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[...]

from modu import *

[...]

x = sqrt(4)  # Is sqrt part of modu? A builtin? Defined above?

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from modu import sqrt

[...]

x = sqrt(4)  # sqrt may be part of modu, if not redefined in between

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import modu

[...]

x = modu.sqrt(4)  # sqrt is visibly part of modu‘s namespace

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def foo():

# do something

def decorator(func):

# manipulate func

return func

foo = decorator(foo)  # Manually decorate

@decorator

def bar():

# Do something

# bar() is decorated

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a = 1

a = ‘a string‘

def a():

pass  # Do something

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count = 1

msg = ‘a string‘

def func():

pass  # Do something

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Bad

items = ‘a b c d‘  # This is a string...

items = items.split(‘ ‘)  # ...becoming a list

items = set(items)  # ...and then a set

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# create a concatenated string from 0 to 19 (e.g. "012..1819")

nums = ""

for n in range(20):

nums += str(n)   # slow and inefficient

print nums

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# create a concatenated string from 0 to 19 (e.g. "012..1819")

nums = []

for n in range(20):

nums.append(str(n))

print "".join(nums)  # much more efficient

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# create a concatenated string from 0 to 19 (e.g. "012..1819")

nums = [str(n) for n in range(20)]

print "".join(nums)

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foo = ‘foo‘

bar = ‘bar‘

foobar = foo + bar  # This is good

foo += ‘ooo‘  # This is bad, instead you should do:

foo = ‘‘.join([foo, ‘ooo‘])

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foo = ‘foo‘

bar = ‘bar‘

foobar = ‘%s%s‘ % (foo, bar) # It is OK

foobar = ‘{0}{1}‘.format(foo, bar) # It is better

foobar = ‘{foo}{bar}‘.format(foo=foo, bar=bar) # It is best

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def make_complex(*args):

x, y = args

return dict(**locals())

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def make_complex(x, y):

return {‘x‘: x, ‘y‘: y}

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print ‘one‘; print ‘two‘

if x == 1: print ‘one‘

if  and :

# do something

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print ‘one‘

print ‘two‘

if x == 1:

print ‘one‘

cond1 =

cond2 =

if cond1 and cond2:

# do something

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#不解释了

位置参数，默认参数，*args, **args

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原因：

Python comes with a very rich set of hooks and tools allowing to do almost any kind of tricky tricks

建议：

it is always better to use the most straightforward way to achieve your goal

感受一下:

We consider that a Python developer should know about these nearly infinite possibilities,

because it grows the confidence that no hard-wall will be on the way.

However, knowing how to use them and particularly when not to use them is the most important.

Like a Kungfu master, a Pythonista knows how to kill with a single finger, and never to actually do it.

其实就是告诉你，骚年，这玩意你要去学习去了解去掌握，目的是增强实力保持自信，但是不要去用啊

(说的原子弹吧....)

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for index, item in enumerate(some_list):

# do something with index and item

a, b = b, a

a, (b, c) = 1, (2, 3)

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filename = ‘foobar.txt‘

basename, __, ext = filename.rpartition(‘.‘)

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four_nones = [None] * 4

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four_lists = [[] for __ in xrange(4)]

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letters = [‘s‘, ‘p‘, ‘a‘, ‘m‘]

word = ‘‘.join(letters)

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d = {‘s‘: [], ‘p‘: [], ‘a‘: [], ‘m‘: []}

l = [‘s‘, ‘p‘, ‘a‘, ‘m‘]

def lookup_dict(d): #O(1)

return ‘s‘ in d

def lookup_list(l): #O(n)

return ‘s‘ in l

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import this

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$ pip install pep8

$ pep8 optparse.py

optparse.py:69:11: E401 multiple imports on one line

optparse.py:77:1: E302 expected 2 blank lines, found 1

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if attr == True:

print ‘True!‘

if attr == None:

print ‘attr is None!‘

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# Just check the value

if attr:

print ‘attr is truthy!‘

# or check for the opposite

if not attr:

print ‘attr is falsey!‘

# or, since None is considered false, explicitly check for it

if attr is None:

print ‘attr is None!‘

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d = {‘hello‘: ‘world‘}

if d.has_key(‘hello‘):

print d[‘hello‘]    # prints ‘world‘

else:

print ‘default_value‘

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d = {‘hello‘: ‘world‘}

print d.get(‘hello‘, ‘default_value‘) # prints ‘world‘

print d.get(‘thingy‘, ‘default_value‘) # prints ‘default_value‘

# Or:

if ‘hello‘ in d:

print d[‘hello‘]

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# Filter elements greater than 4

a = [3, 4, 5]

b = []

for i in a:

if i > 4:

b.append(i)

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a = [3, 4, 5]

b = [i for i in a if i > 4]

b = filter(lambda x: x > 4, a)

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# Add three to all list members.

a = [3, 4, 5]

for i in range(len(a)):

a[i] += 3

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a = [3, 4, 5]

a = [i + 3 for i in a]

# Or:

a = map(lambda i: i + 3, a)

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for i, item in enumerate(a):

print i, item

# prints

# 0 3

# 1 4

# 2 5

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f = open(‘file.txt‘)

a = f.read()

print a

f.close()

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with open(‘file.txt‘) as f:

for line in f:

print line

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my_very_big_string = """For a long time I used to go to bed early. Sometimes,

when I had put out my candle, my eyes would close so quickly that I had not even

time to say “I’m going to sleep.”"""

from some.deep.module.inside.a.module import a_nice_function, another_nice_function,

yet_another_nice_function

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#受教了....

my_very_big_string = (

"For a long time I used to go to bed early. Sometimes, "

"when I had put out my candle, my eyes would close so quickly "

"that I had not even time to say “I’m going to sleep.”"

)

from some.deep.module.inside.a.module import (

a_nice_function, another_nice_function, yet_another_nice_function)

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1.A README file

at the root directory should give general information to the users and the maintainers.

reStructuredText 或 Markdown

2.An INSTALL file

is less necessary with python

setup.py

3.A LICENSE file

should always be present and specify the license under which the software is made available to the public

4.A TODO file or a TODO section in README

should list the planned development for the code.

5.A CHANGELOG file or section in README

should compile a short overview of the changes in the code base for the latest versions.

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Sphinx(听说最强大....)

reStructuredText

Markdown(俺的最爱...)

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def square_and_rooter(x):

"""Returns the square root of self times self."""

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不用三引号注解代码块

每一行加#来注释

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1.A testing unit should focus on one tiny bit of functionality and prove it correct.

2.Each test unit must be fully independent

3.Try hard to make tests that run fast

4.Learn your tools and learn how to run a single test or a test case

5.Always run the full test suite before a coding session, and run it again after.

6.It is a good idea to implement a hook that runs all tests before pushing code to a shared repository.

7.If you are in the middle of a development session and have to interrupt your work,

it is a good idea to write a broken unit test about what you want to develop next.

8.The first step when you are debugging your code is to write a new test pinpointing the bug.

9.Use long and descriptive names for testing functions

10.When something goes wrong or has to be changed, and if your code has a good set of tests,

you or other maintainers will rely largely on the testing suite to fix the problem or modify a given behavior.

11.Another use of the testing code is as an introduction to new developers.

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import unittest

def fun(x):

return x + 1

class MyTest(unittest.TestCase):

def test(self):

self.assertEqual(fun(3), 4)

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def square(x):

"""Squares x.

>>> square(2)

4

>>> square(-2)

4

"""

return x * x

if __name__ == ‘__main__‘:

import doctest

doctest.testmod()

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def append_to(element, to=[]):

to.append(element)

return to

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my_list = append_to(12)

print my_list

my_other_list = append_to(42)

print my_other_list

[12]

[42]

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[12]

[12, 42]

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def append_to(element, to=None):

if to is None:

to = []

to.append(element)

return to

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def create_multipliers():

return [lambda x : i * x for i in range(5)] #

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for multiplier in create_multipliers():

print multiplier(2) # 任意一个返回的函数被调用时，内部循环i=4

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from functools import partial

from operator import mul

def create_multipliers():

return [partial(mul, i) for i in range(5)]

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Requests

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ZeroMQ

RabbitMQ

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WSGI

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5

Django

Flask

Werkzeug

Tornado

Pyramid

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Nginx

1

Gunicorn

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4

PasS (Platform as a service)

Heroku

DotCloud

Gondor

1

Jinja2

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2

lxml

Requests

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2

Clint

docopt

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8

Qt

Cocoa

wxPython

GTk

Tk

Kivy

PyjamasDesktop (pyjs Desktop)

Camelot

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DB-API

SQLAlchemy

Django ORM

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Twisted

PyZMQ

gevent

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Fabric

Salt

Psutil

Chef

Puppet

Blueprint

Buildout

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5

Jenkins

Buildbot

Mule?

Tox

Travis-CI

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GIL

Cython

Pyrex

Shedskin

Numba

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2

Threading

Multiprocessing

1

IPython

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NumPy

Numba

SciPy

Matplotlib

Pandas

Rpy2

PsychoPy

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Python Imaging Library(PIL)

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untangle

xmltodict