Python之其他数据类型
1.可命名元组:namedtuple 由nametuple可创建一个包含tuple所有功能以及其他功能的类型
class Mytuple(__builtin__.tuple)
| Mytuple(x, y)
|
| Method resolution order:
| Mytuple
| __builtin__.tuple
| __builtin__.object
|
| Methods defined here:
|
| __getnewargs__(self)
| Return self as a plain tuple. Used by copy and pickle.
|
| __getstate__(self)
| Exclude the OrderedDict from pickling
|
| __repr__(self)
| Return a nicely formatted representation string
|
| _asdict(self)
| Return a new OrderedDict which maps field names to their values
|
| _replace(_self, **kwds)
| Return a new Mytuple object replacing specified fields with new values
|
| ----------------------------------------------------------------------
| Class methods defined here:
|
| _make(cls, iterable, new=<built-in method __new__ of type object>, len=<built-in function len>) from __builtin__.type
| Make a new Mytuple object from a sequence or iterable
|
| ----------------------------------------------------------------------
| Static methods defined here:
|
| __new__(_cls, x, y)
| Create new instance of Mytuple(x, y)
|
| ----------------------------------------------------------------------
| Data descriptors defined here:
|
| __dict__
| Return a new OrderedDict which maps field names to their values
|
| x
| Alias for field number 0
|
| y
| Alias for field number 1
|
| ----------------------------------------------------------------------
| Data and other attributes defined here:
|
| _fields = ('x', 'y')
|
| ----------------------------------------------------------------------
| Methods inherited from __builtin__.tuple:
|
| __add__(...)
| x.__add__(y) <==> x+y
|
| __contains__(...)
| x.__contains__(y) <==> y in x
|
| __eq__(...)
| x.__eq__(y) <==> x==y
|
| __ge__(...)
| x.__ge__(y) <==> x>=y
|
| __getattribute__(...)
| x.__getattribute__('name') <==> x.name
|
| __getitem__(...)
| x.__getitem__(y) <==> x[y]
|
| __getslice__(...)
| x.__getslice__(i, j) <==> x[i:j]
|
| Use of negative indices is not supported.
|
| __gt__(...)
| x.__gt__(y) <==> x>y
|
| __hash__(...)
| x.__hash__() <==> hash(x)
|
| __iter__(...)
| x.__iter__() <==> iter(x)
|
| __le__(...)
| x.__le__(y) <==> x<=y
|
| __len__(...)
| x.__len__() <==> len(x)
|
| __lt__(...)
| x.__lt__(y) <==> x<y
|
| __mul__(...)
| x.__mul__(n) <==> x*n
|
| __ne__(...)
| x.__ne__(y) <==> x!=y
|
| __rmul__(...)
| x.__rmul__(n) <==> n*x
|
| __sizeof__(...)
| T.__sizeof__() -- size of T in memory, in bytes
|
| count(...)
| T.count(value) -> integer -- return number of occurrences of value
|
| index(...)
| T.index(value, [start, [stop]]) -> integer -- return first index of value.
| Raises ValueError if the value is not present. Mytuple Mytuple 2.双向对列(deque)
class deque(object):
"""
deque([iterable[, maxlen]]) --> deque object Build an ordered collection with optimized access from its endpoints.
"""
def append(self, *args, **kwargs): # real signature unknown
""" Add an element to the right side of the deque. """
pass def appendleft(self, *args, **kwargs): # real signature unknown
""" Add an element to the left side of the deque. """
pass def clear(self, *args, **kwargs): # real signature unknown
""" Remove all elements from the deque. """
pass def count(self, value): # real signature unknown; restored from __doc__
""" D.count(value) -> integer -- return number of occurrences of value """
return 0 def extend(self, *args, **kwargs): # real signature unknown
""" Extend the right side of the deque with elements from the iterable """
pass def extendleft(self, *args, **kwargs): # real signature unknown
""" Extend the left side of the deque with elements from the iterable """
pass def pop(self, *args, **kwargs): # real signature unknown
""" Remove and return the rightmost element. """
pass def popleft(self, *args, **kwargs): # real signature unknown
""" Remove and return the leftmost element. """
pass def remove(self, value): # real signature unknown; restored from __doc__
""" D.remove(value) -- remove first occurrence of value. """
pass def reverse(self): # real signature unknown; restored from __doc__
""" D.reverse() -- reverse *IN PLACE* """
pass def rotate(self, *args, **kwargs): # real signature unknown
""" Rotate the deque n steps to the right (default n=1). If n is negative, rotates left. """
pass def __copy__(self, *args, **kwargs): # real signature unknown
""" Return a shallow copy of a deque. """
pass def __delitem__(self, y): # real signature unknown; restored from __doc__
""" x.__delitem__(y) <==> del x[y] """
pass def __eq__(self, y): # real signature unknown; restored from __doc__
""" x.__eq__(y) <==> x==y """
pass def __getattribute__(self, name): # real signature unknown; restored from __doc__
""" x.__getattribute__('name') <==> x.name """
pass def __getitem__(self, y): # real signature unknown; restored from __doc__
""" x.__getitem__(y) <==> x[y] """
pass def __ge__(self, y): # real signature unknown; restored from __doc__
""" x.__ge__(y) <==> x>=y """
pass def __gt__(self, y): # real signature unknown; restored from __doc__
""" x.__gt__(y) <==> x>y """
pass def __iadd__(self, y): # real signature unknown; restored from __doc__
""" x.__iadd__(y) <==> x+=y """
pass def __init__(self, iterable=(), maxlen=None): # known case of _collections.deque.__init__
"""
deque([iterable[, maxlen]]) --> deque object Build an ordered collection with optimized access from its endpoints.
# (copied from class doc)
"""
pass def __iter__(self): # real signature unknown; restored from __doc__
""" x.__iter__() <==> iter(x) """
pass def __len__(self): # real signature unknown; restored from __doc__
""" x.__len__() <==> len(x) """
pass def __le__(self, y): # real signature unknown; restored from __doc__
""" x.__le__(y) <==> x<=y """
pass def __lt__(self, y): # real signature unknown; restored from __doc__
""" x.__lt__(y) <==> x<y """
pass @staticmethod # known case of __new__
def __new__(S, *more): # real signature unknown; restored from __doc__
""" T.__new__(S, ...) -> a new object with type S, a subtype of T """
pass def __ne__(self, y): # real signature unknown; restored from __doc__
""" x.__ne__(y) <==> x!=y """
pass def __reduce__(self, *args, **kwargs): # real signature unknown
""" Return state information for pickling. """
pass def __repr__(self): # real signature unknown; restored from __doc__
""" x.__repr__() <==> repr(x) """
pass def __reversed__(self): # real signature unknown; restored from __doc__
""" D.__reversed__() -- return a reverse iterator over the deque """
pass def __setitem__(self, i, y): # real signature unknown; restored from __doc__
""" x.__setitem__(i, y) <==> x[i]=y """
pass def __sizeof__(self): # real signature unknown; restored from __doc__
""" D.__sizeof__() -- size of D in memory, in bytes """
pass maxlen = property(lambda self: object(), lambda self, v: None, lambda self: None) # default
"""maximum size of a deque or None if unbounded""" __hash__ = None deque 3.单向队列(先进先出FIFO)
class Queue:
"""Create a queue object with a given maximum size. If maxsize is <= 0, the queue size is infinite.
"""
def __init__(self, maxsize=0):
self.maxsize = maxsize
self._init(maxsize)
# mutex must be held whenever the queue is mutating. All methods
# that acquire mutex must release it before returning. mutex
# is shared between the three conditions, so acquiring and
# releasing the conditions also acquires and releases mutex.
self.mutex = _threading.Lock()
# Notify not_empty whenever an item is added to the queue; a
# thread waiting to get is notified then.
self.not_empty = _threading.Condition(self.mutex)
# Notify not_full whenever an item is removed from the queue;
# a thread waiting to put is notified then.
self.not_full = _threading.Condition(self.mutex)
# Notify all_tasks_done whenever the number of unfinished tasks
# drops to zero; thread waiting to join() is notified to resume
self.all_tasks_done = _threading.Condition(self.mutex)
self.unfinished_tasks = 0 def task_done(self):
"""Indicate that a formerly enqueued task is complete. Used by Queue consumer threads. For each get() used to fetch a task,
a subsequent call to task_done() tells the queue that the processing
on the task is complete. If a join() is currently blocking, it will resume when all items
have been processed (meaning that a task_done() call was received
for every item that had been put() into the queue). Raises a ValueError if called more times than there were items
placed in the queue.
"""
self.all_tasks_done.acquire()
try:
unfinished = self.unfinished_tasks - 1
if unfinished <= 0:
if unfinished < 0:
raise ValueError('task_done() called too many times')
self.all_tasks_done.notify_all()
self.unfinished_tasks = unfinished
finally:
self.all_tasks_done.release() def join(self):
"""Blocks until all items in the Queue have been gotten and processed. The count of unfinished tasks goes up whenever an item is added to the
queue. The count goes down whenever a consumer thread calls task_done()
to indicate the item was retrieved and all work on it is complete. When the count of unfinished tasks drops to zero, join() unblocks.
"""
self.all_tasks_done.acquire()
try:
while self.unfinished_tasks:
self.all_tasks_done.wait()
finally:
self.all_tasks_done.release() def qsize(self):
"""Return the approximate size of the queue (not reliable!)."""
self.mutex.acquire()
n = self._qsize()
self.mutex.release()
return n def empty(self):
"""Return True if the queue is empty, False otherwise (not reliable!)."""
self.mutex.acquire()
n = not self._qsize()
self.mutex.release()
return n def full(self):
"""Return True if the queue is full, False otherwise (not reliable!)."""
self.mutex.acquire()
n = 0 < self.maxsize == self._qsize()
self.mutex.release()
return n def put(self, item, block=True, timeout=None):
"""Put an item into the queue. If optional args 'block' is true and 'timeout' is None (the default),
block if necessary until a free slot is available. If 'timeout' is
a non-negative number, it blocks at most 'timeout' seconds and raises
the Full exception if no free slot was available within that time.
Otherwise ('block' is false), put an item on the queue if a free slot
is immediately available, else raise the Full exception ('timeout'
is ignored in that case).
"""
self.not_full.acquire()
try:
if self.maxsize > 0:
if not block:
if self._qsize() == self.maxsize:
raise Full
elif timeout is None:
while self._qsize() == self.maxsize:
self.not_full.wait()
elif timeout < 0:
raise ValueError("'timeout' must be a non-negative number")
else:
endtime = _time() + timeout
while self._qsize() == self.maxsize:
remaining = endtime - _time()
if remaining <= 0.0:
raise Full
self.not_full.wait(remaining)
self._put(item)
self.unfinished_tasks += 1
self.not_empty.notify()
finally:
self.not_full.release() def put_nowait(self, item):
"""Put an item into the queue without blocking. Only enqueue the item if a free slot is immediately available.
Otherwise raise the Full exception.
"""
return self.put(item, False) def get(self, block=True, timeout=None):
"""Remove and return an item from the queue. If optional args 'block' is true and 'timeout' is None (the default),
block if necessary until an item is available. If 'timeout' is
a non-negative number, it blocks at most 'timeout' seconds and raises
the Empty exception if no item was available within that time.
Otherwise ('block' is false), return an item if one is immediately
available, else raise the Empty exception ('timeout' is ignored
in that case).
"""
self.not_empty.acquire()
try:
if not block:
if not self._qsize():
raise Empty
elif timeout is None:
while not self._qsize():
self.not_empty.wait()
elif timeout < 0:
raise ValueError("'timeout' must be a non-negative number")
else:
endtime = _time() + timeout
while not self._qsize():
remaining = endtime - _time()
if remaining <= 0.0:
raise Empty
self.not_empty.wait(remaining)
item = self._get()
self.not_full.notify()
return item
finally:
self.not_empty.release() def get_nowait(self):
"""Remove and return an item from the queue without blocking. Only get an item if one is immediately available. Otherwise
raise the Empty exception.
"""
return self.get(False) # Override these methods to implement other queue organizations
# (e.g. stack or priority queue).
# These will only be called with appropriate locks held # Initialize the queue representation
def _init(self, maxsize):
self.queue = deque() def _qsize(self, len=len):
return len(self.queue) # Put a new item in the queue
def _put(self, item):
self.queue.append(item) # Get an item from the queue
def _get(self):
return self.queue.popleft() Queue.Queue
Python之其他数据类型的更多相关文章
- python 基础之数据类型
一.python中的数据类型之列表 1.列表 列表是我们最以后最常用的数据类型之一,通过列表可以对数据实现最方便的存储.修改等操作 二.列表常用操作 >切片>追加>插入>修改& ...
- Python学习 之 数据类型(邹琪鲜 milo)
1.Python中的数据类型:数字.字符串.列表.元组.字典 2.数字类型包括整型.长整型.浮点型.复数型 type(number):获取number的数据类型 整型(int):范围:-2,147,4 ...
- (八)python的简单数据类型和变量
什么是数据类型? 程序的本质就是驱使计算机去处理各种状态的变化,这些状态分为很多种. 例如英雄联盟游戏,一个人物角色有名字,钱,等级,装备等特性,大家第一时间会想到这么表示 名字:德玛西亚------ ...
- Python基础之数据类型
Python基础之数据类型 变量赋值 Python中的变量不需要声明,变量的赋值操作既是变量声明和定义的过程. 每个变量在内存中创建,都包括变量的标识,名称和数据这些信息. 每个变量在使用前都必须赋值 ...
- Python学习之数据类型
整数 Python可以处理任意大小的整数,在程序中的表示方法和数学上的写法一模一样,例如:1,100,-8080,0,等等. 用十六进制表示整数比较方便,十六进制用0x前缀和0-9,a-f表示,例如: ...
- python的组合数据类型及其内置方法说明
python中,数据结构是通过某种方式(例如对元素进行编号),组织在一起数据结构的集合. python常用的组合数据类型有:序列类型,集合类型和映射类型 在序列类型中,又可以分为列表和元组,字符串也属 ...
- python学习第九讲,python中的数据类型,字符串的使用与介绍
目录 python学习第九讲,python中的数据类型,字符串的使用与介绍 一丶字符串 1.字符串的定义 2.字符串的常见操作 3.字符串操作 len count index操作 4.判断空白字符,判 ...
- python学习第八讲,python中的数据类型,列表,元祖,字典,之字典使用与介绍
目录 python学习第八讲,python中的数据类型,列表,元祖,字典,之字典使用与介绍.md 一丶字典 1.字典的定义 2.字典的使用. 3.字典的常用方法. python学习第八讲,python ...
- python学习第七讲,python中的数据类型,列表,元祖,字典,之元祖使用与介绍
目录 python学习第七讲,python中的数据类型,列表,元祖,字典,之元祖使用与介绍 一丶元祖 1.元祖简介 2.元祖变量的定义 3.元祖变量的常用操作. 4.元祖的遍历 5.元祖的应用场景 p ...
- python学习第六讲,python中的数据类型,列表,元祖,字典,之列表使用与介绍
目录 python学习第六讲,python中的数据类型,列表,元祖,字典,之列表使用与介绍. 二丶列表,其它语言称为数组 1.列表的定义,以及语法 2.列表的使用,以及常用方法. 3.列表的常用操作 ...
随机推荐
- [转]在C#代码中应用Log4Net系列教程(附源代码)
Log4Net应该可以说是DotNet中最流行的开源日志组件了.以前需要苦逼写的日志类,在Log4Net中简单地配置一下就搞定了.没用过Log4Net,真心不知道原来日志组件也可以做得这么灵活,当然这 ...
- 如何访问linux服务器上的mysql8.0
首先安装好了mysql-connector 1.1. 下载: 官网下载zip包,我下载的是64位的: 下载地址:https://dev.mysql.com/downloads/mysql/ 下载zip ...
- thinkPHP使用中踩的坑,记录一下(不停更)
版本3.2.3 1.数据库操作中的连贯操作table(),在查询的时候可以切换表,但是在插入,更新的时候请不要使用.例如 D('user')->table('auth')->add($da ...
- 博客的页面定制CSS
我目前的博客CSS其实也是借用了别家的,来源:https://www.cnblogs.com/Penn000/p/6947472.html 注意使用的模板是:darkgreentrip 复制粘贴使用就 ...
- 【arc075f】AtCoder Regular Contest 075 F - Mirrored
题意 给定一个数x,问有多少个正整数y,使得rev(y)-y==x 其中rev(x)表示x按位翻转之后得到的数. x<=1e9 做法 首先通过打表发现,这个答案不会很大. 这就说明解相当地松弛. ...
- Hdfs的列存储和行存储
列可以分开存储,对于重复性高的数据压缩比会高,但是在元组(行shi)恢复会比较消耗性能 于传统列存储不同 是行组会存储于同一节点中,列扫描会比较快(因为只需扫描一个行组)
- TensorFlow的安装 (python3.6在有pip的条件下如何安装TensorFlow)
1.Window,MacOS,Linux都已支持Tensorflow. 2.Window用户只能使用python3.5(64bit).MacOS,Linux支持python2.7和python ...
- PHP1.6--数组
一.数组的键值操作函数 1.array_values() 函数作用是返回数组中所有元素的值,只有一个参数,规定传人给定数组,返回一个包含给定数组中所有值的数组,但不保留键名 被返回的数组将使用顺序的数 ...
- HDU 3714
最大值最小问题,三分....竟然排第六当时..... #include<stdio.h> #include<string.h> #define max 10000+10 #de ...
- NACOS集群搭建遇到的问题
搭建NACOS官网教程: https://nacos.io/zh-cn/docs/cluster-mode-quick-start.html 这里说的很详细了.也有中文的.我就记录一下在搭建集群的时候 ...