Several debuggers for Python are described below, and the built-in function
breakpoint()
allows you to drop into any of them.
The pdb module is a simple but adequate console-mode debugger for Python. It is part of the standard Python library, and is
documentedintheLibraryReferenceManual
. You can also write your own debugger by using the code for pdb as an example.
The IDLE interactive development environment, which is part of the standard Python distribution (normally available as
Tools/scripts/idle3
), includes a graphical debugger.
PythonWin is a Python IDE that includes a GUI debugger based on pdb. The PythonWin debugger colors breakpoints and has quite a few cool features such as debugging non-PythonWin programs. PythonWin is available as part of
pywin32
project and as a part of the
ActivePython
distribution.
Eric
is an IDE built on PyQt and the Scintilla editing component.
You don’t need the ability to compile Python to C code if all you want is a stand-alone program that users can download and run without having to install the Python distribution first. There are a number of tools that determine the set of modules required by a program and bind these modules together with a Python binary to produce a single executable.
One is to use the freeze tool, which is included in the Python source tree as
Tools/freeze
. It converts Python byte code to C arrays; with a C compiler you can embed all your modules into a new program, which is then linked with the standard Python modules.
It works by scanning your source recursively for import statements (in both forms) and looking for the modules in the standard Python path as well as in the source directory (for built-in modules). It then turns the bytecode for modules written in Python into C code (array initializers that can be turned into code objects using the marshal module) and creates a custom-made config file that only contains those built-in modules which are actually used in the program. It then compiles the generated C code and links it with the rest of the Python interpreter to form a self-contained binary which acts exactly like your script.
The following packages can help with the creation of console and GUI executables:
It can be a surprise to get the
UnboundLocalError
in previously working code when it is modified by adding an assignment statement somewhere in the body of a function.
This code:
>>> x=10>>> defbar():... print(x)...>>> bar()10
works, but this code:
>>> x=10>>> deffoo():... print(x)... x+=1
results in an
UnboundLocalError
:
>>> foo()Traceback (most recent call last):...UnboundLocalError: local variable 'x' referenced before assignment
This is because when you make an assignment to a variable in a scope, that variable becomes local to that scope and shadows any similarly named variable in the outer scope. Since the last statement in foo assigns a new value to
x
, the compiler recognizes it as a local variable. Consequently when the earlier
print(x)
attempts to print the uninitialized local variable and an error results.
In the example above you can access the outer scope variable by declaring it global:
This explicit declaration is required in order to remind you that (unlike the superficially analogous situation with class and instance variables) you are actually modifying the value of the variable in the outer scope:
>>> print(x)11
You can do a similar thing in a nested scope using the
nonlocal
关键词：
In Python, variables that are only referenced inside a function are implicitly global. If a variable is assigned a value anywhere within the function’s body, it’s assumed to be a local unless explicitly declared as global.
Though a bit surprising at first, a moment’s consideration explains this. On one hand, requiring
global
for assigned variables provides a bar against unintended side-effects. On the other hand, if
global
was required for all global references, you’d be using
global
all the time. You’d have to declare as global every reference to a built-in function or to a component of an imported module. This clutter would defeat the usefulness of the
global
declaration for identifying side-effects.
This gives you a list that contains 5 lambdas that calculate
x**2
. You might expect that, when called, they would return, respectively,
0
,
1
,
4
,
9
，和
16
. However, when you actually try you will see that they all return
16
:
>>> squares[2]()16>>> squares[4]()16
This happens because
x
is not local to the lambdas, but is defined in the outer scope, and it is accessed when the lambda is called — not when it is defined. At the end of the loop, the value of
x
is
4
, so all the functions now return
4**2
, i.e.
16
. You can also verify this by changing the value of
x
and see how the results of the lambdas change:
>>> x=8>>> squares[2]()64
In order to avoid this, you need to save the values in variables local to the lambdas, so that they don’t rely on the value of the global
x
:
这里，
n=x
creates a new variable
n
local to the lambda and computed when the lambda is defined so that it has the same value that
x
had at that point in the loop. This means that the value of
n
将是
0
in the first lambda,
1
in the second,
2
in the third, and so on. Therefore each lambda will now return the correct result:
>>> squares[2]()4>>> squares[4]()16
Note that this behaviour is not peculiar to lambdas, but applies to regular functions too.
The canonical way to share information across modules within a single program is to create a special module (often called config or cfg). Just import the config module in all modules of your application; the module then becomes available as a global name. Because there is only one instance of each module, any changes made to the module object get reflected everywhere. For example:
config.py:
x=0# Default value of the 'x' configuration setting
mod.py:
importconfigconfig.x=1
main.py:
importconfigimportmodprint(config.x)
Note that using a module is also the basis for implementing the singleton design pattern, for the same reason.
In general, don’t use
frommodulenameimport*
. Doing so clutters the importer’s namespace, and makes it much harder for linters to detect undefined names.
Import modules at the top of a file. Doing so makes it clear what other modules your code requires and avoids questions of whether the module name is in scope. Using one import per line makes it easy to add and delete module imports, but using multiple imports per line uses less screen space.
It’s good practice if you import modules in the following order:
third-party library modules (anything installed in Python’s site-packages directory) – e.g.
dateutil
,
requests
,
PIL.Image
locally developed modules
It is sometimes necessary to move imports to a function or class to avoid problems with circular imports. Gordon McMillan says:
Circular imports are fine where both modules use the “import <module>” form of import. They fail when the 2nd module wants to grab a name out of the first (“from module import name”) and the import is at the top level. That’s because names in the 1st are not yet available, because the first module is busy importing the 2nd.
In this case, if the second module is only used in one function, then the import can easily be moved into that function. By the time the import is called, the first module will have finished initializing, and the second module can do its import.
It may also be necessary to move imports out of the top level of code if some of the modules are platform-specific. In that case, it may not even be possible to import all of the modules at the top of the file. In this case, importing the correct modules in the corresponding platform-specific code is a good option.
Only move imports into a local scope, such as inside a function definition, if it’s necessary to solve a problem such as avoiding a circular import or are trying to reduce the initialization time of a module. This technique is especially helpful if many of the imports are unnecessary depending on how the program executes. You may also want to move imports into a function if the modules are only ever used in that function. Note that loading a module the first time may be expensive because of the one time initialization of the module, but loading a module multiple times is virtually free, costing only a couple of dictionary lookups. Even if the module name has gone out of scope, the module is probably available in
sys.modules
.
This type of bug commonly bites neophyte programmers. Consider this function:
deffoo(mydict={}):# Danger: shared reference to one dict for all calls...computesomething...mydict[key]=valuereturnmydict
The first time you call this function,
mydict
contains a single item. The second time,
mydict
contains two items because when
foo()
begins executing,
mydict
starts out with an item already in it.
It is often expected that a function call creates new objects for default values. This is not what happens. Default values are created exactly once, when the function is defined. If that object is changed, like the dictionary in this example, subsequent calls to the function will refer to this changed object.
By definition, immutable objects such as numbers, strings, tuples, and
None
, are safe from change. Changes to mutable objects such as dictionaries, lists, and class instances can lead to confusion.
Because of this feature, it is good programming practice to not use mutable objects as default values. Instead, use
None
as the default value and inside the function, check if the parameter is
None
and create a new list/dictionary/whatever if it is. For example, don’t write:
deffoo(mydict={}):...
but:
deffoo(mydict=None):ifmydictisNone:mydict={}# create a new dict for local namespace
This feature can be useful. When you have a function that’s time-consuming to compute, a common technique is to cache the parameters and the resulting value of each call to the function, and return the cached value if the same value is requested again. This is called “memoizing”, and can be implemented like this:
# Callers can only provide two parameters and optionally pass _cache by keyworddefexpensive(arg1,arg2,*,_cache={}):if(arg1,arg2)in_cache:return_cache[(arg1,arg2)]# Calculate the valueresult=...expensivecomputation..._cache[(arg1,arg2)]=result# Store result in the cachereturnresult
You could use a global variable containing a dictionary instead of the default value; it’s a matter of taste.
Collect the arguments using the
*
and
**
specifiers in the function’s parameter list; this gives you the positional arguments as a tuple and the keyword arguments as a dictionary. You can then pass these arguments when calling another function by using
*
and
**
:
参数
are defined by the names that appear in a function definition, whereas
arguments
are the values actually passed to a function when calling it. Parameters define what
kind of arguments
a function can accept. For example, given the function definition:
deffunc(foo,bar=None,**kwargs):pass
foo
,
bar
and
kwargs
are parameters of
func
. However, when calling
func
，例如：
you might be wondering why appending an element to
y
changed
x
也。
There are two factors that produce this result:
Variables are simply names that refer to objects. Doing
y=x
doesn’t create a copy of the list – it creates a new variable
y
that refers to the same object
x
refers to. This means that there is only one object (the list), and both
x
and
y
refer to it.
Lists are
可变
, which means that you can change their content.
After the call to
append()
, the content of the mutable object has changed from
[]
to
[10]
. Since both the variables refer to the same object, using either name accesses the modified value
[10]
.
If we instead assign an immutable object to
x
:
>>> x=5# ints are immutable>>> y=x>>> x=x+1# 5 can't be mutated, we are creating a new object here>>> x6>>> y5
we can see that in this case
x
and
y
are not equal anymore. This is because integers are
immutable
, and when we do
x=x+1
we are not mutating the int
5
by incrementing its value; instead, we are creating a new object (the int
6
) and assigning it to
x
(that is, changing which object
x
refers to). After this assignment we have two objects (the ints
6
and
5
) and two variables that refer to them (
x
now refers to
6
but
y
still refers to
5
).
Some operations (for example
y.append(10)
and
y.sort()
) mutate the object, whereas superficially similar operations (for example
y=y+[10]
and
sorted(y)
) create a new object. In general in Python (and in all cases in the standard library) a method that mutates an object will return
None
to help avoid getting the two types of operations confused. So if you mistakenly write
y.sort()
thinking it will give you a sorted copy of
y
, you’ll instead end up with
None
, which will likely cause your program to generate an easily diagnosed error.
However, there is one class of operations where the same operation sometimes has different behaviors with different types: the augmented assignment operators. For example,
+=
mutates lists but not tuples or ints (
a_list
+=
[1,
2,
3]
相当于
a_list.extend([1,2,3])
and mutates
a_list
，而
some_tuple+=(1,2,3)
and
some_int+=1
create new objects).
In other words:
If we have a mutable object (
list
,
dict
,
set
, etc.), we can use some specific operations to mutate it and all the variables that refer to it will see the change.
If we have an immutable object (
str
,
int
,
tuple
, etc.), all the variables that refer to it will always see the same value, but operations that transform that value into a new value always return a new object.
If you want to know if two variables refer to the same object or not, you can use the
is
operator, or the built-in function
id()
.
Remember that arguments are passed by assignment in Python. Since assignment just creates references to objects, there’s no alias between an argument name in the caller and callee, and so no call-by-reference per se. You can achieve the desired effect in a number of ways.
By returning a tuple of the results:
>>> deffunc1(a,b):... a='new-value'# a and b are local names... b=b+1# assigned to new objects... returna,b# return new values...>>> x,y='old-value',99>>> func1(x,y)('new-value', 100)
This is almost always the clearest solution.
By using global variables. This isn’t thread-safe, and is not recommended.
By passing a mutable (changeable in-place) object:
>>> deffunc2(a):... a[0]='new-value'# 'a' references a mutable list... a[1]=a[1]+1# changes a shared object...>>> args=['old-value',99]>>> func2(args)>>> args['new-value', 100]
By passing in a dictionary that gets mutated:
>>> deffunc3(args):... args['a']='new-value'# args is a mutable dictionary... args['b']=args['b']+1# change it in-place...>>> args={'a':'old-value','b':99}>>> func3(args)>>> args{'a': 'new-value', 'b': 100}
You have two choices: you can use nested scopes or you can use callable objects. For example, suppose you wanted to define
linear(a,b)
which returns a function
f(x)
that computes the value
a*x+b
. Using nested scopes:
gives a callable object where
taxes(10e6)==0.3*10e6+2
.
The callable object approach has the disadvantage that it is a bit slower and results in slightly longer code. However, note that a collection of callables can share their signature via inheritance:
For an instance
x
of a user-defined class,
dir(x)
returns an alphabetized list of the names containing the instance attributes and methods and attributes defined by its class.
Generally speaking, it can’t, because objects don’t really have names. Essentially, assignment always binds a name to a value; the same is true of
def
and
class
statements, but in that case the value is a callable. Consider the following code:
>>> classA:... pass...>>> B=A>>> a=B()>>> b=a>>> print(b)<__main__.A object at 0x16D07CC>>>> print(a)<__main__.A object at 0x16D07CC>
Arguably the class has a name: even though it is bound to two names and invoked through the name
B
the created instance is still reported as an instance of class
A
. However, it is impossible to say whether the instance’s name is
a
or
b
, since both names are bound to the same value.
Generally speaking it should not be necessary for your code to “know the names” of particular values. Unless you are deliberately writing introspective programs, this is usually an indication that a change of approach might be beneficial.
In comp.lang.python, Fredrik Lundh once gave an excellent analogy in answer to this question:
The same way as you get the name of that cat you found on your porch: the cat (object) itself cannot tell you its name, and it doesn’t really care – so the only way to find out what it’s called is to ask all your neighbours (namespaces) if it’s their cat (object)…
….and don’t be surprised if you’ll find that it’s known by many names, or no name at all!
Before this syntax was introduced in Python 2.5, a common idiom was to use logical operators:
[expression]and[on_true]or[on_false]
However, this idiom is unsafe, as it can give wrong results when
on_true
has a false boolean value. Therefore, it is always better to use the
...if...else...
form.
Yes. Usually this is done by nesting
lambda
在
lambda
. See the following three examples, slightly adapted from Ulf Bartelt:
fromfunctoolsimportreduce# Primes < 1000print(list(filter(None,map(lambday:y*reduce(lambdax,y:x*y!=0,map(lambdax,y=y:y%x,range(2,int(pow(y,0.5)+1))),1),range(2,1000)))))# First 10 Fibonacci numbersprint(list(map(lambdax,f=lambdax,f:(f(x-1,f)+f(x-2,f))ifx>1else1:f(x,f),range(10))))# Mandelbrot setprint((lambdaRu,Ro,Iu,Io,IM,Sx,Sy:reduce(lambdax,y:x+'\n'+y,map(lambday,Iu=Iu,Io=Io,Ru=Ru,Ro=Ro,Sy=Sy,L=lambdayc,Iu=Iu,Io=Io,Ru=Ru,Ro=Ro,i=IM,Sx=Sx,Sy=Sy:reduce(lambdax,y:x+y,map(lambdax,xc=Ru,yc=yc,Ru=Ru,Ro=Ro,i=i,Sx=Sx,F=lambdaxc,yc,x,y,k,f=lambdaxc,yc,x,y,k,f:(k<=0)or(x*x+y*y>=4.0)or1+f(xc,yc,x*x-y*y+xc,2.0*x*y+yc,k-1,f):f(xc,yc,x,y,k,f):chr(64+F(Ru+x*(Ro-Ru)/Sx,yc,0,0,i)),range(Sx))):L(Iu+y*(Io-Iu)/Sy),range(Sy))))(-2.1,0.7,-1.2,1.2,30,80,24))# \___ ___/ \___ ___/ | | |__ lines on screen# V V | |______ columns on screen# | | |__________ maximum of "iterations"# | |_________________ range on y axis# |____________________________ range on x axis
A slash in the argument list of a function denotes that the parameters prior to it are positional-only. Positional-only parameters are the ones without an externally usable name. Upon calling a function that accepts positional-only parameters, arguments are mapped to parameters based solely on their position. For example,
divmod()
is a function that accepts positional-only parameters. Its documentation looks like this:
>>> help(divmod)Help on built-in function divmod in module builtins:divmod(x, y, /) Return the tuple (x//y, x%y). Invariant: div*y + mod == x.
The slash at the end of the parameter list means that both parameters are positional-only. Thus, calling
divmod()
with keyword arguments would lead to an error:
>>> divmod(x=3,y=4)Traceback (most recent call last):
File "<stdin>", line 1, in <module>TypeError: divmod() takes no keyword arguments
To specify an octal digit, precede the octal value with a zero, and then a lower or uppercase “o”. For example, to set the variable “a” to the octal value “10” (8 in decimal), type:
>>> a=0o10>>> a8
Hexadecimal is just as easy. Simply precede the hexadecimal number with a zero, and then a lower or uppercase “x”. Hexadecimal digits can be specified in lower or uppercase. For example, in the Python interpreter:
It’s primarily driven by the desire that
i%j
have the same sign as
j
. If you want that, and also want:
i==(i//j)*j+(i%j)
then integer division has to return the floor. C also requires that identity to hold, and then compilers that truncate
i//j
need to make
i%j
have the same sign as
i
.
There are few real use cases for
i%j
当
j
is negative. When
j
is positive, there are many, and in virtually all of them it’s more useful for
i%j
到
>=0
. If the clock says 10 now, what did it say 200 hours ago?
-190%12==2
is useful;
-190%12==-10
is a bug waiting to bite.
For integers, use the built-in
int()
type constructor, e.g.
int('144')
==
144
。同样，
float()
converts to a floating-point number, e.g.
float('144')==144.0
.
By default, these interpret the number as decimal, so that
int('0144')==144
holds true, and
int('0x144')
引发
ValueError
.
int(string,
base)
takes the base to convert from as a second optional argument, so
int('0x144',16)==324
. If the base is specified as 0, the number is interpreted using Python’s rules: a leading ‘0o’ indicates octal, and ‘0x’ indicates a hex number.
Do not use the built-in function
eval()
if all you need is to convert strings to numbers.
eval()
will be significantly slower and it presents a security risk: someone could pass you a Python expression that might have unwanted side effects. For example, someone could pass
__import__('os').system("rm-rf$HOME")
which would erase your home directory.
eval()
also has the effect of interpreting numbers as Python expressions, so that e.g.
eval('09')
gives a syntax error because Python does not allow leading ‘0’ in a decimal number (except ‘0’).
To convert, e.g., the number
144
到字符串
'144'
, use the built-in type constructor
str()
. If you want a hexadecimal or octal representation, use the built-in functions
hex()
or
oct()
. For fancy formatting, see the
f-strings
and
格式字符串语法
sections, e.g.
"{:04d}".format(144)
产生
'0144'
and
"{:.3f}".format(1.0/3.0)
产生
'0.333'
.
You can’t, because strings are immutable. In most situations, you should simply construct a new string from the various parts you want to assemble it from. However, if you need an object with the ability to modify in-place unicode data, try using an
io.StringIO
object or the
array
模块：
The best is to use a dictionary that maps strings to functions. The primary advantage of this technique is that the strings do not need to match the names of the functions. This is also the primary technique used to emulate a case construct:
defa():passdefb():passdispatch={'go':a,'stop':b}# Note lack of parens for funcsdispatch[get_input()]()# Note trailing parens to call function
可以使用
S.rstrip("\r\n")
to remove all occurrences of any line terminator from the end of the string
S
without removing other trailing whitespace. If the string
S
represents more than one line, with several empty lines at the end, the line terminators for all the blank lines will be removed:
For simple input parsing, the easiest approach is usually to split the line into whitespace-delimited words using the
split()
method of string objects and then convert decimal strings to numeric values using
int()
or
float()
.
split()
supports an optional “sep” parameter which is useful if the line uses something other than whitespace as a separator.
For more complicated input parsing, regular expressions are more powerful than C’s
sscanf
and better suited for the task.
Note that while a backslash will “escape” a quote for the purposes of determining where the raw string ends, no escaping occurs when interpreting the value of the raw string. That is, the backslash remains present in the value of the raw string:
That’s a tough one, in general. First, here are a list of things to remember before diving further:
Performance characteristics vary across Python implementations. This FAQ focuses on
CPython
.
Behaviour can vary across operating systems, especially when talking about I/O or multi-threading.
You should always find the hot spots in your program
before
attempting to optimize any code (see the
profile
模块)。
Writing benchmark scripts will allow you to iterate quickly when searching for improvements (see the
timeit
模块)。
It is highly recommended to have good code coverage (through unit testing or any other technique) before potentially introducing regressions hidden in sophisticated optimizations.
That being said, there are many tricks to speed up Python code. Here are some general principles which go a long way towards reaching acceptable performance levels:
Making your algorithms faster (or changing to faster ones) can yield much larger benefits than trying to sprinkle micro-optimization tricks all over your code.
Use the right data structures. Study documentation for the
内置类型
和
collections
模块。
When the standard library provides a primitive for doing something, it is likely (although not guaranteed) to be faster than any alternative you may come up with. This is doubly true for primitives written in C, such as builtins and some extension types. For example, be sure to use either the
list.sort()
built-in method or the related
sorted()
function to do sorting (and see the
Sorting Techniques
for examples of moderately advanced usage).
Abstractions tend to create indirections and force the interpreter to work more. If the levels of indirection outweigh the amount of useful work done, your program will be slower. You should avoid excessive abstraction, especially under the form of tiny functions or methods (which are also often detrimental to readability).
If you have reached the limit of what pure Python can allow, there are tools to take you further away. For example,
Cython
can compile a slightly modified version of Python code into a C extension, and can be used on many different platforms. Cython can take advantage of compilation (and optional type annotations) to make your code significantly faster than when interpreted. If you are confident in your C programming skills, you can also
write a C extension module
yourself.
str
and
bytes
objects are immutable, therefore concatenating many strings together is inefficient as each concatenation creates a new object. In the general case, the total runtime cost is quadratic in the total string length.
To accumulate many
str
objects, the recommended idiom is to place them into a list and call
str.join()
at the end:
The type constructor
tuple(seq)
converts any sequence (actually, any iterable) into a tuple with the same items in the same order.
例如，
tuple([1,2,3])
产生
(1,2,3)
and
tuple('abc')
产生
('a','b','c')
. If the argument is a tuple, it does not make a copy but returns the same object, so it is cheap to call
tuple()
when you aren’t sure that an object is already a tuple.
The type constructor
list(seq)
converts any sequence or iterable into a list with the same items in the same order. For example,
list((1,2,3))
产生
[1,2,3]
and
list('abc')
产生
['a','b','c']
. If the argument is a list, it makes a copy just like
seq[:]
would.
Python sequences are indexed with positive numbers and negative numbers. For positive numbers 0 is the first index 1 is the second index and so forth. For negative indices -1 is the last index and -2 is the penultimate (next to last) index and so forth. Think of
seq[-n]
as the same as
seq[len(seq)-n]
.
Using negative indices can be very convenient. For example
S[:-1]
is all of the string except for its last character, which is useful for removing the trailing newline from a string.
As with removing duplicates, explicitly iterating in reverse with a delete condition is one possibility. However, it is easier and faster to use slice replacement with an implicit or explicit forward iteration. Here are three variations.:
Lists are equivalent to C or Pascal arrays in their time complexity; the primary difference is that a Python list can contain objects of many different types.
The
array
module also provides methods for creating arrays of fixed types with compact representations, but they are slower to index than lists. Also note that
NumPy
and other third party packages define array-like structures with various characteristics as well.
To get Lisp-style linked lists, you can emulate
cons cells
using tuples:
lisp_list=("like",("this",("example",None)))
If mutability is desired, you could use lists instead of tuples. Here the analogue of a Lisp
car
is
lisp_list[0]
and the analogue of
cdr
is
lisp_list[1]
. Only do this if you’re sure you really need to, because it’s usually a lot slower than using Python lists.
The reason is that replicating a list with
*
doesn’t create copies, it only creates references to the existing objects. The
*3
creates a list containing 3 references to the same list of length two. Changes to one row will show in all rows, which is almost certainly not what you want.
The suggested approach is to create a list of the desired length first and then fill in each element with a newly created list:
A=[None]*3foriinrange(3):A[i]=[None]*2
This generates a list containing 3 different lists of length two. You can also use a list comprehension:
w,h=2,3A=[[None]*wforiinrange(h)]
Or, you can use an extension that provides a matrix datatype;
NumPy
is the best known.
This is because of a combination of the fact that augmented assignment operators are
assignment
operators, and the difference between mutable and immutable objects in Python.
This discussion applies in general when augmented assignment operators are applied to elements of a tuple that point to mutable objects, but we’ll use a
list
and
+=
as our exemplar.
If you wrote:
>>> a_tuple=(1,2)>>> a_tuple[0]+=1Traceback (most recent call last):...TypeError: 'tuple' object does not support item assignment
The reason for the exception should be immediately clear:
1
is added to the object
a_tuple[0]
points to (
1
), producing the result object,
2
, but when we attempt to assign the result of the computation,
2
, to element
0
of the tuple, we get an error because we can’t change what an element of a tuple points to.
Under the covers, what this augmented assignment statement is doing is approximately this:
>>> result=a_tuple[0]+1>>> a_tuple[0]=resultTraceback (most recent call last):...TypeError: 'tuple' object does not support item assignment
It is the assignment part of the operation that produces the error, since a tuple is immutable.
When you write something like:
>>> a_tuple=(['foo'],'bar')>>> a_tuple[0]+=['item']Traceback (most recent call last):...TypeError: 'tuple' object does not support item assignment
The exception is a bit more surprising, and even more surprising is the fact that even though there was an error, the append worked:
>>> a_tuple[0]['foo', 'item']
To see why this happens, you need to know that (a) if an object implements an
__iadd__()
magic method, it gets called when the
+=
augmented assignment is executed, and its return value is what gets used in the assignment statement; and (b) for lists,
__iadd__()
相当于调用
extend()
on the list and returning the list. That’s why we say that for lists,
+=
is a “shorthand” for
list.extend()
:
>>> a_list=[]>>> a_list+=[1]>>> a_list[1]
这相当于：
>>> result=a_list.__iadd__([1])>>> a_list=result
The object pointed to by a_list has been mutated, and the pointer to the mutated object is assigned back to
a_list
. The end result of the assignment is a no-op, since it is a pointer to the same object that
a_list
was previously pointing to, but the assignment still happens.
Thus, in our tuple example what is happening is equivalent to:
>>> result=a_tuple[0].__iadd__(['item'])>>> a_tuple[0]=resultTraceback (most recent call last):...TypeError: 'tuple' object does not support item assignment
The
__iadd__()
succeeds, and thus the list is extended, but even though
result
points to the same object that
a_tuple[0]
already points to, that final assignment still results in an error, because tuples are immutable.
The technique, attributed to Randal Schwartz of the Perl community, sorts the elements of a list by a metric which maps each element to its “sort value”. In Python, use the
key
argument for the
list.sort()
方法：
A class is the particular object type created by executing a class statement. Class objects are used as templates to create instance objects, which embody both the data (attributes) and code (methods) specific to a datatype.
A class can be based on one or more other classes, called its base class(es). It then inherits the attributes and methods of its base classes. This allows an object model to be successively refined by inheritance. You might have a generic
Mailbox
class that provides basic accessor methods for a mailbox, and subclasses such as
MboxMailbox
,
MaildirMailbox
,
OutlookMailbox
that handle various specific mailbox formats.
Self is merely a conventional name for the first argument of a method. A method defined as
meth(self,a,b,c)
should be called as
x.meth(a,b,c)
对于某些实例
x
of the class in which the definition occurs; the called method will think it is called as
meth(x,a,b,c)
.
使用内置函数
isinstance(obj,cls)
. You can check if an object is an instance of any of a number of classes by providing a tuple instead of a single class, e.g.
isinstance(obj,(class1,class2,...))
, and can also check whether an object is one of Python’s built-in types, e.g.
isinstance(obj,str)
or
isinstance(obj,(int,float,complex))
.
注意，
isinstance()
also checks for virtual inheritance from an
抽象基类
. So, the test will return
True
for a registered class even if hasn’t directly or indirectly inherited from it. To test for “true inheritance”, scan the
MRO
of the class:
>>> c=C()>>> isinstance(c,C)# directTrue>>> isinstance(c,P)# indirectTrue>>> isinstance(c,Mapping)# virtualTrue# Actual inheritance chain>>> type(c).__mro__(<class 'C'>, <class 'P'>, <class 'object'>)# Test for "true inheritance">>> Mappingintype(c).__mro__False
Note that most programs do not use
isinstance()
on user-defined classes very often. If you are developing the classes yourself, a more proper object-oriented style is to define methods on the classes that encapsulate a particular behaviour, instead of checking the object’s class and doing a different thing based on what class it is. For example, if you have a function that does something:
defsearch(obj):ifisinstance(obj,Mailbox):...# code to search a mailboxelifisinstance(obj,Document):...# code to search a documentelif...
A better approach is to define a
search()
method on all the classes and just call it:
classMailbox:defsearch(self):...# code to search a mailboxclassDocument:defsearch(self):...# code to search a documentobj.search()
Delegation is an object oriented technique (also called a design pattern). Let’s say you have an object
x
and want to change the behaviour of just one of its methods. You can create a new class that provides a new implementation of the method you’re interested in changing and delegates all other methods to the corresponding method of
x
.
Python programmers can easily implement delegation. For example, the following class implements a class that behaves like a file but converts all written data to uppercase:
Here the
UpperOut
class redefines the
write()
method to convert the argument string to uppercase before calling the underlying
self._outfile.write()
method. All other methods are delegated to the underlying
self._outfile
object. The delegation is accomplished via the
__getattr__()
method; consult
语言参考
for more information about controlling attribute access.
Note that for more general cases delegation can get trickier. When attributes must be set as well as retrieved, the class must define a
__setattr__()
method too, and it must do so carefully. The basic implementation of
__setattr__()
is roughly equivalent to the following:
In the example,
super()
will automatically determine the instance from which it was called (the
self
value), look up the
方法分辨次序
(MRO) with
type(self).__mro__
, and return the next in line after
Derived
in the MRO:
Base
.
You could assign the base class to an alias and derive from the alias. Then all you have to change is the value assigned to the alias. Incidentally, this trick is also handy if you want to decide dynamically (e.g. depending on availability of resources) which base class to use. Example:
Both static data and static methods (in the sense of C++ or Java) are supported in Python.
For static data, simply define a class attribute. To assign a new value to the attribute, you have to explicitly use the class name in the assignment:
classC:count=0# number of times C.__init__ calleddef__init__(self):C.count=C.count+1defgetcount(self):returnC.count# or return self.count
c.count
also refers to
C.count
for any
c
这样
isinstance(c,
C)
holds, unless overridden by
c
itself or by some class on the base-class search path from
c.__class__
back to
C
.
Caution: within a method of C, an assignment like
self.count=42
creates a new and unrelated instance named “count” in
self
’s own dict. Rebinding of a class-static data name must always specify the class whether inside a method or not:
C.count=314
Static methods are possible:
classC:@staticmethoddefstatic(arg1,arg2,arg3):# No 'self' parameter!...
However, a far more straightforward way to get the effect of a static method is via a simple module-level function:
defgetcount():returnC.count
If your code is structured so as to define one class (or tightly related class hierarchy) per module, this supplies the desired encapsulation.
Variable names with double leading underscores are “mangled” to provide a simple but effective way to define class private variables. Any identifier of the form
__spam
(at least two leading underscores, at most one trailing underscore) is textually replaced with
_classname__spam
，其中
classname
is the current class name with any leading underscores stripped.
The identifier can be used unchanged within the class, but to access it outside the class, the mangled name must be used:
In particular, this does not guarantee privacy since an outside user can still deliberately access the private attribute; many Python programmers never bother to use private variable names at all.
The
del
statement does not necessarily call
__del__()
– it simply decrements the object’s reference count, and if this reaches zero
__del__()
被调用。
If your data structures contain circular links (e.g. a tree where each child has a parent reference and each parent has a list of children) the reference counts will never go back to zero. Once in a while Python runs an algorithm to detect such cycles, but the garbage collector might run some time after the last reference to your data structure vanishes, so your
__del__()
method may be called at an inconvenient and random time. This is inconvenient if you’re trying to reproduce a problem. Worse, the order in which object’s
__del__()
methods are executed is arbitrary. You can run
gc.collect()
to force a collection, but there
are
pathological cases where objects will never be collected.
Despite the cycle collector, it’s still a good idea to define an explicit
close()
method on objects to be called whenever you’re done with them. The
close()
method can then remove attributes that refer to subobjects. Don’t call
__del__()
directly –
__del__()
should call
close()
and
close()
should make sure that it can be called more than once for the same object.
Another way to avoid cyclical references is to use the
weakref
module, which allows you to point to objects without incrementing their reference count. Tree data structures, for instance, should use weak references for their parent and sibling references (if they need them!).
Finally, if your
__del__()
method raises an exception, a warning message is printed to
sys.stderr
.
Python does not keep track of all instances of a class (or of a built-in type). You can program the class’s constructor to keep track of all instances by keeping a list of weak references to each instance.
The
id()
builtin returns an integer that is guaranteed to be unique during the lifetime of the object. Since in CPython, this is the object’s memory address, it happens frequently that after an object is deleted from memory, the next freshly created object is allocated at the same position in memory. This is illustrated by this example:
>>> id(1000)13901272>>> id(2000)13901272
The two ids belong to different integer objects that are created before, and deleted immediately after execution of the
id()
call. To be sure that objects whose id you want to examine are still alive, create another reference to the object:
The
is
operator tests for object identity. The test
aisb
相当于
id(a)==id(b)
.
The most important property of an identity test is that an object is always identical to itself,
aisa
始终返回
True
. Identity tests are usually faster than equality tests. And unlike equality tests, identity tests are guaranteed to return a boolean
True
or
False
.
However, identity tests can
only
be substituted for equality tests when object identity is assured. Generally, there are three circumstances where identity is guaranteed:
1) Assignments create new names but do not change object identity. After the assignment
new=old
, it is guaranteed that
newisold
.
2) Putting an object in a container that stores object references does not change object identity. After the list assignment
s[0]=x
, it is guaranteed that
s[0]isx
.
3) If an object is a singleton, it means that only one instance of that object can exist. After the assignments
a=None
and
b=None
, it is guaranteed that
aisb
因为
None
is a singleton.
In most other circumstances, identity tests are inadvisable and equality tests are preferred. In particular, identity tests should not be used to check constants such as
int
and
str
which aren’t guaranteed to be singletons:
Likewise, new instances of mutable containers are never identical:
>>> a=[]>>> b=[]>>> aisbFalse
In the standard library code, you will see several common patterns for correctly using identity tests:
1) As recommended by
PEP 8
, an identity test is the preferred way to check for
None
. This reads like plain English in code and avoids confusion with other objects that may have boolean values that evaluate to false.
2) Detecting optional arguments can be tricky when
None
is a valid input value. In those situations, you can create a singleton sentinel object guaranteed to be distinct from other objects. For example, here is how to implement a method that behaves like
dict.pop()
:
3) Container implementations sometimes need to augment equality tests with identity tests. This prevents the code from being confused by objects such as
float('NaN')
that are not equal to themselves.
For example, here is the implementation of
collections.abc.Sequence.__contains__()
:
When subclassing an immutable type, override the
__new__()
method instead of the
__init__()
method. The latter only runs
after
an instance is created, which is too late to alter data in an immutable instance.
All of these immutable classes have a different signature than their parent class:
fromdatetimeimportdateclassFirstOfMonthDate(date):"Always choose the first day of the month"def__new__(cls,year,month,day):returnsuper().__new__(cls,year,month,1)classNamedInt(int):"Allow text names for some numbers"xlat={'zero':0,'one':1,'ten':10}def__new__(cls,value):value=cls.xlat.get(value,value)returnsuper().__new__(cls,value)classTitleStr(str):"Convert str to name suitable for a URL path"def__new__(cls,s):s=s.lower().replace(' ','-')s=''.join([cforcinsifc.isalnum()orc=='-'])returnsuper().__new__(cls,s)
The
cached_property
approach only works with methods that do not take any arguments. It does not create a reference to the instance. The cached method result will be kept only as long as the instance is alive.
The advantage is that when an instance is no longer used, the cached method result will be released right away. The disadvantage is that if instances accumulate, so too will the accumulated method results. They can grow without bound.
The
lru_cache
approach works with methods that have
hashable
arguments. It creates a reference to the instance unless special efforts are made to pass in weak references.
The advantage of the least recently used algorithm is that the cache is bounded by the specified
maxsize
. The disadvantage is that instances are kept alive until they age out of the cache or until the cache is cleared.
This example shows the various techniques:
classWeather:"Lookup weather information on a government website"def__init__(self,station_id):self._station_id=station_id# The _station_id is private and immutabledefcurrent_temperature(self):"Latest hourly observation"# Do not cache this because old results# can be out of date.@cached_propertydeflocation(self):"Return the longitude/latitude coordinates of the station"# Result only depends on the station_id@lru_cache(maxsize=20)defhistoric_rainfall(self,date,units='mm'):"Rainfall on a given date"# Depends on the station_id, date, and units.
The above example assumes that the
station_id
never changes. If the relevant instance attributes are mutable, the
cached_property
approach can’t be made to work because it cannot detect changes to the attributes.
To make the
lru_cache
approach work when the
station_id
is mutable, the class needs to define the
__eq__()
and
__hash__()
methods so that the cache can detect relevant attribute updates:
classWeather:"Example with a mutable station identifier"def__init__(self,station_id):self.station_id=station_iddefchange_station(self,station_id):self.station_id=station_iddef__eq__(self,other):returnself.station_id==other.station_iddef__hash__(self):returnhash(self.station_id)@lru_cache(maxsize=20)defhistoric_rainfall(self,date,units='cm'):'Rainfall on a given date'# Depends on the station_id, date, and units.
When a module is imported for the first time (or when the source file has changed since the current compiled file was created) a
.pyc
file containing the compiled code should be created in a
__pycache__
subdirectory of the directory containing the
.py
file. The
.pyc
file will have a filename that starts with the same name as the
.py
file, and ends with
.pyc
, with a middle component that depends on the particular
python
binary that created it. (See
PEP 3147
了解细节。)
One reason that a
.pyc
file may not be created is a permissions problem with the directory containing the source file, meaning that the
__pycache__
subdirectory cannot be created. This can happen, for example, if you develop as one user but run as another, such as if you are testing with a web server.
除非
PYTHONDONTWRITEBYTECODE
environment variable is set, creation of a .pyc file is automatic if you’re importing a module and Python has the ability (permissions, free space, etc…) to create a
__pycache__
subdirectory and write the compiled module to that subdirectory.
Running Python on a top level script is not considered an import and no
.pyc
will be created. For example, if you have a top-level module
foo.py
that imports another module
xyz.py
, when you run
foo
(by typing
pythonfoo.py
as a shell command), a
.pyc
will be created for
xyz
因为
xyz
is imported, but no
.pyc
file will be created for
foo
since
foo.py
isn’t being imported.
If you need to create a
.pyc
file for
foo
– that is, to create a
.pyc
file for a module that is not imported – you can, using the
py_compile
and
compileall
模块。
The
py_compile
module can manually compile any module. One way is to use the
compile()
function in that module interactively:
This will write the
.pyc
到
__pycache__
subdirectory in the same location as
foo.py
(or you can override that with the optional parameter
cfile
).
You can also automatically compile all files in a directory or directories using the
compileall
module. You can do it from the shell prompt by running
compileall.py
and providing the path of a directory containing Python files to compile:
A module can find out its own module name by looking at the predefined global variable
__name__
. If this has the value
'__main__'
, the program is running as a script. Many modules that are usually used by importing them also provide a command-line interface or a self-test, and only execute this code after checking
__name__
:
The problem is that the interpreter will perform the following steps:
main imports
foo
Empty globals for
foo
are created
foo
is compiled and starts executing
foo
imports
bar
Empty globals for
bar
are created
bar
is compiled and starts executing
bar
imports
foo
(which is a no-op since there already is a module named
foo
)
The import mechanism tries to read
foo_var
from
foo
globals, to set
bar.foo_var=foo.foo_var
The last step fails, because Python isn’t done with interpreting
foo
yet and the global symbol dictionary for
foo
is still empty.
The same thing happens when you use
importfoo
, and then try to access
foo.foo_var
in global code.
There are (at least) three possible workarounds for this problem.
Guido van Rossum recommends avoiding all uses of
from<module>import...
, and placing all code inside functions. Initializations of global variables and class variables should use constants or built-in functions only. This means everything from an imported module is referenced as
<module>.<name>
.
Jim Roskind suggests performing steps in the following order in each module:
exports (globals, functions, and classes that don’t need imported base classes)
import
statements
active code (including globals that are initialized from imported values).
Van Rossum doesn’t like this approach much because the imports appear in a strange place, but it does work.
Matthias Urlichs recommends restructuring your code so that the recursive import is not necessary in the first place.
For reasons of efficiency as well as consistency, Python only reads the module file on the first time a module is imported. If it didn’t, in a program consisting of many modules where each one imports the same basic module, the basic module would be parsed and re-parsed many times. To force re-reading of a changed module, do this:
Warning: this technique is not 100% fool-proof. In particular, modules containing statements like
frommodnameimportsome_objects
will continue to work with the old version of the imported objects. If the module contains class definitions, existing class instances will
not
be updated to use the new class definition. This can result in the following paradoxical behaviour:
>>> importimportlib>>> importcls>>> c=cls.C()# Create an instance of C>>> importlib.reload(cls)<module 'cls' from 'cls.py'>>>> isinstance(c,cls.C)# isinstance is false?!?False
The nature of the problem is made clear if you print out the “identity” of the class objects:
