What does the "yield" keyword do?

Asked : Nov 17

Viewed : 37 times

What is the use of the `yield` keyword in Python? What does it do?

For example, I'm trying to understand this code1:

``````def _get_child_candidates(self, distance, min_dist, max_dist):
if self._leftchild and distance - max_dist < self._median:
yield self._leftchild
if self._rightchild and distance + max_dist >= self._median:
yield self._rightchild
``````

And this is the caller:

``````result, candidates = [], [self]
while candidates:
node = candidates.pop()
distance = node._get_dist(obj)
if distance <= max_dist and distance >= min_dist:
result.extend(node._values)
candidates.extend(node._get_child_candidates(distance, min_dist, max_dist))
return result
``````

What happens when the method `_get_child_candidates` is called? Is a list returned? A single element? Is it called again? When will subsequent calls stop?

1. This piece of code was written by Jochen Schulz (jrschulz), who made a great Python library for metric spaces. This is the link to the complete source: Module mspace.

python iterator generator yield coroutine

Nov 17

To understand what `yield` does, you must understand what generators are. And before you can understand generators, you must understand iterables.

Iterables

When you create a list, you can read its items one by one. Reading its items one by one is called iteration:

``````>>> mylist = [1, 2, 3]
>>> for i in mylist:
...    print(i)
1
2
3
``````

`mylist` is an iterable. When you use a list comprehension, you create a list, and so an iterable:

``````>>> mylist = [x*x for x in range(3)]
>>> for i in mylist:
...    print(i)
0
1
4
``````

Everything you can use "`for... in...`" on is an iterable; `lists`, `strings`, files...

These iterables are handy because you can read them as much as you wish, but you store all the values in memory and this is not always what you want when you have a lot of values.

Generators

Generators are iterators, a kind of iterable you can only iterate over once. Generators do not store all the values in memory, they generate the values on the fly:

``````>>> mygenerator = (x*x for x in range(3))
>>> for i in mygenerator:
...    print(i)
0
1
4
``````

It is just the same except you used `()` instead of `[]`. BUT, you cannot perform `for i in mygenerator` a second time since generators can only be used once: they calculate 0, then forget about it and calculate 1, and end calculating 4, one by one.

Yield

`yield` is a keyword that is used like `return`, except the function will return a generator.

``````>>> def create_generator():
...    mylist = range(3)
...    for i in mylist:
...        yield i*i
...
>>> mygenerator = create_generator() # create a generator
>>> print(mygenerator) # mygenerator is an object!
<generator object create_generator at 0xb7555c34>
>>> for i in mygenerator:
...     print(i)
0
1
4
``````

Here it's a useless example, but it's handy when you know your function will return a huge set of values that you will only need to read once.

To master `yield`, you must understand that when you call the function, the code you have written in the function body does not run. The function only returns the generator object, this is a bit tricky.

Then, your code will continue from where it left off each time `for` uses the generator.

Now the hard part:

The first time the `for` calls the generator object created from your function, it will run the code in your function from the beginning until it hits `yield`, then it'll return the first value of the loop. Then, each subsequent call will run another iteration of the loop you have written in the function and return the next value. This will continue until the generator is considered empty, which happens when the function runs without hitting `yield`. That can be because the loop has come to an end, or because you no longer satisfy an `"if/else"`.

Generator:

``````# Here you create the method of the node object that will return the generator
def _get_child_candidates(self, distance, min_dist, max_dist):

# Here is the code that will be called each time you use the generator object:

# If there is still a child of the node object on its left
# AND if the distance is ok, return the next child
if self._leftchild and distance - max_dist < self._median:
yield self._leftchild

# If there is still a child of the node object on its right
# AND if the distance is ok, return the next child
if self._rightchild and distance + max_dist >= self._median:
yield self._rightchild

# If the function arrives here, the generator will be considered empty
# there is no more than two values: the left and the right children
``````

Caller:

``````# Create an empty list and a list with the current object reference
result, candidates = list(), [self]

# Loop on candidates (they contain only one element at the beginning)
while candidates:

# Get the last candidate and remove it from the list
node = candidates.pop()

# Get the distance between obj and the candidate
distance = node._get_dist(obj)

# If distance is ok, then you can fill the result
if distance <= max_dist and distance >= min_dist:
result.extend(node._values)

# Add the children of the candidate in the candidate's list
# so the loop will keep running until it will have looked
# at all the children of the children of the children, etc. of the candidate
candidates.extend(node._get_child_candidates(distance, min_dist, max_dist))

return result
``````

This code contains several smart parts:

The loop iterates on a list, but the list expands while the loop is being iterated. It's a concise way to go through all these nested data even if it's a bit dangerous since you can end up with an infinite loop. In this case, `candidates.extend(node._get_child_candidates(distance, min_dist, max_dist))` exhaust all the values of the generator, but `while` keeps creating new generator objects which will produce different values from the previous ones since it's not applied on the same node.

The `extend()` method is a list object method that expects an iterable and adds its values to the list.

Usually we pass a list to it:

``````>>> a = [1, 2]
>>> b = [3, 4]
>>> a.extend(b)
>>> print(a)
[1, 2, 3, 4]
``````

But in your code, it gets a generator, which is good because:

1. You don't need to read the values twice.
2. You may have a lot of children and you don't want them all stored in memory.

And it works because Python does not care if the argument of a method is a list or not. Python expects iterables so it will work with strings, lists, tuples, and generators! This is called duck typing and is one of the reasons why Python is so cool. But this is another story, for another question...

You can stop here, or read a little bit to see an advanced use of a generator:

Controlling a generator exhaustion

``````>>> class Bank(): # Let's create a bank, building ATMs
...    crisis = False
...    def create_atm(self):
...        while not self.crisis:
...            yield "\$100"
>>> hsbc = Bank() # When everything's ok the ATM gives you as much as you want
>>> corner_street_atm = hsbc.create_atm()
>>> print(corner_street_atm.next())
\$100
>>> print(corner_street_atm.next())
\$100
>>> print([corner_street_atm.next() for cash in range(5)])
['\$100', '\$100', '\$100', '\$100', '\$100']
>>> hsbc.crisis = True # Crisis is coming, no more money!
>>> print(corner_street_atm.next())
<type 'exceptions.StopIteration'>
>>> wall_street_atm = hsbc.create_atm() # It's even true for new ATMs
>>> print(wall_street_atm.next())
<type 'exceptions.StopIteration'>
>>> hsbc.crisis = False # The trouble is, even post-crisis the ATM remains empty
>>> print(corner_street_atm.next())
<type 'exceptions.StopIteration'>
>>> brand_new_atm = hsbc.create_atm() # Build a new one to get back in business
>>> for cash in brand_new_atm:
...    print cash
\$100
\$100
\$100
\$100
\$100
\$100
\$100
\$100
\$100
...
``````

Note: For Python 3, use`print(corner_street_atm.__next__())` or `print(next(corner_street_atm))`

It can be useful for various things like controlling access to a resource.

Itertools, your best friend

The itertools module contains special functions to manipulate iterables. Ever wish to duplicate a generator? Chain two generators? Group values in a nested list with a one-liner? `Map / Zip` without creating another list?

Then just `import itertools`.

An example? Let's see the possible orders of arrival for a four-horse race:

``````>>> horses = [1, 2, 3, 4]
>>> races = itertools.permutations(horses)
>>> print(races)
<itertools.permutations object at 0xb754f1dc>
>>> print(list(itertools.permutations(horses)))
[(1, 2, 3, 4),
(1, 2, 4, 3),
(1, 3, 2, 4),
(1, 3, 4, 2),
(1, 4, 2, 3),
(1, 4, 3, 2),
(2, 1, 3, 4),
(2, 1, 4, 3),
(2, 3, 1, 4),
(2, 3, 4, 1),
(2, 4, 1, 3),
(2, 4, 3, 1),
(3, 1, 2, 4),
(3, 1, 4, 2),
(3, 2, 1, 4),
(3, 2, 4, 1),
(3, 4, 1, 2),
(3, 4, 2, 1),
(4, 1, 2, 3),
(4, 1, 3, 2),
(4, 2, 1, 3),
(4, 2, 3, 1),
(4, 3, 1, 2),
(4, 3, 2, 1)]
``````

Understanding the inner mechanisms of iteration

Iteration is a process implying iterables (implementing the `__iter__()` method) and iterators (implementing the `__next__()` method). Iterables are any objects you can get an iterator from. Iterators are objects that let you iterate on iterables.

There is more about it in this article about how `for` loops work.

Yield is a keyword that is used to return a value from a function without destroying the states of its local variable. Any function that contains a yield keyword is generally called a generator.

When we replace return with yield in a function, it causes the function to hand back the iterator to its caller, which leads to the yield preventing the function from exiting until the next time the function is called. When called, it will start executing from the point where it stopped earlier. And how does this happen? Let’s see that.

When we use the yield keyword to return data from a function, it starts storing the states of the local variable, as a result, the overhead of memory allocation for the variable in consecutive calls is saved. Also, as the old state is retained in consecutive calls, the flow starts from the last yield statement executed, which in turn, saves time.

Note: A generator yields values and cannot be called like a simple function, instead it is called like an iterable, i.e. by using a loop, such as a for loop. Iterable functions can be simply created using the yield keyword.

``````#Code to generate cubes from 1 to 300 using yield and hence a generator

>>def cubes(): # An infinite generator function
i = 1
while True:
yield i*i*i
i += 1                # Next execution resumes from this point
>>for n in cubes():
if n > 300:
break
print(n)``````

Output:

1

9

27

81

243

Observe that the function cubes() are printed until the first yield. Now, if you iterate again, it doesn’t start from the beginning, it starts from where it left off.

return sends a specific value back to its caller function, whereas yield produces a sequence of values. We should use yield when our need is to iterate over a sequence, but don’t wish to store the entire sequence in memory.

The yield keyword in Python is not so often used or not so well known, but it is of greater use if one uses it correctly.

If the compiler detects the yield keyword anywhere inside a function, that function no longer returns via the return statement. Instead, it immediately returns a lazy 'pending list' object called a generator. A generator is iterable. iterable is anything like a list or set or range or direct-view, with a built-in protocol for visiting each element in a certain order.

So basically, a function with 'yield' is not a normal function anymore, instead, it becomes a generator. Every time the code is executed to "yield", it returns the right side of "yield", then it continues to loop the code.

``````def makeSqure(n):
i = 1
while i < n:
yield i * i
i += 1
print(list(makeSqure(5)))``````

output

``[1, 4, 9, 16]``

In the example above, the "yield" statement suspends the function's execution and sends a value back to the caller in each iteration, but retains enough state to enable the function to resume where it is left off. When resumed, the function continues execution immediately after the last yield run.

Think of it this way:

An iterator is just a fancy-sounding term for an object that has a `next()` method. So a yield-ed function ends up being something like this:

Original version:

``````def some_function():
for i in xrange(4):
yield i

for i in some_function():
print i
``````

This is basically what the Python interpreter does with the above code:

``````class it:
def __init__(self):
# Start at -1 so that we get 0 when we add 1 below.
self.count = -1

# The __iter__ method will be called once by the 'for' loop.
# The rest of the magic happens on the object returned by this method.
# In this case it is the object itself.
def __iter__(self):
return self

# The next method will be called repeatedly by the 'for' loop
# until it raises StopIteration.
def next(self):
self.count += 1
if self.count < 4:
return self.count
else:
# A StopIteration exception is raised
# to signal that the iterator is done.
# This is caught implicitly by the 'for' loop.
raise StopIteration

def some_func():
return it()

for i in some_func():
print i
``````

For more insight as to what's happening behind the scenes, the `for` the loop can be rewritten to this:

``````iterator = some_func()
try:
while 1:
print iterator.next()
except StopIteration:
pass
``````

Does that make more sense or just confuse you more? :)
I should note that this is an oversimplification for illustrative purposes. :)