Generators
Generators
Generators provide an efficient way to produce values one at a time without storing the entire sequence in memory.
Range vs List
range(100)
def make_list(num):
result = []
for item in range(num):
result.append(item * 2)
return result
print(make_list(100)) # Using a custom function
print(list(range(100))) # Using built-in range
Use a generator and actually generate these, without taking space in memory.
Iterables vs Generators
-
Iterable → any object you can loop over.
It has a dunder iter method.(iter) -
Generator → a special type of iterable created with yield.
-
Everything that is a generator is iterable.
- You can iterate over them, but not everything that is iterable is a generator. range is a generator list is an iterable, but not a generator
- So generator is a subset of an iterable
Creating a Generator
def generator_func(num):
for i in range(num):
yield i #a generator uses yield instead of return to make a generator
g = generator_func(1)
next(g) #if the range is out of num it gives error of stop iteration
print(next(g)) #can call generator with next, while iterating
for i in generator_func(1):
print(i)
yield returns values one at a time. next() retrieves the next value until StopIteration is raised.
Implementing a Custom For Loop
def special_for(iterable):
iterator = iter(iterable)
while True:
try:
print(iterator)
print(next(iterator))
except StopIteration:
break
special_for([1,2,3]) #the list is stored at same memory place
Custom Generator Class(range function)
class MyGen():
current = 0
def __init__(self, first, last):
self.first = first
self.last = last
def __iter__(self):
return self
def __next__ (self):
if MyGen.current < self.last:
num = MyGen.current
MyGen.current += 1
return num
raise StopIteration
gen = MyGen(0,100)
for i in gen:
print(i)