3.1. Generator About

  • Processes one element at a time

  • Does not remember previous element

  • Does not know next element

  • Can be used only once

  • Save memory (does not require more memory for processing large data)

  • Uses around 10% more CPU than regular processing

  • Typical usage: streams, processing larger than memory files or data

  • Cannot use len() as of generators don't have length

  • Previous element is overridden by current on next()

  • Functions (list, dict, tuple, frozenset, set, sum, all, any, etc) will evaluate generator instantly

3.1.1. Example

Generator Expression:

>>> result = (x for x in range(0,5))

Generator Function:

>>> def mygenerator():
...    yield 'something'

3.1.2. Inspect

>>> from inspect import isgeneratorfunction, isgenerator
>>>
>>>
>>> result = (x for x in range(0,5))
>>>
>>> isgenerator(result)
True
>>> isgeneratorfunction(result)
False

>>> from inspect import isgeneratorfunction, isgenerator
>>>
>>>
>>> def mygenerator():
...    yield 'something'
>>>
>>>
>>> isgenerator(mygenerator)
False
>>> isgeneratorfunction(mygenerator)
True
>>>
>>> result = mygenerator()
>>>
>>> isgenerator(result)
True
>>> isgeneratorfunction(result)
False