Python 3.7.x Notes-6 Classes

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Classes

Classes provide a means of bundling data and functionality together. Creating a new class creates a new type of object, allowing new instances of that type to be made. Each class instance can have attributes attached to it for maintaining its state. Class instances can also have methods (defined by its class) for modifying its state.
Compared with other programming languages, Python’s class mechanism adds classes with a minimum of new syntax and semantics. It is a mixture of the class mechanisms found in C++ and Modula-3. Python classes provide all the standard features of Object Oriented Programming
Unlike C++ and Modula-3, built-in types can be used as base classes for extension by the user. Also, like in C++, most built-in operators with special syntax (arithmetic operators, subscripting etc.) can be redefined for class instances.

1. A Word About Names and Objects

Objects have individuality, and multiple names (in multiple scopes) can be bound to the same object. This is known as aliasing in other languages.

2. Python Scopes and Namespaces

Before introducing classes, I first have to tell you something about Python’s scope rules. Class definitions play some neat tricks with namespaces, and you need to know how scopes and namespaces work to fully understand what’s going on.
A namespace is a mapping from names to objects. Most namespaces are currently implemented as Python dictionaries, but that’s normally not noticeable in any way (except for performance), and it may change in the future.
By the way, I use the word attribute for any name following a dot — for example, in the expression z.real, real is an attribute of the object z.
Namespaces are created at different moments and have different lifetimes. The namespace containing the built-in names is created when the Python interpreter starts up, and is never deleted. The global namespace for a module is created when the module definition is read in
The local namespace for a function is created when the function is called, and deleted when the function returns or raises an exception that is not handled within the function.
A scope is a textual region of a Python program where a namespace is directly accessible.
If a name is declared global, then all references and assignments go directly to the middle scope containing the module’s global names. To rebind variables found outside of the innermost scope, the nonlocal statement can be used;
A special quirk of Python is that – if no global statement is in effect – assignments to names always go into the innermost scope.
The global statement can be used to indicate that particular variables live in the global scope and should be rebound there; the nonlocal statement indicates that particular variables live in an enclosing scope and should be rebound there.

Scopes and Namespaces Example

def scope_test():
    def do_local():
        spam = "local spam"

    def do_nonlocal():
        nonlocal spam
        spam = "nonlocal spam"

    def do_global():
        global spam
        spam = "global spam"

    spam = "test spam"
    do_local()
    print("After local assignment:", spam)
    do_nonlocal()
    print("After nonlocal assignment:", spam)
    do_global()
    print("After global assignment:", spam)

scope_test()
print("In global scope:", spam)
After local assignment: test spam
After nonlocal assignment: nonlocal spam
After global assignment: nonlocal spam
In global scope: global spam

3.A First Look at Classes

Classes introduce a little bit of new syntax, three new object types, and some new semantics.
Class Definition Syntax

class ClassName:
    <statement-1>
    .
    .
    .
    <statement-N>

When a class definition is entered, a new namespace is created, and used as the local scope — thus, all assignments to local variables go into this new namespace.

Class Objects
Class objects support two kinds of operations: attribute references and instantiation.
Attribute references use the standard syntax used for all attribute references in Python: obj.name.

class MyClass:
    """A simple example class"""
    i = 12345

    def f(self):
        return 'hello world'

then MyClass.i and MyClass.f are valid attribute references, returning an integer and a function object, respectively.
Class instantiation uses function notation.

x = MyClass()

The instantiation operation (“calling” a class object) creates an empty object.

def __init__(self):
    self.data = []
>>> class Complex:
...     def __init__(self, realpart, imagpart):
...         self.r = realpart
...         self.i = imagpart
...
>>> x = Complex(3.0, -4.5)
>>> x.r, x.i
(3.0, -4.5)

Instance Objects
Now what can we do with instance objects? The only operations understood by instance objects are attribute references.
Method Objects
Usually, a method is called right after it is bound:

x.f()

Class and Instance Variables
Generally speaking, instance variables are for data unique to each instance and class variables are for attributes and methods shared by all instances of the class:

class Dog:

    kind = 'canine'         # class variable shared by all instances

    def __init__(self, name):
        self.name = name    # instance variable unique to each instance

>>> d = Dog('Fido')
>>> e = Dog('Buddy')
>>> d.kind                  # shared by all dogs
'canine'
>>> e.kind                  # shared by all dogs
'canine'
>>> d.name                  # unique to d
'Fido'
>>> e.name                  # unique to e
'Buddy'

4.Random Remarks

Data attributes override method attributes with the same name; to avoid accidental name conflicts, which may cause hard-to-find bugs in large programs, it is wise to use some kind of convention that minimizes the chance of conflicts. Possible conventions include capitalizing method names, prefixing data attribute names with a small unique string (perhaps just an underscore), or using verbs for methods and nouns for data attributes.
Any function object that is a class attribute defines a method for instances of that class. It is not necessary that the function definition is textually enclosed in the class definition: assigning a function object to a local variable in the class is also ok. For example:

# Function defined outside the class
def f1(self, x, y):
    return min(x, x+y)

class C:
    f = f1

    def g(self):
        return 'hello world'

    h = g

5. Inheritance

class DerivedClassName(BaseClassName):
    <statement-1>
    .
    .
    .
    <statement-N>

The name BaseClassName must be defined in a scope containing the derived class definition. In place of a base class name, other arbitrary expressions are also allowed. This can be useful, for example, when the base class is defined in another module:
Execution of a derived class definition proceeds the same as for a base class. When the class object is constructed, the base class is remembered. This is used for resolving attribute references: if a requested attribute is not found in the class, the search proceeds to look in the base class. This rule is applied recursively if the base class itself is derived from some other class.

Python has two built-in functions that work with inheritance:
a. Use isinstance() to check an instance’s type: isinstance(obj, int) will be True only if obj.class is int or some class derived from int.

b. Use issubclass() to check class inheritance: issubclass(bool, int) is True since bool is a subclass of int. However, issubclass(float, int) is False since float is not a subclass of int.

Multiple Inheritance
Python supports a form of multiple inheritance as well. A class definition with multiple base classes looks like this:

class DerivedClassName(Base1, Base2, Base3):
    <statement-1>
    .
    .
    .
    <statement-N>

6. Private Variables

“Private” instance variables that cannot be accessed except from inside an object don’t exist in Python. However, there is a convention that is followed by most Python code: a name prefixed with an underscore (e.g. _spam) should be treated as a non-public part of the API (whether it is a function, a method or a data member).

7. Odds and Ends

Sometimes it is useful to have a data type similar to the Pascal “record” or C “struct”, bundling together a few named data items. An empty class definition will do nicely:

class Employee:
    pass

john = Employee()  # Create an empty employee record

# Fill the fields of the record
john.name = 'John Doe'
john.dept = 'computer lab'
john.salary = 1000

8. Iterators

By now you have probably noticed that most container objects can be looped over using a for statement:

for element in [1, 2, 3]:
    print(element)
for element in (1, 2, 3):
    print(element)
for key in 'one':1, 'two':2:
    print(key)
for char in "123":
    print(char)
for line in open("myfile.txt"):
    print(line, end='')

9. Generators

Generators are a simple and powerful tool for creating iterators. They are written like regular functions but use the yield statement whenever they want to return data. Each time next() is called on it, the generator resumes where it left off (it remembers all the data values and which statement was last executed). An example shows that generators can be trivially easy to create:

def reverse(data):
    for index in range(len(data)-1, -1, -1):
        yield data[index]
>>>
>>> for char in reverse('golf'):
...     print(char)
...
f
l
o
g

10. Generator Expressions

Some simple generators can be coded succinctly as expressions using a syntax similar to list comprehensions but with parentheses instead of square brackets. These expressions are designed for situations where the generator is used right away by an enclosing function.

>>> sum(i*i for i in range(10))                 # sum of squares
285

>>> xvec = [10, 20, 30]
>>> yvec = [7, 5, 3]
>>> sum(x*y for x,y in zip(xvec, yvec))         # dot product
260

>>> from math import pi, sin
>>> sine_table = x: sin(x*pi/180) for x in range(0, 91)

>>> unique_words = set(word  for line in page  for word in line.split())

>>> valedictorian = max((student.gpa, student.name) for student in graduates)

>>> data = 'golf'
>>> list(data[i] for i in range(len(data)-1, -1, -1))
['f', 'l', 'o', 'g']

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