Python Generators vs Iterators
Generators and Iterators are Python constructs that lets you loop through data in chunks instead of loading them into memory. This post discusses the subtle differences between them
In Python, both generators and iterators ensure data is not loaded into memory on the whole but rather processed chunk by chunk. But when to use a generator and iterator.
A generator is a Python function that
yeild a result. Every generator is an iterator. A generator function returns a generator objectdef generate(n):
for i in range(n):
yield i
x = generate(3)
print (x)
print (next(x))
print (next(x))
print (next(x))
print (next(x)) # raises StopIteration
An iterator is a Python object which returns an iterable via
__iter__ method. An iterable has __next__Python Iterators and Iterables talks about iterators in detail, but for now, let's look at a simple example
class GenerateN:
def __init__(self, n):
self.n = n
self.i = -1
def __iter__(self):
return self
def __next__(self):
self.i += 1
return self.i
g = GenerateN(3)
print (g)
print (next(g))
print (next(g))
print (next(g))
print (next(g)) # raises StopIteration
- Generators are function, and Iterators are object-oriented.
- Use generators when you have a large stream of data and you want to loop over them.
- Use iterators to generate a sequence of data.
- Generators are excellent for large loops since it only works on one value at a time.
Last modified 3mo ago