python--第三天总结

【collection系列】
1、计数器（counter）

Counter是对字典类型的补充，用于追踪值的出现次数。

ps：具备字典的所有功能 + 自己的功能

1. c = Counter('abcdeabcdabcaba')
2. print c
3. 输出：Counter({'a': 5, 'b': 4, 'c': 3, 'd': 2, 'e': 1})

import collections  或from collections import Counter

2、有序字典(orderedDict )
from collections import OrderedDict
orderdDict是对字典类型的补充，他记住了字典元素添加的顺序

3、默认字典(defaultdict)

学前需求：

1. 有如下值集合 [11,22,33,44,55,66,77,88,99,90...]，将所有大于 66 的值保存至字典的第一个key中，将小于 66 的值保存至第二个key的值中。
2. 即： {'k1': 大于66 , 'k2': 小于66}

values = [11, 22, 33,44,55,66,77,88,99,90]

my_dict = {}

for value in  values:
    if value>66:
        if my_dict.has_key('k1'):
            my_dict['k1'].append(value)
        else:
            my_dict['k1'] = [value]
    else:
        if my_dict.has_key('k2'):
            my_dict['k2'].append(value)
        else:
            my_dict['k2'] = [value]

原生字典解决方法

from collections import defaultdict

values = [11, 22, 33,44,55,66,77,88,99,90]

my_dict = defaultdict(list)

for value in  values:
    if value>66:
        my_dict['k1'].append(value)
    else:
        my_dict['k2'].append(value)

defaultdict字典解决方法

defaultdict是对字典的类型的补充，他默认给字典的值设置了一个类型。

class defaultdict(dict):
    """
    defaultdict(default_factory[, ...]) --> dict with default factory

    The default factory is called without arguments to produce
    a new value when a key is not present, in __getitem__ only.
    A defaultdict compares equal to a dict with the same items.
    All remaining arguments are treated the same as if they were
    passed to the dict constructor, including keyword arguments.
    """
    def copy(self): # real signature unknown; restored from __doc__
        """ D.copy() -> a shallow copy of D. """
        pass

    def __copy__(self, *args, **kwargs): # real signature unknown
        """ D.copy() -> a shallow copy of D. """
        pass

    def __getattribute__(self, name): # real signature unknown; restored from __doc__
        """ x.__getattribute__('name') <==> x.name """
        pass

    def __init__(self, default_factory=None, **kwargs): # known case of _collections.defaultdict.__init__
        """
        defaultdict(default_factory[, ...]) --> dict with default factory

        The default factory is called without arguments to produce
        a new value when a key is not present, in __getitem__ only.
        A defaultdict compares equal to a dict with the same items.
        All remaining arguments are treated the same as if they were
        passed to the dict constructor, including keyword arguments.

        # (copied from class doc)
        """
        pass

    def __missing__(self, key): # real signature unknown; restored from __doc__
        """
        __missing__(key) # Called by __getitem__ for missing key; pseudo-code:
          if self.default_factory is None: raise KeyError((key,))
          self[key] = value = self.default_factory()
          return value
        """
        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

    default_factory = property(lambda self: object(), lambda self, v: None, lambda self: None)  # default
    """Factory for default value called by __missing__()."""

defaultdict

defaultdict

冒泡算法

#!/usr/bin/env python
#-*- coding: utf-8 -*-

values = [11, 22, 33,44,55,66,77,88,99,90]

my_dict = {}

for value in values:
    if value > 66:
        if my_dict.has_key('k1'):
            my_dict['k1'].append(value)
        else:
            my_dict['k1'] = [value]

    else:
        if my_dict.has_key('k2'):
            my_dict['k2'].append(value)
        else:
            my_dict['k2'] = [value]
print  my_dict

4、可命名元组(namedtuple)

根据nametuple可以创建一个包含tuple所有功能以及其他功能的类型。

1. import collections
2. Mytuple = collections.namedtuple('Mytuple',['x', 'y', 'z'])

#创建一个扩展tuple的类，Mytuple
tuple
Mytuple = collections.namedtuple('Mytuple',['x', 'y'])

old = tuple(1,2)  <==> old = (1,2)
(1,2)
new = Mytuple(1,2)
{'x':1,'y':2 }

5、双向队列(deque)

一个线程安全的双向队列

注：既然有双向队列，也有单项队列（先进先出 FIFO )

【Queue.Queue】

队列，FIFO
栈 ，弹夹

【迭代器和生成器】

一、迭代器

对于Python 列表的 for 循环，他的内部原理：查看下一个元素是否存在，如果存在，则取出，如果不存在，则报异常 StopIteration。（python内部对异常已处理）

erable 迭代
迭代器 只有循环才能取出值

二、生成器

range不是生成器 和 xrange 是生成器

readlines不是生成器 和 xreadlines 是生成器

1. >>> print range(10)
2. [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
3. >>> print xrange(10)
4. xrange(10)

-------[函数]

1. 内置函数
2. 自定义函数
3. 导入函数

【 内置函数如下：】

help()
dir()
vars()
type()

reload(temp)
id([12])
is
------------------
cmp(2,3)
abs()
bool()
divmod(10，3),分页
max()
min()
sum()
pow(2, 11)
------------------
len()
all()  #接受一个序列，判断，序列中所有值都是真，返回真，否则假(空 为False)
any() #有一个为真，返回真
------------------
chr()
ord()
hex()
oct()
bin()

ASCII码转换
ord('A') --> 65
chr(65)

------------------
print range(10)
print xrange(10)
for i in xrange(10):
    print i
for k,v in enumerate([1,2,3,4]，执行数字起始值):
    print k,v

------------------
print apply(Function,('aaaa')) #执行函数
print map(lambda x:x+1,[1,2,3]) #all
print filter(lambda x: x==1,[1,23,4]) #True序列
print reduce(lambda x,y:x+y,[1,2,3]) #累加
print zip(x, y,z)

【自定义函数】

函数的定义主要有如下要点：

1、def 定义函数的关键字
2、函数名，日后通过函数名调用该函数
3、函数声明，不自动执行；调用后才执行
4、函数的参数
5、函数的返回值

返回值：
    1、未明确制定返回值，返回 None
    2、返回值可以赋值给某个变量

函数的有三中不同的参数：
    普通参数
    默认参数
    动态参数
    
# 形式参数，形参
# 实际参数，实参
参数可以有n个，传入制定个数的参数
    
    
默认参数，
    1、不传，则使用默认
    2、默认参数必须放在参数列表的最后
    
# 形式参数，形参
# 实际参数，实参

format

动态参数一

def func(*args):
    pass
1、接受多个参数
2、内部自动构造元组
3、序列，*，避免内部构造元组
def func(*args):
    print args[0]
    
func(11,22,33,44,55)

动态参数二
def func(**kwargs)：
    print kwargs

1、func（k1=123,k2='sss'）
2、
    dic = ｛"k1"：123｝
    func(**dic)
    
func(11,22,33,44,55)
   
def func(*args,**kwargs):
    
s = "i am {0},age{1}"
s.format('alex','18')

s = 'i am {name,age{age}}'
s.format(name='alex',age=19)

[模块导入]

Python只所有应用越来越广泛，在一定程度上也依赖于其为程序员提供了大量的模块以供使用，如果想要使用模块，则需要导入。导入模块有一下几种方法：

1. import module
2. from module.xx.xx import xx
3. from module.xx.xx import xx as rename   
4. from module.xx.xx import *

那么问题来了，导入模块时是根据那个路径作为基准来进行的呢？即：sys.path

【文件操作】

操作文件时，一般需要经历如下步骤：

• 打开文件
• 操作文件

一、打开文件

1. 文件句柄 = file('文件路径', '模式')

 

注：python中打开文件有两种方式，即：open(...) 和  file(...) ，本质上前者在内部会调用后者来进行文件操作，推荐使用 open。

打开文件时，需要指定文件路径和以何等方式打开文件，打开后，即可获取该文件句柄，日后通过此文件句柄对该文件操作。

打开文件的模式有：

• r，只读模式（默认）。
• w，只写模式。【不可读；不存在则创建；存在则删除内容；】
• a，追加模式。【可读；   不存在则创建；存在则只追加内容；】

"+" 表示可以同时读写某个文件

• r+，可读写文件。【可读；可写；可追加】
• w+，无意义
• a+，同a

"U"表示在读取时，可以将 \r \n \r\n自动转换成 \n （与 r 或 r+ 模式同使用）

• rU
• r+U

"b"表示处理二进制文件（如：FTP发送上传ISO镜像文件，linux可忽略，windows处理二进制文件时需标注）

• rb
• wb
• ab

二、操作操作

class file(object):

    def close(self): # real signature unknown; restored from __doc__
        关闭文件
        """
        close() -> None or (perhaps) an integer.  Close the file.

        Sets data attribute .closed to True.  A closed file cannot be used for
        further I/O operations.  close() may be called more than once without
        error.  Some kinds of file objects (for example, opened by popen())
        may return an exit status upon closing.
        """

    def fileno(self): # real signature unknown; restored from __doc__
        文件描述符
         """
        fileno() -> integer "file descriptor".

        This is needed for lower-level file interfaces, such os.read().
        """
        return 0    

    def flush(self): # real signature unknown; restored from __doc__
        刷新文件内部缓冲区
        """ flush() -> None.  Flush the internal I/O buffer. """
        pass

    def isatty(self): # real signature unknown; restored from __doc__
        判断文件是否是同意tty设备
        """ isatty() -> true or false.  True if the file is connected to a tty device. """
        return False

    def next(self): # real signature unknown; restored from __doc__
        获取下一行数据，不存在，则报错
        """ x.next() -> the next value, or raise StopIteration """
        pass

    def read(self, size=None): # real signature unknown; restored from __doc__
        读取指定字节数据
        """
        read([size]) -> read at most size bytes, returned as a string.

        If the size argument is negative or omitted, read until EOF is reached.
        Notice that when in non-blocking mode, less data than what was requested
        may be returned, even if no size parameter was given.
        """
        pass

    def readinto(self): # real signature unknown; restored from __doc__
        读取到缓冲区，不要用，将被遗弃
        """ readinto() -> Undocumented.  Don't use this; it may go away. """
        pass

    def readline(self, size=None): # real signature unknown; restored from __doc__
        仅读取一行数据
        """
        readline([size]) -> next line from the file, as a string.

        Retain newline.  A non-negative size argument limits the maximum
        number of bytes to return (an incomplete line may be returned then).
        Return an empty string at EOF.
        """
        pass

    def readlines(self, size=None): # real signature unknown; restored from __doc__
        读取所有数据，并根据换行保存值列表
        """
        readlines([size]) -> list of strings, each a line from the file.

        Call readline() repeatedly and return a list of the lines so read.
        The optional size argument, if given, is an approximate bound on the
        total number of bytes in the lines returned.
        """
        return []

    def seek(self, offset, whence=None): # real signature unknown; restored from __doc__
        指定文件中指针位置
        """
        seek(offset[, whence]) -> None.  Move to new file position.

        Argument offset is a byte count.  Optional argument whence defaults to
        0 (offset from start of file, offset should be >= 0); other values are 1
        (move relative to current position, positive or negative), and 2 (move
        relative to end of file, usually negative, although many platforms allow
        seeking beyond the end of a file).  If the file is opened in text mode,
        only offsets returned by tell() are legal.  Use of other offsets causes
        undefined behavior.
        Note that not all file objects are seekable.
        """
        pass

    def tell(self): # real signature unknown; restored from __doc__
        获取当前指针位置
        """ tell() -> current file position, an integer (may be a long integer). """
        pass

    def truncate(self, size=None): # real signature unknown; restored from __doc__
        截断数据，仅保留指定之前数据
        """
        truncate([size]) -> None.  Truncate the file to at most size bytes.

        Size defaults to the current file position, as returned by tell().
        """
        pass

    def write(self, p_str): # real signature unknown; restored from __doc__
        写内容
        """
        write(str) -> None.  Write string str to file.

        Note that due to buffering, flush() or close() may be needed before
        the file on disk reflects the data written.
        """
        pass

    def writelines(self, sequence_of_strings): # real signature unknown; restored from __doc__
        将一个字符串列表写入文件
        """
        writelines(sequence_of_strings) -> None.  Write the strings to the file.

        Note that newlines are not added.  The sequence can be any iterable object
        producing strings. This is equivalent to calling write() for each string.
        """
        pass

    def xreadlines(self): # real signature unknown; restored from __doc__
        可用于逐行读取文件，非全部
        """
        xreadlines() -> returns self.

        For backward compatibility. File objects now include the performance
        optimizations previously implemented in the xreadlines module.
        """
        pass

三、with

为了避免打开文件后忘记关闭，可以通过管理上下文，即：

with open('log','r') as f:

...

如此方式，当with代码块执行完毕时，内部会自动关闭并释放文件资源。

在Python 2.7 后，with又支持同时对多个文件的上下文进行管理，即：

1. with open('log1') as obj1, open('log2') as obj2:
2. pass

==其它

入口文件常用的 vars
__file__
__doc__
__name__