PEP 323 – Copyable Iterators
- Alex Martelli <aleaxit at gmail.com>
- Standards Track
Table of Contents
This PEP has been deferred. Copyable iterators are a nice idea, but after four years, no implementation or widespread interest has emerged.
This PEP suggests that some iterator types should support shallow
copies of their instances by exposing a
__copy__ method which meets
some specific requirements, and indicates how code using an iterator
might exploit such a
__copy__ method when present.
Update and Comments
__copy__ was included in Py2.4’s
__copy__ methods to existing iterators will change the
tee(). Currently, the copied iterators remain
tied to the original iterator. If the original advances, then
so do all of the copies. Good practice is to overwrite the
original so that anomalies don’t result:
Code that doesn’t follow that practice may observe a semantic
change if a
__copy__ method is added to an iterator.
In Python up to 2.3, most built-in iterator types don’t let the user copy their instances. User-coded iterators that do let their clients call copy.copy on their instances may, or may not, happen to return, as a result of the copy, a separate iterator object that may be iterated upon independently from the original.
Currently, “support” for copy.copy in a user-coded iterator type is
almost invariably “accidental” – i.e., the standard machinery of the
copy method in Python’s standard library’s copy module does build and
return a copy. However, the copy will be independently iterable with
respect to the original only if calling
.next() on an instance of that
class happens to change instance state solely by rebinding some
attributes to new values, and not by mutating some attributes’
For example, an iterator whose “index” state is held as an integer
attribute will probably give usable copies, since (integers being
.next() presumably just rebinds that attribute. On the
other hand, another iterator whose “index” state is held as a list
attribute will probably mutate the same list object when
executes, and therefore copies of such an iterator will not be
iterable separately and independently from the original.
Given this existing situation,
copy.copy(it) on some iterator object
isn’t very useful, nor, therefore, is it at all widely used. However,
there are many cases in which being able to get a “snapshot” of an
iterator, as a “bookmark”, so as to be able to keep iterating along
the sequence but later iterate again on the same sequence from the
bookmark onwards, is useful. To support such “bookmarking”, module
itertools, in 2.4, has grown a ‘tee’ function, to be used as:
it, bookmark = itertools.tee(it)
The previous value of ‘it’ must not be used again, which is why this typical usage idiom rebinds the name. After this call, ‘it’ and ‘bookmark’ are independently-iterable iterators on the same underlying sequence as the original value of ‘it’: this satisfies application needs for “iterator copying”.
However, when itertools.tee can make no hypotheses about the nature of the iterator it is passed as an argument, it must save in memory all items through which one of the two ‘teed’ iterators, but not yet both, have stepped. This can be quite costly in terms of memory, if the two iterators get very far from each other in their stepping; indeed, in some cases it may be preferable to make a list from the iterator so as to be able to step repeatedly through the subsequence, or, if that is too costy in terms of memory, save items to disk, again in order to be able to iterate through them repeatedly.
This PEP proposes another idea that will, in some important cases,
itertools.tee to do its job with minimal cost in terms of
memory; user code may also occasionally be able to exploit the idea in
order to decide whether to copy an iterator, make a list from it, or
use an auxiliary disk file.
The key consideration is that some important iterators, such as those
which built-in function iter builds over sequences, would be
intrinsically easy to copy: just get another reference to the same
sequence, and a copy of the integer index. However, in Python 2.3,
those iterators don’t expose the state, and don’t support
The purpose of this PEP, therefore, is to have those iterator types
expose a suitable
__copy__ method. Similarly, user-coded iterator
types that can provide copies of their instances, suitable for
separate and independent iteration, with limited costs in time and
space, should also expose a suitable
__copy__ method. While
copy.copy also supports other ways to let a type control the way
its instances are copied, it is suggested, for simplicity, that
iterator types that support copying always do so by exposing a
__copy__ method, and not in the other ways
Having iterators expose a suitable
__copy__ when feasible will afford
easy optimization of itertools.tee and similar user code, as in:
def tee(it): it = iter(it) try: copier = it.__copy__ except AttributeError: # non-copyable iterator, do all the needed hard work # [snipped!] else: return it, copier()
Note that this function does NOT call “copy.copy(it)”, which (even after this PEP is implemented) might well still “just happen to succeed”. for some iterator type that is implemented as a user-coded class. without really supplying an adequate “independently iterable” copy object as its result.
Any iterator type X may expose a method
__copy__ that is callable
without arguments on any instance x of X. The method should be
exposed if and only if the iterator type can provide copyability with
reasonably little computational and memory effort. Furthermore, the
new object y returned by method
__copy__ should be a new instance
of X that is iterable independently and separately from x, stepping
along the same “underlying sequence” of items.
For example, suppose a class Iter essentially duplicated the functionality of the iter builtin for iterating on a sequence:
class Iter(object): def __init__(self, sequence): self.sequence = sequence self.index = 0 def __iter__(self): return self def next(self): try: result = self.sequence[self.index] except IndexError: raise StopIteration self.index += 1 return result
To make this Iter class compliant with this PEP, the following addition to the body of class Iter would suffice:
def __copy__(self): result = self.__class__(self.sequence) result.index = self.index return result
__copy__, in this case, does not even try to copy the
sequence; if the sequence is altered while either or both of the
original and copied iterators are still stepping on it, the iteration
behavior is quite likely to go awry anyway – it is not
responsibility to change this normal Python behavior for iterators
which iterate on mutable sequences (that might, perhaps, be the
specification for a
__deepcopy__ method of iterators, which, however,
this PEP does not deal with).
Consider also a “random iterator”, which provides a nonterminating sequence of results from some method of a random instance, called with given arguments:
class RandomIterator(object): def __init__(self, bound_method, *args): self.call = bound_method self.args = args def __iter__(self): return self def next(self): return self.call(*self.args) def __copy__(self): import copy, new im_self = copy.copy(self.call.im_self) method = new.instancemethod(self.call.im_func, im_self) return self.__class__(method, *self.args)
This iterator type is slightly more general than its name implies, as
it supports calls to any bound method (or other callable, but if the
callable is not a bound method, then method
__copy__ will fail). But
the use case is for the purpose of generating random streams, as in:
import random def show5(it): for i, result in enumerate(it): print '%6.3f'%result, if i==4: break print normit = RandomIterator(random.Random().gauss, 0, 1) show5(normit) copit = normit.__copy__() show5(normit) show5(copit)
which will display some output such as:
-0.536 1.936 -1.182 -1.690 -1.184 0.666 -0.701 1.214 0.348 1.373 0.666 -0.701 1.214 0.348 1.373
the key point being that the second and third lines are equal, because
the normit and copit iterators will step along the same “underlying
sequence”. (As an aside, note that to get a copy of
we must use
copy.copy, NOT try getting at a
__copy__ method directly,
because for example instances of
random.Random support copying via
__setstate__, NOT via
__copy__; indeed, using
copy.copy is the normal way to get a shallow copy of any object –
copyable iterators are different because of the already-mentioned
uncertainty about the result of
copy.copy supporting these “copyable
Besides adding to the Python docs a recommendation that user-coded
iterator types support a
__copy__ method (if and only if it can be
implemented with small costs in memory and runtime, and produce an
independently-iterable copy of an iterator object), this PEP’s
implementation will specifically include the addition of copyability
to the iterators over sequences that built-in iter returns, and also
to the iterators over a dictionary returned by the methods
iterkeys, itervalues, and iteritems of built-in type dict.
Iterators produced by generator functions will not be copyable.
However, iterators produced by the new “generator expressions” of
Python 2.4 (PEP 289) should be copyable if their underlying
iterator[s] are; the strict limitations on what is possible in a
generator expression, compared to the much vaster generality of a
generator, should make that feasible. Similarly, the iterators
produced by the built-in function
enumerate, and certain functions
suppiled by module itertools, should be copyable if the underlying
The implementation of this PEP will also include the optimization of the new itertools.tee function mentioned in the Motivation section.
The main use case for (shallow) copying of an iterator is the same as
for the function
itertools.tee (new in 2.4). User code will not
directly attempt to copy an iterator, because it would have to deal
separately with uncopyable cases; calling
internally perform the copy when appropriate, and implicitly fallback
to a maximally efficient non-copying strategy for iterators that are
not copyable. (Occasionally, user code may want more direct control,
specifically in order to deal with non-copyable iterators by other
strategies, such as making a list or saving the sequence to disk).
A tee’d iterator may serve as a “reference point”, allowing processing of a sequence to continue or resume from a known point, while the other independent iterator can be freely advanced to “explore” a further part of the sequence as needed. A simple example: a generator function which, given an iterator of numbers (assumed to be positive), returns a corresponding iterator, each of whose items is the fraction of the total corresponding to each corresponding item of the input iterator. The caller may pass the total as a value, if known in advance; otherwise, the iterator returned by calling this generator function will first compute the total.
def fractions(numbers, total=None): if total is None: numbers, aux = itertools.tee(numbers) total = sum(aux) total = float(total) for item in numbers: yield item / total
The ability to tee the numbers iterator allows this generator to precompute the total, if needed, without necessarily requiring O(N) auxiliary memory if the numbers iterator is copyable.
As another example of “iterator bookmarking”, consider a stream of numbers with an occasional string as a “postfix operator” now and then. By far most frequent such operator is a ‘+’, whereupon we must sum all previous numbers (since the last previous operator if any, or else since the start) and yield the result. Sometimes we find a ‘*’ instead, which is the same except that the previous numbers must instead be multiplied, not summed.
def filter_weird_stream(stream): it = iter(stream) while True: it, bookmark = itertools.tee(it) total = 0 for item in it: if item=='+': yield total break elif item=='*': product = 1 for item in bookmark: if item=='*': yield product break else: product *= item else: total += item
Similar use cases of itertools.tee can support such tasks as “undo” on a stream of commands represented by an iterator, “backtracking” on the parse of a stream of tokens, and so on. (Of course, in each case, one should also consider simpler possibilities such as saving relevant portions of the sequence into lists while stepping on the sequence with just one iterator, depending on the details of one’s task).
Here is an example, in pure Python, of how the ‘enumerate’
built-in could be extended to support
__copy__ if its underlying
iterator also supported
class enumerate(object): def __init__(self, it): self.it = iter(it) self.i = -1 def __iter__(self): return self def next(self): self.i += 1 return self.i, self.it.next() def __copy__(self): result = self.__class__.__new__() result.it = self.it.__copy__() result.i = self.i return result
Here is an example of the kind of “fragility” produced by “accidental copyability” of an iterator – the reason why one must NOT use copy.copy expecting, if it succeeds, to receive as a result an iterator which is iterable-on independently from the original. Here is an iterator class that iterates (in preorder) on “trees” which, for simplicity, are just nested lists – any item that’s a list is treated as a subtree, any other item as a leaf.
class ListreeIter(object): def __init__(self, tree): self.tree = [tree] self.indx = [-1] def __iter__(self): return self def next(self): if not self.indx: raise StopIteration self.indx[-1] += 1 try: result = self.tree[-1][self.indx[-1]] except IndexError: self.tree.pop() self.indx.pop() return self.next() if type(result) is not list: return result self.tree.append(result) self.indx.append(-1) return self.next()
Now, for example, the following code:
import copy x = [ [1,2,3], [4, 5, [6, 7, 8], 9], 10, 11,  ] print 'showing all items:', it = ListreeIter(x) for i in it: print i, if i==6: cop = copy.copy(it) print print 'showing items >6 again:' for i in cop: print i, print
does NOT work as intended – the “cop” iterator gets consumed, and
exhausted, step by step as the original “it” iterator is, because
the accidental (rather than deliberate) copying performed by
copy.copy shares, rather than duplicating the “index” list, which
is the mutable attribute
it.indx (a list of numerical indices).
Thus, this “client code” of the iterator, which attempts to iterate
twice over a portion of the sequence via a
copy.copy on the
iterator, is NOT correct.
Some correct solutions include using
itertools.tee, i.e., changing
the first for loop into:
for i in it: print i, if i==6: it, cop = itertools.tee(it) break for i in it: print i,
(note that we MUST break the loop in two, otherwise we’d still be looping on the ORIGINAL value of it, which must NOT be used further after the call to tee!!!); or making a list, i.e.
for i in it: print i, if i==6: cop = lit = list(it) break for i in lit: print i,
(again, the loop must be broken in two, since iterator ‘it’
gets exhausted by the call
Finally, all of these solutions would work if Listiter supplied
__copy__ method, as this PEP recommends:
def __copy__(self): result = self.__class__.new() result.tree = copy.copy(self.tree) result.indx = copy.copy(self.indx) return result
There is no need to get any “deeper” in the copy, but the two mutable “index state” attributes must indeed be copied in order to achieve a “proper” (independently iterable) iterator-copy.
The recommended solution is to have class Listiter supply this
__copy__ method AND have client code use
the split-in-two-parts loop as shown above). This will make
client code maximally tolerant of different iterator types it
might be using AND achieve good performance for tee’ing of this
specific iterator type at the same time.
This document has been placed in the public domain.
Last modified: 2022-01-21 11:03:51 GMT