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Why subclassing a str or int behaves differently from subclising a list or dict?


Why subclassing a str or int behaves differently from subclising a list or dict?

By : user2172583
Date : October 23 2020, 08:10 AM
seems to work fine Initializing self.argument = argument does not really initialize the object itself (the list), it's just setting an arbitrary attribute called argument.
If you are using a half-decent IDE you should see a warning saying Call to __init__ of super class is missing.
code :
class ListChild(list):
    def __init__(self, argument):
        super().__init__(argument)
        # self.argument = argument

print(ListChild([1, 2, 3]))
# [1, 2, 3]
class IntChild(int):
    def __init__(self, argument):
        super().__init__()

print(IntChild(3))
# 3


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why the list behaves differently in python and Java?

why the list behaves differently in python and Java?


By : huabo
Date : March 29 2020, 07:55 AM
This might help you A python for loop evaluates the expression that yields the iterable over which to loop once only. You can manipulate the lst object in the loop without affecting the outcome of what for is looping over. This differs from the Java for construct (which is a very different construct from the Python for statement, which is really a Foreach construct), which evaluates the 3 associated expressions for each iteration.
In your first example, you created a range() result, and once that is created it is not updated for each loop iteration.
code :
>>> L = [1, 2, 3]
>>> for i in L:
...     L.append(4)
...     print L
...     if len(L) > 30:
...         break
... 
[1, 2, 3, 4]
[1, 2, 3, 4, 4]
[1, 2, 3, 4, 4, 4]
[1, 2, 3, 4, 4, 4, 4]
[1, 2, 3, 4, 4, 4, 4, 4]
[1, 2, 3, 4, 4, 4, 4, 4, 4]
[1, 2, 3, 4, 4, 4, 4, 4, 4, 4]
[1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4]
[1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4]
[1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]
[1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]
[1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]
[1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]
[1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]
[1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]
[1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]
[1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]
[1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]
[1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]
[1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]
[1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]
[1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]
[1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]
[1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]
[1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]
[1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]
[1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]
[1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]
Why does python list L += x behaves differently than L = L + x?

Why does python list L += x behaves differently than L = L + x?


By : Krlos
Date : March 29 2020, 07:55 AM
Hope this helps Using +=, you are modifying the list in plac, like when you use a class method that append x to L (like .append, .extend…). This is the __iadd__ method.
R counting rows of dataframes in list: for loop behaves differently from apply?

R counting rows of dataframes in list: for loop behaves differently from apply?


By : user3683313
Date : March 29 2020, 07:55 AM
wish help you to fix your issue Your code has a problem it needs to be 1:length(pglist) not just length(pglist) in the for() part. You looped for i in only length(pglist). Also taking the return expression out of the loop is necessary.
code :
countrows <- function(pglist) {
  dfsizes <- rep(NA, length(pglist))
  for (i in 1:length(pglist)) {
    dfsizes[i] <- nrow(pglist[[i]])
  }  
  return(dfsizes)
}

newvector <- countrows(dflist)
newvector
Why pytorch DataLoader behaves differently on numpy array and list?

Why pytorch DataLoader behaves differently on numpy array and list?


By : Alexandr Simonov
Date : March 29 2020, 07:55 AM
This might help you This is because how batching is handled in torch.utils.data.DataLoader. collate_fn argument decides how samples from samples are merged into a single batch. Default for this argument is undocumented torch.utils.data.default_collate.
This function handles batching by assuming numbers/tensors/ndarrays are primitive data to batch and lists/tuples/dicts containing these primitives as structure to be (recursively) preserved. This allow you to have a semantic batching like this:
Why does List.contains(Object) behaves differently?

Why does List.contains(Object) behaves differently?


By : user3041190
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , According to the documentation list contains is supposed to use equals(). Meaning, it should return true if of the values within the object are the same. However, in the example below contains(Object) behave differently:
code :
List<Dog> dogs = new ArrayList<>();
        Dog fido = new Dog("fido", 2, "black");
        dogs.add(fido);
        System.out.println(dogs.contains(new Dog("fido", 2, "black")));
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