I have seen source code where more than one methods are called on an object eg x.y().z() Can someone please explain this to me, does this mean that z() is inside y() or what?
This calls the method y() on object x, then the method z() is called on the result of y() and that entire line is the result of method z().
For example
friendsFavePizzaToping = person.getBestFriend().getFavoritePizzaTopping()
This would result in friendsFavePizzaTopping would be the person's best friend's favorite pizza topping.
Important to note: getBestFriend() must return an object that has the method getFavoritePizzaTopping(). If it does not, an AttributeError will be thrown.
Each method is evaluated in turn, left to right. Consider:
>>> s='HELLO'
>>> s.lower()
'hello'
>>> s='HELLO '
>>> s.lower()
'hello '
>>> s.lower().strip()
'hello'
>>> s.lower().strip().upper()
'HELLO'
>>> s.lower().strip().upper().replace('H', 'h')
'hELLO'
The requirement is that the object to the left in the chain has to have availability of the method on the right. Often that means that the objects are similar types -- or at least share compatible methods or an understood cast.
As an example, consider this class:
class Foo:
def __init__(self, name):
self.name=name
def m1(self):
return Foo(self.name+'=>m1')
def m2(self):
return Foo(self.name+'=>m2')
def __repr__(self):
return '{}: {}'.format(id(self), self.name)
def m3(self):
return .25 # return is no longer a Foo
Notice that as a type of immutable, each return from Foo is a new object (either a new Foo for m1, m2 or a new float). Now try those methods:
>>> foo
4463545376: init
>>> foo.m1()
4463545304: init=>m1
^^^^ different object id
>>> foo
4463545376: init
^^^^ foo still the same because you need to assign it to change
Now assign:
>>> foo=foo.m1().m2()
>>> foo
4464102576: init=>m1=>m2
Now use m3() and it will be a float; not a Foo anymore:
>>> foo=foo.m1().m2().m3()
>>> foo
.25
Now a float -- can't use foo methods anymore:
>>> foo.m1()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'float' object has no attribute 'm1'
But you can use float methods:
>>> foo.as_integer_ratio()
(1, 4)
In the case of:
x.y().z()
You're almost always looking at immutable objects. Mutable objects don't return anything that would HAVE a function like that (for the most part, but I'm simplifying). For instance...
class x:
def __init__(self):
self.y_done = False
self.z_done = False
def y(self):
new_x = x()
new_x.y_done = True
return new_x
def z(self):
new_x = x()
new_x.z_done = True
return new_x
You can see that each of x.y and x.z returns an x object. That object is used to make the consecutive call, e.g. in x.y().z(), x.z is not called on x, but on x.y().
x.y().z() =>
tmp = x.y()
result = tmp.z()
In #dawg's excellent example, he's using strings (which are immutable in Python) whose methods return strings.
string = 'hello'
string.upper() # returns a NEW string with value "HELLO"
string.upper().replace("E","O") # returns a NEW string that's based off "HELLO"
string.upper().replace("E","O") + "W"
# "HOLLOW"
The . "operator" is Python syntax for attribute access. x.y is (nearly) identical to
getattr(x, 'y')
so x.y() is (nearly) identical to
getattr(x, 'y')()
(I say "nearly identical" because it's possible to customize attribute access for a user-defined class. From here on out, I'll assume no such customization is done, and you can assume that x.y is in fact identical to getattr(x, 'y').)
If the thing that x.y() returns has an attribute z such that
foo = getattr(x, 'y')
bar = getattr(foo(), 'z')
is legal, then you can chain the calls together without needing the name foo in the middle:
bar = getattr(getattr(x, 'y')(), 'z')
Converting back to dot notation gives you
bar = getattr(x.y(), 'z')
or simply
bar = x.y().z()
x.y().z() means that the x object has the method y() and the result of x.y() object has the method z() . Now if you first want to apply the method y() on x and then on the result want to apply the z() method, you will write x.y().z(). This is like,
val = x.y()
result = val.z()
Example:
my_dict = {'key':'value'}
my_dict is a dict type object. my_dict.get('key') returns 'value' which is a str type object. now I can apply any method of str type object on it. which will be like,
my_dict.get('key').upper()
This will return 'VALUE'.
That is (sometimes a sign of) bad code.
It violates The law of Demeter. Here is a quote from Wikipedia explaining what is meant:
Each unit should have only limited knowledge about other units: only units "closely" related to the current unit.
Each unit should only talk to its friends; don't talk to strangers.
Only talk to your immediate friends.
Suppose you have a car, which itself has an engine:
class Car:
def __init__(self):
self._engine=None
#property
def engine(self):
return self._engine
#engine.setter
def engine(self, value):
self._engine = value
class Porsche_engine:
def start(self):
print("starting")
So if you make a new car and set the engine to Porsche you could do the following:
>>> from car import *
>>> c=Car()
>>> e=Porsche_engine()
>>> c.engine=e
>>> c.engine.start()
starting
If you are maing this call from an Object, it has not only knowledge of a Car object, but has too knowledge of Engine, which is bad design.
Additionally: if you do not know whether a Car has an engine, calling directly start
>>> c=Car()
>>> c.engine.start()
May result in an Error
AttributeError: 'NoneType' object has no attribute 'start'
Edit:
To avoid (further) misunterstandings and misreadings, from what I am saying.
There are two usages:
1) as I pointed out, Objects calling methods on other objects, returned from a third object is a violation of LoD. This is one way to read the question.
2) an exception to that is method chaining, which is not bad design.
And a better design would be, if the Car itself had a start()-Method which delegates to the engine.
Related
I was reading the famous book on python named 'python cookbook' by David Beazley and Brian K. Jones. In their 'classes and objects' chapter, they have written the following code:
class Point:
def __init__(self,x,y):
self.x = x
self.y = y
def __repr__(self):
return 'Point({0.x!r}, {0.y!r})'.format(self)
def __str__(self):
return '({0.x!s}, {0.y!s})'.format(self)
Now, only I got to understand that the repr() method is used to represent an object to a developer and str() to a blind user. But in both the methods, what is being happened after 'return'? There are tuples in both cases for sure. But what kinda member the tuples have? I haven't seen anything like this before.
Both __repr__ and __str__ in your example return formatted strings, not tuples. These functions are called when either repr() or str() is called on the object, explicitly or implicitly.
For example, a print(Point(1,2)) would print the result of __str__ because it calls str() on the object internally, but print([Point(1,2)]) would print the result of __repr__ instead.
For your own objects, these methods can return anything you want, just as long as those are just strings.
Here's a breakdown of the specific formatting syntax. With the str.format method you can fill-in placeholders in the given string instance that are of the form {...}. These work with positional arguments in the following way:
>>> '{}:{}'.format(1, 2)
'1:2'
>>> '{0}:{1}'.format(1, 2)
'1:2'
>>> '{1}:{0}'.format(1, 2)
'2:1'
That means you can specify the number of the positional argument inside the curly braces. The string can refer to a positional argument multiple times:
>>> '{0}:{1}:{0}'.format(1, 2)
'1:2:1'
Using .x notation let's you access attributes of that argument. For example:
>>> class Test:
... x = 1
... y = 2
...
>>> '{0.x}:{0.y}'.format(Test)
'1:2'
Using !r you can force to use the __repr__ of that object rather than the __str__. For example:
>>> class Test:
... def __str__(self):
... return '__str__'
... def __repr__(self):
... return '__repr__'
...
>>> '{0}'.format(Test())
'__str__'
>>> '{0!s}'.format(Test())
'__str__'
>>> '{0!r}'.format(Test())
'__repr__'
So combining all this information we get the following:
'Point({0.x!r}, {0.y!r})'.format(self)
Here a format string with two placeholders is defined ({0.x!r} and {0.y!r}). They should be filled in with the first positional argument's x and y attributes (recall that {0} will be substituted with the first argument, so {0.x} requests the x attribute of that argument). And finally the !r requests the __repr__ of that object rather than the __str__ (which is the default).
The same reasoning applies to the __str__ method.
By the way, the formatting syntax also allows keyword arguments and they can be referred to by their name:
>>> '{first}:{second}'.format(first=1, second=2)
'1:2'
>>> '{second}:{first}'.format(first=1, second=2)
'2:1'
You might want to try this in an interpreter to see what is going on:
>>> class Point:
def __init__(self, x, y):
"""Initialize self."""
self.x, self.y = x, y
def __repr__(self):
"""Return repr(self)."""
return f'{type(self).__name__}({self.x!r}, {self.y!r})'
def __str__(self):
"""Return str(self)."""
return f'({self.x!s}, {self.y!s})'
>>> Point(1, 2) # causes implicit call to __repr__
Point(1, 2)
>>> print(_) # causes implicit call to __str__
(1, 2)
>>>
class A(object):
def __init__(self, value):
self.value = value
x = A(1)
y = A(2)
q = [x, y]
q.remove(y)
I want to remove from the list a specific object which was added before to it and to which I still have a reference. I do not want an equality test. I want an identity test. This code seems to work in both CPython and IronPython, but does the language guarantee this behavior or is it just a fluke?
The list.remove method documentation is this: same as del s[s.index(x)], which implies that an equality test is performed.
So will an object be equal to itself if you don't override __cmp__, __eq__ or __ne__?
Yes. In your example q.remove(y) would remove the first occurrence of an object which compares equal with y. However, the way the class A is defined, you shouldn't† ever have a variable compare equal with y - with the exception of any other names which are also bound to the same y instance.
The relevant section of the docs is here:
If no __cmp__(), __eq__() or __ne__() operation is defined, class
instances are compared by object identity ("address").
So comparison for A instances is by identity (implemented as memory address in CPython). No other object can have an identity equal to id(y) within y's lifetime, i.e. for as long as you hold a reference to y (which you must, if you're going to remove it from a list!)
† Technically, it is still possible to have objects at other memory locations which are comparing equal - mock.ANY is one such example. But these objects need to override their comparison operators to force the result.
In python, by default an object is always equal to itself (the only exception I can think of is float("nan"). An object of a user-defined class will not be equal to any other object unless you define a comparison function.
See also http://docs.python.org/reference/expressions.html#notin
The answer is yes and no.
Consider the following example
>>> class A(object):
def __init__(self, value):
self.value = value
>>> x = A(1)
>>> y = A(2)
>>> z = A(3)
>>> w = A(3)
>>> q = [x, y,z]
>>> id(y) #Second element in the list and y has the same reference
46167248
>>> id(q[1]) #Second element in the list and y has the same reference
46167248
>>> q.remove(y) #So it just compares the id and removes it
>>> q
[<__main__.A object at 0x02C19AB0>, <__main__.A object at 0x02C19B50>]
>>> q.remove(w) #Fails because though z and w contain the same value yet they are different object
Traceback (most recent call last):
File "<pyshell#11>", line 1, in <module>
q.remove(w)
ValueError: list.remove(x): x not in list
It will remove from the list iff they are the same object. If they are different object with same value it won;t remove it.
Assume that we have a test()class defined as below :
>>> class test():
pass
Normally when I run the below code I make Obj as an object of my test() class :
>>> obj=test()
>>>
>>> obj
<__main__.test object at 0x00000000031B2390>
>>> type(obj)
<class '__main__.test'>
>>>
As you see above obj has two features. It has a value and a type.
In the below, I assign Obj value as a string to another variable called var1 :
>>> var1='<__main__.test object at 0x00000000031B2390>'
>>>
>>> type(var1)
<class 'str'>
>>>
As you see above , obj1 and var1 are equal in value, but are different in type. And again, as you know we can change type of an object to string using str() function as below :
>>> Obj=str(Obj)
>>> Obj
'<__main__.test object at 0x00000000031B2390>'
>>>
Now, I want to know if is there any way to reverse above function? I mean, Is there any way to make a string-type variable as a object?
I mean is there any way to make Var1 equal to Obj?
In the other word, assume that I know <__main__.test object at 0x00000000031B2390> is the value of an object of a class. But I don't know neither the name of the object nor the name of the class. Now I want to create another object of that class. Is there any way?
You are confusing the representation of an object (obj.__repr__(), or repr(obj)) with its value (which doesn't necessarily have a sensible meaning for all types of object *). Here is an example of an object with a misleading representation:
>>> a = 1
>>> a
1
>>> class FakeInt(object):
def __repr__(self):
return "1"
>>> b = FakeInt()
>>> b
1
>>> a == b
False
>>> a + b
Traceback (most recent call last):
File "<pyshell#22>", line 1, in <module>
a + b
TypeError: unsupported operand type(s) for +: 'int' and 'FakeInt'
a looks like b, but isn't equal to it and cannot be used in the same way.
It is conventional to implement __repr__ and __eq__ instance methods such that eval(repr(obj)) == obj. This allows you to create new objects that are equal to the existing ones, for example:
>>> class SillyString(object):
def __init__(self, s):
self.string = s
def __repr__(self):
return "SillyString({0.string!r})".format(self)
def __eq__(self, other):
return self.string == other.string
>>> s = SillyString("foo")
>>> repr(s)
"SillyString('foo')"
>>> eval(repr(s)) == s
True # equal to s
>>> eval(repr(s)) is s
False # but not identical to s, i.e. a separate object
However this seems pointless for your test objects, because they don't have attributes or methods to copy anyway.
* For example, here is an object definition:
def some_obj(object):
def __init__(self, foo, bar):
self.foo = foo
self.bar = bar
What should its "value" be? foo? bar? Some combination of the two?
There are a fair few misconceptions here.
First, it is untrue to say that a variable has two things, a value and a type. It has one thing only: a value. That value has a type. Variables in Python do not have types, they are simply names pointing at things.
Secondly, assigning a string that looks like the repr of an object does not somehow make the value equal to the object. A string is just a string.
Consider the following (broken) code:
import functools
class Foo(object):
def __init__(self):
def f(a,self,b):
print a+b
self.g = functools.partial(f,1)
x=Foo()
x.g(2)
What I want to do is take the function f and partially apply it, resulting in a function g(self,b). I would like to use this function as a method, however this does not currently work and instead I get the error
Traceback (most recent call last):
File "test.py", line 8, in <module>
x.g(2)
TypeError: f() takes exactly 3 arguments (2 given)
Doing x.g(x,2) however works, so it seem the issue is that g is considered a "normal" function instead of a method of the class. Is there a way to get x.g to behave like a method (i.e implicitly pass the self parameter) instead of a function?
There are two issues at hand here. First, for a function to be turned into a method it must be stored on the class, not the instance. A demonstration:
class Foo(object):
def a(*args):
print 'a', args
def b(*args):
print 'b', args
Foo.b = b
x = Foo()
def c(*args):
print 'c', args
x.c = c
So a is a function defined in the class definition, b is a function assigned to the class afterwards, and c is a function assigned to the instance. Take a look at what happens when we call them:
>>> x.a('a will have "self"')
a (<__main__.Foo object at 0x100425ed0>, 'a will have "self"')
>>> x.b('as will b')
b (<__main__.Foo object at 0x100425ed0>, 'as will b')
>>> x.c('c will only recieve this string')
c ('c will only recieve this string',)
As you can see there is little difference between a function defined along with the class, and one assigned to it later. I believe there is actually no difference as long as there is no metaclass involved, but that is for another time.
The second problem comes from how a function is actually turned into a method in the first place; the function type implements the descriptor protocol. (See the docs for details.) In a nutshell, the function type has a special __get__ method which is called when you perform an attribute lookup on the class itself. Instead of you getting the function object, the __get__ method of that function object is called, and that returns a bound method object (which is what supplies the self argument).
Why is this a problem? Because the functools.partial object is not a descriptor!
>>> import functools
>>> def f(*args):
... print 'f', args
...
>>> g = functools.partial(f, 1, 2, 3)
>>> g
<functools.partial object at 0x10042f2b8>
>>> g.__get__
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'functools.partial' object has no attribute '__get__'
There are a number of options you have at this point. You can explicitly supply the self argument to the partial:
import functools
class Foo(object):
def __init__(self):
def f(self, a, b):
print a + b
self.g = functools.partial(f, self, 1)
x = Foo()
x.g(2)
...or you would imbed the self and value of a in a closure:
class Foo(object):
def __init__(self):
a = 1
def f(b):
print a + b
self.g = f
x = Foo()
x.g(2)
These solutions are of course assuming that there is an as yet unspecified reason for assigning a method to the class in the constructor like this, as you can very easily just define a method directly on the class to do what you are doing here.
Edit: Here is an idea for a solution assuming the functions may be created for the class, instead of the instance:
class Foo(object):
pass
def make_binding(name):
def f(self, *args):
print 'Do %s with %s given %r.' % (name, self, args)
return f
for name in 'foo', 'bar', 'baz':
setattr(Foo, name, make_binding(name))
f = Foo()
f.foo(1, 2, 3)
f.bar('some input')
f.baz()
Gives you:
Do foo with <__main__.Foo object at 0x10053e3d0> given (1, 2, 3).
Do bar with <__main__.Foo object at 0x10053e3d0> given ('some input',).
Do baz with <__main__.Foo object at 0x10053e3d0> given ().
This will work. But I'm not sure if this is what you are looking for
class Foo(object):
def __init__(self):
def f(a,self,b):
print a+b
self.g = functools.partial(f,1, self) # <= passing `self` also.
x = Foo()
x.g(2)
this is simply a concrete example of what i believe is the most correct (and therefore pythonic :) way to solve -- as the best solution (definition on a class!) was never revealed -- #MikeBoers explanations are otherwise solid.
i've used this pattern quite a bit (recently for an proxied API), and it's survived untold production hours without the slightest irregularity.
from functools import update_wrapper
from functools import partial
from types import MethodType
class Basic(object):
def add(self, **kwds):
print sum(kwds.values())
Basic.add_to_one = MethodType(
update_wrapper(partial(Basic.add, a=1), Basic.add),
None,
Basic,
)
x = Basic()
x.add(a=1, b=9)
x.add_to_one(b=9)
...yields:
10
10
...the key take-home-point here is MethodType(func, inst, cls), which creates an unbound method from another callable (you can even use this to chain/bind instance methods to unrelated classes... when instantiated+called the original instance method will receive BOTH self objects!)
note the exclusive use of keyword arguments! while there might be a better way to handle, args are generally a PITA because the placement of self becomes less predictable. also, IME anyway, using *args, and **kwds in the bottom-most function has proven very useful later on.
functools.partialmethod() is available since python 3.4 for this purpose.
import functools
class Foo(object):
def __init__(self):
def f(a,self,b):
print a+b
self.g = functools.partialmethod(f,1)
x=Foo()
x.g(2)
I have a variable x in python. How can i find the string 'x' from the variable. Here is my attempt:
def var(v,c):
for key in c.keys():
if c[key] == v:
return key
def f():
x = '321'
print 'Local var %s = %s'%(var(x,locals()),x)
x = '123'
print 'Global var %s = %s'%(var(x,locals()),x)
f()
The results are:
Global var x = 123
Local var x = 321
The above recipe seems a bit un-pythonesque. Is there a better/shorter way to achieve the same result?
Q: I have a variable x in python. How can i find the string 'x' from the variable.
A: If I am understanding your question properly, you want to go from the value of a variable to its name. This is not really possible in Python.
In Python, there really isn't any such thing as a "variable". What Python really has are "names" which can have objects bound to them. It makes no difference to the object what names, if any, it might be bound to. It might be bound to dozens of different names, or none.
Consider this example:
foo = 1
bar = foo
baz = foo
Now, suppose you have the integer object with value 1, and you want to work backwards and find its name. What would you print? Three different names have that object bound to them, and all are equally valid.
print(bar is foo) # prints True
print(baz is foo) # prints True
In Python, a name is a way to access an object, so there is no way to work with names directly. You might be able to search through locals() to find the value and recover a name, but that is at best a parlor trick. And in my above example, which of foo, bar, and baz is the "correct" answer? They all refer to exactly the same object.
P.S. The above is a somewhat edited version of an answer I wrote before. I think I did a better job of wording things this time.
I believe the general form of what you want is repr() or the __repr__() method of an object.
with regards to __repr__():
Called by the repr() built-in function
and by string conversions (reverse
quotes) to compute the “official”
string representation of an object.
See the docs here: object.repr(self)
stevenha has a great answer to this question. But, if you actually do want to poke around in the namespace dictionaries anyway, you can get all the names for a given value in a particular scope / namespace like this:
def foo1():
x = 5
y = 4
z = x
print names_of1(x, locals())
def names_of1(var, callers_namespace):
return [name for (name, value) in callers_namespace.iteritems() if var is value]
foo1() # prints ['x', 'z']
If you're working with a Python that has stack frame support (most do, CPython does), it isn't required that you pass the locals dict into the names_of function; the function can retrieve that dictionary from its caller's frame itself:
def foo2():
xx = object()
yy = object()
zz = xx
print names_of2(xx)
def names_of2(var):
import inspect
callers_namespace = inspect.currentframe().f_back.f_locals
return [name for (name, value) in callers_namespace.iteritems() if var is value]
foo2() # ['xx', 'zz']
If you're working with a value type that you can assign a name attribute to, you can give it a name, and then use that:
class SomeClass(object):
pass
obj = SomeClass()
obj.name = 'obj'
class NamedInt(int):
__slots__ = ['name']
x = NamedInt(321)
x.name = 'x'
Finally, if you're working with class attributes and you want them to know their names (descriptors are the obvious use case), you can do cool tricks with metaclass programming like they do in the Django ORM and SQLAlchemy declarative-style table definitions:
class AutonamingType(type):
def __init__(cls, name, bases, attrs):
for (attrname, attrvalue) in attrs.iteritems():
if getattr(attrvalue, '__autoname__', False):
attrvalue.name = attrname
super(AutonamingType,cls).__init__(name, bases, attrs)
class NamedDescriptor(object):
__autoname__ = True
name = None
def __get__(self, instance, instance_type):
return self.name
class Foo(object):
__metaclass__ = AutonamingType
bar = NamedDescriptor()
baaz = NamedDescriptor()
lilfoo = Foo()
print lilfoo.bar # prints 'bar'
print lilfoo.baaz # prints 'baaz'
There are three ways to get "the" string representation of an object in python:
1: str()
>>> foo={"a":"z","b":"y"}
>>> str(foo)
"{'a': 'z', 'b': 'y'}"
2: repr()
>>> foo={"a":"z","b":"y"}
>>> repr(foo)
"{'a': 'z', 'b': 'y'}"
3: string interpolation:
>>> foo={"a":"z","b":"y"}
>>> "%s" % (foo,)
"{'a': 'z', 'b': 'y'}"
In this case all three methods generated the same output, the difference is that str() calls dict.__str__(), while repr() calls dict.__repr__(). str() is used on string interpolation, while repr() is used by Python internally on each object in a list or dict when you print the list or dict.
As Tendayi Mawushe mentiones above, string produced by repr isn't necessarily human-readable.
Also, the default implementation of .__str__() is to call .__repr__(), so if the class does not have it's own overrides to .__str__(), the value returned from .__repr__() is used.