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====== 1. Whetting Your Appetite ======
Created Monday 02 January 2012
If you do much work on computers, eventually you find that there’s** some task you’d like to automate**. For example, you may wish to perform a search-and-replace over a large number of text files, or rename and rearrange a bunch of photo files in a complicated way. Perhaps you’d like to write a small custom database, or a specialized GUI application, or a simple game.
If you’re a professional software developer, you may have to work with several C/C++/Java libraries but find the usual write/compile/test/re-compile cycle is too slow. Perhaps you’re writing a test suite for such a library and find writing the testing code a tedious task. Or maybe you’ve written a program that could use an extension language, and you don’t want to design and implement a whole new language for your application.
Python is just the language for you.
You could write a __Unix shell script__ or Windows batch files for some of these tasks, but shell scripts are best at moving around files and changing text data, not well-suited for GUI applications or games. You could write a C/C++/Java program, but it can take a lot of development time to get even a first-draft program. Python is simpler to use, available on Windows, Mac OS X, and Unix operating systems, and will help you __get the job done more quickly__.
Python is simple to use, but it is a real programming language, offering __much more structure__ and support for large programs than shell scripts or batch files can offer. On the other hand, Python also offers much __more error checking__ than C, and, being a very-high-level language, it has __high-level data types__ built in, such as flexible arrays and dictionaries. Because of its more general data types Python is applicable to a much larger problem domain than Awk or even Perl, yet many things are at least as easy in Python as in those languages.
Python allows you to split your program into __modules__ that can be reused in other Python programs. It comes with a large collection of **standard modules** that you can use as the basis of your programs — or as examples to start learning to program in Python. Some of these modules provide things like file I/O, system calls, sockets, and even interfaces to graphical user interface toolkits like Tk.
Python is __an interpreted language__, which can save you considerable time during program development because no compilation and linking is necessary. The interpreter can be __used interactively__, which makes it easy to experiment with features of the language, to write throw-away programs, or to test functions during bottom-up program development. It is also a handy desk calculator.
Python enables programs to be written __compactly and readably__. Programs written in Python are typically much shorter than equivalent C, C++, or Java programs, for several reasons:
* the high-level __data types__ allow you to express complex operations in a single statement;
* statement grouping is done by indentation instead of beginning and ending brackets;
* no variable or argument declarations are necessary.
Python is extensible: if you know how to program in C it is easy to add a new built-in function or module to the interpreter, either to perform critical operations at maximum speed, or to link Python programs to libraries that may only be available in binary form (such as a vendor-specific graphics library). Once you are really hooked, you can link the Python interpreter into an application written in C and use it as an extension or command language for that application.
By the way, the language is named after the BBC show “Monty Python’s Flying Circus” and has nothing to do with reptiles. Making references to Monty Python skits in documentation is not only allowed, it is encouraged!
Now that you are all excited about Python, you’ll want to examine it in some more detail. Since __the best way to learn a language is to use it__, the tutorial invites you to play with the Python interpreter as you read.
In the next chapter, the mechanics of using the interpreter are explained. This is rather mundane information, but essential for trying out the examples shown later.
The rest of the tutorial introduces various features of the Python language and system through examples, beginning with simple expressions, statements and data types, through functions and modules, and finally touching upon advanced concepts like exceptions and user-defined classes.

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====== 2.1. Invoking the Interpreter ======
Created Monday 02 January 2012
The Python interpreter is usually installed as /usr/local/bin/python on those machines where it is available; putting /usr/local/bin in your Unix shell’s search path makes it possible to start it by typing the command python to the shell. Since the choice of the directory where the interpreter lives is an installation option, other places are possible; check with your local Python guru or system administrator. (E.g., /usr/local/python is a popular alternative location.)
On Windows machines, the Python installation is usually placed in C:\Python27, though you can change this when you’re running the installer. To add this directory to your path, you can type the following command into the command prompt in a DOS box:
set path=%path%;C:\python27
Typing an __end-of-file__ character (Control-D on Unix, Control-Z on Windows) at the **primary prompt** causes the interpreter to exit with a __zero __exit status. If that doesn’t work, you can exit the interpreter by typing the following command: __quit() or exit() or raise SystemExit().__
The interpreter’s __line-editing__ features usually aren’t very sophisticated. On Unix, whoever installed the interpreter may have enabled support for the** GNU readline library**, which adds more elaborate interactive editing and history features. Perhaps the quickest check to see whether command line editing is supported is typing Control-P to the first Python prompt you get. If it beeps, you have command line editing; see Appendix Interactive Input Editing and History Substitution for an introduction to the keys. If nothing appears to happen, or if ^P is echoed, command line editing isn’t available; you’ll only be able to use backspace to remove characters from the current line.
The interpreter operates somewhat__ like the Unix shell__: when called with standard input connected to a tty device, it reads and executes commands interactively; when called with a file name argument or with a file as standard input, it reads and executes a script from that file.
当解释器和标准输入相连时，它进入了读一行---执行---再读一行的**执行循环模式**，当遇到EOF时退出。
A second way of starting the interpreter is__ python -c command [arg] ...__, which executes the statement(s) in command, analogous to the **shell’s -c option**. Since Python statements often contain spaces or other characters that are special to the shell, it is usually advised to quote command in its entirety with single quotes.
将-c后的参数作为命令执行，command一般用字符串引用，而且中间__可以换行__(因此，可以输入语句块)。
Some Python **modules are also useful as scripts(主要用于测试)**. These can be invoked using __python -m module [arg] ...__, which executes the source file for module as if you had spelled out its** full name(只要提供模块名称即可，python会自动在搜索路径中查找)** on the command line.
一般情况下，-c/-m不同时使用。
When a script file is used, it is sometimes useful to be able to run the script and **enter interactive mode afterwards**. This can be done by passing -i __before__ the script.
将__-i选项__放在脚本参数的前面，这样解释器在执行__完__脚本中的语句时，会自动进入__交互式__模式，此时的环境是__执行脚本时__的环境。
===== 2.1.1. Argument Passing =====
When known to the interpreter, the script name and additional arguments thereafter are turned into **a list of strings** and assigned to the __argv__ variable in the__ sys__ module. You can access this list by executing** import sys**. The length of the list is__ at least one__; when no script and no arguments are given, sys.argv[0] is an empty string. When the script name is given as __'-'__ (meaning standard input), sys.argv[0] is set to '-'. When -c command is used, sys.argv[0] is set to '-c'. When -m module is used, sys.argv[0] is set to the** full name** of the located module. Options found after -c command or -m module __are not consumed__ by the Python interpreter’s option processing but left in __sys.argv__ for the command or module to handle.
* 解释器要向脚本传递__脚本名称和附加参数__，脚本名称放在sys.argv[0]中，附加参数从sys.argv[1]开始存放。
* 脚本名称可能为空、'-'、-c 以及module full name，脚本__名称后的所有选项或参数__都会放在sys.argv[1:]中。因此，在调用解释器时，要注意不能像通常的GUN程序那样**无序地**指定参数。
===== 2.1.2. Interactive Mode =====
When commands are** read from a tty**, the interpreter is said to be__ in interactive mode__. In this mode it prompts for the next command with **the primary prompt**, usually three greater-than signs (>>>); for continuation lines it prompts with** the secondary prompt**, by default three dots (...). The interpreter prints a welcome message stating its version number and a copyright notice before printing the first prompt:
//python//
Python 2.7 (#1, Feb 28 2010, 00:02:06)
Type "help", "copyright", "credits" or "license" for more information.
>>>
Continuation lines are needed when entering a __multi-line construct__. As an example, take a look at this if statement:
>>>
>>> the_world_is_flat = 1
>>> if the_world_is_flat__:__
... print "Be careful not to fall off!"
...
Be careful not to fall off! #__交互式模式中使用一个空白行表明多行语句块缩进的结束。__
===== 2.2. The Interpreter and Its Environment =====
==== 2.2.1. Error Handling ====
When an error occurs, the interpreter prints __an error message and a stack trace__. In interactive mode, it then returns to the primary prompt; when input came from a file, it exits with a nonzero exit status after printing the stack trace. (Exceptions handled by an except clause in a try statement are not errors in this context.) Some errors are unconditionally fatal and cause an exit with a nonzero exit; this applies to internal inconsistencies and some cases of running out of memory. All error messages are written to the** standard error** stream; normal output from executed commands is written to** standard output**.
Typing the interrupt character (usually Control-C or DEL) to the primary or secondary prompt __cancels the input__ and returns to the primary prompt. [1] Typing an interrupt while a command is executing raises the__ KeyboardInterrupt__ exception, which may be handled by a try statement.
==== 2.2.2. Executable Python Scripts ====
On BSD’ish Unix systems, Python scripts can be made **directly executable**, like shell scripts, by putting the line
#! /usr/bin/__env__ python
(assuming that the interpreter is on the user’s PATH) at the beginning of the script and giving the file an executable mode. The #! must be the __first two __characters of the file. On some platforms, this first line must end with a Unix-style line ending ('\n'), not a Windows ('\r\n') line ending. Note that the hash, or pound, character, '#', is used to start a comment in Python.
The script can be given an executable mode, or permission, using the chmod command:
$ chmod +x myscript.py
On Windows systems, there is no notion of an “executable mode”. The Python installer automatically __associates .py files with python.exe __so that a double-click on a Python file will run it as a script. The extension can also be .pyw, in that case, the console window that normally appears is suppressed.
==== 2.2.3. Source Code Encoding ====
**特别适用于使用UTF8编码但是不设置**__文档字节编码标记__**的编辑器生成的源文件。**
It is possible to use encodings different than ASCII in Python source files. The best way to do it is to put one more special comment line __right after__ the #! line to define the source file encoding:
__# -*- coding: encoding -*-__
With that declaration, all characters in the source file will be treated as having the //encoding// encoding, and it will be possible to directly write__ Unicode string literals__ in the selected encoding. The list of possible encodings can be found in the Python Library Reference, in the section on codecs.
目前，python__只支持字符串字面量或注释__使用Unicode编码，所有的关键字和标示符__必须使用__ASCII编码。在指定编码时，**UTF8, UTF-8, utf8, utf-8**是等价的。
For example, to write **Unicode literals** including the Euro currency symbol, the ISO-8859-15 encoding can be used, with the Euro symbol having the __ordinal value__ 164. This script will print the value 8364 (the** Unicode codepoint **corresponding to the Euro symbol) and then exit:
# -*- coding: iso-8859-15 -*-
currency = u"€"
print ord(currency)
If your editor supports saving files as UTF-8 with a__ UTF-8 byte order mark__ (aka **BOM**), you can use that instead of an encoding declaration. IDLE supports this capability if Options/General/Default Source Encoding/UTF-8 is set. Notice that this signature is not understood in older Python releases (2.2 and earlier), and also not understood by the operating system for script files with #! lines (only used on Unix systems).
By using UTF-8 (either through the signature or an encoding declaration), characters of most languages in the world can be used simultaneously in** string literals and comments**. Using non-ASCII characters in __identifiers__ is not supported. To display all these characters properly, your editor must recognize that the file is UTF-8, and it must use a __font__ that supports all the characters in the file.
==== 2.2.4. The Interactive Startup File ====
When you use Python interactively, it is frequently handy to have some **standard commands **executed every time the interpreter is started. You can do this by setting an environment variable named __PYTHONSTARTUP__ to the **name of a file** containing your start-up commands. This is similar to the .profile feature of the Unix shells.
非交互式脚本启动时__不会读取__这个环境变量指定的文件中的语句，但是可以通过如下的代码__明确地执行__它们。由于解释器是一条条执行启动文件中的初始化语句因此可能在文件的开头需要**导入相应的模块**。
This file is __only read__ in interactive sessions, not when Python reads commands from a script, and not when /dev/tty is given as the explicit source of commands (which otherwise behaves like an interactive session). It is executed in __the same namespace__ where interactive commands are executed, so that objects that it defines or imports can be used without qualification in the interactive session. You can also change the prompts __sys.ps1__ and __sys.ps2__ in this file (必须在文件的开头导入sys module).
If you want to read an additional start-up file from the current directory, you can program this in the global start-up file using code like if os.path.isfile('.pythonrc.py'): __execfile__('.pythonrc.py').
如果还需要读取其它的初始化文件，则可以在PYTHONSTARTUP文件中指定并用内置命令execfile执行它们。
If you want to use the startup file in a script(在**脚本文件里**明确使用启动文件，__适用于非交互式脚本__), you must do this explicitly in the script:
import os
filename = os.environ.get('PYTHONSTARTUP')
if filename and os.path.isfile(filename):
execfile(filename)
==== 2.2.5. The Customization Modules ====
Python provides __two hooks__ to let you customize it: sitecustomize and usercustomize. To see how it works, you need first to find the location of your **user site-packages directory**. Start Python and run this code:
>>>
>>> import site
>>> site.getusersitepackages()
'/home/user/.local/lib/python3.2/site-packages'
Now you can create a file named **usercustomize.py** in that directory and put anything you want in it. It will affect every invocation of Python, unless it is started with the __-s__ option to disable the automatic import.
sitecustomize works in the same way(使用的是site.getsitepackages), but is typically created by an administrator of the computer in the __global site-packages__ directory, and is imported before usercustomize. See the documentation of the site module for more details.
Footnotes
[1] A problem with the GNU Readline package may prevent this.

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====== 10. Brief Tour of the Standard Library ======
Created Wednesday 04 January 2012
===== 10.1. Operating System Interface =====
The__ os module__ provides dozens of functions for interacting with the operating system:
>>>
>>> import os
>>> os.getcwd() # Return the current working directory
'C:\\Python26'
>>> os.chdir('/server/accesslogs') # Change current working directory
>>> __os.system__('mkdir today') # Run the command mkdir in the system shell
0
Be sure to use the** import os **style instead of from os import *. This will keep os.open() from shadowing the __built-in open()__ function which operates much differently.
The __built-in dir() and help()__ functions are useful as interactive aids for working with large modules like os:
>>>
>>> import os
>>> dir(os)
<returns a list of **all module functions**>
>>> help(os)
<returns an extensive** manual page** created from the __module's docstrings__>
For daily file and directory management tasks, the __shutil __module provides a higher level interface that is easier to use:
>>>
>>> import **shutil #shell utility**
>>> shutil.copyfile('data.db', 'archive.db')
>>> shutil.move('/build/executables', 'installdir')
===== 10.2. File Wildcards =====
The __glob__ module provides a function for making file lists from directory **wildcard** searches:
>>>
>>> import glob
>>> glob.glob('*.py')
['primes.py', 'random.py', 'quote.py']
glob模块使用的通配符语法和bash使用的一样。
===== 10.3. Command Line Arguments =====
Common utility scripts often need to** process command line arguments**. These arguments are stored in the__ sys module’s argv__ attribute as a list. For instance the following output results from running python demo.py one two three at the command line:
>>>
>>> import sys
>>> print sys.argv
['demo.py', 'one', 'two', 'three']
The__ getopt__ module processes sys.argv using the conventions of the Unix getopt() function. More powerful and flexible command line processing is provided by the __argparse__ module.
===== 10.4. Error Output Redirection and Program Termination =====
The sys module also has attributes for __stdin, stdout, and stderr__. The latter is useful for emitting warnings and error messages to make them visible even when stdout has been redirected:
>>>
>>> **sys.stderr.write**('Warning, log file not found starting a new one\n')
Warning, log file not found starting a new one
The most direct way to terminate a script is to use __sys.exit()__.
===== 10.5. String Pattern Matching =====
The __re __module provides regular expression tools for advanced string processing. For **complex matching and manipulation**, regular expressions offer succinct, optimized solutions:
>>>
>>> import re
>>> re.findall(__r__'\bf[a-z]*', 'which foot or hand fell fastest')
['foot', 'fell', 'fastest']
>>> re.sub(r'(\b[a-z]+) \1', r'\1', 'cat in the the hat')
'cat in the hat'
When only simple capabilities are needed, string methods are preferred because they are easier to read and debug:
>>>
>>> 'tea for too'.**replace**('too', 'two')
'tea for two'
===== 10.6. Mathematics =====
The __math__ module gives access to the underlying **C library functions** for floating point math:
>>>
>>> import math
>>> math.cos(math.pi / 4.0)
0.70710678118654757
>>> math.log(1024, 2)
10.0
The __random__ module provides tools for making random selections:
>>>
>>> import random
>>> random.choice(['apple', 'pear', 'banana'])
'apple'
>>> random.sample(xrange(100), 10) # sampling without replacement
[30, 83, 16, 4, 8, 81, 41, 50, 18, 33]
>>> random.random() # random float
0.17970987693706186
>>> random.randrange(6) # random integer chosen from range(6)
4
===== 10.7. Internet Access =====
There are a number of modules for accessing the internet and processing internet protocols. Two of the simplest are __urllib2__ for retrieving data from urls and __smtplib __for sending mail:
>>>
>>> import urllib2
>>> for line in urllib2.**urlopen**('http://tycho.usno.navy.mil/cgi-bin/timer.pl'):
... if 'EST' in line or 'EDT' in line: # look for Eastern Time
... print line
<BR>Nov. 25, 09:43:32 PM EST
>>> import smtplib
>>> __server__ = smtplib.**SMTP**('localhost')
>>> server.sendmail('soothsayer@example.org', 'jcaesar@example.org',
... """To: jcaesar@example.org
... From: soothsayer@example.org
...
... Beware the Ides of March.
... """)
>>> server.quit()
(Note that the second example needs a mailserver running on localhost.)
===== 10.8. Dates and Times =====
The __datetime __module supplies classes for manipulating dates and times in both simple and complex ways. While date and time arithmetic is supported, the focus of the implementation is on efficient member extraction for output formatting and manipulation. The module also supports objects that are **timezone **aware.
>>>
>>> # dates are easily constructed and formatted
>>> from datetime import date
>>> now = date.today()
>>> now
datetime.date(2003, 12, 2)
>>> now.__strftime__("%m-%d-%y. %d %b %Y is a %A on the %d day of %B.")
'12-02-03. 02 Dec 2003 is a Tuesday on the 02 day of December.'
>>> # dates support __calendar arithmetic__
>>> birthday = date(1964, 7, 31)
>>> age = now - birthday
>>> age.days
14368
===== 10.9. Data Compression =====
Common data archiving and compression formats are directly supported by modules including: __zlib, gzip, bz2, zipfile and tarfile__.
>>>
>>> import zlib
>>> s = 'witch which has which witches wrist watch'
>>> len(s)
41
>>> t = zlib.**compress(s)**
>>> len(t)
37
>>> zlib.**decompress(t)**
'witch which has which witches wrist watch'
>>> zlib.**crc32(s)**
226805979
===== 10.10. Performance Measurement =====
Some Python users develop a deep interest in knowing the relative performance of __different approaches to the same problem__. Python provides a **measurement tool** that answers those questions immediately.
For example, it may be tempting to use the tuple packing and unpacking feature instead of the traditional approach to swapping arguments. The __timeit__ module quickly demonstrates a modest performance advantage:
>>>
>>> from __timeit__ import Timer
>>> Timer('t=a; a=b; b=t', 'a=1; b=2').timeit()
0.57535828626024577
>>> Timer('a,b = b,a', 'a=1; b=2').timeit()
0.54962537085770791
In contrast to timeit‘s fine level of granularity, the __profile__ and __pstats__ modules provide tools for identifying time critical sections in larger blocks of code.
===== 10.11. Quality Control =====
One approach for developing high quality software is to **write tests for each function** as it is developed and to__ run those tests frequently __during the development process.
The __doctest__ module provides a tool for scanning a module and **validating tests embedded in a program’s docstrings**. Test construction is as simple as __cutting-and-pasting__ a typical call along with its results into the docstring. This improves the documentation by **providing the user with an example **and it allows the doctest module to make sure the code remains true to the documentation:
def average(values):
"""Computes the arithmetic mean of a list of numbers.
>>> print average([20, 30, 70])
40.0
"""
return sum(values, 0.0) / len(values)
import __doctest__
doctest.testmod() # automatically validate the** embedded tests**
The __unittest __module is not as effortless as the doctest module, but it allows a more comprehensive set of tests to be maintained **in a separate file**:
import unittest
class TestStatisticalFunctions(unittest.TestCase):
def test_average(self):
self.assertEqual(average([20, 30, 70]), 40.0)
self.assertEqual(round(average([1, 5, 7]), 1), 4.3)
self.assertRaises(ZeroDivisionError, average, [])
self.assertRaises(TypeError, average, 20, 30, 70)
unittest.main() # Calling from the command line invokes all tests
===== 10.12. Batteries Included =====
Python has a “batteries included” philosophy. This is best seen through the sophisticated and robust capabilities of its larger packages. For example:
* The __xmlrpclib__ and __SimpleXMLRPCServer__ modules make implementing remote procedure calls into an almost trivial task. Despite the modules names, no direct knowledge or handling of XML is needed.
* The __email__ package is a library for managing email messages, including MIME and other RFC 2822-based message documents. Unlike smtplib and poplib which actually send and receive messages, the email package has a complete toolset for __building or decoding complex message structures __(including attachments) and for implementing** internet encoding** and header protocols.
* The__ xml.dom__ and __xml.sax__ packages provide robust support for parsing this popular data interchange format. Likewise, the__ csv __module supports direct reads and writes in a common database format. Together, these modules and packages greatly simplify data interchange between Python applications and other tools.
* Internationalization is supported by a number of modules including **gettext, locale**, and the **codecs** package.

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====== 11. Brief Tour of the Standard Library – Part II ======
Created Thursday 05 January 2012
This second tour covers more advanced modules that support __professional programming__ needs. These modules **rarely occur **in small scripts.
===== 11.1. Output Formatting =====
The __repr __module provides a version of repr() customized for abbreviated displays of large or deeply nested containers:
>>>
>>> import repr
>>> repr.repr(set('supercalifragilisticexpialidocious'))
"set(['a', 'c', 'd', 'e', 'f', 'g', ...])"
The __pprint__ module offers more sophisticated control over printing both built-in and user defined objects in a way that is** readable by the interpreter.** When the result is longer than one line, the “pretty printer” adds line breaks and indentation to more clearly reveal data structure:
>>>
>>> import pprint
>>> t = [[[['black', 'cyan'], 'white', ['green', 'red']], [['magenta',
... 'yellow'], 'blue']]]
...
>>> pprint.pprint(t, width=30)
[[[['black', 'cyan'],
'white',
['green', 'red']],
[['magenta', 'yellow'],
'blue']]]
The __textwrap__ module formats paragraphs of text to fit **a given screen width**:
>>>
>>> import textwrap
>>> doc = """The wrap() method is just like fill() except that it returns
... a list of strings instead of one big string with newlines to separate
... the wrapped lines."""
...
>>> print textwrap.fill(doc, width=40)
The wrap() method is just like fill()
except that it returns a list of strings
instead of one big string with newlines
to separate the wrapped lines.
The__ locale__ module accesses a database of __culture specific data formats__. The grouping attribute of locale’s format function provides a direct way of formatting numbers with group separators:
>>>
>>> import locale
>>> locale.setlocale(locale.LC_ALL, 'English_United States.1252')
'English_United States.1252'
>>> conv = locale.localeconv() # get a mapping of conventions
>>> x = 1234567.8
>>> locale.format("%d", x, grouping=True)
'1,234,567'
>>> locale.format_string("%s%.*f", (conv['currency_symbol'],
... conv['frac_digits'], x), grouping=True)
'$1,234,567.80'
===== 11.2. Templating =====
The __string __module includes a versatile **Template class** with a simplified syntax suitable for editing by end-users. This allows users to customize their applications without having to alter the application.
The format uses placeholder names formed by $ with valid Python identifiers (alphanumeric characters and underscores). Surrounding the__ placeholder __with braces allows it to be followed by more alphanumeric letters with no intervening spaces. Writing $$ creates a single escaped $:
>>>
>>> from string import __Template__
>>> t = Template('${village}folk send $$10 to $cause.')
>>> t.__substitute__(village='Nottingham', cause='the ditch fund')
'Nottinghamfolk send $10 to the ditch fund.'
The substitute() method raises a **KeyError** when a placeholder is not supplied in **a dictionary or a keyword** argument. For mail-merge style applications, user supplied data may be incomplete and the __safe_substitute()__ method may be more appropriate — it will** leave placeholders unchanged** if data is missing:
>>> t = Template('Return the $item to $owner.')
>>> d = dict(item='unladen swallow')
>>> t.substitute(d)
Traceback (most recent call last):
. . .
**KeyError: 'owner'**
>>> t.__safe_substitute__(d)
'Return the unladen swallow to $owner.'
**Template **subclasses can specify __a custom delimiter__. For example, a batch renaming utility for a photo browser may elect to use percent signs for placeholders such as the current date, image sequence number, or file format:
>>>
>>> import** time**, **os.path**
>>> photofiles = ['img_1074.jpg', 'img_1076.jpg', 'img_1077.jpg']
>>> class BatchRename(Template):
... __delimiter = '%'__
>>> fmt = raw_input('Enter rename style (%d-date %n-seqnum %f-format): ')
Enter rename style (%d-date %n-seqnum %f-format): Ashley_%n%f
>>> t = BatchRename(fmt)
>>> date = time.strftime('%d%b%y')
>>> for i, filename in enumerate(photofiles):
... base, ext = __os.path.splitext__(filename)
... newname = t.substitute(d=date, n=i, f=ext)
... print '{0} --> {1}'.format(filename, newname)
img_1074.jpg --> Ashley_0.jpg
img_1076.jpg --> Ashley_1.jpg
img_1077.jpg --> Ashley_2.jpg
Another application for templating is separating program logic from the details of multiple output formats. This makes it possible to substitute custom templates for XML files, plain text reports, and HTML web reports.
===== 11.3. Working with Binary Data Record Layouts =====
The __struct __module provides pack() and unpack() functions for working with __variable length binary record formats__. The following example shows how to loop through header information in a ZIP file without using the zipfile module. Pack codes "H" and "I" represent** two and four byte** unsigned numbers respectively. The "<" indicates that they are** standard size** and in** little-endian** byte order:
import struct
data = open('myfile.zip', 'rb').read()
start = 0
for i in range(3): # show the first 3 file headers
start += 14
fields = struct.unpack('<IIIHH', data[start:start+16])
crc32, comp_size, uncomp_size, filenamesize, extra_size = fields
start += 16
filename = data[start:start+filenamesize]
start += filenamesize
extra = data[start:start+extra_size]
print filename, hex(crc32), comp_size, uncomp_size
start += extra_size + comp_size # skip to the next header
===== 11.4. Multi-threading =====
Threading is a technique for __decoupling tasks __which are __not sequentially dependent__. Threads can be used to improve the responsiveness of applications that accept user input while other tasks run in the** background**. A related use case is running I/O in parallel with computations in another thread.
The following code shows how the high level __threading__ module can run tasks in background while the main program continues to run:
import__ threading, zipfile__
class AsyncZip(threading.Thread):
def __init__(self, infile, outfile):
threading.Thread.__init__(self)
self.infile = infile
self.outfile = outfile
def run(self):
f = zipfile.ZipFile(self.outfile, 'w', zipfile.ZIP_DEFLATED)
f.write(self.infile)
f.close()
print 'Finished background zip of: ', self.infile
__background__ = AsyncZip('mydata.txt', 'myarchive.zip')
__background.start()__
print 'The main program continues to run in foreground.'
__background.join()__ # Wait for the background task to finish
print 'Main program waited until background was done.'
The principal challenge of multi-threaded applications is__ coordinating threads that share data or other resources__. To that end, the threading module provides a number of__ synchronization primitives__ including locks, events, condition variables, and semaphores.
While those tools are powerful, minor design errors can result in problems that are difficult to reproduce. So, __the preferred approach to task coordination is to concentrate all access to a resource in a single thread and then use the Queue module to feed that thread with requests from other threads__. Applications using Queue.Queue objects for inter-thread communication and coordination are easier to design, more readable, and more reliable.
===== 11.5. Logging =====
The __logging__ module offers a full featured and flexible logging system. At its simplest, log messages are sent to a file or to sys.stderr:
import logging
logging.debug('Debugging information')
logging.info('Informational message')
logging.warning('Warning:config file %s not found', 'server.conf')
logging.error('Error occurred')
logging.critical('Critical error -- shutting down')
This produces the following output:
WARNING:root:Warning:config file server.conf not found
ERROR:root:Error occurred
CRITICAL:root:Critical error -- shutting down
By default, **informational and debugging messages are suppressed** and the output is sent to __standard error__. Other output options include routing messages through email, datagrams, sockets, or to an HTTP Server. **New filters** can select different routing based on message priority: DEBUG, INFO, WARNING, ERROR, and CRITICAL.
The logging system can be configured directly from Python or can be loaded from a user editable configuration file for customized logging without altering the application.
===== 11.6. Weak References =====
Python does __automatic memory management__ (reference counting for most objects and garbage collection to eliminate cycles). The memory is freed shortly after the__ last reference __to it has been eliminated.
This approach works fine for most applications but occasionally there is a need to** track objects** only as long as they are being used by something else. Unfortunately, just tracking them creates a reference that makes them__ permanent__. __The weakref module provides tools for tracking objects without creating a reference__. When the object is no longer needed, it is automatically removed from a weakref table and a callback is triggered for weakref objects. Typical applications include caching objects that are expensive to create:
>>>
>>> import __weakref, gc__
>>> class A:
... def __init__(self, value):
... self.value = value
... def __repr__(self):
... return str(self.value)
...
>>> a = A(10) # create **a reference**
>>> d =__ weakref.WeakValueDictionary()__
>>> d['primary'] = a # __does not create a reference__
>>> d['primary'] # fetch the object if it is still alive
10
>>> del a # remove the one reference
>>>__ gc.collect()__ # run garbage collection right away
0
>>> d['primary'] # entry was automatically removed
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
d['primary'] # entry was automatically removed
File "C:/python26/lib/weakref.py", line 46, in __getitem__
o = self.data[key]()
KeyError: 'primary'
===== 11.7. Tools for Working with Lists =====
Many data structure needs can be met with the built-in list type. However, sometimes there is a need for** alternative implementations** with different performance trade-offs.
The __array__ module provides an array() object that is like a list that stores only homogeneous data and stores it more compactly. The following example shows an array of numbers stored as **two byte unsigned binary numbers** (typecode "H") rather than the usual** 16 bytes per entry** for regular lists of Python int objects:
>>>
>>> from array import array
>>> a = array('H', [4000, 10, 700, 22222])
>>> sum(a)
26932
>>> a[1:3]
array('H', [10, 700])
The __collections __module provides a__ deque() __object that is like a list with faster appends and pops from the left side but slower lookups in the middle. These objects are well suited for implementing queues and __breadth first tree searches__:
>>>
>>> from collections import deque
>>> d = deque(["task1", "task2", "task3"])
>>> d.append("task4")
>>> print "Handling", d.popleft()
Handling task1
unsearched = deque([starting_node])
def breadth_first_search(unsearched):
node = unsearched.popleft()
for m in __gen_moves__(node):
if is_goal(m):
return m
unsearched.append(m)
In addition to alternative list implementations, the library also offers other tools such as the __bisect __module with functions for manipulating sorted lists:
>>>
>>> import bisect
>>> scores = [(100, 'perl'), (200, 'tcl'), (400, 'lua'), (500, 'python')]
>>> bisect.insort(scores, (300, 'ruby'))
>>> scores
[(100, 'perl'), (200, 'tcl'), (300, 'ruby'), (400, 'lua'), (500, 'python')]
The __heapq__ module provides functions for implementing heaps based on regular lists. __The lowest valued entry is always kept at position zero__. This is useful for applications which repeatedly access the smallest element but do not want to run a full list sort:
>>>
>>> from heapq import **heapify, heappop, heappush**
>>> data = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0]
>>> heapify(data) # rearrange the list into heap order
>>> heappush(data, -5) # add a new entry
>>> [heappop(data) for i in range(3)] # fetch the three smallest entries
[-5, 0, 1]
===== 11.8. Decimal Floating Point Arithmetic =====
The __decimal__ module offers a Decimal datatype for **decimal floating point** arithmetic. Compared to the built-in float implementation of **binary floating point**, the class is especially helpful for
* financial applications and other uses which require__ exact decimal representation__,
* control over precision,
* control over rounding to meet legal or regulatory requirements,
* tracking of significant decimal places, or
* applications where the user expects the results to match calculations done by hand.
For example, calculating a 5% tax on a 70 cent phone charge gives different results in decimal floating point and binary floating point. The difference becomes significant if the results are rounded to the nearest cent:
>>>
>>> from decimal import *
>>> x = Decimal('0.70') * Decimal('1.05')
>>> x
Decimal('0.7350')
>>> x.quantize(Decimal('0.01')) # round to nearest cent
Decimal('0.74')
>>> round(.70 * 1.05, 2) # same calculation with floats
0.73
The Decimal result keeps a trailing zero, automatically inferring four place significance from multiplicands with two place significance. Decimal reproduces mathematics as done by hand and avoids issues that can arise when binary floating point cannot exactly represent decimal quantities.
Exact representation enables the Decimal class to perform modulo calculations and equality tests that are unsuitable for binary floating point:
>>>
>>> Decimal('1.00') % Decimal('.10')
Decimal('0.00')
>>> 1.00 % 0.10
0.09999999999999995
>>> sum([Decimal('0.1')]*10) == Decimal('1.0')
True
>>> sum([0.1]*10) == 1.0
False
The decimal module provides arithmetic with as much precision as needed:
>>>
>>> getcontext().prec = 36
>>> Decimal(1) / Decimal(7)
Decimal('0.142857142857142857142857142857142857')

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====== 12. What Now ======
Created Thursday 05 January 2012
Reading this tutorial has probably reinforced your interest in using Python — you should be eager to apply Python to solving your real-world problems. Where should you go to learn more?
This tutorial is part of Python’s documentation set. Some other documents in the set are:
* The Python Standard Library:
You should browse through this manual, which gives complete (though terse) reference material about__ types, functions, and the modules__ in the standard library. The standard Python distribution includes a lot of additional code. There are modules to read Unix mailboxes, retrieve documents via HTTP, generate random numbers, parse command-line options, write CGI programs, compress data, and many other tasks. Skimming through the Library Reference will give you an idea of what’s available.
* Installing Python Modules explains how to install external modules written by other Python users.
* The Python Language Reference: A detailed explanation of Python’s__ syntax and semantics__. It’s heavy reading, but is useful as a complete guide to the language itself.
More Python resources:
http://www.python.org: The major Python Web site. It contains code, documentation, and pointers to Python-related pages around the Web. This Web site is mirrored in various places around the world, such as Europe, Japan, and Australia; a mirror may be faster than the main site, depending on your geographical location.
http://docs.python.org: Fast access to Python’s documentation.
http://pypi.python.org: The__ Python Package Index__, previously also nicknamed the Cheese Shop, is **an index of user-created Python modules **that are available for download. Once you begin releasing code, you can register it here so that others can find it.
http://aspn.activestate.com/ASPN/Python/Cookbook/: The Python Cookbook is a sizable collection of code examples, larger modules, and useful scripts. Particularly notable contributions are collected in a book also titled Python Cookbook (O’Reilly & Associates, ISBN 0-596-00797-3.)
For Python-related questions and problem reports, you can post to the newsgroup comp.lang.python, or send them to the mailing list at python-list@python.org. The newsgroup and mailing list are gatewayed, so messages posted to one will automatically be forwarded to the other. There are around 120 postings a day (with peaks up to several hundred), asking (and answering) questions, suggesting new features, and announcing new modules. Before posting, be sure to check the list of Frequently Asked Questions (also called the FAQ), or look for it in the Misc/ directory of the Python source distribution. Mailing list archives are available at http://mail.python.org/pipermail/. The FAQ answers many of the questions that come up again and again, and may already contain the solution for your problem.

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====== 3. An Informal Introduction to Python ======
Created Monday 02 January 2012
In the following examples, input and output are distinguished by the presence or absence of prompts (>>> and ...): to repeat the example, you must type everything after the prompt, when the prompt appears; lines that do not begin with a prompt are output from the interpreter. Note that a secondary prompt on a line by itself in an example means you __must type a blank line__; this is used to **end a multi-line command**.
在交互式模式中，用空行来结束多行缩进的语句块。
Many of the examples in this manual, even those entered at the interactive prompt, include comments. Comments in Python start with the hash character, #, and extend to the end of the physical line. A comment may appear at the start of a line or following whitespace or code, but not within a string literal. A hash character within a string literal is just a hash character. Since __comments are to clarify code__ and are not interpreted by Python, they may be omitted when typing in examples.
Some examples:
# this is the first comment
SPAM = 1 # and this is the second comment
# ... and now a third!
STRING = "# This is not a comment."
===== 3.1. Using Python as a Calculator =====
Let’s try some simple Python commands. Start the interpreter and wait for the primary prompt, >>>. (It shouldn’t take long.)
==== 3.1.1. Numbers ====
The interpreter acts as a simple calculator: you can type an expression at it and it will write the value. Expression syntax is straightforward: the operators +, -, * and / work just like in most other languages (for example, Pascal or C); parentheses can be used for grouping. For example:
>>>
>>> 2+2
4
>>> # This is a comment
... 2+2
4
>>> 2+2 # and a comment on the same line as code
4
>>> (50-5*6)/4
5
>>> #__ Integer division returns the floor__:
... 7/3
2
>>> 7/-3
-3
The equal sign ('=') is used to assign a value to a variable. Afterwards, no result is displayed before the next interactive prompt:
>>>
>>> width = 20
>>> height = 5*9
>>> width * height
900
__A value can be assigned to several variables simultaneously__:
>>>
>>> x = y = z = 0 # Zero x, y and z
>>> x
0
>>> y
0
>>> z
0
__Variables must be “defined” (assigned a value) before they can be used__, or an error will occur:
>>>
>>> # try to access an undefined variable
... n
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
__NameError__: name 'n' is not defined
There is** full support** for floating point; operators with mixed type operands convert the integer operand to floating point:
>>>
>>> 3 * 3.75 / 1.5
7.5
>>> 7.0 / 2
3.5
__Complex numbers __are also supported; imaginary numbers are written with a suffix of __j or J__. Complex numbers with a nonzero real component are written as__ (real+imagj)__, or can be created with the **complex(real, imag)** function.
>>>
>>> 1j * 1J
(-1+0j)
>>> 1j * complex(0,1)
(-1+0j)
>>> 3+1j*3
(3+3j)
>>> (3+1j)*3
(9+3j)
>>> (1+2j)/(1+1j)
(1.5+0.5j)
Complex numbers are always represented as two__ floating point__ numbers, the real and imaginary part. To extract these parts from a complex number z, use z.real and z.imag.
>>>
>>> a=1.5+0.5j
>>>__ a.real__
1.5
>>> __a.imag__
0.5
The** conversion functions **to floating point and integer __(float(), int() and long())__ don’t work for complex numbers — there is no one correct way to convert a complex number to a real number. Use __abs(z)__ to get its magnitude (as a float) or z.real to get its real part.
>>>
>>> a=3.0+4.0j
>>> float(a)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
TypeError: can't convert complex to float; use abs(z)
>>> a.real
3.0
>>> a.imag
4.0
>>>__ abs__(a) # __sqrt__(a.real**2 + a.imag**2)
5.0
In interactive mode, the** last printed expression** is assigned to the variable _____. This means that when you are using Python as a desk calculator, it is somewhat easier to continue calculations, for example:
>>>
>>> tax = 12.5 / 100
>>> price = 100.50
>>> price * tax
12.5625
>>> price + _
113.0625
>>> __round__(_, 2)
113.06
This variable should be treated as** read-only** by the user. Don’t explicitly assign a value to it — __you would create an independent local variable with the same name masking the built-in variable with its magic behavior__.
==== 3.1.2. Strings ====
转义字符在引号(单、双)中__都起__作用。
Besides numbers, Python can also manipulate strings, which can be expressed in several ways. They can be enclosed in single quotes or double quotes:
>>>
>>> 'spam eggs'
'spam eggs'
>>> 'doesn__\'__t'
"doesn't" #注意最外层为双引号，因为里面没有双引号
>>> "doesn't"
"doesn't" #同上
>>> '"Yes," he said.'
'"Yes," he said.' #注意最外层为单引号
>>> "\"Yes,\" he said."
'"Yes," he said.' #同上
>>>__ '"Isn\'t," she said.' #这里的转义字符是起作用的。__
'"Isn\'t," she said.' #结果中包含转义字符是因为python的字符串中包含双引号时，最外层要用单引号显示。
The interpreter prints the result of string operations in the same way as they are typed for input: inside quotes, and __with quotes__ and other funny characters __escaped by backslashes__, to show the precise value. The string is enclosed in __double quotes __if the string contains a single quote and no double quotes, else it’s enclosed in single quotes. The** print** statement produces a more readable output for such input strings.
String literals can__ span multiple lines __in several ways. Continuation lines can be used, with a backslash as the last character on the line indicating that the next line is a **logical continuation** of the line:
注意：和C语言一样，python的字符串字面量中__不能直接输入换行__，而只能使用转义字符\n，如果要**跨行输入**则必须在当前行尾__添加一转义字符\__。(而bash的字符串中可以直接输入换行。)
但是使用特殊的__三引号形式__，则其中可以换行。
hello = "This is a rather long string containing\n\
several lines of text just as you would do in C.\n\
Note that whitespace at the beginning of the line is\
significant."
print hello
Note that newlines still need to be embedded in the string using \n – **the newline following the trailing backslash is discarded**. This example would print the following:
This is a rather long string containing
several lines of text just as you would do in C.
Note that whitespace at the beginning of the line is significant.
Or, strings can be surrounded in a pair of matching __triple-quotes: """ or '''__. **End of lines do not need to be escaped when using triple-quotes**, but they will be included in the string.
print """
Usage: thingy [OPTIONS]
-h Display this usage message
-H hostname Hostname to connect to
"""
produces the following output:
Usage: thingy [OPTIONS]
-h Display this usage message
-H hostname Hostname to connect to
If we make the string literal __a “raw” string__, \n sequences are not converted to newlines, but the backslash at the end of the line, and the newline character in the source, are both included in the string as data. Thus, the example:
原生形式的字符串字面两会忽略其中的转义字符，而且其中可以直接输入换行。
hello =__ r"__This is a rather long string containing\n\
several lines of text much as you would do in C."
print hello
would print:
This is a rather long string containing\n\
several lines of text much as you would do in C.
例如：
>>> hello = r'dfasf\tkjdk\n__\ __ #结尾的转义字符是必需的。
... dfdjf dlfjdl\
... dfdsjfl'
>>> print hello #由于是原生字符串字面量，因此python__不会解释__其中的转义字符。
dfasf\tkjdk\n\
dfdjf dlfjdl\
dfdsjfl
>>> hello
__'dfasf\\tkjdk\\n\\\ndfdjf dlfjdl\\\ndfdsjfl' #注意，其中的各字符已经被恰当地转义__
>>>
Strings can be concatenated (glued together) with the **+ operator**, and repeated with *:
>>>
>>> word =__ 'Help' + 'A'__
>>> word
'HelpA'
>>> '<' + __word*5 __+ '>'
'<HelpAHelpAHelpAHelpAHelpA>'
Two string literals__ next to each other are automatically concatenated__; the first line above could also have been written word = 'Help' 'A'; this__ only works__ with two literals, not with arbitrary string expressions:
>>>
>>> 'str' 'ing' # <- This is ok
'string'
>>> 'str'.strip() + 'ing' # <- This is ok
'string'
>>> **'str'.strip()** 'ing' # <- This is invalid； 因为通过空格连接两相邻字符串的做法只适合于__两字符串字面量__，而不适合__字符串表达式__。
File "<stdin>", line 1, in ?
'str'.strip() 'ing'
^
SyntaxError: invalid syntax
Strings can be **subscripted (indexed)**; like in C, the first character of a string has subscript (index) 0. There is __no separate character type__; a character is simply a string of size one. Like in Icon, substrings can be specified with the** slice notation**: two indices separated by a colon.
python中__没有字符(char)类型__，而只有字符串(string)字面量类型，前者只是后者的特殊形式。
>>>
>>> word[4] #字符串分片的结果还是一个字符串。
'A'
>>> word[0:2]
'He'
>>> word[2:4]
'lp'
Slice indices have useful defaults; an omitted first index defaults to zero, an omitted second index defaults to **the size of the string **being sliced.
>>>
>>> word[:2] # The first two characters
'He'
>>> word[2:] # Everything except the first two characters
'lpA'
Unlike a C string, __Python strings cannot be changed__. Assigning to an indexed position in the string results in an error:
>>>
>>> word[0] = 'x'
Traceback (most recent call last):
File "<stdin>", line 1, in ?
TypeError: object does not support** item assignment**
>>> word[:1] = 'Splat'
Traceback (most recent call last):
File "<stdin>", line 1, in ?
TypeError: object does not support **slice assignment**
However, **creating a new string **with the combined content is easy and efficient:
>>>
>>> 'x' + word[1:]
'xelpA'
>>> 'Splat' + word[4]
'SplatA'
Here’s a useful invariant of slice operations: s[:i] + s[i:] equals s.
>>>
>>> word[:2] + word[2:]
'HelpA'
>>> word[:3] + word[3:]
'HelpA'
__Degenerate slice indices__ are handled gracefully: an index that is too large is replaced by the string size, an upper bound smaller than the lower bound returns an empty string.
>>>
>>> word[1:100]
'elpA'
>>> word[10:]
''
>>> word[2:1]
'l'
Indices may be__ negative numbers, to start counting from the right__. For example:
>>>
>>> word[-1] # The last character
'A'
>>> word[-2] # The last-but-one character
'p'
>>> word[-2:] # The last two characters
'pA'
>>> word[:-2] # Everything** except** the last two characters
'Hel'
But note that__ -0 is really the same as 0__, so it does not count from the right!
>>>
>>> word[-0] # (since -0 equals 0)
'H'
__Out-of-range negative slice indices are truncated, but don’t try this for single-element (non-slice) indices:__
>>>
>>> word[-100:]
'HelpA'
>>> word[-10] # error
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IndexError: string index out of range
One way to remember how slices work is to think of the indices as pointing between characters, with the left edge of the first character numbered 0. Then the right edge of the last character of a string of n characters has index n, for example:
+---+---+---+---+---+
| H | e | l | p | A |
+---+---+---+---+---+
__ 0__ 1 2 3 4 5
-5 -4 -3 -2 -1
The first row of numbers gives the position of th//e indices 0...5// in the string; the second row gives the corresponding negative indices. The slice from i to j consists of all characters between the edges labeled i and j, respectively.
For non-negative indices, the length of a slice is the difference of the indices, if both are within bounds. For example, **the length of word[1:3] is 2**.
The built-in function__ len() __returns the length of a string:
>>>
>>> s = 'supercalifragilisticexpialidocious'
>>> len(s)
34
===== See also =====
__Sequence Types __— **str, unicode, list, tuple, bytearray, buffer, xrange**
Strings, and the Unicode strings described in the next section, are examples of sequence types, and support the **common operations** supported by such types.
String Methods
Both strings and Unicode strings support a large number of methods for basic** transformations and searching**.
__String Formatting__
Information about string formatting with __str.format()__ is described here.
String Formatting Operations
The old formatting operations invoked when strings and Unicode strings are the left operand of the __% __operator are described in more detail here.
==== 3.1.3. Unicode Strings ====
Starting with Python 2.0 a new data type for** storing text data **is available to the programmer: the Unicode object. It can be used to store and manipulate Unicode data (see http://www.unicode.org/) and integrates well with the existing string objects, providing **auto-conversions** where necessary.
Unicode has the advantage of providing __one ordinal(序数，编号) for every character __in every script used in modern and ancient texts. Previously, there were only 256 possible ordinals for script characters. Texts were typically bound to** a code page** which mapped the ordinals to script characters. This lead to very much confusion especially with respect to internationalization (usually written as i18n — 'i' + 18 characters + 'n') of software.__ Unicode solves these problems by defining one code page for all scripts.__
Creating Unicode strings in Python is just as simple as creating normal strings:
>>>
>>> __u__'Hello World !'
u'Hello World !'
The small 'u' in front of the quote indicates that a Unicode string is supposed to be created. If you want to include special characters in the string, you can do so by using the __Python Unicode-Escape encoding__. The following example shows how:
>>>
>>> u'Hello\u0020World !' #该字符串中的各字符将以其在unicode字符集中的__序号来存储__。注意，__字符在Unicode中的序号是惟一的，但是在不同Unicode字符集编码中的值可能是不同的。__
u'Hello World !'
The **escape sequence** \u0020 indicates to insert the **Unicode character** with the ordinal value 0x0020 (the space character) at the given position.
Other characters are interpreted by using their respective ordinal values directly as Unicode ordinals. If you have literal strings in the standard Latin-1 encoding that is used in many Western countries, you will find it convenient that__ the lower 256 characters of Unicode are the same as the 256 characters__ of Latin-1.
For experts, there is also a raw mode just like the one for normal strings. You have to prefix the opening quote with ‘ur’ to have Python use the__ Raw-Unicode-Escape encoding__. It will only apply the above \uXXXX conversion if there is __an uneven number(非偶数)__ of backslashes in front of the small ‘u’.
>>>
>>> ur'Hello\u0020World !' #如果u前面有奇数个反斜线则\uXXXX会被认为是Unicode-Escape，否则将忽略其转义功能。
u'Hello World !'
>>> ur'Hello\\u0020World !'
u'Hello\\\\u0020World !'
The raw mode is most useful when you have to enter lots of backslashes, as can be necessary in__ regular expressions__.
Apart from these** standard encodings**, Python provides a whole set of other ways of creating Unicode strings on the basis of a __known encoding__.
The built-in function** unicode()** provides access to all__ registered Unicode codecs__ (COders and DECoders，注意这里是Unicode__字符集的编码形式__). Some of the more well known encodings which these codecs can convert are **Latin-1, ASCII, UTF-8, and UTF-16**. The latter two are__ variable-length __encodings that store each Unicode character in one or more bytes.
The default encoding is normally set to ASCII, which passes through characters in the range 0 to 127 and **rejects any other characters with an error**.
__ When a Unicode string is printed, written to a file, or converted with str(), conversion takes place using this default encoding.__
这是因为Unicode字符串是__未编码__存储在解释器内部的，而当将其打印或保存时必须对其进行编码。普通字符串都是编码保存的。
>>>
>>> u"abc" #未编码unicode字符串
__u__'abc'
>>>__ str(u"abc") #转换为缺省编码的普通字符串__
'abc' #注意，没有u前缀
>>> u"äöü"
u'\xe4\xf6\xfc' #显示的是三个字符在Unicode字符集中的__序号，注意它们不是ASCII编码。__
>>> str(u"äöü") #将Unicode编码的字符串转换为缺省编码(ASCII)的字符串。
Traceback (most recent call last):
File "<stdin>", line 1, in ?
__UnicodeEncodeError__: 'ascii' codec can't encode characters in position 0-2: ordinal not in range(128)
To convert __a Unicode string into an 8-bit string using a specific encoding__, Unicode objects provide an __encode()__ method that takes one argument, the name of the encoding. Lowercase names for encodings are preferred.
>>>
>>> u"äöü".encode(__'utf-8'__) #由于该三个字符不在ASCII字符集中，因此不能使用缺省的ASCII编码。这里指定__编码形式为utf-8__.
'\xc3\xa4\xc3\xb6\xc3\xbc' #对Unicode字符串采用utf-8编码后得到的普通字符串
If you have data in a specific encoding and want to produce a corresponding Unicode string from it, you can use the __unicode()__ function with the encoding name as the second argument.
>>>
>>> unicode(**'\xc3\xa4\xc3\xb6\xc3\xbc'**, 'utf-8') #__第一个参数是采用第二个参数代表的编码方法编码得到的字符串,结果是一个Unicode字符串。__
u'\xe4\xf6\xfc'
===== 3.1.4. Lists =====
Python knows a number of compound data types, used to **group together** other values. The most versatile（多才多艺的） is the list, which can be written as a list of comma-separated values (items) between square brackets. List items need not all have the same type.
>>>
>>> a = ['spam', 'eggs', 100, 1234]
>>> a
['spam', 'eggs', 100, 1234]
Like string indices, list indices start at 0, and lists can be sliced, concatenated and so on:
>>>
>>> a[0]
'spam'
>>> a[3]
1234
>>> a[-2]
100
>>> a[1:-1] #包括第二个参数的值。
['eggs', 100]
>>>** a[:2] + ['bacon', 2*2]**
['spam', 'eggs', 'bacon', 4]
>>> __3*a[:3]__ + ['Boo!']
['spam', 'eggs', 100, 'spam', 'eggs', 100, 'spam', 'eggs', 100, 'Boo!']
__All slice operations return a new list __containing the requested elements. This means that the following slice returns **a shallow copy(潜复制) **of the list a:
>>>
>>>__ a[:]__
['spam', 'eggs', 100, 1234]
Unlike strings, which are immutable, it is possible to change individual elements of a list:
>>>
>>> a
['spam', 'eggs', 100, 1234]
>>> a[2] = a[2] + 23
>>> a
['spam', 'eggs', 123, 1234]
Assignment to slices is also possible, and this can even change the size of the list or clear it entirely:
>>>
>>> # Replace some items:
... a[0:2] = [1, 12]
>>> a
[1, 12, 123, 1234]
>>> # Remove some:
... __a[0:2] = []__
>>> a
[123, 1234]
>>> # Insert some:
... __a[1:1] = ['bletch', 'xyzzy'] __
>>> a
[123, 'bletch', 'xyzzy', 1234]
>>> # Insert (a copy of) itself at the beginning
>>> a[:0] = a #也可以是__a[0:0]__
>>> a
[123, 'bletch', 'xyzzy', 1234, 123, 'bletch', 'xyzzy', 1234]
>>> **# Clear the list:** replace all items with an empty list
>>> a[:] = []
>>> a
[]
The built-in function __len() __also applies to lists:
>>>
>>> a = ['a', 'b', 'c', 'd']
>>> len(a)
4
It is possible to **nest lists** (create lists containing other lists), for example:
>>>
>>> q = [2, 3]
>>> p = [1, q, 4]
>>> len(p)
3
>>> p[1]
[2, 3]
>>> p[1][0]
2
>>>** p[1].append('xtra')** # See section 5.1
>>> p
[1, [2, 3, 'xtra'], 4]
>>> q
[2, 3, 'xtra']
Note that in the last example, p[1] and q really refer to the same object! We’ll come back to object semantics later.
==== 3.2. First Steps Towards Programming ====
Of course, we can use Python for more complicated tasks than adding two and two together. For instance, we can write an initial sub-sequence of the Fibonacci series as follows:
>>>
>>> # Fibonacci series:
... # the sum of two elements defines the next
... __a, b = 0, 1__
>>> while b < 10:
... print b
... a, b = b, a+b
...
1
1
2
3
5
8
This example introduces several new features.
* The first line contains a **multiple assignment**: the variables a and b simultaneously get the new values 0 and 1. On the last line this is used again, demonstrating that the expressions on the right-hand side are all evaluated first before any of the assignments take place. The right-hand side expressions are evaluated from the left to the right.
* The **while loop **executes as long as the condition (here: b < 10) remains true. In Python, like in C, any non-zero integer value is true; zero is false. The condition may also be a string or list value, in fact any sequence; anything with a non-zero length is true, empty sequences are false. The test used in the example is a simple comparison. The** standard comparison operators** are written the same as in C: < (less than), > (greater than), == (equal to), <= (less than or equal to), >= (greater than or equal to) and != (not equal to).
* The body of the loop is indented:__ indentation is Python’s way of grouping statements__. At the interactive prompt, you have to type a tab or space(s) for each indented line. In practice you will prepare more complicated input for Python with a text editor; all decent text editors have an auto-indent facility. When a compound statement is entered interactively, it must be__ followed by a blank line__ to indicate completion (since the parser cannot guess when you have typed the last line). Note that each line within a basic block must be indented by **the same amount**.
* The** print statement **writes the value of the expression(s) it is given. It differs from just writing the expression you want to write (as we did earlier in the calculator examples) in the way it handles multiple expressions and strings. Strings are printed __without quotes, and a space__ is inserted between items, so you can format things nicely, like this:
>>>
>>> i = 256*256
>>> print 'The value of i is', i
The value of i is 65536
__ A trailing comma__ avoids the newline after the output:
>>>
>>> a, b = 0, 1
>>> while b < 1000:
... print __b,__
... a, b = b, a+b
...
1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987
Note that the interpreter inserts a newline before it prints the next prompt if the last line was not completed

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====== 4. More Control Flow Tools ======
Created Tuesday 03 January 2012
Besides the **while** statement just introduced, Python knows the usual control flow statements known from other languages, with some twists.
===== 4.1. if Statements =====
Perhaps the most well-known statement type is the if statement. For example:
>>>
>>> x = int(__raw_input__("Please enter an integer: "))
Please enter an integer: 42
>>> if x < 0:
... x = 0
... print 'Negative changed to zero'
... elif x == 0:
... print 'Zero'
... elif x == 1:
... print 'Single'
... else:
... print 'More'
...
More
There can be zero or more elif parts, and the __else__ part is optional. The keyword __‘elif‘__ is short for ‘else if’, and is useful to avoid excessive indentation. An if ... elif ... elif ... sequence is a substitute for the switch or case statements found in other languages.
python没有switch...case...语句结构。
===== 4.2. for Statements =====
The for statement in Python differs a bit from what you may be used to in C or Pascal. Rather than always iterating over an arithmetic progression of numbers (like in Pascal), or giving the user the ability to define both the iteration step and halting condition (as C), __Python’s for statement iterates over the items of any sequence __(a list or a string), in the order that they appear in the sequence. For example (no pun intended):
>>>
>>> # Measure some strings:
... a = ['cat', 'window', 'defenestrate']
>>> for x in a:
... print x, len(x)
...
cat 3
window 6
defenestrate 12
It is not safe to modify the sequence being iterated over in the loop (this can only happen for mutable sequence types, such as lists). If you need to modify the list you are iterating over (for example, to duplicate selected items) you must __iterate over a copy__. The slice notation makes this particularly convenient:
>>>
>>> for x in __a[:]: # make a slice copy of the entire list__
... if len(x) > 6: a.insert(0, x)
...
>>> a
['defenestrate', 'cat', 'window', 'defenestrate']
===== 4.3. The range() Function =====
If you do need to iterate over a sequence of numbers, the built-in function __range() __comes in handy. It generates lists containing arithmetic progressions:
>>>
>>> range(10)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
The given end point is never part of the generated list; range(10) generates a list of 10 values, the legal indices for items of a sequence of length 10. It is possible to let the range start at another number, or to specify a different increment (even negative; sometimes this is called the ‘step’):
>>>
>>> range(5, 10) #第二个参数不会包含在结果序列中。
[5, 6, 7, 8, 9]
>>> range(0, 10, 3)
[0, 3, 6, 9]
>>> range(-10, -100, -30)
[-10, -40, -70]
To iterate over the indices of a sequence, you can combine range() and len() as follows:
>>>
>>> a = ['Mary', 'had', 'a', 'little', 'lamb']
>>> for i in range(len(a)):
... print i, a[i]
...
0 Mary
1 had
2 a
3 little
4 lamb
In most such cases, however, it is convenient to use the __enumerate()__ function, see Looping Techniques.
===== 4.4. break and continue Statements, and else Clauses on Loops =====
* The break statement, like in C, breaks out of the** smallest enclosing** for or while loop.
* The continue statement, also borrowed from C, continues with the next iteration of the loop.
Loop statements may have an __else clause__; it is executed when the loop terminates through__ exhaustion of the list__ (with for) or when the condition becomes false (with while), but **not when the loop is terminated by a break statement**. This is exemplified by the following loop, which searches for prime numbers:
>>>
>>> for n in range(2, 10):
... for x in range(2, n):
... if n % x == 0:
... print n, 'equals', x, '*', n/x
... break
... else: #注意，python的loop循环可以使用一个else语句块，该else不属于上面的if，这可以**通过缩进来表示**。
... # loop fell through without finding a factor
... print n, 'is a prime number'
...
2 is a prime number
3 is a prime number
4 equals 2 * 2
5 is a prime number
6 equals 2 * 3
7 is a prime number
8 equals 2 * 4
9 equals 3 * 3
(Yes, this is the correct code. Look closely: the else clause belongs to the for loop, not the if statement.)
===== 4.5. pass Statements =====
The pass statement does nothing. It can be used when a statement is __required syntactically__ but the program requires no action. For example:
>>>
>>> while **True:**
... pass # Busy-wait for keyboard interrupt (Ctrl+C)
...
This is commonly used for creating minimal classes:
>>>
>>> class MyEmptyClass:
... pass
...
Another place pass can be used is as a__ place-holder for a function or conditional body__ when you are working on new code, allowing you to keep thinking at a more abstract level. The pass is silently ignored:
>>>
>>> def initlog(*args):
... pass # Remember to implement this!
...
===== 4.6. Defining Functions =====
We can create a function that writes the Fibonacci series to an arbitrary boundary:
>>>
>>> def fib(n): # write Fibonacci series up to n
... """Print a Fibonacci series up to n."""
... a, b = 0, 1
... while a < n:
... print __a,__
... a, b = b, a+b #右边的表达式**从左向右**执行后复制给左边。
...
>>> # Now call the function we just defined:
... fib(2000)
0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 1597
The keyword** def** introduces a function definition. It must be followed by the function name and the **parenthesized list of formal parameters**. The statements that form the body of the function start at the next line, and must be indented.
The first statement of the function body can optionally be **a string literal**; this string literal is the function’s __documentation string__, or__ docstring__. (More about docstrings can be found in the section Documentation Strings.) There are tools which use docstrings to automatically produce** online or printed documentation**, or to let the user interactively browse through code; it’s good practice to include docstrings in code that you write, so make a habit of it.
__The execution of a function introduces a new symbol table used for the local variables of the function.More precisely, all variable assignments in a function store the value in the local symbol table; whereas variable references first look in the local symbol table, then in the local symbol tables of enclosing functions, then in the global symbol table, and finally in the table of built-in names. Thus, global variables cannot be directly assigned a value within a function (unless named in a global statement), although they may be referenced.__
python中的函数可以嵌套定义，每个层次的函数都有**自己的符号表**。
The actual parameters (arguments) to a function call are introduced in the **local symbol table **of the called function when it is called; thus, __arguments are passed using call by value__ (where the value is __always an object reference__, not the value of the object). [1] When a function calls another function, a new local symbol table is created for that call.
函数的参数只能是值传递，值总是一个__对象引用__。
__A function definition introduces the function name in the current symbol table__. The value of the function name has a type that is recognized by the interpreter as a user-defined function. This value can be assigned to another name which can then also be used as a function. __This serves as a general renaming mechanism__:
python中的所有对象都是一个类型，所有类型也是一个对象。
>>>
>>> fib
<function fib at 10042ed0> #fib引用的是一个函数对象，其在内存中的地址为10042ed0，该值是对象的标示(identifer)
>>> f = fib
>>> f(100)
0 1 1 2 3 5 8 13 21 34 55 89
Coming from other languages, you might object that **fib is not a function** but a procedure since it doesn’t return a value. In fact, even functions without a return statement do return a value, albeit a rather boring one. This value is called __None__ (it’s a built-in name). Writing the value None is normally suppressed by the interpreter if it would be the only value written. You can see it if you really want to using print:
没有使用return语句返回值的函数，其返回的值为None。
>>>
>>> fib(0)
>>> print fib(0)
None
It is simple to write a function that returns a list of the numbers of the Fibonacci series, instead of printing it:
>>>
>>> def fib2(n): # return Fibonacci series up to n
... """Return a list containing the Fibonacci series up to n."""
... result = []
... a, b = 0, 1
... while a < n:
... result.append(a) # see below
... a, b = b, a+b
... **return result**
...
>>> **f100** = fib2(100) # call it
>>> f100 # write the result
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]
This example, as usual, demonstrates some new Python features:
* The return statement returns with a value from a function. __return without an expression argument returns None__. Falling off the end of a function also returns __None__.
* The statement result.append(a) calls a method of the list object result. __A method__ is a function that ‘belongs’ to an object and is named obj.methodname, where obj is some object (this may be an expression), and methodname is the name of a method that is defined by the object’s type. __Different types define different methods__. Methods of different types may have the same name without causing ambiguity. (It is possible to define your own object types and methods, using classes, see Classes) The method append() shown in the example is defined for list objects; it adds a new element at the end of the list. In this example it is equivalent to result = result + [a], but more efficient.
===== 4.7. More on Defining Functions =====
It is also possible to define functions with **a variable number** of arguments. There are three forms, which can be combined.
==== 4.7.1. Default Argument Values ====
The most useful form is to specify __a default value __for one or more arguments. This creates a function that can be called with fewer arguments than it is defined to allow. For example:
def ask_ok(prompt, retries=4, complaint='Yes or no, please!'):
while __True__:
ok = __raw_input__(prompt)
__ if ok in ('y', 'ye', 'yes'):__
return __True__
if ok in ('n', 'no', 'nop', 'nope'):
return False
retries = retries - 1
if retries < 0:
__ raise IOError('refusenik user')__
print complaint
This function can be called in several ways:
* giving only the__ mandatory__ argument: ask_ok('Do you really want to quit?')
* giving one of the optional arguments: ask_ok('OK to overwrite the file?', 2)
* or even giving all arguments: ask_ok('OK to overwrite the file?', 2, 'Come on, only yes or no!')
This example also introduces the__ in__ keyword. This tests whether or not a sequence contains a certain value.
__The default values are evaluated at the point of function definition in the defining scope__, so that
i = 5
def f(arg=i): #函数参数的__初始值在函数定义时确定，解释器会一直保存这个状态信息__。
print arg
i = 6
f()
will print 5.
Important warning: __The default value is evaluated only once__. This makes a difference when the default is** a mutable object** such as a list, dictionary, or instances of most classes. For example, the following function** accumulates** the arguments passed to it on subsequent calls:
def f(a, L=[]): #L的缺省值在定义时确定，这里为一个可变对象------空列表，Python会一直保存这个定义信息。
L.append(a)
return L
print f(1) #缺省值只会在函数第一次调用时被求值一次，而且__会在以后的多次调用过程中传递__。
print f(2)
print f(3)
This will print
[1]
[1, 2]
[1, 2, 3]
If you don’t want** the default to be shared between subsequent call**s, you can write the function like this instead:
def f(a, __L=None__): #这里的None标示，L值未定义。
if L is None:
L = []
L.append(a)
return L
==== 4.7.2. Keyword Arguments ====
Functions can also be called using keyword arguments of the form** kwarg=value**. For instance, the following function:
def parrot(voltage, state='a stiff', action='voom', type='Norwegian Blue'):
print "-- This parrot wouldn't", action,
print "if you put", voltage, "volts through it."
print "-- Lovely plumage, the", type
print "-- It's", state, "!"
accepts** one required argument **(voltage) and **three optional arguments** (state, action, and type). This function can be called in any of the following ways:
parrot(1000) # 1 positional argument
parrot(voltage=1000) # 1 keyword argument
parrot(voltage=1000000, action='VOOOOOM') # 2 keyword arguments
parrot(action='VOOOOOM', voltage=1000000) # 2 keyword arguments
parrot('a million', 'bereft of life', 'jump') # 3 positional arguments
parrot('a thousand', state='pushing up the daisies') # 1 positional, 1 keyword
but all the following calls would be invalid:
parrot() # required argument missing
parrot(voltage=5.0, 'dead') # __non-keyword argument after a keyword argument__
parrot(110, voltage=220) # **duplicate** value for the same argument
parrot(actor='John Cleese') # unknown keyword argument
**关键字参数必须放在位置参数之后。**
In a function call, __keyword arguments must follow positional arguments__. All the keyword arguments passed must match one of the arguments accepted by the function (e.g. actor is not a valid argument for the parrot function), and **their order is not important**. This also includes non-optional arguments (e.g. parrot(voltage=1000) is valid too). No argument may receive a value more than once. Here’s an example that fails due to this restriction:
>>>
>>> def function(a):
... pass
...
>>> function(0, a=0)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
**TypeError**: function() got multiple values for keyword argument 'a'
When a__ final __formal parameter of the form__ **name__ is present, it receives **a dictionary** (see Mapping Types — dict) containing all keyword arguments except for those corresponding to a formal parameter. This may be combined with a formal parameter of the form__ *name__ (described in the next subsection) which receives** a tuple **containing the positional arguments beyond the formal parameter list. (*name __must occur before__ **name.) For example, if we define a function like this:
def cheeseshop(kind, *arguments, **keywords):
print "-- Do you have any", kind, "?"
print "-- I'm sorry, we're all out of", kind
f**or arg in arguments**:
print arg
print "-" * 40
keys =** sorted**(__keywords.keys()__)
for kw in keys:
print kw, ":", keywords[kw]
It could be called like this:
cheeseshop("Limburger", "It's very runny, sir.",
"It's really very, VERY runny, sir.",
shopkeeper='Michael Palin',
client="John Cleese",
sketch="Cheese Shop Sketch")
and of course it would print:
-- Do you have any Limburger ?
-- I'm sorry, we're all out of Limburger
It's very runny, sir.
It's really very, VERY runny, sir.
----------------------------------------
client : John Cleese
shopkeeper : Michael Palin
sketch : Cheese Shop Sketch
Note that the list of keyword argument names is created by sorting the result of the keywords dictionary’s keys() method before printing its contents; if this is not done, the order in which the arguments are printed is undefined.
==== 4.7.3. Arbitrary Argument Lists ====
Finally, the least frequently used option is to specify that a function can be called with an arbitrary number of arguments. These arguments will be wrapped up** in a tuple** (see Tuples and Sequences). Before the variable number of arguments, zero or more normal arguments may occur.
def write_multiple_items(file, separator, *args):
file.write(__separator.join(args)__)
==== 4.7.4. Unpacking Argument Lists ====
The reverse situation occurs when the arguments are **already in a list or tuple but need to be unpacked for a function call** requiring separate positional arguments. For instance, the built-in range() function expects separate start and stop arguments. If they are not available separately, write the function call with the__ *-operator to unpack__ the arguments out of a list or tuple:
>>>
>>> range(3, 6) # normal call with separate arguments
[3, 4, 5]
>>> args = [3, 6]
>>> range(*args) # call with arguments **unpacked from a list**
[3, 4, 5]
In the same fashion, **dictionaries can deliver keyword arguments with the **-operator**:
>>>
>>> def parrot(voltage, state='a stiff', action='voom'):
... print "-- This parrot wouldn't", action,
... print "if you put", voltage, "volts through it.",
... print "E's", state, "!"
...
>>> d = {"voltage": "four million", "state": "bleedin' demised", "action": "VOOM"}
>>> parrot(__**d__)
-- This parrot wouldn't VOOM if you put four million volts through it. E's bleedin' demised !
==== 4.7.5. Lambda Forms ====
By popular demand, a few features commonly found in__ functional programming__ languages like Lisp have been added to Python. With the lambda keyword, **small anonymous functions** can be created. Here’s a function that returns the sum of its two arguments:** lambda a, b: a+b**. Lambda forms can be used wherever **function objects are required**. They are syntactically restricted to __a single expression__. Semantically, they are just syntactic sugar for a normal function definition. Like nested function definitions, lambda forms can reference variables from the containing scope:
>>>
>>> def make_incrementor(n):
... return lambda x: x + n
...
>>> f = make_incrementor(42)
>>> f(0)
42
>>> f(1)
43
==== 4.7.6. Documentation Strings ====
There are emerging conventions about the** content and formatting **of documentation strings.
The first line __should always be a short, concise summary of the object’s purpose__. For brevity, it should not explicitly state the object’s name or type, since these are available by other means (except if the name happens to be a verb describing a function’s operation). This line should begin with a capital letter and end with a period.
If there are more lines in the documentation string,__ the second line should be blank__, visually separating the __summary__ from the rest of the __description__. The following lines should be one or more paragraphs describing the object’s** calling conventions**, its side effects, etc.
The Python parser does** not strip** indentation from multi-line string literals in Python, so tools that process documentation have to strip indentation if desired. This is done using the following convention. The **first non-blank line after the first line** of the string determines the amount of indentation for the entire documentation string. (We can’t use the first line since it is generally adjacent to the string’s opening quotes so its indentation is not apparent in the string literal.) Whitespace “equivalent” to this indentation is then stripped from the start of all lines of the string. **Lines that are indented less should not occur**, but if they occur all their leading whitespace should be stripped. Equivalence of whitespace should be tested after expansion of tabs (to 8 spaces, normally).
Here is an example of a multi-line docstring:
>>>
>>> def my_function():
... """Do nothing, but document it.
...
... No, really, it doesn't do anything.
... """
... pass
...
>>> print my_function.____doc____
Do nothing, but document it.
No, really, it doesn't do anything.
===== 4.8. Intermezzo: Coding Style =====
Now that you are about to write longer, more complex pieces of Python, it is a good time to talk about coding style. Most languages can be written (or more concise, formatted) in different styles; some are more readable than others. Making it easy for others to read your code is always a good idea, and adopting a nice coding style helps tremendously for that.
For Python, PEP 8 has emerged as the style guide that most projects adhere to; it promotes a very readable and eye-pleasing coding style. Every Python developer should read it at some point; here are the most important points extracted for you:
* Use 4-space indentation, and no tabs.
4 spaces are a good compromise between small indentation (allows greater nesting depth) and large indentation (easier to read). Tabs introduce confusion, and are best left out.
* Wrap lines so that they don’t exceed__ 79__ characters.
This helps users with small displays and makes it possible to have several code files side-by-side on larger displays.
* Use blank lines to__ separate __functions and classes, and larger blocks of code inside functions.
* When possible, put comments on a line of __their own__.
* Use__ docstrings__.
* Use spaces around operators and after commas, but **not directly inside bracketing constructs**: a = f(1, 2) + g(3, 4).
* Name your classes and functions consistently; the convention is to use__ CamelCase for classes and lower_case_with_underscores for functions and methods.__ Always use self as the name for the first method argument (see A First Look at Classes for more on classes and methods).
* Don’t use fancy encodings if your code is meant to be used in international environments. Plain ASCII works best in any case.
Footnotes
[1] Actually, call by object reference would be a better description, since if a mutable object is passed, the caller will see any changes the callee makes to it (items inserted into a list).

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====== se5. Data Structures ======
Created Tuesday 03 January 2012
http://docs.python.org/tutorial/datastructures.html
This chapter describes some things you’ve learned about already in more detail, and adds some new things as well.
===== 5.1. More on Lists =====
The list data type has some more methods. Here are all of the methods of list objects:
list.append(x)
Add an item to the end of the list; equivalent to a[len(a):] = [x].
list.extend(L)
Extend the list by appending all the items in the given list; equivalent to a[len(a):] = L.
list.insert(i, x)
Insert an item at a given position. The first argument is the index of the element before which to insert, so a.insert(0, x) inserts at the front of the list, and a.insert(len(a), x) is equivalent to a.append(x).
**list.remove(x)**
Remove the __first item__ from the list whose value is x. It is **an error** if there is no such item.
list.pop([i])
**Remove** the item at the given position in the list, and return it. If no index is specified, a.pop() removes and returns the// last item// in the list. (The square brackets around the i in the method signature denote that the parameter is optional, not that you should type square brackets at that position. You will see this notation frequently in the Python Library Reference.)
list.index(x)
Return the index in the list of the first item whose value is x. It is __an error__ if there is no such item.
list.count(x)
Return the number of times x appears in the list.
list.sort()
Sort the items of the list,__ in place.__
list.reverse()
Reverse the elements of the list, in place.
An example that uses most of the list methods:
>>>
>>> a = [66.25, 333, 333, 1, 1234.5]
>>> print a.count(333), a.count(66.25), a.count('x')
2 1 0
>>> a.insert(2, -1)
>>> a.append(333)
>>> a
[66.25, 333, -1, 333, 1, 1234.5, 333]
>>> a.index(333)
1
__>>> a.remove(333)__
>>> a
[66.25, -1, 333, 1, 1234.5, 333]
>>> a.reverse()
>>> a
[333, 1234.5, 1, 333, -1, 66.25]
>>> a.sort()
>>> a
[-1, 1, 66.25, 333, 333, 1234.5]
==== 5.1.1. Using Lists as Stacks ====
The list methods make it very easy to use a list as a stack, where the last element added is the first element retrieved (“last-in, first-out”). To add an item to the top of the stack, use append(). To retrieve an item from the top of the stack, use pop() without an explicit index. For example:
>>>
>>> stack = [3, 4, 5]
>>> stack.append(6)
>>> stack.append(7)
>>> stack
[3, 4, 5, 6, 7]
>>> stack.pop()
7
>>> stack
[3, 4, 5, 6]
>>> stack.pop()
6
>>> stack.pop()
5
>>> stack
[3, 4]
==== 5.1.2. Using Lists as Queues ====
It is also possible to use a list as a queue, where the first element added is the first element retrieved (“first-in, first-out”); however, lists are __not efficient __for this purpose. While appends and pops from the** end** of list are fast, doing inserts or pops from the beginning of a list is slow (because all of the other elements have to be **shifted** by one).
To implement a queue, use collections.deque which was designed to have fast appends and pops from both ends. For example:
>>>
>>> __from collections import deque__
>>> queue = deque(["Eric", "John", "Michael"])
>>> queue.append("Terry") # Terry arrives
>>> queue.append("Graham") # Graham arrives
>>> queue.popleft() # The first to arrive now leaves
'Eric'
>>> queue.popleft() # The second to arrive now leaves
'John'
>>> queue # Remaining queue in order of arrival
deque(['Michael', 'Terry', 'Graham'])
==== 5.1.3. Functional Programming Tools ====
There are three built-in functions that are very useful when used with lists:__ filter(), map(), and reduce()__.
filter(function, sequence) r__eturns a sequence consisting of those items from the sequence for which function(item) is true__. If sequence is a string or tuple, the result will be of the same type; otherwise, it is **always a list.** For example, to compute a sequence of numbers not divisible by 2 and 3:
>>>
>>> def f(x): return x % 2 != 0 and x % 3 != 0
...
>>> filter(f, range(2, 25))
[5, 7, 11, 13, 17, 19, 23]
map(function, sequence) __calls function(item) for each of the sequence’s items and returns a list of the return values__. For example, to compute some cubes:
>>>
>>> def cube(x): return x*x*x
...
>>> map(cube, range(1, 11))
[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000]
More than one sequence may be passed; __the function must then have as many arguments as there are sequences __and is called with the corresponding item from each sequence (or None if some sequence is shorter than another). For example:
>>>
>>> seq = range(8)
>>> def add(x, y): return x+y
...
>>> map(add, seq, seq)
[0, 2, 4, 6, 8, 10, 12, 14]
reduce(function, sequence)__ returns a single value constructed by calling the binary function function on the first two items of the sequence__, then on the result and the next item, and so on. For example, to compute the sum of the numbers 1 through 10:
>>>
>>> def add(x,y): return x+y
...
>>> reduce(add, range(1, 11))
55
If there’s only one item in the sequence, its value is returned; if the sequence is empty, an exception is raised.
A third argument can be passed to indicate the **starting value**. In this case the starting value is returned for an empty sequence, and the function is first applied to the starting value and the first sequence item, then to the result and the next item, and so on. For example,
>>>
>>> def sum(seq):
... def add(x,y): return x+y
... return reduce(add, seq, 0)
...
>>> sum(range(1, 11))
55
>>> sum([])
0
Don’t use this example’s definition of sum(): since summing numbers is such a common need, a built-in function __sum(sequence)__ is already provided, and works exactly like this.
New in version 2.3.
==== 5.1.4. List Comprehensions ====
List comprehensions provide a concise way to create lists. Common applications are to make new lists where each element is the result of some operations applied to each member of another sequence or iterable, or to create a subsequence of those elements that __satisfy a certain condition__.
For example, assume we want to create a list of squares, like:
>>>
>>> squares = []
>>> for x in range(10):
... squares.append(x**2)
...
>>> squares
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
We can obtain the same result with:
squares = [x**2 for x in range(10)]
This is also equivalent to __squares = map(lambda x: x**2, range(10))__, but it’s more concise and readable.
A list comprehension consists of brackets containing an expression followed by a__ for__ clause, then zero or more__ for or if __clauses. The result will be a new list resulting from evaluating the expression in the context of the for and if clauses which follow it. For example, this listcomp combines the elements of two lists if they are not equal:
>>>
>>> [(x, y) for x in [1,2,3] for y in [3,1,4] if x != y] #注意这两个for的__嵌套关系__。
[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]
and it’s equivalent to:
>>>
>>> combs = []
>>> for x in [1,2,3]:
... for y in [3,1,4]:
... if x != y:
... combs.append((x, y))
...
>>> combs
[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]
Note how the order of the for and if statements is the same in both these snippets.
If the expression is a tuple (e.g. the (x, y) in the previous example), it must be parenthesized.
>>>
>>> vec = [-4, -2, 0, 2, 4]
>>> # create a new list with the values doubled
>>> [x*2 for x in vec]
[-8, -4, 0, 4, 8]
>>> #__ filter the list__ to exclude negative numbers
>>> [x for x in vec if x >= 0]
[0, 2, 4]
>>> #** apply a function** to all the elements
>>> [abs(x) for x in vec]
[4, 2, 0, 2, 4]
>>> # call a method on each element
>>> freshfruit = [' banana', ' loganberry ', 'passion fruit ']
>>> [**weapon.strip()** for weapon in freshfruit]
['banana', 'loganberry', 'passion fruit']
>>> # create** a list of 2-tuples** like (number, square)
>>> [(x, x**2) for x in range(6)]
[(0, 0), (1, 1), (2, 4), (3, 9), (4, 16), (5, 25)]
>>> #__ the tuple must be parenthesized__, otherwise an error is raised
>>> [x, x**2 for x in range(6)]
File "<stdin>", line 1
[x, x**2 for x in range(6)]
^
SyntaxError: invalid syntax
>>> # flatten a list using a listcomp with two 'for'
>>> vec =[ [ 1,2,3], [4,5,6], [7,8,9] ]
>>> [num for elem in vec for num in elem]
[1, 2, 3, 4, 5, 6, 7, 8, 9]
List comprehensions can contain **complex expressions and nested functions**:
>>>
>>> from math import pi
>>> [__str(round(pi, i))__ for i in range(1, 6)]
['3.1', '3.14', '3.142', '3.1416', '3.14159']
==== 5.1.4.1. Nested List Comprehensions ====
The initial expression in a list comprehension can be any arbitrary expression, including another list comprehension.
Consider the following example of a 3x4 matrix implemented as a list of 3 lists of length 4:
>>>
>>> matrix = [
... [1, 2, 3, 4],
... [5, 6, 7, 8],
... [9, 10, 11, 12],
... ]
The following list comprehension will **transpose rows and columns**:
>>>
>>> [[row[i] for row in matrix] for i in range(4)]
[ [1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12] ]
As we saw in the previous section, __the nested listcomp is evaluated in the context of the for that follows it__, so this example is equivalent to:
>>>
>>> transposed = []
>>> for i in range(4):
... transposed.append([row[i] for row in matrix])
...
>>> transposed
[ [1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12] ]
which, in turn, is the same as:
>>>
>>> transposed = []
>>> for i in range(4):
... # the following 3 lines implement the nested listcomp
... transposed_row = []
... for row in matrix:
... transposed_row.**append**(row[i])
... transposed.append(transposed_row)
...
>>> transposed
[ [1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12] ]
In the real world, you __should prefer built-in functions to complex flow statements__. The zip() function would do a great job for this use case:
>>>
>>>__ zip(*matrix)__
[(1, 5, 9), (2, 6, 10), (3, 7, 11), (4, 8, 12)]
See Unpacking Argument Lists for details on the asterisk in this line.
===== 5.2. The del statement =====
There is a way to remove an item from a list given__ its index__ instead of its value: the **del** statement. This differs from the **pop()** method which returns a value. The del statement can also be used to __remove slices__ from a list or clear the entire list (which we did earlier by assignment of an empty list to the slice). For example:
>>>
>>> a = [-1, 1, 66.25, 333, 333, 1234.5]
>>> del a[0]
>>> a
[1, 66.25, 333, 333, 1234.5]
>>> del a[2:4]
>>> a
[1, 66.25, 1234.5]
>>> __del a[:]__
>>> a
[]
del can also be used to__ delete entire variables__:
>>>
>>> del a
Referencing the name a hereafter is an error (at least until another value is assigned to it). We’ll find other uses for del later.
===== 5.3. Tuples and Sequences =====
We saw that lists and strings have many** common properties**, such as indexing and slicing operations. They are two examples of__ sequence data types__ (see Sequence Types —__ str, unicode, list, tuple, bytearray, buffer, xrange__). Since Python is an evolving language, other sequence data types may be added. There is also another standard sequence data type: the tuple.
A tuple consists of a number of values **separated by commas**, for instance:
>>>
>>> t = 12345, 54321, 'hello!'
>>> t[0]
12345
>>> t
(12345, 54321, 'hello!')
>>> # Tuples may be nested:
... u = t, (1, 2, 3, 4, 5)
>>> u
((12345, 54321, 'hello!'), (1, 2, 3, 4, 5))
As you see, on__ output tuples are always enclosed in parentheses__, so that nested tuples are interpreted correctly; they may be input with or without surrounding parentheses, although often parentheses are necessary anyway (if the tuple is part of a larger expression).
Tuples have many uses. For example: (x, y) coordinate pairs, employee records from a database, etc. __Tuples, like strings, are immutable__: it is not possible to assign to the individual items of a tuple (you can simulate much of the same effect with slicing and concatenation, though). It is also possible to create tuples which contain mutable objects, such as lists.
A special problem is the construction of tuples containing 0 or 1 items: the syntax has some extra quirks to accommodate these. Empty tuples are constructed by an empty pair of parentheses; a tuple __with one item__ is constructed by__ following a value with a comma__ (it is not sufficient to enclose a single value in parentheses). Ugly, but effective. For example:
>>>
>>> empty = __()__
>>> singleton = __'hello', __ # <-- note trailing comma
>>> len(empty)
0
>>> len(singleton)
1
>>> singleton
('hello',)
注意：singleton = ('aa')是不对的，需是 singleton = ('aa', )
The statement t = 12345, 54321, 'hello!' is an example of __tuple packing__: the values 12345, 54321 and 'hello!' are packed together in a tuple. The reverse operation is also possible:
>>>
>>> x, y, z = t
This is called, appropriately enough, __sequence unpacking__ and __works for any sequence__ on the right-hand side. Sequence unpacking requires the list of variables on the left to have__ the same number __of elements as the length of the sequence. Note that multiple assignment is really just a combination of tuple packing and sequence unpacking.
===== 5.4. Sets =====
Python also includes a data type for sets. A set is an __unordered collection with no duplicate elements__. Basic uses include membership testing and eliminating duplicate entries. Set objects also support mathematical operations like** union, intersection, difference, and symmetric difference**.
Here is a brief demonstration:
>>>
>>> basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana']
>>> fruit = __set__(basket) # create a set without duplicates
>>> fruit
set(['orange', 'pear', 'apple', 'banana'])
>>> 'orange' in fruit # fast membership testing
True
>>> 'crabgrass' in fruit
False
>>> # Demonstrate** set operations **on unique letters from two words
...
>>> a = set('abracadabra')
>>> b = set('alacazam')
>>> a # unique letters in a
set(['a', 'r', 'b', 'c', 'd'])
>>> a__ - __b # letters in a but not in b
set(['r', 'd', 'b'])
>>> a__ | __b # letters in either a or b
set(['a', 'c', 'r', 'd', 'b', 'm', 'z', 'l'])
>>> a__ &__ b # letters in both a and b
set(['a', 'c'])
>>> a__ ^ __b # letters in a or b but not both
set(['r', 'd', 'b', 'm', 'z', 'l'])
===== 5.5. Dictionaries =====
Another useful data type built into Python is the dictionary (see __Mapping Types — dict__). Dictionaries are sometimes found in other languages as “associative memories” or **“associative arrays”**. Unlike sequences, which are** indexed **by a range of numbers, dictionaries are __indexed by keys,__ which can be __any immutable__ type; strings and numbers can always be keys. Tuples can be used as keys if they contain only strings, numbers, or tuples; if a tuple contains any mutable object either directly or indirectly, it cannot be used as a key. You can’t use lists as keys, since lists can be modified in place using index assignments, slice assignments, or methods like append() and extend().
It is best to think of a dictionary as __an unordered set of key: value pairs,__ with the requirement that the** keys are unique **(within one dictionary). A pair of braces creates an empty dictionary: {}. Placing a comma-separated list of **key:value** pairs within the braces adds initial key:value pairs to the dictionary; this is also the way dictionaries are written on output.
The main operations on a dictionary are** storing** a value with some key and** extracting** the value given the key. It is also possible to **delete **a key:value pair with del. If you store using a key that is already in use, the old value associated with that key is forgotten. It is an **error** to extract a value using a non-existent key.
The keys() method of a dictionary object returns a __list __of all the keys used in the dictionary,** in arbitrary order** (if you want it sorted, just apply the sorted() function to it). To check whether a single key is in the dictionary, use the__ in__ keyword.
Here is a small example using a dictionary:
>>>
>>> tel = {'jack': 4098, 'sape': 4139}
>>>** tel['guido'] = 4127**
>>> tel
{'sape': 4139, 'guido': 4127, 'jack': 4098}
>>> tel['jack']
4098
>>>__ del __tel['sape']
>>> tel['irv'] = 4127
>>> tel
{'guido': 4127, 'irv': 4127, 'jack': 4098}
>>> tel.**keys()**
['guido', 'irv', 'jack']
>>> 'guido' __in__ tel
True
The dict() constructor builds dictionaries directly from **lists of key-value pairs** stored as tuples. When the__ pairs form a pattern__, list comprehensions can compactly specify the key-value list.
>>>
>>> dict([('sape', 4139), ('guido', 4127), ('jack', 4098)])
{'sape': 4139, 'jack': 4098, 'guido': 4127}
>>> __dict([(x, x**2) for x in (2, 4, 6)])__ # use a list comprehension
{2: 4, 4: 16, 6: 36}
Later in the tutorial, we will learn about __Generator Expressions__ which are even better suited for the task of supplying key-values pairs to the dict() constructor.
When the keys are simple strings, it is sometimes easier to specify pairs using keyword arguments:
>>>
>>> dict(sape=4139, guido=4127, jack=4098)
{'sape': 4139, 'jack': 4098, 'guido': 4127}
===== 5.6. Looping Techniques =====
When looping through dictionaries, the key and corresponding value can be retrieved at the same time using the __iteritems() __method.
>>>
>>> knights = {'gallahad': 'the pure', 'robin': 'the brave'}
>>> for k, v in knights.__iteritems()__:
... print k, v
...
gallahad the pure
robin the brave
When looping through a sequence, the position index and corresponding value can be retrieved at the same time using the **enumerate() **function.
>>>
>>> for i, v in__ enumerate(['tic', 'tac', 'toe'])__:
... print i, v
...
0 tic
1 tac
2 toe
To loop over two or more sequences at the same time, the entries can__ be paired with the zip()__ function.
>>>
>>> questions = ['name', 'quest', 'favorite color']
>>> answers = ['lancelot', 'the holy grail', 'blue']
>>> for q, a in__ zip__(questions, answers):
... print 'What is your {0}? It is {1}.'.__format__(q, a)
...
What is your name? It is lancelot.
What is your quest? It is the holy grail.
What is your favorite color? It is blue.
To loop over a sequence in reverse, first specify the sequence in a forward direction and then call the __reversed() __function.
>>>
>>> for i in** reversed**(xrange(1,10,2)):
... print i
...
9
7
5
3
1
To loop over a sequence in sorted order, use the __sorted()__ function which returns **a new **sorted list while leaving the source unaltered.
>>>
>>> basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana']
>>> for f in sorted(set(basket)):
... print f
...
apple
banana
orange
pear
===== 5.7. More on Conditions =====
The conditions used in while and if statements can__ contain any operators, not just comparisons__.
The comparison operators__ in __and __not in__ check whether a value occurs (does not occur) in a sequence. The operators__ is and is not__ compare whether two objects are really **the same object**; this only matters for mutable objects like lists. All comparison operators have __the same priority__, which is lower than that of all numerical operators.
Comparisons can be chained. For example, __a < b == c__ tests whether a is less than b and moreover b equals c.
Comparisons may be combined using the **Boolean operators**__ and __and__ or, __and the outcome of a comparison (or of any other Boolean expression) may be negated with __not__. These have lower priorities than comparison operators; between them, **not has the highest priority and or the lowest**, so that A and not B or C is equivalent to (A and (not B)) or C. As always, parentheses can be used to express the desired composition.
优先级：算术运算符>关系运算符>逻辑运算符。关系运算符的优先级相同，从左到右结合。逻辑运算符中的not优先级最高，or优先级最低。
The Boolean operators __and__ and __or __are so-called __short-circuit operators__: their arguments are evaluated from left to right, and evaluation stops as soon as the outcome is determined. For example, if A and C are true but B is false, A and B and C does not evaluate the expression C. When used as a general value and not as a Boolean, the return value of a short-circuit operator is the** last evaluated argument**.
逻辑运算符返回的结果为__最后一个执行的表达式__，而不一定是True或False.
It is possible to assign the result of a comparison or other Boolean expression to a variable. For example,
>>>
>>> string1, string2, string3 = '', 'Trondheim', 'Hammer Dance'
>>> non_null = string1 or string2 or string3 #返回结果为最后一个表达式。
>>> non_null
'Trondheim'
Note that in Python, unlike C, assignment cannot occur inside expressions. C programmers may grumble about this, but it avoids a common class of problems encountered in C programs: typing = in an expression when == was intended.
===== 5.8. Comparing Sequences and Other Types =====
Sequence objects may be compared to other objects with __the same sequence type__. The comparison uses__ lexicographical ordering__: first the first two items are compared, and if they differ this determines the outcome of the comparison; if they are equal, the next two items are compared, and so on, until either sequence is exhausted. If two items to be compared are** themselves sequences of the same type**, the lexicographical comparison is carried out__ recursively__. If all items of two sequences compare equal, the sequences are considered equal. If one sequence is an initial sub-sequence of the other, __the shorter sequence is the smaller__ (lesser) one. Lexicographical ordering for strings uses the **ASCII ordering** for individual characters. Some examples of comparisons between sequences of the same type:
(1, 2, 3) < (1, 2, 4)
[1, 2, 3] < [1, 2, 4]
'ABC' < 'C' < 'Pascal' < 'Python'
(1, 2, 3, 4) < (1, 2, 4)
(1, 2) < (1, 2, -1)
(1, 2, 3) == (1.0, 2.0, 3.0)
(1, 2, ('aa', 'ab')) < (1, 2, ('abc', 'a'), 4)
Note that **comparing objects of different types is legal**. The outcome is deterministic but __arbitrary__: **the types are ordered by their name**. Thus, a list is always smaller than a string, a string is always smaller than a tuple, etc. [1] Mixed numeric types are compared according to their __numeric value, so 0 equals 0.0__, etc.
Footnotes
[1] The rules for comparing objects of different types should not be relied upon; they may change in a future version of the language.

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====== 6. Modules ======
Created Wednesday 04 January 2012
If you quit from the Python interpreter and enter it again, the** definitions** you have made (functions and variables) are lost. Therefore, if you want to write a somewhat longer program, you are better off using a text editor to prepare the input for the interpreter and running it with that file as input instead. This is known as __creating a script__. As your program gets longer, you may want to __split it into several files__ for easier maintenance. You may also want to use a __handy function__ that you’ve written in several programs without copying its definition into each program.
To support this, Python has a way to put definitions in a file and use them in a script or in an interactive instance of the interpreter. Such __a file is called a module__; definitions from a module can be imported into other modules or into the __main module__ (the collection of variables that you have access to in a script executed at __the top level__ and in calculator mode).
__A module is a file containing Python definitions and statements.__ The file name is the module name with the suffix **.py** appended. Within a module, the module’s name (as a string) is available as the value of the global variable** __name__**. For instance, use your favorite text editor to create a file called fibo.py in the current directory with the following contents:
# Fibonacci numbers module
def fib(n): # write Fibonacci series up to n
a, b = 0, 1
while b < n:
print b,
a, b = b, a+b
def fib2(n): # return Fibonacci series up to n
result = []
a, b = 0, 1
while b < n:
result.append(b)
a, b = b, a+b
return result
Now enter the Python interpreter and import this module with the following command:
>>>
>>> import fibo
This does not enter the names of the functions defined in **fibo** directly in the __current symbol table__; it only enters the module name fibo there. Using the module name you can access the functions:
>>>
>>> fibo.fib(1000)
1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987
>>> fibo.fib2(100)
[1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]
>>> fibo.**__name__**
'fibo'
If you intend to use a function often you can assign it to __a local name__:
>>>
>>> fib = fibo.fib
>>> fib(500)
1 1 2 3 5 8 13 21 34 55 89 144 233 377
===== 6.1. More on Modules =====
A module can contain executable statements as well as function definitions. These statements are intended to __initialize the module__. They are executed only the __first time__ the module is imported somewhere. [1]
Each module has__ its own private symbol table__, which is used as the __global symbol table by all functions defined in the module__. Thus, the author of a module can **use global variables in the module without worrying about accidental clashes with a user’s global variables.** On the other hand, if you know what you are doing you can touch a module’s global variables with the same notation used to refer to its functions, modname.itemname.
Modules can import other modules. It is customary but not required to** place all import statements at the beginning of a module **(or script, for that matter). The imported module names are placed in the importing module’s __global symbol table__.
There is a variant of the import statement that imports names from a module __directly __into the importing module’s symbol table. For example:
>>>
>>> from fibo import fib, fib2
>>> fib(500)
1 1 2 3 5 8 13 21 34 55 89 144 233 377
This does __not introduce the module name __from which the imports are taken in the local symbol table (so in the example, fibo is not defined).
There is even a variant to import all names that a module defines:
>>>
>>> from fibo import *
>>> fib(500)
1 1 2 3 5 8 13 21 34 55 89 144 233 377
This imports all names except those **beginning with an underscore **(_).
Note that in general the practice of importing * from a module or package is __frowned upon__, since it often causes poorly readable code. However, it is okay to use it to save typing in interactive sessions.
Note
For efficiency reasons, __each module is only imported once per interpreter session__. Therefore, if you change your modules, you must restart the interpreter – or, if it’s just one module you want to test interactively, use reload(), e.g. __reload(modulename)__.
==== 6.1.1. Executing modules as scripts ====
When you run a Python module with
python** fibo.py** <arguments>
__the code in the module will be executed__, just as if you imported it, but with the ____name__ set to "__main__"__. That means that by adding this code at the end of your module:
if** __name__ == "__main__"**:
import sys
fib(int(sys.argv[1]))
只有在python__命令行__上将该模块当作脚本来执行时，__name__ 变量的值才为 "__main__".
you can make the file usable as a script as well as an importable module, because the code that parses the command line only runs if the module is executed as the “main” file:
$ python fibo.py 50
1 1 2 3 5 8 13 21 34
__If the module is imported, the code is not run__:
>>>
>>> import fibo
>>>
This is often used either to provide a convenient user interface to a module, or for testing purposes (running the module as a script executes a test suite).
==== 6.1.2. The Module Search Path ====
When a module named spam is imported,__ the interpreter searches for a file named spam.py__ in the directory containing the input script and then in the list of directories specified by the environment variable__ PYTHONPATH__. This has the same syntax as the shell variable PATH, that is, **a list of directory names**. When PYTHONPATH is not set, or when the file is not found there, the search continues in an installation-dependent default path; on Unix, this is usually .:/usr/local/lib/python.
Actually, modules are searched in the list of directories given by the variable__ sys.path__ which is initialized from the directory containing the input script (or the current directory), PYTHONPATH and the installation- dependent default. This allows Python programs that know what they’re doing to modify or replace the module search path. Note that because the directory containing the script being run is on the search path,__ it is important that the script not have the same name as a standard module__, or Python will attempt to load the script as a module when that module is imported. This will generally be an error. See section Standard Modules for more information.
==== 6.1.3. “Compiled” Python files ====
As an important speed-up of the start-up time for short programs that use a lot of standard modules, if a file called__ spam.pyc __exists in the directory where spam.py is found, this is assumed to contain an already-“__byte-compiled__” version of the module spam.
The **modification time** of the version of spam.py used to create spam.pyc is recorded in spam.pyc, and the .pyc file is ignored if these don’t match.
Normally, you don’t need to do anything to create the spam.pyc file. Whenever spam.py is successfully compiled, an attempt is made to write the compiled version to spam.pyc. It is not an error if this attempt fails; if for any reason the file is not written completely, the resulting spam.pyc file will be recognized as invalid and thus ignored later. The contents of the spam.pyc file are platform independent, so __a Python module directory can be shared__ by machines of different architectures.
Some tips for experts:
When the Python interpreter is invoked with the__ -O __flag, optimized code is generated and stored in__ .pyo__ files. The optimizer currently doesn’t help much; it only removes __assert statements__. When -O is used, all bytecode is optimized; .pyc files are ignored and .py files are compiled to optimized bytecode.
Passing two -O flags to the Python interpreter (__-OO__) will cause the bytecode compiler to perform optimizations that could in some rare cases result in malfunctioning programs. Currently **only __doc__ strings are removed from the bytecode**, resulting in more compact .pyo files. Since some programs may rely on having these available, you should only use this option if you know what you’re doing.
A program __doesn’t run any faster__ when it is read from a .pyc or .pyo file than when it is read from a .py file; the only thing that’s faster about .pyc or .pyo files is the speed with which they are__ loaded__.
When a script is run by giving its name on the command line, the bytecode for the script is never written to a .pyc or .pyo file. Thus, the startup time of a script may be reduced by moving most of its code to a module and having __a small bootstrap script__ that imports that module. It is also possible to name a .pyc or .pyo file directly on the command line.
It is possible to have a file called spam.pyc (or spam.pyo when -O is used) without a file spam.py for the same module. This can be used to __distribute a library__ of Python code in a form that is moderately hard to reverse engineer.
The module **compileall** can create .pyc files (or .pyo files when -O is used) for all modules in a directory.
===== 6.2. Standard Modules =====
Python comes with a library of standard modules, described in a separate document, the **Python Library Reference** (“Library Reference” hereafter). Some modules are built into the interpreter; these provide access to operations that are not part of the core of the language but are nevertheless built in, either for efficiency or to provide access to operating system primitives such as system calls. The set of such modules is a configuration option which also depends on the underlying platform For example, the winreg module is only provided on Windows systems. One particular module deserves some attention: __sys__, which is built into every Python interpreter. The variables sys.ps1 and sys.ps2 define the strings used as primary and secondary prompts:
>>>
>>> import sys
>>> sys.ps1
'>>> '
>>> sys.ps2
'... '
>>> sys.ps1 = 'C> '
C> print 'Yuck!'
Yuck!
C>
These two variables are only defined if the interpreter is in interactive mode.
The variable __sys.path__ is a list of strings that determines the __interpreter’s search path__ for modules. It is initialized to a default path taken from the environment variable PYTHONPATH, or from a built-in default if PYTHONPATH is not set. You can modify it using standard list operations:
>>>
>>> import sys
>>> sys.path.append('/ufs/guido/lib/python')
===== 6.3. The dir() Function =====
The built-in function dir() is used to find out__ which names a module defines__. It returns a sorted list of strings:
>>>
>>> import fibo, sys
>>> dir(fibo)
['__name__', 'fib', 'fib2']
>>> dir(sys)
['__displayhook__', '__doc__', '__excepthook__', '__name__', '__stderr__',
'__stdin__', '__stdout__', '_getframe', 'api_version', 'argv',
'builtin_module_names', 'byteorder', 'callstats', 'copyright',
'displayhook', 'exc_clear', 'exc_info', 'exc_type', 'excepthook',
'exec_prefix', 'executable', 'exit', 'getdefaultencoding', 'getdlopenflags',
'getrecursionlimit', 'getrefcount', 'hexversion', 'maxint', 'maxunicode',
'meta_path', **'modules', 'path'**, 'path_hooks', 'path_importer_cache',
'platform', 'prefix', **'ps1', 'ps2'**, 'setcheckinterval', 'setdlopenflags',
'setprofile', 'setrecursionlimit', 'settrace', **'stderr', 'stdin', 'stdout'**,
'version', 'version_info', 'warnoptions']
Without arguments, dir() lists the names you have defined __currently__:
>>>
>>> a = [1, 2, 3, 4, 5]
>>> import fibo
>>> fib = fibo.fib
>>> dir()
[__'__builtins__'__, '__doc__', '__file__',** '__name__'**, 'a', 'fib', 'fibo', 'sys']
dir()的参数__为对象的标示符__，当参数为空时，列出__当前命名空间__中的符号。
Note that it lists all types of names: variables, modules, functions, etc.
dir() does not list the names of __built-in functions and variables__. If you want a list of those, they are defined in the **standard module __builtin__**:
>>>
>>> import ____builtin____
>>> dir(____builtin____)
['ArithmeticError', 'AssertionError', 'AttributeError', 'DeprecationWarning',
'EOFError', 'Ellipsis', 'EnvironmentError', 'Exception', __'False'__,
'FloatingPointError', 'FutureWarning', 'IOError', 'ImportError',
'IndentationError', 'IndexError', 'KeyError', 'KeyboardInterrupt',
'LookupError', 'MemoryError', 'NameError', __'None'__, 'NotImplemented',
'NotImplementedError', 'OSError', 'OverflowError',
'PendingDeprecationWarning', 'ReferenceError', 'RuntimeError',
'RuntimeWarning', 'StandardError', 'StopIteration', 'SyntaxError',
'SyntaxWarning', 'SystemError', 'SystemExit', 'TabError', __'True'__,
'TypeError', 'UnboundLocalError', 'UnicodeDecodeError',
'UnicodeEncodeError', 'UnicodeError', 'UnicodeTranslateError',
'UserWarning', 'ValueError', 'Warning', 'WindowsError',
'ZeroDivisionError', __'_'__, '__debug__',** '__doc__'**, '__import__',
** '__name__'**,__ 'abs'__, 'apply', 'basestring',__ 'bool'__, 'buffer',
__ 'callable'__, 'chr', 'classmethod', 'cmp', 'coerce', 'compile',
**'complex'**, 'copyright', 'credits', 'delattr',__ 'dict', 'dir'__, 'divmod',
** 'enumerate'**, 'eval', __'execfile'__,__ 'exit'__, __'file'__, __'filter', 'float',__
'frozenset', 'getattr', __'globals'__, 'hasattr', 'hash', 'help', 'hex',
'id', __'input'__, 'int', 'intern',__ 'isinstance', 'issubclass'__, __'iter'__,
__ 'len'__, 'license', 'list', **'locals'**, 'long', 'map', 'max', 'memoryview',
'min', __'object'__, 'oct', __'open'__, 'ord', 'pow', 'property',__ 'quit'__, 'range',
__'raw_input'__, 'reduce', __'reload', 'repr', __'reversed', __'round',__ 'set',
'setattr', 'slice', 'sorted', 'staticmethod', 'str', 'sum', 'super',
'tuple', __'type',__ 'unichr', __'unicode'__, 'vars', 'xrange', 'zip']
===== 6.4. Packages =====
Packages are a way of structuring Python’s **module namespace **by using “__dotted module names__”. For example, the module name A.B designates a submodule named B in a package named A.
Just like the** use of modules saves the authors of different modules from having to worry about each other’s global variable names**, the use of dotted module names saves the authors of __multi-module packages like NumPy or the Python Imaging Library from having to worry about each other’s module names__.
模块隔离了各个函数或全局变量的命名空间，包机制隔离了各个包中同名的模块。
Suppose you want to design a collection of modules (a “package”) for the uniform handling of sound files and sound data. There are many different sound file formats (usually recognized by their extension, for example: .wav, .aiff, .au), so you may need to create and maintain **a growing collection of modules** for the conversion between the various file formats. There are also many different operations you might want to perform on sound data (such as mixing, adding echo, applying an equalizer function, creating an artificial stereo effect), so in addition you will be writing **a never-ending **__stream__** of modules** to perform these operations. Here’s a possible __structure__ for your package (expressed in terms of a hierarchical filesystem):
sound/ **Top-level **package #sound目录应该放**在sys.path的一个目录**中。
____init__.py __ Initialize the sound package
formats/ **Subpackage** for file format conversions
____init__.py__
wavread.py
wavwrite.py
aiffread.py
aiffwrite.py
auread.py
auwrite.py
...
effects/ Subpackage for sound effects
____init____.py
echo.py
surround.py
reverse.py
...
filters/ Subpackage for filters
____init____.py
equalizer.py
vocoder.py
karaoke.py
...
When** importing the package**, Python searches through the directories on __sys.path __looking for the package subdirectory（**package是一个目录名**，当导入一个package时，python在sys.path列表中搜索含有此名的目录）.
__The __init__.py files are required to make Python treat the directories as containing packages__; this is done to prevent directories with a common name, such as// string//, from unintentionally hiding valid modules that occur later on the module search path.
只有当目录中有____init____.py时，python才将该目录当作一个package。这样一个普通目录(其中没有__init__.py)不会被当作package，例如一个package目录pa与同名的普通目录pa放在不同的sys.path中时，python会只在前者中搜索module。
In the simplest case, __init__.py can just be **an empty file**, but it can also execute initialization code for the package or set the all variable, described later.
__package中的__init__.py会在导入该package或package中的子包、模块等时被执行。实验如下：__
import sound #导入一个包。创建一个新的命名空间sound，__在其中__执行__init__.py。
import sound.filters #__导入一个子包时会同时导入路径中的父包，创建多个命名空间__。这会创建两个命名空间:sound和sound.filters，sound空间中包含其__init__.py执行的结果；sound.filters中包含filters包的__init__.py执行的结果。
import sound.filters.vocoder #__导入模块时会同时导入路径中的各包，创建多个命名空间__。这会创建sound、sound.filters、sound.filters.vocoder三个命名空间。
from sound import * #__在当前命名空间中执行sound的__init__.py文件__(因此，__会将其中定义的符号导入到当前命名空间__)，同时将文件中____all__列表__中的所有符号导入到当前空间中。注意不会创建sound命名空间。
from sound.filters import * #先在一个临时空间中执行sound的__init__.py文件，然后__在当前命名空间中执行filters的__init__.py文件__(因此，__会将其中定义的符号导入到当前命名空间__)，同时将文件中____all__列表__中的所有符号导入到当前空间中。注意不会创建sound和sound.filters命名空间(前者被丢弃，后者被合并到当前空间)。
from sound import filters #先后__在不同空间中执行sound和filters的__init__.py文件，__但是只有filters的空间被保留，不会创建sound空间。
from sound import filters.vocoder #__错误的语法！！！，import右边不能有路径形式指定的包/模块以及模块中定义的符号。__
from sound.filters.vocoder import decode() #先后在不同空间中执行路径中各包的__init__.py文件但是空间__均不保留__。
注意：使用from package import item时，python会先执行package的__init__.py文件，然后__在其结果中查找是否有item符号__，若无就认为item是一个子包或模块，若是子包则创建一个命名空间在其中执行子包的__init__.py文件，若是模块则创建一个命名空间在其中执行模块文件。
Users of the package can import individual modules from the package, for example:
__import sound.effects.echo #这种形式只能导入subpackage或module.__
This loads the **submodule** sound.effects.echo. It must__ be referenced with its full name__.
**sound.effects.echo.echofilter**(input, output, delay=0.7, atten=4)
An alternative way of importing the submodule is:
__from sound.effects import echo #导入包中的module文件。__
This also loads the submodule echo, and makes it available **without its package prefix**, so it can be used as follows:
echo.echofilter(input, output, delay=0.7, atten=4)
Yet another variation is to import the **desired function or variable **directly:
__from sound.effects.echo import echofilter __
Again, this loads the submodule echo, but this makes its function echofilter() directly available:
echofilter(input, output, delay=0.7, atten=4)
Note that when using__ from package import item__, the item can be either a __submodule __(or __subpackage__) of the package, or some__ other name__ defined in the package, like a function, class or variable__(item不能是路径形式__). The import statement first tests whether the item is defined **in the package(也就是package中__init__.py中定义的符号item)**; if not, it assumes it is **a module** and attempts to load its file. If it fails to find it, an ImportError exception is raised.
Contrarily, when using syntax like __import item.subitem.subsubitem__, each item except for the last **must be a package**; the last item can be a module or a package but can’t be a class or function or variable defined in the previous item.
==== 6.4.1. Importing * From a Package ====
Now what happens when the user writes__ from sound.effects import *__? Ideally, one would hope that this somehow goes out to the filesystem, finds which submodules are present in the package, and imports them all. This could take a long time and importing sub-modules might have unwanted side-effects that should only happen when the sub-module is explicitly imported.
The only solution is for the package author to __provide an explicit index of the package__. The import statement uses the following convention: if a package’s __init__.py code defines a list named ____all____, it is taken to be the list of module names that should be imported when **from package import *** is encountered. It is up to the package author to keep this list up-to-date when a new version of the package is released. Package authors may also decide not to support it, if they don’t see a use for importing * from their package. For example, the file sounds/effects/__init__.py could contain the following code:
__all__ = ["echo", "surround", "reverse"]
This would mean that __from sound.effects import *__ would import the three named submodules of the sound package。
在两个不同空间中执行sound和effects的__init__.py文件，然后前一个空间被丢弃，后一个空间的符号会__合并到当前空间中__，同时effects中__all__列表中的包、模块等也会被导入到当前空间中。sound和sound.effects名称都不可访问。
If** __all__** is not defined, the statement** from sound.effects import * **__does not import all submodules __from the package **sound.effects** into the current namespace; it only ensures that the__ package sound.effects has been imported__ (possibly running any initialization code in __init__.py) and then imports whatever **names are defined in the package**. This includes **any names defined (and submodules explicitly loaded) by __init__.py.** It also includes any submodules of the package that were explicitly loaded by previous import statements. Consider this code:
import sound.effects.echo
import sound.effects.surround
from sound.effects import *
In this example, the echo and surround modules are imported __in the current namespace__ because they are defined in th**e sound.effects** package when the from...import statement is executed. (This **also works** when __all__ is defined.)
在包的__init__.py文件中，也可以导入一些包、模块、函数等符号，这些符号会保留在__init__.py的包环境中(对于from ... import ....是保留在__当前环境__中)。
Although certain modules are designed to export only names that follow certain patterns when you use import *, it is still considered bad practise in production code.
Remember, there is nothing wrong with using **from Package import specific_submodule**! In fact, this is the recommended notation unless the importing module needs to use submodules with the same name from different packages.
==== 6.4.2. Intra-package References ====
The submodules often need to__ refer to each other__. For example, the surround module might use the echo module. In fact, such references are so common that the__ import statement first looks in the containing package before looking in the standard module search path__. Thus, the surround module can simply use import echo or from echo import echofilter. If the imported module is not found in the** current package** (the package of which the current module is a submodule), the import statement looks for a** top-level** module with the given name.
使用from package import item形式时，pyton会现在package的__init__.py结果中查找item符号，若没有则在package的子目录中查找名为item的package或module，若两者都没有会产生导入错误。
在子模块文件中导入其它模块时，python会现在该模块所在的package及其父package中__逐级向上查找直到__top-level package，**然后才在标准搜索路径**中查找。
When packages are structured into subpackages (as with the sound package in the example), you can use **absolute imports** to refer to submodules of __siblings__ packages. For example, if the module** sound.filters.vocoder** needs to use the echo module in the **sound.effects** package, it can use from sound.effects import echo.
上面的absolute imports 意思是__从当前模块所在的包目录结构的顶层package开始逐级指定，__也被成为implicit relative imports.
Starting with Python 2.5, in addition to the implicit relative imports described above, you can write __explicit relative imports__ with the **from module import name** form of import statement. These explicit relative imports use **leading dots** to indicate the current and parent packages involved in the relative import. From the surround module for example, you might use:
from . import echo
from .. import formats
from ..filters import equalizer
Note that both explicit and implicit relative imports **are based on the name of the current module**. Since the name of the__ main module __is always "__main__", modules intended for use as the main module of a Python application should always use absolute imports.
注意：包间的引用也就是上面的两种导入方式，__只适合于包中__的模块文件或__init__.py文件，因为相对路径是相对于当前module的。所以在非包模块如交互式module(名为__main__)中使用上述语法是错误的。
==== 6.4.3. Packages in Multiple Directories ====
Packages support one more special attribute, ____path. This is initialized to be a list containing the name of the directory holding the package’s __init__.py before the code in that file is executed. This variable can be modified; doing so affects future searches for modules and subpackages contained in the package.
While this feature is not often needed, it can be used to extend the set of modules found in a package.
Footnotes
[1] In fact function definitions are also ‘statements’ that are ‘executed’; the execution of a module-level function enters the function name in the module’s global symbol table.

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====== python学习笔记之module && package ======
Created Wednesday 04 January 2012
http://arganzheng.iteye.com/blog/986301
===== module =====
__import只能导入模块，不能导入模块中的对象__（类、函数、变量等）。如一个模块A（A.py）中有个函数getName，另一个模块不能通过import A.getName将getName导入到本模块，只能用import A。如果想只导入特定的类、函数、变量则用from A import getName即可。
import一个module时，会执行该module的所有方法，并且将该module添加到importing module的命名空间中。A module's body executes immediately__ the first time__ the module is imported in a given run of a program...An import statement creates__ a new namespace__ containing all the attributes of the module. 如：
fibo.py
# Fibonacci numbers module
def fib(n): # write Fibonacci series up to n
a, b = 0, 1
while b < n:
print b,
a, b = b, a+b
def fib2(n): # return Fibonacci series up to n
result = []
a, b = 0, 1
while b < n:
result.append(b)
a, b = b, a+b
return result
print "EOF"
In [1]: import fibo #第一次导入fibo模块，模块中的所有代码被执行一次。
EOF
In [2]: **import fibo #再次导入时，不再执行模块文件中的代码**
In [3]: fibo.
fibo.__builtins__ fibo.__doc__ fibo.__hash__ fibo.__package__ fibo.__setattr__ fibo.fib
fibo.__class__ fibo.__file__ fibo.__init__ fibo.__reduce__ fibo.__sizeof__ fibo.fib2
fibo.__delattr__ fibo.__format__ fibo.__name__ fibo.__reduce_ex__ fibo.__str__ fibo.py
fibo.__dict__ fibo.__getattribute__ fibo.__new__ fibo.__repr__ fibo.__subclasshook__ fibo.pyc
In [3]: fibo.____name____
Out[3]: 'fibo'
In [4]: fibo.fib(100)
1 1 2 3 5 8 13 21 34 55 89
In [5]: fibo.fib2(100)
Out[5]: [1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]
In [6]: **from fibo import fib #将fib导入到当前命名空间，**__模块名称并没有__**导入到当前命名空间。**
In [7]: fib(100)
1 1 2 3 5 8 13 21 34 55 89
In [8]: fib2(100)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
/home/forrest/study/python/<ipython console> in <module>()
NameError: name 'fib2' is not defined
In [9]: from fibo import *
In [10]: fib2(100)
Out[10]: [1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]
会将fibo添加在当前module的名字空间，并且__执行fibo.py定义的函数__（定义函数表示将函数名添加到module的命名空间）这样就可以通过fibo访问fibo中定义的方法。并且会执行module中的statement。上面只执行一次，说明python只加载了一次。
下面这段话道出了python module的本质，其实也是整个python语言的本质——绑定。
1. 变量定义：赋值邦定，对一个x = y ==>定义一个变量x，他的值是y，并且将这个变量邦定在其命名空间上（如果是全局变量，那么是该变量所在module）。如果是函数内部变量，运行时才会执行，并且是邦定在函数自身的命名空间上。
2. 函数定义：def functionName: 定义一个函数对象，并将其绑定它自身的命名空间中。
3. 类定义：class clsName: 定义一个类，并将该类对象邦定在其命名空间中。
Attributes of a module object are normally bound by statements __in the module body__. When a statement in the body binds a variable (a global variable), what gets bound is an attribute of the **module object**. The normal purpose of a module body is exactly that of creating the module's attributes: def statements create and bind functions, class statements create and bind classes, and assignment statements can bind attributes of any type.
You can also bind and unbind module attributes outside the body (i.e., in other modules), generally using attribute reference syntax M.name (where M is any expression whose value is the module, and identifier name is the attribute name). For clarity, however, it's usually best to limit yourself to binding module attributes in the module's own body.
===== package =====
包通常总是一个目录，目录下为首的一个文件便是 __init__.py。然后是一些模块文件和目录，假如子目录中也有 __init_.py 那么它就是这个包的__子包__了。差不多就像这样吧：
Package1/
__init__.py
Module1.py
Module2.py
Package2/
__init__.py
Module1.py
Module2.py
我们可以就这样导入一个包：
import Package1
或者调入一个子模块和子包：
from Package1 import Module1
from Package1 import **Package2 #from...import形式可以导入子包、模块、模块中的符号。**
import Packag1.Module1
import Packag1.Package2 #import形式，只能导入**子包或者模块**，而不能导入**模块中的函数**。
可以深入好几层包结构：
from Package1.Package2 import Module1
import Package1.Package2.Module1
_init_.py文件
The _init.py files are required to make Python treat the directories as containing packages. In the simplest case, __init.py can just be an empty file, but it can also execute initialization code for the package or set the __all_ variable, described later.
_init.py 控制着__包的导入行为__。假如 __init_.py 为空，那么仅仅导入包是什么都做不了的。注意__无论是导入包还是导入包中的module，路径中的各包的__init__.py都会被执行__。
>>> import Package1 # __#创建了一个新的命名空间，在该空间中执行包的__init__.py文件。__
>>> Package1.Module1 #__但是该空间中并没有导入Module1，或并没有这个符号。__
Traceback (most recent call last):
File "<pyshell#1>", line 1, in ?
Package1.Module1
AttributeError: 'module' object has no attribute 'Module1'
我们需要在 _init_.py 里把 Module1 预先导入：
import Module1 #最会将Module1导入到__所在的包空间__。
测试：
>>> import Package1
>>> Package1.Module1 #包空间中含有Module1符号。
<module 'Package1.Module1' from
'Module.pyc'>
_init.py 中还有一个重要的变量，叫做** __all__**。我们有时会使出一招**"全部导入"**，也就是这样：
from PackageName import * #前面已说了：无路是from还是import都会执行路径中各包的__init__.py文件(还是在**各包空间**)。
这时 import 就会把注册在包 _init.py 文件中 __all_ 列表中的**子模块和子包**导入到**当前作用域**中来。比如：
__all__ = ['Module1', 'Module2', 'Package2']
测试：
>>> from Package1 import * #__将Package1的__init__.py中定义的符号以及其中__all__列表中定义的符号导入到当前的命名空间中。如果__init__.py中也导入了符号(用import或from...import..语句)，这些符号也将导入到当前空间中。__
>>> Module2
<module 'Package1.Module2' from 'Module.pyc'>
_init_.py其实就是一个普通的python文件，它会在__package被导入或包中的子包、模块等被导入__时执行。
print ">>>in package1.__init__.py"
def say_hello():
print "Hi, my name is Forrest!"
测试：
In [1]: import package1 #导入包时，建立一个命名空间，在其中__init__.py文件。
**>>>in package1.__init__.py**
In [2]:** package1.say_hello() ** #该包定义的函数。__可以在__init__.py文件中定义属于该包的符号__。
Hi, my name is Forrest!
__多级package——_init_.py依次被执行__
In [1]: import package1.package2 **#路径中各个包的__init__.py都将会在各自的包空间执行，但是只有最后的package2空间被保留**。使用路径形式时，package1的__init__.py中__不必__导入package2符号或将其放到__all__中。
<<<in package1.__init__.py>>>
<<<in package1.package2.__init__.py>>>
In [4]: **package1.package2**.foo_bar()
foobar!
将package/_init_.py改成如下：
print "<<<in package1.__init__.py>>>"

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====== 7. Input and Output ======
Created Wednesday 04 January 2012
There are several ways to present the output of a program; data can be **printed in a human-readable form**, or written to** a file **for future use. This chapter will discuss some of the possibilities.
===== 7.1. Fancier Output Formatting =====
So far we’ve encountered two ways of writing values: **expression statements and the print statement**. (A third way is using the __write() method of file objects__; the standard output file can be referenced as **sys.stdout**. See the Library Reference for more information on this.)
Often you’ll want more control over the** formatting of your output **than simply printing space-separated values. There are two ways to format your output; the first way is to do all the string handling yourself;** using string slicing and concatenation operations** you can create any layout you can imagine. The string types have some methods that perform useful operations for padding strings to a given column width; these will be discussed shortly. The second way is to use the __str.format()__ method.
The string module contains a** Template class **which offers yet another way to substitute values into strings.
One question remains, of course: how do you **convert values to strings**? Luckily, Python has ways to convert any value to a string: pass it to the__ repr() or str()__ functions.
The str() function is meant to **return representations of values which are fairly human-readable, while repr() is meant to generate representations which can be read by the interpreter **(or will force a SyntaxError if there is not equivalent syntax). For objects which don’t have a particular representation for human consumption, str() will return the same value as repr(). Many values, such as** numbers **or structures like** lists **and **dictionaries**, have the same representation using either function.__ Strings__ and__ floating point__ numbers, in particular, have two distinct representations.
Some examples:
>>>
>>> s = 'Hello, world.'
>>> str(s)
**'Hello, world.'**
>>> repr(s)
__"'Hello, world.'"__
>>> str(1.0/7.0)
'0.142857142857'
>>> repr(1.0/7.0)
__'0.14285714285714285'__
>>> x = 10 * 3.25
>>> y = 200 * 200
>>> s = 'The value of x is ' + repr(x) + ', and y is ' + repr(y) + '...'
>>> print s
The value of x is 32.5, and y is 40000...
>>> # __The repr() of a string adds string quotes and backslashes:__
... hello = 'hello, world\n'
>>> hellos = repr(hello)
>>> print hellos __#如果是str则，print的结果没有引号和转义字符。__
**'hello, world\n'**
>>> # __The argument to repr() may be any Python object:__
... repr((x, y, ('spam', 'eggs')))
"(32.5, 40000, ('spam', 'eggs'))"
Here are two ways to write a table of squares and cubes:
>>>
>>> for x in range(1, 11):
... print repr(x).__rjust(2)__, repr(x*x).rjust(3)__,__
... # Note trailing comma on previous line
... print repr(x*x*x).rjust(4)
...
1 1 1
2 4 8
3 9 27
4 16 64
5 25 125
6 36 216
7 49 343
8 64 512
9 81 729
10 100 1000
字符串的rjust(n)方法用于表示，__字符串至少占n位字符空间，不足n位的右对齐__。
>>> for x in range(1,11):
... print '{0:2d} {1:3d} {2:4d}'__.format__(x, x*x, x*x*x)
...
1 1 1
2 4 8
3 9 27
4 16 64
5 25 125
6 36 216
7 49 343
8 64 512
9 81 729
10 100 1000
(Note that in the first example, one space between each column was added by the way print works: **it always adds spaces between its arguments.**)
This example demonstrates the__ str.rjust() __method of string objects, which **right-justifies a string in a field of a given width by padding it with spaces on the left.** There are similar methods __str.ljust() and str.center()__. These methods do not write anything, they just __return a new string__. If the input string is too long, they don’t truncate it, but return it unchanged; this will mess up your column lay-out but that’s usually better than the alternative, which would be lying about a value. (If you really want truncation you can always add a slice operation, as in __x.ljust(n)[:n]__.)
There is another method, __str.zfill()__, which pads a numeric string on the left with zeros. It understands about **plus and minus** signs:
>>>
>>> '12'.zfill(5)
'00012'
>>> '-3.14'.zfill(7)
'-003.14' #符号和小数点各占一位。
>>> '3.14159265359'.zfill(5)
'3.14159265359'
Basic usage of the__ str.format() __method looks like this:
>>>
>>> print 'We are the {} who say "{}!"'.format('knights', 'Ni')
We are the knights who say "Ni!"
The brackets and characters within them (called __format fields__) are** replaced **with the objects passed into the str.format() method. A number in the brackets refers to the **position of the object** passed into the str.format() method.
>>>
>>> print '{0} and {1}'.format('spam', 'eggs')
spam and eggs
>>> print '{1} and {0}'.format('spam', 'eggs')
eggs and spam
If **keyword arguments** are used in the str.format() method, their values are referred to by using the name of the argument.
>>>
>>> print 'This {food} is {adjective}.'.format(
... __food='spam', adjective='absolutely horrible'__)
This spam is absolutely horrible.
Positional and keyword arguments can be **arbitrarily combined**:
>>>
>>> print 'The story of {0}, {1}, and {other}.'.format('Bill', 'Manfred',
... other='Georg')
The story of Bill, Manfred, and Georg.
__'!s' __(apply str()) and__ '!r'__ (apply repr()) can be used to **convert the value before it is formatted**.
>>>
>>> import math
>>> print 'The value of PI is approximately {}.'.format(math.pi)
The value of PI is approximately 3.14159265359.
>>> print 'The value of PI is approximately__ {!r}__.'.format(math.pi)
The value of PI is approximately 3.141592653589793.
An optional__ ':' and format specifier __can follow the field name. This allows greater control over how the value is formatted. The following example rounds Pi to three places after the decimal.
>>>
>>> import math
>>> print 'The value of PI is approximately__ {0:.3f}__.'.format(**math.pi**)
The value of PI is approximately 3.142.
Passing an integer after the ':' will cause that field to be a** minimum number of characters wide**. This is useful for making tables pretty.
>>>
>>> table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 7678}
>>> for name, phone in table.items():
... print '{0:10} ==> {1:10d}'.format(name, phone)
...
Jack ==> 4098
Dcab ==> 7678
Sjoerd ==> 4127
If you have a really long format string that you don’t want to split up, it would be nice if you could reference the variables to be **formatted by name instead of by position**. This can be done by simply passing the __dict__ and __using square brackets '[]' to access the keys__
>>>
>>> table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 8637678}
>>> print ('Jack: {__0[Jack]__:d}; Sjoerd: {0[Sjoerd]:d}; '
... 'Dcab: {0[Dcab]:d}'.format(table)) #因为只有一个参数所以所有的位置参数为0. {n[key]:format-spec},n表示format()的第n个参数，它是一个dict，n[key]表示该key对应的值。
Jack: 4098; Sjoerd: 4127; Dcab: 8637678
This could also be done by passing the table as **keyword arguments with the ‘**’ notation**.
>>>
>>> table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 8637678}
>>> print 'Jack: {Jack:d}; Sjoerd: {Sjoerd:d}; Dcab: {Dcab:d}'.format(**table)
Jack: 4098; Sjoerd: 4127; Dcab: 8637678
This is particularly useful in combination with the__ built-in function vars()__, which returns a dictionary containing **all local variables**.
For a complete overview of string formatting with str.format(), see Format String Syntax.
==== 7.1.1. Old string formatting ====
The % operator can also be used for string formatting. It interprets the __left argument much like a sprintf()-style format string__ to be applied to the right argument, and returns the string resulting from this formatting operation. For example:
>>>
>>> import math
>>> print 'The value of PI is approximately %5.3f.' % math.pi
The value of PI is approximately 3.142.
Since str.format() is quite new, a lot of Python code still uses the % operator. However, because this old style of formatting will eventually be** removed from** the language, __str.format() should generally be used__.
More information can be found in the String Formatting Operations section.
===== 7.2. Reading and Writing Files =====
open() returns a __file object__, and is most commonly used with two arguments: open(filename, mode).
>>>
>>> f = open('/tmp/workfile', 'w')
>>> print f
<open file '/tmp/workfile', mode 'w' at 80a0960>
The first argument is a string containing the filename. The second argument is another string containing a few characters describing the way in which the file will be used. mode can be__ 'r'__ when the file will only be read,__ 'w'__ for only writing (an existing file with the same name will be erased), and__ 'a'__ opens the file for appending; any data written to the file is automatically added to the end. __'r+'__ opens the file for both reading and writing. The mode argument is optional; 'r' will be assumed if it’s omitted.
On Windows,__ 'b' __appended to the mode opens the file in binary mode, so there are also modes like '__rb', 'wb', and 'r+b'__. Python on Windows makes a distinction between text and binary files; the __end-of-line__ characters in text files are automatically altered slightly when data is read or written. This behind-the-scenes modification to file data is **fine for ASCII text files**, but it’ll corrupt binary data like that in JPEG or EXE files. Be very careful to __use binary mode__ when reading and writing such files. On Unix, it doesn’t hurt to append a 'b' to the mode, so you can use it platform-independently for all binary files.
==== 7.2.1. Methods of File Objects ====
The rest of the examples in this section will assume that **a file object** called f has already been created.
To read a file’s contents, call__ f.read(size)__, which reads some quantity of data and** returns it as a string**. size is an optional numeric argument. When size is omitted or negative, the __entire __contents of the file will be read and returned; it’s your problem if the file is twice as large as your machine’s memory. Otherwise, **at most size bytes **are read and returned. If the end of the file has been reached, f.read() will return __an empty string ("")__.
>>>
>>> f.read()
'This is the entire file.\n'
>>> f.read()
''
__f.readline()__ reads a single line from the file; a newline character (\n)__ is left__ at the end of the string, and is only omitted on the** last line** of the file if the file doesn’t end in a newline. This makes the return value unambiguous; if f.readline() returns **an empty string**, the end of the file has been reached, while a blank line is represented by '\n', a string containing only a single newline.
>>>
>>> f.readline()
'This is the first line of the file.\n'
>>> f.readline()
'Second line of the file\n'
>>> f.readline()
''
__f.readlines()__ returns __a list__ containing all the lines of data in the file. If given an optional parameter** sizehint**, it reads that many** bytes **from the file and enough more** to complete** a line, and returns the lines from that. This is often used to allow efficient reading of a large file by lines, but without having to load the entire file in memory. __Only complete lines will be returned__.
>>>
>>> f.readlines()
['This is the first line of the file.\n', 'Second line of the file\n']
An alternative approach to reading lines is __to loop over the file object.__ This is memory efficient, fast, and leads to simpler code:
>>>
>>> for line in f: #line字符串包含行尾的换行符，所以用print打印该行时可以省略换行符。
print__ line,__
This is the first line of the file.
Second line of the file
The alternative approach is simpler but does not provide as **fine-grained** control. Since the two approaches manage line buffering differently, they should not be mixed.
__f.write(string)__ writes the contents of __string__ to the file, returning__ None__.
>>>
>>> f.write('This is a test\n')
To write something other than a string, it needs to be **converted to a string first**:
>>>
>>> value = ('the answer', 42)
>>> s = str(value)
>>> f.write(s)
__f.tell()__ returns an integer giving the **file object’s current position** in the file, measured in **bytes **from the beginning of the file. To change the file object’s position, use __f.seek(offset, from_what)__. The position is computed from **adding offset to a reference point;** the reference point is selected by the from_what argument. A from_what value of 0 measures from the beginning of the file, 1 uses the current file position, and 2 uses the end of the file as the reference point. from_what can be omitted and __defaults to 0__, using the beginning of the file as the reference point.
>>>
>>> f = open('/tmp/workfile', **'r+'**)
>>> f.write('0123456789abcdef')
>>> f.seek(5) # Go to the 6th byte in the file
>>> f.read(1)
'5'
>>> f.seek(-3, 2) # Go to the 3rd byte before the end
>>> f.read(1)
'd'
When you’re done with a file, call __f.close()__ to close it and free up any system resources taken up by the open file. After calling f.close(), attempts to use the file object will automatically fail.
>>>
>>> f.close()
>>> f.read()
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: I/O operation on closed file
It is good practice to use the __with keyword when dealing with file objects__. This has the advantage that the file is properly closed after its suite finishes, even if an exception is raised on the way. It is also much shorter than writing equivalent **try-finally** blocks:
>>>
>>>__ with open('/tmp/workfile', 'r') as f:__
... read_data = f.read()
>>> f.closed
True
File objects have some additional methods, such as **isatty()** and **truncate()** which are less frequently used; consult the Library Reference for a complete guide to file objects.
==== 7.2.2. The pickle Module ====
Strings can easily be written to and read from a file. Numbers take a bit more effort, since the** read() method only returns strings**, which will have to be passed to a function like int(), which takes a string like '123' and returns its numeric value 123. However, when you want to save **more complex data types** like lists, dictionaries, or class instances, things get a lot more complicated.
Rather than have users be constantly writing and debugging code__ to save complicated data types__, Python provides a standard module called** pickle**. This is an amazing module that** can take almost any Python object** (even some forms of Python code!), and convert it to__ a string representation__; this process is called **pickling**.__ Reconstructing __the object from the string representation is called unpickling. Between pickling and unpickling, the string representing the object may have been** stored in a file or data**, or sent over a network connection to some distant machine.
If you have an object x, and a file object f that’s been **opened for writing**, the simplest way to pickle the object takes only one line of code:
__pickle.dump(x, f)__
To unpickle the object again, if f is a file object which has been opened for reading:
__x = pickle.load(f)__
(There are other variants of this, used when pickling many objects or when you don’t want to write the pickled data to a file; consult the complete documentation for pickle in the Python Library Reference.)
pickle is the__ standard way__ to make Python objects which can be** stored and reused **by other programs or by a future invocation of the same program; the technical term for this is__ a persistent object__. Because pickle is so widely used, many authors who write Python extensions take care to ensure that new data types such as matrices can be properly pickled and unpickled.

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====== 8. Errors and Exceptions ======
Created Wednesday 04 January 2012
Until now error messages haven’t been more than mentioned, but if you have tried out the examples you have probably seen some. There are (at least) two distinguishable kinds of errors: __syntax errors and exceptions__.
===== 8.1. Syntax Errors =====
Syntax errors, also known as __parsing errors__, are perhaps the most common kind of complaint you get while you are still learning Python:
>>>
>>> while True print 'Hello world'
** File "<stdin>", line 1, in ?**
while True print 'Hello world'
^
SyntaxError: invalid syntax
The parser repeats the offending line and displays a little ‘arrow’ pointing at the earliest point in the line where the error was detected. The error is caused by (or at least detected at) __the token preceding the arrow__: in the example, the error is detected at the keyword print, since a colon (':') is missing before it. File name and line number are printed so you know where to look in case the input came from a script.
===== 8.2. Exceptions =====
Even if a statement or expression is syntactically correct, it may cause an error when an attempt is made to execute it. **Errors detected during execution are called exceptions** and are not unconditionally fatal: you will soon learn how to **handle them** in Python programs. Most exceptions are not handled by programs, however, and result in error messages as shown here:
>>>
>>> 10 * (1/0)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
**ZeroDivisionError**: integer division or modulo by zero
>>> 4 + spam*3
Traceback (most recent call last):
File "<stdin>", line 1, in ?
**NameError**: name 'spam' is not defined
>>> '2' + 2
Traceback (most recent call last):
File "<stdin>", line 1, in ?
**TypeError**: cannot concatenate 'str' and 'int' objects
The__ last line__ of the error message indicates what happened. Exceptions come in __different types__, and the type is printed as part of the message: the types in the example are ZeroDivisionError, NameError and TypeError. The string printed as the exception type is the name of the built-in exception that occurred. This is true for all built-in exceptions, but need not be true for user-defined exceptions (although it is a useful convention). **Standard exception names are built-in identifiers** (not reserved keywords).
The rest of the line provides detail based on the type of exception and what caused it.
The preceding part of the error message __shows the context__ where the exception happened, in the__ form of a stack traceback__. In general it contains a stack traceback listing source lines; however, it will not display lines read from standard input.
Built-in Exceptions lists the built-in exceptions and their meanings.
===== 8.3. Handling Exceptions =====
It is possible to write programs that **handle selected exceptions**. Look at the following example, which asks the user for input until a valid integer has been entered, but allows the user to** interrupt** the program (using Control-C or whatever the operating system supports); note that __a user-generated interruption is signalled by raising the KeyboardInterrupt exception__.
>>>
>>> while True:
... try:
... x = int(**raw_input**("Please enter a number: "))
... break
... except **ValueError**:
... print "Oops! That was no valid number. Try again..."
...
The try statement works as follows.
* First, the try clause (the statement(s) between the try and except keywords) is executed.
* If no exception occurs, __the except clause is skipped__ and execution of the try statement is finished.
* If an exception occurs during execution of the try clause, __the rest of the clause is skipped__. Then if its **type matches** the exception named after the except keyword, the except clause is executed, and then execution continues **after** the try statement.
* If an exception occurs which does not match the exception named in the except clause, it is__ passed on to outer try statements__; if no handler is found, it is an __unhandled exception__ and** execution stops** with a message as shown above.
A try statement may have more than one except clause, to specify handlers for different exceptions. __At most one handler will be executed__. Handlers only handle exceptions that occur in the corresponding try clause, not in other handlers of the same try statement. An except clause may name** multiple exceptions** as a parenthesized tuple, for example:
... except (RuntimeError, TypeError, NameError):
... pass
The last except clause may __omit __the exception name(s), to__ serve as a wildcard__. Use this with extreme caution, since it is easy to **mask **a real programming error in this way! It can also be used to print an error message and then __re-raise__ the exception (allowing a caller to handle the exception as well):
import sys
try:
f = open('myfile.txt')
s = f.readline()
i = int(s.strip())
except **IOError **as** (errno, strerror)**:
print "I/O error({0}): {1}".format(errno, strerror)
except** ValueError**:
print "Could not convert data to an integer."
except:
print "Unexpected error:", **sys.exc_info()[0]**
__raise__
The try ... except statement has an optional __else clause__, which, when present, must follow all except clauses. It is useful for code that **must be executed if the try clause **__does not__** raise an exception**. For example:
for arg in **sys.argv[1:]**:
try:
f = open(arg, 'r')
except IOError:
print 'cannot open', arg
else:
print arg, 'has', len(f.readlines()), 'lines'
f.close()
The use of the else clause is better than adding additional code to the try clause because it avoids accidentally catching an exception that wasn’t raised by the code being protected by the try ... except statement.
When an exception occurs, it may have an associated value, also known as the** exception’s argument**. The presence and type of the argument depend on the exception type.
The except clause may **specify a variable after the exception name** (or tuple). The variable is __bound to an exception instance__ with the arguments stored in __instance.args__. For convenience, the exception instance defines __str__() so the arguments can be** printed directly** without having to reference .args.
One may also instantiate an exception first before raising it and add any attributes to it as desired.
>>>
>>> try:
... raise Exception(__'spam', 'eggs'__)
... except Exception as **inst**:
... print type(inst) # the exception instance
... print inst.__args __ # arguments stored in .args
... **print inst ** #** __str__ allows args to printed directly**
... x, y = inst # **__getitem__ allows args to be unpacked directly**
... print 'x =', x
... print 'y =', y
...
<type 'exceptions.Exception'>
('spam', 'eggs')
('spam', 'eggs')
x = spam
y = eggs
If an exception has an argument, it is printed as the last part (‘detail’) of the message for unhandled exceptions.
Exception handlers don’t just handle exceptions if they occur **immediately in** the try clause, but also if they occur inside functions that__ are called__ (even indirectly) in the try clause. For example:
>>>
>>> def this_fails():
... x = 1/0
...
>>> try:
... this_fails()
... except ZeroDivisionError as detail:
... print 'Handling run-time error:', detail
...
Handling run-time error: integer division or modulo by zero
===== 8.4. Raising Exceptions =====
The** raise** statement allows the programmer to force a specified exception to occur. For example:
>>>
>>> raise NameError('HiThere')
Traceback (most recent call last):
File "<stdin>", line 1, in ?
NameError: HiThere
The sole argument to raise indicates** the exception** to be raised. This must be either __an exception instance or an exception class__ (a class that derives from Exception).
If you need to determine whether an exception was raised but don’t intend to handle it, a simpler form of the raise statement allows you to __re-raise__ the exception:
>>>
>>> try:
... raise NameError('HiThere')
... except NameError:
... print 'An exception flew by!'
... __raise__
...
An exception flew by!
Traceback (most recent call last):
File "<stdin>", line 2, in ?
NameError: HiThere
===== 8.5. User-defined Exceptions =====
Programs may name their own exceptions by __creating a new exception class__ (see Classes for more about Python classes). Exceptions should typically be derived from the __Exception class__, either directly or indirectly. For example:
>>>
>>> class MyError(Exception):
... def __init__(self, value):
... self.value = value
... def ____str____(self):
... return repr(self.value)
...
>>> try:
... raise MyError(2*2)
... except MyError __as e__:
... print 'My exception occurred, value:', **e.value**
...
My exception occurred, value: 4
>>> raise MyError('oops!')
Traceback (most recent call last):
File "<stdin>", line 1, in ?
__main__.MyError: 'oops!'
In this example, the default __init__() of Exception has been** overridden**. The new behavior simply creates the value attribute. This__ replaces the default behavior of creating the args attribute__.
Exception classes can be defined which do anything any other class can do, but are usually kept simple, often only** offering a number of attributes** that allow __information about the error to be extracted by handlers__ for the exception.
When creating a module that can raise several distinct errors, a common practice is to__ create a base class__ for exceptions defined by that module, and __subclass__ that to create specific exception classes for different error conditions:
class Error(Exception):
"""Base class for exceptions in this module."""
pass
class InputError(Error):
"""Exception raised for errors in the input.
Attributes:
expr -- input expression in which the error occurred
msg -- explanation of the error
"""
def __init__(self, expr, msg):
self.__expr __= expr
self.__msg __= msg
class TransitionError(Error):
"""Raised when an operation attempts a state transition that's not
allowed.
Attributes:
prev -- state at beginning of transition
next -- attempted new state
msg -- explanation of why the specific transition is not allowed
"""
def __init__(self, prev, next, msg):
self.prev = prev
self.next = next
self.msg = msg
Most exceptions are defined with names that __end in “Error,” __similar to the naming of the standard exceptions.
Many standard modules define their own exceptions to report errors that may occur in functions they define. More information on classes is presented in chapter Classes.
===== 8.6. Defining Clean-up Actions =====
The try statement has another optional clause which is intended to define__ clean-up actions__ that __must be executed under all circumstances__. For example:
>>>
>>> try:
... raise KeyboardInterrupt
... finally:
... print 'Goodbye, world!'
...
Goodbye, world!
KeyboardInterrupt
__A finally clause is always executed before leaving the try statement__, （finally子句总是在控制流即将离开try子句前执行，不管异常是否发生。）whether an exception has occurred or not. When an exception has occurred in the try clause and has not been handled by an except clause (or it has occurred in a except or **else** clause), it is __re-raised after the finally clause has been executed__. The finally clause is also executed “on the way out” when any other clause of the try statement is left via a break, continue or return statement. A more complicated example (having **except and finally** clauses in the same try statement works as of Python 2.5):
>>>
>>> def divide(x, y):
... try:
... result = x / y
... except ZeroDivisionError:
... print "division by zero!"
... __else: #try子句成功执行后才执行。__
... print "result is", result
... __finally:__
... print "executing finally clause"
...
>>> divide(2, 1)
result is 2 #else子句在finally子句__之前__执行。
executing finally clause
>>> divide(2, 0) #产生的异常**先被捕获然后执行**finally子句(不会再重新触发异常)
division by zero!
**executing finally clause**
>>> divide("2", "1")
executing finally clause #产生未捕获的异常时，fianlly子句执行完后__会自动重新触发__异常。
Traceback (most recent call last):
File "<stdin>", line 1, in ?
File "<stdin>", line 3, in divide
TypeError: unsupported operand type(s) for /: 'str' and 'str'
As you can see, **the finally clause is executed in any event**. The TypeError raised by dividing two strings is not handled by the except clause and therefore __re-raised__ after the finally clause has been executed.
In real world applications, the finally clause is useful for** releasing external resources** (such as files or network connections), regardless of whether the use of the resource was successful.
===== 8.7. Predefined Clean-up Actions =====
Some objects define s**tandard clean-up actions **to be undertaken when the object is no longer needed, regardless of whether or not the operation using the object succeeded or failed. Look at the following example, which tries to open a file and print its contents to the screen.
for line in open("myfile.txt"):
print line
The problem with this code is that it** leaves the file open** for an indeterminate amount of time after the code has finished executing. This is not an issue in simple scripts, but can be a problem for larger applications. The __with statement __allows objects like files to be used in a way that **ensures they are always cleaned up promptly and correctly**.
with open("myfile.txt") __as f__:
for line in f:
print line
After the statement is executed,** the file f is always closed**, even if a problem was encountered while processing the lines. Other objects which provide predefined clean-up actions will indicate this in their documentation.

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====== 9. Classes ======
Created Wednesday 04 January 2012
Compared with other programming languages, Python’s class mechanism adds classes with a minimum of new syntax and semantics. It is a mixture of the class mechanisms found in C++ and Modula-3. Python classes provide** all the standard features of Object Oriented Programming**: the class inheritance mechanism allows** multiple base classes**, a derived class can **override any **methods of its base class or classes, and a method can call the method of a base class with the same name. Objects can contain arbitrary amounts and kinds of data. As is true for modules, classes partake of the__ dynamic nature__ of Python: they are created at runtime, and can be modified further after creation.
In C++ terminology, normally class members (including the data members) are public (except see below Private Variables), and all member functions are virtual. As in Modula-3, there are no shorthands for referencing the object’s members from its methods: the method function is declared with an __explicit first argument representing the object__, which is provided implicitly by the call. As in Smalltalk,__ classes themselves are objects__. This provides semantics for **importing and renaming**. Unlike C++ and Modula-3, built-in types can be used as base classes for extension by the user. Also, like in C++, most **built-in operators **with special syntax (arithmetic operators, subscripting etc.) can be redefined for class instances.
(Lacking universally accepted terminology to talk about classes, I will make occasional use of Smalltalk and C++ terms. I would use Modula-3 terms, since its object-oriented semantics are closer to those of Python than C++, but I expect that few readers have heard of it.)
===== 9.1. A Word About Names and Objects =====
Objects have individuality, and **multiple names (in multiple scopes) can be bound to the same object**. This is known as __aliasing__ in other languages. This is usually not appreciated on a first glance at Python, and can be safely ignored when dealing with immutable basic types (numbers, strings, tuples). However, aliasing has a possibly surprising effect on the semantics of Python code involving **mutable objects** such as lists, dictionaries, and most other types. This is usually used to the benefit of the program, since __aliases behave like pointers in some respects.__ For example, passing an object is cheap since only a pointer is passed by the implementation; and if a function modifies an object passed as an argument, **the caller will see the change** — this eliminates the need for two different argument passing mechanisms as in Pascal.
===== 9.2. Python Scopes and Namespaces =====
Before introducing classes, I first have to tell you something about Python’s __scope rules__. Class definitions play some neat tricks with namespaces, and you need to know how scopes and namespaces work to fully understand what’s going on. Incidentally, knowledge about this subject is useful for any advanced Python programmer.
Let’s begin with some definitions.
__A namespace is a mapping from names to objects.__ Most namespaces are currently implemented as Python **dictionaries**, but that’s normally not noticeable in any way (except for performance), and it may change in the future. Examples of namespaces are: **the set of **__built-in names__ (containing functions such as abs(), and built-in exception names); the__ global names__ in a module; and the__ local names__ in a function invocation. In a sense __the set of attributes of an object__ also form a namespace.
The important thing to know about namespaces is that __there is absolutely no relation between names in different namespaces__; for instance, two different modules may both define a function maximize without confusion — users of the modules must prefix it with the module name.
By the way, I use the word__ attribute__ for any name following a dot — for example, in the expression z.real, real is an attribute of the object z.
Strictly speaking, **references to names in modules are attribute references**: in the expression modname.funcname, modname is a__ module object__ and funcname is an attribute of it.
In this case there happens to be a straightforward mapping between__ the module’s attributes and the global names defined in the module: they share the same namespace! __[1]
Attributes may be read-only or writable. In the latter case, assignment to attributes is possible. Module attributes are writable: you can write modname.the_answer = 42. __Writable attributes may also be deleted with the del statement__. For example, del modname.the_answer will remove the attribute the_answer from the object named by modname.
Namespaces are** created at different moments and have different lifetime**s. The namespace containing the__ built-in__ names is created **when the Python interpreter starts up**, and is never deleted.__ The global namespace for a module__ is created when **the module definition is read in**; normally, module namespaces also last until the interpreter quits.
The statements executed by the** top-level** invocation of the interpreter, either read from a script file or interactively, are considered part of a module called ____main, so they have their own global namespace. (The built-in names actually also live in a module; this is called__ __builtin____.)
__The local namespace__ for a function is created when **the function is called**, and deleted when the function returns or raises an exception that is not handled within the function. (Actually, forgetting would be a better way to describe what actually happens.) Of course, recursive invocations each have __their own__ local namespace.
__A scope__ is a** textual region** of a Python program where a namespace is **directly accessible**. “Directly accessible” here means that an unqualified reference to a name attempts to find the name in the namespace.
__Although scopes are determined statically, they are used dynamically.__ At any time during execution, there are at least **three nested scopes** whose namespaces are directly accessible:
* the innermost scope, which is searched first, contains the** local names**
* the scopes of any **enclosing functions**, which are searched starting with the nearest enclosing scope, contains __non-local__, but also __non-global__ names
* the next-to-last scope contains the __current module’s global names__
* the outermost scope (searched last) is the namespace__ containing built-in names__
If a name is declared **global**, then all references and assignments go directly to the middle scope containing the__ module’s global names__. Otherwise, all variables found outside of the innermost scope are** read-only** (an attempt to write to such a variable will **simply create **a new local variable in the innermost scope, leaving the identically named outer variable unchanged).
Usually, the local scope references the local names of the (textually) current function. __Outside__ functions, the local scope references the same namespace as the **global scope**: the module’s namespace. Class definitions place yet another namespace in the local scope.
It is important to realize that __scopes are determined textually__: the global scope of a function defined in a module is that __module’s namespace__, no matter from where or by what alias the function is called.
On the other hand, __the actual search for names is done dynamically__, at run time — however, the language definition is evolving towards static name resolution, at “compile” time, so don’t rely on dynamic name resolution! (In fact, local variables are already determined statically.)
A special quirk of Python is that – __if no global statement is in effect – assignments to names always go into the innermost scope__.
__Assignments do not copy data — they just bind names to objects__.
The same is true for deletions: __the statement del x removes the binding of x from the namespace referenced by the local scope__.
In fact, all operations that introduce new names use the local scope: in particular, import statements and function definitions bind the module or function name __in the local scope__. (The global statement can be used to indicate that particular variables live in the global scope.)
===== 9.3. A First Look at Classes =====
Classes introduce a little bit of new syntax, three new object types, and some new semantics.
==== 9.3.1. Class Definition Syntax ====
The simplest form of class definition looks like this:
class ClassName:
<statement-1>
.
.
.
<statement-N>
Class definitions, like function definitions (def statements) must be executed before they have any effect. (You could conceivably place a class definition in a branch of an if statement, or inside a function.)
In practice, the statements inside a class definition will usually be function definitions, but other statements are allowed, and sometimes useful — we’ll come back to this later. The function definitions inside a class normally have __a peculiar form of argument list__, dictated by the calling conventions for methods — again, this is explained later.
When a class definition is entered, __a new namespace is created, and used as the local scope__ — thus, all assignments to local variables go into this new namespace(类定义中的所有赋值语句产生的名称和对象，都位于class定义时产生的namespace。). In particular, function definitions bind the name of the new function here.
When a class definition is left normally (via the end), __a class object__ is created. This is basically **a wrapper around the contents of the namespace created by the class definition**; we’ll learn more about class objects in the next section. The original local scope (the one in effect just before the class definition was entered) is reinstated, and the class object is bound here to the class name given in the class definition header (ClassName in the example).
类定义结束后，即在当前namespace中产生了一个__类对象(不是类实例)__，定义时的__类名与该对象在当前namspace中被绑定__。类对象其实是对类命名空间的一种封装。
==== 9.3.2. Class Objects ====
Class objects support two kinds of operations:** attribute references and instantiation**.
类对象支持两类操作：属性引用和实例化。
__Attribute references__ use the standard syntax used for all attribute references in Python: obj.name. Valid attribute names are all the names that were in the** class’s namespace** when the class object was created. So, if the class definition looked like this:
class MyClass:
"""A simple example class"""
i = 12345
def f(self):
return 'hello world'
#符号i和f都在类空间中定义。
then MyClass.i and MyClass.f are valid attribute references, returning an integer and a function object, respectively. Class attributes can also be assigned to, so you can change the value of MyClass.i by assignment. **__doc__** is also a valid attribute, returning the docstring belonging to the class: "A simple example class".
__Class instantiation__ uses function notation. Just pretend that the class object is a parameterless function that** returns a new instance of the class**. For example (assuming the above class):
x = MyClass()
creates a new instance of the class and assigns this object to the** local variable x**.
The instantiation operation (“calling” a class object) creates an empty object. Many classes like to create objects with instances customized to __a specific initial state__. Therefore a class may define a special method named __init__(), like this:
def** __init__**(self):
self.data = []
When a class defines an __init__() method, class instantiation__ automatically invokes __init__() __for the newly-created class instance. So in this example, a new, initialized instance can be obtained by:
x = MyClass() #生成一个实例对象，用位于当前命名空间中的符号x与该对象绑定。
Of course, the __init__() method may have arguments for greater flexibility. In that case,__ arguments given to the class instantiation operator are passed on to __init__()__. For example,
>>>
>>> class Complex:
... def __init__(self, realpart, imagpart): #符号__init__位于__类对象空间__中。
... self.r = realpart #__self表示符号r位于实例对象命名空间中__。
... self.i = imagpart
...
>>> x = Complex(3.0, -4.5)
>>> x.r, x.i
(3.0, -4.5)
==== 9.3.3. Instance Objects ====
Now what can we do with instance objects? __The only operations understood by instance objects are attribute references__. There are two kinds of valid attribute names**, data attributes and methods**.
data attributes correspond to **“instance variables” **in Smalltalk, and to “data members” in C++. __Data attributes need not be declared; like local variables__, they spring into existence__ when they are first assigned to__. For example, if x is the instance of MyClass created above, the following piece of code will print the value 16, without leaving a trace:
**x.counter** = 1 #不用管x对象中是否包含conter属性。
while x.counter < 10:
x.counter = x.counter * 2
print x.counter
del x.counter
The other kind of instance attribute reference is a method. __A method is a function that “belongs to” an object.__ (In Python, the term method is not unique to class instances: other object types can have methods as well. For example, list objects have methods called append, insert, remove, sort, and so on. However, in the following discussion, we’ll use the term method exclusively to mean methods of class instance objects, unless explicitly stated otherwise.)
Valid method names of an instance object __depend on its class__. By definition, all attributes of a class that are function objects define corresponding methods of its instances. So in our example, x.f is a valid method reference, since MyClass.f is a function, but x.i is not, since MyClass.i is not.
But x.f is not the same thing as MyClass.f —__ it is a method object, not a function object__.
方法对象和函数对象是两个不同的概念。
==== 9.3.4. Method Objects ====
Usually, a method is called right after it is bound:
x.f()
In the MyClass example, this will return the string 'hello world'. However, it is not necessary to call a method right away: __x.f is a method object, and can be stored away and called at a later time. __For example:
xf = x.f
while True:
print xf()
will continue to print hello world until the end of time.
What exactly happens when a method is called? You may have noticed that x.f() was called without an argument above, even though the function definition for f() specified an argument. What happened to the argument? Surely Python **raises an exception** when a function that requires an argument is called without any — even if the argument isn’t actually used...
Actually, you may have guessed the answer:__ the special thing about methods is that the object(实例对象) is passed as the first argument of the function__. In our example, the **call x.f() is exactly equivalent to MyClass.f(x)**. In general, calling a method with a list of n arguments is equivalent to calling the corresponding function with an argument list that is created by inserting the method’s object before the first argument.
If you still don’t understand how methods work, a look at the implementation can perhaps clarify matters. When an** instance attribute** is referenced that isn’t a data attribute, __its class is searched__. If the name denotes a valid class attribute that is a function object, a method object is created by packing (pointers to) the instance object and the function object just found together in an abstract object: this is the method object. When the method object is called with an argument list, a new argument list is constructed from the instance object and the argument list, and the function object is called with this new argument list.
===== 9.4. Random Remarks =====
__Data attributes override method attributes with the same name;__ to avoid accidental name conflicts, which may cause hard-to-find bugs in large programs, it is wise to use some kind of convention that minimizes the chance of conflicts.
Possible conventions include__ capitalizing method names__, prefixing data attribute names with a small unique string (perhaps just an underscore), or__ using verbs for methods and nouns for data attributes.__
Data attributes may be referenced by methods as well as by** ordinary users (“clients”) of an object**.（实例对象的数据属性不但可以被实例方法所使用，也可以被普通用户如调用函数使用。） In other words, classes are not usable to implement __pure abstract data types__. In fact, nothing in Python makes it possible to enforce data hiding — it is all based upon convention. (On the other hand, the Python implementation, written in C, can completely hide implementation details and control access to an object if necessary; this can be used by extensions to Python written in C.)
**Clients should use data attributes with care** — clients may mess up invariants maintained by the methods by stamping on their data attributes. Note that __clients may add data attributes of their own to an instance object__ without affecting the validity of the methods, as long as name conflicts are avoided — again, a naming convention can save a lot of headaches here.
客户可以使用对象的数据属性，因此就有可能破坏本来**由类方法**负责维护的对象状态的完整性。
There is __no shorthand__ for referencing data attributes (or other methods!) from within methods. I find that this actually increases the readability of methods: there is no chance of **confusing local variables(位于类对象中的命名空间) and instance variables(位于实例对象的命名空间)** when glancing through a method.
Often, the first argument of a method is called **self**. This is nothing more than a convention: the name self has absolutely no special meaning to Python. Note, however, that by not following the convention your code may be less readable to other Python programmers, and it is also conceivable that a class browser program might be written that relies upon such a convention.
__Any function object that is a class attribute defines a method for instances of that class.__ It is not necessary that the function definition is textually enclosed in the class definition: assigning a function object to **a local variable in the class **is also ok. For example:
# Function defined outside the class
def f1(self, x, y): #self不可省。
return min(x, x+y)
class C:
__f = f1__
def g(self):
return 'hello world'
h = g
Now **f, g and h are all attributes of class C** that refer to __function objects__, and consequently they are __all methods of instances of C__ — h being exactly equivalent to g. Note that this practice usually only serves to confuse the reader of a program.
Methods may call other methods by using __method attributes of the self__ argument:
class Bag:
def __init__(self):
__self.__data = []
def add(self, x):
self.data.append(x)
def addtwice(self, x):
__self.add__(x)
self.add(x)
Methods may reference __global names __in the same way as ordinary functions. The global scope associated with a method is__ the module __containing the class definition. (The class itself is never used as a global scope.) While one rarely encounters a good reason for using global data in a method, there are many legitimate uses of the global scope: for one thing, functions and modules imported into the global scope(类定义所在的module) can be used by methods, as well as functions and classes defined in it. Usually, the class containing the method is itself defined in this global scope, and in the next section we’ll find some good reasons why a method would want to reference its own class.
类定义中的属性可以使用全局变量，该变量位于类定义所在的module。类属性和函数可以使用模块导入的函数或其它模块。
__Each value is an object__, and therefore has a class (also called its type). It is stored as **object.__class__**.
在Python中所有的值都是一个有类型的对象，它的内部保存的变量__calss__指示了类型名称。
===== 9.5. Inheritance =====
Of course, a language feature would not be worthy of the name “class” without supporting inheritance. The syntax for a derived class definition looks like this:
class DerivedClassName(BaseClassName):
<statement-1>
.
.
.
<statement-N>
The name BaseClassName must be defined in a scope containing the derived class definition. In place of a base class name, **other arbitrary expressions** are also allowed. This can be useful, for example, when the base class is defined in another module:
class DerivedClassName(__modname.BaseClassName__):
Execution of a derived class definition proceeds the same as for a base class. When the class object is constructed, **the base class is remembered**. This is used for __resolving attribute references__: if a requested attribute is not found in the class, the search proceeds to look in the base class. This rule is applied__ recursively__ if the base class itself is derived from some other class.
There’s nothing special about instantiation of derived classes: DerivedClassName() creates a** new instance** of the class. __Method references__ are resolved as follows: the corresponding class attribute is searched, descending down the chain of base classes if necessary, and the method reference is valid if this yields a function object.
Derived classes may __override methods__ of their base classes. Because methods have no special privileges when calling other methods of the same object, a method of a base class that calls another method defined in the same base class may end up calling a method of a derived class that overrides it. (For C++ programmers: __all methods in Python are effectively virtual__.)
python中__所有的方法都是虚方法__，这样都存在多态性。因此基类中一个方法调用**同类中**定义的另一个方法时，实际调用的可能是__子类中重载的该方法__。如果要确信实际调用的是本类定义的方法，可以使用BaseClassName.methodname(self, arguments)的形式。
An overriding method in a derived class may in fact want to **extend **rather than simply replace the base class method of the **same** name. There is a simple way to __call the base class method directly__: just call **BaseClassName.methodname(self, arguments).** This is occasionally useful to clients as well. (Note that this only works if the base class is accessible as BaseClassName in the global scope.)
Python has two built-in functions that work with inheritance:
* Use isinstance() to check an instance’s type: isinstance(obj, int) will be True only if __obj.class__ is int or some class derived from int.
* Use issubclass() to check class inheritance: issubclass(bool, int) is True since bool is a subclass of int. However, issubclass(unicode, str) is False since unicode is not a subclass of str (they only share a common ancestor, **basestring**).
==== 9.5.1. Multiple Inheritance ====
Python supports a limited form of multiple inheritance as well. A class definition with multiple base classes looks like this:
class DerivedClassName(Base1, Base2, Base3):
<statement-1>
.
.
.
<statement-N>
For **old-style **classes, the only rule is__ depth-first, left-to-right__. Thus, if an attribute is not found in DerivedClassName, it is searched in Base1, then (recursively) in the base classes of Base1, and only if it is not found there, it is searched in Base2, and so on.
多重继承的子类在属性或方法解析时，使用的是深度优先，自左向右。
(To some people breadth first — searching Base2 and Base3 before the **base classes of Base1** — looks more natural. However, this would require you to know whether a particular attribute of Base1 is actually defined in Base1 or in one of its base classes before you can figure out the consequences of a __name conflict__ with an attribute of Base2. The depth-first rule makes no differences between** direct and inherited **attributes of Base1.)
For new-style classes, the method resolution order changes dynamically to support cooperative calls to__ super()__. This approach is known in some other multiple-inheritance languages as__ call-next-method__ and is more powerful than the super call found in single-inheritance languages.
With new-style classes, **dynamic ordering** is necessary because all cases of multiple inheritance exhibit one or more diamond relationships (where at least one of the parent classes can be accessed through multiple paths from the bottommost class). For example, all new-style classes inherit from object, so any case of multiple inheritance provides __more than one path to reach object__. To keep the base classes from being accessed more than once, the dynamic algorithm __linearizes the search order__ in a way that preserves the left-to-right ordering specified in each class, that calls each parent only once, and that is monotonic (meaning that a class can be subclassed without affecting the precedence order of its parents). Taken together, these properties make it possible to design reliable and extensible classes with multiple inheritance. For more detail, see http://www.python.org/download/releases/2.3/mro/.
===== 9.6. Private Variables =====
“Private” instance variables that cannot be accessed except from inside an object __don’t exist__ in Python. However, there is a convention that is followed by most Python code: a name prefixed with an underscore (e.g. _spam) should be treated as a **non-public **part of the API (whether it is a function, a method or a data member). It should be considered an **implementation detail** and subject to change without notice.
Since there is a valid use-case for class-private members (namely to avoid name clashes of names with names defined by subclasses), there is limited support for such a mechanism, called __name mangling ['mæŋgəl]v.碾压,损坏, 糟蹋, 乱切n.碾压机__. Any identifier of the form **__spam** (at least two leading underscores, at most one trailing underscore) is textually replaced with** _classname__spam**, where classname is the current class name with leading underscore(s) stripped. This mangling is done without regard to the syntactic position of the identifier, as long as it occurs within the definition of a class.
Name mangling is helpful for __letting subclasses override methods without breaking intraclass method calls__. For example:
命名切换非常适合于**子类想重载父类的方法**但又不破坏**父类方法调用原定义于父类中的方法的情况（正常情况下，类中定义的所有函数都是虚函数，因此父类中方法调用的可能是子类重载的方法。）。**
class Mapping:
def init(self, iterable):
self.items_list = []
self.__update(iterable) #注意，使用的是命名切换的方法名称。__update()实际为_Mapping__update()。这样__update方法__就可以当作私有方法__。
def update(self, iterable): #update()为即将在子类中重载的方法。
for item in iterable:
self.items_list.append(item)
____update = update __ # private copy of original update() method， __update__不随__update的重载而改变。
class MappingSubclass(Mapping):
def update(self, keys, values):
# provides new signature for update()
# but does not break init()
for item in zip(keys, values):
self.items_list.append(item)
Note that the mangling rules are designed mostly to __avoid accidents__; it still is possible to access or modify a variable that is considered private. This can even be useful in special circumstances, such as in the debugger.
Notice that code passed to **exec, eval() or execfile()** does not consider the classname of the invoking class to be the current class; this is similar to the effect of the global statement, the effect of which is likewise restricted to code that is byte-compiled together. The same restriction applies to **getattr(), setattr() and delattr()**, as well as when referencing dict directly.
===== 9.7. Odds and Ends =====
Sometimes it is useful to have a data type similar to the Pascal “record” or __C “struct”__, bundling together a few named data items. __An empty class __definition will do nicely:
class Employee:
pass
john = Employee() # Create an empty employee record
# Fill the fields of the record
john.name = 'John Doe'
john.dept = 'computer lab'
john.salary = 1000
空类实例可以当作一个__结构体__来使用。(这是由于python中对象的数据属性可以__动态添加__。)
A piece of Python code that expects **a particular abstract data type** can often be passed a class that emulates the methods of that data type instead. For instance, if you have a function that formats some data from a file object, you can define a class with methods read() and readline() that get the data from a string buffer instead, and pass it as an argument.
Instance method objects have attributes, too: m.im_self is the instance object with the method m(), and m.im_func is the function object corresponding to the method.
===== 9.8. Exceptions Are Classes Too =====
User-defined exceptions are identified by classes as well. Using this mechanism it is possible __to create extensible hierarchies of exceptions__.
There are two new valid (semantic) forms for the raise statement:
* raise Class, instance
* raise instance
In the first form, instance must be an instance of Class or of a class derived from it. The second form is a shorthand for:
raise instance.class, instance
A class in an except clause is compatible with an exception if it is the same class or a base class thereof (but not the other way around — an except clause listing a derived class is not compatible with a base class). For example, the following code will print B, C, D in that order:
也就是说：__子类实例也是父类的实例，但是父类实例不是子类的实例。__
class B:
pass
class C(B):
pass
class D(C):
pass
D类实例是C类实例是B类实例，反之则不行。
for c in [B, C, D]:
try:
raise c()
except D:
print "D"
except C:
print "C"
except B:
print "B"
Note that if the except clauses were reversed (with except B first), it would have printed B, B, B — the first matching except clause is triggered.
When an error message is printed for an unhandled exception, the exception’s __class name__ is printed, then a colon and a space, and finally the__ instance converted to a string__ using the built-in function** str()**.
===== 9.9. Iterators =====
By now you have probably noticed that most __container objects__ can be looped over using a for statement:
for element in [1, 2, 3]:
print element
for element in (1, 2, 3):
print element
for key in {'one':1, 'two':2}:
print key
for char in "123":
print char
for line in **open("myfile.txt")**:
print line__，__
This style of access is clear, concise, and convenient. __The use of iterators pervades and unifies Python__. Behind the scenes, the for statement calls** iter() **on the container object. The function returns an** iterator object** that defines the method** next()** which accesses elements in the container one at a time. When there are no more elements, next() raises a __StopIteration exception __which tells the for loop to terminate. This example shows how it all works:
>>>
>>> s = 'abc'
>>> it = iter(s)
>>> it
<iterator object at 0x00A1DB50>
>>> it.next()
'a'
>>> it.next()
'b'
>>> it.next()
'c'
>>> it.next()
Traceback (most recent call last):
File "<stdin>", line 1, in ?
it.next()
**StopIteration**
Having seen the mechanics behind the iterator protocol, it is easy to **add iterator behavior to your classes**. Define an__ iter()__ method which returns an object with a__ next()__ method. **If the class defines next(), then iter() can just return self**:
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]
>>>
>>> rev = Reverse('spam')
>>> iter(rev)
<main.**Reverse object** at 0x00A1DB50>
>>> for char in rev:
... print char
...
m
a
p
s
===== 9.10. Generators =====
Generators are a simple and powerful tool __for creating iterators__. They are written like regular functions but use the** yield statement** whenever they want to return data. Each time next() is called, the generator resumes where it left-off (it remembers all the data values and which statement was last executed). An example shows that generators can be trivially easy to create:
def reverse(data):
for index in range(len(data)-1, -1, -1):
__ yield data[index]__
>>>
>>> for char in reverse('golf'): #reverse()产生一个__生成器对象__。
... print char
...
f
l
o
g
Anything that can be done with generators can also be done with class based iterators as described in the previous section. What makes generators so compact is that the** iter() and next() methods are created automatically.**
Another key feature is that the __local variables and execution state are automatically saved between calls__. This made the function easier to write and much more clear than an approach using instance variables like self.index and self.data.
In addition to automatic method creation and saving program state, when generators terminate, they **automatically raise StopIteration**. In combination, these features make it easy to create iterators with no more effort than writing a regular function.
===== 9.11. Generator Expressions =====
Some simple generators can be coded succinctly as expressions using a syntax similar to __list comprehensions__ but __with parentheses__** instead of brackets**. These expressions are designed for situations where the generator is used right away __by an enclosing function__. Generator expressions are more compact but less versatile than full generator definitions and tend to be more memory friendly than equivalent list comprehensions.
Examples:
>>>
>>> sum(i*i for i in range(10)) # sum of squares
285
>>> xvec = [10, 20, 30]
>>> yvec = [7, 5, 3]
>>> sum(x*y for x,y in zip(xvec, yvec)) # dot product
260
>>> from math import __pi, sin__
>>> sine_table = dict(**(x, sin(x*pi/180))** for x in range(0, 91))
>>> unique_words = set(__word for line in page for word in line.split()__)
>>> valedictorian = max((student.gpa, student.name) for student in graduates)
>>> data = 'golf'
>>> list(data[i] for i in range(len(data)-1,-1,-1))
['f', 'l', 'o', 'g']
Footnotes
[1] Except for one thing.** Module objects** have a secret read-only attribute called __dict__ which returns the dictionary used to implement the** module’s namespace**; the name dict is an attribute but not a global name. Obviously, using this violates the abstraction of namespace implementation, and should be restricted to things like post-mortem debuggers.