Generators:
- Generators are solution to overhead of Iterators.
- We have to implement a class with __iter__() and __next__() method, keep track of internal states, raise StopIteration when there was no values to be returned etc.
Create a generator:
- It is fairly simple to create a generator in Python.
- It is as easy as defining a normal function with yield statement instead of a return statement.
- If a function contains at least one yield statement (it may contain other yield or return statements), it becomes a generator function.
- Both yield and return will return some value from a function.
# A simple generator function
def my_gen():
n = 1
print('This is printed first')
yield n
n += 1
print('This is printed second')
yield n
n += 1
print('This is printed at last')
yield n
return
obj = my_gen()
next(obj)
next(obj)
next(obj)
- One interesting thing to note in the above example is that, the value of variable n is remembered between each call.
- Unlike normal functions, the local variables are not destroyed when the function yields. Furthermore, the generator object can be iterated only once.
- To restart the process we need to create another generator object using something like a = my_gen().
Note: One final thing to note is that we can use generators with for loops directly.
# A simple generator function
def my_gen():
n = 1
print('This is printed first')
# Generator function contains yield statements
yield n
n += 1
print('This is printed second')
yield n
n += 1
print('This is printed at last')
yield n
# Using for loop
for item in my_gen():
print(item)
