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Is it possible to define a functionn as a variable?

working on some code has a func. that goes through if statements I want to assign this to a vaiable that i can use to redefine a list.
everything! cries in python
Code here
Expected : Works
Actual: don't Works

Because of the fact that functions are a type of object (a callable object) a variable can be assigned an instance of that object.
def bar():
return 'foo'
x = bar
print(x) #O <function at #number>
print(x()) #Output: 'foo'

Yeah you can define a function and assign that function to a variable. Then you can call that variable by providing args like any other function.
For example.
>>> def example_function(x, y):
... return x + y
...
>>> func_var = example_function
>>> func_var(10, 20)
30
Will work. It doesn't really accomplish anything though.
If you provide an example of what you want to accomplish I can give a better example.

What determines output when executing single variable in python interpreter?

So take the complex built-in type as an example. I am making my own version of Complex for educational purposes, however as of now, they aren't behaving the same way.
When I run
>>> a = (2+3j)
>>> a
(2+3j)
>>> from complex import Complex # My version of the type
>>> b = Complex(2, 3)
>>> b
<complex.Complex object at 0x10caeb0f0>
I want my class to output the same thing. I always thought that this was the purpose of the str magic method, but that only gets invoked when something is trying to convert the instance to string, which isn't happening in the example above. How to do this?

Use __repr__:
class Complex:
def __init__(self, i, j):
self.i = i
self.j = j
def __repr__(self):
return f'({self.i}, {self.j}j)'
b = Complex(2, 3)
print(b)
Output:
(2, 3j)

generate consecutive numbers in python while input doesnt change

i need to get consecutive numbers while an input number doesnt change.
so i get give(5)->1, give(5)->2, and so on, but then: give(6)->1 again, starting the count.
So far I solved it with an iterator function count() and a function give(num) like this:
def count(start=1):
n=start
while True:
yield n
n +=1
def give(num):
global last
global a
if num==last:
ret=a.next()
else:
a=count()
ret=a.next()
last=num
return ret
It works, but its ugly: I have two globals and have to set them before I call give(num). I'd like to be able to call give(num) without setting previously the 'a=count()' and 'last=999' variables. I'm positive there's better way to do this...
edit: ty all for incredibly fast and varied responses, i've got a lot to study here..

The obvious thing to do is to make give into an object rather than a function.* Any object can be made callable by defining a __call__ method.
While we're at it, your code can be simplified quite a bit, so let's do that.
class Giver(object):
def __init__(self):
self.last, self.a = object(), count()
def __call__(self, num):
if num != self.last:
self.a = count(1)
self.last = num
return self.a.next()
give = Giver()
So:
>>> give(5)
1
>>> give(5)
2
>>> give(6)
1
>>> give(5)
1
This also lets you create multiple separate givers, each with its own, separate current state, if you have any need to do that.
If you want to expand it with more state, the state just goes into the instance variables. For example, you can replace last and a with a dictionary mapping previously-seen values to counters:
class Giver(object):
def __init__(self):
self.counters = defaultdict(count)
def __call__(self, num):
return next(self.counters[num])
And now:
>>> give(5)
1
>>> give(5)
2
>>> give(6)
1
>>> give(5)
3
* I sort of skipped a step here. You can always remove globals by putting the variables and everything that uses them (which may just be one function) inside a function or other scope, so they end up as free variables in the function's closure. But in your case, I think this would just make your code look "uglier" (in the same sense you thought it was ugly). But remember that objects and closures are effectively equivalent in what they can do, but different in what they look like—so when one looks horribly ugly, try the other.

Just keep track of the last returned value for each input. You can do this with an ordinary dict:
_counter = {}
def give(n):
_counter[n] = _counter.get(n, 0) + 1
return _counter[n]
The standard library has a Counter class that makes things a bit easier:
import collections
_counter = collections.Counter()
def give(n):
_counter[n] += 1
return _counter[n]
collections.defaultdict(int) works too.

You can achieve this with something like this:
def count(start=1):
n = start
while True:
yield n
n += 1
def give(num):
if num not in give.memo:
give.memo[num] = count()
return next(give.memo[num])
give.memo = {}
Which produces:
>>> give(5)
1
>>> give(5)
2
>>> give(5)
3
>>> give(6)
1
>>> give(5)
4
>>>
The two key points are using a dict to keep track of multiple iterators simultaneously, and setting a variable on the function itself. You can do this because functions are themselves objects in python. This is the equivalent of a static local variable in C.

You can basically get what you want via combination of defaultdict and itertools.count:
from collections import defaultdict
from itertools import count
_counters = defaultdict(count)
next(_counters[5])
Out[116]: 0
next(_counters[5])
Out[117]: 1
next(_counters[5])
Out[118]: 2
next(_counters[5])
Out[119]: 3
next(_counters[6])
Out[120]: 0
next(_counters[6])
Out[121]: 1
next(_counters[6])
Out[122]: 2
If you need the counter to start at one, you can get that via functools.partial:
from functools import partial
_counters = defaultdict(partial(count,1))
next(_counters[5])
Out[125]: 1
next(_counters[5])
Out[126]: 2
next(_counters[5])
Out[127]: 3
next(_counters[6])
Out[128]: 1

Adding a second answer because this is rather radically different from my first.
What you are basically trying to accomplish is a coroutine - a generator that preserves state that at arbitrary time, values can be sent into. PEP 342 gives us a way to do that with the "yield expression". I'll jump right into how it looks:
from collections import defaultdict
from itertools import count
from functools import partial
def gen(x):
_counters = defaultdict(partial(count,1))
while True:
out = next(_counters[x])
sent = yield out
if sent:
x = sent
If the _counters line is confusing, see my other answer.
With a coroutine, you can send data into the generator. So you can do something like the following:
g = gen(5)
next(g)
Out[159]: 1
next(g)
Out[160]: 2
g.send(6)
Out[161]: 1
next(g)
Out[162]: 2
next(g)
Out[163]: 3
next(g)
Out[164]: 4
g.send(5)
Out[165]: 3
Notice how the generator preserves state and can switch between counters at will.

In my first answer, I suggested that one solution was to transform the closure into an object. But I skipped a step—you're using global variables, not a closure, and that's part of what you didn't like about it.
Here's a simple way to transform any global state into encapsulated state:
def make_give():
last, a = None, None
def give(num):
nonlocal last
nonlocal a
if num != last:
a = count()
last=num
return a.next()
return give
give = make_give()
Or, adapting my final version of Giver:
def make_giver():
counters = defaultdict(count)
def give(self, num):
return next(counters[num])
return give
If you're curious how this works:
>>> give.__closure__
(<cell at 0x10f0e2398: NoneType object at 0x10b40fc50>, <cell at 0x10f0e23d0: NoneType object at 0x10b40fc50>)
>>> give.__code__.co_freevars
('a', 'last')
Those cell objects are essentially references into the stack frame of the make_give call that created the give function.
This doesn't always work quite as well in Python 2.x as in 3.x. While closure cells work the same way, if you assign to a variable inside the function body and there's no global or nonlocal statement, it automatically becomes local, and Python 2 had no nonlocal statement. So, the second version works fine, but for the first version, you'd have to do something like state = {'a': None, 'last': None} and then write state['a'] = count instead of a = count.
This trick—creating a closure just to hide local variables—is very common in a few other languages, like JavaScript. In Python (partly because of the long history without the nonlocal statement, and partly because Python has alternatives that other languages don't), it's less common. It's usually more idiomatic to stash the state in a mutable default parameter value, or an attribute on the function—or, if there's a reasonable class to make the function a method of, as an attribute on the class instances. There are plenty of cases where a closure is pythonic, this just isn't usually one of them.

Assign function arguments to `self`

I've noticed that a common pattern I use is to assign SomeClass.__init__() arguments to self attributes of the same name. Example:
class SomeClass():
def __init__(self, a, b, c):
self.a = a
self.b = b
self.c = c
In fact it must be a common task for others as well as PyDev has a shortcut for this - if you place the cursor on the parameter list and click Ctrl+1 you're given the option to Assign parameters to attributes which will create that boilerplate code for you.
Is there a different, short and elegant way to perform this assignment?

You could do this, which has the virtue of simplicity:
>>> class C(object):
def __init__(self, **kwargs):
self.__dict__ = dict(kwargs)
This leaves it up to whatever code creates an instance of C to decide what the instance's attributes will be after construction, e.g.:
>>> c = C(a='a', b='b', c='c')
>>> c.a, c.b, c.c
('a', 'b', 'c')
If you want all C objects to have a, b, and c attributes, this approach won't be useful.
(BTW, this pattern comes from Guido his own bad self, as a general solution to the problem of defining enums in Python. Create a class like the above called Enum, and then you can write code like Colors = Enum(Red=0, Green=1, Blue=2), and henceforth use Colors.Red, Colors.Green, and Colors.Blue.)
It's a worthwhile exercise to figure out what kinds of problems you could have if you set self.__dict__ to kwargs instead of dict(kwargs).

I sympathize with your sense that boilerplate code is a bad thing. But in this case, I'm not sure there even could be a better alternative. Let's consider the possibilities.
If you're talking about just a few variables, then a series of self.x = x lines is easy to read. In fact, I think its explicitness makes that approach preferable from a readability standpoint. And while it might be a slight pain to type, that alone isn't quite enough to justify a new language construct that might obscure what's really going on. Certainly using vars(self).update() shenanigans would be more confusing than it's worth in this case.
On the other hand, if you're passing nine, ten, or more parameters to __init__, you probably need to refactor anyway. So this concern really only applies to cases that involve passing, say, 5-8 parameters. Now I can see how eight lines of self.x = x would be annoying both to type and to read; but I'm not sure that the 5-8 parameter case is common enough or troublesome enough to justify using a different method. So I think that, while the concern you're raising is a good one in principle, in practice, there are other limiting issues that make it irrelevant.
To make this point more concrete, let's consider a function that takes an object, a dict, and a list of names, and assigns values from the dict to names from the list. This ensures that you're still being explicit about which variables are being assigned to self. (I would never suggest a solution to this problem that didn't call for an explicit enumeration of the variables to be assigned; that would be a rare-earth bug magnet):
>>> def assign_attributes(obj, localdict, names):
... for name in names:
... setattr(obj, name, localdict[name])
...
>>> class SomeClass():
... def __init__(self, a, b, c):
... assign_attributes(self, vars(), ['a', 'b', 'c'])
Now, while not horribly unattractive, this is still harder to figure out than a straightforward series of self.x = x lines. And it's also longer and more trouble to type than one, two, and maybe even three or four lines, depending on circumstances. So you only get certain payoff starting with the five-parameter case. But that's also the exact moment that you begin to approach the limit on human short-term memory capacity (= 7 +/- 2 "chunks"). So in this case, your code is already a bit challenging to read, and this would only make it more challenging.

Mod for #pcperini's answer:
>>> class SomeClass():
def __init__(self, a, b=1, c=2):
for name,value in vars().items():
if name != 'self':
setattr(self,name,value)
>>> s = SomeClass(7,8)
>>> print s.a,s.b,s.c
7 8 2

Your specific case could also be handled with a namedtuple:
>>> from collections import namedtuple
>>> SomeClass = namedtuple("SomeClass", "a b c")
>>> sc = SomeClass(1, "x", 200)
>>> print sc
SomeClass(a=1, b='x', c=200)
>>> print sc.a, sc.b, sc.c
1 x 200

Decorator magic!!
>>> class SomeClass():
#ArgsToSelf
def __init__(a, b=1, c=2, d=4, e=5):
pass
>>> s=SomeClass(6,b=7,d=8)
>>> print s.a,s.b,s.c,s.d,s.e
6 7 2 8 5
while defining:
>>> import inspect
>>> def ArgsToSelf(f):
def act(self, *args, **kwargs):
arg_names,_,_,defaults = inspect.getargspec(f)
defaults=list(defaults)
for arg in args:
setattr(self, arg_names.pop(0),arg)
for arg_name,arg in kwargs.iteritems():
setattr(self, arg_name,arg)
defaults.pop(arg_names.index(arg_name))
arg_names.remove(arg_name)
for arg_name,arg in zip(arg_names,defaults):
setattr(self, arg_name,arg)
return f(*args, **kwargs)
return act
Of course you could define this decorator once and use it throughout your project.Also, This decorator works on any object function, not only __init__.

You can do it via setattr(), like:
[setattr(self, key, value) for key, value in kwargs.items()]
Is not very beautiful, but can save some space :)
So, you'll get:
kwargs = { 'd':1, 'e': 2, 'z': 3, }
class P():
def __init__(self, **kwargs):
[setattr(self, key, value) for key, value in kwargs.items()]
x = P(**kwargs)
dir(x)
['__doc__', '__init__', '__module__', 'd', 'e', 'z']

For that simple use-case I must say I like putting things explicitly (using the Ctrl+1 from PyDev), but sometimes I also end up using a bunch implementation, but with a class where the accepted attributes are created from attributes pre-declared in the class, so that I know what's expected (and I like it more than a namedtuple as I find it more readable -- and it won't confuse static code analysis or code-completion).
I've put on a recipe for it at: http://code.activestate.com/recipes/577999-bunch-class-created-from-attributes-in-class/
The basic idea is that you declare your class as a subclass of Bunch and it'll create those attributes in the instance (either from default or from values passed in the constructor):
class Point(Bunch):
x = 0
y = 0
p0 = Point()
assert p0.x == 0
assert p0.y == 0
p1 = Point(x=10, y=20)
assert p1.x == 10
assert p1.y == 20
Also, Alex Martelli also provided a bunch implementation: http://code.activestate.com/recipes/52308-the-simple-but-handy-collector-of-a-bunch-of-named/ with the idea of updating the instance from the arguments, but that'll confuse static code-analysis (and IMO can make things harder to follow) so, I'd only use that approach for an instance that's created locally and thrown away inside that same scope without passing it anywhere else).

I solved it for myself using locals() and __dict__:
>>> class Test:
... def __init__(self, a, b, c):
... l = locals()
... for key in l:
... self.__dict__[key] = l[key]
...
>>> t = Test(1, 2, 3)
>>> t.a
1
>>>

Disclaimer
Do not use this: I was simply trying to create the answer closest to OPs initial intentions. As pointed out in comments, this relies on entirely undefined behavior, and explicitly prohibited modifications of the symbol table.
It does work though, and has been tested under extremely basic circumstances.
Solution
class SomeClass():
def __init__(self, a, b, c):
vars(self).update(dict((k,v) for k,v in vars().iteritems() if (k != 'self')))
sc = SomeClass(1, 2, 3)
# sc.a == 1
# sc.b == 2
# sc.c == 3
Using the vars() built-in function, this snippet iterates through all of the variables available in the __init__ method (which should, at this point, just be self, a, b, and c) and set's self's variables equal to the same, obviously ignoring the argument-reference to self (because self.self seemed like a poor decision.)

One of the problems with #user3638162's answer is that locals() contain the 'self' variable. Hence, you end up with an extra self.self. If one doesn't mind the extra self, that solution can simply be
class X:
def __init__(self, a, b, c):
self.__dict__.update(locals())
x = X(1, 2, 3)
print(x.a, x.__dict__)
The self can be removed after construction by del self.__dict__['self']
Alternatively, one can remove the self during construction using dictionary comprehensions introduced in Python3
class X:
def __init__(self, a, b, c):
self.__dict__.update(l for l in locals().items() if l[0] != 'self')
x = X(1, 2, 3)
print(x.a, x.__dict__)

Is it possible to change an instance's method implementation without changing all other instances of the same class? [duplicate]

This question already has answers here:
Override a method at instance level
(11 answers)
Closed 3 years ago.
I do not know python very much (never used it before :D), but I can't seem to find anything online. Maybe I just didn't google the right question, but here I go:
I want to change an instance's implementation of a specific method. When I googled for it, I found you could do it, but it changes the implementation for all other instances of the same class, for example:
def showyImp(self):
print self.y
class Foo:
def __init__(self):
self.x = "x = 25"
self.y = "y = 4"
def showx(self):
print self.x
def showy(self):
print "y = woohoo"
class Bar:
def __init__(self):
Foo.showy = showyImp
self.foo = Foo()
def show(self):
self.foo.showx()
self.foo.showy()
if __name__ == '__main__':
b = Bar()
b.show()
f = Foo()
f.showx()
f.showy()
This does not work as expected, because the output is the following:
x = 25
y = 4
x = 25
y = 4
And I want it to be:
x = 25
y = 4
x = 25
y = woohoo
I tried to change Bar's init method with this:
def __init__(self):
self.foo = Foo()
self.foo.showy = showyImp
But I get the following error message:
showyImp() takes exactly 1 argument (0 given)
So yeah... I tried using setattr(), but seems like it's the same as self.foo.showy = showyImp.
Any clue? :)

Since Python 2.6, you should use the types module's MethodType class:
from types import MethodType
class A(object):
def m(self):
print 'aaa'
a = A()
def new_m(self):
print 'bbb'
a.m = MethodType(new_m, a)
As another answer pointed out, however, this will not work for 'magic' methods of new-style classes, such as __str__().

This answer is outdated; the answer below works with modern Python
Everything you wanted to know about Python Attributes and Methods.
Yes, this is an indirect answer, but it demonstrates a number of techniques and explains some of the more intricate details and "magic".
For a "more direct" answer, consider python's new module. In particular, look at the instancemethod function which allows "binding" a method to an instance -- in this case, that would allow you to use "self" in the method.
import new
class Z(object):
pass
z = Z()
def method(self):
return self
z.q = new.instancemethod(method, z, None)
z is z.q() # true

If you ever need to do it for a special method (which, for a new-style class -- which is what you should always be using and the only kind in Python 3 -- is looked up on the class, not the instance), you can just make a per-instance class, e.g....:
self.foo = Foo()
meths = {'__str__': lambda self: 'peekaboo!'}
self.foo.__class__ = type('yFoo', (Foo,), meths)
Edit: I've been asked to clarify the advantages of this approach wrt new.instancemethod...:
>>> class X(object):
... def __str__(self): return 'baah'
...
>>> x=X()
>>> y=X()
>>> print x, y
baah baah
>>> x.__str__ = new.instancemethod(lambda self: 'boo!', x)
>>> print x, y
baah baah
As you can see, the new.instancemethod is totally useless in this case. OTOH...:
>>> x.__class__=type('X',(X,),{'__str__':lambda self:'boo!'})
>>> print x, y
boo! baah
...assigning a new class works great for this case and every other. BTW, as I hope is clear, once you've done this to a given instance you can then later add more method and other class attributes to its x.__class__ and intrinsically affect only that one instance!

If you're binding to the instance, you shouldn't include the self argument:
>>> class Foo(object):
... pass
...
>>> def donothing():
... pass
...
>>> f = Foo()
>>> f.x = donothing
>>> f.x()
>>>
You do need the self argument if you're binding to a class though:
>>> def class_donothing(self):
... pass
...
>>> foo.y = class_donothing
>>> f.y()
>>>

Your example is kind of twisted and complex, and I don't quite see what it has to do with your question. Feel free to clarify if you like.
However, it's pretty easy to do what you're looking to do, assuming I'm reading your question right.
class Foo(object):
def bar(self):
print('bar')
def baz():
print('baz')
In an interpreter ...
>>> f = Foo()
>>> f.bar()
bar
>>> f.bar = baz
>>> f.bar()
baz
>>> g = Foo()
>>> g.bar()
bar
>>> f.bar()
baz

Do Not Do This.
Changing one instance's methods is just wrong.
Here are the rules of OO Design.
Avoid Magic.
If you can't use inheritance, use delegation.
That means that every time you think you need something magic, you should have been writing a "wrapper" or Facade around the object to add the features you want.
Just write a wrapper.