iterator is a more general concept: any object whose class has a __next__ method (next in Python 2) and an __iter__ method that does return self. Every generator is an iterator, but not vice versa. A generator is built by calling a function that has one or more yield expressions (yield statements, in Python 2.5 … Read more
“If I have seen further…” The erat2 function from the cookbook can be further sped up (by about 20-25%): erat2a import itertools as it def erat2a( ): D = { } yield 2 for q in it.islice(it.count(3), 0, None, 2): p = D.pop(q, None) if p is None: D[q*q] = q yield q else: # … Read more
To write a lazy function, just use yield: def read_in_chunks(file_object, chunk_size=1024): “””Lazy function (generator) to read a file piece by piece. Default chunk size: 1k.””” while True: data = file_object.read(chunk_size) if not data: break yield data with open(‘really_big_file.dat’) as f: for piece in read_in_chunks(f): process_data(piece) Another option would be to use iter and a helper … Read more
John’s answer is good (that list comprehensions are better when you want to iterate over something multiple times). However, it’s also worth noting that you should use a list if you want to use any of the list methods. For example, the following code won’t work: def gen(): return (something for something in get_some_stuff()) print … Read more
Note: this post assumes Python 3.x syntax.† A generator is simply a function which returns an object on which you can call next, such that for every call it returns some value, until it raises a StopIteration exception, signaling that all values have been generated. Such an object is called an iterator. Normal functions return … Read more