黑体下划线： 重要部分汉字： 笔记、注释
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Instant Python
[If you like this tutorial, please check out my book
Beginning
Python.]
This is a minimal crash-course in the programming language
Python
. To
learn more, take a look at the documentation at the Python web site,
www.python.org
; especially the
tutorial
. If you wonder
why you should be interested, check out the
comparison
page where Python is compared to other languages.
This introduction has been translated into several languages, among
them
Portuguese,
Italian
,
Spanish
,
Russian
,
French
,
Lithuanian,
Japanese
,
German
and
Greek,
and is currently being translated into Norwegian, Polish, and Korean.
Since this document still might undergo changes, these translations
may not always be up to date.
Note: To get the examples working properly,
write the
programs in a text file and then run that with the interpreter; do
not try to run them directly in the interactive interpreter -
not all of them will work. (Please do not ask me for details on
this! I get swamped with emails on the subject... Check the
documentation
, or send an
email to
help@python.org
).
The Basics
To begin with, think of Python as pseudo-code. It's almost true.
Variables don't have types, so you don't have to declare them. They
appear when you assign to them, and disappear when you don't use them
anymore. Assignment is done by the = operator. Equality
is tested by the == operator. You can assign several
variables at once:
x,y,z = 1,2,3
first, second = second, first
a = b = 123
Blocks are indicated through indentation, and only through
indentation. (No BEGIN/END or braces.) Some
common control structures are:
if x 10 and x = 0:
print "x is still not negative."
x = x-1
The first two examples are equivalent.
The index variable given in the for loop iterates
through the elements of a list (written as in the example).
To make an "ordinary" for loop (that is, a counting
loop), use the built-in function range().
# Print out the values from 0 to 99 inclusive.
for value in range(100):
print value
(The line beginning with "#"
is a comment, and is ignored by the
interpreter.)
Okay; now you know enough to (in theory) implement any algorithm in
Python. Let's add some basic user interaction. To get input
from the user (from a text prompt), use the builtin function
input.
x = input("Please enter a number: ")
print "The square of that number is", x*x
The input function displays the prompt given (which
may be empty) and lets the user enter any valid Python value. In this
case we were expecting a number – if something else (like a string) is
entered, the program would crash. To avoid that we would need some
error checking. I won't go into that here; suffice it to say that if
you want the user input stored verbatim as a string (so that
anything can be entered), use the function
raw_input instead. If you wanted to convert the input
string s to an integer, you could then use
int(s).
Note: If you want to input a string with
input, the user has to write the quotes explicitly. In
Python, strings can be enclosed in either single or double quotes.
So, we have control structures, input and output covered – now we
need some snazzy data structures. The most important ones are
lists and dictionaries. Lists are written with
brackets, and can (naturally) be nested:
name = ["Cleese", "John"]
x = [[1,2,3],[y,z],[[[]]]]
One of the nice things about lists is that you can access their
elements separately or in groups, through indexing and
slicing. Indexing is done (as in many other languages) by
appending the index in brackets to the list. (Note that the first
element has index 0).
print name[1], name[0] # Prints "John Cleese"
name[0] = "Smith"
Slicing is almost like indexing, except that you indicate both the
start and stop index of the result, with a colon (":")
separating them:
x = ["spam","spam","spam","spam","spam","eggs","and","spam"]
print x[5:7] # Prints the list ["eggs","and"]
Notice that the end is non-inclusive. If one of the indices is
dropped, it is assumed that you want everything in that direction.
I.e. list[:3] means "every element from the beginning of
list up to element 3, non-inclusive." (It could be argued
that it would actually mean element 4, since the counting starts at
0... Oh, well.) list[3:] would, on the other hand, mean
"every element from list, starting at element 3
(inclusive) up to, and including, the last one." For really
interesting results, you can use negative numbers too:
list[-3] is the third element from the end of
the list...
While on the subject of indexing, you might be interested to know
that the built-in function len gives you the length of a
list.
Now, then – what about dictionaries? To put it simply, they are
like lists, only their contents are not ordered. How do you index them
then? Well, every element has a key, or a "name" which is
used to look up the element just like in a real dictionary. A couple
of example dictionaries:
{ "Alice" : 23452532, "Boris" : 252336,
"Clarice" : 2352525, "Doris" : 23624643}
person = { 'first name': "Robin", 'last name': "Hood",
'occupation': "Scoundrel" }
Now, to get person's occupation, we use the expression
person["occupation"]. If we wanted to change his last
name, we could write:
person['last name'] = "of Locksley"
Simple, isn't it? Like lists, dictionaries can hold other
dictionaries. Or lists, for that matter. And naturally lists can hold
dictionaries too. That way, you can easily make some quite advanced
data structures.
Functions
Next step: Abstraction. We want to give a name to a piece of code,
and call it with a couple of parameters. In other words – we want to
define a function (or "procedure"). That's easy. Use the keyword
def like this:
def square(x):
return x*x
print square(2) # Prints out 4
For those of you who understand it:
When you pass a parameter to a function, you bind the parameter
to the value, thus creating a new reference. If you change
the "contents" of this parameter name (i.e. rebind it) that
won't affect the original. This works just like in Java,
for instance. Let's take a look at an example:
def change(some_list):
some_list[1] = 4
x = [1,2,3]
change(x)
print x # Prints out [1,4,3]
As you can see, it is the original list that is passed in,
and if the function changes it, these changes carry over to
the place where the function was called. Note, however
the behaviour in the following example:
def nochange(x):
x = 0
y = 1
nochange(y)
print y # Prints out 1
Why is there no change now? Because we don't change the
value! The value that is passed in is the number 1 — we
can't change a number in the same way that we change a list. The
number 1 is (and will always be) the number 1. What we did do
is change the contents of the local variable (parameter)
x, and this does not carry over to the
environment.
For those of you who didn't understand this: Don't worry — it's
pretty easy if you don't think too much about it :)
Python has all kinds of nifty things like named arguments
and default arguments and can handle a variable number of
arguments to a single function. For more info on this, see the Python
tutorial's
section 4.7
.
If you know how to use functions in general, this is basically what
you need to know about them in Python. (Oh, yes... The
return keyword halts the execution of the function and
returns the value given.)
One thing that might be useful to know, however, is that functions
are values in Python. So if you have a function like
square, you could do something like:
queeble = square
print queeble(2) # Prints out 4 *也可以理解为函数指针
To call a function without arguments you must remember to write
doit() and not doit. The latter, as shown,
only returns the function itself, as a value. (This goes for methods
in objects too... See below.)
Objects and Stuff...
I assume you know how object-oriented programming works.
(Otherwise, this section might not make much sense. No problem...
Start playing without the objects :).) In Python you
define classes with the (surprise!) class keyword, like
this:
class Basket:
# Always remember the *self* argument
def __init__(self,contents=None):
self.contents = contents or []
def add(self,element):
self.contents.append(element)
def print_me(self):
result = ""
for element in self.contents:
result = result + " " + `element`
print "Contains:"+result
New things here:
All methods (functions in an object) receive an additional
argument at the start of the argument list, containing the
object itself. (Called self in this example, which
is customary.)
- Methods are called like this:
object.method(arg1,arg2).
- Some method names, like __init__ (with two
underscores before and after), are pre-defined,
and mean special things. __init__ is the name of
the constructor of the class, i.e. it is the function
that is called when you create an instance.
- Some arguments can be optional and given a default
value (as mentioned before, under the section on functions).
This is done by writing the definition like:
def spam(age=32): ...
Here, spam can be called with one or zero
parameters. If none is used, then the parameter
age will have the value 32.
- "Short circuit logic." This is a clever bit... See below.
- Backquotes convert an object to its string representation. (So
if element contains the number 1, then
`element` is the same as "1" whereas
'element' is a literal string.)
- The addition sign + is used also for concatenating
lists, and strings are really just lists of characters (which
means that you can use indexing and slicing and the
len function on them. Cool, huh?)
No methods or member variables are protected (or private or the like)
in Python. Encapsulation is pretty much a matter of programming style.
(If you really need it, there are naming-conventions that
will allow some privacy :)).
Now, about that short-circuit logic...
All values in Python can be used as logic values. Some of the more
"empty" ones, like [], 0, ""
and None represent logical falsity, while most other
values (like [0], 1 or "Hello,
world") represent logical truth.
Now, logical expressions like a and b are evaluated
like this: First, check if a is true. If it is
not, then simply return it. If it is, then
simply return b (which will represent the truth value of
the expression.) The corresponding logic for a or b is:
If a is true, then return it. If it isn't, then return
b.
This mechanism makes and and or behave
like the boolean operators they are supposed to implement, but they
also let you write short and sweet little conditional expressions.
For instance, the statement
if a:
print a
else:
print b
Could instead be written:
print a or b
Actually, this is somewhat of a Python idiom, so you might as well
get used to it. This is what we do in the method
Basket.__init__. The argument contents has a
default value of None (which is, among other things,
false). Therefore, to check if it had a value, we could write:
if contents:
self.contents = contents
else:
self.contents = []
Of course, now you know there is a better way. And why don't we
give it the default value of [] in the first place?
Because of the way Python works, this would give all the Baskets the
same empty list as default contents. As soon as one of them started to
fill up, they all would contain the same elements, and the default
would not be empty anymore... To learn more about this, you should
read the documentation and look for the difference between
identity and equality.
Another way of doing the above is:
def __init__(self, contents=[]):
self.contents = contents[:]
Can you guess how this works? Instead of using the same empty list
everywhere, we use the expression contents[:] to make a
copy. (We simply slice the entire thing.)
So, to actually make a Basket and to use it (i.e. to
call some methods on it) we would do something like this:
b = Basket(['apple','orange'])
b.add("lemon")
b.print_me()
There are other magic methods than __init__. One such
method is __str__ which defines how the object wants to
look if it is treated like a string. We could use this in our basket
instead of print_me:
def __str__(self):
result = ""
for element in self.contents:
result = result + " " + `element`
return "Contains:"+result
Now, if we wanted to print the basket b, we could just
use:
print b
Cool, huh?
Subclassing is done like this:
class SpamBasket(Basket):
# ...
Python allows multiple inheritance, so you can have several
superclasses in the parentheses, separated by commas. Classes are
instantiated like this: x = Basket(). Constructors are,
as I said,
made by defining the special member function __init__.
Let's say that SpamBasket had a constructor
__init__(self,type). Then you could make a spam basket
thus: y = SpamBasket("apples").
If you, in the constructor
of SpamBasket, needed to call the constructor of one or more
superclasses, you could call it like this: Basket.__init__(self).
Note, that in addition to supplying the ordinary parameters, you have to
explicitly supply self, since the superclass __init__
doesn't know which instance it is dealing with.
For more about the wonders of object-oriented programming in
Python, see the tutorial's
section 9
.
A Jedi Mind Trick
(This section is only here because I think it is cool. It is
definitely not necessary to read it to start learning
Python. See the end of the section for a note about changes
for Python 2.1.)
Do you like mind boggling concepts? Then, if you're really daring,
you might want to check out Guido van Rossum's essay on
metaclasses
.
If, however, you would rather not have your brain explode,
you might be satisfied with this little trick.
Python uses dynamic as opposed to lexical namespaces. That means
that if you have a function like this:
def orange_juice():
return x*2
... where a variable (in this case x) is not bound to
an argument and is not given a value inside the function, Python will
use the value it has where and when the function is called. In this
case:
x = 3
y = orange_juice()
# y is now 6
x = 1
y = orange_juice()
# y is now 2
Usually, this is the sort of behaviour you want (though the example
above is a bit contrived – you seldom access variables like this.)
However, sometimes it can be nice to have something like
a static namespace, that is, storing some values from the environment
in the function when it is created. The way of doing this in Python
is by means of default arguments.
x = 4
def apple_juice(x=x):
return x*2
Here, the argument x is given a default value which is
the same as the value of the variable x at the
time when the function is defined. So, as long as nobody supplies an
argument for the function, it will work like this:
x = 3
y = apple_juice()
# y is now 8
x = 1
y = apple_juice()
# y is now 8
So – the value of x is not changed. If this was all we
wanted, we might just as well have written
def tomato_juice():
x = 4
return x*2
or even
def carrot_juice():
return 8
However, the point is that the value of x is
picked up from the environment at the time when the function
is defined. How is this useful? Let's take an example – a function
which composes two other functions.
We want a function that works like this:
from math import sin, cos
sincos = compose(sin,cos)
x = sincos(3)
Where compose is the function we want to make, and
x has the value -0.836021861538, which is
the same as sin(cos(3)). Now, how do we do this?
(Note that we are using functions as arguments here... That's a pretty
neat trick in itself.)
Clearly, compose takes two functions as parameters,
and returns a function which again takes one parameter. So, a skeleton
solution might be:
def compose(fun1, fun2):
def inner(x):
pass # ...
return inner
We might be tempted to put return fun1(fun2(x)) inside
the function inner and leave it at that. No, no, no. That
would behave very strangely. Imagine the following scenario:
from math import sin, cos
# Wrong version
def compose(fun1, fun2):
def inner(x):
return fun1(fun2(x))
return inner
def fun1(x):
return x + " world!"
def fun2(x):
return "Hello,"
sincos = compose(sin,cos) # Using the wrong version
x = sincos(3)
Now, what value would x have? That's right:
"Hello, world". Why is that? Because when it's called, it
picks up the values of fun1 and fun2 from the
environment, not the ones that were around when it was created.
To get a working solution, all we have to do is use the technique
I described earlier:
def compose(fun1, fun2):
def inner(x, fun1=fun1, fun2=fun2):
return fun1(fun2(x))
return inner
Now we only have to hope that nobody supplies the resulting
function with more than one argument, as that would wreck it
:). And by the way, since we have no need for the name
inner, and it contains only an expression, we might as
well use an anonymous function, using the keyword
lambda:
def compose(f1, f2):
return lambda x, f1=f1, f2=f2: f1(f2(x))
Terse, yet clear. You've gotta love it :)
(And if you didn't understand any of that, don't worry. At least
I hope it has convinced you that Python is more than "just a scripting
language"... :))
A Note About Python 2.1 and Nested Scopes
With the advent of Python 2.1, the language now (optionally) has
statically nested scopes or namespaces. That means that you can do the
things described in this section without some of the trickyness. Now
you can simply write the following:
# This magic won't be necessary in Python 2.2:
from __future__ import nested_scopes
def compose(fun1, fun2):
def inner(x):
return fun1(fun2(x))
return inner
... and it will work as it should.
And Now...
Just a few things near the end. Most useful functions and classes are
put in modules, which are really text-files containing Python
code. You can import these and use them in your own programs. For
instance, to use the method split from the standard
module string, you can do either:
import string
x = string.split(y)
Or...
from string import split
x = split(y)
Note: The string module isn't
used much anymore; rather than the code below, you should use
x=y.split().
For more information on the standard library modules, take a look
at
www.python.org/doc/lib
.
They contain a lot of useful stuff.
All the code in the module/script is run when it is imported. If
you want your program to be both an importable module and a runnable
program, you might want to add something like this at the end of it:
if __name__ == "__main__": go()
This is a magic way of saying that if this module is run as an
executable script (that is, it is not being imported into another
script), then the function go should be called. Of
course, you could do anything after the colon there... :)
And for those of you who want to make an executable script on UN*X,
use the following first line to make it run by itself:
#!/usr/bin/env python
Finally, a brief mention of an important concept: Exceptions. Some
operations (like dividing something by zero or reading from a
non-existent file) produce an error condition or exception.
You can even make your own and raise them at the appropriate times.
If nothing is done about the exception, your program ends and
prints out an error message. You can avoid this with a
try/except-statement. For instance:
def safe_division(a,b):
try:
return a/b
except ZeroDivisionError:
return None
ZeroDivisionError is a standard exception. In this
case, you could have checked if b was zero, but
in many cases, that strategy is not feasible. And besides, if we
didn't have the try-clause in safe_division,
thereby making it a risky function to call, we could still do
something like:
try:
unsafe_division(a,b)
except ZeroDivisionError:
print "Something was divided by zero in unsafe_division"
In cases where you normally would not have a specific
problem, but it might occur, using exceptions lets you avoid
costly testing etc.
Well – that's it. Hope you learned something. Now go and
play
. And remember the Python motto
of learning: "Use the source, Luke." (Translation: Read all the code
you can get your hands on :)) To get you started, here is
an
example
. It is Hoare's well known
QuickSort algorithm. A syntax-coloured version is
here
.
One thing might be worth mentioning about this example. The
done variable controls whether or not the
partition has finished moving about the elements. So when
one of the two inner loops wants to end the entire swapping sequence,
it sets done to 1 and then exits
with break. Why do the inner loops use done?
Because, when the first inner loop ends with a break,
whether or not the next loop is to be started depends on whether the
main loop is finished, that is, whether or not done has
been set to 1:
while not done:
while not done:
# Iterates until a break
while not done:
# Only executed if the first didn't set "done" to 1
An equivalent, possibly clearer, but in my
opinion less pretty version would be:
while not done:
while 1:
# Iterates until a break
if not done:
while 1:
# Only executed if the first didn't set "done" to 1
The only reason I used the done variable in the first
loop was that I liked preserving the symmetry between the two. That
way one could reverse their order and the algorithm would still work.
Some more examples can be found on
Joe Strout's
tidbit
page.
