Can't set attribute for subclasses of namedtuple

Question

It looks like this or this are somewhat related threads, but still haven't figured things out :)

I'm trying to create a subclass of namedtuple and provide different initializers so that I can construct objects in different ways. For example:

>>> from collections import namedtuple
>>> class C(namedtuple("C", "x, y")) :
...     __slots__ = ()
...     def __init__(self, obj) : # Initialize a C instance by copying values from obj
...         self.x = obj.a
...         self.y = obj.b
...     def __init__(self, x, y) : # Initialize a C instance from the parameters
...         self.x = x
...         self.y = y

However, that doesn't work:

>>> c = C(1, 2)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<stdin>", line 7, in __init__
AttributeError: can't set attribute

After some poking around (for example, see this thread) I tried to use constructors instead of initializers:

>>> from collections import namedtuple
>>> class C(namedtuple("C", "x, y")) :
...     __slots__ = ()
...     def __new__(cls, obj) :
...       self = super(C, cls).__new__(cls, obj.a, obj.b)
...     def __new__(cls, x, y) :
...       self = super(C, cls).__new__(cls, x, y)

which seemed to construct an object but then I can't read its attributes:

>>> c = C(1,2)
>>> c.x, c.y
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'NoneType' object has no attribute 'x'

Where am I going wrong here? How can I create a subclass with multiple constructors or initializers?

Answer 1

I suggest you use the the _replace method

from collections import namedtuple
C = namedtuple('C', 'x, y')
c = C(x=10, y=20)
# c.x = 30 won't work
c = c._replace(x=30)

Answer 2

Named tuples are immutable, so you cannot manipulate them in the __init__ initializer. Your only option is to override the __new__ method:

class C(namedtuple('C', 'x, y')):
    __slots__ = ()
    def __new__(cls, obj):
        return super(C, cls).__new__(cls, obj.x, obj.y)

Note that because __new__ is a factory method for new instances, you do need to return the newly created instance. If you do not use return in the __new__ method, the default return value is None, which gives you your error.

Demo with an object with x and y attributes:

>>> class C(namedtuple('C', 'x, y')):
...     __slots__ = ()
...     def __new__(cls, obj):
...         return super(C, cls).__new__(cls, obj.x, obj.y)
... 
>>> O.x, O.y
(10, 20)
>>> C(O)
C(x=10, y=20)

Python does not support method overloading; generally you either use optional keyword arguments or extra class methods as factory methods.

The datetime module, for example, has several such factory methods to let you create objects that do not fit the standard constructor. datetime.datetime.fromtimestamp() creates a datetime.datetime instance from a single numeric value, and so does datetime.datetime.fromordinal(); except that they interpret the number in different ways.

If you wanted to support variable arguments, do:

class C(namedtuple('C', 'x, y')):
    __slots__ = ()

    def __new__(cls, x, y=None):
        if y is None:
            # assume attributes
            x, y = x.x, x.y
        return super(C, cls).__new__(cls, x, y)

Here, y is an optional argument, defaulting to None if not supplied by the caller:

>>> C(3, 5):
C(x=3, y=5)
>>> C(O)
C(x=10, y=20)

The alternative, using a class method, would be:

class C(namedtuple('C', 'x, y')):
    @classmethod
    def from_attributes(cls, obj):
        return cls(obj.x, obj.y)

Now there are two factory methods; one default and one named:

>>> C(3, 5):
C(x=3, y=5)
>>> C.from_attributes(O)
C(x=10, y=20)

Answer 3

Two things: one, you're not really getting much out of namedtuple here, as far as i can tell. So maybe you should just switch to a normal class. Also, you can't overload the

Second, other possibilities which might help with your problem:

Factory design pattern - instead of putting the different parameters in the constructor, have a class that takes different kinds of parameters and calls the constructor with appropriate arguments, outside the object. recordtype - a mutable namedtuple, that allows defaults but would also let you write your subclass the way you originally wanted. bunch - not exactly a named tuple, but lets you create somewhat arbitrary objects.

Answer 4

There is a workaround to changing the attribute of a namedtuple.

import collections

def updateTuple(NamedTuple,nameOfNamedTuple):
    ## Convert namedtuple to an ordered dictionary, which can be updated
    NamedTuple_asdict = NamedTuple._asdict()

    ## Make changes to the required named attributes
    NamedTuple_asdict['path']= 'www.google.com'

    ## reconstruct the namedtuple using the updated ordered dictionary
    updated_NamedTuple = collections.namedtuple(nameOfNamedTuple, NamedTuple_asdict.keys())(**NamedTuple_asdict)

    return updated_NamedTuple

Tuple = collections.namedtuple("Tuple", "path")
NamedTuple = Tuple(path='www.yahoo.com')
NamedTuple = updateTuple(NamedTuple, "Tuple")

Answer 5

I create a named tuple then later calculate leverage based on foods list in the named tuple. Finally, I replace the named tuple in the list

def get_leverage(antecedent, consequent):
     #Compute support for antecedent AND consequent
    supportAC = np.logical_and(antecedent, consequent).mean()
    # Compute support for antecedent
    supportA = antecedent.mean()
    # Compute support for consequent
    supportC = consequent.mean()
    # Return leverage
    return supportAC - supportA*supportC


DataRecord = namedtuple("DataRecord", "foods support confidence lift leverage conviction")
lstData=[]
for record in association_results:
    foods=[i for i in record.items]
    support=round(record.support,2)
    confidence=round(record.ordered_statistics[0].confidence,2)
    lift=round(record.ordered_statistics[0].lift,2)    
    dataRecord = DataRecord(foods,support,confidence,lift,0,0)
    lstData.append(dataRecord)


print(lstData[:3])


for index,record in enumerate(lstData):
    old_food=""
    lstLeverage=[]
    for food in record.foods:
        #print(food)
        if len(food)>0 and len(old_food)>0:
            leverage=get_leverage(onehot[food],onehot[old_food])
            lstLeverage.append(leverage)
        old_food=food
    #print(np.mean(lstLeverage))
    record=record._replace(leverage=round(np.mean(lstLeverage),3))
    lstData[index]=record
    #print(record)

print(lstData[:3])