CS 150 - Spring 2017 - Lecture 11 - Objects and references

  • objects
       - almost everything in Python is actually an object!
          - ints, floats, bools, etc.
       - what does this mean?
          - they have data
          - they also have methods

          >>> x = -10
          >>> x.bit_length()
          4
          >>> x.__abs__()
          10
          >>> (-10).__abs__() # we need parenthesis to avoid confusion with floating point numbers
          10
       
       - we can see all the methods associated with an object using "help" just like we did for functions
          - for ints
          
          >>> help(int)

          gives all of the methods that can be called on an int object

          >>> x = 10
          >>> help(x)

          - for strings

          >>> help(str)


  • references
       - objects reside in memory
          - anytime we create a new int, float, string, list, etc. it is allocated in memory

       - variables are references to objects in memory
          - a variable does NOT hold the value itself, but it is a references to where that values resides
          - arm/hand metaphor
             - think of variables like your hands
             - you can point them at things and then ask questions:
                - what is the name of the thing that my right hand points to?
                - for the thing that my left hand points to, do something?
             - you can point them to new things (i.e. assignment)
             - you can point point them at the same thing (i.e. assignment one to the other)
             - but they are separate things!
                - similarly, variables are separate things
                - they can reference the same thing, but they are not the same thing
                
       - when you ask for the value of a variable, it's actually getting the value of the things it references in memory

  • assignment =
       - when we say x = y what we're actually saying is let x reference the same thing that y references
       - x and y are still separate variables
          - if we say x = z that does NOT change the value of y

  • mutable objects
       - if an object is mutable (i.e. it has methods that change the object) then we have to be careful when we have multiple things referencing the same object (this is called "aliasing")

          >>> x = [1, 2, 3, 4, 5]
          >>> y = x

          - what does this look like in memory?
             - there is one list object
             - both x and y are references to that same object (drawn as arrows in class)
          
          - what happens when I call a method that changes/mutates the object?

             >>> y.reverse()
             >>> y
             [5, 4, 3, 2, 1]
             >>> x
             [5, 4, 3, 2, 1]

             x and y are references to the same object!

             >>> x[0] = 0
             >>> x
             [0, 4, 3, 2, 1]
             >>> y
             [0, 4, 3, 2, 1]

             things that mutate the object done on either variable will affect this object

             >>> y[0] = 15
             >>> x
             [15, 4, 3, 2, 1]
             >>> y
             [15, 4, 3, 2, 1]

       - If we change what one of them references using assignment, then it will NOT affect the original object
          >>> y = [0] * 5
          >>> y
          [0, 0, 0, 0, 0]
          >>> x
          [0, 4, 3, 2, 1]

          there are now two separate objects and x and y each reference a different object

       - why hasn't this problem come up with ints/floats/bools/strings? We said they're objects?
          >>> x = 10
          >>> y = x

          - what does the memory picture look like?
             - we just have one int object!
             - x and y are both references to that one int object
          - it seems like we could have the same problem as with lists...
          - ints/floats/bools/strings are not mutable!
             - there is no way for us to change the underlying object
                - therefore, even though two variables reference the same int, it's as if they referenced separate ints
          - draw the memory picture for the following:

             >>> x = 10
             >>> y = x
             >>> y = 15
             >>> x
             10
             >>> y
             15

             - we now have 2 objects (10 and 15)
             - just like with lists, changing what y references does not affect x


  • parameter passing and references
       - some terminology:
          - when we define a functions parameters we are defining the "formal parameters"
          
          def my_function(a, b):
             return a+b

             - a and b are formal parameters
             - until the function is actually called, they don't represent actual values
          - when we call a function, the values that we give to the function are called the "actual parameters" (sometimes also called the arguments)

             >>> x = 10
             >>> y = 20
             >>> my_function(x, y)
             30

             - the values 10 and 20 are the actual parameters (or similarly, x and y become the actual parameters)

       - when a function is called the following happens:
          - the values of the actual parameters are determined (we evaluate the expression representing each parameter)
             - the value is just an object (could be an int, float, string, etc)
          - these values are then "bound" or assigned to the formal parameters
             - it's very similar to assignment
             - the formal parameters represent new variables
             - the formal parameters will then REFERENCE the same objects as those passed in through the actual parameters
       
       - let's consider the picture for our function above
          - x and y are both references to int objects
          - when we call my_function, the formal parameters a and b, will represent two new variables
          - these variables will reference the same thing as their actual parameters
             - think of it like running the statements:
             a = x
             b = y
             
                - a will reference the same thing as x
                - b will reference the same thing as y

       - for non-mutable objects, this whole story doesn't really matter. Why?
          - they could be references to the same thing or copies, if we can't mutate the object, the behavior is the same
       - how does this change for mutable objects?

          def changer(a):
             a[0] = 100

          >>> x = [1, 2, 3, 4, 5]
          >>> changer(x)

          - what does the picture look like for this function call?
             - x was a variable that references a list object
             - when the function was called, the formal parameter a will represent a new variable
             - a will reference the same thing as x
                - think of it like running the statement
                a = x
          - because the object is mutable and since the formal parameter references the same object as what was passed in, changes made to the object referenced by a will also be seen in x
       - notice, however, that operations that do not change/mutate the object will NOT be seen outside the function
          def no_changer(a):
             a = [0]*5

          >>> x = [1, 2, 3, 4, 5]
          >>> no_changer(x)

          - in this case, we're assigning a to some new object
             - we can't change what x references!
             - x and a will no longer reference the same object
             - any changes to a after this will not affect x

  • why is variable assignment and parameter passing done based on the references (i.e. a shallow copy) rather than a deep copy of the whole object?
       - performance (it takes work to copy the object)
          - often we just want the value and don't need to mutate the underlying object

  • slicing revisited
       - what does slicing do with respect to objects?
          >>> x = [1, 2, 3, 4, 5]
          >>> y = x[0:2]
          >>> y
          [1, 2]
          >>> y[0] = 100
          >>> x
          [1, 2, 3, 4, 5]

       - what does this say that slicing does?
          - slicing creates a new list object
       
       - given this, how could we create a deep copy of other_list?
          >>> my_list = [1, 2, 3, 4, 5]
          >>> other_list = my_list[:]
          >>> other_list[3] = 100
          >>> other_list
          [1, 2, 3, 100, 5]
          >>> my_list
          [1, 2, 3, 4, 5]

       - besides accessing a slice, we can also assign to a slice
          >>> x = [1, 2, 3, 4]
          >>> y = [5, 6, 7, 8]
          >>> x[0:2] = y[0:2]
          >>> x
          [5, 6, 3, 4]
          >>> y
          [5, 6, 7, 8]
       
       - slicing creates a new list, so there is no sharing
          >>> x[0] = 100
          >>> x
          [100, 6, 3, 4]
          >>> y
          [5, 6, 7, 8]
       
       - in fact, we can slip in a larger slice if we'd like
          >>> x = [1, 2, 3, 4]
          >>> y = [5, 6, 7, 8]
          >>> x[0:2] = y[0:4]
          >>> x
          [5, 6, 7, 8, 3, 4]
          >>> y
          [5, 6, 7, 8]

       - be careful, though... assigning to a slice is different than assigning to an index
          >>> x = [1, 2, 3, 4]
          >>> y = [5, 6, 7, 8]
          >>> x[0] = y[0:2]
          >>> x
          [[5, 6], 2, 3, 4]

  • swap
       - given the following function:

          def swap(a, b):
             temp = a
             a = b
             b = temp
          
       - what would be in x and y after the following statements:
          >>> x = 10
          >>> y = 20
          >>> swap(x, y)