default behavior

[wheres pythonmonks]

Why is the default value of an int zero?

How do I build an "int1" type that has a default value of 1?
[Hopefully no speed penalty.]
I am thinking about applications with collections.defaultdict.
What if I want to make a defaultdict of defaultdicts of lists? [I
guess my Perl background is showing -- I miss auto-vivification.]

W

 

[Paul Rubin]

How do I build an "int1" type that has a default value of 1?
[Hopefully no speed penalty.]
I am thinking about applications with collections.defaultdict.

You can supply an arbitary function to collections.defaultdict.
It doesn't have to be a class. E.g.

d = collections.defaultdict(lambda: 1)

will do what you are asking.

 

[wheres pythonmonks]

Thanks. I presume this will work for my nested example as well. Thanks again.

How do I build an "int1" type that has a default value of 1?
[Hopefully no speed penalty.]
I am thinking about applications with collections.defaultdict.

You can supply an arbitary function to collections.defaultdict.
It doesn't have to be a class.  E.g.

   d = collections.defaultdict(lambda: 1)

will do what you are asking.

 

[John Nagle]

Why is the default value of an int zero?


How do I build an "int1" type that has a default value of 1?

.... def __init__(self) :
.... self.val = 1
.... def __call__(self) :
.... return(self.val)
....1

This isn't useful; you'd also have to define all the numeric operators
for this type. And then there are mixed-type conversion issues.

Inheriting from "int" is not too helpful, because you can't assign
to the value of the base class. "self=1" won't do what you want.

[Hopefully no speed penalty.]
In your dreams. Although all numbers in CPython are "boxed",
so there's more of a speed penalty with "int" itself than you
might expect. There are some C libraries for handling large
arrays if you really need to crunch numbers.

John Nagle

 

[Peter Otten]

How do I build an "int1" type that has a default value of 1?
[Hopefully no speed penalty.]
I am thinking about applications with collections.defaultdict.

from collections import defaultdict
d = defaultdict(1 .conjugate)
d["x"] += 2
d["x"]

3

Isn't that beautiful? Almost like home

It is also fast:

$ python -m timeit -s"one = lambda: 1" "one()"
1000000 loops, best of 3: 0.213 usec per loop
$ python -m timeit -s"one = 1 .conjugate" "one()"
10000000 loops, best of 3: 0.0972 usec per loop

Micro-optimisation, the best excuse for ugly code...

Peter

 

[Peter Otten]

from collections import defaultdict
d = defaultdict(1 .conjugate)
d["x"] += 2
d["x"]

3

Isn't that beautiful? Almost like home

It is also fast:

$ python -m timeit -s"one = lambda: 1" "one()"
1000000 loops, best of 3: 0.213 usec per loop
$ python -m timeit -s"one = 1 .conjugate" "one()"
10000000 loops, best of 3: 0.0972 usec per loop

Micro-optimisation, the best excuse for ugly code...

Nice one, but if you are going to micro-optimise why not save a few
keystrokes while you're at it and use '1 .real' instead?

real is a property, not a method. conjugate() was the first one that worked
that was not __special__. I think it has the added benefit that it's likely
to confuse the reader...

Peter

 

[wheres pythonmonks]

Instead of defaultdict for hash of lists, I have seen something like:


m={}; m.setdefault('key', []).append(1)

Would this be preferred in some circumstances?
Also, is there a way to upcast a defaultdict into a dict? I have also
heard some people use exceptions on dictionaries to catch key
existence, so passing in a defaultdict (I guess) could be hazardous to
health. Is this true?

W

 

[Steven D'Aprano]

Instead of defaultdict for hash of lists, I have seen something like:


m={}; m.setdefault('key', []).append(1)

Would this be preferred in some circumstances?

Sure, why not? Whichever you prefer.

setdefault() is a venerable old technique, dating back to Python 2.0, and
not a newcomer like defaultdict.

Also, is there a way to upcast a defaultdict into a dict?

"Upcast"? Surely it is downcasting. Or side-casting. Or type-casting.
Whatever. *wink*

Whatever it is, the answer is Yes:

from collections import defaultdict as dd
x = dd(int)
x[1] = 'a'
x

dict(x)

{1: 'a'}

I have also heard some people use
exceptions on dictionaries to catch key existence, so passing in a
defaultdict (I guess) could be hazardous to health. Is this true?

Yes, it is true that some people use exceptions on dicts to catch key
existence. The most common reason to do so is to catch the non-existence
of a key so you can add it:

try:
mydict[x] = mydict[x] + 1
except KeyError:
mydict[x] = 1


If mydict is a defaultdict with the appropriate factory, then the change
is perfectly safe because mydict[x] will not raise an exception when x is
missing, but merely return 0, so it will continue to work as expected and
all is good.

Of course, if you pass it an defaultdict with an *inappropriate* factory,
you'll get an error. So don't do that Seriously, you can't expect to
just randomly replace a variable with some arbitrarily different variable
and expect it to work. You need to know what the code is expecting, and
not break those expectations too badly.

And now you have at least three ways of setting missing values in a dict.
And those wacky Perl people say that Python's motto is "only one way to
do it"

 

[Peter Otten]

Instead of defaultdict for hash of lists, I have seen something like:


m={}; m.setdefault('key', []).append(1)

Would this be preferred in some circumstances?

In some circumstances, sure. I just can't think of them at the moment.
Maybe if your code has to work in Python 2.4.

Also, is there a way to upcast a defaultdict into a dict?
dict(some_defaultdict)

I have also
heard some people use exceptions on dictionaries to catch key
existence, so passing in a defaultdict (I guess) could be hazardous to
health. Is this true?

A problem could arise when you swap a "key in dict" test with a
"try...except KeyError". This would be an implementation detail for a dict
but affect the contents of a defaultdict:
.... for c in "abc":
.... try: d[c]
.... except KeyError: d[c] = c
........ for c in "abc":
.... if c not in d:
.... d[c] = c
....defaultdict(<function <lambda> at 0x7fd4ce32a6e0>, {'a': 'a', 'c': 'c', 'b':
'b'})

Peter

 

[Steven D'Aprano]

Sorry, doesn't the following make a copy?

from collections import defaultdict as dd x = dd(int)
x[1] = 'a'
x

dict(x)

{1: 'a'}

I was hoping not to do that -- e.g., actually reuse the same underlying
data.

It does re-use the same underlying data.

from collections import defaultdict as dd
x = dd(list)
x[1].append(1)
x

y = dict(x)
x[1].append(42)
y

{1: [1, 42]}

Both the defaultdict and the dict are referring to the same underlying
key:value pairs. The data itself isn't duplicated. If they are mutable
items, a change to one will affect the other (because they are the same
item). An analogy for C programmers would be that creating dict y from
dict y merely copies the pointers to the keys and values, it doesn't copy
the data being pointed to.

(That's pretty much what the CPython implementation does. Other
implementations may do differently, so long as the visible behaviour
remains the same.)

Maybe dict(x), where x is a defaultdict is smart? I agree that a
defaultdict is safe to pass to most routines, but I guess I could
imagine that a try/except block is used in a bit of code where on the
key exception (when the value is absent) populates the value with a
random number. In that application, a defaultdict would have no random
values.

If you want a defaultdict with a random default value, it is easy to
provide:

import random
z = dd(random.random)
z[2] += 0
z

defaultdict(<built-in method random of Random object at 0xa01e4ac>, {2:
0.30707092626033605})


The point which I tried to make, but obviously failed, is that any piece
of code has certain expectations about the data it accepts. If take a
function that expects an int between -2 and 99, and instead decide to
pass a Decimal between 100 and 150, then you'll have problems: if you're
lucky, you'll get an exception, if you're unlucky, it will silently give
the wrong results. Changing a dict to a defaultdict is no different.

If you have code that *relies* on getting a KeyError for missing keys:

def who_is_missing(adict):
for person in ("Fred", "Barney", "Wilma", "Betty"):
try:
adict[person]
except KeyError:
print person, "is missing"

then changing adict to a defaultdict will cause the function to
misbehave. That's not unique to dicts and defaultdicts.

Besides a slightly different favor, does the following have applications
not covered by defaultdict?

m.setdefault('key', []).append(1)

defaultdict calls a function of no arguments to provide a default value.
That means, in practice, it almost always uses the same default value for
any specific dict.

setdefault takes an argument when you call the function. So you can
provide anything you like at runtime.

I think I am unclear on the difference between that and:

m['key'] = m.get('key',[]).append(1)

Have you tried it? I guess you haven't, or you wouldn't have thought they
did the same thing.

Hint -- what does [].append(1) return?

 

[wheres pythonmonks]

Hint -- what does [].append(1) return?

Again, apologies from a Python beginner. It sure seems like one has
to do gymnastics to get good behavior out of the core-python:

Here's my proposed fix:

m['key'] = (lambda x: x.append(1) or x)(m.get('key',[]))

Yuck! So I guess I'll use defaultdict with upcasts to dict as needed.

On a side note: does up-casting always work that way with shared
(common) data from derived to base? (I mean if the data is part of
base's interface, will b = base(child) yield a new base object that
shares data with the child?)

Thanks again from a Perl-to-Python convert!

W

Sorry, doesn't the following make a copy?

from collections import defaultdict as dd x = dd(int)
x[1] = 'a'
x
defaultdict(<type 'int'>, {1: 'a'})
dict(x)
{1: 'a'}

I was hoping not to do that -- e.g., actually reuse the same underlying
data.

It does re-use the same underlying data.

from collections import defaultdict as dd
x = dd(list)
x[1].append(1)
x

y = dict(x)
x[1].append(42)
y

{1: [1, 42]}

Both the defaultdict and the dict are referring to the same underlying
key:value pairs. The data itself isn't duplicated. If they are mutable
items, a change to one will affect the other (because they are the same
item). An analogy for C programmers would be that creating dict y from
dict y merely copies the pointers to the keys and values, it doesn't copy
the data being pointed to.

(That's pretty much what the CPython implementation does. Other
implementations may do differently, so long as the visible behaviour
remains the same.)

Maybe dict(x), where x is a defaultdict is smart?  I agree that a
defaultdict is safe to pass to most routines, but I guess I could
imagine that a try/except block is used in a bit of code where on the
key exception (when the value is absent)  populates the value with a
random number.  In that application, a defaultdict would have no random
values.

If you want a defaultdict with a random default value, it is easy to
provide:

import random
z = dd(random.random)
z[2] += 0
z

defaultdict(<built-in method random of Random object at 0xa01e4ac>, {2:
0.30707092626033605})


The point which I tried to make, but obviously failed, is that any piece
of code has certain expectations about the data it accepts. If take a
function that expects an int between -2 and 99, and instead decide to
pass a Decimal between 100 and 150, then you'll have problems: if you're
lucky, you'll get an exception, if you're unlucky, it will silently give
the wrong results. Changing a dict to a defaultdict is no different.

If you have code that *relies* on getting a KeyError for missing keys:

def who_is_missing(adict):
   for person in ("Fred", "Barney", "Wilma", "Betty"):
       try:
           adict[person]
       except KeyError:
           print person, "is missing"

then changing adict to a defaultdict will cause the function to
misbehave. That's not unique to dicts and defaultdicts.

Besides a slightly different favor, does the following have applications
not covered by defaultdict?

m.setdefault('key', []).append(1)

defaultdict calls a function of no arguments to provide a default value.
That means, in practice, it almost always uses the same default value for
any specific dict.

setdefault takes an argument when you call the function. So you can
provide anything you like at runtime.

I think I am unclear on the difference between that and:

m['key'] = m.get('key',[]).append(1)

Have you tried it? I guess you haven't, or you wouldn't have thought they
did the same thing.

Hint -- what does [].append(1) return?

 

[Steven D'Aprano]

Hint -- what does [].append(1) return?

Again, apologies from a Python beginner. It sure seems like one has to
do gymnastics to get good behavior out of the core-python:

Here's my proposed fix:

m['key'] = (lambda x: x.append(1) or x)(m.get('key',[]))

Yuck!

Yuk is right. What's wrong with the simple, straightforward solution?

L = m.get('key', [])
L.append(1)
m['key'] = L


Not everything needs to be a one-liner. But if you insist on making it a
one-liner, that's what setdefault and defaultdict are for.

So I guess I'll use defaultdict with upcasts to dict as needed.

You keep using that term "upcast". I have no idea what you think it
means, so I have no idea whether or not Python does it. Perhaps you
should explain what you think "upcasting" is.

On a side note: does up-casting always work that way with shared
(common) data from derived to base? (I mean if the data is part of
base's interface, will b = base(child) yield a new base object that
shares data with the child?)

Of course not. It depends on the implementation of the class.

 

[wheres pythonmonks]

I think of an upcast as casting to the base-class (casting up the
inheritance tree).
http://en.wiktionary.org/wiki/upcast
But really, what I am thinking of doing is overriding the virtual
methods of a derived class with the base class behavior in an object
that I can then pass into methods that are base/derived agnostic.

defaultdict is the way to go.

W

<ps>
<rant>

Sadly, there are guidelines that I program by that are perhaps anti-pythonic:

1. Don't use "extra" variables in code. Don't use global variables.
Keep the scopes of local variables at a minimum to reduce state (the
exception being for inner loops) or variables explicitly identified as
part of the algorithm before implementation. [In python, just about
everything is a variable which is terrifying to me. I never want the
Alabama version of math.pi i.e.,
http://www.snopes.com/religion/pi.asp, or math.sin being "666".]

2. Use built-in functions/features as much as possible, as this are
the most tested. Don't roll your own -- you're not that good, instead
master the language. (How often do I invent a noun in English? Not
even "upcast"!) [Plus, guys with phds probably already did what you
need.] Use only very well known libraries -- numpy is okay (I hope!)
for example. An exception can be made while interfacing external
data, because others who create data may not have abided by rule #2.
In most cases (except gui programming, which again tackles the
external interfacing program) the more heavy-weight your API, the more
wrong you are.

3. In interpreted languages, avoid function calls, unless the
function does something significant. [e.g., Functional call overhead
tends to be worse that a dictionary lookup -- and yes I used timeit,
the overhead can be 100%.] Small functions and methods (and
callbacks) hamper good interpreted code. When writing functions, make
them operate on lists/dicts.

It is because of the above that I stopped writing object-oriented Perl.

So I want "big" functions that do a lot of work with few variable
names. Ideally, I'd create only variables that are relevant based on
the description of the algorithm. [Oh yeah, real programming is done
before the implementation in python or C++.]

My problems are compounded by the lack of indention-based scope, but I
see this as simply enforcing the full use of functional-programming
approaches.

</rant>
</ps>

Hint -- what does [].append(1) return?

Again, apologies from a Python beginner.  It sure seems like one has to
do gymnastics to get good behavior out of the core-python:

Here's my proposed fix:

 m['key'] = (lambda x: x.append(1) or x)(m.get('key',[]))

Yuck!

Yuk is right. What's wrong with the simple, straightforward solution?

L = m.get('key', [])
L.append(1)
m['key'] = L


Not everything needs to be a one-liner. But if you insist on making it a
one-liner, that's what setdefault and defaultdict are for.

So I guess I'll use defaultdict with upcasts to dict as needed.

You keep using that term "upcast". I have no idea what you think it
means, so I have no idea whether or not Python does it. Perhaps you
should explain what you think "upcasting" is.

On a side note:  does up-casting always work that way with shared
(common) data from derived to base?  (I mean if the data is part of
base's interface, will  b = base(child) yield a new base object that
shares data with the child?)

Of course not. It depends on the implementation of the class.

 

[John Posner]

Caveat -- there's another description of defaultdict here:

http://docs.python.org/library/collections.html#collections.defaultdict

... and it's bogus. This other description claims that __missing__ is a
method of defaultdict, not of dict.

Following is a possible replacement for the bogus description. Comments
welcome. I intend to submit a Python doc bug, and I'd like to have a
clean alternative to propose.

--------------

class collections.defaultdict([default_factory[, ...]])

defaultdict is a dict subclass that can guarantee success on key
lookups: if a key does not currently exist in a defaultdict object, a
"default value factory" is called to provide a value for that key. The
"default value factory" is a callable object (typically, a function)
that takes no arguments. You specify this callable as the first argument
to defaultdict(). Additional defaultdict() arguments are the same as for
dict().

The "default value factory" callable is stored as an attribute,
default_factory, of the newly created defaultdict object. If you call
defaultdict() with no arguments, or with None as the first argument, the
default_factory attribute is set to None. You can reassign the
default_factory attribute of an existing defaultdict object to another
callable, or to None.

When a lookup of a non-existent key is performed in a defaultdict
object, its default_factory attribute is evaluated, and the resulting
object is called:

* If the call produces a value, that value is returned as the result of
the lookup. In addition, the key-value pair is inserted into the
defaultdict.

* If the call raises an exception, it is propagated unchanged.

* If the default_factory attribute evaluates to None, a KeyError
exception is raised, with the non-existent key as its argument. (The
defaultdict behaves exactly like a standard dict in this case.)

 

[Ethan Furman]

Caveat -- there's another description of defaultdict here:

http://docs.python.org/library/collections.html#collections.defaultdict

... and it's bogus. This other description claims that __missing__ is a
method of defaultdict, not of dict.

Following is a possible replacement for the bogus description. Comments
welcome. I intend to submit a Python doc bug, and I'd like to have a
clean alternative to propose.

--------------

class collections.defaultdict([default_factory[, ...]])

defaultdict is a dict subclass that can guarantee success on key
lookups: if a key does not currently exist in a defaultdict object, a
"default value factory" is called to provide a value for that key. The
"default value factory" is a callable object (typically, a function)
that takes no arguments. You specify this callable as the first argument
to defaultdict(). Additional defaultdict() arguments are the same as for
dict().

The "default value factory" callable is stored as an attribute,
default_factory, of the newly created defaultdict object. If you call
defaultdict() with no arguments, or with None as the first argument, the
default_factory attribute is set to None. You can reassign the
default_factory attribute of an existing defaultdict object to another
callable, or to None.

When a lookup of a non-existent key is performed in a defaultdict
object, its default_factory attribute is evaluated, and the resulting
object is called:

* If the call produces a value, that value is returned as the result of
the lookup. In addition, the key-value pair is inserted into the
defaultdict.

* If the call raises an exception, it is propagated unchanged.

* If the default_factory attribute evaluates to None, a KeyError
exception is raised, with the non-existent key as its argument. (The
defaultdict behaves exactly like a standard dict in this case.)

I think mentioning how __missing__ plays into all this would be helpful.
Perhaps in the first paragraph, after the colon:

if a key does not currently exist in a defaultdict object, __missing__
will be called with that key, which in turn will call a "default value
factory" to provide a value for that key.

~Ethan~

 

[Ethan Furman]

__missing__ isn't a method of dict:

--> print dir(dict())
['__class__', '__cmp__', '__contains__', '__delattr__', '__delitem__',
'__doc__', '__eq__', '__ge__', '__getattribute__', '__getitem__',
'__gt__', '__hash__', '__init__', '__iter__', '__le__', '__len__',
'__lt__', '__ne__', '__new__', '__reduce__', '__reduce_ex__',
'__repr__', '__setattr__', '__setitem__', '__str__', 'clear', 'copy',
'fromkeys', 'get', 'has_key', 'items', 'iteritems', 'iterkeys',
'itervalues', 'keys', 'pop', 'popitem', 'setdefault', 'update',
'values']

I will agree that the current defaultdict description does not make it
clear that __missing__ can be defined for *any* subclass of dict,
although the dict description does go over this... is that the confusion
you are talking about? If not, could you explain?

~Ethan~

 

[Ethan Furman]

I just went and read the entry that had the bogus claim -- personally, I
didn't see any confusion. I would like to point out the __missing__ is
*not* part of dicts (tested on 2.5 and 2.6 -- don't have 2.7 installed yet).

I beg your pardon but you are wrong. __missing__ is available for all
*subclasses* of dict since Python 2.5. See
http://svn.python.org/view/python/b...jects/dictobject.c?revision=81031&view=markup
... def __missing__(self, key):
... print "__missing__", key
... raise KeyError(key)
...

m = mydict()
m[1]

__missing__ 1
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 4, in __missing__
KeyError: 1

Perhaps punctuation will help clarify my intent:

__missing__ is *not* part of (dict)s, as shown by dir(dict()):

['__class__', '__cmp__', '__contains__', '__delattr__', '__delitem__',
'__doc__', '__eq__', '__ge__', '__getattribute__', '__getitem__',
'__gt__', '__hash__', '__init__', '__iter__', '__le__', '__len__',
'__lt__', '__ne__', '__new__', '__reduce__', '__reduce_ex__',
'__repr__', '__setattr__', '__setitem__', '__str__', 'clear', 'copy',
'fromkeys', 'get', 'has_key', 'items', 'iteritems', 'iterkeys',
'itervalues', 'keys', 'pop', 'popitem', 'setdefault', 'update',
'values']

And, just to state what is hopefully obvious, if you don't create
__missing__ yourself, it still isn't in the subclass:

--> class somedict(dict):
.... "Is __missing__ defined if I don't define it? Nope."
....
--> sd = somedict()
--> sd[1]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
KeyError: 1
--> dir(sd)
['__class__', '__cmp__', '__contains__', '__delattr__', '__delitem__',
'__dict__', '__doc__', '__eq__', '__ge__', '__getattribute__',
'__getitem__', '__gt__', '__hash__', '__init__', '__iter__', '__le__',
'__len__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__',
'__reduce_ex__', '__repr__', '__setattr__', '__setitem__', '__str__',
'__weakref__', 'clear', 'copy', 'fromkeys', 'get', 'has_key', 'items',
'iteritems', 'iterkeys', 'itervalues', 'keys', 'pop', 'popitem',
'setdefault', 'update', 'values']

~Ethan~

 

[Ethan Furman]

Indeed, that's correct. Can we agree, that __missing__ is an optional
feature of the dict interface, that can be implemented in subclasses of
dict?

Absolutely.

~Ethan~

 

[John Posner]

I just went and read the entry that had the bogus claim --
personally, I didn't see any confusion. I would like to point out the
__missing__ is *not* part of dicts (tested on 2.5 and 2.6 -- don't
have 2.7 installed yet).

I beg your pardon but you are wrong. __missing__ is available for all
*subclasses* of dict since Python 2.5. See
http://svn.python.org/view/python/b...jects/dictobject.c?revision=81031&view=markup

class mydict(dict):

... def __missing__(self, key):
... print "__missing__", key
... raise KeyError(key)
...

m = mydict()
m[1]

__missing__ 1
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 4, in __missing__
KeyError: 1

Perhaps punctuation will help clarify my intent:

__missing__ is *not* part of (dict)s, as shown by dir(dict()):

['__class__', '__cmp__', '__contains__', '__delattr__', '__delitem__',
'__doc__', '__eq__', '__ge__', '__getattribute__', '__getitem__',
'__gt__', '__hash__', '__init__', '__iter__', '__le__', '__len__',
'__lt__', '__ne__', '__new__', '__reduce__', '__reduce_ex__',
'__repr__', '__setattr__', '__setitem__', '__str__', 'clear', 'copy',
'fromkeys', 'get', 'has_key', 'items', 'iteritems', 'iterkeys',
'itervalues', 'keys', 'pop', 'popitem', 'setdefault', 'update',
'values']

And, just to state what is hopefully obvious, if you don't create
__missing__ yourself, it still isn't in the subclass:

Right, a __missing__ method does not magically appear in the subclass.
Rather, the subclass is allowed (but not required) to define a method
named __missing__, which will "magically" be called in certain situations.

Here's a dict subclass that uses the "magic" for a purpose that has
nothing to do with default values:

class BadKeyTrackerDict(dict):
def __init__(self, *args, **kwargs):
dict.__init__(self, *args, **kwargs)
self.bad_keys = set([])

def __missing__(self, key):
"""
add missing key to "bad keys" set
"""
self.bad_keys.add(key)
raise KeyError

Note that "defaultdict" is nowhere in sight here. It's the dict class
(or type) itself that provides the magic -- but only for its subclasses.

--> class somedict(dict):
... "Is __missing__ defined if I don't define it? Nope."
...
--> sd = somedict()
--> sd[1]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
KeyError: 1
--> dir(sd)
['__class__', '__cmp__', '__contains__', '__delattr__', '__delitem__',
'__dict__', '__doc__', '__eq__', '__ge__', '__getattribute__',
'__getitem__', '__gt__', '__hash__', '__init__', '__iter__', '__le__',
'__len__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__',
'__reduce_ex__', '__repr__', '__setattr__', '__setitem__', '__str__',
'__weakref__', 'clear', 'copy', 'fromkeys', 'get', 'has_key', 'items',
'iteritems', 'iterkeys', 'itervalues', 'keys', 'pop', 'popitem',
'setdefault', 'update', 'values']

~Ethan~

-John

 

[John Posner]

Following is a possible replacement for the bogus description. Comments
welcome. I intend to submit a Python doc bug, and I'd like to have a
clean alternative to propose.

After some off-list discussion with Ethan Furman (many thanks!), the
Python Doc bug is submitted: #9536 at bugs.python.org.

-John