from: http://hi.baidu.com/wxipi/blog/item/dcbf1f500991975b1038c298.html
from: http://yunhaozou.org/perl-shell/314.html
from: http://roclinux.cn/?p=2338
看到好多同学在求Python教程，其实完全没必要，
Python非常的简单，简单到恐怕用自然语言来看python就能看懂了...
这里偶就简单的写个教程吧。
首先是数据结构，python所有的数据都是对象，包括0,1之类的：
数只有整数和浮点之分，整数是直接支持高精度的，因此，想知道12345的12345次方是多
少，直接输入print 12345**12345就行了。
不过浮点数不是高精度的，表达范围跟C的float一样。
接下来是字符串，可以用''／""框起来，还有""" """对这里就不提了。
没有字符的概念，单个字符也是一个字符串。
字符串可以用类似matlab的下标方式操作，诸如a="abcd"
那么a[0]=='a',a[1:3]=='bc',a[-1]='d',a[1:]='bcd',a[1::2]=='bd'等等等等
字符串是不可更改的。
接下来就是容器类：tuple/list/dict，分别用(),[],{}表示。比如
t=(1,2,"a"),l=［1,2,"a"]。无论是tuple还是list，都可以用类似字符串下标的形式访问
，例如：
l[1]==2，l[-1]=='a'。
tuple和list的区别在于,tuple和字符串一样不可修改，而list可以修改：
l[0]='p' l变成['p',2,'a']
l[0:2]=[3,4] l变成[3,4,'a']
l[::2]=[5,6] l变成[5,2,6]
del l[1:] l变成[5]
而dict则完成一个key->value的映射：
d={1:2,"a":4}，那么d[1]==2,d['a']==4。
接下来是Python灵活性的体现：list comprehension
例如:a=[1,2,3,4,5,6,7,8]
那么[x+1 for x in a]->[2,3,4,5,6,7,8,9]
[x*2 for x in a if x>4]->[10,12,14,16]
list comprehension将操作和过滤融合在一起，而且读起来就像是自然语言这么自然，非
常的舒服。
接下来讲分支／循环。这里要提一下python以缩进来取代C里面的{}对。
同样的缩进表示这段代码处于同一个层次。
下面是if分支：
if 条件:
true分支
elif 条件2:
true分支
else:
else分支
接下来是循环：
for i in [1,2,3,4]:
循环体
或者：
while 条件:
循环体
接下来是函数：
def funcname(arg1,arg2):
函数体
变参和默认值：
def funcname(arg1, arg2="default value", *varargs): pass
至于**kwargs偶就不提了。
好了基础部分全部说完，下面才是我最感兴趣的部分，也是Python最核心的东西，就是namespace。
很多初学者学Python总是把Python和C，java等语言对比，
面对a=1这样的语句，把a看作一个变量名，1是一个值，认为这是一个赋值过程。
让我们回顾一下赋值的定义：
赋值是指将一个数值存储到一个空间中。例如以下C代码：
int a,b;
a=1;b=1;
这个例子中，a和b都有各自的空间。里面都存储了一个值，为1。这两个数值是互不相关的。
而在python中，这里要狠狠强调一下：Python里面不存在赋值过程，也不存在变量！！！
a=1这个过程，在Python中解释为一个name对一个对象的绑定。
也就是说"a"这个名字指向了1这个对象。
由于Python里面不存在变量的概念，所以自然也没有变量的申明。Python里面就是一大堆
的对象，以及各种名字指向这些对象：
a=1
b=2
c=a+b
在Python中，解释为让"a"指向对象1，让"b"指向对象2；对"a"所指对象以及"b"所指对象
运用add操作，
让"c"指向得到的结果。
在Python中，你不需要关心对象的产生和销毁。例如这个上面例子中，产生的对象3，如果
没有名字指向它，就会被自动销毁（这里偶为了便于理解撒了个谎。事实上为了性能问题，
0-100这些对象是永远存在的）
以上为name的解释
下面解释space：
每一个name都有其存在的空间。
只有模块，类，函数有其自己的子空间，而只有模块，类的子空间可以被访问。
例如：
def func():
a=1
这个a就存在于func的空间中。
在func以外，这个a是无法被访问到的。
另一个经常引起困扰的例子：
a=1
def func():
print a 1.结果为1
a=2
print a 2.结果为2
print a 3.结果为1
func()
print a 4.结果还是1
这个例子中，第一个print a执行的时候，func的子空间中不存在a，这个时候就自动的访
问上一层空间，
也就是全局空间，得到了a。
随后的a=2赋值，在func的子空间中创造了a的name，并指向2。
于是在第二个print a执行的时候，直接打印了func子空间中的a；
在第三个、第四个print a执行的都是全局a的结果。
恩，不知道说清楚了没有。
因为我比较懒，没有说class,以及module，所以目前看来namespace似乎没啥。
其实namespace是Python的关键中的关键。理解了这个才能驾驭Python～
如果这些都充分理解了，下面就可以自己去翻翻python library reference，看看python
丰富的库了～
Preliminary fluff
So, you want to learn the Python programming language but can’t find a concise and yet full-featured tutorial. This tutorial will attempt to teach you Python in 10 minutes. It’s probably not so much a tutorial as it is a cross between a tutorial and a cheatsheet, so it will just show you some basic concepts to start you off. Obviously, if you want to really learn a language you need to program in it for a while. I will assume that you are already familiar with programming and will, therefore, skip most of the non-language-specific stuff. The important keywords will be highlighted so you can easily spot them. Also, pay attention because, due to the terseness of this tutorial, some things will be introduced directly in code and only briefly commented on.
Properties
Python is strongly typed (i.e. types are enforced), dynamically, implicitly typed (i.e. you don’t have to declare variables), case sensitive (i.e. var and VAR are two different variables) and object-oriented (i.e. everything is an object).
Getting help
Help in Python is always available right in the interpreter. If you want to know how an object works, all you have to do is call help(<object>)! Also useful are dir(), which shows you all the object’s methods, and <object>.doc, which shows you its documentation string:
>>> help(5)
Help on int object:
(etc etc)
>>> dir(5)
['__abs__', '__add__', ...]
>>> abs.__doc__
‘abs(number) -> number\n\nReturn the absolute value of the argument.’
Syntax
Python has no mandatory statement termination characters and blocks are specified by indentation. Indent to begin a block, dedent to end one. Statements that expect an indentation level end in a colon (:). Comments start with the pound (#) sign and are single-line, multi-line strings are used for multi-line comments. Values are assigned (in fact, objects are bound to names) with the equals sign (”=”), and equality testing is done using two equals signs (”==“). You can increment/decrement values using the += and -= operators respectively by the right-hand amount. This works on many datatypes, strings included. You can also use multiple variables on one line. For example:
>>> myvar = 3
>>> myvar += 2
>>> myvar
5
>>> myvar -= 1
>>> myvar
4
“”"This is a multiline comment.
The following lines concatenate the two strings.”"”
>>> mystring = ”Hello”
>>> mystring += ” world.”
>>> print mystring
Hello world.
# This swaps the variables in one line(!).
# It doesn’t violate strong typing because values aren’t
# actually being assigned, but new objects are bound to
# the old names.
>>> myvar, mystring = mystring, myvar
Data types
The data structures available in python are lists, tuples and dictionaries. Sets are available in the sets library (but are built-in in Python 2.5 and later). Lists are like one-dimensional arrays (but you can also have lists of other lists), dictionaries are associative arrays (a.k.a. hash tables) and tuples are immutable one-dimensional arrays (Python “arrays” can be of any type, so you can mix e.g. integers, strings, etc in lists/dictionaries/tuples). The index of the first item in all array types is 0. Negative numbers count from the end towards the beginning, -1 is the last item. Variables can point to functions. The usage is as follows:
>>> sample = [1, ["another", "list"], (“a”, ”tuple”)]
>>> mylist = ["List item 1", 2, 3.14]
>>> mylist[0] = ”List item 1 again”
>>> mylist[-1] = 3.14
>>> mydict = {“Key 1″: ”Value 1″, 2: 3, ”pi”: 3.14}
>>> mydict["pi"] = 3.15
>>> mytuple = (1, 2, 3)
>>> myfunction = len
>>> print myfunction(mylist)
3
You can access array ranges using a colon (:). Leaving the start index empty assumes the first item, leaving the end index assumes the last item. Negative indexes count from the last item backwards (thus -1 is the last item) like so:
>>> mylist = ["List item 1", 2, 3.14]
>>> print mylist[:]
['List item 1', 2, 3.1400000000000001]
>>> print mylist[0:2]
['List item 1', 2]
>>> print mylist[-3:-1]
['List item 1', 2]
>>> print mylist[1:]
[2, 3.14]
Strings
Its strings can use either single or double quotation marks, and you can have quotation marks of one kind inside a string that uses the other kind (i.e. “He said ‘hello’.” is valid). Multiline strings are enclosed in triple double (or single) quotes(”“”). Pythonsupports Unicode out of the box, using the syntax u“This is a unicode string”. To fill a string with values, you use the % (modulo) operator and a tuple. Each %s gets replaced with an item from the tuple, left to right, and you can also use dictionary substitutions, like so:
>>>print ”Name: %s\nNumber: %s\nString: %s” % (myclass.name, 3, 3 * ”-”)
Name: Poromenos
Number: 3
String: —
strString = ”"”This is
a multiline
string.”"”
# WARNING: Watch out for the trailing s in “%(key)s”.
>>> print ”This %(verb)s a %(noun)s.” % {“noun”: ”test”, ”verb”: ”is”}
This is a test.
Flow control statements
Flow control statements are if, for, and while. There is no select; instead, use if. Use for to enumerate through members of a list. To obtain a list of numbers, userange(<number>). These statements’ syntax is thus:
rangelist = range(10)
>>> print rangelist
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
for number in rangelist:
# Check if number is one of
# the numbers in the tuple.
if number in (3, 4, 7, 9):
# “Break” terminates a for without
# executing the “else” clause.
break
else:
# “Continue” starts the next iteration
# of the loop. It’s rather useless here,
# as it’s the last statement of the loop.
continue
else:
# The “else” clause is optional and is
# executed only if the loop didn’t “break”.
pass # Do nothing
if rangelist[1] == 2:
print ”The second item (lists are 0-based) is 2″
elif rangelist[1] == 3:
print ”The second item (lists are 0-based) is 3″
else:
print ”Dunno”
while rangelist[1] == 1:
pass
Functions
Functions are declared with the “def” keyword. Optional arguments are set in the function declaration after the mandatory arguments by being assigned a default value. For named arguments, the name of the argument is assigned a value. Functions can return a tuple (and using tuple unpacking you can effectively return multiple values).Lambda functions are ad hoc functions that are comprised of a single statement. Parameters are passed by reference, but immutable types (tuples, ints, strings, etc)cannot be changed. This is because only the memory location of the item is passed, and binding another object to a variable discards the old one, so immutable types are replaced. For example:
# Same as def f(x): return x + 1
functionvar = lambda x: x + 1
>>> print functionvar(1)
2
# an_int and a_string are optional, they have default values
# if one is not passed (2 and “A default string”, respectively).
def passing_example(a_list, an_int=2, a_string=”A default string”):
a_list.append(“A new item”)
an_int = 4
return a_list, an_int, a_string
>>> my_list = [1, 2, 3]
>>> my_int = 10
>>> print passing_example(my_list, my_int)
([1, 2, 3, 'A new item'], 4, ”A default string”)
>>> my_list
[1, 2, 3, 'A new item']
>>> my_int
10
Classes
Python supports a limited form of multiple inheritance in classes. Private variables and methods can be declared (by convention, this is not enforced by the language) by adding at least two leading underscores and at most one trailing one (e.g. “__spam”). We can also bind arbitrary names to class instances. An example follows:
class MyClass:
common = 10
def __init__(self):
self.myvariable = 3
def myfunction(self, arg1, arg2):
return self.myvariable
# This is the class instantiation
>>> classinstance = MyClass()
>>> classinstance.myfunction(1, 2)
3
# This variable is shared by all classes.
>>> classinstance2 = MyClass()
>>> classinstance.common
10
>>> classinstance2.common
10
# Note how we use the class name
# instead of the instance.
>>> MyClass.common = 30
>>> classinstance.common
30
>>> classinstance2.common
30
# This will not update the variable on the class,
# instead it will bind a new object to the old
# variable name.
>>> classinstance.common = 10
>>> classinstance.common
10
>>> classinstance2.common
30
>>> MyClass.common = 50
# This has not changed, because “common” is
# now an instance variable.
>>> classinstance.common
10
>>> classinstance2.common
50
# This class inherits from MyClass. Multiple
# inheritance is declared as:
# class OtherClass(MyClass1, MyClass2, MyClassN)
class OtherClass(MyClass):
# The “self” argument is passed automatically
# and refers to the class instance, so you can set
# instance variables as above, but from inside the class.
def __init__(self, arg1):
self.myvariable = 3
print arg1
>>> classinstance = OtherClass(“hello”)
hello
>>> classinstance.myfunction(1, 2)
3
# This class doesn’t have a .test member, but
# we can add one to the instance anyway. Note
# that this will only be a member of classinstance.
>>> classinstance.test = 10
>>> classinstance.test
10
Exceptions
Exceptions in Python are handled with try-except [exceptionname] blocks:
def some_function():
try:
# Division by zero raises an exception
10 / 0
except ZeroDivisionError:
print ”Oops, invalid.”
else:
# Exception didn’t occur, we’re good.
pass
finally:
# This is executed after the code block is run
# and all exceptions have been handled, even
# if a new exception is raised while handling.
print ”We’re done with that.”
>>> some_function()
Oops, invalid.
We’re done with that.
Importing
External libraries are used with the import [libname] keyword. You can also use from [libname] import [funcname] for individual functions. Here is an example:
import random
from time import clock
randomint = random.randint(1, 100)
>>> print randomint
64
File I/O
Python has a wide array of libraries built in. As an example, here is how serializing(converting data structures to strings using the pickle library) with file I/O is used:
import pickle
mylist = ["This", "is", 4, 13327]
# Open the file C:\binary.dat for writing. The letter r before the
# filename string is used to prevent backslash escaping.
myfile = file(r”C:\binary.dat”, ”w”)
pickle.dump(mylist, myfile)
myfile.close()
myfile = file(r”C:\text.txt”, ”w”)
myfile.write(“This is a sample string”)
myfile.close()
myfile = file(r”C:\text.txt”)
>>> print myfile.read()
‘This is a sample string’
myfile.close()
# Open the file for reading.
myfile = file(r”C:\binary.dat”)
loadedlist = pickle.load(myfile)
myfile.close()
>>> print loadedlist
['This', 'is', 4, 13327]
Miscellaneous
- Conditions can be chained.
1 < a < 3checks that a is both less than 3 and more than 1. - You can use
delto delete variables or items in arrays.
- List comprehensions provide a powerful way to create and manipulate lists. They consist of an expression followed by a
forclause followed by zero or moreiforforclauses, like so:
>>> lst1 = [1, 2, 3]
>>> lst2 = [3, 4, 5]
>>> print [x * y for x in lst1 for y in lst2]
[3, 4, 5, 6, 8, 10, 9, 12, 15]
>>> print [x for x in lst1 if 4 > x > 1]
[2, 3]
# Check if an item has a specific property.
# “any” returns true if any item in the list is true.
>>> any([i % 3 for i in [3, 3, 4, 4, 3]])
True
# This is because 4 % 3 = 1, and 1 is true, so any()
# returns True.
# Check how many items have this property.
>>> sum(1 for i in [3, 3, 4, 4, 3] if i == 4)
2
>>> del lst1[0]
>>> print lst1
[2, 3]
>>> del lst1
Global variables are declared outside of functions and can be read without any special declarations, but if you want to write to them you must declare them at the beginning of the function with the “global” keyword, otherwise Python will bind that object to a new local variable (be careful of that, it’s a small catch that can get you if you don’t know it). For example:
number = 5
def myfunc():
# This will print 5.
print number
def anotherfunc():
# This raises an exception because the variable has not
# been bound before printing. Python knows that it an
# object will be bound to it later and creates a new, local
# object instead of accessing the global one.
print number
number = 3
def yetanotherfunc():
global number
# This will correctly change the global.
number = 3
Epilogue
This tutorial is not meant to be an exhaustive list of all (or even a subset) of Python. Python has a vast array of libraries and much much more functionality which you will have to discover through other means, such as the excellent book Dive into Python. I hope I have made your transition in Python easier. Please leave comments if you believe there is something that could be improved or added or if there is anything else you would like to see (classes, error handling, anything).