Home

Pythonic

• In Python 3 all strings are Unicode while in Python 2 strings are bytes by default
• All generators are iterators BUT not all iterators are generators
• Generators are usually attached to functions have next() method and yield
• Iterator creates a list generator generates ONE item at a time for computation
• Python supports MULTIPLE INHERITANCE

ADANTAGES Creates item for computation one at a time and does not consumes more memory.

GIL Python's GIL is intended to serialize access to interpreter internals from different threads. On multi-core systems, it means that multiple threads can't effectively make use of multiple cores. (If the GIL didn't lead to this problem, most people wouldn't care about the GIL - it's only being raised as an issue because of the increasing prevalence of multi-core systems.) Note that Python's GIL is only really an issue for CPython, the reference implementation. Jython and IronPython don't have a GIL. As a Python developer, you don't generally come across the GIL unless you're writing a C extension. C extension writers need to release the GIL when their extensions do blocking I/O, so that other threads in the Python process get a chance to run.

class Base1:
    pass

class Base2:
    pass

class MultiDerived(Base1, Base2):
    pass

class Base:
    pass

class Derived1(Base):
    pass

class Derived2(Derived1):
    pass

Python Operator Overloading Python operators work for built-in classes. But same operator behaves differently with different types. For example, the + operator will, perform arithmetic addition on two numbers, merge two lists and concatenate two strings. This feature in Python, that allows same operator to have different meaning according to the context is called operator overloading.

POLYMORPHISM WITH PYTHON A common real example in Python is file-like objects. Besides actual files, several other types, including StringIO and BytesIO, are file-like. A method that acts as files can also act on them because they support the required methods (e.g. read, write).

ENCAPSULATION (Accessability) private attributes are defined by special syntax self.__a same with methods. def __methodName(self):

Major Difference between Python 2 and Python 3:

1. print is now a function instead of keyword
2. xrange (iterable) is now replaced with range()
3. python 3 supports Unicode
4. raw_input() is replaced by input() method

Abstract methods

An abstract method is a method defined in a base class, but that may not provide any implementation. In Java, it would describe the methods of an interface. So the simplest way to write an abstract method in Python is:

class Pizza(object):
    def get_radius(self):
        raise NotImplementedError

Any class inheriting from Pizza should implement and override the get_radius method, otherwise an exception would be raised. This particular way of implementing abstract method has a drawback. If you write a class that inherits from Pizza and forget to implement get_radius, the error will only be raised when you'll try to use that method.

>>> Pizza()
<__main__.Pizza object at 0x7fb747353d90>
>>> Pizza().get_radius()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<stdin>", line 3, in get_radius
NotImplementedError

There's a way to triggers this way earlier, when the object is being instantiated, using the abc module that's provided with Python.

import abc
 
class BasePizza(object):
    __metaclass__  = abc.ABCMeta
 
    @abc.abstractmethod
    def get_radius(self):
         """Method that should do something."""

Using abc and its special class, as soon as you'll try to instantiate BasePizza or any class inheriting from it, you'll get a TypeError.

>>> BasePizza()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: Can't instantiate abstract class BasePizza with abstract methods get_radius

Mixing static, class and abstract methods

When building classes and inheritances, the time will come where you will have to mix all these methods decorators. So here's some tips about it. Keep in mind that declaring a method as being abstract, doesn't freeze the prototype of that method. That means that it must be implemented, but i can be implemented with any argument list.

import abc
 
class BasePizza(object):
    __metaclass__  = abc.ABCMeta
 
    @abc.abstractmethod
    def get_ingredients(self):
         """Returns the ingredient list."""
 
class Calzone(BasePizza):
    def get_ingredients(self, with_egg=False):
        egg = Egg() if with_egg else None
        return self.ingredients + egg

This is valid, since Calzone fulfil the interface requirement we defined for BasePizza objects. That means that we could also implement it as being a class or a static method, for example:

import abc
 
class BasePizza(object):
    __metaclass__  = abc.ABCMeta
 
    @abc.abstractmethod
    def get_ingredients(self):
         """Returns the ingredient list."""
 
class DietPizza(BasePizza):
    @staticmethod
    def get_ingredients():
        return None

This is also correct and fulfil the contract we have with our abstract BasePizza class. The fact that the get_ingredients method don't need to know about the object to return result is an implementation detail, not a criteria to have our contract fulfilled.

Therefore, you can't force an implementation of your abstract method to be a regular, class or static method, and arguably you shouldn't. Starting with Python 3 (this won't work as you would expect in Python 2, see issue5867), it's now possible to use the @staticmethod and @classmethod decorators on top of @abstractmethod:

import abc
 
class BasePizza(object):
    __metaclass__  = abc.ABCMeta
 
    ingredient = ['cheese']
 
    @classmethod
    @abc.abstractmethod
    def get_ingredients(cls):
         """Returns the ingredient list."""
         return cls.ingredients

Don't misread this: if you think this going to force your subclasses to implement get_ingredients as a class method, you are wrong. This simply implies that your implementation of get_ingredients in the BasePizza class is a class method.

An implementation in an abstract method? Yes! In Python, contrary to methods in Java interfaces, you can have code in your abstract methods and call it via super():

import abc
 
class BasePizza(object):
    __metaclass__  = abc.ABCMeta
 
    default_ingredients = ['cheese']
 
    @classmethod
    @abc.abstractmethod
    def get_ingredients(cls):
         """Returns the ingredient list."""
         return cls.default_ingredients
 
class DietPizza(BasePizza):
    def get_ingredients(self):
        return ['egg'] + super(DietPizza, self).get_ingredients()

In such a case, every pizza you will build by inheriting from BasePizza will have to override the get_ingredients method, but will be able to use the default mechanism to get the ingredient list by using super().

Why do we need Abstract methods in the first place ?