Develop Reference

Python: How can I set the default behaviour of NoneType objects?

I have created my own class that inherits from python's default list class. A simplified version is the following, that contains the __abs__ method so I can use python's abs function.
class DataSet(list):
def __abs__(self):
result = []
for i in self:
result.append(abs(i))
return result
Suppose have a DataSet that sometimes contains a NoneType value, for example
>>> dataset = DataSet([1, 2, 3, None, -1, -2, -3])
If I want to know the absolute value of this DataSet, I use the function
>>> abs_dataset = abs(dataset)
The result that I want to get is
[1, 2, 3, None, 1, 2, 3]
but because there is a value of type NoneType in the dataset, I get the error
TypeError: bad operand type for abs(): 'NoneType'
For this one case it can be fixed by modifying the DataSet's __abs__ function and to check for None in the individual elements of the DataSet, but in my case I have more cases where a None value can occur and I also want to implement more builtin functions than only abs.
Is there a method to set this default behaviour of default python functions like abs to None values?

You can do something like this.
class DataSet(list):
def __abs__(self):
# Here if i is 0 then it'll be 0.
# No need to check for `None`.
return [abs(i) if i else i for i in self]
dataset = DataSet([1, 2, 3, None, -1, -2, -3])
print(abs(dataset))
# [1, 2, 3, None, 1, 2, 3]
Edits:
As mentioned by #juanpa.arrivillaga, if you want to filter the None type elements then you can do something like [abs(i) for i in self if i is not None] inside list comprehension.

There isn't. And for a good reason. Other code can rely on None type bahaviour.
What you can do is provide method on DataSet which provide filtered list without None values and use it for your methods.

you can edit those methods in order to make sure there will be no None in your dataset.
__setattr__
__setitem__
append
extend
insert
just override them with a check for None, if there is change the value to 0/cancel the operation.

"Pythonic" class level recursion?

I am creating a class that inherits from collections.UserList that has some functionality very similar to NumPy's ndarray (just for exercise purposes). I've run into a bit of a roadblock regarding recursive functions involving the modification of class attributes:
Let's take the flatten method, for example:
class Array(UserList):
def __init__(self, initlist):
self.data = initlist
def flatten(self):
# recursive function
...
Above, you can see that there is a singular parameter in the flatten method, being the required self parameter. Ideally, a recursive function should take a parameter which is passed recursively through the function. So, for example, it might take a lst parameter, making the signature:
Array.flatten(self, lst)
This solves the problem of having to set lst to self.data, which consequently will not work recursively, because self.data won't be changed. However, having that parameter in the function is going to be ugly in use and hinder the user experience of an end user who may be using the function.
So, this is the solution I've come up with:
def flatten(self):
self.data = self.__flatten(self.data)
def __flatten(self, lst):
...
return result
Another solution could be to nest __flatten in flatten, like so:
def flatten(self):
def __flatten(lst):
...
return result
self.data = __flatten(self.data)
However, I'm not sure if nesting would be the most readable as flatten is not the only recursive function in my class, so it could get messy pretty quickly.
Does anyone have any other suggestions? I'd love to know your thoughts, thank you!

A recursive method need not take any extra parameters that are logically unnecessary for the method to work from the caller's perspective; the self parameter is enough for recursion on a "child" element to work, because when you call the method on the child, the child is bound to self in the recursive call. Here is an example:
from itertools import chain
class MyArray:
def __init__(self, data):
self.data = [
MyArray(x) if isinstance(x, list) else x
for x in data]
def flatten(self):
return chain.from_iterable(
x.flatten() if isinstance(x, MyArray) else (x,)
for x in self.data)
Usage:
>>> a = MyArray([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
>>> list(a.flatten())
[1, 2, 3, 4, 5, 6, 7, 8]

Since UserList is an iterable, you can use a helper function to flatten nested iterables, which can deal likewise with lists and Array objects:
from collections import UserList
from collections.abc import Iterable
def flatten_iterable(iterable):
for item in iterable:
if isinstance(item, Iterable):
yield from flatten_iterable(item)
else:
yield item
class Array(UserList):
def __init__(self, initlist):
self.data = initlist
def flatten(self):
self.data = list(flatten_iterable(self.data))
a = Array([[1, 2], [3, 4]])
a.flatten(); print(a) # prints [1, 2, 3, 4]
b = Array([Array([1, 2]), Array([3, 4])])
b.flatten(); print(b) # prints [1, 2, 3, 4]

barrier of abstraction
Write array as a separate module. flatten can be generic like the example implementation here. This differs from a_guest's answer in that only lists are flattened, not all iterables. This is a choice you get to make as the module author -
# array.py
from collections import UserList
def flatten(t): # generic function
if isinstance(t, list):
for v in t:
yield from flatten(v)
else:
yield t
class array(UserList):
def flatten(self):
return list(flatten(self.data)) # specialization of generic function
why modules are important
Don't forget you are the module user too! You get to reap the benefits from both sides of the abstraction barrier created by the module -
As the author, you can easily expand, modify, and test your module without worrying about breaking other parts of your program
As the user, you can rely on the module's features without having to think about how the module is written or what the underlying data structures might be
# main.py
from array import array
t = array([1,[2,3],4,[5,[6,[7]]]]) # <- what is "array"?
print(t.flatten())
[1, 2, 3, 4, 5, 6, 7]
As the user, we don't have to answer "what is array?" anymore than you have to answer "what is dict?" or "what is iter?" We use these features without having to understand their implementation details. Their internals may change over time, but if the interface stays the same, our programs will continue to work without requiring change.
reusability
Good programs are reusable in many ways. See python's built-in functions for proof of this, or see the the guiding principles of the Unix philosophy -
Write programs that do one thing and do it well.
Write programs to work together.
If you wanted to use flatten in other areas of our program, we can reuse it easily -
# otherscript.py
from array import flatten
result = flatten(something)

Typically, all methods of a class have at least one argument which is called self in order to be able to reference the actual object this method is called on.
If you don't need self in your function, but you still want to include it in a class, you can use #staticmethod and just include a normal function like this:
class Array(UserList):
def __init__(self, initlist):
self.data = initlist
#staticmethod
def flatten():
# recursive function
...
Basically, #staticmethod allows you to make any function a method that can be called on a class or an instance of a class (object). So you can do this:
arr = Array()
arr.flatten()
as well as this:
Array.flatten()
Here is some further reference from Pyhon docs: https://docs.python.org/3/library/functions.html#staticmethod

python class initialize custom class referencing to existing list

I have class MyList that inherits from list. When I pass instance of list ie. [0, 3, 5, 1] to MyList, how to construct MyList to avoid copy and have self have no-copy reference to other content.
I have tried with:
other.__class__ = MyList : gives TypeError
and with
super(MyList, cls).__new__(other) : gives TypeError
and with
super(MyList, other) : gives TypeError
lastly with
self[:] = other[:] : gives id(self) != id(other)
Also simple MyList([0, 1, 3, 4]) would not solve problem when I do some operations in-place inside MyList.
class MyList(list):
def __new__(cls, other):
other.__class__ = MyList
return other
# add bunch of methods that work inplace on list
def merge(self,):
pass
def sort(self,):
pass
def find(self, x):
pass
def nextNonMember(self, x):
pass
Alternative way that I want to avoid is:
class MyNotSoFancyList(object):
def __init__(self, other):
self.list = other
I expect to have this behavior:
t = [0, 1, 3, 100, 20, 4]
o = MyList(t)
o.sort()
assert(t == o)
Question is probably not so trivial one for me when I dont know Python on "low" level. It seems its not possible. Thus I wanted to ask, maybe someone knows some trick xD.
EDIT
Until now there was one hint in message to be deleted. Need some time to digest it, so will keep it here:
#RobertGRZELKA I think I kinda got to a conclusion with myself that this simply can't be done. As when you create an object of the class, it instantiates a new list in memory and references it. So if want to reference another list, there is no point in the new object. Bottom line I believe you will have to have the reference as an attribute of the class, implement your methods, and then override the list methods you are going to use so that they work on the referenced list. Tell me when you read that and I will delete this answer – Tomerikoo 2 hours ago

Try this
class MyList(list):
def __init__(self,value):
self.extend(value)
I dont really understand why you would want it unless you want to add more methods to the list object. but that should give you a list
t = [0, 1, 3, 100, 20, 4]
o = MyList(t)
o.sort()
t.sort()
assert(t==o)

How do __getitem__, __setitem__, work with slices?

I'm running Python 2.7.10.
I need to intercept changes in a list. By "change" I mean anything that modifies the list in the shallow sense (the list is not changed if it consists of the same objects in the same order, regardless of the state of those objects; otherwise, it is). I don't need to find out how the list has changed, only that it has. So I just make sure I can detect that, and let the base method do its work. This is my test program:
class List(list):
def __init__(self, data):
list.__init__(self, data)
print '__init__(', data, '):', self
def __getitem__(self, key):
print 'calling __getitem__(', self, ',', key, ')',
r = list.__getitem__(self, key)
print '-->', r
return r
def __setitem__(self, key, data):
print 'before __setitem__:', self
list.__setitem__(self, key, data)
print 'after __setitem__(', key, ',', data, '):', self
def __delitem__(self, key):
print 'before __delitem__:', self
list.__delitem__(self, key)
print 'after __delitem__(', key, '):', self
l = List([0,1,2,3,4,5,6,7]) #1
x = l[5] #2
l[3] = 33 #3
x = l[3:7] #4
del l[3] #5
l[0:4]=[55,66,77,88] #6
l.append(8) #7
Cases #1, #2, #3, and #5 work as I expected; #4, #6, and #7 don't. The program prints:
__init__( [0, 1, 2, 3, 4, 5, 6, 7] ): [0, 1, 2, 3, 4, 5, 6, 7]
calling __getitem__( [0, 1, 2, 3, 4, 5, 6, 7] , 5 ) --> 5
before __setitem__: [0, 1, 2, 3, 4, 5, 6, 7]
after __setitem__( 3 , 33 ): [0, 1, 2, 33, 4, 5, 6, 7]
before __delitem__: [0, 1, 2, 33, 4, 5, 6, 7]
after __delitem__( 3 ): [0, 1, 2, 4, 5, 6, 7]
I'm not terribly surprised by #7: append is probably implemented in an ad-hoc way. But for #4 and #6 I am confused. The __getitem__ documentation says: "Called to implement evaluation of self[key]. For sequence types, the accepted keys should be integers and slice objects." (my emphasys). And for __setitem__: " Same note as for __getitem__()", which I take to mean that key can also be a slice.
What's wrong with my reasoning? I'm prepared, if necessary, to override every list-modifying method (append, extend, insert, pop, etc.), but what should override to catch something like #6?
I am aware of the existence of __setslice__, etc. But those methods are deprecated since 2.0 ...
Hmmm. I read again the docs for __getslice__, __setslice__, etc., and I find this bone-chilling statement:
"(However, built-in types in CPython currently still implement __getslice__(). Therefore, you have to override it in derived classes when implementing slicing.)"
Is this the explanation? Is this saying "Well, the methods are deprecated, but in order to achieve the same functionality in 2.7.10 as you had in 2.0 you still have to override them"? Alas, then why did you deprecate them? How will things work in the future? Is there a "list" class - that I am not aware of - that I could extend and would not present this inconvenience? What do I really need to override to make sure I catch every list-modifying operation?

The problem is that you're subclassing a builtin, and so have to deal with a few wrinkles. Before I delve into that issue, I'll go straight to the "modern" way:
How will things work in the future? Is there a "list" class - that I am not aware of - that I could extend and would not present this inconvenience?
Yes, there's the stdlib Abstract Base Classes. You can avoid the ugly complications caused by subclassing builtin list by using the ABCs instead. For something list-like, try subclassing MutableSequence:
from collections import MutableSequence
class MyList(MutableSequence):
...
Now you should only need to deal with __getitem__ and friends for slicing behaviour.
If you want to push ahead with subclassing the builtin list, read on...
Your guess is correct, you will need to override __getslice__ and __setslice__. The language reference explains why and you already saw that:
However, built-in types in CPython currently still implement __getslice__(). Therefore, you have to override it in derived classes when implementing slicing.
Note that l[3:7] will hook into __getslice__, whereas the otherwise equivalent l[3:7:] will hook into __getitem__, so you have to handle the possibility of receiving slices in both methods... groan!

Python: Class design - List Transformations (critique my code)

I have been thinking about a class which could be useful for list transformations.
Here is my current implementation:
class ListTransform(object):
"""Specs: stores original list + transformations.
Transformations are stored in a list.
Every transformation is a func call, with
one parameter, transformations are done in place.
"""
def __init__(self, _list):
self.orig_list = _list
self.reset()
def addtransform(self,t):
self.transforms.append(t)
def reset(self, ts = []):
self.transforms = ts
def getresult(self):
li = self.orig_list[:] # start from a copy from the original
# call all the in-place transform functions in order
for transform in self.transforms:
transform(li)
return li
def pick_transform(pickindexes):
"""Only includes elements with specific indexes
"""
def pt(li):
newli = []
for idx in pickindexes:
newli.append(li[idx])
del li[:] # clear all the elements
li.extend(newli)
return pt
def map_transform(fn_for_every_element):
"""Creates a transformation, which will call a specific
function for every element in a list
"""
def mt(li):
newli = map(fn_for_every_element, li)
del li[:] # clear
li.extend(newli)
return mt
# example:
# the object which stores the original list and the transformations
li = ListTransform([0,10,20,30,40,50,60,70,80,90])
# transformations
li.addtransform(map_transform(lambda x: x + (x/10)))
li.addtransform(pick_transform([5,6,7]))
# getting result, prints 55, 66, 77
print li.getresult()
This works well, however, the feeling of implementing something in a substandard manner bothers me.
What Python features would you use in this implementation, I haven't used? How would you improve the overall design/ideas behind this class? How would you improve the code?
Also, since reinventing the wheel feels awkward: what are the standard tools replacing this class?
Thanks.

Having a general scope and not a particular use case in mind, I would look at this in a more "functional" way:
Don't make the tranformations in place -- rather return new lists. This is how standard functions in functional programming work (and also map(), filter() and reduce() in Python).
Concentrate on the transformations rather than on the data. In particular, I would not create a class like your ListTransform at all, but rather only have some kind of transformation objects that can be chained.
To code this having functional programming in mind, the transforms would simply be functions, just like in your design. All you would need in addition is some kind of composition for the transforms:
def compose(f, g):
return lambda lst: f(g(lst))
(For the sake of simplicity the given implementation has only two parameters instead of an arbitrary number.) Your example would now be very simple:
from functools import partial
map_transform = partial(map, lambda x: x + (x/10))
pick_transform = lambda lst: [lst[i] for i in (5,6,7)]
transform = compose(pick_transform, map_transform)
print transform([0,10,20,30,40,50,60,70,80,90])
# [55, 66, 77]
An alternative would be to implement the transforms as classes instead of functions.

Do not use an empty list as default argument. Use None and test for it:
def some_method(self, arg=None):
if arg is None:
arg = []
do_your_thing_with(arg)
I's a well known Python's beginner pitfall.

You could extend the list class itself, and apply the transforms lazily as the elements are needed. Here is a short implementation - it does not allow for index manipulation on the transforms, but you can apply any mapping transform in a stack.
class ListTransform(list):
def __init__(self, *args):
list.__init__(self, *args)
self.transforms = []
def __getitem__(self, index):
return reduce(lambda item, t: t(item), self.transforms, list.__getitem__(self, index))
def __iter__(self):
for index in xrange(len(self)):
yield self[index]
def __repr__(self):
return "'[%s]'" % ", ".join(repr(item) for item in self)
__str__ = lambda s: repr(s).strip("'")
And you are ready to go:
>>> a = ListTransform( range(10))
>>> a
'[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]'
>>> a.transforms.append(lambda x: 2 * x)>>> a
'[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]'
>>> a.transforms.append(lambda x: x + 5)
>>> a
'[5, 7, 9, 11, 13, 15, 17, 19, 21, 23]'
>>> a.append(0)
>>> a
'[5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 5]'
Ok - I may have overreached with the "reduce" call in the getitem method - but that is the fun part. :-)
Feel free to rewrite it in more lines for readability:
def __getitem__(self, index):
item = list.__getitem__(self, index)
for t in self.transforms:
item = t(item)
return item
If you like the idea, you could include a "filter" member to create filtering functions for the items, and check for the number of parameters on the transforms to allow them to work with indexes, and even reach other list items.