OOP is one of the major paradigms in programming.
The traditional programming paradigm (think Fortran, C, MATLAB, etc.) is called procedural.
It works as follows
In contrast, in the OOP paradigm
(Functions in this context are referred to as methods)
Python is a pragmatic language that blends object-oriented and procedural styles, rather than taking a purist approach.
However, at a foundational level, Python is object-oriented.
In particular, in Python, everything is an object.
In this lecture, we explain what that statement means and why it matters.
These concepts are defined and discussed sequentially below.
s = 'This is a string' type(s)
x = 42 # Now let's create an integer type(x)
The type of an object matters for many expressions.
For example, the addition operator between two strings means concatenation
'300' + 'cc'
On the other hand, between two numbers it means ordinary addition
300 + 400
Consider the following expression
'300' + 400
Here we are mixing types, and it’s unclear to Python whether the user wants to
'300'to an integer and then add it to
400to string and then concatenate it with
Some languages might try to guess but Python is strongly typed
To avoid the error, you need to clarify by changing the relevant type.
int('300') + 400 # To add as numbers, change the string to an integer
The identity of an object can be obtained via the
y = 2.5 z = 2.5 id(y)
In this example,
z happen to have the same value (i.e.,
2.5), but they are not the same object.
The identity of an object is in fact just the address of the object in memory.
In fact, it contains more, as the following example shows
x = 42 x
When Python creates this integer object, it stores with it various auxiliary information, such as the imaginary part, and the type.
Any name following a dot is called an attribute of the object to the left of the dot.
__class__are attributes of
We see from this example that objects have attributes that contain auxiliary information.
They also have attributes that act like functions, called methods.
These attributes are important, so let’s discuss them in-depth.
Formally, methods are attributes of objects that are callable (i.e., can be called as functions)
x = ['foo', 'bar'] callable(x.append)
Methods typically act on the data contained in the object they belong to, or combine that data with other data
x = ['a', 'b'] x.append('c') s = 'This is a string' s.upper()
A great deal of Python functionality is organized around method calls.
For example, consider the following piece of code
x = ['a', 'b'] x = 'aa' # Item assignment using square bracket notation x
It doesn’t look like there are any methods used here, but in fact the square bracket assignment notation is just a convenient interface to a method call.
What actually happens is that Python calls the
__setitem__ method, as follows
x = ['a', 'b'] x.__setitem__(0, 'aa') # Equivalent to x = 'aa' x
(If you wanted to you could modify the
__setitem__ method, so that square bracket assignment does something totally different)
In Python, everything in memory is treated as an object.
This includes not just lists, strings, etc., but also less obvious things, such as
Consider, for example, functions.
When Python reads a function definition, it creates a function object and stores it in memory.
The following code illustrates
def f(x): return x**2 f
We can see that
f has type, identity, attributes and so on—just like any other object.
It also has methods.
One example is the
__call__ method, which just evaluates the function
Another is the
__dir__ method, which returns a list of attributes.
Modules loaded into memory are also treated as objects
import math id(math)
This uniform treatment of data in Python (everything is an object) helps keep the language simple and consistent.