diff options
| -rw-r--r-- | Doc/library/itertools.rst | 65 | ||||
| -rw-r--r-- | Lib/test/test_itertools.py | 72 |
2 files changed, 36 insertions, 101 deletions
diff --git a/Doc/library/itertools.rst b/Doc/library/itertools.rst index d8c33316b4..3cef985762 100644 --- a/Doc/library/itertools.rst +++ b/Doc/library/itertools.rst @@ -35,18 +35,11 @@ equivalent result. Likewise, the functional tools are designed to work well with the high-speed functions provided by the :mod:`operator` module. -The module author welcomes suggestions for other basic building blocks to be -added to future versions of the module. - Whether cast in pure python form or compiled code, tools that use iterators are -more memory efficient (and faster) than their list based counterparts. Adopting +more memory efficient (and often faster) than their list based counterparts. Adopting the principles of just-in-time manufacturing, they create data when and where needed instead of consuming memory with the computer equivalent of "inventory". -The performance advantage of iterators becomes more acute as the number of -elements increases -- at some point, lists grow large enough to severely impact -memory cache performance and start running slowly. - .. seealso:: @@ -598,55 +591,35 @@ which incur interpreter overhead. .. testcode:: - def take(n, seq): - return list(islice(seq, n)) + def take(n, iterable): + "Return first n items of the iterable as a list" + return list(islice(iterable, n)) - def enumerate(iterable): - return izip(count(), iterable) + def enumerate(iterable, start=0): + return izip(count(start), iterable) - def tabulate(function): + def tabulate(function, start=0): "Return function(0), function(1), ..." - return imap(function, count()) - - def iteritems(mapping): - return izip(mapping.iterkeys(), mapping.itervalues()) + return imap(function, count(start)) def nth(iterable, n): - "Returns the nth item or raise StopIteration" - return islice(iterable, n, None).next() - - def all(seq, pred=None): - "Returns True if pred(x) is true for every element in the iterable" - for elem in ifilterfalse(pred, seq): - return False - return True - - def any(seq, pred=None): - "Returns True if pred(x) is true for at least one element in the iterable" - for elem in ifilter(pred, seq): - return True - return False - - def no(seq, pred=None): - "Returns True if pred(x) is false for every element in the iterable" - for elem in ifilter(pred, seq): - return False - return True - - def quantify(seq, pred=None): - "Count how many times the predicate is true in the sequence" - return sum(imap(pred, seq)) - - def padnone(seq): + "Returns the nth item or empty list" + return list(islice(iterable, n, n+1)) + + def quantify(iterable, pred=bool): + "Count how many times the predicate is true" + return sum(imap(pred, iterable)) + + def padnone(iterable): """Returns the sequence elements and then returns None indefinitely. Useful for emulating the behavior of the built-in map() function. """ - return chain(seq, repeat(None)) + return chain(iterable, repeat(None)) - def ncycles(seq, n): + def ncycles(iterable, n): "Returns the sequence elements n times" - return chain.from_iterable(repeat(seq, n)) + return chain.from_iterable(repeat(iterable, n)) def dotproduct(vec1, vec2): return sum(imap(operator.mul, vec1, vec2)) diff --git a/Lib/test/test_itertools.py b/Lib/test/test_itertools.py index 2d07d8df0c..1a4e9363b8 100644 --- a/Lib/test/test_itertools.py +++ b/Lib/test/test_itertools.py @@ -1185,52 +1185,32 @@ Samuele [22] [25, 26, 27, 28] ->>> def take(n, seq): -... return list(islice(seq, n)) +>>> def take(n, iterable): +... "Return first n items of the iterable as a list" +... return list(islice(iterable, n)) ->>> def enumerate(iterable): -... return izip(count(), iterable) +>>> def enumerate(iterable, start=0): +... return izip(count(start), iterable) ->>> def tabulate(function): +>>> def tabulate(function, start=0): ... "Return function(0), function(1), ..." -... return imap(function, count()) - ->>> def iteritems(mapping): -... return izip(mapping.iterkeys(), mapping.itervalues()) +... return imap(function, count(start)) >>> def nth(iterable, n): -... "Returns the nth item" +... "Returns the nth item or empty list" ... return list(islice(iterable, n, n+1)) ->>> def all(seq, pred=None): -... "Returns True if pred(x) is true for every element in the iterable" -... for elem in ifilterfalse(pred, seq): -... return False -... return True - ->>> def any(seq, pred=None): -... "Returns True if pred(x) is true for at least one element in the iterable" -... for elem in ifilter(pred, seq): -... return True -... return False - ->>> def no(seq, pred=None): -... "Returns True if pred(x) is false for every element in the iterable" -... for elem in ifilter(pred, seq): -... return False -... return True - ->>> def quantify(seq, pred=None): -... "Count how many times the predicate is true in the sequence" -... return sum(imap(pred, seq)) - ->>> def padnone(seq): +>>> def quantify(iterable, pred=bool): +... "Count how many times the predicate is true" +... return sum(imap(pred, iterable)) + +>>> def padnone(iterable): ... "Returns the sequence elements and then returns None indefinitely" -... return chain(seq, repeat(None)) +... return chain(iterable, repeat(None)) ->>> def ncycles(seq, n): -... "Returns the sequence elements n times" -... return chain(*repeat(seq, n)) +>>> def ncycles(iterable, n): +... "Returns the seqeuence elements n times" +... return chain(*repeat(iterable, n)) >>> def dotproduct(vec1, vec2): ... return sum(imap(operator.mul, vec1, vec2)) @@ -1315,24 +1295,6 @@ perform as purported. >>> nth('abcde', 3) ['d'] ->>> all([2, 4, 6, 8], lambda x: x%2==0) -True - ->>> all([2, 3, 6, 8], lambda x: x%2==0) -False - ->>> any([2, 4, 6, 8], lambda x: x%2==0) -True - ->>> any([1, 3, 5, 9], lambda x: x%2==0,) -False - ->>> no([1, 3, 5, 9], lambda x: x%2==0) -True - ->>> no([1, 2, 5, 9], lambda x: x%2==0) -False - >>> quantify(xrange(99), lambda x: x%2==0) 50 |