Related
Python's built-in open function returns a "file-like object". These file objects can be obtained in other ways as well, and may not actually represent files.
I haven't been able to find what I want to know about these.
https://docs.python.org/3/glossary.html#term-file-object states that all file objects all have 1 thing in common: they expose "a file-oriented API". But I can't find any documentation about this api.
A file object's write method seems to return an integer, but what does it represent? Is the return value guaranteed to be an integer? What methods and properties are file objects guaranteed to have?
People are so unlikely to use the return value of write that I wouldn't be surprised if any particular file-like object just returned None. That said, there is something resembling a spec.
The behavior a file-like object's methods should provide is documented in the io module docs, under the abstract base classes. While many file-like objects will not be instances of those ABCs, and many file-like objects will not provide all methods in the nearest ABC, methods they do provide should match the ABC docs.
For a binary file-like object, write should return the number of bytes written, as documented under RawIOBase.write.
For a text file-like object, write should return the number of characters written, as documented under TextIOBase.write.
When you call the object.__repr__() method in Python you get something like this back:
<__main__.Test object at 0x2aba1c0cf890>
Is there any way to get a hold of the memory address if you overload __repr__(), other then calling super(Class, obj).__repr__() and regexing it out?
The Python manual has this to say about id():
Return the "identity'' of an object.
This is an integer (or long integer)
which is guaranteed to be unique and
constant for this object during its
lifetime. Two objects with
non-overlapping lifetimes may have the
same id() value. (Implementation note:
this is the address of the object.)
So in CPython, this will be the address of the object. No such guarantee for any other Python interpreter, though.
Note that if you're writing a C extension, you have full access to the internals of the Python interpreter, including access to the addresses of objects directly.
You could reimplement the default repr this way:
def __repr__(self):
return '<%s.%s object at %s>' % (
self.__class__.__module__,
self.__class__.__name__,
hex(id(self))
)
Just use
id(object)
There are a few issues here that aren't covered by any of the other answers.
First, id only returns:
the “identity” of an object. This is an integer (or long integer) which is guaranteed to be unique and constant for this object during its lifetime. Two objects with non-overlapping lifetimes may have the same id() value.
In CPython, this happens to be the pointer to the PyObject that represents the object in the interpreter, which is the same thing that object.__repr__ displays. But this is just an implementation detail of CPython, not something that's true of Python in general. Jython doesn't deal in pointers, it deals in Java references (which the JVM of course probably represents as pointers, but you can't see those—and wouldn't want to, because the GC is allowed to move them around). PyPy lets different types have different kinds of id, but the most general is just an index into a table of objects you've called id on, which is obviously not going to be a pointer. I'm not sure about IronPython, but I'd suspect it's more like Jython than like CPython in this regard. So, in most Python implementations, there's no way to get whatever showed up in that repr, and no use if you did.
But what if you only care about CPython? That's a pretty common case, after all.
Well, first, you may notice that id is an integer;* if you want that 0x2aba1c0cf890 string instead of the number 46978822895760, you're going to have to format it yourself. Under the covers, I believe object.__repr__ is ultimately using printf's %p format, which you don't have from Python… but you can always do this:
format(id(spam), '#010x' if sys.maxsize.bit_length() <= 32 else '#18x')
* In 3.x, it's an int. In 2.x, it's an int if that's big enough to hold a pointer—which is may not be because of signed number issues on some platforms—and a long otherwise.
Is there anything you can do with these pointers besides print them out? Sure (again, assuming you only care about CPython).
All of the C API functions take a pointer to a PyObject or a related type. For those related types, you can just call PyFoo_Check to make sure it really is a Foo object, then cast with (PyFoo *)p. So, if you're writing a C extension, the id is exactly what you need.
What if you're writing pure Python code? You can call the exact same functions with pythonapi from ctypes.
Finally, a few of the other answers have brought up ctypes.addressof. That isn't relevant here. This only works for ctypes objects like c_int32 (and maybe a few memory-buffer-like objects, like those provided by numpy). And, even there, it isn't giving you the address of the c_int32 value, it's giving you the address of the C-level int32 that the c_int32 wraps up.
That being said, more often than not, if you really think you need the address of something, you didn't want a native Python object in the first place, you wanted a ctypes object.
Just in response to Torsten, I wasn't able to call addressof() on a regular python object. Furthermore, id(a) != addressof(a). This is in CPython, don't know about anything else.
>>> from ctypes import c_int, addressof
>>> a = 69
>>> addressof(a)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: invalid type
>>> b = c_int(69)
>>> addressof(b)
4300673472
>>> id(b)
4300673392
You can get something suitable for that purpose with:
id(self)
With ctypes, you can achieve the same thing with
>>> import ctypes
>>> a = (1,2,3)
>>> ctypes.addressof(a)
3077760748L
Documentation:
addressof(C instance) -> integer
Return the address of the C instance internal buffer
Note that in CPython, currently id(a) == ctypes.addressof(a), but ctypes.addressof should return the real address for each Python implementation, if
ctypes is supported
memory pointers are a valid notion.
Edit: added information about interpreter-independence of ctypes
I know this is an old question but if you're still programming, in python 3 these days... I have actually found that if it is a string, then there is a really easy way to do this:
>>> spam.upper
<built-in method upper of str object at 0x1042e4830>
>>> spam.upper()
'YO I NEED HELP!'
>>> id(spam)
4365109296
string conversion does not affect location in memory either:
>>> spam = {437 : 'passphrase'}
>>> object.__repr__(spam)
'<dict object at 0x1043313f0>'
>>> str(spam)
"{437: 'passphrase'}"
>>> object.__repr__(spam)
'<dict object at 0x1043313f0>'
You can get the memory address/location of any object by using the 'partition' method of the built-in 'str' type.
Here is an example of using it to get the memory address of an object:
Python 3.8.3 (default, May 27 2020, 02:08:17)
[GCC 9.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> object.__repr__(1)
'<int object at 0x7ca70923f0>'
>>> hex(int(object.__repr__(1).partition('object at ')[2].strip('>'), 16))
0x7ca70923f0
>>>
Here, I am using the built-in 'object' class' '__repr__' method with an object/item such as 1 as an argument to return the string and then I am partitioning that string which will return a tuple of the string before the string that I provided, the string that I provided and then the string after the string that I provided, and as the memory location is positioned after 'object at', I can get the memory address as it has partitioned it from that part.
And then as the memory address was returned as the third item in the returned tuple, I can access it with index 2 from the tuple. But then, it has a right angled bracket as a suffix in the string that I obtained, so I use the 'strip' function to remove it, which will return it without the angled bracket. I then transformed the resulted string into an integer with base 16 and then turn it into a hex number.
While it's true that id(object) gets the object's address in the default CPython implementation, this is generally useless... you can't do anything with the address from pure Python code.
The only time you would actually be able to use the address is from a C extension library... in which case it is trivial to get the object's address since Python objects are always passed around as C pointers.
If the __repr__ is overloaded, you may consider __str__ to see the memory address of the variable.
Here is the details of __repr__ versus __str__ by Moshe Zadka in StackOverflow.
There is a way to recovery the value from the 'id' command, here it the TL;DR.
ctypes.cast(memory_address,ctypes.py_object).value
source
I constantly see people state that "Everything in Python is an object.", but I haven't seen "thing" actually defined. This saying would lead me to believe that all tokens of any kind are also considered to be objects, including operators, punctuators, whitespace, etc. Is that actually the case? Is there a more concise way of stating what a Python object actually is?
Thanks
Anything that can be assigned to a variable is an object.
That includes functions, classes, and modules, and of course int's, str's, float's, list's, and everything else. It does not include whitespace, punctuation, or operators.
Just to mention it, there is the operator module in the standard library which includes functions that implement operators; those functions are objects. That doesn't mean + or * are objects.
I could go on and on, but this is simple and pretty complete.
Some values are obviously objects; they are instances of a class, have attributes, etc.
>>> i = 3
>>> type(i)
<type 'int'>
>>> i.denominator
1
Other values are less obviously objects. Types are objects:
>>> type(int)
<type 'type'>
>>> int.__mul__(3, 5)
15
Even type is an object (of type type, oddly enough):
>>> type(type)
<type 'type'>
Modules are objects:
>>> import sys
>>> type(sys)
<type 'module'>
Built-in functions are objects:
>>> type(sum)
<type 'builtin_function_or_method'>
In short, if you can reference it by name, it's an object.
What is generally meant is that most things, for example functions and methods are objects. Modules too. Classes (not just their instances) themselves are objects. and int/float/strings are objects. So, yes, things generally tend to be objects in Python. Cyphase is correct, I just wanted to give some examples of things that might not be immediately obvious as objects.
Being objects then a number of properties are observable on things that you would consider special case, baked-in stuff in other languages. Though __dict__, which allows arbitrary attribute assignment in Python, is often missing on things intended for large volume instantiations like int.
Therefore, at least on pure-Python objects, a lot of magic can happen, from introspection to things like creating a new class on the fly.
Kinda like turtles all the way down.
You're not going to find a rigorous definition like C++11's, because Python does not have a formal specification like C++11, it has a reference manual like pre-ISO C++. The Data model chapter is as rigorous as it gets:
Objects are Python’s abstraction for data. All data in a Python program is represented by objects or by relations between objects. (In a sense, and in conformance to Von Neumann’s model of a “stored program computer,” code is also represented by objects.)
Every object has an identity, a type and a value. An object’s identity never changes once it has been created; you may think of it as the object’s address in memory. …
The glossary also has a shorter definition:
Any data with state (attributes or value) and defined behavior (methods).
And it's true that everything in Python has methods and (other) attributes. Even if there are no public methods, there's a set of special methods and values inherited from the object base class, like the __str__ method.
This wasn't true in versions of Python before 2.2, which is part of the reason we have multiple words for nearly the same thing—object, data, value; type, class… But from then on, the following kinds of things are identical:
Objects.
Things that can be returned or yielded by a function.
Things that can be stored in a variable (including a parameter).
Things that are instances of type object (usually indirectly, through a subclass or two).
Things that can be the value resulting from an expression.
Things represented by pointers to PyObject structs in CPython.
… and so on.
That's what "everything is an object" means.
It also means that Python doesn't have "native types" and "class types" like Java, or "value types" and "reference types" like C#; there's only one kind of thing, objects.
This saying would lead me to believe that all tokens of any kind are also considered to be objects, including operators, punctuators, whitespace, etc. Is that actually the case?
No. Those things don't have values, so they're not objects.1
Also, variables are not objects. Unlike C-style variables, Python variables are not memory locations with a type containing a value, they're just names bound to a value in some namespace.2 And that's why you can't pass around references to variables; there is no "thing" to reference.3
Assignment targets are also not objects. They sometimes look a lot like values, and even the core devs sometimes refer to things like the a, b in a, b = 1, 2 loosely as a tuple object—but there is no tuple there.4
There's also a bit of apparent vagueness with things like elements of a numpy.array (or an array.array or ctypes.Structure). When you write a[0] = 3, the 3 object doesn't get stored in the array the way it would with a list. Instead, numpy stores some bytes that Python doesn't even understand, but that it can use to do "the same thing a 3 would do" in array-wide operations, or to make a new copy of the 3 object if you later ask for a[0] = 3.
But if you go back to the definition, it's pretty clear that this "virtual 3" is not an object—while it has a type and value, it does not have an identity.
1. At the meta level, you can write an import hook that can act on imported code as a byte string, a decoded Unicode string, a list of token tuples, an AST node, a code object, or a module, and all of those are objects… But at the "normal" level, from within the code being imported, tokens, etc. are not objects.
2. Under the covers, there's almost always a string object to represent that name, stored in a dict or tuple that represents the namespace, as you can see by calling globals() or dir(self). But that's not what the variable is.
3. A closure cell is sort of a way of representing a reference to a variable, but really, it's the cell itself that's an object, and the variables at different scopes are just a slightly special kind of name for that cell.
4. However, in a[0] = 3, although a[0] isn't a value, a and 0 are, because that assignment is equivalent to the expression a.__setitem__(0, 3), except that it's not an expression.
I read on another Stack Overflow question that Python was just like Ruby, as it relates to "everything's an object," and everything in Python was an object, just like Ruby.
Is this true? Is everything an object in Python like Ruby?
How are the two different in this respect or are they really the same? For example, can you take a number and do the Ruby stuff I've seen like:
y = 5.plus 6
Can that be done the same way in Python?
DiveIntoPython - Everything Is an Object
Everything in Python is an object, and almost everything has attributes and methods. All functions have a built-in attribute __doc__, which returns the doc string defined in the function's source code. The sys module is an object which has (among other things) an attribute called path. And so forth.
Still, this begs the question. What is an object? Different programming languages define “object” in different ways. In some, it means that all objects must have attributes and methods; in others, it means that all objects are subclassable. In Python, the definition is looser; some objects have neither attributes nor methods (more on this in Chapter 3), and not all objects are subclassable (more on this in Chapter 5). But everything is an object in the sense that it can be assigned to a variable or passed as an argument to a function (more in this in Chapter 4).
Ruby Docs - To Ruby From Python
As with Python, in Ruby,... Everything is an object
So there you have it from Ruby's own website: in Python everything is an object.
While everything is an object in Python, it differs from Ruby in its approach to resolving names and interacting with objects.
For example, while Ruby provides you with a 'to_s' method on the Object base class, in order to expose that functionality, Python integrates it into the string type itself - you convert a type to a string by constructing a string from it. Instead of 5.to_s, you have str(5).
Don't be fooled, though. There's still a method behind the scenes - which is why this code works:
(5).__str__()
So in practice, the two are fundamentally similar, but you use them differently. Length for sequences like lists and tuples in Python is another example of this principle at work - the actual feature is built upon methods with special names, but exposed through a simpler, easier-to-use interface (the len function).
The Python equivalent to what you wrote in your question would thus be:
(5).__add__(6)
The other difference that's important is how global functions are implemented. In Python, globals are represented by a dictionary (as are locals). This means that the following:
foo(5)
Is equivalent to this in Python:
globals()["foo"].__call__(5)
While Ruby effectively does this:
Object.foo(5)
This has a large impact on the approach used when writing code in both languages. Ruby libraries tend to grow through the addition of methods to existing types like Object, while Python libraries tend to grow through the addition of global functions to a given module.
"everything" is a tad of an overbid, for both Python and Ruby -- for example, if is not "an object", rather it's a keyword used to start a conditional statement or (in Python) inside list comprehensions and generator expressions. The enthusiasm of finding out that functions, classes, methods, and all sort of such things that aren't really objects in (say) C++, are objects in Ruby or Python, causes such enthusiasm. Other things may be objects in Ruby but not Python or viceversa (code blocks, regular expressions, ...).
In answer to your second question, yes:
>>> (1).__add__(2)
3
Yep, as far as I know everything is an object in Python. Certainly the primitive and builtin types (int, long, str, float, etc.) can be subclassed - and in fact the types themselves are objects. Functions are objects, classes are objects, even code blocks are objects in a sense... I can't think of anything in Python that can't be treated as an object.
To add a comment to other people's excellent answers: everything is an object, but some – notably strings and numeric types – are immutable. This means that these types behave the way they do in languages like C or Java (where integers, etc. are not objects) with respect to assignment, parameter passing, etc, and you never have to worry about traps caused by pass-by-reference. It's rather a good solution :-)
Hello and answer is out of the bat not everything, reference is more complete than that and offers many more avenues, within Python 3.8.5 for example Delimiters, Operators and Keywords are not objects. stackoverflow.com/a/66374328/11554034
Have explained it with some detail in that link feel free to check it along.
Anyway, next one says that statement you can correct it by saying (something more correct, although if still can be more completed feel free):
"Everything in a logical line that is not NEWLINE, INDENT, DEDENT, Space bar Character, Operator, Keyword or Delimiter is an object in Python."
Cheers.
Yes, everything is object in Python as long as I researched.
The documentation says below:
Objects are Python’s abstraction for data. All data in a Python
program is represented by objects or by relations between objects.
Every object has an identity, a type and a value.
And, I also checked the type of each value and if each of them is the instance of a particular class as shown below:
from types import FunctionType
class Person:
pass
def test():
pass
print(type("Hello"), isinstance("Hello", str))
print(type(100), isinstance(100, int))
print(type(100.23), isinstance(100.23, float))
print(type(100 + 2j), isinstance(100 + 2j, complex))
print(type(True), isinstance(True, bool))
print(type(None), isinstance(None, type(None)))
print(type([]), isinstance([], list))
print(type(()), isinstance((), tuple))
print(type({}), isinstance({}, dict))
print(type({""}), isinstance({""}, set))
print(type(Person), isinstance(Person, type))
print(type(test), isinstance(test, FunctionType))
Output:
<class 'str'> True
<class 'int'> True
<class 'float'> True
<class 'complex'> True
<class 'bool'> True
<class 'NoneType'> True
<class 'list'> True
<class 'tuple'> True
<class 'dict'> True
<class 'set'> True
<class 'type'> True
<class 'function'> True
I was looking at the builtin object methods in the Python documentation, and I was interested in the documentation for object.__repr__(self). Here's what it says:
Called by the repr() built-in function
and by string conversions (reverse
quotes) to compute the “official”
string representation of an object. If
at all possible, this should look like
a valid Python expression that could
be used to recreate an object with the
same value (given an appropriate
environment). If this is not possible,
a string of the form <...some useful
description...> should be returned.
The return value must be a string
object. If a class defines repr()
but not str(), then repr() is
also used when an “informal” string
representation of instances of that
class is required.
This is typically used for debugging,
so it is important that the
representation is information-rich and
unambiguous
The most interesting part to me, was...
If at all possible, this should look like a valid Python expression that could be used to recreate an object with the same value
... but I'm not sure exactly what this means. It says it should look like an expression which can be used to recreate the object, but does that mean it should just be an example of the sort of expression you could use, or should it be an actual expression, that can be executed (eval etc..) to recreate the object? Or... should it be just a rehasing of the actual expression which was used, for pure information purposes?
In general I'm a bit confused as to exactly what I should be putting here.
>>> from datetime import date
>>>
>>> repr(date.today()) # calls date.today().__repr__()
'datetime.date(2009, 1, 16)'
>>> eval(_) # _ is the output of the last command
datetime.date(2009, 1, 16)
The output is a string that can be parsed by the python interpreter and results in an equal object.
If that's not possible, it should return a string in the form of <...some useful description...>.
It should be a Python expression that, when eval'd, creates an object with the exact same properties as this one. For example, if you have a Fraction class that contains two integers, a numerator and denominator, your __repr__() method would look like this:
# in the definition of Fraction class
def __repr__(self):
return "Fraction(%d, %d)" % (self.numerator, self.denominator)
Assuming that the constructor takes those two values.
Guideline: If you can succinctly provide an exact representation, format it as a Python expression (which implies that it can be both eval'd and copied directly into source code, in the right context). If providing an inexact representation, use <...> format.
There are many possible representations for any value, but the one that's most interesting for Python programmers is an expression that recreates the value. Remember that those who understand Python are the target audience—and that's also why inexact representations should include relevant context. Even the default <XXX object at 0xNNN>, while almost entirely useless, still provides type, id() (to distinguish different objects), and indication that no better representation is available.
"but does that mean it should just be an example of the sort of expression you could use, or should it be an actual expression, that can be executed (eval etc..) to recreate the object? Or... should it be just a rehasing of the actual expression which was used, for pure information purposes?"
Wow, that's a lot of hand-wringing.
An "an example of the sort of expression you could use" would not be a representation of a specific object. That can't be useful or meaningful.
What is the difference between "an actual expression, that can ... recreate the object" and "a rehasing of the actual expression which was used [to create the object]"? Both are an expression that creates the object. There's no practical distinction between these. A repr call could produce either a new expression or the original expression. In many cases, they're the same.
Note that this isn't always possible, practical or desirable.
In some cases, you'll notice that repr() presents a string which is clearly not an expression of any kind. The default repr() for any class you define isn't useful as an expression.
In some cases, you might have mutual (or circular) references between objects. The repr() of that tangled hierarchy can't make sense.
In many cases, an object is built incrementally via a parser. For example, from XML or JSON or something. What would the repr be? The original XML or JSON? Clearly not, since they're not Python. It could be some Python expression that generated the XML. However, for a gigantic XML document, it might not be possible to write a single Python expression that was the functional equivalent of parsing XML.
'repr' means representation.
First, we create an instance of class coordinate.
x = Coordinate(3, 4)
Then if we input x into console, the output is
<__main__.Coordinate at 0x7fcd40ab27b8>
If you use repr():
>>> repr(x)
Coordinate(3, 4)
the output is as same as 'Coordinate(3, 4)', except it is a string. You can use it to recreate a instance of coordinate.
In conclusion, repr() method is print out a string, which is the representation of the object.
To see how the repr works within a class, run the following code, first with and then without the repr method.
class Coordinate (object):
def __init__(self,x,y):
self.x = x
self.y = y
def getX(self):
# Getter method for a Coordinate object's x coordinate.
# Getter methods are better practice than just accessing an attribute directly
return self.x
def getY(self):
# Getter method for a Coordinate object's y coordinate
return self.y
def __repr__(self): #remove this and the next line and re-run
return 'Coordinate(' + str(self.getX()) + ',' + str(self.getY()) + ')'
>>>c = Coordinate(2,-8)
>>>print(c)
I think the confusion over here roots from the english. I mean __repr__(); short for 'representation' of the value I'm guessing, like #S.Lott said
"What is the difference between "an actual expression, that can ... recreate the object" and "a rehasing of the actual expression which was used [to create the object]"? Both are an expression that creates the object. There's no practical distinction between these. A repr call could produce either a new expression or the original expression. In many cases, they're the same."
But in some cases they might be different. E.g; coordinate points, you might want c.coordinate to return: 3,5 but c.__repr__ to return Coordinate(3, 5). Hope that makes more sense...