PEP 3106 -- Revamping dict.keys(), .values() and .items()

PEP: 3106 Title: Revamping dict.keys(), .values() and .items() Author: Guido van Rossum Status: Final Type: Standards Track Created: 19-Dec-2006 Post-History:

Abstract This PEP proposes to change the .keys(), .values() and .items() methods of the built-in dict type to return a set-like or unordered container object whose contents are derived from the underlying dictionary rather than a list which is a copy of the keys, etc.; and to remove the .iterkeys(), .itervalues() and .iteritems() methods. The approach is inspired by that taken in the Java Collections Framework .

Introduction It has long been the plan to change the .keys(), .values() and .items() methods of the built-in dict type to return a more lightweight object than a list, and to get rid of .iterkeys(), .itervalues() and .iteritems(). The idea is that code that currently (in 2.x) reads: for k, v in d.iteritems(): ... should be rewritten as: for k, v in d.items(): ... (and similar for .itervalues() and .iterkeys(), except the latter is redundant since we can write that loop as for k in d .) Code that currently reads: a = d.keys() # assume we really want a list here (etc.) should be rewritten as a = list(d.keys()) There are (at least) two ways to accomplish this. The original plan was to simply let .keys(), .values() and .items() return an iterator, i.e. exactly what iterkeys(), itervalues() and iteritems() return in Python 2.x. However, the Java Collections Framework suggests that a better solution is possible: the methods return objects with set behavior (for .keys() and .items()) or multiset (== bag) behavior (for .values()) that do not contain copies of the keys, values or items, but rather reference the underlying dict and pull their values out of the dict as needed. The advantage of this approach is that one can still write code like this: a = d.items() for k, v in a: ... # And later, again: for k, v in a: ... Effectively, iter(d.keys()) (etc.) in Python 3.0 will do what d.iterkeys() (etc.) does in Python 2.x; but in most contexts we don't have to write the iter() call because it is implied by a for-loop. The objects returned by the .keys() and .items() methods behave like sets. The object returned by the values() method behaves like a much simpler unordered collection -- it cannot be a set because duplicate values are possible. Because of the set behavior, it will be possible to check whether two dicts have the same keys by simply testing: if a.keys() == b.keys(): ... and similarly for .items(). These operations are thread-safe only to the extent that using them in a thread-unsafe way may cause an exception but will not cause corruption of the internal representation. As in Python 2.x, mutating a dict while iterating over it using an iterator has an undefined effect and will in most cases raise a RuntimeError exception. (This is similar to the guarantees made by the Java Collections Framework.) The objects returned by .keys() and .items() are fully interoperable with instances of the built-in set and frozenset types; for example: set(d.keys()) == d.keys() is guaranteed to be True (except when d is being modified simultaneously by another thread).

Specification I'm using pseudo-code to specify the semantics: class dict: # Omitting all other dict methods for brevity. # The .iterkeys(), .itervalues() and .iteritems() methods # will be removed. def keys(self): return d_keys(self) def items(self): return d_items(self) def values(self): return d_values(self) class d_keys: def __init__(self, d): self.__d = d def __len__(self): return len(self.__d) def __contains__(self, key): return key in self.__d def __iter__(self): for key in self.__d: yield key # The following operations should be implemented to be # compatible with sets; this can be done by exploiting # the above primitive operations: # # <, <=, ==, !=, >=, > (returning a bool) # &, |, ^, - (returning a new, real set object) # # as well as their method counterparts (.union(), etc.). # # To specify the semantics, we can specify x == y as: # # set(x) == set(y) if both x and y are d_keys instances # set(x) == y if x is a d_keys instance # x == set(y) if y is a d_keys instance # # and so on for all other operations. class d_items: def __init__(self, d): self.__d = d def __len__(self): return len(self.__d) def __contains__(self, (key, value)): return key in self.__d and self.__d[key] == value def __iter__(self): for key in self.__d: yield key, self.__d[key] # As well as the set operations mentioned for d_keys above. # However the specifications suggested there will not work if # the values aren't hashable. Fortunately, the operations can # still be implemented efficiently. For example, this is how # intersection can be specified: def __and__(self, other): if isinstance(other, (set, frozenset, d_keys)): result = set() for item in other: if item in self: result.add(item) return result if not isinstance(other, d_items): return NotImplemented d = {} if len(other) < len(self): self, other = other, self for item in self: if item in other: key, value = item d[key] = value return d.items() # And here is equality: def __eq__(self, other): if isinstance(other, (set, frozenset, d_keys)): if len(self) != len(other): return False for item in other: if item not in self: return False return True if not isinstance(other, d_items): return NotImplemented # XXX We could also just compare the underlying dicts... if len(self) != len(other): return False for item in self: if item not in other: return False return True def __ne__(self, other): # XXX Perhaps object.__ne__() should be defined this way. result = self.__eq__(other) if result is not NotImplemented: result = not result return result class d_values: def __init__(self, d): self.__d = d def __len__(self): return len(self.__d) def __contains__(self, value): # This is slow, and it's what "x in y" uses as a fallback # if __contains__ is not defined; but I'd rather make it # explicit that it is supported. for v in self: if v == value: return True return False def __iter__(self): for key in self.__d: yield self.__d[key] def __eq__(self, other): if not isinstance(other, d_values): return NotImplemented if len(self) != len(other): return False # XXX Sometimes this could be optimized, but these are the # semantics: we can't depend on the values to be hashable # or comparable. olist = list(other) for x in self: try: olist.remove(x) except ValueError: return False assert olist == [] return True def __ne__(self, other): result = self.__eq__(other) if result is not NotImplemented: result = not result return result Notes: The view objects are not directly mutable, but don't implement __hash__(); their value can change if the underlying dict is mutated. The only requirements on the underlying dict are that it implements __getitem__(), __contains__(), __iter__(), and __len__(). We don't implement .copy() -- the presence of a .copy() method suggests that the copy has the same type as the original, but that's not feasible without copying the underlying dict. If you want a copy of a specific type, like list or set, you can just pass one of the above to the list() or set() constructor. The specification implies that the order in which items are returned by .keys(), .values() and .items() is the same (just as it was in Python 2.x), because the order is all derived from the dict iterator (which is presumably arbitrary but stable as long as a dict isn't modified). This can be expressed by the following invariant: list(d.items()) == list(zip(d.keys(), d.values()))