Field
## eg1:
def countIter(n):
while n>0:
yield n
n -= 1
print("next end")
for i in countIter(5):
print(i)
# "yield" key mean return a next(), when a iterator was used, next() would be used.
# One benefit of "yield" is void to store data with a list, save memory.
# 05-04: when you execute countIter() return a iterator. So, 'for i in countIter(5)' means countIter.next...
## eg2:
def read_file(fpath):
BLOCK_SIZE = 1024
with open(fpath, 'rb') as f:
while True:
block = f.read(BLOCK_SIZE)
if block:
yield block
else:
return
使用field的函数，返回迭代器，该函数并不先执行获得结果而是在使用的使用执行。
见下面例子，我们并没有得到一个[5,4,3,2,1]，而是一个迭代器，迭代到3的时候break，并没有执行一个函数获得[5,4,3,2,1]。
## eg3:
for i in countIter(5):
if( i<3 ):
break;
print(i)
# result: 5,4,3
ps：迭代递归
其实这里面涉及到一个概念，就是什么是迭代什么是递归。
譬如说，计算机只能执行加1的计算的话，如果需要计算4+7的话
迭代：
int result = 4;
for(i=1;i<=7,i++){
result = result + i;
}
return result;
可以看出需要有个存储的变量迭代的加1.
递归：
sum(a,b){
if(b=0)
return a;
sum(a+1, b-1);
}
return sum(4,7);
可以看出只传递计算过程，而没有存储一个变量。
xrange()
和field一样，这也是个懒加载函数，range()会直接返回一个链表，而xrange()是使用时才会生成。
Type
class A:
pass
class B(A):
pass
isinstance(A(), A) # returns True
type(A()) == A # returns True
isinstance(B(), A) # returns True
type(B()) == A # returns False
type方法不把子类当作是父类，isinstance中子类实例是父类。
for
array = [1, 2, 3, 4, 5]
for i, e in enumerate(array):
# index 'i' and its corresponding element 'e'.
序列乘法
写测试时候常用。
nums = [0] * 100 # [0,0,0,....]
strs = 100 * "a" # "aaaaaa..."
object
class object:
""" The most base type """
def __delattr__(self, *args, **kwargs): # real signature unknown
""" Implement delattr(self, name). """
pass
def __dir__(self): # real signature unknown; restored from __doc__
"""
__dir__() -> list
default dir() implementation
"""
return []
def __getattribute__(self, *args, **kwargs): # real signature unknown
""" Return getattr(self, name). """
pass
@staticmethod # known case of __new__
def __new__(cls, *more): # known special case of object.__new__
""" Create and return a new object. See help(type) for accurate signature. """
pass
def __setattr__(self, *args, **kwargs): # real signature unknown
""" Implement setattr(self, name, value). """
pass
__class__ = None # (!) forward: type, real value is ''
__dict__ = {}
__doc__ = ''# 文档字符串，object和function都有
__module__ = '' # code单元归属
object中attribute的存储在__dict__里面，”For instance, a.x has a lookup chain starting with a.__dict__['x'], then type(a).__dict__['x'], and continuing through the base classes of type(a) excluding metaclasses. “。
和java object差别是，java中class field在编译期间就声明了，py中可以__setattr__，这个特性在mock数据时候很有用。
关于Python DataModel，写的很赞。
生成式
列表生成式是循环的语法糖，来自Haskell，相当于lambda-map+filter。
results = [x for x in range(20) if x % 2 == 0]
# [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]
集合、字典生成式
>>> # Set Comprehensions
>>> some_list = [1, 2, 3, 4, 5, 2, 5, 1, 4, 8]
>>> even_set = { x for x in some_list if x % 2 == 0 }
>>> even_set
set([8, 2, 4])
>>> # Dict Comprehensions
>>> d = { x: x % 2 == 0 for x in range(1, 11) }
>>> d
{1: False, 2: True, 3: False, 4: True, 5: False, 6: True, 7: False, 8: True, 9: False, 10: True}
This work is licensed under a CC A-S 4.0 International License.