements there to demonstrate the order in which things happen very clearly.
In the middle, you see:
g1 of Class1 is defined
Class1.attr1 = <property object at 0x105115c10>
attr1 of Class1 is defined
We have created an object of type property and assigned it to a class attribute.
Continuing:
addProperty of Class1 is defined
Metaclass1 __new__(<class '__main__.MetaClass1'>)
Metaclass1 __init__(<class '__main__.Class1'>)
Class is defined
The metaclass got instantiated, being passed first itself (__new__) and then the class it will work on (__init__). This happened right as we stepped out of the class definition. I have only included the metaclass to show what will happen with the type metaclass by default.
Then:
Class1 __new__(<class '__main__.Class1'>)
Class1 __init__(<__main__.Class1 object at 0x105124c10>)
Instance is created
attr1 value
self. attr2 = <property object at 0x105115cb0>
<property object at 0x105115cb0>
Class1 is instantiated, providing first its type to __new__ and then its instance to __init__.
We see that attr1 is instantiated properly, but attr2 is not. That is because setattr is being called once the class instance is already constructed and is just saying attr2 is an instance of the class property and not defining attr2 as the actual runtime construct of a property.
Which is made more clear if we run:
print(c1.attr2.fget(c1))
print(c1.attr1.fget(c1))
attr2 (a property object) isn't aware of the class or instance of the containing attribute's parent. The function it wraps still needs to be given the instance to work on.
attr1 doesn't know what to do with that, because as far as it is concerned it is a string object, and has no concept of how the runtime is mapping its getter.
The fundamental reason why what you tried doesn't work is that a property, a use case of a descriptor, by design must be stored as a class variable, not as an instance attribute.
Excerpt from the documentation of descriptor:
To use the descriptor, it must be stored as a class variable in
another class:
To create a class with dynamically named properties that has access to a parent class, one elegant approach is to create the class within a method of the main class, and use setattr to create class attributes with dynamic names and property objects. A class created in the closure of a method automatically has access to the self object of the parent instance, avoiding having to manage a clunky _parent attribute like you do in your attempt:
class Evolution_Base:
def __init__(self, property_list):
self.property_list = property_list
self._difference = None
#property
def difference(self):
if not self._difference:
class Value_Differences:
pass
for name in self.property_list:
# use default value to store the value of name in each iteration
def func(obj, prop_name=name):
return self._get_df_change(prop_name) # access self via closure
setattr(Value_Differences, name, property(func))
self._difference = Value_Differences()
return self._difference
def _get_df_change(self, df_name):
return f'df change of {df_name}' # simplified return value for demo purposes
so that:
evolution = Evolution_Base(['abc', 'xyz'])
print(evolution.difference.abc)
print(evolution.difference.xyz)
would output:
df change of abc
df change of xyz
Demo: https://replit.com/#blhsing/ExtralargeNaturalCoordinate
Responding directly to your question, you can create a class:
class FooBar:
def __init__(self, props):
def make_prop(name):
return property(lambda accessor_self: self._prop_impl(name))
self.accessor = type(
'Accessor',
tuple(),
{p: make_prop(p) for p in props}
)()
def _prop_impl(self, arg):
return arg
o = FooBar(['foo', 'bar'])
assert o.accessor.foo == o._prop_impl('foo')
assert o.accessor.bar == o._prop_impl('bar')
Further, it would be beneficiary to cache created class to make equivalent objects more similar and eliminate potential issues with equality comparison.
That said, I am not sure if this is desired. There's little benefit of replacing method call syntax (o.f('a')) with property access (o.a). I believe it can be detrimental on multiple accounts: dynamic properties are confusing, harder to document, etc., finally while none of this is strictly guaranteed in crazy world of dynamic python -- they kind of communicate wrong message: that the access is cheap and does not involve computation and that perhaps you can attempt to write to it.
I think that when you define the function func in the loop, it closes over the current value of the name variable, not the value of the name variable at the time the property is accessed. To fix this, you can use a lambda function to create a closure that captures the value of name at the time the property is defined.
class Value_Differences():
def __init__(self, parent : Evolution_Base, property_list = []):
self._parent = parent
for name in property_list:
setattr(self, name, property(fget = lambda self, name=name: self._parent._get_df_change(name)))
Does this help you ?
The simple question is really, how do I setattr for a property function?
In python we can set dynamic attributes like this:
class DynamicProperties():
def __init__(self, property_list):
self.property_list = property_list
def add_properties(self):
for name in self.property_list:
setattr(self.__class__, name, property(fget=lambda self: 1))
dync = DynamicProperties(['a', 'b'])
dync.add_properties()
print(dync.a) # prints 1
print(dync.b) # prints 1
Correct me if I am wrong but from reviewing your code, you want to create a dynamic attributes then set their value to a specific function call within the same class, where the passed in data is passed in attributes in the constructor " init " this is achievable, an example:
class DynamicProperties():
def __init__(self, property_list, data1, data2):
self.property_list = property_list
self.data1 = data1
self.data2 = data2
def add_properties(self):
for name in self.property_list:
setattr(self.__class__, name, property(fget=lambda self: self.change(self.data1, self.data2) ))
def change(self, data1, data2):
return data1 - data2
dync = DynamicProperties(['a', 'b'], 1, 2)
dync.add_properties()
print(dync.a == dync.change(1, 2)) # prints true
print(dync.b == dync.change(1,2)) # prints true
You just have to add more complexity to the member, __getattr__ / __setattr__ gives you the string, so it can be interpreted as needed. The biggest "problem" doing this is that the return might no be consistent and piping it back to a library that expect an object to have a specific behavior can cause soft errors.
This example is not the same as yours, but it has the same concept, manipulate columns with members. To get a copy with changes a set is not needed, with a copy, modify and return, the new instance can be created with whatever needed.
For example, the __getattr__ in this line will:
Check and interpret the string xyz_mull_0
Validate that the members and the operand exists
Make a copy of data_a
Modify the copy and return it
var = data_a.xyz_mull_0()
This looks more complex that it actually is, with the same instance members its clear what it is doing, but the _of modifier needs a callback, this is because the __getattr__ can only have one parameter, so it needs to save the attr and return a callback to be called with the other instance that then will call back to the __getattr__ and complete the rest of the function.
import re
class FlexibleFrame:
operand_mod = {
'sub': lambda a, b: a - b,
'add': lambda a, b: a + b,
'div': lambda a, b: a / b,
'mod': lambda a, b: a % b,
'mull': lambda a, b: a * b,
}
#staticmethod
def add_operand(name, func):
if name not in FlexibleFrame.operand_mod.keys():
FlexibleFrame.operand_mod[name] = func
# This makes this class subscriptable
def __getitem__(self, item):
return self.__dict__[item]
# Uses:
# -> object.value
# -> object.member()
# -> object.<name>_<operand>_<name|int>()
# -> object.<name>_<operand>_<name|int>_<flow>()
def __getattr__(self, attr):
if re.match(r'^[a-zA-Z]+_[a-zA-Z]+_[a-zA-Z0-9]+(_of)?$', attr):
seg = attr.split('_')
var_a, operand, var_b = seg[0:3]
# If there is a _of: the second operand is from the other
# instance, the _of is removed and a callback is returned
if len(seg) == 4:
self.__attr_ref = '_'.join(seg[0:3])
return self.__getattr_of
# Checks if this was a _of attribute and resets it
if self.__back_ref is not None:
other = self.__back_ref
self.__back_ref = None
self.__attr_ref = None
else:
other = self
if var_a not in self.__dict__:
raise AttributeError(
f'No match of {var_a} in (primary) {__class__.__name__}'
)
if operand not in FlexibleFrame.operand_mod.keys():
raise AttributeError(
f'No match of operand {operand}'
)
# The return is a copy of self, if not the instance
# is getting modified making x = a.b() useless
ret = FlexibleFrame(**self.__dict__)
# Checks if the second operand is a int
if re.match(r'^\d+$', var_b) :
ref_b_num = int(var_b)
for i in range(len(self[var_a])):
ret[var_a][i] = FlexibleFrame.operand_mod[operand](
self[var_a][i], ref_b_num
)
elif var_b in other.__dict__:
for i in range(len(self[var_a])):
# out_index = operand[type](in_a_index, in_b_index)
ret[var_a][i] = FlexibleFrame.operand_mod[operand](
self[var_a][i], other[var_b][i]
)
else:
raise AttributeError(
f'No match of {var_b} in (secondary) {__class__.__name__}'
)
# This swaps the .member to a .member()
# it also adds and extra () in __getattr_of
return lambda: ret
# return ret
if attr in self.__dict__:
return self[attr]
raise AttributeError(
f'No match of {attr} in {__class__.__name__}'
)
def __getattr_of(self, other):
self.__back_ref = other
return self.__getattr__(self.__attr_ref)()
def __init__(self, **kwargs):
self.__back_ref = None
self.__attr_ref = None
#TODO: Check if data columns match in size
# if not, implement column_<name>_filler=<default>
for i in kwargs:
self.__dict__[i] = kwargs[i]
if __name__ == '__main__':
data_a = FlexibleFrame(**{
'abc': [i for i in range(10)],
'nmv': [i for i in range(10)],
'xyz': [i for i in range(10)],
})
data_b = FlexibleFrame(**{
'fee': [i + 10 for i in range(10)],
'foo': [i + 10 for i in range(10)],
})
FlexibleFrame.add_operand('set', lambda a, b: b)
var = data_a.xyz_mull_0()
var = var.abc_set_xyz()
var = var.xyz_add_fee_of(data_b)
As a extra thing, lambdas in python have this thing, so it can make difficult using them when self changes.
It seems you're bending the language to do weird things. I'd take it as a smell that your code is probably getting convoluted but I'm not saying there would never be a use-case for it so here is a minimal example of how to do it:
class Obj:
def _df_change(self, arg):
print('change', arg)
class DynAttributes(Obj):
def __getattr__(self, name):
return self._df_change(name)
class Something:
difference = DynAttributes()
a = Something()
b = Obj()
assert a.difference.hello == b._df_change('hello')
When calling setattr , use self.__class__ instead of self
Code sample:
class A:
def __init__(self,names : List[str]):
for name in names:
setattr(self.__class__,name,property(fget=self.__create_getter(name)))
def __create_getter(self,name: str):
def inner(self):
print(f"invoking {name}")
return 10
return inner
a = A(['x','y'])
print(a.x + 1)
print(a.y + 2)
I think a code sample will better speak for itself:
class SomeClass:
example = create_get_method()
Yes, that's all – ideally.
In that case, create_get_method would add a get_example() to SomeClass in a way that it can be accessed via an instance of SomeClass:
obj = SomeClass()
obj.get_example() <- returns the value of self.example
(Of course, the idea is to implement a complex version of get_contact, that's why I want to do that in a non-repetitive way, and this is a simplified version that represents well the issue.)
I don't know if that's possible, because it require to have access to the property name (example) and the class (SomeClass) since these can not be guessed in advance (that function will be used on many and various classes).
I know it's something possible, because that's kind of what SQLAlchemy does with their relationship() function on a class:
class Model(BaseModel):
id = ...
contact_id = db.Integer(db.ForeignKey..)
contact = relationship('contact') <-- This !
How can this be done?
Objects bound to class-level variables can have a __set_name__ method that will be called immediately after the class object has been created. It will be called with two arguments, the class object, and the name of the variable the object is saved as in the class.
You could use this to create your extra getter method, though I'm not sure why exactly you want to (you could make the object a descriptor instead, which would probably be better than adding a separate getter function to the parent class).
class create_get_method:
def __set_name__(self, owner, name):
def getter(self):
return getattr(self, name)
getter_name = f"get_{name}"
getter.__name__ = getter_name
setattr(owner, getter_name, getter)
# you might also want a __get__ method here to give a default value (like None)
Here's how that would work:
>>> class Test:
... example = create_get_method()
...
>>> t = Test()
>>> print(t.get_example())
<__main__.create_get_method at 0x000001E0B4D41400>
>>> t.example = "foo"
>>> print(t.get_example())
foo
You could change the value returned by default (in the first print call), so that the create_get_method object isn't as exposed. Just add a __get__ method to the create_get_method class.
You can do this with a custom non-data descriptor, like a property, except that you don't need a __set__ method:
class ComplicatedDescriptor:
def __init__(self, name):
self.name = name
def __get__(self, owner, type):
# Here, `owner` is the instance of `SomeClass` that contains this descriptor
# Use `owner` to do some complicated stuff, like DB lookup or whatever
name = f'_{self.name}'
# These two lines for demo only
value = owner.__dict__.get(name, 0)
value += 1
setattr(owner, name, value)
return value
Now you can have any number of classes that use this descriptor:
class SomeClass:
example = ComplicatedDescriptor('example')
Now you can do something like:
>>> inst0 = SomeClass()
>>> inst1 = SomeClass()
>>> inst0.example
1
>>> inst1.example
1
>>> inst1.example
2
>>> inst0.example
2
The line name = f'_{self.name} is necessary because the descriptor here is a non-data descriptor: it has no __set__ method, so if you create inst0.__dict__['example'], the lookup will no longer happen: inst0.example will return inst0.__dict__['example'] instead of calling SomeClass.example.__get__(inst0, type(inst0)). One workaround is to store the value under the attribute name _example. The other is to make your descriptor into a data descriptor:
class ComplicatedDescriptor_v2:
def __init__(self, name):
self.name = name
def __get__(self, owner, type):
# Here, `owner` is the instance of `SomeClass` that contains this descriptor
# Use `owner` to do some complicated stuff, like DB lookup or whatever
# These two lines for demo only
value = owner.__dict__.get(self.name, 0)
value += 1
owner.__dict__[self.name] = value
return value
def __set__(self, *args):
raise AttributeError(f'{self.name} is a read-only attribute')
The usage is generally identical:
class SomeClass:
example = ComplicatedDescriptor_v2('example')
Except that now you can't accidentally override your attribute:
>>> inst = SomeClass()
>>> inst.example
1
>>> inst.example
2
>>> inst.example = 0
AttributeError: example is a read-only attribute
Descriptors are a fairly idiomatic way to get and set values in python. They are preferred to getters and setters in almost all cases. The simplest cases are handled by the built-in property. That being said, if you wanted to explicitly have a getter method, I would recommend doing something very similar, but just returning a method instead of calling __get__ directly.
For example:
def __get__(self, owner, type):
def enclosed():
# Use `owner` to do some complicated stuff, like DB lookup or whatever
name = f'_{self.name}'
# These two lines for demo only
value = owner.__dict__.get(name, 0)
value += 1
setattr(owner, name, value)
return value
return enclosed
There is really no point to doing something like this unless you plan on really just want to be able to call inst.example().
Suppose we have the following class hierarchy:
class ClassA:
#property
def foo(self): return "hello"
class ClassB(ClassA):
#property
def bar(self): return "world"
If I explore __dict__ on ClassB like so, I only see the bar attribute:
for name,_ in ClassB.__dict__.items():
if name.startswith("__"):
continue
print(name)
Output is bar
I can roll my own means to get attributes on not only the specified type but its ancestors. However, my question is whether there's already a way in python for me to do this without re-inventing a wheel.
def return_attributes_including_inherited(type):
results = []
return_attributes_including_inherited_helper(type,results)
return results
def return_attributes_including_inherited_helper(type,attributes):
for name,attribute_as_object in type.__dict__.items():
if name.startswith("__"):
continue
attributes.append(name)
for base_type in type.__bases__:
return_attributes_including_inherited_helper(base_type,attributes)
Running my code as follows...
for attribute_name in return_attributes_including_inherited(ClassB):
print(attribute_name)
... gives back both bar and foo.
Note that I'm simplifying some things: name collisions, using items() when for this example I could use dict, skipping over anything that starts with __, ignoring the possibility that two ancestors themselves have a common ancestor, etc.
EDIT1 - I tried to keep the example simple. But I really want both the attribute name and the attribute reference for each class and ancestor class. One of the answers below has me on a better track, I'll post some better code when I get it to work.
EDIT2 - This does what I want and is very succinct. It's based on Eli's answer below.
def get_attributes(type):
attributes = set(type.__dict__.items())
for type in type.__mro__:
attributes.update(type.__dict__.items())
return attributes
It gives back both the attribute names and their references.
EDIT3 - One of the answers below suggested using inspect.getmembers. This appears very useful because it's like dict only it operates on ancestor classes as well.
Since a large part of what I was trying to do was find attributes marked with a particular descriptor, and include ancestors classes, here is some code that would help do that in case it helps anyone:
class MyCustomDescriptor:
# This is greatly oversimplified
def __init__(self,foo,bar):
self._foo = foo
self._bar = bar
pass
def __call__(self,decorated_function):
return self
def __get__(self,instance,type):
if not instance:
return self
return 10
class ClassA:
#property
def foo(self): return "hello"
#MyCustomDescriptor(foo="a",bar="b")
def bar(self): pass
#MyCustomDescriptor(foo="c",bar="d")
def baz(self): pass
class ClassB(ClassA):
#property
def something_we_dont_care_about(self): return "world"
#MyCustomDescriptor(foo="e",bar="f")
def blah(self): pass
# This will get attributes on the specified type (class) that are of matching_attribute_type. It just returns the attributes themselves, not their names.
def get_attributes_of_matching_type(type,matching_attribute_type):
return_value = []
for member in inspect.getmembers(type):
member_name = member[0]
member_instance = member[1]
if isinstance(member_instance,matching_attribute_type):
return_value.append(member_instance)
return return_value
# This will return a dictionary of name & instance of attributes on type that are of matching_attribute_type (useful when you're looking for attributes marked with a particular descriptor)
def get_attribute_name_and_instance_of_matching_type(type,matching_attribute_type):
return_value = {}
for member in inspect.getmembers(ClassB):
member_name = member[0]
member_instance = member[1]
if isinstance(member_instance,matching_attribute_type):
return_value[member_name] = member_instance
return return_value
You should use python's inspect module for any such introspective capabilities.
.
.
>>> class ClassC(ClassB):
... def baz(self):
... return "hiya"
...
>>> import inspect
>>> for attr in inspect.getmembers(ClassC):
... print attr
...
('__doc__', None)
('__module__', '__main__')
('bar', <property object at 0x10046bf70>)
('baz', <unbound method ClassC.baz>)
('foo', <property object at 0x10046bf18>)
Read more about the inspect module here.
You want to use dir:
for attr in dir(ClassB):
print attr
Sadly there isn't a single composite object. Every attribute access for a (normal) python object first checks obj.__dict__, then the attributes of all it's base classes; while there are some internal caches and optimizations, there isn't a single object you can access.
That said, one thing that could improve your code is to use cls.__mro__ instead of cls.__bases__... instead of the class's immediate parents, cls.__mro__ contains ALL the ancestors of the class, in the exact order Python would search, with all common ancestors occuring only once. That would also allow your type-searching method to be non-recursive. Loosely...
def get_attrs(obj):
attrs = set(obj.__dict__)
for cls in obj.__class__.__mro__:
attrs.update(cls.__dict__)
return sorted(attrs)
... does a fair approximation of the default dir(obj) implementation.
Here is a function I wrote, back in the day. The best answer is using the inspect module, as using __dict__ gives us ALL functions (ours + inherited) and (ALL?) data members AND properties. Where inspect gives us enough information to weed out what we don't want.
def _inspect(a, skipFunctionsAlways=True, skipMagic = True):
"""inspects object attributes, removing all the standard methods from 'object',
and (optionally) __magic__ cruft.
By default this routine skips __magic__ functions, but if you want these on
pass False in as the skipMagic parameter.
By default this routine skips functions, but if you want to see all the functions,
pass in False to the skipFunctionsAlways function. This works together with the
skipMagic parameter: if the latter is True, you won't see __magic__ methods.
If skipFunctionsAlways = False and skipMagic = False, you'll see all the __magic__
methods declared for the object - including __magic__ functions declared by Object
NOT meant to be a comprehensive list of every object attribute - instead, a
list of every object attribute WE (not Python) defined. For a complete list
of everything call inspect.getmembers directly"""
objType = type(object)
def weWantIt(obj):
#return type(a) != objType
output= True
if (skipFunctionsAlways):
output = not ( inspect.isbuiltin(obj) ) #not a built in
asStr = ""
if isinstance(obj, types.MethodType):
if skipFunctionsAlways: #never mind, we don't want it, get out.
return False
else:
asStr = obj.__name__
#get just the name of the function, we don't want the whole name, because we don't want to take something like:
#bound method LotsOfThings.bob of <__main__.LotsOfThings object at 0x103dc70>
#to be a special method because it's module name is special
#WD-rpw 02-23-2008
#TODO: it would be great to be able to separate out superclass methods
#maybe by getting the class out of the method then seeing if that attribute is in that class?
else:
asStr = str(obj)
if (skipMagic):
output = (asStr.find("__") == -1 ) #not a __something__
return (output)
for value in inspect.getmembers( a, weWantIt ):
yield value
{k: getattr(ClassB, k) for k in dir(ClassB)}
Proper values (instead of <property object...>) will be presented when using ClassB instance.
And of course You can filter this by adding things like if not k.startswith('__') in the end.
I use pickle in order to save a list of objects. After saving the list with pickle and loading this very same list again I compare the newly loaded list with the original list. Strangely those two objects differ. Why is this the case? Shouldn't it be the same?
I already tried to use only instance attributes that are defined in the init-function and no class attributes but the error remains.
import pickle as p
class Person(object):
def __init__(self, name=None, job=None, quote=None):
self.name = name
self.job = job
self.quote = quote
personList = [Person("Payne N. Diaz", "coach", "Without exception, there is no rule!"),
Person("Mia Serts", "bicyclist", "If the world didn't suck, we'd all fall off!"),
Person("Don B. Sanosi", "teacher", "Work real hard while you wait and good things will come to you!")]
with open('test_list.p', 'wb') as handle:
p.dump(personList, handle, protocol=p.HIGHEST_PROTOCOL)
with open('test_list.p', 'rb') as handle:
personList2 = p.load(handle)
print(personList == personList2)
I expect that True will be printed out but the result printed is False.
You haven't defined an explicit way to compare Person objects. Therefore, the only way Python can compare them is by their IDs (ie their memory address). The address of the items you've loaded from the pickle will be different from the originals - as they must be, because they are new objects - so the lists will not compare equal.
You can declare an explicit __eq__ method on the Person class:
class Person(object):
def __init__(self, name=None, job=None, quote=None):
self.name = name
self.job = job
self.quote = quote
def __eq__(self, other):
return (self.name == other.name and self.job == other.job and self.quote == other.quote)
Now your comparison will return True as expected.
Running your code and printing personList1 and personList2; it appears that you are checking if the objects are the same, not if the contents are the same.
False
[<__main__.Person object at 0x000001A8EAA264A8>, <__main__.Person object at 0x000001A8EAA26A20>, <__main__.Person object at 0x000001A8EAA26F98>]
[<__main__.Person object at 0x000001A8EAA26240>, <__main__.Person object at 0x000001A8EAA260F0>, <__main__.Person object at 0x000001A8EAA26908>]
Where if you change your print statement to the following, it will yield true because it is checking the contents.
print(personList[0].name == personList2[0].name)