The Vanishing Pattern: from iterators to generators in Python

TheVanishing Pattern
from
iterators to
generators in
Python Luciano Ramalho
ramalho@turing.com.br
@ramalhoorg

@ramalhoorg
Demo: laziness
in the Django Shell
2

>>> from django.db import connection
>>> q = connection.queries
>>> q
[]
>>> from municipios.models import *
>>> res = Municipio.objects.all()[:5]
>>> q
[]
>>> for m in res: print m.uf, m.nome
...
GO Abadia de Goiás
MG Abadia dos Dourados
GO Abadiânia
MG Abaeté
PA Abaetetuba
>>> q
[{'time': '0.000', 'sql': u'SELECT "municipios_municipio"."id",
"municipios_municipio"."uf", "municipios_municipio"."nome",
"municipios_municipio"."nome_ascii", "municipios_municipio"."meso_regiao_id",
"municipios_municipio"."capital", "municipios_municipio"."latitude",
"municipios_municipio"."longitude", "municipios_municipio"."geohash" FROM
"municipios_municipio" ORDER BY "municipios_municipio"."nome_ascii" ASC LIMIT 5'}]

>>> q
[]
>>> q
[]
...
GO Abadia de Goiás
GO Abadiânia
MG Abaeté
PA Abaetetuba
>>> q
this expression makes
a Django QuerySet

>>> q
[]
>>> q
[]
...
GO Abadia de Goiás
GO Abadiânia
MG Abaeté
PA Abaetetuba
>>> q
a Django QuerySet
QuerySets are “lazy”:
no database access
so far

>>> q
[]
>>> q
[]
...
GO Abadia de Goiás
GO Abadiânia
MG Abaeté
PA Abaetetuba
>>> q
a Django QuerySet
QuerySets are “lazy”:
no database access
so far
the query is made
only when we
iterate over the
results

@ramalhoorg
QuerySet is a lazy iterable
7

@ramalhoorg
QuerySet is a lazy iterable
technical term
8

@ramalhoorg
Lazy
• Avoids unnecessary work, by postponing it as long as possible
• The opposite of eager
9
In Computer Science, being
“lazy” is often a good thing!

@ramalhoorg
Now, back to basics...
10

@ramalhoorg
Iteration: C and Python
#include <stdio.h>
int main(int argc, char *argv[]) {
int i;
for(i = 0; i < argc; i++)
printf("%sn", argv[i]);
return 0;
}
import sys
for arg in sys.argv:
print arg

@ramalhoorg
Iteration: Java (classic)
class Arguments {
public static void main(String[] args) {
for (int i=0; i < args.length; i++)
System.out.println(args[i]);
}
}
$ java Arguments alfa bravo charlie
alfa
bravo
charlie

@ramalhoorg
Iteration: Java ≥1.5
$ java Arguments2 alfa bravo charlie
alfa
bravo
charlie
• Enhanced for (a.k.a. foreach)
since
2004
class Arguments2 {
for (String arg : args)
System.out.println(arg);
}
}

@ramalhoorg
Iteration: Java ≥1.5
• Enhanced for (a.k.a. foreach)
class Arguments2 {
for (String arg : args)
System.out.println(arg);
}
}
since
2004
import sys
for arg in sys.argv:
print arg
since
1991

@ramalhoorg
You can iterate over many
Python objects
• strings
• ﬁles
• XML: ElementTree nodes
• not limited to built-in types:
• Django QuerySet
• etc.
15

@ramalhoorg
So, what is an iterable?
• Informal, recursive definition:
• iterable: fit to be iterated
• just as:
edible: fit to be eaten
16

@ramalhoorg
The for loop statement is
not the only construct that
handles iterables...
17

List comprehension
● Compreensão de lista ou abrangência
● Exemplo: usar todos os elementos:
– L2 = [n*10 for n in L]
List comprehension
• An expression that builds a list from any iterable
>>> s = 'abracadabra'
>>> l = [ord(c) for c in s]
>>> l
[97, 98, 114, 97, 99, 97, 100, 97, 98, 114, 97]
input: any iterable object
output: a list (always)

@ramalhoorg
Set comprehension
• An expression that builds a set from any iterable
>>> set(s)
{'b', 'r', 'a', 'd', 'c'}
>>> {ord(c) for c in s}
{97, 98, 99, 100, 114}
19

@ramalhoorg
Dict comprehensions
• An expression that builds a dict from any iterable
>>> {c:ord(c) for c in s}
{'a': 97, 'r': 114, 'b': 98, 'c': 99, 'd': 100}
20

@ramalhoorg
Syntactic support for iterables
• Tuple unpacking,
parallel assignment
>>> a, b, c = 'XYZ'
>>> a
'X'
>>> b
'Y'
>>> c
'Z'
21
>>> l = [(c, ord(c)) for c in 'XYZ']
>>> l
[('X', 88), ('Y', 89), ('Z', 90)]
>>> for char, code in l:
... print char, '->', code
...
X -> 88
Y -> 89
Z -> 90

@ramalhoorg
Syntactic support for iterables (2)
• Function calls: exploding arguments with *
>>> import math
>>> def hypotenuse(a, b):
... return math.sqrt(a*a + b*b)
...
>>> hypotenuse(3, 4)
5.0
>>> sides = (3, 4)
>>> hypotenuse(sides)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: hypotenuse() takes exactly 2 arguments (1 given)
>>> hypotenuse(*sides)
5.0 22

@ramalhoorg
Built-in iterable types
• basestring
• str
• unicode
• dict
• ﬁle
• frozenset
• list
• set
• tuple
• xrange
23

@ramalhoorg
Built-in functions that take iterable
arguments
• all
• any
• ﬁlter
• iter
• len
• map
• max
• min
• reduce
• sorted
• sum
• zip
unrelated to compression

@ramalhoorg
Classic iterables in Python
25

@ramalhoorg
Iterator is...
• a classic design pattern
Design Patterns
Gamma, Helm, Johnson &Vlissides
Addison-Wesley,
ISBN 0-201-63361-2
26

@ramalhoorg
Head First Design
Patterns Poster
O'Reilly,
ISBN 0-596-10214-3
27

@ramalhoorg
Head First Design
Patterns Poster
O'Reilly,
ISBN 0-596-10214-3
28
“The Iterator Pattern
provides a way to access
the elements of an
aggregate object
sequentially without
exposing the underlying
representation.”

An iterable Train class
>>> train = Train(4)
>>> for car in train:
... print(car)
car #1
car #2
car #3
car #4
>>>

@ramalhoorg
class Train(object):
def __init__(self, cars):
self.cars = cars
def __len__(self):
return self.cars
def __iter__(self):
return TrainIterator(self)
class TrainIterator(object):
def __init__(self, train):
self.train = train
self.current = 0
def __next__(self): # Python 3
if self.current < len(self.train):
self.current += 1
return 'car #%s' % (self.current)
else:
raise StopIteration()
An iterable
Train with
iterator
iterable
iterator

@ramalhoorg
Iterable ABC
• collections.Iterable abstract base class
• A concrete subclass of Iterable must
implement .__iter__
• .__iter__ returns an Iterator
• You don’t usually call .__iter__ directly
• when needed, call iter(x)
31

@ramalhoorg
Iterator ABC
• Iterator provides
.next
or
.__next__
• .__next__ returns the next item
• You don’t usually call .__next__ directly
• when needed, call next(x)
Python 3
Python 2
Python ≥ 2.6
32

@ramalhoorg
for car in train:
• calls iter(train) to
obtain a TrainIterator
• makes repeated calls to
next(aTrainIterator)
until it raises StopIteration
self.cars = cars
def __len__(self):
return self.cars
def __iter__(self):
self.train = train
self.current = 0
self.current += 1
else:
Train with
iterator
1
1
2
... print(car)
car #1
car #2
car #3
2

@ramalhoorg34 Richard Bartz/Wikipedia

@ramalhoorg
Iterable duck-like
creatures
35

@ramalhoorg
Design patterns in dynamic
languages
• Dynamic languages: Lisp, Smalltalk, Python,
Ruby, PHP, JavaScript...
• Many features not found in C++, where most
of the original 23 Design Patterns were
identiﬁed
• Java is more dynamic than C++, but much
more static than Lisp, Python etc.
36
Gamma, Helm, Johnson, Vlissides
a.k.a. the Gang of Four (GoF)

Peter Norvig:
“Design Patterns in Dynamic Languages”

@ramalhoorg
Dynamic types
• No need to declare types or interfaces
• It does not matter what an object claims do be, only what it is
capable of doing
38

@ramalhoorg
Duck typing
39
“In other words, don't
check whether it is-a duck:
check whether it quacks-
like-a duck, walks-like-a
duck, etc, etc, depending
on exactly what subset of
duck-like behaviour you
need to play your
language-games with.”
Alex Martelli
comp.lang.python (2000)

@ramalhoorg
A Python iterable is...
• An object from which the iter function can produce an iterator
• The iter(x) call:
• invokes x.__iter__() to obtain an iterator
• but, if x has no __iter__:
• iter makes an iterator which tries to fetch items from x by doing
x[0], x[1], x[2]...
sequence protocol
Iterable interface
40

@ramalhoorg
Train: a sequence of cars
train = Train(4)
41
train[0] train[1] train[2] train[3]

>>> len(train)
4
>>> train[0]
'car #1'
>>> train[3]
'car #4'
>>> train[-1]
'car #4'
>>> train[4]
...
IndexError: no car at 4
... print(car)
car #1
car #2
car #3
car #4

self.cars = cars
def __getitem__(self, key):
index = key if key >= 0 else self.cars + key
if 0 <= index < len(self): # index 2 -> car #3
return 'car #%s' % (index + 1)
else:
raise IndexError('no car at %s' % key)
if __getitem__ exists,
iteration “just works”

@ramalhoorg
The sequence protocol at work
>>> t = Train(4)
>>> len(t)
4
>>> t[0]
'car #1'
>>> t[3]
'car #4'
>>> t[-1]
'car #4'
>>> for car in t:
... print(car)
car #1
car #2
car #3
car #4
__len__
__getitem__
__getitem__

@ramalhoorg
Protocol
• protocol: a synonym for interface used in dynamic
languages like Smalltalk, Python, Ruby, Lisp...
• not declared, and not enforced by static checks
45

self.cars = cars
def __len__(self):
return self.cars
else:
Sequence protocol
__len__ and __getitem__
implement the immutable
sequence protocol

import collections
class Train(collections.Sequence):
self.cars = cars
def __len__(self):
return self.cars
else:
Sequence ABC
• collections.Sequence abstract base class
abstract
methods
Python ≥ 2.6

import collections
self.cars = cars
def __len__(self):
return self.cars
else:
Sequence ABC
implement
these 2

import collections
self.cars = cars
def __len__(self):
return self.cars
else:
Sequence ABC
inherit these 5

@ramalhoorg
Sequence ABC
>>> 'car #2' in train
True
>>> 'car #7' in train
False
>>> for car in reversed(train):
... print(car)
car #4
car #3
car #2
car #1
>>> train.index('car #3')
2 50

@ramalhoorg51 U.S. NRC/Wikipedia

@ramalhoorg
Iteration in C (example 2)
#include <stdio.h>
int main(int argc, char *argv[]) {
int i;
for(i = 0; i < argc; i++)
printf("%d : %sn", i, argv[i]);
return 0;
}
$ ./args2 alfa bravo charlie
0 : ./args2
1 : alfa
2 : bravo
3 : charlie

@ramalhoorg
Iteration in Python (ex. 2)
import sys
for i in range(len(sys.argv)):
print i, ':', sys.argv[i]
$ python args2.py alfa bravo charlie
0 : args2.py
1 : alfa
2 : bravo
3 : charlie 54
not Pythonic

@ramalhoorg
import sys
for i, arg in enumerate(sys.argv):
print i, ':', arg
0 : args2.py
1 : alfa
2 : bravo
3 : charlie 55
Pythonic!

@ramalhoorg
import sys
for i, arg in enumerate(sys.argv):
print i, ':', arg
0 : args2.py
1 : alfa
2 : bravo
3 : charlie
this returns a lazy
iterable object
that object yields tuples
(index, item)
on demand, at each
iteration
56

@ramalhoorg
What
enumerate does >>> e = enumerate('Turing')
>>> e
<enumerate object at 0x...>
>>>
enumerate builds an
enumerate object
57

@ramalhoorg
What
enumerate does
isso constroi
um gerador
and that is iterable
>>> e = enumerate('Turing')
>>> e
>>> for item in e:
... print item
...
(0, 'T')
(1, 'u')
(2, 'r')
(3, 'i')
(4, 'n')
(5, 'g')
>>>
58
enumerate builds an
enumerate object

@ramalhoorg
What
enumerate does
isso constroi
um gerador
the enumerate object
produces an
(index, item) tuple
for each next(e) call
>>> e = enumerate('Turing')
>>> e
>>> next(e)
(0, 'T')
>>> next(e)
(1, 'u')
>>> next(e)
(2, 'r')
>>> next(e)
(3, 'i')
>>> next(e)
(4, 'n')
>>> next(e)
(5, 'g')
>>> next(e)
Traceback (most recent...):
...
StopIteration
• The enumerator object is an
example of a generator

@ramalhoorg
Iterator x generator
• By deﬁnition (in GoF) an iterator retrieves successive items
from an existing collection
• A generator implements the iterator interface (next) but
produces items not necessarily in a collection
• a generator may iterate over a collection, but return the items
decorated in some way, skip some items...
• it may also produce items independently of any existing data
source (eg. Fibonacci sequence generator)
60

@ramalhoorg
Very simple
generators
62

@ramalhoorg
Generator
function
• Any function that has the
yield keyword in its body
is a generator function
63
>>> def gen_123():
... yield 1
... yield 2
... yield 3
...
>>> for i in gen_123(): print(i)
1
2
3
>>>
the keyword gen was considered
for defining generator functions,
but def prevailed

@ramalhoorg
• When invoked, a
generator function
returns a
generator object
Generator
function
64
>>> def gen_123():
... yield 1
... yield 2
... yield 3
...
>>> for i in gen_123(): print(i)
1
2
3
>>> g = gen_123()
>>> g
<generator object gen_123 at ...>

@ramalhoorg
Generator
function
>>> def gen_123():
... yield 1
... yield 2
... yield 3
...
>>> g = gen_123()
>>> g
<generator object gen_123 at ...>
>>> next(g)
1
>>> next(g)
2
>>> next(g)
3
>>> next(g)
...
StopIteration
• Generator objects implement the
Iterator interface
65

@ramalhoorg
Generator
behavior
• Note how the output of
the generator function is
interleaved with the
output of the calling code
66
>>> def gen_AB():
... print('START')
... yield 'A'
... print('CONTINUE')
... yield 'B'
... print('END.')
...
>>> for c in gen_AB():
... print('--->', c)
...
START
---> A
CONTINUE
---> B
END.
>>>

@ramalhoorg
Generator
behavior
• The body is executed only
when next is called, and it
runs only up to the following
yield
>>> def gen_AB():
... print('START')
... yield 'A'
... yield 'B'
... print('END.')
...
>>> g = gen_AB()
>>> next(g)
START
'A'
>>>

@ramalhoorg
Generator
behavior
• When the body of the function
returns, the generator object
throws StopIteration
• The for statement catches
that for you
68
>>> def gen_AB():
... print('START')
... yield 'A'
... yield 'B'
... print('END.')
...
>>> g = gen_AB()
>>> next(g)
START
'A'
>>> next(g)
CONTINUE
'B'
>>> next(g)
END.
StopIteration

for car in train:
obtain a generator
• makes repeated calls to next(generator) until
the function returns, which raises StopIteration
self.cars = cars
def __iter__(self):
for i in range(self.cars):
# index 2 is car #3
yield 'car #%s' % (i+1)
Train with
generator
function 1
1
2
... print(car)
car #1
car #2
car #3
2

Classic iterator
x generator
self.cars = cars
def __len__(self):
return self.cars
def __iter__(self):
self.train = train
self.current = 0
self.current += 1
else:
self.cars = cars
def __iter__(self):
2 classes,
12 lines of code
1 class,
3 lines of code

self.cars = cars
def __iter__(self):
The pattern
just vanished

self.cars = cars
def __iter__(self):
“When I see patterns in my
programs, I consider it a sign of
trouble. The shape of a program
should reﬂect only the problem it
needs to solve. Any other regularity
in the code is a sign, to me at least,
that I'm using abstractions that
aren't powerful enough -- often that
I'm generating by hand the
expansions of some macro that I
need to write.”
Paul Graham
Revenge of the nerds (2002)

Generator expression (genexp)
>>> g = (c for c in 'ABC')
>>> g
<generator object <genexpr> at 0x10045a410>
>>> for l in g:
... print(l)
...
A
B
C
>>>

@ramalhoorg
• When evaluated,
returns a generator object
>>> g = (n for n in [1, 2, 3])
>>> g
<generator object <genexpr> at 0x...>
>>> next(g)
1
>>> next(g)
2
>>> next(g)
3
>>> next(g)
StopIteration
Generator expression (genexp)

for car in train:
obtain a generator
• makes repeated calls to next(generator) until
the function returns, which raises StopIteration
1
2
self.cars = cars
def __iter__(self):
return ('car #%s' % (i+1)
for i in range(self.cars))
Train with generator expression
... print(car)
car #1
car #2
car #3

self.cars = cars
def __iter__(self):
return ('car #%s' % (i+1) for i in range(self.cars))
Generator function
x genexpclass Train(object):
self.cars = cars
def __iter__(self):

@ramalhoorg
Built-in functions that return
iterables, iterators or generators
• dict
• enumerate
• frozenset
• list
• reversed
• set
• tuple
78

@ramalhoorg
• boundless generators
• count(), cycle(), repeat()
• generators which combine several iterables:
• chain(), tee(), izip(), imap(), product(), compress()...
• generators which select or group items:
• compress(), dropwhile(), groupby(), iﬁlter(), islice()...
• generators producing combinations of items:
• product(), permutations(), combinations()...
The itertools module Don’t reinvent the
wheel, use itertools!
this was not
reinvented:
ported from
Haskell
great for
MapReduce

@ramalhoorg
Generators in Python 3
• Several functions and methods of the standard library that used to
return lists, now return generators and other lazy iterables in Python 3
• dict.keys(), dict.items(), dict.values()...
• range(...)
• like xrange in Python 2.x (more than a generator)
• If you really need a list, just pass the generator to the list constructor.
Eg.: list(range(10))
80

@ramalhoorg
A practical example using
generator functions
• Generator functions to decouple reading and writing logic in a database
conversion tool designed to handle large datasets
https://github.com/ramalho/isis2json
81

@ramalhoorg
Main loop writes
JSON ﬁle

@ramalhoorg
Another loop reads
the input records

@ramalhoorg
One implementation:
same loop reads/writes

@ramalhoorg
But what if we need to read
another format?

@ramalhoorg
Functions in the
script
• iterMstRecords*
• iterIsoRecords*
• writeJsonArray
• main

* generator functions

@ramalhoorg
main:
read command
line arguments

main: determine
input format
selected generator function is passed
as an argument
input generator function is selected
based on the input file extension

@ramalhoorg
writeJsonArray:
write JSON records
89

writeJsonArray:
iterates over one of the input
generator functions
selected generator function received as
an argument...
and called to produce input
generator

@ramalhoorg
iterIsoRecords:
read records
from ISO-2709
format ﬁle
generator function!
91

@ramalhoorg
iterIsoRecords
yields one record,
structured as a dict
creates a new dict in each
iteration
92

@ramalhoorg
iterMstRecords:
read records
from ISIS
.MST ﬁle
generator function!

iterIsoRecordsiterMstRecords
yields one record,
structured as a dict
creates a new dict in each
iteration

@ramalhoorg
We did not cover
• other generator methods:
• gen.close(): causes a GeneratorExit exception to be raised
within the generator body, at the point where it is paused
• gen.throw(e): causes any exception e to be raised within the
generator body, at the point it where is paused
Mostly useful for long-running processes.
Often not needed in batch processing scripts.
98

@ramalhoorg
We did not cover
• generator delegation with yield from
• sending data into a generator function with the
gen.send(x) method (instead of next(gen)),
and using yield as an expression to get the
data sent
• using generator functions as coroutines
not useful in the
context of iteration
Python ≥ 3.3
“Coroutines are not
related to iteration”
David Beazley
99

@ramalhoorg
How to learn generators
• Forget about .send() and coroutines: that is a completely different
subject. Look into that only after mastering and becoming really
confortable using generators for iteration.
• Study and use the itertools module
• Don’t worry about .close() and .throw() initially. You can be
productive with generators without using these methods.
• yield from is only available in Python 3.3, and only relevant if you
need to use .close() and .throw()
100

Q & A
Luciano Ramalho
luciano@ramalho.org
@ramalhoorg
https://github.com/ramalho/isis2json

The Vanishing Pattern: from iterators to generators in Python

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