Usually there's no value returned, but if so it is set to the exception object's value attribute. When a return statement is reached, the generator function raises a StopIteration exception. The next time next() is called, the execution resumes from the yield statement with all the same values for the local variables. This will run the code up until a yield statement, which acts like a return statement and makes the next() call return a value. To run the code inside a generator function, you must call Python's built-in next() function and pass it the generator object. You can see this in the interactive shell: > aGeneratorFunction('Eve') Instead, calling a generator function returns a generator object. Calling a generator function does not run it's code. What you need to know is that one does not simply call a generator function. When you run this code, the output looks like this: Calling aGeneratorFunction('Bob'): Print('type(excObj.value) is', type(excObj.value)) GeneratorObj = aGeneratorFunction('Bob') # Does not run the code, but returns a generator object. Print("Calling aGeneratorFunction('Bob'):") Return 86 # Raises StopIteration with value 86. You can tell a function is a generator function because it'll have the yield keyword somewhere in the function body: # A generator (aka generator function): Now let's take a look at a generator function. A function takes arguments for its parameters, runs some code, and then returns some return value.
When you run this code, the output looks like this: Calling aRegularFunction('Alice'): ReturnedValue = aRegularFunction('Alice') Print("Calling aRegularFunction('Alice'):") This blog post assumes you have a basic beginner's level of understanding of Python.įirst, let's take a look at a regular function that you're already familiar with: # A regular function: py file of all the examples from whatIsAGenerator.py. (Iterables are beyond the scope of this blog post, but see The Iterator Protocol: How "For Loops" Work in Python for more details.) Using a generator is like using the readline() method to read a text file one line at a time, instead of using the read() method to read in the entire file all at once.Īs a side note, generator functions return generator objects and generator objects are iterables. This is especially true if you are creating large lists with hundreds of thousands of items or more. If you find yourself creating a large list of values with, say, a list comprehension or for loop, but you only need one value at a time, you should consider using a generator instead.
Generator functions let you create one value at a time with any sort of data, not just ranges of integers. This was fixed in Python 3, where range() now produces one integer at a time. This takes up a large amount of memory even though the for loop only needs one integer at a time. For example, in Python 2 running for i in range(1000000): the range() function would create and return a list with one million integers in it. This can keep your program from requiring the large amounts of memory needed if you generated all the values in the series at once. Let's learn how they work by re-creating the built-in range() function. Generators in Python (also called generator functions) are used to create a series of values one at a time.
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