I am working with a Python object that implements __add__, but does not subclass int. MyObj1 + MyObj2 works fine, but sum([MyObj1, MyObj2]) led to a TypeError, becausesum() first attempts 0 + MyObj. In order to use sum(), my object needs __radd__ to handle MyObj + 0 or I need to provide an empty object as the start parameter. The object in question is not designed to be empty.
Before anyone asks, the object is not list-like or string-like, so use of join() or itertools would not help.
Edit for details: the module has a SimpleLocation and a CompoundLocation. I'll abbreviate Location to Loc. A SimpleLoc contains one right-open interval, i.e. [start, end). Adding SimpleLoc yields a CompoundLoc, which contains a list of the intervals, e.g. [[3, 6), [10, 13)]. End uses include iterating through the union, e.g. [3, 4, 5, 10, 11, 12], checking length, and checking membership.
The numbers can be relatively large (say, smaller than 2^32 but commonly 2^20). The intervals probably won't be extremely long (100-2000, but could be longer). Currently, only the endpoints are stored. I am now tentatively thinking of attempting to subclass set such that the location is constructed as set(xrange(start, end)). However, adding sets will give Python (and mathematicians) fits.
Questions I've looked at:
python's sum() and non-integer values
why there's a start argument in python's built-in sum function
TypeError after overriding the __add__ method
I'm considering two solutions. One is to avoid sum() and use the loop offered in this comment. I don't understand why sum() begins by adding the 0th item of the iterable to 0 rather than adding the 0th and 1st items (like the loop in the linked comment); I hope there's an arcane integer optimization reason.
My other solution is as follows; while I don't like the hard-coded zero check, it's the only way I've been able to make sum() work.
# ...
def __radd__(self, other):
# This allows sum() to work (the default start value is zero)
if other == 0:
return self
return self.__add__(other)
In summary, is there another way to use sum() on objects that can neither be added to integers nor be empty?
Instead of sum, use:
import operator
from functools import reduce
reduce(operator.add, seq)
in Python 2 reduce was built-in so this looks like:
import operator
reduce(operator.add, seq)
Reduce is generally more flexible than sum - you can provide any binary function, not only add, and you can optionally provide an initial element while sum always uses one.
Also note: (Warning: maths rant ahead)
Providing support for add w/r/t objects that have no neutral element is a bit awkward from the algebraic points of view.
Note that all of:
naturals
reals
complex numbers
N-d vectors
NxM matrices
strings
together with addition form a Monoid - i.e. they are associative and have some kind of neutral element.
If your operation isn't associative and doesn't have a neutral element, then it doesn't "resemble" addition. Hence, don't expect it to work well with sum.
In such case, you might be better off with using a function or a method instead of an operator. This may be less confusing since the users of your class, seeing that it supports +, are likely to expect that it will behave in a monoidic way (as addition normally does).
Thanks for expanding, I'll refer to your particular module now:
There are 2 concepts here:
Simple locations,
Compound locations.
It indeed makes sense that simple locations could be added, but they don't form a monoid because their addition doesn't satisfy the basic property of closure - the sum of two SimpleLocs isn't a SimpleLoc. It's, generally, a CompoundLoc.
OTOH, CompoundLocs with addition looks like a monoid to me (a commutative monoid, while we're at it): A sum of those is a CompoundLoc too, and their addition is associative, commutative and the neutral element is an empty CompoundLoc that contains zero SimpleLocs.
If you agree with me (and the above matches your implementation), then you'll be able to use sum as following:
sum( [SimpleLoc1, SimpleLoc2, SimpleLoc3], start=ComplexLoc() )
Indeed, this appears to work.
I am now tentatively thinking of attempting to subclass set such that the location is constructed as set(xrange(start, end)). However, adding sets will give Python (and mathematicians) fits.
Well, locations are some sets of numbers, so it makes sense to throw a set-like interface on top of them (so __contains__, __iter__, __len__, perhaps __or__ as an alias of +, __and__ as the product, etc).
As for construction from xrange, do you really need it? If you know that you're storing sets of intervals, then you're likely to save space by sticking to your representation of [start, end) pairs. You could throw in an utility method that takes an arbitrary sequence of integers and translates it to an optimal SimpleLoc or CompoundLoc if you feel it's going to help.
I think that the best way to accomplish this is to provide the __radd__ method, or pass the start object to sum explicitly.
In case you really do not want to override __radd__ or provide a start object, how about redefining sum()?
>>> from __builtin__ import sum as builtin_sum
>>> def sum(iterable, startobj=MyCustomStartObject):
... return builtin_sum(iterable, startobj)
...
Preferably use a function with a name like my_sum(), but I guess that is one of the things you want to avoid (even though globally redefining builtin functions is probably something that a future maintainer will curse you for)
Actually, implementing __add__ without the concept of an "empty object" makes little sense. sum needs a start parameter to support the sums of empty and one-element sequences, and you have to decide what result you expect in these cases:
sum([o1, o2]) => o1 + o2 # obviously
sum([o1]) => o1 # But how should __add__ be called here? Not at all?
sum([]) => ? # What now?
You could use an object that's universally neutral wrt. addition:
class Neutral:
def __add__(self, other):
return other
print(sum("A BC D EFG".split(), Neutral())) # ABCDEFG
You could so something like:
from operator import add
try:
total = reduce(add, whatever) # or functools.reduce in Py3.x
except TypeError as e:
# I'm not 100% happy about branching on the exception text, but
# figure this msg isn't likely to be changed after so long...
if e.args[0] == 'reduce() of empty sequence with no initial value':
pass # do something appropriate here if necessary
else:
pass # Most likely that + isn't usable between objects...
Related
As the title asks. Python has a lot of special methods, __add__, __len__, __contains__ et c. Why is there no __max__ method that is called when doing max? Example code:
class A:
def __max__():
return 5
a = A()
max(a)
It seems like range() and other constructs could benefit from this. Am I missing some other effective way to do max?ยจ
Addendum 1:
As a trivial example, max(range(1000000000)) takes a long time to run.
I have no authoritative answer but I can offer my thoughts on the subject.
There are several built-in functions that have no corresponding special method. For example:
max
min
sum
all
any
One thing they have in common is that they are reduce-like: They iterate over an iterable and "reduce" it to one value. The point here is that these are more of a building block.
For example you often wrap the iterable in a generator (or another comprehension, or transformation like map or filter) before applying them:
sum(abs(val) for val in iterable) # sum of absolutes
any(val > 10 for val in iterable) # is one value over 10
max(person.age for person in iterable) # the oldest person
That means most of the time it wouldn't even call the __max__ of the iterable but try to access it on the generator (which isn't implemented and cannot be implemented).
So there is simply not much of a benefit if these were implemented. And in the few cases when it makes sense to implement them it would be more obvious if you create a custom method (or property) because it highlights that it's a "shortcut" or that it's different from the "normal result".
For example these functions (min, etc.) have O(n) run-time, so if you can do better (for example if you have a sorted list you could access the max in O(1)) it might make sense to document that explicitly.
Some operations are not basic operations. Take max as an example, it is actually an operation based on comparison. In other words, when you get a max value, you are actually getting a biggest value.
So in this case, why should we implement a specified max function but not override the behave of comparison?
Think in another direction, what does max really mean? For example, when we execute max(list), what are we doing?
I think we are actually checking list's elements, and the max operation is not related to list itself at all.
list is just a container which is unnecessary in max operation. It is list or set or something else, it doesn't matter. What really useful is the elements inside this container.
So if we define a __max__ action for list, we are actually doing another totally different operation. We are asking a container to give us advice about max value.
I think in this case, as it is a totally different operation, it should be a method of container instead of overriding built-in function's behave.
In python 2, the built in function map seems to call the __len__ when length is overwritten. Is that correct - if so, why are we computing the length of the iterable to map? Iterables don't need to have length overwritten (e.g.), and the map function works even when length is not pre-defined by the iterable.
Map is defined here; it does specify that there is length-dependent functionality in the event that multiple iterables are passed. However,
I'm interested in the case that only one iterable is passed
Even if multiple iterables were passed (not my question), it seems like an odd design choice to explicitely check the length, instead of just iterating until you run out and then returning None
I am concerned because according to several 1 2 extremely highly upvoted questions,
map(f, iterable)
is basically equivalent to:
[f(x) for x in iterable]
But I am running into simple examples where that isn't true.
For Example
class Iterable:
def __iter__(self):
self.iterable = [1,2,3,4,5].__iter__()
return self
def next(self):
return self.iterable.next()
#def __len__(self):
# self.iterable = None
# return 5
def foo(x): return x
print( [foo(x) for x in Iterable()] )
print( map(foo,Iterable()) )
Behaves as it should, but if you uncomment the overloading of len, it very much does not.
In this case, it raises an AttributeError because the iterable is None. While the unit behaviour is silly, I see no requirement of invariance in the specification of len. Surely, it's good practice to not modify the state in a call to len, but the reason should not be because of unexpectable behaviour in builtin functions. In more realistic cases, my len function may just be slow, and I don't expect to worry about it being called by map, or maybe it isn't thread safe, etc..
Implementation Dependent?
Since map is a builtin function, it may have implementation-specific features outside the spec, but cpython implements it on line 918 of bltinmodule.c, which indeed states:
/* Do a first pass to obtain iterators for the arguments, and set len
* to the largest of their lengths.
*/
And then calls _PyObject_LengthHint, which is defined in Object/abstract.c, and indeed seems to look for an overwritten len. This doesn't clarify to me whether this is just implementation dependent, or if I'm missing some reason that map purposefully looks for the iterable's length against my instinct.
(Note I haven't tested this in python 3, that is why I specified python 2. In python3, map returns a generator, so at least a few of my claims aren't true)
I am concerned because according to several 1 2 extremely
highly upvoted questions,
map(f, iterable)
is basically equivalent to:
[f(x) for x in iterable]
But I am running into simple examples where that isn't true.
But calling _PyObject_LengthHint is supposed to be basically equivalent to not calling it. An object's __len__ or __length_hint__ is not supposed to mutate the object like this. You might as well say that map(f, iterable) and [f(x) for x in iterable] are inequivalent because if f uses stack inspection to determine whether it's being called from map and does something different, the two snippets behave differently.
As for why map does this, it's trying preallocate the list to the right size to avoid needing to resize the list. Resizes only slow things down by a constant factor, but if you can avoid the constant factor, why not? It would be perfectly reasonable for list comprehensions to do this in a future Python version.
I'm not quite sure of what you're asking here. I'm going to assume that your question is "Why is the result of map(f, iterable) not always equivalent to [f(x) for x in iterable] ?"
From your research, it's clear that there is degree of implementation dependence in the builtin map function, which (while it does come off a bit strange) makes total sense for a custom implementation of an iterable object.
The point of the specification of the __len__ being a bit too lax is a good one though. It seems that modification of object state should be labeled as very bad in a method like this.
However, it does seem like your interpretation of the equivalence between map(f, iterable) and [f(x) for x in iterable] might be making incorrect assumptions. This is true, of course, in cases where the implementation of f, map and iterable do not modify the underlying mechanisms of the evaluation. When they do, all bets are off. Basically, context is important. Take this, for instance:
def map(function, iterable):
return None
def some_fun(x):
return x + 1
a = [1,2,3]
>>> map(some_fun, a)
None
>>> [some_fun(x) for x in a]
[2, 3, 4]
Here, obviously, the map function result and the list comprehension are not the same thing (if you only look at the last 2 evals). Heck, they do completely different things. This is completely due to context. Therefore, unless clearly stated, in most cases it is completely reasonable to assume that both map and iterable's implementation do not circumvent the mechanisms in Python. However, if there is context, it comes first.
Here's what I have so far:
def is_ordered(collection):
if isinstance(collection, set):
return False
if isinstance(collection, list):
return True
if isinstance(collection, dict):
return False
raise Exception("unknown collection")
Is there a much better way to do this?
NB: I do mean ordered and not sorted.
Motivation:
I want to iterate over an ordered collection. e.g.
def most_important(priorities):
for p in priorities:
print p
In this case the fact that priorities is ordered is important. What kind of collection it is is not. I'm trying to live duck-typing here. I have frequently been dissuaded by from type checking by Pythonistas.
If the collection is truly arbitrary (meaning it can be of any class whatsoever), then the answer has to be no.
Basically, there are two possible approaches:
know about every possible class that can be presented to your method, and whether it's ordered;
test the collection yourself by inserting into it every possible combination of keys, and seeing whether the ordering is preserved.
The latter is clearly infeasible. The former is along the lines of what you already have, except that you have to know about every derived class such as collections.OrderedDict; checking for dict is not enough.
Frankly, I think the whole is_ordered check is a can of worms. Why do you want to do this anyway?
Update: In essence, you are trying to unittest the argument passed to you. Stop doing that, and unittest your own code. Test your consumer (make sure it works with ordered collections), and unittest the code that calls it, to ensure it is getting the right results.
In a statically-typed language you would simply restrict yourself to specific types. If you really want to replicate that, simply specify the only types you accept, and test for those. Raise an exception if anything else is passed. It's not pythonic, but it reliably achieves what you want to do
Well, you have two possible approaches:
Anything with an append method is almost certainly ordered; and
If it only has an add method, you can try adding a nonce-value, then iterating over the collection to see if the nonce appears at the end (or, perhaps at one end); you could try adding a second nonce and doing it again just to be more confident.
Of course, this won't work where e.g. the collection is empty, or there is an ordering function that doesn't result in addition at the ends.
Probably a better solution is simply to specify that your code requires ordered collections, and only pass it ordered collections.
I think that enumerating the 90% case is about as good as you're going to get (if using Python 3, replace basestring with str). Probably also want to consider how you would handle generator expressions and similar ilk, too (again, if using Py3, skip the xrangor):
generator = type((i for i in xrange(0)))
enumerator = type(enumerate(range(0)))
xrangor = type(xrange(0))
is_ordered = lambda seq : isinstance(seq,(tuple, list, collections.OrderedDict,
basestring, generator, enumerator, xrangor))
If your callers start using itertools, then you'll also need to add itertools types as returned by islice, imap, groupby. But the sheer number of these special cases really starts to point to a code smell.
What if the list is not ordered, e.g. [1,3,2]?
I have some functions in my code that accept either an object or an iterable of objects as input. I was taught to use meaningful names for everything, but I am not sure how to comply here. What should I call a parameter that can a sinlge object or an iterable of objects? I have come up with two ideas, but I don't like either of them:
FooOrManyFoos - This expresses what goes on, but I could imagine that someone not used to it could have trouble understanding what it means right away
param - Some generic name. This makes clear that it can be several things, but does explain nothing about what the parameter is used for.
Normally I call iterables of objects just the plural of what I would call a single object. I know this might seem a little bit compulsive, but Python is supposed to be (among others) about readability.
I have some functions in my code that accept either an object or an iterable of objects as input.
This is a very exceptional and often very bad thing to do. It's trivially avoidable.
i.e., pass [foo] instead of foo when calling this function.
The only time you can justify doing this is when (1) you have an installed base of software that expects one form (iterable or singleton) and (2) you have to expand it to support the other use case. So. You only do this when expanding an existing function that has an existing code base.
If this is new development, Do Not Do This.
I have come up with two ideas, but I don't like either of them:
[Only two?]
FooOrManyFoos - This expresses what goes on, but I could imagine that someone not used to it could have trouble understanding what it means right away
What? Are you saying you provide NO other documentation, and no other training? No support? No advice? Who is the "someone not used to it"? Talk to them. Don't assume or imagine things about them.
Also, don't use Leading Upper Case Names.
param - Some generic name. This makes clear that it can be several things, but does explain nothing about what the parameter is used for.
Terrible. Never. Do. This.
I looked in the Python library for examples. Most of the functions that do this have simple descriptions.
http://docs.python.org/library/functions.html#isinstance
isinstance(object, classinfo)
They call it "classinfo" and it can be a class or a tuple of classes.
You could do that, too.
You must consider the common use case and the exceptions. Follow the 80/20 rule.
80% of the time, you can replace this with an iterable and not have this problem.
In the remaining 20% of the cases, you have an installed base of software built around an assumption (either iterable or single item) and you need to add the other case. Don't change the name, just change the documentation. If it used to say "foo" it still says "foo" but you make it accept an iterable of "foo's" without making any change to the parameters. If it used to say "foo_list" or "foo_iter", then it still says "foo_list" or "foo_iter" but it will quietly tolerate a singleton without breaking.
80% of the code is the legacy ("foo" or "foo_list")
20% of the code is the new feature ("foo" can be an iterable or "foo_list" can be a single object.)
I guess I'm a little late to the party, but I'm suprised that nobody suggested a decorator.
def withmany(f):
def many(many_foos):
for foo in many_foos:
yield f(foo)
f.many = many
return f
#withmany
def process_foo(foo):
return foo + 1
processed_foo = process_foo(foo)
for processed_foo in process_foo.many(foos):
print processed_foo
I saw a similar pattern in one of Alex Martelli's posts but I don't remember the link off hand.
It sounds like you're agonizing over the ugliness of code like:
def ProcessWidget(widget_thing):
# Infer if we have a singleton instance and make it a
# length 1 list for consistency
if isinstance(widget_thing, WidgetType):
widget_thing = [widget_thing]
for widget in widget_thing:
#...
My suggestion is to avoid overloading your interface to handle two distinct cases. I tend to write code that favors re-use and clear naming of methods over clever dynamic use of parameters:
def ProcessOneWidget(widget):
#...
def ProcessManyWidgets(widgets):
for widget in widgets:
ProcessOneWidget(widget)
Often, I start with this simple pattern, but then have the opportunity to optimize the "Many" case when there are efficiencies to gain that offset the additional code complexity and partial duplication of functionality. If this convention seems overly verbose, one can opt for names like "ProcessWidget" and "ProcessWidgets", though the difference between the two is a single easily missed character.
You can use *args magic (varargs) to make your params always be iterable.
Pass a single item or multiple known items as normal function args like func(arg1, arg2, ...) and pass iterable arguments with an asterisk before, like func(*args)
Example:
# magic *args function
def foo(*args):
print args
# many ways to call it
foo(1)
foo(1, 2, 3)
args1 = (1, 2, 3)
args2 = [1, 2, 3]
args3 = iter((1, 2, 3))
foo(*args1)
foo(*args2)
foo(*args3)
Can you name your parameter in a very high-level way? people who read the code are more interested in knowing what the parameter represents ("clients") than what their type is ("list_of_tuples"); the type can be defined in the function documentation string, which is a good thing since it might change, in the future (the type is sometimes an implementation detail).
I would do 1 thing,
def myFunc(manyFoos):
if not type(manyFoos) in (list,tuple):
manyFoos = [manyFoos]
#do stuff here
so then you don't need to worry anymore about its name.
in a function you should try to achieve to have 1 action, accept the same parameter type and return the same type.
Instead of filling the functions with ifs you could have 2 functions.
Since you don't care exactly what kind of iterable you get, you could try to get an iterator for the parameter using iter(). If iter() raises a TypeError exception, the parameter is not iterable, so you then create a list or tuple of the one item, which is iterable and Bob's your uncle.
def doIt(foos):
try:
iter(foos)
except TypeError:
foos = [foos]
for foo in foos:
pass # do something here
The only problem with this approach is if foo is a string. A string is iterable, so passing in a single string rather than a list of strings will result in iterating over the characters in a string. If this is a concern, you could add an if test for it. At this point it's getting wordy for boilerplate code, so I'd break it out into its own function.
def iterfy(iterable):
if isinstance(iterable, basestring):
iterable = [iterable]
try:
iter(iterable)
except TypeError:
iterable = [iterable]
return iterable
def doIt(foos):
for foo in iterfy(foos):
pass # do something
Unlike some of those answering, I like doing this, since it eliminates one thing the caller could get wrong when using your API. "Be conservative in what you generate but liberal in what you accept."
To answer your original question, i.e. what you should name the parameter, I would still go with "foos" even though you will accept a single item, since your intent is to accept a list. If it's not iterable, that is technically a mistake, albeit one you will correct for the caller since processing just the one item is probably what they want. Also, if the caller thinks they must pass in an iterable even of one item, well, that will of course work fine and requires very little syntax, so why worry about correcting their misapprehension?
I would go with a name explaining that the parameter can be an instance or a list of instances. Say one_or_more_Foo_objects. I find it better than the bland param.
I'm working on a fairly big project now and we're passing maps around and just calling our parameter map. The map contents vary depending on the function that's being called. This probably isn't the best situation, but we reuse a lot of the same code on the maps, so copying and pasting is easier.
I would say instead of naming it what it is, you should name it what it's used for. Also, just be careful that you can't call use in on a not iterable.
i am a python newbie, and i am not sure why python implemented len(obj), max(obj), and min(obj) as a static like functions (i am from the java language) over obj.len(), obj.max(), and obj.min()
what are the advantages and disadvantages (other than obvious inconsistency) of having len()... over the method calls?
why guido chose this over the method calls? (this could have been solved in python3 if needed, but it wasn't changed in python3, so there gotta be good reasons...i hope)
thanks!!
The big advantage is that built-in functions (and operators) can apply extra logic when appropriate, beyond simply calling the special methods. For example, min can look at several arguments and apply the appropriate inequality checks, or it can accept a single iterable argument and proceed similarly; abs when called on an object without a special method __abs__ could try comparing said object with 0 and using the object change sign method if needed (though it currently doesn't); and so forth.
So, for consistency, all operations with wide applicability must always go through built-ins and/or operators, and it's those built-ins responsibility to look up and apply the appropriate special methods (on one or more of the arguments), use alternate logic where applicable, and so forth.
An example where this principle wasn't correctly applied (but the inconsistency was fixed in Python 3) is "step an iterator forward": in 2.5 and earlier, you needed to define and call the non-specially-named next method on the iterator. In 2.6 and later you can do it the right way: the iterator object defines __next__, the new next built-in can call it and apply extra logic, for example to supply a default value (in 2.6 you can still do it the bad old way, for backwards compatibility, though in 3.* you can't any more).
Another example: consider the expression x + y. In a traditional object-oriented language (able to dispatch only on the type of the leftmost argument -- like Python, Ruby, Java, C++, C#, &c) if x is of some built-in type and y is of your own fancy new type, you're sadly out of luck if the language insists on delegating all the logic to the method of type(x) that implements addition (assuming the language allows operator overloading;-).
In Python, the + operator (and similarly of course the builtin operator.add, if that's what you prefer) tries x's type's __add__, and if that one doesn't know what to do with y, then tries y's type's __radd__. So you can define your types that know how to add themselves to integers, floats, complex, etc etc, as well as ones that know how to add such built-in numeric types to themselves (i.e., you can code it so that x + y and y + x both work fine, when y is an instance of your fancy new type and x is an instance of some builtin numeric type).
"Generic functions" (as in PEAK) are a more elegant approach (allowing any overriding based on a combination of types, never with the crazy monomaniac focus on the leftmost arguments that OOP encourages!-), but (a) they were unfortunately not accepted for Python 3, and (b) they do of course require the generic function to be expressed as free-standing (it would be absolutely crazy to have to consider the function as "belonging" to any single type, where the whole POINT is that can be differently overridden/overloaded based on arbitrary combination of its several arguments' types!-). Anybody who's ever programmed in Common Lisp, Dylan, or PEAK, knows what I'm talking about;-).
So, free-standing functions and operators are just THE right, consistent way to go (even though the lack of generic functions, in bare-bones Python, does remove some fraction of the inherent elegance, it's still a reasonable mix of elegance and practicality!-).
It emphasizes the capabilities of an object, not its methods or type. Capabilites are declared by "helper" functions such as __iter__ and __len__ but they don't make up the interface. The interface is in the builtin functions, and beside this also in the buit-in operators like + and [] for indexing and slicing.
Sometimes, it is not a one-to-one correspondance: For example, iter(obj) returns an iterator for an object, and will work even if __iter__ is not defined. If not defined, it goes on to look if the object defines __getitem__ and will return an iterator accessing the object index-wise (like an array).
This goes together with Python's Duck Typing, we care only about what we can do with an object, not that it is of a particular type.
Actually, those aren't "static" methods in the way you are thinking about them. They are built-in functions that really just alias to certain methods on python objects that implement them.
>>> class Foo(object):
... def __len__(self):
... return 42
...
>>> f = Foo()
>>> len(f)
42
These are always available to be called whether or not the object implements them or not. The point is to have some consistency. Instead of some class having a method called length() and another called size(), the convention is to implement len and let the callers always access it by the more readable len(obj) instead of obj.methodThatDoesSomethingCommon
I thought the reason was so these basic operations could be done on iterators with the same interface as containers. However, it actually doesn't work with len:
def foo():
for i in range(10):
yield i
print len(foo())
... fails with TypeError. len() won't consume and count an iterator; it only works with objects that have a __len__ call.
So, as far as I'm concerned, len() shouldn't exist. It's much more natural to say obj.len than len(obj), and much more consistent with the rest of the language and the standard library. We don't say append(lst, 1); we say lst.append(1). Having a separate global method for length is an odd, inconsistent special case, and eats a very obvious name in the global namespace, which is a very bad habit of Python.
This is unrelated to duck typing; you can say getattr(obj, "len") to decide whether you can use len on an object just as easily--and much more consistently--than you can use getattr(obj, "__len__").
All that said, as language warts go--for those who consider this a wart--this is a very easy one to live with.
On the other hand, min and max do work on iterators, which gives them a use apart from any particular object. This is straightforward, so I'll just give an example:
import random
def foo():
for i in range(10):
yield random.randint(0, 100)
print max(foo())
However, there are no __min__ or __max__ methods to override its behavior, so there's no consistent way to provide efficient searching for sorted containers. If a container is sorted on the same key that you're searching, min/max are O(1) operations instead of O(n), and the only way to expose that is by a different, inconsistent method. (This could be fixed in the language relatively easily, of course.)
To follow up with another issue with this: it prevents use of Python's method binding. As a simple, contrived example, you can do this to supply a function to add values to a list:
def add(f):
f(1)
f(2)
f(3)
lst = []
add(lst.append)
print lst
and this works on all member functions. You can't do that with min, max or len, though, since they're not methods of the object they operate on. Instead, you have to resort to functools.partial, a clumsy second-class workaround common in other languages.
Of course, this is an uncommon case; but it's the uncommon cases that tell us about a language's consistency.