Chainz is a lightweight library to provide chaining, functional methods to iterables.
To install: pip install chainz
Basic example:
from chainz import Chain
Chain(xrange(10))\
.map(lambda x: x + 1)\
.filter(lambda x: x % 2 == 0)\
.omit(lambda x: x % 3 == 0)\
.reduce(lambda x, y: x + y)
# 30
The fundamental class in chainz
is Chain
, which accepts an
iterable as an argument to its constructor. It is itself an iterable,
just exposing the supplied iterable. It exposes functional methods like
map
, filter
, and flatten
, which return the chain so as to be
chainable. These methods alter the chain; chain.map(f)
is the same
as chain = chain.map(f)
.
Some methods, such as reduce
and for_each
, are "sinks", in that
they consume the iterable. These methods do not return the chain, to
make it clear that once they are called, the chain is done.
All non-sink methods are lazy, so they don't result in any evaluation.
Only by using a sink method, or consuming the iterable in another way
(such as list(chain)
or [x for x in chain]
), do you actually
evaluate the iterable.
You can think of Chain
as a way to wrap itertools
in a more
chainable fashion.
By default, a Chain
will stop whenever there's an exception. Often
that is not what you want. When you are processing a long list of items
(something for which Chain
was specifically created for), you just
want to note what went wrong, and move on to the next item. The method
on_error
allows just that. It takes a function
f(exception, object)
which itself takes two parameter. The first
parameter is the raised exception. The second parameter is the object
that caused the exception.
def handle_error(exception, obj):
print("%s caused exception: %s" % (obj, exception))
def double(x):
if x == 1:
raise Exception('Bad')
return x*2
chain = Chain(xrange(3)).on_error(handle_error).map(double)
list(chain)
# "1 caused exception: Exception('Bad')"
# [0, 2]
Please see the docs/
directory for auto-generated (and thus
up-to-date) documentation. This is generated from the doc strings, so
introspection can also be helpful (eg, print Chain.reduce.__doc__
).