前言
断断续续两个周, 终于比对着敲了一遍.
相对源文档的一些python2实现,转成了python3.
有一些问题尚未解决,如有了解,也请不吝赐教,我会尽快更新.
原文文档
GIthub源文件
后续: python 推导式 Mutation/Immutation virtualenv Collections
Learning Code
# Python 进阶
# 1 可选参数
# 使用:函数装饰器,猴子补丁(程序运行时(runtime)修改某些代码)
# *args
def test_asterisk(f_arg, *arg_vars):
print('f_arg', f_arg)
for arg in arg_vars:
print('arg in arg_vars', arg)
test_asterisk('yasoob', 'python', 'eggs', 'test')
# **dargs: 只能单独使用
def test_kvps(**arg_vars):
for (key, v) in arg_vars.items():
print("{0} == {1}".format(key, v))
test_kvps(**{'name': 'yasoob'})
# 同时使用时的顺序不能改变
def test_args(arg1, *arg2, **arg3):
print('f_arg', arg1)
for arg in arg2:
print('arg in arg_vars', arg)
for (key, v) in arg3.items():
print("{0} == {1}".format(key, v))
test_args('yasoob', 'python', 'eggs', 'test', 123123, **{'name': 'yasoob'})
# 2 Debugging
'''
python -m pdb my_script.py
c:continue 继续执行
w:where 显示当前正在执行的代码行的上下文信息
a:args 打印当前函数的参数列表
s:step 执行当前代码行,并停在第一个能停的地方(相当于单步进入)
n:next 继续执行到当前函数的下一行,或者当前行直接返回(单步跳过)
p:print p expression
'''
# 3 生成器(Generators)
'''
迭代(Iteration):当我们使用一个循环来遍历某个东西的过程
迭代器(Iterator): 遍历一个容器(特别是列表)的对象,
定义了next(Python2) 或者__next__方法的对象
可迭代对象(Iterable): 能提供迭代器的任意对象,
定义了可以返回一个迭代器的__iter__方法,或者可以支持下标索引的__getitem__方法
生成器(Generators): 生成器是只迭代一次的迭代器.这是因为它们并没有把所有的值存在内存中,而是在运行时生成值.
通过yield每次返回一个单次运行的值, 而不是直接返回占用大量空间的一个值
调用:用for循环,或可进行迭代的函数或结构
next(): 它允许我们获取一个序列的下一个元素. yield所有值后会触发 StopIteration exception
'''
def fibon(n):
a = b = 1
for i in range(n):
yield a
(a, b) = (b, a+b)
i = 0
for x in fibon(10):
i += 1
print('fibon({0})'.format(i), x)
test_string = 'te'
test = iter(test_string)
print(next(test))
print(next(test))
# print(next(test) ) # StopIteration
# iter and next implement
class Reverse:
"""Iterator for looping over a sequence backwards."""
def __init__(self, data):
self.data = data
self.index = len(data)
def __iter__(self):
return self
def __next__(self):
if self.index == 0:
raise StopIteration
self.index = self.index - 1
return self.data[self.index]
# 4 Map:n个输入源返回n个结果 将函数映射到集合的每个元素,多与lambda连用
# map(function_to_apply, list_of_inputs)
# lambda:匿名函数
# 参数:操作(参数)
items = [1, 2, 3, 4, 5]
squard = list(map(lambda x: x**2, items))
print(squard)
# 将多个函数映射到集合
def multiply(x): return (x**2)
def add(x): return (x*2)
funcs = [multiply, add]
for i in range(5):
# 函数作为lambda的操作对象
value = map(lambda x: x(i), funcs)
print(list(value))
# Filter: 过滤表中的元素, 返回所有符合要求的元素
# filter(function, iterable)
# 可用推导式替换,推导式的可读性更好
pr = filter(lambda x: 1==1, range(-5,5))
print(list(pr))
# Reduce 多个输入源返回一个结果,对一个列表计算返回结果:第一个元素与第二个计算,其结果与第三个元素运算
# reduce(function, iterable[, initializer])
from functools import reduce
pro = reduce(lambda x,y:x*y, range(1, 5))
print(pro)
# 5 数据结构
# strings, list, tuple, dictionary
# number:int float bool
# string 'name' 不可变,不可以对其中的字符赋值; 多用list替代 可切片
# list [1, 2, 3] 可变,key必须是数字,可以对其组成元素进行增删改 可切片
# tuple (0, 1, 2) 不可变,常用于return返回的结果,形参,字典键, 可切片
# dict {'name':'zhangsan', 'age':20} 可变,key可以是string等非数字 {}
# set {1,2,3} 元素不可以重复,不能切片, 运算的单位是集合
# set 集合:不能包含重复的值 不能切片
some_list = ['a', 'b', 'c', 'b', 'd', 'm', 'n', 'n']
dup = set([x for x in some_list if some_list.count(x) > 1])
print(dup)
# set intersection 交集
valid = set(['yellow', 'red', 'blue', 'green', 'black'])
input_set = set(['red', 'brown'])
print(input_set.intersection(valid))
# set difference 差集
print(input_set.difference(valid))
# 6 三元运算符
# 如果条件为真,返回真 否则返回假
# condition_is_true if condition else condition_is_false
is_fat = True
print('fat' if is_fat else 'not fat')
# 结合元组使用 true means 1, 因为元组要先建数据,所以两个表达式都会执行
print(('skinny','fat')[is_fat])
# 7 装饰器
# 一切皆对象:对象可以作为赋值给变量或是作为参数传递给函数(类似js)
# 不同语言对对象的定义不同,python中的对象只要有属性或方法就可以,不要求可子类化,
def hi(name='benji'):
return 'hi '+name
print(hi())
greet = hi #greet不是调用hi函数,而是分配到新的内存
print(greet())
del hi
#print(hi()) #NameError: name 'hi' is not defined
print(greet())
# 嵌套函数
def hi2(name='benji'):
print('context is in hi()')
def greet2():
print('context is in greet()')
greet2()
print('context is in hi() again')
hi2()
# greet2() #NameError: name 'greet2' is not defined
# 返回函数
def hi3(name='benji'):
def greet3(): return 'greet3 ' + name
def welcome3(): return 'welcome3'
if name == 'benji':
return welcome3
else:
return greet3
a = hi3()
print(a) #<function hi3.<locals>.greet3 at 0x00DAD7C8>
print(a())
# 函数作为参数
def fun_as_var(func):
print('fun_as_var')
func()
fun_as_var(hi2)
# Python 装饰器: 封装一个函数, 围绕函数,做一些操作
# @decorator: 以单个函数作为参数的一个包裹函数
from functools import wraps
def new_decorator(a_func):
@wraps(a_func) #恢复被装饰函数的名字和注释文档
def wrap_func():
print('before para function in new_decoration ')
a_func()
print('after para function in new_decoration ')
return wrap_func
def a_func():
print('in function needed to be decorated')
new_decorator(a_func)()
@new_decorator
def a_func_with_deco():
print('in a_func_with_deco, function needed to be decorated')
a_func_with_deco()
print(a_func_with_deco.__name__)#wrap_func restore by functools.wraps
# decorator sample
from functools import wraps
def decorator_name(f):
@wraps(f)
def decorated(*args, **kwargs):
print('run in decorator_name')
if not can_run:
return 'function will not run'
return f(*args, **kwargs)
return decorated
@decorator_name
def func(*arg2, **arg3):
for arg in arg2:
print('arg in arg_vars', arg)
for (key, v) in arg3.items():
print("{0} == {1}".format(key, v))
return 'function is running'
can_run = True
print(func(12,'test','asdf'))
# 使用场景
'''# 授权
def require_auth(f):
@warps(f)
def decorated(*args, **kwargs):
auth = request.authorization
if not auth or not check_auth(auth.username, auth.password):
authenticate()
return f(*args, **kwargs)
return decorated
'''
# 日志
def logit_easy(func):
@wraps(func)
def with_logging(*args, **kwargs):
print(func.__name__ + ' was called')
return func(*args, **kwargs)
return with_logging
@logit_easy
def addition_func(x):
return x+x
print(addition_func(4))
# 带参数的装饰器
# 装饰器方法本身需要接收函数作为入参,为避免形参冲突,再嵌套一层函数用来接收其他入参
from functools import wraps
def logit(logfile='out.log'):
def logging_decorator(func):
@wraps(func)
def warp_function(*args, **kwargs):
log_string = func.__name__ + ' was called.'
print(log_string)
with open(logfile, 'a') as opened_file:
opened_file.write(log_string+'\n')
return func(*args, **kwargs)
return warp_function
return logging_decorator
@logit()
def myfunc1():
pass
myfunc1()
@logit(logfile='func2.log')
def myfunc2():
pass
myfunc2()
# Decorate Class
# 装饰类代码比装饰函数简洁,易于拓展,包裹函数可以通过类属性获取新功能的参数,不需要嵌套函数
# __call__()方法能够让类的实例对象,像函数一样被调用
class logitClass(object):
def __init__(self, logfile='out2.log'):
self.logfile = logfile
def __call__(self, func):
@wraps(func)
def warp_function(*args, **kwargs):
log_string = func.__name__ + ' was called'
print(log_string, self.logfile)
with open(self.logfile, 'a') as opened_file:
opened_file.write(log_string+'\n')
self.notify()
return func(*args, **kwargs)
return warp_function
def notify(self):
print('super notify')
# 包裹函数的语法与之前一致
@logitClass()
def myclass1func():
pass
myclass1func()
class email_logit(logitClass):
def __init__(self, email='[email protected]', *args, **kwargs):
self.email = email
print('email_logit',args)
for i in args:
print('email_logit',i)
logitClass.__init__(self, *args, **kwargs)
def notify(self):
print('this is in child class email logit')
# ??? 子类如何设置log文件名称
# invalid @email_logit('email.log')
@email_logit()
def myclassEmail():
pass
myclassEmail()
相关链接
Python 进阶
英文版: Python Tips
The Python Debugger
ipython_memory_usage IPython tool to report memory usage deltas for every command you type.
virtual DOC
ctypes A foreign function library for Python
swig tutorial
Python/C API This manual documents the API used by C and C++ programmers who want to write extension modules or embed Python
smartcd Alter your bash (or zsh) environment as you cd
coroutine Coroutines.pdf