Is there a simple way of testing if the generator has no items, like peek, hasNext, isEmpty, something along those lines?

The simple answer to your question: no, there is no simple way. There are a whole lot of work-arounds.

There really shouldn’t be a simple way, because of what generators are: a way to output a sequence of values without holding the sequence in memory. So there’s no backward traversal.

You could write a has_next function or maybe even slap it on to a generator as a method with a fancy decorator if you wanted to.

Suggestion:

def peek(iterable):
    try:
        first = next(iterable)
    except StopIteration:
        return None
    return first, itertools.chain([first], iterable)

Usage:

res = peek(mysequence)
if res is None:
    # sequence is empty.  Do stuff.
else:
    first, mysequence = res
    # Do something with first, maybe?
    # Then iterate over the sequence:
    for element in mysequence:
        # etc.

A simple way is to use the optional parameter for next() which is used if the generator is exhausted (or empty). For example:

iterable = some_generator()

_exhausted = object()

if next(iterable, _exhausted) == _exhausted:
    print('generator is empty')

Edit: Corrected the problem pointed out in mehtunguh’s comment.

next(generator, None) is not None

Or replace None but whatever value you know it’s not in your generator.

Edit: Yes, this will skip 1 item in the generator. Often, however, I check whether a generator is empty only for validation purposes, then don’t really use it. Or otherwise I do something like:

def foo(self):
    if next(self.my_generator(), None) is None:
        raise Exception("Not initiated")

    for x in self.my_generator():
        ...

That is, this works if your generator comes from a function, as in generator().

The best approach, IMHO, would be to avoid a special test. Most times, use of a generator is the test:

thing_generated = False

# Nothing is lost here. if nothing is generated, 
# the for block is not executed. Often, that's the only check
# you need to do. This can be done in the course of doing
# the work you wanted to do anyway on the generated output.
for thing in my_generator():
    thing_generated = True
    do_work(thing)

If that’s not good enough, you can still perform an explicit test. At this point, thing will contain the last value generated. If nothing was generated, it will be undefined – unless you’ve already defined the variable. You could check the value of thing, but that’s a bit unreliable. Instead, just set a flag within the block and check it afterward:

if not thing_generated:
    print "Avast, ye scurvy dog!"

I hate to offer a second solution, especially one that I would not use myself, but, if you absolutely had to do this and to not consume the generator, as in other answers:

def do_something_with_item(item):
    print item

empty_marker = object()

try:
     first_item = my_generator.next()     
except StopIteration:
     print 'The generator was empty'
     first_item = empty_marker

if first_item is not empty_marker:
    do_something_with_item(first_item)
    for item in my_generator:
        do_something_with_item(item)

Now I really don’t like this solution, because I believe that this is not how generators are to be used.

I realize that this post is 5 years old at this point, but I found it while looking for an idiomatic way of doing this, and did not see my solution posted. So for posterity:

import itertools

def get_generator():
    """
    Returns (bool, generator) where bool is true iff the generator is not empty.
    """
    gen = (i for i in [0, 1, 2, 3, 4])
    a, b = itertools.tee(gen)
    try:
        a.next()
    except StopIteration:
        return (False, b)
    return (True, b)

Of course, as I’m sure many commentators will point out, this is hacky and only works at all in certain limited situations (where the generators are side-effect free, for example). YMMV.

Sorry for the obvious approach, but the best way would be to do:

for item in my_generator:
     print item

Now you have detected that the generator is empty while you are using it. Of course, item will never be displayed if the generator is empty.

This may not exactly fit in with your code, but this is what the idiom of the generator is for: iterating, so perhaps you might change your approach slightly, or not use generators at all.

All you need to do to see if a generator is empty is to try to get the next result. Of course if you’re not ready to use that result then you have to store it to return it again later.

Here’s a wrapper class that can be added to an existing iterator to add an __nonzero__ test, so you can see if the generator is empty with a simple if. It can probably also be turned into a decorator.

class GenWrapper:
    def __init__(self, iter):
        self.source = iter
        self.stored = False

    def __iter__(self):
        return self

    def __nonzero__(self):
        if self.stored:
            return True
        try:
            self.value = next(self.source)
            self.stored = True
        except StopIteration:
            return False
        return True

    def __next__(self):  # use "next" (without underscores) for Python 2.x
        if self.stored:
            self.stored = False
            return self.value
        return next(self.source)

Here’s how you’d use it:

with open(filename, 'r') as f:
    f = GenWrapper(f)
    if f:
        print 'Not empty'
    else:
        print 'Empty'

Note that you can check for emptiness at any time, not just at the start of the iteration.

Prompted by Mark Ransom, here’s a class that you can use to wrap any iterator so that you can peek ahead, push values back onto the stream and check for empty. It’s a simple idea with a simple implementation that I’ve found very handy in the past.

class Pushable:

    def __init__(self, iter):
        self.source = iter
        self.stored = []

    def __iter__(self):
        return self

    def __bool__(self):
        if self.stored:
            return True
        try:
            self.stored.append(next(self.source))
        except StopIteration:
            return False
        return True

    def push(self, value):
        self.stored.append(value)

    def peek(self):
        if self.stored:
            return self.stored[-1]
        value = next(self.source)
        self.stored.append(value)
        return value

    def __next__(self):
        if self.stored:
            return self.stored.pop()
        return next(self.source)

Just fell on this thread and realized that a very simple and easy to read answer was missing:

def is_empty(generator):
    for item in generator:
        return False
    return True

If we are not suppose to consume any item then we need to re-inject the first item into the generator:

def is_empty_no_side_effects(generator):
    try:
        item = next(generator)
        def my_generator():
            yield item
            yield from generator
        return my_generator(), False
    except StopIteration:
        return (_ for _ in []), True

Example:

>>> g=(i for i in [])
>>> g,empty=is_empty_no_side_effects(g)
>>> empty
True
>>> g=(i for i in range(10))
>>> g,empty=is_empty_no_side_effects(g)
>>> empty
False
>>> list(g)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

>>> gen = (i for i in [])
>>> next(gen)
Traceback (most recent call last):
  File "<pyshell#43>", line 1, in <module>
    next(gen)
StopIteration

At the end of generator StopIteration is raised, since in your case end is reached immediately, exception is raised. But normally you shouldn’t check for existence of next value.

another thing you can do is:

>>> gen = (i for i in [])
>>> if not list(gen):
    print('empty generator')

If you need to know before you use the generator, then no, there is no simple way. If you can wait until after you have used the generator, there is a simple way:

was_empty = True

for some_item in some_generator:
    was_empty = False
    do_something_with(some_item)

if was_empty:
    handle_already_empty_generator_case()

Simply wrap the generator with itertools.chain, put something that will represent the end of the iterable as the second iterable, then simply check for that.

Ex:

import itertools

g = some_iterable
eog = object()
wrap_g = itertools.chain(g, [eog])

Now all that’s left is to check for that value we appended to the end of the iterable, when you read it then that will signify the end

for value in wrap_g:
    if value == eog: # DING DING! We just found the last element of the iterable
        pass # Do something

In my case I needed to know if a host of generators was populated before I passed it on to a function, which merged the items, i.e., zip(...). The solution is similar, but different enough, from the accepted answer:

Definition:

def has_items(iterable):
    try:
        return True, itertools.chain([next(iterable)], iterable)
    except StopIteration:
        return False, []

Usage:

def filter_empty(iterables):
    for iterable in iterables:
        itr_has_items, iterable = has_items(iterable)
        if itr_has_items:
            yield iterable


def merge_iterables(iterables):
    populated_iterables = filter_empty(iterables)
    for items in zip(*populated_iterables):
        # Use items for each "slice"

My particular problem has the property that the iterables are either empty or has exactly the same number of entries.

I found only this solution as working for empty iterations as well.

def is_generator_empty(generator):
    a, b = itertools.tee(generator)
    try:
        next(a)
    except StopIteration:
        return True, b
    return False, b

is_empty, generator = is_generator_empty(generator)

Or if you do not want to use exception for this try to use

def is_generator_empty(generator):
    a, b = itertools.tee(generator)
    for item in a:
        return False, b
    return True, b

is_empty, generator = is_generator_empty(generator)

In the marked solution you are not possible to use it for empty generators like

def get_empty_generator():
    while False:
        yield None 

generator = get_empty_generator()

This is an old and answered question, but as no one has shown it before, here it goes:

for _ in generator:
    break
else:
    print('Empty')

You can read more here

Here is my simple approach that i use to keep on returning an iterator while checking if something was yielded I just check if the loop runs:

        n = 0
        for key, value in iterator:
            n+=1
            yield key, value
        if n == 0:
            print ("nothing found in iterator)
            break

Here’s a simple decorator which wraps the generator, so it returns None if empty. This can be useful if your code needs to know whether the generator will produce anything before looping through it.

def generator_or_none(func):
    """Wrap a generator function, returning None if it's empty. """

    def inner(*args, **kwargs):
        # peek at the first item; return None if it doesn't exist
        try:
            next(func(*args, **kwargs))
        except StopIteration:
            return None

        # return original generator otherwise first item will be missing
        return func(*args, **kwargs)

    return inner

Usage:

import random

@generator_or_none
def random_length_generator():
    for i in range(random.randint(0, 10)):
        yield i

gen = random_length_generator()
if gen is None:
    print('Generator is empty')

One example where this is useful is in templating code – i.e. jinja2

{% if content_generator %}
  <section>
    <h4>Section title</h4>
    {% for item in content_generator %}
      {{ item }}
    {% endfor %
  </section>
{% endif %}

using islice you need only check up to the first iteration to discover if it is empty.

from itertools import islice

def isempty(iterable):
    return list(islice(iterable,1)) == []

What about using any()? I use it with generators and it’s working fine. Here there is guy explaining a little about this

Use the peek function in cytoolz.

from cytoolz import peek
from typing import Tuple, Iterable

def is_empty_iterator(g: Iterable) -> Tuple[Iterable, bool]:
    try:
        _, g = peek(g)
        return g, False
    except StopIteration:
        return g, True

The iterator returned by this function will be equivalent to the original one passed in as an argument.

I solved it by using the sum function. See below for an example I used with glob.iglob (which returns a generator).

def isEmpty():
    files = glob.iglob(search)
    if sum(1 for _ in files):
        return True
    return False

*This will probably not work for HUGE generators but should perform nicely for smaller lists

I am reading the Python cookbook at the moment and am currently looking at generators. I’m finding it hard to get my head round.

As I come from a Java background, is there a Java equivalent? The book was speaking about ‘Producer / Consumer’, however when I hear that I think of threading.

What is a generator and why would you use it? Without quoting any books, obviously (unless you can find a decent, simplistic answer direct from a book). Perhaps with examples, if you’re feeling generous!

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 a single value using return, just like in Java. In Python, however, there is an alternative, called yield. Using yield anywhere in a function makes it a generator. Observe this code:

>>> def myGen(n):
...     yield n
...     yield n + 1
... 
>>> g = myGen(6)
>>> next(g)
6
>>> next(g)
7
>>> next(g)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
StopIteration

As you can see, myGen(n) is a function which yields n and n + 1. Every call to next yields a single value, until all values have been yielded. for loops call next in the background, thus:

>>> for n in myGen(6):
...     print(n)
... 
6
7

Likewise there are generator expressions, which provide a means to succinctly describe certain common types of generators:

>>> g = (n for n in range(3, 5))
>>> next(g)
3
>>> next(g)
4
>>> next(g)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
StopIteration

Note that generator expressions are much like list comprehensions:

>>> lc = [n for n in range(3, 5)]
>>> lc
[3, 4]

Observe that a generator object is generated once, but its code is not run all at once. Only calls to next actually execute (part of) the code. Execution of the code in a generator stops once a yield statement has been reached, upon which it returns a value. The next call to next then causes execution to continue in the state in which the generator was left after the last yield. This is a fundamental difference with regular functions: those always start execution at the “top” and discard their state upon returning a value.

There are more things to be said about this subject. It is e.g. possible to send data back into a generator (reference). But that is something I suggest you do not look into until you understand the basic concept of a generator.

Now you may ask: why use generators? There are a couple of good reasons:

• Certain concepts can be described much more succinctly using generators.
• Instead of creating a function which returns a list of values, one can write a generator which generates the values on the fly. This means that no list needs to be constructed, meaning that the resulting code is more memory efficient. In this way one can even describe data streams which would simply be too large to fit in memory.

• Generators allow for a natural way to describe infinite streams. Consider for example the Fibonacci numbers:

  >>> def fib():
  ...     a, b = 0, 1
  ...     while True:
  ...         yield a
  ...         a, b = b, a + b
  ... 
  >>> import itertools
  >>> list(itertools.islice(fib(), 10))
  [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]
  

  This code uses itertools.islice to take a finite number of elements from an infinite stream. You are advised to have a good look at the functions in the itertools module, as they are essential tools for writing advanced generators with great ease.

  ^† About Python <=2.6: in the above examples next is a function which calls the method __next__ on the given object. In Python <=2.6 one uses a slightly different technique, namely o.next() instead of next(o). Python 2.7 has next() call .next so you need not use the following in 2.7:

>>> g = (n for n in range(3, 5))
>>> g.next()
3

A generator is effectively a function that returns (data) before it is finished, but it pauses at that point, and you can resume the function at that point.

>>> def myGenerator():
...     yield 'These'
...     yield 'words'
...     yield 'come'
...     yield 'one'
...     yield 'at'
...     yield 'a'
...     yield 'time'

>>> myGeneratorInstance = myGenerator()
>>> next(myGeneratorInstance)
These
>>> next(myGeneratorInstance)
words

and so on. The (or one) benefit of generators is that because they deal with data one piece at a time, you can deal with large amounts of data; with lists, excessive memory requirements could become a problem. Generators, just like lists, are iterable, so they can be used in the same ways:

>>> for word in myGeneratorInstance:
...     print word
These
words
come
one
at 
a 
time

Note that generators provide another way to deal with infinity, for example

>>> from time import gmtime, strftime
>>> def myGen():
...     while True:
...         yield strftime("%a, %d %b %Y %H:%M:%S +0000", gmtime())    
>>> myGeneratorInstance = myGen()
>>> next(myGeneratorInstance)
Thu, 28 Jun 2001 14:17:15 +0000
>>> next(myGeneratorInstance)
Thu, 28 Jun 2001 14:18:02 +0000   

The generator encapsulates an infinite loop, but this isn’t a problem because you only get each answer every time you ask for it.

First of all, the term generator originally was somewhat ill-defined in Python, leading to lots of confusion. You probably mean iterators and iterables (see here). Then in Python there are also generator functions (which return a generator object), generator objects (which are iterators) and generator expressions (which are evaluated to a generator object).

According to the glossary entry for generator it seems that the official terminology is now that generator is short for “generator function”. In the past the documentation defined the terms inconsistently, but fortunately this has been fixed.

It might still be a good idea to be precise and avoid the term “generator” without further specification.

Generators could be thought of as shorthand for creating an iterator. They behave like a Java Iterator. Example:

>>> g = (x for x in range(10))
>>> g
<generator object <genexpr> at 0x7fac1c1e6aa0>
>>> g.next()
0
>>> g.next()
1
>>> g.next()
2
>>> list(g)   # force iterating the rest
[3, 4, 5, 6, 7, 8, 9]
>>> g.next()  # iterator is at the end; calling next again will throw
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
StopIteration

Hope this helps/is what you are looking for.

Update:

As many other answers are showing, there are different ways to create a generator. You can use the parentheses syntax as in my example above, or you can use yield. Another interesting feature is that generators can be “infinite” — iterators that don’t stop:

>>> def infinite_gen():
...     n = 0
...     while True:
...         yield n
...         n = n + 1
... 
>>> g = infinite_gen()
>>> g.next()
0
>>> g.next()
1
>>> g.next()
2
>>> g.next()
3
...

There is no Java equivalent.

Here is a bit of a contrived example:

#! /usr/bin/python
def  mygen(n):
    x = 0
    while x < n:
        x = x + 1
        if x % 3 == 0:
            yield x

for a in mygen(100):
    print a

There is a loop in the generator that runs from 0 to n, and if the loop variable is a multiple of 3, it yields the variable.

During each iteration of the for loop the generator is executed. If it is the first time the generator executes, it starts at the beginning, otherwise it continues from the previous time it yielded.

I like to describe generators, to those with a decent background in programming languages and computing, in terms of stack frames.

In many languages, there is a stack on top of which is the current stack “frame”. The stack frame includes space allocated for variables local to the function including the arguments passed in to that function.

When you call a function, the current point of execution (the “program counter” or equivalent) is pushed onto the stack, and a new stack frame is created. Execution then transfers to the beginning of the function being called.

With regular functions, at some point the function returns a value, and the stack is “popped”. The function’s stack frame is discarded and execution resumes at the previous location.

When a function is a generator, it can return a value without the stack frame being discarded, using the yield statement. The values of local variables and the program counter within the function are preserved. This allows the generator to be resumed at a later time, with execution continuing from the yield statement, and it can execute more code and return another value.

Before Python 2.5 this was all generators did. Python 2.5 added the ability to pass values back in to the generator as well. In doing so, the passed-in value is available as an expression resulting from the yield statement which had temporarily returned control (and a value) from the generator.

The key advantage to generators is that the “state” of the function is preserved, unlike with regular functions where each time the stack frame is discarded, you lose all that “state”. A secondary advantage is that some of the function call overhead (creating and deleting stack frames) is avoided, though this is a usually a minor advantage.

The only thing I can add to Stephan202’s answer is a recommendation that you take a look at David Beazley’s PyCon ’08 presentation “Generator Tricks for Systems Programmers,” which is the best single explanation of the how and why of generators that I’ve seen anywhere. This is the thing that took me from “Python looks kind of fun” to “This is what I’ve been looking for.” It’s at http://www.dabeaz.com/generators/.

It helps to make a clear distinction between the function foo, and the generator foo(n):

def foo(n):
    yield n
    yield n+1

foo is a function. foo(6) is a generator object.

The typical way to use a generator object is in a loop:

for n in foo(6):
    print(n)

The loop prints

# 6
# 7

Think of a generator as a resumable function.

yield behaves like return in the sense that values that are yielded get “returned” by the generator. Unlike return, however, the next time the generator gets asked for a value, the generator’s function, foo, resumes where it left off — after the last yield statement — and continues to run until it hits another yield statement.

Behind the scenes, when you call bar=foo(6) the generator object bar is defined for you to have a next attribute.

You can call it yourself to retrieve values yielded from foo:

next(bar)    # Works in Python 2.6 or Python 3.x
bar.next()   # Works in Python 2.5+, but is deprecated. Use next() if possible.

When foo ends (and there are no more yielded values), calling next(bar) throws a StopInteration error.

This post will use Fibonacci numbers as a tool to build up to explaining the usefulness of Python generators.

This post will feature both C++ and Python code.

Fibonacci numbers are defined as the sequence: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, ….

Or in general:

F0 = 0
F1 = 1
Fn = Fn-1 + Fn-2

This can be transferred into a C++ function extremely easily:

size_t Fib(size_t n)
{
    //Fib(0) = 0
    if(n == 0)
        return 0;

    //Fib(1) = 1
    if(n == 1)
        return 1;

    //Fib(N) = Fib(N-2) + Fib(N-1)
    return Fib(n-2) + Fib(n-1);
}

But if you want to print the first six Fibonacci numbers, you will be recalculating a lot of the values with the above function.

For example: Fib(3) = Fib(2) + Fib(1), but Fib(2) also recalculates Fib(1). The higher the value you want to calculate, the worse off you will be.

So one may be tempted to rewrite the above by keeping track of the state in main.

// Not supported for the first two elements of Fib
size_t GetNextFib(size_t &pp, size_t &p)
{
    int result = pp + p;
    pp = p;
    p = result;
    return result;
}

int main(int argc, char *argv[])
{
    size_t pp = 0;
    size_t p = 1;
    std::cout << "0 " << "1 ";
    for(size_t i = 0; i <= 4; ++i)
    {
        size_t fibI = GetNextFib(pp, p);
        std::cout << fibI << " ";
    }
    return 0;
}

But this is very ugly, and it complicates our logic in main. It would be better to not have to worry about state in our main function.

We could return a vector of values and use an iterator to iterate over that set of values, but this requires a lot of memory all at once for a large number of return values.

So back to our old approach, what happens if we wanted to do something else besides print the numbers? We’d have to copy and paste the whole block of code in main and change the output statements to whatever else we wanted to do. And if you copy and paste code, then you should be shot. You don’t want to get shot, do you?

To solve these problems, and to avoid getting shot, we may rewrite this block of code using a callback function. Every time a new Fibonacci number is encountered, we would call the callback function.

void GetFibNumbers(size_t max, void(*FoundNewFibCallback)(size_t))
{
    if(max-- == 0) return;
    FoundNewFibCallback(0);
    if(max-- == 0) return;
    FoundNewFibCallback(1);

    size_t pp = 0;
    size_t p = 1;
    for(;;)
    {
        if(max-- == 0) return;
        int result = pp + p;
        pp = p;
        p = result;
        FoundNewFibCallback(result);
    }
}

void foundNewFib(size_t fibI)
{
    std::cout << fibI << " ";
}

int main(int argc, char *argv[])
{
    GetFibNumbers(6, foundNewFib);
    return 0;
}

This is clearly an improvement, your logic in main is not as cluttered, and you can do anything you want with the Fibonacci numbers, simply define new callbacks.

But this is still not perfect. What if you wanted to only get the first two Fibonacci numbers, and then do something, then get some more, then do something else?

Well, we could go on like we have been, and we could start adding state again into main, allowing GetFibNumbers to start from an arbitrary point. But this will further bloat our code, and it already looks too big for a simple task like printing Fibonacci numbers.

We could implement a producer and consumer model via a couple of threads. But this complicates the code even more.

Instead let’s talk about generators.

Python has a very nice language feature that solves problems like these called generators.

A generator allows you to execute a function, stop at an arbitrary point, and then continue again where you left off. Each time returning a value.

Consider the following code that uses a generator:

def fib():
    pp, p = 0, 1
    while 1:
        yield pp
        pp, p = p, pp+p

g = fib()
for i in range(6):
    g.next()

Which gives us the results:

0 1 1 2 3 5

The yield statement is used in conjuction with Python generators. It saves the state of the function and returns the yeilded value. The next time you call the next() function on the generator, it will continue where the yield left off.

This is by far more clean than the callback function code. We have cleaner code, smaller code, and not to mention much more functional code (Python allows arbitrarily large integers).

Source

I believe the first appearance of iterators and generators were in the Icon programming language, about 20 years ago.

You may enjoy the Icon overview, which lets you wrap your head around them without concentrating on the syntax (since Icon is a language you probably don’t know, and Griswold was explaining the benefits of his language to people coming from other languages).

After reading just a few paragraphs there, the utility of generators and iterators might become more apparent.

Experience with list comprehensions has shown their widespread utility throughout Python. However, many of the use cases do not need to have a full list created in memory. Instead, they only need to iterate over the elements one at a time.

For instance, the following summation code will build a full list of squares in memory, iterate over those values, and, when the reference is no longer needed, delete the list:

sum([x*x for x in range(10)])

Memory is conserved by using a generator expression instead:

sum(x*x for x in range(10))

Similar benefits are conferred on constructors for container objects:

s = Set(word  for line in page  for word in line.split())
d = dict( (k, func(k)) for k in keylist)

Generator expressions are especially useful with functions like sum(), min(), and max() that reduce an iterable input to a single value:

max(len(line)  for line in file  if line.strip())

more

I put up this piece of code which explains 3 key concepts about generators:

def numbers():
    for i in range(10):
            yield i

gen = numbers() #this line only returns a generator object, it does not run the code defined inside numbers

for i in gen: #we iterate over the generator and the values are printed
    print(i)

#the generator is now empty

for i in gen: #so this for block does not print anything
    print(i)

I’m starting to learn Python and I’ve come across generator functions, those that have a yield statement in them. I want to know what types of problems that these functions are really good at solving.

Generators give you lazy evaluation. You use them by iterating over them, either explicitly with ‘for’ or implicitly by passing it to any function or construct that iterates. You can think of generators as returning multiple items, as if they return a list, but instead of returning them all at once they return them one-by-one, and the generator function is paused until the next item is requested.

Generators are good for calculating large sets of results (in particular calculations involving loops themselves) where you don’t know if you are going to need all results, or where you don’t want to allocate the memory for all results at the same time. Or for situations where the generator uses another generator, or consumes some other resource, and it’s more convenient if that happened as late as possible.

Another use for generators (that is really the same) is to replace callbacks with iteration. In some situations you want a function to do a lot of work and occasionally report back to the caller. Traditionally you’d use a callback function for this. You pass this callback to the work-function and it would periodically call this callback. The generator approach is that the work-function (now a generator) knows nothing about the callback, and merely yields whenever it wants to report something. The caller, instead of writing a separate callback and passing that to the work-function, does all the reporting work in a little ‘for’ loop around the generator.

For example, say you wrote a ‘filesystem search’ program. You could perform the search in its entirety, collect the results and then display them one at a time. All of the results would have to be collected before you showed the first, and all of the results would be in memory at the same time. Or you could display the results while you find them, which would be more memory efficient and much friendlier towards the user. The latter could be done by passing the result-printing function to the filesystem-search function, or it could be done by just making the search function a generator and iterating over the result.

If you want to see an example of the latter two approaches, see os.path.walk() (the old filesystem-walking function with callback) and os.walk() (the new filesystem-walking generator.) Of course, if you really wanted to collect all results in a list, the generator approach is trivial to convert to the big-list approach:

big_list = list(the_generator)

One of the reasons to use generator is to make the solution clearer for some kind of solutions.

The other is to treat results one at a time, avoiding building huge lists of results that you would process separated anyway.

If you have a fibonacci-up-to-n function like this:

# function version
def fibon(n):
    a = b = 1
    result = []
    for i in xrange(n):
        result.append(a)
        a, b = b, a + b
    return result

You can more easily write the function as this:

# generator version
def fibon(n):
    a = b = 1
    for i in xrange(n):
        yield a
        a, b = b, a + b

The function is clearer. And if you use the function like this:

for x in fibon(1000000):
    print x,

in this example, if using the generator version, the whole 1000000 item list won’t be created at all, just one value at a time. That would not be the case when using the list version, where a list would be created first.

See the “Motivation” section in PEP 255.

A non-obvious use of generators is creating interruptible functions, which lets you do things like update UI or run several jobs “simultaneously” (interleaved, actually) while not using threads.

I find this explanation which clears my doubt. Because there is a possibility that person who don’t know Generators also don’t know about yield

Return

The return statement is where all the local variables are destroyed and the resulting value is given back (returned) to the caller. Should the same function be called some time later, the function will get a fresh new set of variables.

Yield

But what if the local variables aren’t thrown away when we exit a function? This implies that we can resume the function where we left off. This is where the concept of generators are introduced and the yield statement resumes where the function left off.

  def generate_integers(N):
    for i in xrange(N):
    yield i

    In [1]: gen = generate_integers(3)
    In [2]: gen
    <generator object at 0x8117f90>
    In [3]: gen.next()
    0
    In [4]: gen.next()
    1
    In [5]: gen.next()

So that’s the difference between return and yield statements in Python.

Yield statement is what makes a function a generator function.

So generators are a simple and powerful tool for creating iterators. They are written like regular functions, but they use the yield statement whenever they want to return data. Each time next() is called, the generator resumes where it left off (it remembers all the data values and which statement was last executed).

Real World Example

Let’s say you have 100 million domains in your MySQL table, and you would like to update Alexa rank for each domain.

First thing you need is to select your domain names from the database.

Let’s say your table name is domains and column name is domain.

If you use SELECT domain FROM domains it’s going to return 100 million rows which is going to consume lot of memory. So your server might crash.

So you decided to run the program in batches. Let’s say our batch size is 1000.

In our first batch we will query the first 1000 rows, check Alexa rank for each domain and update the database row.

In our second batch we will work on the next 1000 rows. In our third batch it will be from 2001 to 3000 and so on.

Now we need a generator function which generates our batches.

Here is our generator function:

def ResultGenerator(cursor, batchsize=1000):
    while True:
        results = cursor.fetchmany(batchsize)
        if not results:
            break
        for result in results:
            yield result

As you can see, our function keeps yielding the results. If you used the keyword return instead of yield, then the whole function would be ended once it reached return.

return - returns only once
yield - returns multiple times

If a function uses the keyword yield then it’s a generator.

Now you can iterate like this:

db = MySQLdb.connect(host="localhost", user="root", passwd="root", db="domains")
cursor = db.cursor()
cursor.execute("SELECT domain FROM domains")
for result in ResultGenerator(cursor):
    doSomethingWith(result)
db.close()

Buffering. When it is efficient to fetch data in large chunks, but process it in small chunks, then a generator might help:

def bufferedFetch():
  while True:
     buffer = getBigChunkOfData()
     # insert some code to break on 'end of data'
     for i in buffer:    
          yield i

The above lets you easily separate buffering from processing. The consumer function can now just get the values one by one without worrying about buffering.

I have found that generators are very helpful in cleaning up your code and by giving you a very unique way to encapsulate and modularize code. In a situation where you need something to constantly spit out values based on its own internal processing and when that something needs to be called from anywhere in your code (and not just within a loop or a block for example), generators are the feature to use.

An abstract example would be a Fibonacci number generator that does not live within a loop and when it is called from anywhere will always return the next number in the sequence:

def fib():
    first = 0
    second = 1
    yield first
    yield second

    while 1:
        next = first + second
        yield next
        first = second
        second = next

fibgen1 = fib()
fibgen2 = fib()

Now you have two Fibonacci number generator objects which you can call from anywhere in your code and they will always return ever larger Fibonacci numbers in sequence as follows:

>>> fibgen1.next(); fibgen1.next(); fibgen1.next(); fibgen1.next()
0
1
1
2
>>> fibgen2.next(); fibgen2.next()
0
1
>>> fibgen1.next(); fibgen1.next()
3
5

The lovely thing about generators is that they encapsulate state without having to go through the hoops of creating objects. One way of thinking about them is as “functions” which remember their internal state.

I got the Fibonacci example from Python Generators – What are they? and with a little imagination, you can come up with a lot of other situations where generators make for a great alternative to for loops and other traditional iteration constructs.

The simple explanation: Consider a for statement

for item in iterable:
   do_stuff()

A lot of the time, all the items in iterable doesn’t need to be there from the start, but can be generated on the fly as they’re required. This can be a lot more efficient in both

• space (you never need to store all the items simultaneously) and
• time (the iteration may finish before all the items are needed).

Other times, you don’t even know all the items ahead of time. For example:

for command in user_input():
   do_stuff_with(command)

You have no way of knowing all the user’s commands beforehand, but you can use a nice loop like this if you have a generator handing you commands:

def user_input():
    while True:
        wait_for_command()
        cmd = get_command()
        yield cmd

With generators you can also have iteration over infinite sequences, which is of course not possible when iterating over containers.

My favorite uses are “filter” and “reduce” operations.

Let’s say we’re reading a file, and only want the lines which begin with “##”.

def filter2sharps( aSequence ):
    for l in aSequence:
        if l.startswith("##"):
            yield l

We can then use the generator function in a proper loop

source= file( ... )
for line in filter2sharps( source.readlines() ):
    print line
source.close()

The reduce example is similar. Let’s say we have a file where we need to locate blocks of <Location>...</Location> lines. [Not HTML tags, but lines that happen to look tag-like.]

def reduceLocation( aSequence ):
    keep= False
    block= None
    for line in aSequence:
        if line.startswith("</Location"):
            block.append( line )
            yield block
            block= None
            keep= False
        elif line.startsWith("<Location"):
            block= [ line ]
            keep= True
        elif keep:
            block.append( line )
        else:
            pass
    if block is not None:
        yield block # A partial block, icky

Again, we can use this generator in a proper for loop.

source = file( ... )
for b in reduceLocation( source.readlines() ):
    print b
source.close()

The idea is that a generator function allows us to filter or reduce a sequence, producing a another sequence one value at a time.

A practical example where you could make use of a generator is if you have some kind of shape and you want to iterate over its corners, edges or whatever. For my own project (source code here) I had a rectangle:

class Rect():

    def __init__(self, x, y, width, height):
        self.l_top  = (x, y)
        self.r_top  = (x+width, y)
        self.r_bot  = (x+width, y+height)
        self.l_bot  = (x, y+height)

    def __iter__(self):
        yield self.l_top
        yield self.r_top
        yield self.r_bot
        yield self.l_bot

Now I can create a rectangle and loop over its corners:

myrect=Rect(50, 50, 100, 100)
for corner in myrect:
    print(corner)

Instead of __iter__ you could have a method iter_corners and call that with for corner in myrect.iter_corners(). It’s just more elegant to use __iter__ since then we can use the class instance name directly in the for expression.

Basically avoiding call-back functions when iterating over input maintaining state.

See here and here for an overview of what can be done using generators.

Some good answers here, however, I’d also recommend a complete read of the Python Functional Programming tutorial which helps explain some of the more potent use-cases of generators.

• Particularly interesting is that it is now possible to update the yield variable from outside the generator function, hence making it possible to create dynamic and interwoven coroutines with relatively little effort.
• Also see PEP 342: Coroutines via Enhanced Generators for more information.

Since the send method of a generator has not been mentioned, here is an example:

def test():
    for i in xrange(5):
        val = yield
        print(val)

t = test()

# Proceed to 'yield' statement
next(t)

# Send value to yield
t.send(1)
t.send('2')
t.send([3])

It shows the possibility to send a value to a running generator. A more advanced course on generators in the video below (including yield from explination, generators for parallel processing, escaping the recursion limit, etc.)

David Beazley on generators at PyCon 2014

I use generators when our web server is acting as a proxy:

1. The client requests a proxied url from the server
2. The server begins to load the target url
3. The server yields to return the results to the client as soon as it gets them

Piles of stuff. Any time you want to generate a sequence of items, but don’t want to have to ‘materialize’ them all into a list at once. For example, you could have a simple generator that returns prime numbers:

def primes():
    primes_found = set()
    primes_found.add(2)
    yield 2
    for i in itertools.count(1):
        candidate = i * 2 + 1
        if not all(candidate % prime for prime in primes_found):
            primes_found.add(candidate)
            yield candidate

You could then use that to generate the products of subsequent primes:

def prime_products():
    primeiter = primes()
    prev = primeiter.next()
    for prime in primeiter:
        yield prime * prev
        prev = prime

These are fairly trivial examples, but you can see how it can be useful for processing large (potentially infinite!) datasets without generating them in advance, which is only one of the more obvious uses.

Also good for printing the prime numbers up to n:

def genprime(n=10):
    for num in range(3, n+1):
        for factor in range(2, num):
            if num%factor == 0:
                break
        else:
            yield(num)

for prime_num in genprime(100):
    print(prime_num)

I want to change the following code

for directory, dirs, files in os.walk(directory_1):
    do_something()

for directory, dirs, files in os.walk(directory_2):
    do_something()

to this code:

for directory, dirs, files in os.walk(directory_1) + os.walk(directory_2):
    do_something()

I get the error:

unsupported operand type(s) for +: ‘generator’ and ‘generator’

How to join two generators in Python?

I think itertools.chain() should do it.

A example of code:

from itertools import chain

def generator1():
    for item in 'abcdef':
        yield item

def generator2():
    for item in '123456':
        yield item

generator3 = chain(generator1(), generator2())
for item in generator3:
    print item

In Python (3.5 or greater) you can do:

def concat(a, b):
    yield from a
    yield from b

Simple example:

from itertools import chain
x = iter([1,2,3])      #Create Generator Object (listiterator)
y = iter([3,4,5])      #another one
result = chain(x, y)   #Chained x and y

With itertools.chain.from_iterable you can do things like:

def genny(start):
  for x in range(start, start+3):
    yield x

y = [1, 2]
ab = [o for o in itertools.chain.from_iterable(genny(x) for x in y)]
print(ab)

Here it is using a generator expression with nested fors:

a = range(3)
b = range(5)
ab = (i for it in (a, b) for i in it)
assert list(ab) == [0, 1, 2, 0, 1, 2, 3, 4]

One can also use unpack operator *:

concat = (*gen1(), *gen2())

NOTE: Works most efficiently for ‘non-lazy’ iterables. Can also be used with different kind of comprehensions. Preferred way for generator concat would be from the answer from @Uduse

If you want to keep the generators separate but still iterate over them at the same time you can use zip():

NOTE: Iteration stops at the shorter of the two generators

For example:

for (root1, dir1, files1), (root2, dir2, files2) in zip(os.walk(path1), os.walk(path2)):

    for file in files1:
        #do something with first list of files

    for file in files2:
        #do something with second list of files

Lets say that we have to generators (gen1 and gen 2) and we want to perform some extra calculation that requires the outcome of both. We can return the outcome of such function/calculation through the map method, which in turn returns a generator that we can loop upon.

In this scenario, the function/calculation needs to be implemented via the lambda function. The tricky part is what we aim to do inside the map and its lambda function.

General form of proposed solution:

def function(gen1,gen2):
        for item in map(lambda x, y: do_somethin(x,y), gen1, gen2):
            yield item

All those complicated solutions…

just do:

for dir in directory_1, directory_2:
    for directory, dirs, files in os.walk(dir):
        do_something()

If you really want to “join” both generators, then do :

for directory, dirs, files in [
        x for osw in [os.walk(directory_1), os.walk(directory_2)] 
               for x in osw
        ]:
    do_something()