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Home > Programming > Object Oriented Programming > Python 3 Object-Oriented Programming - Second Edition
Python 3 Object-Oriented Programming - Second Edition
Python 3 Object-Oriented Programming - Second Edition

Python 3 Object-Oriented Programming - Second Edition: Building robust and maintainable software with object oriented design patterns in Python

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Python 3 Object-Oriented Programming - Second Edition

Chapter 2. Objects in Python

So, we now have a design in hand and are ready to turn that design into a working program! Of course, it doesn't usually happen this way. We'll be seeing examples and hints for good software design throughout the book, but our focus is object-oriented programming. So, let's have a look at the Python syntax that allows us to create object-oriented software.

After completing this chapter, we will understand:

  • How to create classes and instantiate objects in Python

  • How to add attributes and behaviors to Python objects

  • How to organize classes into packages and modules

  • How to suggest people don't clobber our data

Creating Python classes

We don't have to write much Python code to realize that Python is a very "clean" language. When we want to do something, we just do it, without having to go through a lot of setup. The ubiquitous "hello world" in Python, as you've likely seen, is only one line.

Similarly, the simplest class in Python 3 looks like this:

class MyFirstClass:
    pass

There's our first object-oriented program! The class definition starts with the class keyword. This is followed by a name (of our choice) identifying the class, and is terminated with a colon.

Note

The class name must follow standard Python variable naming rules (it must start with a letter or underscore, and can only be comprised of letters, underscores, or numbers). In addition, the Python style guide (search the web for "PEP 8") recommends that classes should be named using CamelCase notation (start with a capital letter; any subsequent words should also start with a capital).

The class definition line is followed by the class...

Modules and packages

Now, we know how to create classes and instantiate objects, but how do we organize them? For small programs, we can just put all our classes into one file and add a little script at the end of the file to start them interacting. However, as our projects grow, it can become difficult to find the one class that needs to be edited among the many classes we've defined. This is where modules come in. Modules are simply Python files, nothing more. The single file in our small program is a module. Two Python files are two modules. If we have two files in the same folder, we can load a class from one module for use in the other module.

For example, if we are building an e-commerce system, we will likely be storing a lot of data in a database. We can put all the classes and functions related to database access into a separate file (we'll call it something sensible: database.py). Then, our other modules (for example, customer models, product information, and inventory) can import...

Organizing module contents

Inside any one module, we can specify variables, classes, or functions. They can be a handy way to store the global state without namespace conflicts. For example, we have been importing the Database class into various modules and then instantiating it, but it might make more sense to have only one database object globally available from the database module. The database module might look like this:

class Database:
    # the database implementation
    pass

database = Database()

Then we can use any of the import methods we've discussed to access the database object, for example:

from ecommerce.database import database

A problem with the preceding class is that the database object is created immediately when the module is first imported, which is usually when the program starts up. This isn't always ideal since connecting to a database can take a while, slowing down startup, or the database connection information may not yet be available. We could delay creating the...

Who can access my data?

Most object-oriented programming languages have a concept of access control. This is related to abstraction. Some attributes and methods on an object are marked private, meaning only that object can access them. Others are marked protected, meaning only that class and any subclasses have access. The rest are public, meaning any other object is allowed to access them.

Python doesn't do this. Python doesn't really believe in enforcing laws that might someday get in your way. Instead, it provides unenforced guidelines and best practices. Technically, all methods and attributes on a class are publicly available. If we want to suggest that a method should not be used publicly, we can put a note in docstrings indicating that the method is meant for internal use only (preferably, with an explanation of how the public-facing API works!).

By convention, we should also prefix an attribute or method with an underscore character, _. Python programmers will interpret this as "this...

Third-party libraries

Python ships with a lovely standard library, which is a collection of packages and modules that are available on every machine that runs Python. However, you'll soon find that it doesn't contain everything you need. When this happens, you have two options:

  • Write a supporting package yourself

  • Use somebody else's code

We won't be covering the details about turning your packages into libraries, but if you have a problem you need to solve and you don't feel like coding it (the best programmers are extremely lazy and prefer to reuse existing, proven code, rather than write their own), you can probably find the library you want on the Python Package Index (PyPI) at http://pypi.python.org/. Once you've identified a package that you want to install, you can use a tool called pip to install it. However, pip does not come with Python, but Python 3.4 contains a useful tool called ensurepip, which will install it:

python -m ensurepip

This may fail for you on Linux, Mac OS, or other...

Case study

To tie it all together, let's build a simple command-line notebook application. This is a fairly simple task, so we won't be experimenting with multiple packages. We will, however, see common usage of classes, functions, methods, and docstrings.

Let's start with a quick analysis: notes are short memos stored in a notebook. Each note should record the day it was written and can have tags added for easy querying. It should be possible to modify notes. We also need to be able to search for notes. All of these things should be done from the command line.

The obvious object is the Note object; less obvious one is a Notebook container object. Tags and dates also seem to be objects, but we can use dates from Python's standard library and a comma-separated string for tags. To avoid complexity, in the prototype, let's not define separate classes for these objects.

Note objects have attributes for memo itself, tags, and creation_date. Each note will also need a unique integer id so that users...

Exercises

Write some object-oriented code. The goal is to use the principles and syntax you learned in this chapter to ensure you can use it, instead of just reading about it. If you've been working on a Python project, go back over it and see if there are some objects you can create and add properties or methods to. If it's large, try dividing it into a few modules or even packages and play with the syntax.

If you don't have such a project, try starting a new one. It doesn't have to be something you intend to finish, just stub out some basic design parts. You don't need to fully implement everything, often just a print("this method will do something") is all you need to get the overall design in place. This is called top-down design, in which you work out the different interactions and describe how they should work before actually implementing what they do. The converse, bottom-up design, implements details first and then ties them all together. Both patterns are useful at different times...

Summary

In this chapter, we learned how simple it is to create classes and assign properties and methods in Python. Unlike many languages, Python differentiates between a constructor and an initializer. It has a relaxed attitude toward access control. There are many different levels of scope, including packages, modules, classes, and functions. We understood the difference between relative and absolute imports, and how to manage third-party packages that don't come with Python.

In the next chapter, we'll learn how to share implementation using inheritance.

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Key benefits

  • Stop writing scripts and start architecting programs
  • Learn the latest Python syntax and libraries
  • A practical, hands-on tutorial that teaches you all about abstract design patterns and how to implement them in Python 3

Description

Python 3 is more versatile and easier to use than ever. It runs on all major platforms in a huge array of use cases. Coding in Python minimizes development time and increases productivity in comparison to other languages. Clean, maintainable code is easy to both read and write using Python's clear, concise syntax. Object-oriented programming is a popular design paradigm in which data and behaviors are encapsulated in such a way that they can be manipulated together. Many modern programming languages utilize the powerful concepts behind object-oriented programming and Python is no exception. Starting with a detailed analysis of object-oriented analysis and design, you will use the Python programming language to clearly grasp key concepts from the object-oriented paradigm. This book fully explains classes, data encapsulation, inheritance, polymorphism, abstraction, and exceptions with an emphasis on when you can use each principle to develop well-designed software. You'll get an in-depth analysis of many common object-oriented design patterns that are more suitable to Python's unique style. This book will not just teach Python syntax, but will also build your confidence in how to program. You will also learn how to create maintainable applications by studying higher level design patterns. Following this, you'll learn the complexities of string and file manipulation, and how Python distinguishes between binary and textual data. Not one, but two very powerful automated testing systems will be introduced in the book. After you discover the joy of unit testing and just how easy it can be, you'll study higher level libraries such as database connectors and GUI toolkits and learn how they uniquely apply object-oriented principles. You'll learn how these principles will allow you to make greater use of key members of the Python eco-system such as Django and Kivy. This new edition includes all the topics that made Python 3 Object-oriented Programming an instant Packt classic. It's also packed with updated content to reflect recent changes in the core Python library and covers modern third-party packages that were not available on the Python 3 platform when the book was first published.  

Who is this book for?

If you're new to object-oriented programming techniques, or if you have basic Python skills and wish to learn in depth how and when to correctly apply object-oriented programming in Python to design software, this is the book for you.

What you will learn

  • Implement objects in Python by creating classes and defining methods
  • Separate related objects into a taxonomy of classes and describe the properties and behaviors of those objects via the class interface
  • Extend class functionality using inheritance
  • Understand when to use object-oriented features, and more importantly when not to use them
  • Discover what design patterns are and why they are different in Python
  • Uncover the simplicity of unit testing and why it s so important in Python
  • Grasp common concurrency techniques and pitfalls in Python 3
  • Exploit object-oriented programming in key Python technologies such as Kivy and Django.
  • Object-oriented programming concurrently with asyncio

Product Details

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Publication date : Aug 20, 2015
Length: 460 pages
Edition : 1st
Language : English
ISBN-13 : 9781784395957
Category :
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Languages :
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Concepts :
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Language : English
ISBN-13 : 9781784395957
Category :
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Languages :
Python
Concepts :
Object Oriented Programming

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Table of Contents

13 Chapters
Object-oriented Design Chevron down icon Chevron up icon
Object-oriented Design
Introducing object-oriented
Objects and classes
Specifying attributes and behaviors
Hiding details and creating the public interface
Composition
Inheritance
Case study
Exercises
Summary
Objects in Python Chevron down icon Chevron up icon
Objects in Python
Creating Python classes
Modules and packages
Organizing module contents
Who can access my data?
Third-party libraries
Case study
Exercises
Summary
When Objects Are Alike Chevron down icon Chevron up icon
When Objects Are Alike
Basic inheritance
Multiple inheritance
Polymorphism
Abstract base classes
Case study
Exercises
Summary
Expecting the Unexpected Chevron down icon Chevron up icon
Expecting the Unexpected
Raising exceptions
Case study
Exercises
Summary
When to Use Object-oriented Programming Chevron down icon Chevron up icon
When to Use Object-oriented Programming
Treat objects as objects
Adding behavior to class data with properties
Manager objects
Case study
Exercises
Summary
Python Data Structures Chevron down icon Chevron up icon
Python Data Structures
Empty objects
Tuples and named tuples
Dictionaries
Lists
Sets
Extending built-ins
Queues
Case study
Exercises
Summary
Python Object-oriented Shortcuts Chevron down icon Chevron up icon
Python Object-oriented Shortcuts
Python built-in functions
An alternative to method overloading
Functions are objects too
Case study
Exercises
Summary
Strings and Serialization Chevron down icon Chevron up icon
Strings and Serialization
Strings
Regular expressions
Serializing objects
Case study
Exercises
Summary
The Iterator Pattern Chevron down icon Chevron up icon
The Iterator Pattern
Design patterns in brief
Iterators
Comprehensions
Generators
Coroutines
Case study
Exercises
Summary
Python Design Patterns I Chevron down icon Chevron up icon
Python Design Patterns I
The decorator pattern
The observer pattern
The strategy pattern
The state pattern
The singleton pattern
The template pattern
Exercises
Summary
Python Design Patterns II Chevron down icon Chevron up icon
Python Design Patterns II
The adapter pattern
The facade pattern
The flyweight pattern
The command pattern
The abstract factory pattern
The composite pattern
Exercises
Summary
Testing Object-oriented Programs Chevron down icon Chevron up icon
Testing Object-oriented Programs
Why test?
Unit testing
Testing with py.test
Imitating expensive objects
How much testing is enough?
Case study
Exercises
Summary
Concurrency Chevron down icon Chevron up icon
Concurrency
Threads
Multiprocessing
Futures
AsyncIO
Case study
Exercises
Summary

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Santhosh Apr 26, 2018
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Good book for Object oriented python
Amazon Verified review Amazon
adnan baloch Sep 29, 2015
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The title page of the book greets the reader with a photo of idyllic scenery with a mountain in the background. This is not just a photo. This is the place where the readers will find themselves in their minds' eyes once they are done with the book. The book's source code consists of over 200 python source files that are guaranteed to delight beginners and intermediate Python users alike. Happily, the files can be downloaded from the publisher's website for free so go ahead and witness that treasure trove for yourself. The paperback edition of this book is a must have for anyone interested in getting to grips with the object oriented features of Python 3. This second edition has the good fortune of incorporating several years of reader feedback that combines with the author's mature and seasoned grasp of the subject matter to deliver content that is highly relevant to the practical application of Python's power in solving real world computing problems. Data structures, design patterns and unit testing chapters will help the readers stand out for any serious Python development positions. New to this edition is an entire chapter devoted to concurrency that serves as pure icing on the cake especially when you factor in the fact that the world has increasingly shifted to a many-core computing era. Don't be surprised if this voluminous 460 page book turns you into an excellent Python teacher.
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Weston Buckhorn Oct 13, 2015
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This book is laser focused on learning OOP for people who have some programming experience but are stuck in the world of procedural programming. This is perfect for old farts who are addicted to crack-languages like the Basic family of languages. I have included a list of the code you can download below because I want people to buy this book.Ch03\3_01_inheriting_from_object.pyCh03\3_02_simple_contact_class_to_inherit_from.pyCh03\3_03_contact_inherit_supplier.pyCh03\3_04_contact_list_inheritance.pyCh03\3_05_dictionary_long_name.pyCh03\3_06_friend_overrides_init.pyCh03\3_07_friend_overrides_init_super.pyCh03\3_08_send_mail.pyCh03\3_09_send_mail_multi.pyCh03\3_10_friend_address_holder.pyCh03\3_11_friend_multi.pyCh03\3_12_contrived_diamond.pyCh03\3_14_contrived_diamond_super.pyCh03\3_15_friend_multi_super.pyCh03\3_16_polymorphic_audio.pyCh03\3_17.1_abc_container.pyCh03\3_17.2_abc_media.pyCh03\3_17_ducktype_flac.pyCh03\3_18_property.pyCh03\3_19_apartment_ugly_prompt.pyCh03\3_20_validation_function.pyCh03\3_21_apartment_nice_prompt.pyCh03\3_22_house.pyCh03\3_23_purchase_and_rental.pyCh03\3_24_house_rental.pyCh03\3_25_remaining_subclasses.pyCh03\3_26_rudimentary_agent.pyCh03\3_27_type_map.pyCh03\3_28_add_property.pyCh03\final_case_study.pyCh04\04_03_method_calls_excepting.pyCh04\4_01_even_integers.pyCh04\4_02_exception_quits.pyCh04\4_04_try_except.pyCh04\4_05_catch_specific_exception.pyCh04\4_06_catch_multiple_exceptions.pyCh04\4_07_catch_multiple_different.pyCh04\4_08_catch_as_keyword.pyCh04\4_09_finally_and_else.pyCh04\4_10_defining_an_exception.pyCh04\4_11_exception_with_custom_args.pyCh04\4_12_handle_custom_exception.pyCh04\4_13_branching_vs_exceptions.pyCh04\4_14_inventory_mock_object.pyCh04\4_15_inventory_handling.pyCh04\4_16_auth_user.pyCh04\4_17_authenticator.pyCh04\4_18_login.pyCh04\4_19_authorizor.pyCh04\4_20_test_auth.pyCh04\__pycache__\auth.cpython-34.pycCh04\auth.pyCh04\auth.pycCh05\5_01_distances_no_objects.pyCh05\5_02_distances_by_object.pyCh05\5_03_object_polygon_init.pyCh05\5_04_pytho_ugly_as_java.pyCh05\5_05_python_pretty_as_python.pyCh05\5_06_setting_name_in_method.pyCh05\5_07_setting_name_property.pyCh05\5_08_property_arguments.pyCh05\5_09_property_decorator_get.pyCh05\5_11_property_decorator_arguments.pyCh05\5_12_property_decorator_get_set.pyCh05\5_13_read_only_setattr.pyCh05\5_14_read_only_getattribute.pyCh05\5_15_cache_getter.pyCh05\5_16_average_property.pyCh05\5_17_zipsearch.pyCh05\5_18_zipprocessor.pyCh05\5_19_zipreplace_inheritance.pyCh05\5_20_scaleimage_inheritance.pyCh05\5_24_most_basic_document.pyCh05\5_25_document_cursor.pyCh05\5_26_document_using_cursor.pyCh05\5_27_string_property.pyCh05\5_28_Character_class.pyCh05\5_29_document_with_character.pyCh05\Document.pyCh05\zip_processor.pyCh05\zip_processor.pycCh06\6_01_empty_object.pyCh06\6_02_pass_tuple_to_function.pyCh06\6_03_named_tuple.pyCh06\6_04_dict_stocks.pyCh06\6_05_random_key_dict.pyCh06\6_06_setdefault_frequency.pyCh06\6_07_defaultdict_frequency.pyCh06\6_08_defaultdict_custom_function.pyCh06\6_09_counter_frequency.pyCh06\6_09_counter_poll.pyCh06\6_09_list_tuple_frequency.pyCh06\6_10_object_comparison.pyCh06\6_11_song_artist_set.pyCh06\6_12_set_operations.pyCh06\6_13_set_operations2.pyCh06\6_14_oop_pairs.pyCh06\6_15_stupid_adding_integer.pyCh06\6_16_dictsorted.pyCh06\6_17_link_parser.pyCh06\6_18_normalize_url.pyCh06\6_19_visited_links_sets.pyCh06\6_20_collect_remaining_links.pyCh06\6_21_print_collected_links.pyCh06\6_22_dict_link_collector.pyCh06\6_23_queue_link_collector.pyCh06\link_collector.pyCh07\7_01_reversible_objects.pyCh07\7_02_enumerate_line_numbers.pyCh07\7_03.1_zip_to_enumerate.pyCh07\7_03_enumerate_max_min.pyCh07\7_05_read_file.pyCh07\7_05_tdf_processor.pyCh07\7_06_write.pyCh07\7_07_with.pyCh07\7_08_context_manager.pyCh07\7_18_bad_kw_default.pyCh07\7_19_link_downloader.pyCh07\7_20_link_downloader_vararg.pyCh07\7_21_kwarg_options.pyCh07\7_22_all_arguments.pyCh07\7_23_unpacking_arguments.pyCh07\7_24_function_object.pyCh07\7_25_timer.pyCh07\7_26_timer_test.pyCh07\7_27_add_function_to_object.pyCh07\7_28_callable_repeat.pyCh07\7_29_send_email.pyCh07\7_30_send_email_dict_headers.pyCh07\7_32_mailing_list_defaultdict_set.pyCh07\7_33_mailing_list_get_emails.pyCh07\7_34_send_mailing.pyCh07\7_35_load_save.pyCh07\7_36_enter_exit.pyCh07\__pycache__\timer.cpython-34.pycCh07\mailing_list.pyCh07\timer.pyCh07\timer.pycCh08\8_01_string_creation.pyCh08\8_02_format_empty.pyCh08\8_03_format_position.pyCh08\8_04_format_some_positions_broken.pyCh08\8_05_brace_escape.pyCh08\8_06_format_kw_args.pyCh08\8_07_unlabelled_kw.pyCh08\8_08_tuple_dict_format.pyCh08\8_09_tuple_in_dict_format.pyCh08\8_10_object_formatting.pyCh08\8_11_no_format.pyCh08\8_12_currency_format.pyCh08\8_13_tabular.pyCh08\8_14_format_datetime.pyCh08\8_15_encode_bytes.pyCh08\8_16_decode_unicode.pyCh08\8_17_bytearray_replace.pyCh08\8_18_bytearray_index.pyCh08\8_23.1_basic_regex.pyCh08\8_23.2_regex_generic.pyCh08\8_23.3_match_group.pyCh08\8_23_stringio.pyCh08\8_24_basic_pickling.pyCh08\8_25_state_pickling.pyCh08\8_26_json_objects.pyCh08\8_27_template_boilerplate.pyCh08\8_28_template_process.pyCh08\8_29_template_processer_complete.pyCh09\09_08_list_comp_exclude.pyCh09\9_01_iterator.pyCh09\9_06_for_loop_converter.pyCh09\9_07_list_comp_converter.pyCh09\9_09_tdf_list_comp.pyCh09\9_10_set_comprehension.pyCh09\9_11_dict_comprehension.pyCh09\9_13_log_processor.pyCh09\9_14_log_delete_warning_expression.pyCh09\9_15_log_delete_warnings_loop.pyCh09\9_16_log_delete_warnings_object.pyCh09\9_17_log_delete_warnings_generator.pyCh09\9_18_log_delete_warnings_yield_from.pyCh09\9_19_yield_from_filesystem.pyCh09\9_20_basic_count_coroutine.pyCh09\9_21_kernel_log.pyCh09\9_22_load_dataset.pyCh09\9_23_generate_colors.pyCh09\9_24_color_distance.pyCh09\9_25_knearest.pyCh09\9_26_write_results.pyCh09\case_study_machine_learn.pyCh09\kivy_color_checker\main.pyCh09\kivy_color_classifier\main.pyCh10\10_02_simple_socket.pyCh10\10_03_simple_client.pyCh10\10_04_logging_decorator.pyCh10\10_05_gzip_decorator.pyCh10\10_06_calling_decorated_sockets.pyCh10\10_09_logging_decorator.pyCh10\10_10_decorator_syntax.pyCh10\10_11_observer_core.pyCh10\10_12_observer_observing.pyCh10\10_13_strategy_tile.pyCh10\10_15_xml_states.pyCh10\10_16_singleton_using_new.pyCh10\10_17_xml_singletonstates.pyCh10\10_18_create_database_for_template.pyCh10\10_19_template_abstract_noimple.pyCh10\10_20_template_abstract_implemented.pyCh10\10_21_template_concretes.pyCh11\11_01_age_calculator.pyCh11\11_02_age_calculator_adapted.pyCh11\11_03_age_calculator_adapt_date.pyCh11\11_04_email_facade.pyCh11\11_05_flyweight_factory.pyCh11\11_06_flyweight_init.pyCh11\11_07_flyweight_check_serial.pyCh11\11_08_car_class.pyCh11\11_09_window_commands.pyCh11\11_10_window_command_invokers.pyCh11\11_11_window_command_commands.pyCh11\11_12_window_command_function.pyCh11\11_13_document_command_callable.pyCh11\11_14_formatters.pyCh11\11_15_formatter_factories.pyCh11\11_16_composite_folder_methods.pyCh11\11_17_component_hierarchy.pyCh11\11_18_add_child.pyCh12\12_01_simplest_unittest.pyCh12\12_02_assertraises_python.pyCh12\12_03_stats.pyCh12\12_04_test_stats.pyCh12\12_05_skipping_tests.pyCh12\12_06_simplestpytest.pyCh12\12_06_simplestpytest.pycCh12\12_07_class_pytest.pyCh12\12_07_class_pytest.pycCh12\12_08_setup_teardown.pyCh12\12_08_setup_teardown.pycCh12\12_09_funcargs.pyCh12\12_09_funcargs.pycCh12\12_10_funcarg_finalizer.pyCh12\12_10_funcarg_finalizer.pycCh12\12_11_echo_server.pyCh12\12_12_pytest_echo.pyCh12\12_12_pytest_echo.pycCh12\12_13_pytest_simple_skip.pyCh12\12_13_pytest_simple_skip.pycCh12\12_14_pytest_importorskip.pyCh12\12_14_pytest_importorskip.pycCh12\12_15_pytest_skipifmark.pyCh12\12_15_pytest_skipifmark.pycCh12\12_16_mock_flightstatus.pyCh12\12_17_mock_redis.pyCh12\12_30_coverage_unittest.pyCh12\__pycache__\stats.cpython-34.pycCh12\__pycache__\test_mock.cpython-34-PYTEST.pycCh12\casestudy\__pycache__\test_vigenere_cipher.cpython-34-PYTEST.pycCh12\casestudy\__pycache__\vigenere_cipher.cpython-34.pycCh12\casestudy\test_vigenere_cipher.pyCh12\casestudy\test_vigenere_cipher.pycCh12\casestudy\vigenere_cipher.pyCh12\casestudy\vigenere_cipher.pycCh12\casestudy\vigenere_cipher1.pyCh12\stats.pyCh12\stats.pycCh12\test_mock.pyCh13\13_01_two_basic_threads.pyCh13\13_02_thread_wait.pyCh13\13_03_parallel.pyCh13\13_04_prime_factor.pyCh13\13_05_post_search.pyCh13\13_06_futures.pyCh13\13_07_basic_async.pyCh13\13_08_asyncio_dns.pyCh13\13_09_async_multiprocessing.pyCh13\13_10_async_client.pyCh13\case_study\compress_bmp.pyCh13\case_study\decompress_to_bmp.pyThere are no code files present for Chapters 1 and 2.
Amazon Verified review Amazon
Ecki Dec 13, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Das Buch ist für Leser mit Basiskenntnissen in Python geeignet.
Amazon Verified review Amazon
Amazon Customer Sep 12, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Helped with some small issues, great read
Amazon Verified review Amazon

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About the author

Profile icon Dusty Phillips
Dusty Phillips
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Dusty Phillips is a Canadian software developer and an author currently living in New Brunswick. He has been active in the open-source community for 2 decades and has been programming in Python for nearly as long. He holds a master's degree in computer science and has worked for Facebook, the United Nations, and several startups.
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