Pythonic Thinking

Know which version you are using

• 2.7 on most of the machines. still some 2.6
• some minor changes even within 2.7
  

  celery(amqp==2.0.3)

  will not work on <2.7.7
• and we are embracing Python 3

Know which version you are using

• 2to3 will help a lot basically
• and check Python doc before migration
• pyenv for managing and switching Python versions
• make sure your global version is still "system" to ensure libraries will still work
• combine with virtualenv to make development env more isolated

Follow PEP 8 style guide

• For Vim

  Plugin 'klen/python-mode'

• For Sublime Text Editor

  Anaconda

Use list comprehension over map & filer

• Faster(True for 2.*)
• More Pythonic

      
  >>> import timeit
  >>> print(timeit.timeit('[i ** 2 for i in range(1000) if i % 2 == 0]', number=10000))
  0.637861013412
  >>> print(timeit.timeit('map(lambda x: x ** 2, filter(lambda x: x % 2 == 0, range(1000)))', number=10000))
  1.21372795105
  >>> print(timeit.timeit('[i ** 2 for i in xrange(1000) if i % 2 == 0]', number=10000))
  0.5871758461
  >>> print(timeit.timeit('map(lambda x: x ** 2, filter(lambda x: x % 2 == 0, xrange(1000)))', number=10000))
  1.16653990746
      
    

Generator for large comprehensions

• More memory friendly

• yield is more than generator though
  cover this in later section with concurrency & performance
• [] --> ()
• but you can only use it once

Python 3: map & filter

      
  import timeit

  print(timeit.timeit('[i ** 2 for i in range(1000) if i % 2 == 0]', number=10000))
  # 2.831494385987753
  print(timeit.timeit('(i ** 2 for i in range(1000) if i % 2 == 0)', number=10000))
  # 0.007943934993818402
  print(timeit.timeit('map(lambda x: x ** 2, filter(lambda x: x % 2 == 0, range(1000)))', number=10000))
  # 0.008463110993034206
      
    

"map() and filter() return iterators"
-- What's new in Python 3.0

Use True in infinite loop

• More readable, it is not C || C++
• although slower on <3.0

      
>>> import timeit
>>> def test1():
...     idx = 1
...     while True:
...        idx += 1
...        if idx > 1000:
...           break
>>> def test2():
...     idx = 1
...     while 1:
...        idx += 1
...        if idx > 1000:
...            break
...
>>> timeit.timeit(test1, number=1000000)
60.8167519569397
>>> timeit.timeit(test2, number=1000000)
35.87116599082947
      
    

Use True in infinite loop

• In Python 3, True is reserved as constants
• Meaning you can do True = 1 in 2.7

      
# See that Python 3 is generally slower than 2
import timeit
def test1():
  idx = 0
  while 1:
    idx += 1
    if idx > 1000:
      break
def test2():
  idx = 0
  while True:
    idx += 1
    if idx > 1000:
      break
print(timeit.timeit(test1, number=1000000))
# 55.750455176021205
print(timeit.timeit(test2, number=1000000))
# 56.51068397599738
      
    

Use unpacking for swapping variables

• Save code & more readable
• Faster

      
import timeit
def test1():
  x, y = 1, 2
  for _ in range(1000):
    temp = x
    x = y
    y = temp
def test2():
  x, y = 1, 2
  for _ in range(1000):
    y, x = x, y
print(timeit.timeit(test1, number=4000000))
# 147.59938350701123
print(timeit.timeit(test2, number=4000000))
# 133.6212830049917
      
    

Use unpacking for swapping variables

      
## test1
        >>   25 FOR_ITER                24 (to 52)
             28 STORE_FAST               2 (_)

  4          31 LOAD_FAST                0 (x)
             34 STORE_FAST               3 (temp)

  5          37 LOAD_FAST                1 (y)
             40 STORE_FAST               0 (x)

  6          43 LOAD_FAST                3 (temp)
             46 STORE_FAST               1 (y)
             49 JUMP_ABSOLUTE           25
## test2
        >>   25 FOR_ITER                19 (to 47)
             28 STORE_FAST               2 (_)

 12          31 LOAD_FAST                0 (x)
             34 LOAD_FAST                1 (y)
             37 ROT_TWO
             38 STORE_FAST               1 (y)
             41 STORE_FAST               0 (x)
             44 JUMP_ABSOLUTE           25
      
    

Careful use of else

• Really great with try
• May take time to understand when with for or while

Pay attention to finally

• Supposed to do cleanup work
• Control clauses like return or break will silence any exceptions in the try-block

Use zip for loop iterators together

• Iterate 2 iterable at the same time
• Check itertools.izip & itertools.izip_longest
• BTW, itertools is also a very powerful library

      
example = dict((k, v) for k, v in zip(ls1, ls2))
      
    

Functions

Prefer exceptions to returning None

• Python is not C or Go
• Clearer control flow

Understanding closure

• Understanding LEGB
• Cannot assign without declaration
• nonlocal in Python 3

      
def test_non_local():
    def inner():
        nonlocal outvar
        outvar = 5

    outvar = 3
    print(outvar)
    inner()
    print(outvar)

test_non_local()
      
    

Understanding closure

• Viariables in closure only gets evaluated at execution

      
print([func(2) for func in [lambda x: i * x for i in range(5)]])
# [8, 8, 8, 8, 8]
print([func(2) for func in [lambda x, i=i: i * x for i in range(5)]])
# [0, 2, 4, 6, 8]
print([func(2) for func in (lambda x: i * x for i in range(5))])
# [0, 2, 4, 6, 8]
      
    

Generator over returning list

• yield will transform a function to a generator
• Efficiency
• Infinite series

      
def fib_infinite():
    a, b = 0, 1
    while True:
        a, b = a + b, a
        yield a

import itertools
print(list(itertools.islice(fib_infinite(), 20)))
      
    

Prefer kwargs for clarity and extensibility

• User knows, reader knows
• Optional arguments

      
# version 1
def get_user(user_id):
    return User.find(id=user_id)
# version 2
def get_user(user_id, **kwargs):
    encoding = kwargs.pop('encoding', None)
    user = User.find(id=user_id)
    encoder = get_encoder(encoding)
    return encoder.encode(user)
# we can even further improve it if encoder is used by
#   many other functions as well
      
    

Use None for default argument

• Immutability is the KEY!

      
def test_default_arg(default=None):
    default = [] if default is None else default
    print(default)

test_default_arg()
test_default_arg('')
test_default_arg(0)
      
    

Decorator

• One of the greatest features in Python
• Memorization
• Logging
• Profiling
• and etc.

Decorator

      
from functools import wraps
def profile_func(func):
    @wraps(func)
    def profiled_func(*args, **kwargs):
        profile = cProfile.Profile()
        try:
            profile.enable()
            result = func(*args, **kwargs)
            profile.disable()
        finally:
            profile.print_stats()
        return result

    return profiled_func

@profile_func
def demo():
    return 'Hello, world!'

print(demo.__name__)
      
    

Decorator

      
# version 3 of get_user
from functools import wraps
def encode_result(func):
    @wraps(func)
    def inner(*args, **kwargs):
        encoding = kwargs.pop('encoding', None)
        result = func(*args, **kwargs)
        return get_encoder(encoding).encode(result)
    return inner

@encode_result
def get_user(user_id, **kwargs):
    return User.find(id=user_id)
      
    

Decorator

      
# version 3 of get_user
from functools import wraps
def encode_result(encoding=None):
    def wrapper(func):
        @wraps(func)
        def inner(*args, **kwargs):
            result = func(*args, **kwargs)
            return get_encoder(encoding).encode(result)
        return inner
    return wrapper

@encode_result(encoding='json')
def get_user(user_id, **kwargs):
    return User.find(id=user_id)