python example - How do I pass a variable by reference?




object int (20)

The Python documentation seems unclear about whether parameters are passed by reference or value, and the following code produces the unchanged value 'Original'

class PassByReference:
    def __init__(self):
        self.variable = 'Original'
        self.change(self.variable)
        print(self.variable)

    def change(self, var):
        var = 'Changed'

Is there something I can do to pass the variable by actual reference?


Answers

There is a little trick to pass an object by reference, even though the language doesn't make it possible. It works in Java too, it's the list with one item. ;-)

class PassByReference:
    def __init__(self, name):
        self.name = name

def changeRef(ref):
    ref[0] = PassByReference('Michael')

obj = PassByReference('Peter')
print obj.name

p = [obj] # A pointer to obj! ;-)
changeRef(p)

print p[0].name # p->name

It's an ugly hack, but it works. ;-P


Aside from all the great explanations on how this stuff works in Python, I don't see a simple suggestion for the problem. As you seem to do create objects and instances, the pythonic way of handling instance variables and changing them is the following:

class PassByReference:
    def __init__(self):
        self.variable = 'Original'
        self.Change()
        print self.variable

    def Change(self):
        self.variable = 'Changed'

In instance methods, you normally refer to self to access instance attributes. It is normal to set instance attributes in __init__ and read or change them in instance methods. That is also why you pass self als the first argument to def Change.

Another solution would be to create a static method like this:

class PassByReference:
    def __init__(self):
        self.variable = 'Original'
        self.variable = PassByReference.Change(self.variable)
        print self.variable

    @staticmethod
    def Change(var):
        var = 'Changed'
        return var

I found the other answers rather long and complicated, so I created this simple diagram to explain the way Python treats variables and parameters.


Think of stuff being passed by assignment instead of by reference/by value. That way, it is allways clear, what is happening as long as you understand what happens during normal assignment.

So, when passing a list to a function/method, the list is assigned to the parameter name. Appending to the list will result in the list being modified. Reassigning the list inside the function will not change the original list, since:

a = [1, 2, 3]
b = a
b.append(4)
b = ['a', 'b']
print a, b      # prints [1, 2, 3, 4] ['a', 'b']

Since immutable types cannot be modified, they seem like being passed by value - passing an int into a function means assigning the int to the functions parameter. You can only ever reassign that, but it won't change the originial variables value.


You got some really good answers here.

x = [ 2, 4, 4, 5, 5 ]
print x  # 2, 4, 4, 5, 5

def go( li ) :
  li = [ 5, 6, 7, 8 ]  # re-assigning what li POINTS TO, does not
  # change the value of the ORIGINAL variable x

go( x ) 
print x  # 2, 4, 4, 5, 5  [ STILL! ]


raw_input( 'press any key to continue' )

Effbot (aka Fredrik Lundh) has described Python's variable passing style as call-by-object: http://effbot.org/zone/call-by-object.htm

Objects are allocated on the heap and pointers to them can be passed around anywhere.

  • When you make an assignment such as x = 1000, a dictionary entry is created that maps the string "x" in the current namespace to a pointer to the integer object containing one thousand.

  • When you update "x" with x = 2000, a new integer object is created and the dictionary is updated to point at the new object. The old one thousand object is unchanged (and may or may not be alive depending on whether anything else refers to the object).

  • When you do a new assignment such as y = x, a new dictionary entry "y" is created that points to the same object as the entry for "x".

  • Objects like strings and integers are immutable. This simply means that there are no methods that can change the object after it has been created. For example, once the integer object one-thousand is created, it will never change. Math is done by creating new integer objects.

  • Objects like lists are mutable. This means that the contents of the object can be changed by anything pointing to the object. For example, x = []; y = x; x.append(10); print y will print [10]. The empty list was created. Both "x" and "y" point to the same list. The append method mutates (updates) the list object (like adding a record to a database) and the result is visible to both "x" and "y" (just as a database update would be visible to every connection to that database).

Hope that clarifies the issue for you.


given the way python handles values and references to them, the only way you can reference an arbitrary instance attribute is by name:

class PassByReferenceIsh:
    def __init__(self):
        self.variable = 'Original'
        self.change('variable')
        print self.variable

    def change(self, var):
        self.__dict__[var] = 'Changed'

in real code you would, of course, add error checking on the dict lookup.


You can merely use an empty class as an instance to store reference objects because internally object attributes are stored in an instance dictionary. See the example.

class RefsObj(object):
    "A class which helps to create references to variables."
    pass

...

# an example of usage
def change_ref_var(ref_obj):
    ref_obj.val = 24

ref_obj = RefsObj()
ref_obj.val = 1
print(ref_obj.val) # or print ref_obj.val for python2
change_ref_var(ref_obj)
print(ref_obj.val)

There are no variables in Python

The key to understanding parameter passing is to stop thinking about "variables". There are names and objects in Python and together they appear like variables, but it is useful to always distinguish the three.

  1. Python has names and objects.
  2. Assignment binds a name to an object.
  3. Passing an argument into a function also binds a name (the parameter name of the function) to an object.

That is all there is to it. Mutability is irrelevant for this question.

Example:

a = 1

This binds the name a to an object of type integer that holds the value 1.

b = x

This binds the name b to the same object that the name x is currently bound to. Afterwards, the name b has nothing to do with the name x any more.

See sections 3.1 and 4.2 in the Python 3 language reference.


So in the code shown in the question, the statement self.Change(self.variable) binds the name var (in the scope of function Change) to the object that holds the value 'Original' and the assignment var = 'Changed' (in the body of function Change) assigns that same name again: to some other object (that happens to hold a string as well but could have been something else entirely).


While pass by reference is nothing that fits well into python and should be rarely used there are some workarounds that actually can work to get the object currently assigned to a local variable or even reassign a local variable from inside of a called function.

The basic idea is to have a function that can do that access and can be passed as object into other functions or stored in a class.

One way is to use global (for global variables) or nonlocal (for local variables in a function) in a wrapper function.

def change(wrapper):
    wrapper(7)

x = 5
def setter(val):
    global x
    x = val
print(x)

The same idea works for reading and deleting a variable.

For just reading there is even a shorter way of just using lambda: x which returns a callable that when called returns the current value of x. This is somewhat like "call by name" used in languages in the distant past.

Passing 3 wrappers to access a variable is a bit unwieldy so those can be wrapped into a class that has a proxy attribute:

class ByRef:
    def __init__(self, r, w, d):
        self._read = r
        self._write = w
        self._delete = d
    def set(self, val):
        self._write(val)
    def get(self):
        return self._read()
    def remove(self):
        self._delete()
    wrapped = property(get, set, remove)

# left as an exercise for the reader: define set, get, remove as local functions using global / nonlocal
r = ByRef(get, set, remove)
r.wrapped = 15

Pythons "reflection" support makes it possible to get a object that is capable of reassigning a name/variable in a given scope without defining functions explicitly in that scope:

class ByRef:
    def __init__(self, locs, name):
        self._locs = locs
        self._name = name
    def set(self, val):
        self._locs[self._name] = val
    def get(self):
        return self._locs[self._name]
    def remove(self):
        del self._locs[self._name]
    wrapped = property(get, set, remove)

def change(x):
    x.wrapped = 7

def test_me():
    x = 6
    print(x)
    change(ByRef(locals(), "x"))
    print(x)

Here the ByRef class wraps a dictionary access. So attribute access to wrapped is translated to a item access in the passed dictionary. By passing the result of the builtin locals and the name of a local variable this ends up accessing a local variable. The python documentation as of 3.5 advises that changing the dictionary might not work but it seems to work for me.


Here is the simple (I hope) explanation of the concept pass by object used in Python.
Whenever you pass an object to the function, the object itself is passed (object in Python is actually what you'd call a value in other programming languages) not the reference to this object. In other words, when you call:

def change_me(list):
   list = [1, 2, 3]

my_list = [0, 1]
change_me(my_list)

The actual object - [0, 1] (which would be called a value in other programming languages) is being passed. So in fact the function change_me will try to do something like:

[0, 1] = [1, 2, 3]

which obviously will not change the object passed to the function. If the function looked like this:

def change_me(list):
   list.append(2)

Then the call would result in:

[0, 1].append(2)

which obviously will change the object. This answer explains it well.


A simple trick I normally use is to just wrap it in a list:

def Change(self, var):
    var[0] = 'Changed'

variable = ['Original']
self.Change(variable)      
print variable[0]

(Yeah I know this can be inconvenient, but sometimes it is simple enough to do this.)


The problem comes from a misunderstanding of what variables are in Python. If you're used to most traditional languages, you have a mental model of what happens in the following sequence:

a = 1
a = 2

You believe that a is a memory location that stores the value 1, then is updated to store the value 2. That's not how things work in Python. Rather, a starts as a reference to an object with the value 1, then gets reassigned as a reference to an object with the value 2. Those two objects may continue to coexist even though a doesn't refer to the first one anymore; in fact they may be shared by any number of other references within the program.

When you call a function with a parameter, a new reference is created that refers to the object passed in. This is separate from the reference that was used in the function call, so there's no way to update that reference and make it refer to a new object. In your example:

def __init__(self):
    self.variable = 'Original'
    self.Change(self.variable)

def Change(self, var):
    var = 'Changed'

self.variable is a reference to the string object 'Original'. When you call Change you create a second reference var to the object. Inside the function you reassign the reference var to a different string object 'Changed', but the reference self.variable is separate and does not change.

The only way around this is to pass a mutable object. Because both references refer to the same object, any changes to the object are reflected in both places.

def __init__(self):         
    self.variable = ['Original']
    self.Change(self.variable)

def Change(self, var):
    var[0] = 'Changed'

In this case the variable titled var in the method Change is assigned a reference to self.variable, and you immediately assign a string to var. It's no longer pointing to self.variable. The following code snippet shows what would happen if you modify the data structure pointed to by var and self.variable, in this case a list:

>>> class PassByReference:
...     def __init__(self):
...         self.variable = ['Original']
...         self.change(self.variable)
...         print self.variable
...         
...     def change(self, var):
...         var.append('Changed')
... 
>>> q = PassByReference()
['Original', 'Changed']
>>> 

I'm sure someone else could clarify this further.


It is neither pass-by-value or pass-by-reference - it is call-by-object. See this, by Fredrik Lundh:

http://effbot.org/zone/call-by-object.htm

Here is a significant quote:

"...variables [names] are not objects; they cannot be denoted by other variables or referred to by objects."

In your example, when the Change method is called--a namespace is created for it; and var becomes a name, within that namespace, for the string object 'Original'. That object then has a name in two namespaces. Next, var = 'Changed' binds var to a new string object, and thus the method's namespace forgets about 'Original'. Finally, that namespace is forgotten, and the string 'Changed' along with it.


As you can state you need to have a mutable object, but let me suggest you to check over the global variables as they can help you or even solve this kind of issue!

http://docs.python.org/3/faq/programming.html#what-are-the-rules-for-local-and-global-variables-in-python

example:

>>> def x(y):
...     global z
...     z = y
...

>>> x
<function x at 0x00000000020E1730>
>>> y
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'y' is not defined
>>> z
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'z' is not defined

>>> x(2)
>>> x
<function x at 0x00000000020E1730>
>>> y
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'y' is not defined
>>> z
2

A lot of insights in answers here, but i think an additional point is not clearly mentioned here explicitly. Quoting from python documentation https://docs.python.org/2/faq/programming.html#what-are-the-rules-for-local-and-global-variables-in-python

"In Python, variables that are only referenced inside a function are implicitly global. If a variable is assigned a new value anywhere within the function’s body, it’s assumed to be a local. If a variable is ever assigned a new value inside the function, the variable is implicitly local, and you need to explicitly declare it as ‘global’. Though a bit surprising at first, a moment’s consideration explains this. On one hand, requiring global for assigned variables provides a bar against unintended side-effects. On the other hand, if global was required for all global references, you’d be using global all the time. You’d have to declare as global every reference to a built-in function or to a component of an imported module. This clutter would defeat the usefulness of the global declaration for identifying side-effects."

Even when passing a mutable object to a function this still applies. And to me clearly explains the reason for the difference in behavior between assigning to the object and operating on the object in the function.

def test(l):
    print "Received", l , id(l)
    l = [0, 0, 0]
    print "Changed to", l, id(l)  # New local object created, breaking link to global l

l= [1,2,3]
print "Original", l, id(l)
test(l)
print "After", l, id(l)

gives:

Original [1, 2, 3] 4454645632
Received [1, 2, 3] 4454645632
Changed to [0, 0, 0] 4474591928
After [1, 2, 3] 4454645632

The assignment to an global variable that is not declared global therefore creates a new local object and breaks the link to the original object.


Technically, Python always uses pass by reference values. I am going to repeat my other answer to support my statement.

Python always uses pass-by-reference values. There isn't any exception. Any variable assignment means copying the reference value. No exception. Any variable is the name bound to the reference value. Always.

You can think about a reference value as the address of the target object. The address is automatically dereferenced when used. This way, working with the reference value, it seems you work directly with the target object. But there always is a reference in between, one step more to jump to the target.

Here is the example that proves that Python uses passing by reference:

If the argument was passed by value, the outer lst could not be modified. The green are the target objects (the black is the value stored inside, the red is the object type), the yellow is the memory with the reference value inside -- drawn as the arrow. The blue solid arrow is the reference value that was passed to the function (via the dashed blue arrow path). The ugly dark yellow is the internal dictionary. (It actually could be drawn also as a green ellipse. The colour and the shape only says it is internal.)

You can use the id() built-in function to learn what the reference value is (that is, the address of the target object).

In compiled languages, a variable is a memory space that is able to capture the value of the type. In Python, a variable is a name (captured internally as a string) bound to the reference variable that holds the reference value to the target object. The name of the variable is the key in the internal dictionary, the value part of that dictionary item stores the reference value to the target.

Reference values are hidden in Python. There isn't any explicit user type for storing the reference value. However, you can use a list element (or element in any other suitable container type) as the reference variable, because all containers do store the elements also as references to the target objects. In other words, elements are actually not contained inside the container -- only the references to elements are.


I used the following method to quickly convert a couple of Fortran codes to Python. True, it's not pass by reference as the original question was posed, but is a simple work around in some cases.

a=0
b=0
c=0
def myfunc(a,b,c):
    a=1
    b=2
    c=3
    return a,b,c

a,b,c = myfunc(a,b,c)
print a,b,c

Just to show the contrast, compare the following C++ and Java snippets:

In C++: Note: Bad code - memory leaks! But it demonstrates the point.

void cppMethod(int val, int &ref, Dog obj, Dog &objRef, Dog *objPtr, Dog *&objPtrRef)
{
    val = 7; // Modifies the copy
    ref = 7; // Modifies the original variable
    obj.SetName("obj"); // Modifies the copy of Dog passed
    objRef.SetName("objRef"); // Modifies the original Dog passed
    objPtr->SetName("objPtr"); // Modifies the original Dog pointed to 
                               // by the copy of the pointer passed.
    objPtr = new Dog("newObjPtr");  // Modifies the copy of the pointer, 
                                   // leaving the original object alone.
    objPtrRef->SetName("objRefPtr"); // Modifies the original Dog pointed to 
                                    // by the original pointer passed. 
    objPtrRef = new Dog("newObjPtrRef"); // Modifies the original pointer passed
}

int main()
{
    int a = 0;
    int b = 0;
    Dog d0 = Dog("d0");
    Dog d1 = Dog("d1");
    Dog *d2 = new Dog("d2");
    Dog *d3 = new Dog("d3");
    cppMethod(a, b, d0, d1, d2, d3);
    // a is still set to 0
    // b is now set to 7
    // d0 still have name "d0"
    // d1 now has name "objRef"
    // d2 now has name "objPtr"
    // d3 now has name "newObjPtrRef"
}

In Java,

public static void javaMethod(int val, Dog objPtr)
{
   val = 7; // Modifies the copy
   objPtr.SetName("objPtr") // Modifies the original Dog pointed to 
                            // by the copy of the pointer passed.
   objPtr = new Dog("newObjPtr");  // Modifies the copy of the pointer, 
                                  // leaving the original object alone.
}

public static void main()
{
    int a = 0;
    Dog d0 = new Dog("d0");
    javaMethod(a, d0);
    // a is still set to 0
    // d0 now has name "objPtr"
}

Java only has the two types of passing: by value for built-in types, and by value of the pointer for object types.





python reference parameter-passing pass-by-reference