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Home > Programming > Programming Language > Python Unlocked
Python Unlocked
Python Unlocked

Python Unlocked: Become more fluent in Python—learn strategies and techniques for smart and high-performance Python programming

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Python Unlocked

Chapter 2. Namespaces and Classes

In the previous chapter, we covered how objects worked. In this chapter, we will explore how objects are made available to code via reference, specifically how namespaces work, what modules are, and how they are imported. We will also cover topics related to classes, such as language protocols, MRO, and abstract classes. We will discuss the following topics:

  • Namespaces
  • Imports and modules
  • Class multiple inheritance, MRO, super
  • Protocols
  • Abstract classes

How referencing objects work – namespaces

Key 1: Interrelations between objects.

The scope is the visibility of a name within a code block. Namespace is mapping from names to objects. Namespaces are important in order to maintain localization and avoid name collision. Every module has a global namespace. Modules store mapping from variable name to objects in their __dict__ attribute, which is a normal Python dictionary along with information to reload it, package information, and so on.

Every module's global namespace has an implicit reference to the built-in module; hence, objects that are in the built-in module are always available. We can also import other modules in the main script. When we use the syntax import module name, a mapping with module name to module object is created in the global namespace of the current module. For import statements with syntax such as import modname as modrename, mapping is created with a new name to module object.

We are always in the __main__...

Functions with state – closures

Key 2: Creating cheap state-remembering functions.

A closure is a function that has access to variables in an enclosing scope, which has completed its execution. This means that referenced objects are kept alive until the function is in memory. The main utility of such a setup is to easily retain some state, or to create specialized functions whose functioning depends on the initial setup:

>>> def getformatter(start,end):
...     def formatter(istr):
...         print("%s%s%s"%(start,istr,end))
...     return formatter
... 
>>> formatter1 = getformatter("<",">")
>>> formatter2 = getformatter("[","]")
>>> 
>>> formatter1("hello")
<hello>
>>> formatter2("hello")
[hello]
>>> formatter1.__closure__[0].cell_contents
'>'
>>> formatter1.__closure__[1].cell_contents
'<'

We can do...

Understanding import and modules

Key 3: Creating a custom loader for modules.

Import statements get references of other module objects in the current module's namespace. It consists of searching the module, executing code to create a module object, updating caches (sys.modules), updating modules namespace, and creating a reference to new module being imported.

The built-in __import__ function searches and executes the module to create a module object. The importlib library has the implementation, and it also provides a customizable interface to the import mechanism. Various classes interact to get the job done. The __import__ function should return a module object. For example, in the following example, we are creating a module finder, which checks for modules in any path that is given as an argument during construction. Here, an empty file named names.py should be present at the given path. We have loaded the module, then inserted its module object in sys.modules and added a function...

Class inheritance

We already discussed how instances and classes are created. We also discussed how attributes are accessed in a class. Let's dive deeper into how this works for multiple base classes. As type is searched for the presence of an attribute for an instance, if the type inherits from a number of classes, they all are searched as well. There is a defined pattern to this (Method Resolution Order (MRO)). This order plays an important role in determining the method in cases of multiple inheritance and diamond-shaped inheritance.

Method resolution order

Key 4: Understanding MRO.

The methods are searched in the base classes of a class in predefined manner. This sequence or order is known as method resolution order. In Python 3, when an attribute is not found in a class, it is searched in all the base classes of that class. If the attribute is still not found, the base classes of the base classes are searched. This process goes on until we exhaust all base classes. This is similar...

Using language protocols in classes

All objects that provide a specific functionality have certain methods that facilitate that behavior, for example, you can create an object of type worker and expect it to have the submit_work(function, kwargs), and is _completed() methods. Now, we can expect all objects that have these methods to be usable as workers in any application portion. Similarly, the Python language has defined some methods that are needed to add a certain functionality to an object. If an object possesses these methods, it has that functionality.

We will discuss two very import protocols: iteration protocol, and context protocol.

Iteration protocol

For iteration protocol, objects must possess the __iter__ method. If the object possesses it, we can use the object anywhere that we use an iterator object. When we are using the iterator object in a for loop or passing it to the iter built-in function, we are calling its __iter__ method. This method returns another or the same object...

How referencing objects work – namespaces

Key 1: Interrelations between objects.

The scope is the visibility of a name within a code block. Namespace is mapping from names to objects. Namespaces are important in order to maintain localization and avoid name collision. Every module has a global namespace. Modules store mapping from variable name to objects in their __dict__ attribute, which is a normal Python dictionary along with information to reload it, package information, and so on.

Every module's global namespace has an implicit reference to the built-in module; hence, objects that are in the built-in module are always available. We can also import other modules in the main script. When we use the syntax import module name, a mapping with module name to module object is created in the global namespace of the current module. For import statements with syntax such as import modname as modrename, mapping is created with a new name to module object.

We are always in the __main__ module's...

Functions with state – closures

Key 2: Creating cheap state-remembering functions.

A closure is a function that has access to variables in an enclosing scope, which has completed its execution. This means that referenced objects are kept alive until the function is in memory. The main utility of such a setup is to easily retain some state, or to create specialized functions whose functioning depends on the initial setup:

>>> def getformatter(start,end):
...     def formatter(istr):
...         print("%s%s%s"%(start,istr,end))
...     return formatter
... 
>>> formatter1 = getformatter("<",">")
>>> formatter2 = getformatter("[","]")
>>> 
>>> formatter1("hello")
<hello>
>>> formatter2("hello")
[hello]
>>> formatter1.__closure__[0].cell_contents
'>'
>>> formatter1.__closure__[1].cell_contents
'<'

We can do the same by creating a class and using the instance object to save state. The benefit with closures...

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Page 1 of 8

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Key benefits

  • *Write smarter, bug-free, high performance code with minimal effort
  • *Uncover the best tools and options available to Python developers today
  • *Deploy decorators, design patters, and various optimization techniques to use Python 3.5 effectively

Description

Python is a versatile programming language that can be used for a wide range of technical tasks—computation, statistics, data analysis, game development, and more. Though Python is easy to learn, it’s range of features means there are many aspects of it that even experienced Python developers don’t know about. Even if you’re confident with the basics, its logic and syntax, by digging deeper you can work much more effectively with Python – and get more from the language. Python Unlocked walks you through the most effective techniques and best practices for high performance Python programming - showing you how to make the most of the Python language. You’ll get to know objects and functions inside and out, and will learn how to use them to your advantage in your programming projects. You will also find out how to work with a range of design patterns including abstract factory, singleton, strategy pattern, all of which will help make programming with Python much more efficient. Finally, as the process of writing a program is never complete without testing it, you will learn to test threaded applications and run parallel tests. If you want the edge when it comes to Python, use this book to unlock the secrets of smarter Python programming.

Who is this book for?

If you are a Python developer and you think that you don’t know everything about the language yet, then this is the book for you. We will unlock the mysteries and re-introduce you to the hidden features of Python to write efficient programs, making optimal use of the language.

What you will learn

  • *Manipulate object creation processes for instances, classes, and functions
  • *Use the best possible language constructs to write data structures with super speed and maintainability
  • *Make efficient use of design patterns to decrease development time and make your code more maintainable
  • *Write better test cases with an improved understanding of the testing framework of Python and unittests, and discover how to develop new functionalities in it
  • *Write fully-optimized code with the Python language by profiling, compiling C modules, and more
  • *Unlock asynchronous programming to build efficient and scalable applications

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Publication date : Dec 30, 2015
Length: 172 pages
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Language : English
ISBN-13 : 9781785881312
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Last updated date : Feb 11, 2025
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Length: 172 pages
Edition : 1st
Language : English
ISBN-13 : 9781785881312
Category :
Programming
Languages :
Python
Concepts :
Programming Language

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Table of Contents

9 Chapters
1. Objects in Depth Chevron down icon Chevron up icon
1. Objects in Depth
Understanding objects
Making calls to objects
How objects are created
Playing with attributes
Summary
2. Namespaces and Classes Chevron down icon Chevron up icon
2. Namespaces and Classes
How referencing objects work – namespaces
Functions with state – closures
Understanding import and modules
Class inheritance
Using language protocols in classes
Using abstract classes
Summary
3. Functions and Utilities Chevron down icon Chevron up icon
3. Functions and Utilities
Defining functions
Decorating callables
Utilities
Summary
4. Data Structures and Algorithms Chevron down icon Chevron up icon
4. Data Structures and Algorithms
Python built-in data structures
Python library data structures
Third party data structures
Algorithms on scale
Summary
5. Elegance with Design Patterns Chevron down icon Chevron up icon
5. Elegance with Design Patterns
Observer pattern
Strategy pattern
Singleton pattern
Template pattern
Adaptor pattern
Facade pattern
Flyweight pattern
Command pattern
Abstract factory
Registry pattern
State pattern
Summary
6. Test-Driven Development Chevron down icon Chevron up icon
6. Test-Driven Development
Mock for tests
Parameterization
Creating custom test runners
Testing threaded applications
Running test cases in parallel
Summary
7. Optimization Techniques Chevron down icon Chevron up icon
7. Optimization Techniques
Writing optimized code
Profiling to find bottlenecks
Using fast libraries
Using C speeds
Summary
8. Scaling Python Chevron down icon Chevron up icon
8. Scaling Python
Going multithreaded
Using multiple processes
Going asynchronous
Scaling horizontally
Summary
Index Chevron down icon Chevron up icon
Index

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(2 Ratings)
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Eduardo j. Morales Jan 16, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The inners of the interpreter are explained clearly and mind opening to more. Concise and specific.
Amazon Verified review Amazon
Jascha Casadio Jul 23, 2016
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
And yet another Python book. Being such a clean and flexible has brought this 20 years old programming language to be one of the hottest ones out there. Like it or not, most of the active repositories on GitHub are on Python. This is also confirmed by multiple sources listing the most demanded programming languages in the IT world. We Pythonists are lucky because, apart from a nice documentation, there is plenty of books to pick from, even though most of them cover basic features of the language. Python Unlocked does not belong to this large group of titles and focuses itself on intermediate topics.Released at the end of 2015, Python Unlocked is a short book targeting that niche of Pythonists interested in intermediate to advanced topics, such as decorators, abstract classes and unit testing.Let's dive into the content!The very first things that comes to the eye of the reader is poor English writing skills. True, Packt Publishing does not offer authors any kind of proofreading service but someone should at least check if articles are present. The reader, sometimes after a second pass through the sentence, can still grasp what the author wanted to say, but this definitely lowers the overall quality of the title.English apart, the choice of topics somehow got me raising an eyebrow in doubt: abstract classes are definitely a subject an intermediate Python developer wants to know of, but the MRO is probably misplaced here. Unless you are going to work in the core of the language itself, and are smart enough to prefer mixins to multiple inheritance, you can definitely live without knowing about it.Unnecessary complexities are present everywhere. So are unclear explanations. For example, the book starts discussing the internals of an object. The author states When an attribute is searched in an object first, it is searched in its dictionary then its type's (base class's) dictionary. Fine, but what is that __dict__ that I see everywhere? Either you introduce me to it, and explain me what it is or, if I am supposed to know what it represents already then the whole chapter is superfluous.Let's continue. Decorators are callable objects, which replace the original callable objects with some other objects. This is not correct. A decorator can replace the original callable with another one, but it must not. No words about decorators with a variable number of arguments, which is a very interesting aspect of decorators. Only later on, paged ahead, the author dedicates a couple of lines explaining functools.wrap.The examples are usually very short, Hello world! like pieces of code, whose functions are given names like f1, f2 and foo and whose body does little more than print('adf').Overall, a poor effort. The concepts are not properly explained and the examples are too short and focused on unnecessary complexities. Anyone interested in intermediate to advanced Python topics should pick any of the following titles, instead:Fluent PythonEffective Python: 59 Specific Ways to Write Better PythonAs usual, you can find more reviews on my personal blog: books.lostinmalloc.com. Feel free to pass by and share your thoughts!
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About the author

Profile icon Tigeraniya
Tigeraniya
Arun Tigeraniya has a BE in electronics and communication. After his graduation, he worked at various companies as a Python developer. His main professional interests are AI and Big Data. He enjoys writing efficient and testable code, and interesting technical articles. He has worked with open source technology since 2008. He currently works at Jaarvis Labs Limited, India.
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