# 4.2. Generators¶

## 4.2.1. Recap¶

• Comprehensions executes instantly

• Generators are lazy evaluated

>>> data = [x for x in range(0,5)]
>>>
>>> print(data)
[0, 1, 2, 3, 4]
>>> list(data)
[0, 1, 2, 3, 4]

>>> data = (x for x in range(0,5))
>>>
>>> print(data)  # doctest: +ELLIPSIS
<generator object <genexpr> at 0x...>
>>> list(data)
[0, 1, 2, 3, 4]

>>> _ = list(x for x in range(0,5))      # list comprehension
>>> _ = tuple(x for x in range(0,5))     # tuple comprehension
>>> _ = set(x for x in range(0,5))       # set comprehension
>>> _ = dict((x,x) for x in range(0,5))  # dict comprehension

>>> _ = [x for x in range(0,5)]          # list comprehension
>>> _ = (x for x in range(0,5))          # generator expression
>>> _ = {x for x in range(0,5)}          # set comprehension
>>> _ = {x:x for x in range(0,5)}        # dict comprehension


## 4.2.2. Rationale¶

• Create generator object and assign pointer (do not execute)

• Comprehensions will be in the memory until end of a program

• Generators are cleared once they are executed

• Comprehensions - Using values more than one

• Generators - Using values once (for example in the loop iterator)

• Generator will calculate next number for every loop iteration

• Generator forgets previous number

• Generator doesn't know the next number

• Code do not execute instantly

• Sometimes code is not executed at all!

• If you need values evaluated instantly, there is no point in using generators

## 4.2.3. Generator Function¶

Function:

>>> def even(data):
...     result = []
...     for x in data:
...         if x % 2 == 0:
...             result.append(x)
...     return result
>>>
>>>
>>> DATA = [0, 1, 2, 3, 4, 5]
>>>
>>> result = even(DATA)
>>>
>>> print(result)
[0, 2, 4]


Generator:

>>> def even(data):
...     for x in data:
...         if x % 2 == 0:
...             yield x
>>>
>>>
>>> DATA = [0, 1, 2, 3, 4, 5]
>>>
>>> result = even(DATA)
>>>
>>> print(result)  # doctest: +ELLIPSIS
<generator object even at 0x...>
>>> list(result)
[0, 2, 4]


## 4.2.4. Generator Filter¶

>>> DATA = [('Sepal length', 'Sepal width', 'Petal length', 'Petal width', 'Species'),
...         (5.8, 2.7, 5.1, 1.9, 'virginica'),
...         (5.1, 3.5, 1.4, 0.2, 'setosa'),
...         (5.7, 2.8, 4.1, 1.3, 'versicolor'),
...         (6.3, 2.9, 5.6, 1.8, 'virginica'),
...         (6.4, 3.2, 4.5, 1.5, 'versicolor'),
...         (4.7, 3.2, 1.3, 0.2, 'setosa')]
>>>
>>>
>>> def get_values(species):
...     result = []
...     for row in DATA:
...         if row[4] == species:
...             result.append(row)
...     return result
>>>
>>>
>>> data = get_values('setosa')
>>>
>>> print(data)
[(5.1, 3.5, 1.4, 0.2, 'setosa'), (4.7, 3.2, 1.3, 0.2, 'setosa')]
>>>
>>> for row in data:
...     print(row)
(5.1, 3.5, 1.4, 0.2, 'setosa')
(4.7, 3.2, 1.3, 0.2, 'setosa')

>>> DATA = [('Sepal length', 'Sepal width', 'Petal length', 'Petal width', 'Species'),
...         (5.8, 2.7, 5.1, 1.9, 'virginica'),
...         (5.1, 3.5, 1.4, 0.2, 'setosa'),
...         (5.7, 2.8, 4.1, 1.3, 'versicolor'),
...         (6.3, 2.9, 5.6, 1.8, 'virginica'),
...         (6.4, 3.2, 4.5, 1.5, 'versicolor'),
...         (4.7, 3.2, 1.3, 0.2, 'setosa')]
>>>
>>>
>>> def get_values(species):
...     for row in DATA:
...         if row[4] == species:
...             yield row
>>>
>>>
>>> data = get_values('setosa')
>>>
>>> print(data)  # doctest: +ELLIPSIS
<generator object get_values at 0x...>
>>>
>>> for row in data:
...     print(row)
(5.1, 3.5, 1.4, 0.2, 'setosa')
(4.7, 3.2, 1.3, 0.2, 'setosa')


## 4.2.5. Itertools¶

• Learn more at https://docs.python.org/library/itertools.html

• More information in Itertools

• from itertools import *

• count(start=0, step=1)

• cycle(iterable)

• repeat(object[, times])

• accumulate(iterable[, func, *, initial=None])

• chain(*iterables)

• compress(data, selectors)

• islice(iterable, start, stop[, step])

• starmap(function, iterable)

• product(*iterables, repeat=1)

• permutations(iterable, r=None)

• combinations(iterable, r)

• combinations_with_replacement(iterable, r)

• groupby(iterable, key=None)

## 4.2.6. Memory Footprint¶

• sys.getsizeof(obj) returns the size of an obj in bytes

• sys.getsizeof(obj) calls obj.__sizeof__() method

• sys.getsizeof(obj) adds an additional garbage collector overhead if the obj is managed by the garbage collector

>>> from sys import getsizeof
>>>
>>>
>>> gen1 = (x for x in range(0,1))
>>> gen10 = (x for x in range(0,10))
>>> gen100 = (x for x in range(0,100))
>>> gen1000 = (x for x in range(0,1000))
>>>
>>> getsizeof(gen1)
112
>>>
>>> getsizeof(gen10)
112
>>>
>>> getsizeof(gen100)
112
>>>
>>> getsizeof(gen1000)
112

>>> from sys import getsizeof
>>>
>>>
>>> com1 = [x for x in range(0,1)]
>>> com10 = [x for x in range(0,10)]
>>> com100 = [x for x in range(0,100)]
>>> com1000 = [x for x in range(0,1000)]
>>>
>>>
>>> getsizeof(com1)
88
>>>
>>> getsizeof(com10)
184
>>>
>>> getsizeof(com100)
920
>>>
>>> getsizeof(com1000)
8856


Figure 4.3.

## 4.2.7. Inspection¶

>>> from inspect import isgenerator
>>>
>>>
>>> a = [x for x in range(0,5)]
>>> b = (x for x in range(0,5))
>>>
>>> isgenerator(a)
False
>>> isgenerator(b)
True

>>> from inspect import isgenerator
>>>
>>>
>>> data = range(0, 10)
>>>
>>> isgenerator(data)
False


## 4.2.8. Introspection¶

>>> data = (x for x in range(0,10))
>>>
>>>
>>> next(data)
0
>>>
>>> data.gi_code  # doctest: +ELLIPSIS
<code object <genexpr> at 0x..., file "<...>", line 1>
>>>
>>> data.gi_running
False
>>>
>>> data.gi_frame  # doctest: +ELLIPSIS
<frame at 0x..., file '<...>', line 1, code <genexpr>>
>>>
>>> data.gi_frame.f_locals  # doctest: +ELLIPSIS
{'.0': <range_iterator object at 0x...>, 'x': 0}
>>>
>>> data.gi_frame.f_code  # doctest: +ELLIPSIS
<code object <genexpr> at 0x...0, file "<...>", line 1>
>>>
>>> data.gi_frame.f_lineno
1
>>>
>>> data.gi_frame.f_lasti
8
>>>
>>> data.gi_yieldfrom


## 4.2.9. Multiple Yields¶

>>> def run():
...     for x in range(0, 3):
...         yield x
...     for y in range(10, 13):
...         yield y
>>>
>>>
>>> result = run()
>>>
>>> type(result)
<class 'generator'>
>>>
>>> next(result)
0
>>> next(result)
1
>>> next(result)
2
>>> next(result)
10
>>> next(result)
11
>>> next(result)
12
>>> next(result)
Traceback (most recent call last):
StopIteration


## 4.2.10. Yield From¶

• Since Python 3.3: PEP 380 -- Syntax for Delegating to a Subgenerator

• Helps with refactoring generators

• Useful for large generators which can be split into smaller ones

• Delegation call

• yield from terminates on GeneratorExit from other function

• The value of the yield from expression is the first argument to the StopIteration exception raised by the iterator when it terminates

• Return expr in a generator causes StopIteration(expr) to be raised upon exit from the generator

>>> def generator1():
...     for x in range(0, 3):
...         yield x
>>>
>>> def generator2():
...     for x in range(10, 13):
...         yield x
>>>
>>> def run():
...     yield from generator1()
...     yield from generator2()
>>>
>>>
>>> result = run()
>>>
>>> type(result)
<class 'generator'>
>>>
>>> next(result)
0
>>> next(result)
1
>>> next(result)
2
>>> next(result)
10
>>> next(result)
11
>>> next(result)
12
>>> next(result)
Traceback (most recent call last):
StopIteration


The code is equivalent to itertools.chain():

>>> from itertools import chain
>>>
>>>
>>> def generator1():
...     for x in range(0, 3):
...         yield x
>>>
>>> def generator2():
...     for x in range(10, 13):
...         yield x
>>>
>>> def run():
...     for x in chain(generator1(), generator2()):
...         yield x
>>>
>>>
>>> result = run()
>>>
>>> type(result)
<class 'generator'>
>>>
>>> list(result)
[0, 1, 2, 10, 11, 12]


yield from turns ordinary function, into a delegation call:

>>> def worker():
...     return [1, 2, 3]
>>>
>>> def run():
...     yield from worker()
>>>
>>>
>>> result = run()
>>>
>>> next(result)
1
>>> next(result)
2
>>> next(result)
3
>>> next(result)
Traceback (most recent call last):
StopIteration

>>> def worker():
...     return [x for x in range(0,3)]
>>>
>>> def run():
...     yield from worker()
>>>
>>>
>>> result = run()
>>>
>>> next(result)
0
>>> next(result)
1
>>> next(result)
2
>>> next(result)
Traceback (most recent call last):
StopIteration


yield from with sequences:

>>> def run():
...     yield from [0, 1, 2]
>>>
>>>
>>> result = run()
>>>
>>> type(result)
<class 'generator'>
>>>
>>> next(result)
0
>>> next(result)
1
>>> next(result)
2
>>> next(result)
Traceback (most recent call last):
StopIteration


yield from with comprehensions:

>>> def run():
...     yield from [x for x in range(0,3)]
>>>
>>>
>>> result = run()
>>>
>>> type(result)
<class 'generator'>
>>>
>>> next(result)
0
>>> next(result)
1
>>> next(result)
2
>>> next(result)
Traceback (most recent call last):
StopIteration


yield from with generator expressions:

>>> def run():
...     yield from (x for x in range(0,3))
>>>
>>>
>>> result = run()
>>>
>>> type(result)
<class 'generator'>
>>>
>>> next(result)
0
>>> next(result)
1
>>> next(result)
2
>>> next(result)
Traceback (most recent call last):
StopIteration


## 4.2.11. Send¶

• .send() method allows to pass value to the generator

• data = yield will receive this "sent" value

• After running you have to send None value to begin processing

• Sending anything other will raise TypeError

>>> def run():
...     while True:
...         data = yield
...         print(f'Processing {data}')
>>>
>>>
>>> worker = run()
>>>
>>> type(worker)
<class 'generator'>
>>>
>>> worker.send('hello')
Traceback (most recent call last):
TypeError: can't send non-None value to a just-started generator

>>> def run():
...     while True:
...         data = yield
...         print(f'Processing {data}')
>>>
>>>
>>> worker = run()
>>> worker.send(None)
>>>
>>> for x in range(0,3):
...     worker.send(x)
Processing 0
Processing 1
Processing 2

>>> def worker():
...     while True:
...         data = yield
...         print(f'Processing {data}')
>>>
>>> def run(gen):
...     gen.send(None)
...     while True:
...         x = yield
...         gen.send(x)
>>>
>>>
>>> result = run(worker())
>>> result.send(None)
>>>
>>> for x in range(0,3):
...     result.send(x)
Processing 0
Processing 1
Processing 2


## 4.2.12. Conclusion¶

• Python yield keyword creates a generator function.

• It’s useful when the function returns a large amount of data by splitting it into multiple chunks.

• We can also send values to the generator using its send() function.

• The yield from statement is used to create a sub-iterator from the generator function.

## 4.2.13. Assignments¶

"""
* Assignment: Function Generator Chain
* Complexity: easy
* Lines of code: 10 lines
* Time: 8 min

English:
1. Use generator expression to create result
2. In generator use range() to get numbers from 1 to 33 (inclusive) divisible by 3
3. Use filter() to get odd numbers from result
4. Use map() to cube all numbers in result
5. Set result with arithmetic mean of result
6. Compare result with "Tests" section (see below)

Polish:
1. Użyj wyrażenia generatorowego do stworzenia result
2. W generatorze użyj range() aby otrzymać liczby od 1 do 33 (włącznie) podzielne przez 3
3. Użyj filter() aby otrzymać liczby nieparzyste z result
4. Użyj map() aby podnieść wszystkie liczby w result do sześcianu
5. Ustaw result ze średnią arytmetyczną z result
6. Porównaj wyniki z sekcją "Tests" (patrz poniżej)

Hints:
* type cast to list() to expand generator before calculating mean
* mean = sum(...) / len(...)

Tests:
>>> result
11502.0
"""

# Given
def odd(x):
return x % 2

def cube(x):
return x ** 3

result: float


"""
* Assignment: Function Generator Iris
* Complexity: easy
* Lines of code: 8 lines
* Time: 8 min

English:
1. Use code from "Given" section (see below)
2. Write filter for DATA which returns features for given species
3. Implement solution using function
4. Implement solution using generator and yield keyword
5. Compare results of both using sys.getsizeof()
6. What will happen if input data will be bigger?
7. Note, that in different Python versions you'll get slightly
different values for getsizeof generator and function:
a. 112 for generator in Python 3.9
b. 112 for generator in Python 3.8
c. 120 for generator in Python 3.7
8. Compare result with "Tests" section (see below)

Polish:
1. Użyj kodu z sekcji "Given" (patrz poniżej)
2. Napisz filtr dla DATA zwracający features dla danego gatunku species
3. Zaimplementuj rozwiązanie wykorzystując funkcję
4. Zaimplementuj rozwiązanie wykorzystując generator i słowo kluczowe yield
5. Porównaj wyniki obu używając sys.getsizeof()
6. Co się stanie, gdy ilość danych będzie większa?
7. Zwróć uwagę, że w zależności od wersji Python wartości getsizeof
dla funkcji i generatora mogą się nieznaczenie różnić:
a. 112 dla generator w Python 3.9
b. 112 dla generator w Python 3.8
c. 120 dla generator w Python 3.7
8. Porównaj wyniki z sekcją "Tests" (patrz poniżej)

Tests:
>>> from sys import getsizeof
>>> from inspect import isfunction, isgeneratorfunction
>>> assert isfunction(function)
>>> assert isgeneratorfunction(generator)

>>> list(function(DATA, 'setosa'))
[[5.1, 3.5, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2]]
>>> list(generator(DATA, 'setosa'))
[[5.1, 3.5, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2]]

>>> getsizeof(function(DATA, 'setosa'))
88
>>> getsizeof(function(DATA*10, 'setosa'))
248
>>> getsizeof(function(DATA*100, 'setosa'))
1656
>>> getsizeof(generator(DATA, 'setosa'))
112
>>> getsizeof(generator(DATA*10, 'setosa'))
112
>>> getsizeof(generator(DATA*100, 'setosa'))
112
"""

# Given
DATA = [(5.8, 2.7, 5.1, 1.9, 'virginica'),
(5.1, 3.5, 1.4, 0.2, 'setosa'),
(5.7, 2.8, 4.1, 1.3, 'versicolor'),
(6.3, 2.9, 5.6, 1.8, 'virginica'),
(6.4, 3.2, 4.5, 1.5, 'versicolor'),
(4.7, 3.2, 1.3, 0.2, 'setosa')]

def function(data: list, species: str):
...

def generator(data: list, species: str):
...


"""

* Assignment: Function Generator Passwd
* Complexity: medium
* Lines of code: 10 lines
* Time: 8 min

English:
1. Use code from "Given" section (see below)
2. Split DATA by lines and then by colon :
3. Extract system accounts (users with UID [third field] is less than 1000)
4. Return list of system account logins
5. Implement solution using function
6. Implement solution using generator and yield keyword
7. Compare results of both using sys.getsizeof()
8. Compare result with "Tests" section (see below)

Polish:
1. Użyj kodu z sekcji "Given" (patrz poniżej)
2. Podziel DATA po liniach a następnie po dwukropku :
3. Wyciągnij konta systemowe (użytkownicy z UID (trzecie pole) mniejszym niż 1000)
4. Zwróć listę loginów użytkowników systemowych
5. Zaimplementuj rozwiązanie wykorzystując funkcję
6. Zaimplementuj rozwiązanie wykorzystując generator i słowo kluczowe yield
7. Porównaj wyniki obu używając sys.getsizeof()
8. Porównaj wyniki z sekcją "Tests" (patrz poniżej)

Tests:
>>> from sys import getsizeof
>>> from inspect import isfunction, isgeneratorfunction
>>> assert isfunction(function)
>>> assert isgeneratorfunction(generator)
>>> fun = function(DATA)
>>> gen = generator(DATA)
>>> list(fun)
['root', 'bin', 'daemon', 'adm', 'shutdown', 'halt', 'nobody', 'sshd']
>>> list(gen)
['root', 'bin', 'daemon', 'adm', 'shutdown', 'halt', 'nobody', 'sshd']
>>> getsizeof(fun)
120
>>> getsizeof(gen)
112
"""

# Given
DATA = """root:x:0:0:root:/root:/bin/bash
bin:x:1:1:bin:/bin:/sbin/nologin
daemon:x:2:2:daemon:/sbin:/sbin/nologin
adm:x:3:4:adm:/var/adm:/sbin/nologin
shutdown:x:6:0:shutdown:/sbin:/sbin/shutdown
halt:x:7:0:halt:/sbin:/sbin/halt
nobody:x:99:99:Nobody:/:/sbin/nologin
sshd:x:74:74:Privilege-separated SSH:/var/empty/sshd:/sbin/nologin
watney:x:1000:1000:Mark Watney:/home/watney:/bin/bash
jimenez:x:1001:1001:José Jiménez:/home/jimenez:/bin/bash
ivanovic:x:1002:1002:Иван Иванович:/home/ivanovic:/bin/bash
lewis:x:1003:1002:Melissa Lewis:/home/ivanovic:/bin/bash"""

def function(data: str):
...

def generator(data: str):
...