Haven't you always wanted an infinite dict?

IterDicts are similar to regular Python dicts except that they're only populated upon demand. This gives them most of the same advantages of generators, such as the ability to operator on very large (or infinite!) datasets.

Accessing keys that aren't populated yet

When the get or __getitem__ methods are called, an IterDict tries to fetch a key in the normal manner. If that fails, it starts consuming the iterator it was constructed with and adding those items to itself until it finds the key (or the universe dies of heat death):

>>> d = IterDict((a, a) for a in xrange(1000000000000000))  # 1 quadrillion (US)
>>> d[10]
10

>>> list(d)
[you're going to be here a while]

Important differences

A dict consumes its iterator at initialization, and in the case of duplicates the last value wins:

>>> d = dict([(1,1),(1,2)])
>>> d
{1: 2}
>>> del d[1]
>>> d
{}

IterDicts differ in that they stop consuming their iterators as soon as the first instance of a requested key is found:

>>> i = IterDict([(1,1), (1,2)])
>>> i
IterDict<{}, fed by <listiterator object at 0x105bab8d0>>
>>> i[1]
1
>>> i
IterDict<{1: 1}, fed by <listiterator object at 0x105bab8d0>>

For space, time, and complexity reasons, IterDicts don't track keys that were present at one point and have been since deleted. This means that keys may reappear after deletion if the IterDict's iterator yields them again. Continuing the previous example:

>>> del i[1]
>>> i
IterDict<{}, fed by <listiterator object at 0x105bab890>>
>>> i[1]
2
>>> i
IterDict<{1: 2}, fed by <listiterator object at 0x105bab890>>