Python utilizes a system, which is known as “Call by Object Reference” or “Call by assignment”. In the event that you pass arguments like whole numbers, strings or tuples to a function, the passing is like call-by-value because you can not change the value of the immutable objects being passed to the function. Whereas passing mutable objects can be considered as call by reference because when their values are changed inside the function, then it will also be reflected outside the function.
Example 1:
Python3
# Python code to demonstrate# call by valuestring = "Geeks"def test(string): string = "Lazyroar" print("Inside Function:", string) # Driver's codetest(string)print("Outside Function:", string) |
Output
Inside Function: Lazyroar Outside Function: Geeks
Example 2
Python3
# Python code to demonstrate# call by referencedef add_more(list): list.append(50) print("Inside Function", list)# Driver's codemylist = [10,20,30,40]add_more(mylist)print("Outside Function:", mylist) |
Output
Inside Function [10, 20, 30, 40, 50] Outside Function: [10, 20, 30, 40, 50]
Binding Names to Objects
In python, each variable to which we assign a value/container is treated as an object. When we are assigning a value to a variable, we are actually binding a name to an object.
Python3
a = "first"b = "first"# Returns the actual location# where the variable is storedprint(id(a))# Returns the actual location# where the variable is storedprint(id(b))# Returns true if both the variables# are stored in same locationprint(a is b) |
Output
110001234557894 110001234557894 True
Now, let’s try and understand this better with another example.
Example 2:
Python3
a = [10, 20, 30]b = [10, 20, 30]# return the location# where the variable# is storedprint(id(a))# return the location# where the variable# is storedprint(id(b))# returns false if the# location is not sameprint(a is b) |
Output
541190289536222 541190288737777 False
The output of the above two examples are different because the list is mutable and the string is immutable. An immutable variable cannot be changed once created. If we wish to change an immutable variable, such as a string, we must create a new instance and bind the variable to the new instance. Whereas, mutable variable can be changed in place.
Example 3:
Python3
def foo(a): # A new variable is assigned # for the new string a = "new value" print("Inside Function:", a) # Driver's codestring = "old value"foo(string)print("Outside Function:", string) |
Output:
Inside Function: new value Outside Function: old value
In the above example, a string which is an immutable type of object is passed as argument to the function foo. Within the scope of the given function foo, a= “new value” has been bounded to the same object that string has been bound outside. Within the scope of the function foo, we modify “old value”` to “new value”. Once we leave the scope of function foo , a=”new value” is no longer in the name space, and the value that string refers to was never changed.
Example 4: Now, let us look at how mutable variable is passed into the function.
Python3
def foo(a): a[0] = "Nothing" # Driver' codebar = ['Hi', 'how', 'are', 'you', 'doing']foo(bar)print(bar) |
Output:
['Nothing, 'how', 'are', 'you', 'doing']
When we pass a mutable variable into the function foo and modify it to some other name the function foo still points to that object and continue to point to that object during its execution.
