Python 实用宝典

Question 1

What I want is this behavior:

class a:
    list = []

x = a()
y = a()

x.list.append(1)
y.list.append(2)
x.list.append(3)
y.list.append(4)

print(x.list) # prints [1, 3]
print(y.list) # prints [2, 4]

Of course, what really happens when I print is:

print(x.list) # prints [1, 2, 3, 4]
print(y.list) # prints [1, 2, 3, 4]

Clearly they are sharing the data in class a. How do I get separate instances to achieve the behavior I desire?

Question 2

You want this:

class a:
    def __init__(self):
        self.list = []

Declaring the variables inside the class declaration makes them “class” members and not instance members. Declaring them inside the __init__ method makes sure that a new instance of the members is created alongside every new instance of the object, which is the behavior you’re looking for.

Question 3

The accepted answer works but a little more explanation does not hurt.

Class attributes do not become instance attributes when an instance is created. They become instance attributes when a value is assigned to them.

In the original code no value is assigned to list attribute after instantiation; so it remains a class attribute. Defining list inside __init__ works because __init__ is called after instantiation. Alternatively, this code would also produce the desired output:

>>> class a:
    list = []

>>> y = a()
>>> x = a()
>>> x.list = []
>>> y.list = []
>>> x.list.append(1)
>>> y.list.append(2)
>>> x.list.append(3)
>>> y.list.append(4)
>>> print(x.list)
[1, 3]
>>> print(y.list)
[2, 4]

However, the confusing scenario in the question will never happen to immutable objects such as numbers and strings, because their value cannot be changed without assignment. For example a code similar to the original with string attribute type works without any problem:

>>> class a:
    string = ''


>>> x = a()
>>> y = a()
>>> x.string += 'x'
>>> y.string += 'y'
>>> x.string
'x'
>>> y.string
'y'

So to summarize: class attributes become instance attributes if and only if a value is assigned to them after instantiation, being in the __init__ method or not. This is a good thing because this way you can have static attributes if you never assign a value to an attribute after instantiation.

Question 4

You declared “list” as a “class level property” and not “instance level property”. In order to have properties scoped at the instance level, you need to initialize them through referencing with the “self” parameter in the __init__ method (or elsewhere depending on the situation).

You don’t strictly have to initialize the instance properties in the __init__ method but it makes for easier understanding.

Question 5

Although the accepted anwer is spot on, I would like to add a bit description.

Let’s do a small exercise

first of all define a class as follows:

class A:
    temp = 'Skyharbor'

    def __init__(self, x):
        self.x = x

    def change(self, y):
        self.temp = y

So what do we have here?

We have a very simple class which has an attribute temp which is a string
An __init__ method which sets self.x
A change method sets self.temp

Pretty straight forward so far yeah? Now let’s start playing around with this class. Let’s initialize this class first:

a = A('Tesseract')

Now do the following:

>>> print(a.temp)
Skyharbor
>>> print(A.temp)
Skyharbor

Well, a.temp worked as expected but how the hell did A.temp work? Well it worked because temp is a class attribute. Everything in python is an object. Here A is also an object of class type. Thus the attribute temp is an attribute held by the A class and if you change the value of temp through A (and not through an instance of a), the changed value is going to be reflected in all the instance of A class. Let’s go ahead and do that:

>>> A.temp = 'Monuments'
>>> print(A.temp)
Monuments
>>> print(a.temp)
Monuments

Interesting isn’t it? And note that id(a.temp) and id(A.temp) are still the same.

Any Python object is automatically given a __dict__ attribute, which contains its list of attributes. Let’s investigate what this dictionary contains for our example objects:

>>> print(A.__dict__)
{
    'change': <function change at 0x7f5e26fee6e0>,
    '__module__': '__main__',
    '__init__': <function __init__ at 0x7f5e26fee668>,
    'temp': 'Monuments',
    '__doc__': None
}
>>> print(a.__dict__)
{x: 'Tesseract'}

Note that temp attribute is listed among A class’s attributes while x is listed for the instance.

So how come that we get a defined value of a.temp if it is not even listed for the instance a. Well that’s the magic of __getattribute__() method. In Python the dotted syntax automatically invokes this method so when we write a.temp, Python executes a.__getattribute__('temp'). That method performs the attribute lookup action, i.e. finds the value of the attribute by looking in different places.

The standard implementation of __getattribute__() searches first the internal dictionary (dict) of an object, then the type of the object itself. In this case a.__getattribute__('temp') executes first a.__dict__['temp'] and then a.__class__.__dict__['temp']

Okay now let’s use our change method:

>>> a.change('Intervals')
>>> print(a.temp)
Intervals
>>> print(A.temp)
Monuments

Well now that we have used self, print(a.temp) gives us a different value from print(A.temp).

Now if we compare id(a.temp) and id(A.temp), they will be different.

Question 6

Yes you must declare in the “constructor” if you want that the list becomes an object property and not a class property.

Question 7

So nearly every response here seems to miss a particular point. Class variables never become instance variables as demonstrated by the code below. By utilizing a metaclass to intercept variable assignment at the class level, we can see that when a.myattr is reassigned, the field assignment magic method on the class is not called. This is because the assignment creates a new instance variable. This behavior has absolutely nothing to do with the class variable as demonstrated by the second class which has no class variables and yet still allows field assignment.

class mymeta(type):
    def __init__(cls, name, bases, d):
        pass

    def __setattr__(cls, attr, value):
        print("setting " + attr)
        super(mymeta, cls).__setattr__(attr, value)

class myclass(object):
    __metaclass__ = mymeta
    myattr = []

a = myclass()
a.myattr = []           #NOTHING IS PRINTED
myclass.myattr = [5]    #change is printed here
b = myclass()
print(b.myattr)         #pass through lookup on the base class

class expando(object):
    pass

a = expando()
a.random = 5            #no class variable required
print(a.random)         #but it still works

IN SHORT Class variables have NOTHING to do with instance variables.

More clearly They just happen to be in the scope for lookups on instances. Class variables are in fact instance variables on the class object itself. You can also have metaclass variables if you want as well because metaclasses themselves are objects too. Everything is an object whether it is used to create other objects or not, so do not get bound up in the semantics of other languages usage of the word class. In python, a class is really just an object that is used to determine how to create other objects and what their behaviors will be. Metaclasses are classes that create classes, just to further illustrate this point.

Question 8

To protect your variable shared by other instance you need to create new instance variable each time you create an instance. When you are declaring a variable inside a class it’s class variable and shared by all instance. If you want to make it for instance wise need to use the init method to reinitialize the variable as refer to the instance

From Python Objects and Class by Programiz.com:

__init__() function. This special function gets called whenever a new object of that class is instantiated.

This type of function is also called constructors in Object Oriented Programming (OOP). We normally use it to initialize all the variables.

For example:

class example:
    list=[] #This is class variable shared by all instance
    def __init__(self):
        self.list = [] #This is instance variable referred to specific instance

Question 9

With PEP 557 data classes are introduced into python standard library.

They make use of the @dataclass decorator and they are supposed to be “mutable namedtuples with default” but I’m not really sure I understand what this actually means and how they are different from common classes.

What exactly are python data classes and when is it best to use them?

Question 10

Data classes are just regular classes that are geared towards storing state, more than contain a lot of logic. Every time you create a class that mostly consists of attributes you made a data class.

What the dataclasses module does is make it easier to create data classes. It takes care of a lot of boiler plate for you.

This is especially important when your data class must be hashable; this requires a __hash__ method as well as an __eq__ method. If you add a custom __repr__ method for ease of debugging, that can become quite verbose:

class InventoryItem:
    '''Class for keeping track of an item in inventory.'''
    name: str
    unit_price: float
    quantity_on_hand: int = 0

    def __init__(
            self, 
            name: str, 
            unit_price: float,
            quantity_on_hand: int = 0
        ) -> None:
        self.name = name
        self.unit_price = unit_price
        self.quantity_on_hand = quantity_on_hand

    def total_cost(self) -> float:
        return self.unit_price * self.quantity_on_hand

    def __repr__(self) -> str:
        return (
            'InventoryItem('
            f'name={self.name!r}, unit_price={self.unit_price!r}, '
            f'quantity_on_hand={self.quantity_on_hand!r})'

    def __hash__(self) -> int:
        return hash((self.name, self.unit_price, self.quantity_on_hand))

    def __eq__(self, other) -> bool:
        if not isinstance(other, InventoryItem):
            return NotImplemented
        return (
            (self.name, self.unit_price, self.quantity_on_hand) == 
            (other.name, other.unit_price, other.quantity_on_hand))

With dataclasses you can reduce it to:

from dataclasses import dataclass

@dataclass(unsafe_hash=True)
class InventoryItem:
    '''Class for keeping track of an item in inventory.'''
    name: str
    unit_price: float
    quantity_on_hand: int = 0

    def total_cost(self) -> float:
        return self.unit_price * self.quantity_on_hand

The same class decorator can also generate comparison methods (__lt__, __gt__, etc.) and handle immutability.

namedtuple classes are also data classes, but are immutable by default (as well as being sequences). dataclasses are much more flexible in this regard, and can easily be structured such that they can fill the same role as a namedtuple class.

The PEP was inspired by the attrs project, which can do even more (including slots, validators, converters, metadata, etc.).

If you want to see some examples, I recently used dataclasses for several of my Advent of Code solutions, see the solutions for day 7, day 8, day 11 and day 20.

If you want to use dataclasses module in Python versions < 3.7, then you could install the backported module (requires 3.6) or use the attrs project mentioned above.

Question 11

Overview

The question has been addressed. However, this answer adds some practical examples to aid in the basic understanding of dataclasses.

What exactly are python data classes and when is it best to use them?

code generators: generate boilerplate code; you can choose to implement special methods in a regular class or have a dataclass implement them automatically.
data containers: structures that hold data (e.g. tuples and dicts), often with dotted, attribute access such as classes, namedtuple and others.

“mutable namedtuples with default[s]”

Here is what the latter phrase means:

mutable: by default, dataclass attributes can be reassigned. You can optionally make them immutable (see Examples below).
namedtuple: you have dotted, attribute access like a namedtuple or a regular class.
default: you can assign default values to attributes.

Compared to common classes, you primarily save on typing boilerplate code.

Features

This is an overview of dataclass features (TL;DR? See the Summary Table in the next section).

What you get

Here are features you get by default from dataclasses.

Attributes + Representation + Comparison

import dataclasses


@dataclasses.dataclass
#@dataclasses.dataclass()                                       # alternative
class Color:
    r : int = 0
    g : int = 0
    b : int = 0

These defaults are provided by automatically setting the following keywords to True:

@dataclasses.dataclass(init=True, repr=True, eq=True)

What you can turn on

Additional features are available if the appropriate keywords are set to True.

Order

@dataclasses.dataclass(order=True)
class Color:
    r : int = 0
    g : int = 0
    b : int = 0

The ordering methods are now implemented (overloading operators: < > <= >=), similarly to functools.total_ordering with stronger equality tests.

Hashable, Mutable

@dataclasses.dataclass(unsafe_hash=True)                        # override base `__hash__`
class Color:
    ...

Although the object is potentially mutable (possibly undesired), a hash is implemented.

Hashable, Immutable

@dataclasses.dataclass(frozen=True)                             # `eq=True` (default) to be immutable 
class Color:
    ...

A hash is now implemented and changing the object or assigning to attributes is disallowed.

Overall, the object is hashable if either unsafe_hash=True or frozen=True.

See also the original hashing logic table with more details.

What you don’t get

To get the following features, special methods must be manually implemented:

Unpacking

@dataclasses.dataclass
class Color:
    r : int = 0
    g : int = 0
    b : int = 0

    def __iter__(self):
        yield from dataclasses.astuple(self)

Optimization

@dataclasses.dataclass
class SlottedColor:
    __slots__ = ["r", "b", "g"]
    r : int
    g : int
    b : int

The object size is now reduced:

>>> imp sys
>>> sys.getsizeof(Color)
1056
>>> sys.getsizeof(SlottedColor)
888

In some circumstances, __slots__ also improves the speed of creating instances and accessing attributes. Also, slots do not allow default assignments; otherwise, a ValueError is raised.

See more on slots in this blog post.

Summary Table

+----------------------+----------------------+----------------------------------------------------+-----------------------------------------+
|       Feature        |       Keyword        |                      Example                       |           Implement in a Class          |
+----------------------+----------------------+----------------------------------------------------+-----------------------------------------+
| Attributes           |  init                |  Color().r -> 0                                    |  __init__                               |
| Representation       |  repr                |  Color() -> Color(r=0, g=0, b=0)                   |  __repr__                               |
| Comparision*         |  eq                  |  Color() == Color(0, 0, 0) -> True                 |  __eq__                                 |
|                      |                      |                                                    |                                         |
| Order                |  order               |  sorted([Color(0, 50, 0), Color()]) -> ...         |  __lt__, __le__, __gt__, __ge__         |
| Hashable             |  unsafe_hash/frozen  |  {Color(), {Color()}} -> {Color(r=0, g=0, b=0)}    |  __hash__                               |
| Immutable            |  frozen + eq         |  Color().r = 10 -> TypeError                       |  __setattr__, __delattr__               |
|                      |                      |                                                    |                                         |
| Unpacking+           |  -                   |  r, g, b = Color()                                 |   __iter__                              |
| Optimization+        |  -                   |  sys.getsizeof(SlottedColor) -> 888                |  __slots__                              |
+----------------------+----------------------+----------------------------------------------------+-----------------------------------------+

_{⁺These methods are not automatically generated and require manual implementation in a dataclass.}

_{^* __ne__ is not needed and thus not implemented.}

Additional features

Post-initialization

@dataclasses.dataclass
class RGBA:
    r : int = 0
    g : int = 0
    b : int = 0
    a : float = 1.0

    def __post_init__(self):
        self.a : int =  int(self.a * 255)


RGBA(127, 0, 255, 0.5)
# RGBA(r=127, g=0, b=255, a=127)

Inheritance

@dataclasses.dataclass
class RGBA(Color):
    a : int = 0

Conversions

Convert a dataclass to a tuple or a dict, recursively:

>>> dataclasses.astuple(Color(128, 0, 255))
(128, 0, 255)
>>> dataclasses.asdict(Color(128, 0, 255))
{'r': 128, 'g': 0, 'b': 255}

Limitations

Lacks mechanisms to handle starred arguments
Working with nested dataclasses can be complicated

References

R. Hettinger’s talk on Dataclasses: The code generator to end all code generators
T. Hunner’s talk on Easier Classes: Python Classes Without All the Cruft
Python’s documentation on hashing details
Real Python’s guide on The Ultimate Guide to Data Classes in Python 3.7
A. Shaw’s blog post on A brief tour of Python 3.7 data classes
E. Smith’s github repository on dataclasses

Question 12

From the PEP specification:

A class decorator is provided which inspects a class definition for variables with type annotations as defined in PEP 526, “Syntax for Variable Annotations”. In this document, such variables are called fields. Using these fields, the decorator adds generated method definitions to the class to support instance initialization, a repr, comparison methods, and optionally other methods as described in the Specification section. Such a class is called a Data Class, but there’s really nothing special about the class: the decorator adds generated methods to the class and returns the same class it was given.

The @dataclass generator adds methods to the class that you’d otherwise define yourself like __repr__, __init__, __lt__, and __gt__.

Question 13

Consider this simple class Foo

from dataclasses import dataclass
@dataclass
class Foo:    
    def bar():
        pass

Here is the dir() built-in comparison. On the left-hand side is the Foo without the @dataclass decorator, and on the right is with the @dataclass decorator.

Here is another diff, after using the inspect module for comparison.

Question 14

Can one write something like:

class Test(object):
    def _decorator(self, foo):
        foo()

    @self._decorator
    def bar(self):
        pass

This fails: self in @self is unknown

I also tried:

@Test._decorator(self)

which also fails: Test unknown

I would like to temporarily change some instance variables in the decorator and then run the decorated method, before changing them back.

Question 15

Would something like this do what you need?

class Test(object):
    def _decorator(foo):
        def magic( self ) :
            print "start magic"
            foo( self )
            print "end magic"
        return magic

    @_decorator
    def bar( self ) :
        print "normal call"

test = Test()

test.bar()

This avoids the call to self to access the decorator and leaves it hidden in the class namespace as a regular method.

>>> import stackoverflow
>>> test = stackoverflow.Test()
>>> test.bar()
start magic
normal call
end magic
>>>

edited to answer question in comments:

How to use the hidden decorator in another class

class Test(object):
    def _decorator(foo):
        def magic( self ) :
            print "start magic"
            foo( self )
            print "end magic"
        return magic

    @_decorator
    def bar( self ) :
        print "normal call"

    _decorator = staticmethod( _decorator )

class TestB( Test ):
    @Test._decorator
    def bar( self ):
        print "override bar in"
        super( TestB, self ).bar()
        print "override bar out"

print "Normal:"
test = Test()
test.bar()
print

print "Inherited:"
b = TestB()
b.bar()
print

Output:

Normal:
start magic
normal call
end magic

Inherited:
start magic
override bar in
start magic
normal call
end magic
override bar out
end magic

Question 16

What you’re wanting to do isn’t possible. Take, for instance, whether or not the code below looks valid:

class Test(object):

    def _decorator(self, foo):
        foo()

    def bar(self):
        pass
    bar = self._decorator(bar)

It, of course, isn’t valid since self isn’t defined at that point. The same goes for Test as it won’t be defined until the class itself is defined (which its in the process of). I’m showing you this code snippet because this is what your decorator snippet transforms into.

So, as you can see, accessing the instance in a decorator like that isn’t really possible since decorators are applied during the definition of whatever function/method they are attached to and not during instantiation.

If you need class-level access, try this:

class Test(object):

    @classmethod
    def _decorator(cls, foo):
        foo()

    def bar(self):
        pass
Test.bar = Test._decorator(Test.bar)

Question 17

import functools


class Example:

    def wrapper(func):
        @functools.wraps(func)
        def wrap(self, *args, **kwargs):
            print("inside wrap")
            return func(self, *args, **kwargs)
        return wrap

    @wrapper
    def method(self):
        print("METHOD")

    wrapper = staticmethod(wrapper)


e = Example()
e.method()

Question 18

I use this type of decorator in some debugging situations, it allows overriding class properties by decorating, without having to find the calling function.

class myclass(object):
    def __init__(self):
        self.property = "HELLO"

    @adecorator(property="GOODBYE")
    def method(self):
        print self.property

Here is the decorator code

class adecorator (object):
    def __init__ (self, *args, **kwargs):
        # store arguments passed to the decorator
        self.args = args
        self.kwargs = kwargs

    def __call__(self, func):
        def newf(*args, **kwargs):

            #the 'self' for a method function is passed as args[0]
            slf = args[0]

            # replace and store the attributes
            saved = {}
            for k,v in self.kwargs.items():
                if hasattr(slf, k):
                    saved[k] = getattr(slf,k)
                    setattr(slf, k, v)

            # call the method
            ret = func(*args, **kwargs)

            #put things back
            for k,v in saved.items():
                setattr(slf, k, v)

            return ret
        newf.__doc__ = func.__doc__
        return newf

Note: because I’ve used a class decorator you’ll need to use @adecorator() with the brackets on to decorate functions, even if you don’t pass any arguments to the decorator class constructor.

Question 19

This is one way to access(and have used) self from inside a decorator defined inside the same class:

class Thing(object):
    def __init__(self, name):
        self.name = name

    def debug_name(function):
        def debug_wrapper(*args):
            self = args[0]
            print 'self.name = ' + self.name
            print 'running function {}()'.format(function.__name__)
            function(*args)
            print 'self.name = ' + self.name
        return debug_wrapper

    @debug_name
    def set_name(self, new_name):
        self.name = new_name

Output (tested on Python 2.7.10):

>>> a = Thing('A')
>>> a.name
'A'
>>> a.set_name('B')
self.name = A
running function set_name()
self.name = B
>>> a.name
'B'

The example above is silly, but it works.

Question 20

I found this question while researching a very similar problem. My solution is to split the problem into two parts. First, you need to capture the data that you want to associate with the class methods. In this case, handler_for will associate a Unix command with handler for that command’s output.

class OutputAnalysis(object):
    "analyze the output of diagnostic commands"
    def handler_for(name):
        "decorator to associate a function with a command"
        def wrapper(func):
            func.handler_for = name
            return func
        return wrapper
    # associate mount_p with 'mount_-p.txt'
    @handler_for('mount -p')
    def mount_p(self, slurped):
        pass

Now that we’ve associated some data with each class method, we need to gather that data and store it in a class attribute.

OutputAnalysis.cmd_handler = {}
for value in OutputAnalysis.__dict__.itervalues():
    try:
        OutputAnalysis.cmd_handler[value.handler_for] = value
    except AttributeError:
        pass

Question 21

Here’s an expansion on Michael Speer’s answer to take it a few steps further:

An instance method decorator which takes arguments and acts on a function with arguments and a return value.

class Test(object):
    "Prints if x == y. Throws an error otherwise."
    def __init__(self, x):
        self.x = x

    def _outer_decorator(y):
        def _decorator(foo):
            def magic(self, *args, **kwargs) :
                print("start magic")
                if self.x == y:
                    return foo(self, *args, **kwargs)
                else:
                    raise ValueError("x ({}) != y ({})".format(self.x, y))
                print("end magic")
            return magic

        return _decorator

    @_outer_decorator(y=3)
    def bar(self, *args, **kwargs) :
        print("normal call")
        print("args: {}".format(args))
        print("kwargs: {}".format(kwargs))

        return 27

And then

In [2]:

    test = Test(3)
    test.bar(
        13,
        'Test',
        q=9,
        lollipop=[1,2,3]
    )
    
    start magic
    normal call
    args: (13, 'Test')
    kwargs: {'q': 9, 'lollipop': [1, 2, 3]}
Out[2]:
    27
In [3]:

    test = Test(4)
    test.bar(
        13,
        'Test',
        q=9,
        lollipop=[1,2,3]
    )
    
    start magic
    ---------------------------------------------------------------------------
    ValueError                                Traceback (most recent call last)
    <ipython-input-3-576146b3d37e> in <module>()
          4     'Test',
          5     q=9,
    ----> 6     lollipop=[1,2,3]
          7 )

    <ipython-input-1-428f22ac6c9b> in magic(self, *args, **kwargs)
         11                     return foo(self, *args, **kwargs)
         12                 else:
    ---> 13                     raise ValueError("x ({}) != y ({})".format(self.x, y))
         14                 print("end magic")
         15             return magic

    ValueError: x (4) != y (3)

Question 22

Decorators seem better suited to modify the functionality of an entire object (including function objects) versus the functionality of an object method which in general will depend on instance attributes. For example:

def mod_bar(cls):
    # returns modified class

    def decorate(fcn):
        # returns decorated function

        def new_fcn(self):
            print self.start_str
            print fcn(self)
            print self.end_str

        return new_fcn

    cls.bar = decorate(cls.bar)
    return cls

@mod_bar
class Test(object):
    def __init__(self):
        self.start_str = "starting dec"
        self.end_str = "ending dec" 

    def bar(self):
        return "bar"

The output is:

>>> import Test
>>> a = Test()
>>> a.bar()
starting dec
bar
ending dec

Question 23

You can decorate the decorator:

import decorator

class Test(object):
    @decorator.decorator
    def _decorator(foo, self):
        foo(self)

    @_decorator
    def bar(self):
        pass

Question 24

I have a Implementation of Decorators that Might Help

    import functools
    import datetime


    class Decorator(object):

        def __init__(self):
            pass


        def execution_time(func):

            @functools.wraps(func)
            def wrap(self, *args, **kwargs):

                """ Wrapper Function """

                start = datetime.datetime.now()
                Tem = func(self, *args, **kwargs)
                end = datetime.datetime.now()
                print("Exection Time:{}".format(end-start))
                return Tem

            return wrap


    class Test(Decorator):

        def __init__(self):
            self._MethodName = Test.funca.__name__

        @Decorator.execution_time
        def funca(self):
            print("Running Function : {}".format(self._MethodName))
            return True


    if __name__ == "__main__":
        obj = Test()
        data = obj.funca()
        print(data)

Question 25

Declare in inner class. This solution is pretty solid and recommended.

class Test(object):
    class Decorators(object):
    @staticmethod
    def decorator(foo):
        def magic(self, *args, **kwargs) :
            print("start magic")
            foo(self, *args, **kwargs)
            print("end magic")
        return magic

    @Decorators.decorator
    def bar( self ) :
        print("normal call")

test = Test()

test.bar()

The result:

>>> test = Test()
>>> test.bar()
start magic
normal call
end magic
>>>

Question 26

I know that Python does not support method overloading, but I’ve run into a problem that I can’t seem to solve in a nice Pythonic way.

I am making a game where a character needs to shoot a variety of bullets, but how do I write different functions for creating these bullets? For example suppose I have a function that creates a bullet travelling from point A to B with a given speed. I would write a function like this:

    def add_bullet(sprite, start, headto, speed):
        ... Code ...

But I want to write other functions for creating bullets like:

    def add_bullet(sprite, start, direction, speed):
    def add_bullet(sprite, start, headto, spead, acceleration):
    def add_bullet(sprite, script): # For bullets that are controlled by a script
    def add_bullet(sprite, curve, speed): # for bullets with curved paths
    ... And so on ...

And so on with many variations. Is there a better way to do it without using so many keyword arguments cause its getting kinda ugly fast. Renaming each function is pretty bad too because you get either add_bullet1, add_bullet2, or add_bullet_with_really_long_name.

To address some answers:

No I can’t create a Bullet class hierarchy because thats too slow. The actual code for managing bullets is in C and my functions are wrappers around C API.
I know about the keyword arguments but checking for all sorts of combinations of parameters is getting annoying, but default arguments help allot like acceleration=0

Question 27

What you are asking for is called multiple dispatch. See Julia language examples which demonstrates different types of dispatches.

However, before looking at that, we’ll first tackle why overloading is not really what you want in python.

Why Not Overloading?

First, one needs to understand the concept of overloading and why it’s not applicable to python.

When working with languages that can discriminate data types at compile-time, selecting among the alternatives can occur at compile-time. The act of creating such alternative functions for compile-time selection is usually referred to as overloading a function. (Wikipedia)

Python is a dynamically typed language, so the concept of overloading simply does not apply to it. However, all is not lost, since we can create such alternative functions at run-time:

In programming languages that defer data type identification until run-time the selection among alternative functions must occur at run-time, based on the dynamically determined types of function arguments. Functions whose alternative implementations are selected in this manner are referred to most generally as multimethods. (Wikipedia)

So we should be able to do multimethods in python—or, as it is alternatively called: multiple dispatch.

Multiple dispatch

The multimethods are also called multiple dispatch:

Multiple dispatch or multimethods is the feature of some object-oriented programming languages in which a function or method can be dynamically dispatched based on the run time (dynamic) type of more than one of its arguments. (Wikipedia)

Python does not support this out of the box¹, but, as it happens, there is an excellent python package called multipledispatch that does exactly that.

Solution

Here is how we might use multipledispatch² package to implement your methods:

>>> from multipledispatch import dispatch
>>> from collections import namedtuple  
>>> from types import *  # we can test for lambda type, e.g.:
>>> type(lambda a: 1) == LambdaType
True

>>> Sprite = namedtuple('Sprite', ['name'])
>>> Point = namedtuple('Point', ['x', 'y'])
>>> Curve = namedtuple('Curve', ['x', 'y', 'z'])
>>> Vector = namedtuple('Vector', ['x','y','z'])

>>> @dispatch(Sprite, Point, Vector, int)
... def add_bullet(sprite, start, direction, speed):
...     print("Called Version 1")
...
>>> @dispatch(Sprite, Point, Point, int, float)
... def add_bullet(sprite, start, headto, speed, acceleration):
...     print("Called version 2")
...
>>> @dispatch(Sprite, LambdaType)
... def add_bullet(sprite, script):
...     print("Called version 3")
...
>>> @dispatch(Sprite, Curve, int)
... def add_bullet(sprite, curve, speed):
...     print("Called version 4")
...

>>> sprite = Sprite('Turtle')
>>> start = Point(1,2)
>>> direction = Vector(1,1,1)
>>> speed = 100 #km/h
>>> acceleration = 5.0 #m/s
>>> script = lambda sprite: sprite.x * 2
>>> curve = Curve(3, 1, 4)
>>> headto = Point(100, 100) # somewhere far away

>>> add_bullet(sprite, start, direction, speed)
Called Version 1

>>> add_bullet(sprite, start, headto, speed, acceleration)
Called version 2

>>> add_bullet(sprite, script)
Called version 3

>>> add_bullet(sprite, curve, speed)
Called version 4

_{1. Python 3 currently supports single dispatch}
_{2. Take care not to use multipledispatch in a multi-threaded environment or you will get weird behavior.}

Question 28

Python does support “method overloading” as you present it. In fact, what you just describe is trivial to implement in Python, in so many different ways, but I would go with:

class Character(object):
    # your character __init__ and other methods go here

    def add_bullet(self, sprite=default, start=default, 
                 direction=default, speed=default, accel=default, 
                  curve=default):
        # do stuff with your arguments

In the above code, default is a plausible default value for those arguments, or None. You can then call the method with only the arguments you are interested in, and Python will use the default values.

You could also do something like this:

class Character(object):
    # your character __init__ and other methods go here

    def add_bullet(self, **kwargs):
        # here you can unpack kwargs as (key, values) and
        # do stuff with them, and use some global dictionary
        # to provide default values and ensure that ``key``
        # is a valid argument...

        # do stuff with your arguments

Another alternative is to directly hook the desired function directly to the class or instance:

def some_implementation(self, arg1, arg2, arg3):
  # implementation
my_class.add_bullet = some_implementation_of_add_bullet

Yet another way is to use an abstract factory pattern:

class Character(object):
   def __init__(self, bfactory, *args, **kwargs):
       self.bfactory = bfactory
   def add_bullet(self):
       sprite = self.bfactory.sprite()
       speed = self.bfactory.speed()
       # do stuff with your sprite and speed

class pretty_and_fast_factory(object):
    def sprite(self):
       return pretty_sprite
    def speed(self):
       return 10000000000.0

my_character = Character(pretty_and_fast_factory(), a1, a2, kw1=v1, kw2=v2)
my_character.add_bullet() # uses pretty_and_fast_factory

# now, if you have another factory called "ugly_and_slow_factory" 
# you can change it at runtime in python by issuing
my_character.bfactory = ugly_and_slow_factory()

# In the last example you can see abstract factory and "method
# overloading" (as you call it) in action

Question 29

You can use “roll-your-own” solution for function overloading. This one is copied from Guido van Rossum’s article about multimethods (because there is little difference between mm and overloading in python):

registry = {}

class MultiMethod(object):
    def __init__(self, name):
        self.name = name
        self.typemap = {}
    def __call__(self, *args):
        types = tuple(arg.__class__ for arg in args) # a generator expression!
        function = self.typemap.get(types)
        if function is None:
            raise TypeError("no match")
        return function(*args)
    def register(self, types, function):
        if types in self.typemap:
            raise TypeError("duplicate registration")
        self.typemap[types] = function


def multimethod(*types):
    def register(function):
        name = function.__name__
        mm = registry.get(name)
        if mm is None:
            mm = registry[name] = MultiMethod(name)
        mm.register(types, function)
        return mm
    return register

The usage would be

from multimethods import multimethod
import unittest

# 'overload' makes more sense in this case
overload = multimethod

class Sprite(object):
    pass

class Point(object):
    pass

class Curve(object):
    pass

@overload(Sprite, Point, Direction, int)
def add_bullet(sprite, start, direction, speed):
    # ...

@overload(Sprite, Point, Point, int, int)
def add_bullet(sprite, start, headto, speed, acceleration):
    # ...

@overload(Sprite, str)
def add_bullet(sprite, script):
    # ...

@overload(Sprite, Curve, speed)
def add_bullet(sprite, curve, speed):
    # ...

Most restrictive limitations at the moment are:

methods are not supported, only functions that are not class members;
inheritance is not handled;
kwargs are not supported;
registering new functions should be done at import time thing is not thread-safe

Question 30

A possible option is to use the multipledispatch module as detailed here: http://matthewrocklin.com/blog/work/2014/02/25/Multiple-Dispatch

Instead of doing this:

def add(self, other):
    if isinstance(other, Foo):
        ...
    elif isinstance(other, Bar):
        ...
    else:
        raise NotImplementedError()

You can do this:

from multipledispatch import dispatch
@dispatch(int, int)
def add(x, y):
    return x + y    

@dispatch(object, object)
def add(x, y):
    return "%s + %s" % (x, y)

With the resulting usage:

>>> add(1, 2)
3

>>> add(1, 'hello')
'1 + hello'

Question 31

In Python 3.4 was added PEP-0443. Single-dispatch generic functions.

Here is short API description from PEP.

To define a generic function, decorate it with the @singledispatch decorator. Note that the dispatch happens on the type of the first argument. Create your function accordingly:

from functools import singledispatch
@singledispatch
def fun(arg, verbose=False):
    if verbose:
        print("Let me just say,", end=" ")
    print(arg)

To add overloaded implementations to the function, use the register() attribute of the generic function. This is a decorator, taking a type parameter and decorating a function implementing the operation for that type:

@fun.register(int)
def _(arg, verbose=False):
    if verbose:
        print("Strength in numbers, eh?", end=" ")
    print(arg)

@fun.register(list)
def _(arg, verbose=False):
    if verbose:
        print("Enumerate this:")
    for i, elem in enumerate(arg):
        print(i, elem)

Question 32

This type of behaviour is typically solved (in OOP languages) using Polymorphism. Each type of bullet would be responsible for knowing how it travels. For instance:

class Bullet(object):
    def __init__(self):
        self.curve = None
        self.speed = None
        self.acceleration = None
        self.sprite_image = None

class RegularBullet(Bullet):
    def __init__(self):
        super(RegularBullet, self).__init__()
        self.speed = 10

class Grenade(Bullet):
    def __init__(self):
        super(Grenade, self).__init__()
        self.speed = 4
        self.curve = 3.5

add_bullet(Grendade())

def add_bullet(bullet):
    c_function(bullet.speed, bullet.curve, bullet.acceleration, bullet.sprite, bullet.x, bullet.y) 


void c_function(double speed, double curve, double accel, char[] sprite, ...) {
    if (speed != null && ...) regular_bullet(...)
    else if (...) curved_bullet(...)
    //..etc..
}

Pass as many arguments to the c_function that exist, then do the job of determining which c function to call based on the values in the initial c function. So, python should only ever be calling the one c function. That one c function looks at the arguments, and then can delegate to other c functions appropriately.

You’re essentially just using each subclass as a different data container, but by defining all the potential arguments on the base class, the subclasses are free to ignore the ones they do nothing with.

When a new type of bullet comes along, you can simply define one more property on the base, change the one python function so that it passes the extra property, and the one c_function that examines the arguments and delegates appropriately. Doesn’t sound too bad I guess.

Question 33

By passing keyword args.

def add_bullet(**kwargs):
    #check for the arguments listed above and do the proper things

Question 34

Either use multiple keyword arguments in the definition, or create a Bullet hierarchy whose instances are passed to the function.

Question 35

I think your basic requirement is to have a C/C++ like syntax in python with the least headache possible. Although I liked Alexander Poluektov’s answer it doesn’t work for classes.

The following should work for classes. It works by distinguishing by the number of non keyword arguments (but doesn’t support distinguishing by type):

class TestOverloading(object):
    def overloaded_function(self, *args, **kwargs):
        # Call the function that has the same number of non-keyword arguments.  
        getattr(self, "_overloaded_function_impl_" + str(len(args)))(*args, **kwargs)
    
    def _overloaded_function_impl_3(self, sprite, start, direction, **kwargs):
        print "This is overload 3"
        print "Sprite: %s" % str(sprite)
        print "Start: %s" % str(start)
        print "Direction: %s" % str(direction)
        
    def _overloaded_function_impl_2(self, sprite, script):
        print "This is overload 2"
        print "Sprite: %s" % str(sprite)
        print "Script: "
        print script

And it can be used simply like this:

test = TestOverloading()

test.overloaded_function("I'm a Sprite", 0, "Right")
print
test.overloaded_function("I'm another Sprite", "while x == True: print 'hi'")

Output:

This is overload 3
Sprite: I’m a Sprite
Start: 0
Direction: Right

This is overload 2
Sprite: I’m another Sprite
Script:
while x == True: print ‘hi’

Question 36

The @overload decorator was added with type hints (PEP 484). While this doesn’t change the behaviour of python, it does make it easier to understand what is going on, and for mypy to detect errors.
See: Type hints and PEP 484

Question 37

I think a Bullet class hierarchy with the associated polymorphism is the way to go. You can effectively overload the base class constructor by using a metaclass so that calling the base class results in the creation of the appropriate subclass object. Below is some sample code to illustrate the essence of what I mean.

Updated

The code has been modified to run under both Python 2 and 3 to keep it relevant. This was done in a way that avoids the use Python’s explicit metaclass syntax, which varies between the two versions.

To accomplish that objective, a BulletMetaBase instance of the BulletMeta class is created by explicitly calling the metaclass when creating the Bullet baseclass (rather than using the __metaclass__= class attribute or via a metaclass keyword argument depending on the Python version).

class BulletMeta(type):
    def __new__(cls, classname, bases, classdict):
        """ Create Bullet class or a subclass of it. """
        classobj = type.__new__(cls, classname, bases, classdict)
        if classname != 'BulletMetaBase':
            if classname == 'Bullet':  # Base class definition?
                classobj.registry = {}  # Initialize subclass registry.
            else:
                try:
                    alias = classdict['alias']
                except KeyError:
                    raise TypeError("Bullet subclass %s has no 'alias'" %
                                    classname)
                if alias in Bullet.registry: # unique?
                    raise TypeError("Bullet subclass %s's alias attribute "
                                    "%r already in use" % (classname, alias))
                # Register subclass under the specified alias.
                classobj.registry[alias] = classobj

        return classobj

    def __call__(cls, alias, *args, **kwargs):
        """ Bullet subclasses instance factory.

            Subclasses should only be instantiated by calls to the base
            class with their subclass' alias as the first arg.
        """
        if cls != Bullet:
            raise TypeError("Bullet subclass %r objects should not to "
                            "be explicitly constructed." % cls.__name__)
        elif alias not in cls.registry: # Bullet subclass?
            raise NotImplementedError("Unknown Bullet subclass %r" %
                                      str(alias))
        # Create designated subclass object (call its __init__ method).
        subclass = cls.registry[alias]
        return type.__call__(subclass, *args, **kwargs)


class Bullet(BulletMeta('BulletMetaBase', (object,), {})):
    # Presumably you'd define some abstract methods that all here
    # that would be supported by all subclasses.
    # These definitions could just raise NotImplementedError() or
    # implement the functionality is some sub-optimal generic way.
    # For example:
    def fire(self, *args, **kwargs):
        raise NotImplementedError(self.__class__.__name__ + ".fire() method")

    # Abstract base class's __init__ should never be called.
    # If subclasses need to call super class's __init__() for some
    # reason then it would need to be implemented.
    def __init__(self, *args, **kwargs):
        raise NotImplementedError("Bullet is an abstract base class")


# Subclass definitions.
class Bullet1(Bullet):
    alias = 'B1'
    def __init__(self, sprite, start, direction, speed):
        print('creating %s object' % self.__class__.__name__)
    def fire(self, trajectory):
        print('Bullet1 object fired with %s trajectory' % trajectory)


class Bullet2(Bullet):
    alias = 'B2'
    def __init__(self, sprite, start, headto, spead, acceleration):
        print('creating %s object' % self.__class__.__name__)


class Bullet3(Bullet):
    alias = 'B3'
    def __init__(self, sprite, script): # script controlled bullets
        print('creating %s object' % self.__class__.__name__)


class Bullet4(Bullet):
    alias = 'B4'
    def __init__(self, sprite, curve, speed): # for bullets with curved paths
        print('creating %s object' % self.__class__.__name__)


class Sprite: pass
class Curve: pass

b1 = Bullet('B1', Sprite(), (10,20,30), 90, 600)
b2 = Bullet('B2', Sprite(), (-30,17,94), (1,-1,-1), 600, 10)
b3 = Bullet('B3', Sprite(), 'bullet42.script')
b4 = Bullet('B4', Sprite(), Curve(), 720)
b1.fire('uniform gravity')
b2.fire('uniform gravity')

Output:

creating Bullet1 object
creating Bullet2 object
creating Bullet3 object
creating Bullet4 object
Bullet1 object fired with uniform gravity trajectory
Traceback (most recent call last):
  File "python-function-overloading.py", line 93, in <module>
    b2.fire('uniform gravity') # NotImplementedError: Bullet2.fire() method
  File "python-function-overloading.py", line 49, in fire
    raise NotImplementedError(self.__class__.__name__ + ".fire() method")
NotImplementedError: Bullet2.fire() method

Question 38

Python 3.8 added functools.singledispatchmethod

Transform a method into a single-dispatch generic function.

To define a generic method, decorate it with the @singledispatchmethod decorator. Note that the dispatch happens on the type of the first non-self or non-cls argument, create your function accordingly:

from functools import singledispatchmethod


class Negator:
    @singledispatchmethod
    def neg(self, arg):
        raise NotImplementedError("Cannot negate a")

    @neg.register
    def _(self, arg: int):
        return -arg

    @neg.register
    def _(self, arg: bool):
        return not arg


negator = Negator()
for v in [42, True, "Overloading"]:
    neg = negator.neg(v)
    print(f"{v=}, {neg=}")

Output

v=42, neg=-42
v=True, neg=False
NotImplementedError: Cannot negate a

@singledispatchmethod supports nesting with other decorators such as @classmethod. Note that to allow for dispatcher.register, singledispatchmethod must be the outer most decorator. Here is the Negator class with the neg methods being class bound:

from functools import singledispatchmethod


class Negator:
    @singledispatchmethod
    @staticmethod
    def neg(arg):
        raise NotImplementedError("Cannot negate a")

    @neg.register
    def _(arg: int) -> int:
        return -arg

    @neg.register
    def _(arg: bool) -> bool:
        return not arg


for v in [42, True, "Overloading"]:
    neg = Negator.neg(v)
    print(f"{v=}, {neg=}")

Output:

v=42, neg=-42
v=True, neg=False
NotImplementedError: Cannot negate a

The same pattern can be used for other similar decorators: staticmethod, abstractmethod, and others.

Question 39

Use keyword arguments with defaults. E.g.

def add_bullet(sprite, start=default, direction=default, script=default, speed=default):

In the case of a straight bullet versus a curved bullet, I’d add two functions: add_bullet_straight and add_bullet_curved.

Question 40

overloading methods is tricky in python. However, there could be usage of passing the dict, list or primitive variables.

I have tried something for my use cases, this could help here to understand people to overload the methods.

Let’s take your example:

a class overload method with call the methods from different class.

def add_bullet(sprite=None, start=None, headto=None, spead=None, acceleration=None):

pass the arguments from remote class:

add_bullet(sprite = 'test', start=Yes,headto={'lat':10.6666,'long':10.6666},accelaration=10.6}

OR

add_bullet(sprite = 'test', start=Yes, headto={'lat':10.6666,'long':10.6666},speed=['10','20,'30']}

So, handling is being achieved for list, Dictionary or primitive variables from method overloading.

try it out for your codes.

Question 41

Just a simple decorator

class overload:
    def __init__(self, f):
        self.cases = {}

    def args(self, *args):
        def store_function(f):
            self.cases[tuple(args)] = f
            return self
        return store_function

    def __call__(self, *args):
        function = self.cases[tuple(type(arg) for arg in args)]
        return function(*args)

You can use it like this

@overload
def f():
    pass

@f.args(int, int)
def f(x, y):
    print('two integers')

@f.args(float)
def f(x):
    print('one float')


f(5.5)
f(1, 2)

Modify it to adapt it to your use case.

A clarification of concepts

function dispatch: there are multiple functions with the same name. Which one should be called? two strategies
static/compile-time dispatch (aka. “overloading”). decide which function to call based on the compile-time type of the arguments. In all dynamic languages, there is no compile-time type, so overloading is impossible by definition
dynamic/run-time dispatch: decide which function to call based on the runtime type of the arguments. This is what all OOP languages do: multiple classes have the same methods, and the language decides which one to call based on the type of self/this argument. However, most languages only do it for the this argument only. The above decorator extends the idea to multiple parameters.

To clear up, assume a static language, and define the functions

void f(Integer x):
    print('integer called')

void f(Float x):
    print('float called')

void f(Number x):
    print('number called')


Number x = new Integer('5')
f(x)
x = new Number('3.14')
f(x)

With static dispatch (overloading) you will see “number called” twice, because x has been declared as Number, and that’s all overloading cares about. With dynamic dispatch you will see “integer called, float called”, because those are the actual types of x at the time the function is called.

Question 42

Consider this class:

class foo(object):
    pass

The default string representation looks something like this:

>>> str(foo)
"<class '__main__.foo'>"

How can I make this display a custom string?

Question 43

Implement __str__() or __repr__() in the class’s metaclass.

class MC(type):
  def __repr__(self):
    return 'Wahaha!'

class C(object):
  __metaclass__ = MC

print C

Use __str__ if you mean a readable stringification, use __repr__ for unambiguous representations.

Question 44

class foo(object):
    def __str__(self):
        return "representation"
    def __unicode__(self):
        return u"representation"

Question 45

If you have to choose between __repr__ or __str__ go for the first one, as by default implementation __str__ calls __repr__ when it wasn’t defined.

Custom Vector3 example:

class Vector3(object):
    def __init__(self, args):
        self.x = args[0]
        self.y = args[1]
        self.z = args[2]

    def __repr__(self):
        return "Vector3([{0},{1},{2}])".format(self.x, self.y, self.z)

    def __str__(self):
        return "x: {0}, y: {1}, z: {2}".format(self.x, self.y, self.z)

In this example, repr returns again a string that can be directly consumed/executed, whereas str is more useful as a debug output.

v = Vector3([1,2,3])
print repr(v)    #Vector3([1,2,3])
print str(v)     #x:1, y:2, z:3

Question 46

Ignacio Vazquez-Abrams’ approved answer is quite right. It is, however, from the Python 2 generation. An update for the now-current Python 3 would be:

class MC(type):
  def __repr__(self):
    return 'Wahaha!'

class C(object, metaclass=MC):
    pass

print(C)

If you want code that runs across both Python 2 and Python 3, the six module has you covered:

from __future__ import print_function
from six import with_metaclass

class MC(type):
  def __repr__(self):
    return 'Wahaha!'

class C(with_metaclass(MC)):
    pass

print(C)

Finally, if you have one class that you want to have a custom static repr, the class-based approach above works great. But if you have several, you’d have to generate a metaclass similar to MC for each, and that can get tiresome. In that case, taking your metaprogramming one step further and creating a metaclass factory makes things a bit cleaner:

from __future__ import print_function
from six import with_metaclass

def custom_class_repr(name):
    """
    Factory that returns custom metaclass with a class ``__repr__`` that
    returns ``name``.
    """
    return type('whatever', (type,), {'__repr__': lambda self: name})

class C(with_metaclass(custom_class_repr('Wahaha!'))): pass

class D(with_metaclass(custom_class_repr('Booyah!'))): pass

class E(with_metaclass(custom_class_repr('Gotcha!'))): pass

print(C, D, E)

prints:

Wahaha! Booyah! Gotcha!

Metaprogramming isn’t something you generally need everyday—but when you need it, it really hits the spot!

Question 47

Just adding to all the fine answers, my version with decoration:

from __future__ import print_function
import six

def classrep(rep):
    def decorate(cls):
        class RepMetaclass(type):
            def __repr__(self):
                return rep

        class Decorated(six.with_metaclass(RepMetaclass, cls)):
            pass

        return Decorated
    return decorate


@classrep("Wahaha!")
class C(object):
    pass

print(C)

stdout:

Wahaha!

The down sides:

You can’t declare C without a super class (no class C:)
C instances will be instances of some strange derivation, so it’s probably a good idea to add a __repr__ for the instances as well.

Question 48

I have this code which calculates the distance between two coordinates. The two functions are both within the same class.

However how do I call the function distToPoint in the function isNear?

class Coordinates:
    def distToPoint(self, p):
        """
        Use pythagoras to find distance
        (a^2 = b^2 + c^2)
        """
        ...

    def isNear(self, p):
        distToPoint(self, p)
        ...

Question 49

Since these are member functions, call it as a member function on the instance, self.

def isNear(self, p):
    self.distToPoint(p)
    ...

Question 50

That doesn’t work because distToPoint is inside your class, so you need to prefix it with the classname if you want to refer to it, like this: classname.distToPoint(self, p). You shouldn’t do it like that, though. A better way to do it is to refer to the method directly through the class instance (which is the first argument of a class method), like so: self.distToPoint(p).

Question 51

I’m used to the Java model where you can have one public class per file. Python doesn’t have this restriction, and I’m wondering what’s the best practice for organizing classes.

Question 52

A Python file is called a “module” and it’s one way to organize your software so that it makes “sense”. Another is a directory, called a “package”.

A module is a distinct thing that may have one or two dozen closely-related classes. The trick is that a module is something you’ll import, and you need that import to be perfectly sensible to people who will read, maintain and extend your software.

The rule is this: a module is the unit of reuse.

You can’t easily reuse a single class. You should be able to reuse a module without any difficulties. Everything in your library (and everything you download and add) is either a module or a package of modules.

For example, you’re working on something that reads spreadsheets, does some calculations and loads the results into a database. What do you want your main program to look like?

from ssReader import Reader
from theCalcs import ACalc, AnotherCalc
from theDB import Loader

def main( sourceFileName ):
    rdr= Reader( sourceFileName )
    c1= ACalc( options )
    c2= AnotherCalc( options )
    ldr= Loader( parameters )
    for myObj in rdr.readAll():
        c1.thisOp( myObj )
        c2.thatOp( myObj )
        ldr.laod( myObj )

Think of the import as the way to organize your code in concepts or chunks. Exactly how many classes are in each import doesn’t matter. What matters is the overall organization that you’re portraying with your import statements.

Question 53

Since there is no artificial limit, it really depends on what’s comprehensible. If you have a bunch of fairly short, simple classes that are logically grouped together, toss in a bunch of ’em. If you have big, complex classes or classes that don’t make sense as a group, go one file per class. Or pick something in between. Refactor as things change.

Question 54

I happen to like the Java model for the following reason. Placing each class in an individual file promotes reuse by making classes easier to see when browsing the source code. If you have a bunch of classes grouped into a single file, it may not be obvious to other developers that there are classes there that can be reused simply by browsing the project’s directory structure. Thus, if you think that your class can possibly be reused, I would put it in its own file.

Question 55

It entirely depends on how big the project is, how long the classes are, if they will be used from other files and so on.

For example I quite often use a series of classes for data-abstraction – so I may have 4 or 5 classes that may only be 1 line long (class SomeData: pass).

It would be stupid to split each of these into separate files – but since they may be used from different files, putting all these in a separate data_model.py file would make sense, so I can do from mypackage.data_model import SomeData, SomeSubData

If you have a class with lots of code in it, maybe with some functions only it uses, it would be a good idea to split this class and the helper-functions into a separate file.

You should structure them so you do from mypackage.database.schema import MyModel, not from mypackage.email.errors import MyDatabaseModel – if where you are importing things from make sense, and the files aren’t tens of thousands of lines long, you have organised it correctly.

The Python Modules documentation has some useful information on organising packages.

Question 56

I find myself splitting things up when I get annoyed with the bigness of files and when the desirable structure of relatedness starts to emerge naturally. Often these two stages seem to coincide.

It can be very annoying if you split things up too early, because you start to realise that a totally different ordering of structure is required.

On the other hand, when any .java or .py file is getting to more than about 700 lines I start to get annoyed constantly trying to remember where “that particular bit” is.

With Python/Jython circular dependency of import statements also seems to play a role: if you try to split too many cooperating basic building blocks into separate files this “restriction”/”imperfection” of the language seems to force you to group things, perhaps in rather a sensible way.

As to splitting into packages, I don’t really know, but I’d say probably the same rule of annoyance and emergence of happy structure works at all levels of modularity.

Question 57

I would say to put as many classes as can be logically grouped in that file without making it too big and complex.

Question 58

Why is cls sometimes used instead of self as an argument in Python classes?

For example:

class Person:
    def __init__(self, firstname, lastname):
        self.firstname = firstname
        self.lastname = lastname

    @classmethod
    def from_fullname(cls, fullname):
        cls.firstname, cls.lastname = fullname.split(' ', 1)

Question 59

The distinction between "self" and "cls" is defined in PEP 8 . As Adrien said, this is not a mandatory. It’s a coding style. PEP 8 says:

Function and method arguments:

Always use self for the first argument to instance methods.

Always use cls for the first argument to class methods.

Question 60

It’s used in case of a class method. Check this reference for further details.

EDIT: As clarified by Adrien, it’s a convention. You can actually use anything but cls and self are used (PEP8).

Question 61

cls implies that method belongs to the class while self implies that the method is related to instance of the class,therefore member with cls is accessed by class name where as the one with self is accessed by instance of the class…it is the same concept as static member and non-static members in java if you are from java background.

Question 62

This is very good question but not as wanting as question. There is difference between ‘self’ and ‘cls’ used method though analogically they are at same place

def moon(self, moon_name):
    self.MName = moon_name

#but here cls method its use is different 

@classmethod
def moon(cls, moon_name):
    instance = cls()
    instance.MName = moon_name

Now you can see both are moon function but one can be used inside class while other function name moon can be used for any class.

For practical programming approach :

While designing circle class we use area method as cls instead of self because we don’t want area to be limited to particular class of circle only .

Question 63

Instead of accepting a self parameter, class methods take a cls parameter that points to the class—and not the object instance—when the method is called. Since the class method only has access to this cls argument, it can’t modify object instance state. That would require access to self . However, class methods can still modify class state that applies across all instances of the class.

–Python Tricks

Question 64

I want to create a dynamic object (inside another object) in Python and then add attributes to it.

I tried:

obj = someobject
obj.a = object()
setattr(obj.a, 'somefield', 'somevalue')

but this didn’t work.

Any ideas?

edit:

I am setting the attributes from a for loop which loops through a list of values, e.g.

params = ['attr1', 'attr2', 'attr3']
obj = someobject
obj.a = object()

for p in params:
   obj.a.p # where p comes from for loop variable

In the above example I would get obj.a.attr1, obj.a.attr2, obj.a.attr3.

I used the setattr function because I didn’t know how to do obj.a.NAME from a for loop.

How would I set the attribute based on the value of p in the example above?

问题：如何避免实例之间共享类数据？

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总览

特征

你得到什么

您可以开启什么

你没有得到什么

汇总表

附加功能

参考资料

Overview

Features

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What you can turn on

What you don’t get

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Additional features

References

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问题：Python函数重载

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为什么不超载？

多次派遣

解

Why Not Overloading?

Multiple dispatch

Solution

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问题：Python类中使用的“ cls”变量是什么？