Textbook in PDF format

Take your Python skills to the next level to develop scalable, real-world applications for local as well as cloud deployment
Key FeaturesAll code examples have been tested with Python 3.7 and Python 3.8 and are expected to work with any future 3.x releaseLearn how to build modular and object-oriented applications in PythonDiscover how to use advanced Python techniques for the cloud and clustersBook Description
Python is a multipurpose language that can be used for multiple use cases. Python for Geeks will teach you how to advance in your career with the help of expert tips and tricks.
You'll start by exploring the different ways of using Python optimally, both from the design and implementation point of view. Next, you'll understand the life cycle of a large-scale Python project. As you advance, you'll focus on different ways of creating an elegant design by modularizing a Python project and learn best practices and design patterns for using Python. You'll also discover how to scale out Python beyond a single thread and how to implement multiprocessing and multithreading in Python. In addition to this, you'll understand how you can not only use Python to deploy on a single machine but also use clusters in private as well as in public cloud computing environments. You'll then explore data processing techniques, focus on reusable, scalable data pipelines, and learn how to use these advanced techniques for network automation, serverless functions, and machine learning. Finally, you'll focus on strategizing web development design using the techniques and best practices covered in the book.
By the end of this Python book, you'll be able to do some serious Python programming for large-scale complex projects.
What you will learnUnderstand how to design and manage complex Python projectsStrategize test-driven development (TDD) in PythonExplore multithreading and multiprogramming in PythonUse Python for data processing with Apache Spark and Google Cloud Platform (GCP)Deploy serverless programs on public clouds such as GCPUse Python to build web applications and application programming interfacesApply Python for network automation and serverless functionsGet to grips with Python for data analysis and machine learningWho this book is for
This book is for intermediate-level Python developers in any field who are looking to build their skills to develop and manage large-scale complex projects. Developers who want to create reusable modules and Python libraries and cloud developers building applications for cloud deployment will also find this book useful. Prior experience with Python will help you get the most out of this book.
Table of ContentsOptimal Python Development LifecycleUsing Modularization to Elegantly Design Complex ProjectsAdvanced Object Oriented Python ProgrammingPython Libraries for Advanced ProgrammingTesting and Automation with PythonAdvanced Tips and Tricks in PythonMultiprocessing and MultithreadingScaling Out Python to ClustersPython Programming for the CloudUsing Python for Web Development and REST APIUsing Python for micro-services developmentBuilding serverless functions using PythonPython and Machine LearningUsing python for network automation
Cover
Title Page
Copyright and Credits
Dedication
Contributors
Preface
Section 1: Python, beyond the Basics
Chapter 1: Optimal Python Development Life Cycle
Python culture and community
Different phases of a Python project
Strategizing the development process
Iterating through the phases
Aiming for MVP first
Strategizing development for specialized domains
Effectively documenting Python code
Python comments
Docstring
Functional or class-level documentation
Developing an effective naming scheme
Methods
Variables
Constant
Classes
Packages
Modules
Import conventions
Arguments
Useful tools
Exploring choices for source control
What does not belong to the source control repository?
Understanding strategies for deploying the code
Batch development
Python development environments
IDLE
Sublime Text
PyCharm
Visual Studio Code
PyDev
Spyder
Summary
Questions
Further reading
Answers
Chapter 2: Using Modularization to Handle Complex Projects
Technical requirements
Introduction to modules and packages
Importing modules
Using the import statement
Using the __import__ statement
Using the importlib.import_module statement
Absolute versus relative import
Loading and initializing a module
Standard modules
Writing reusable modules
Building packages
Naming
Package initialization file
Building a package
Accessing packages from any location
Sharing a package
Building a package as per the PyPA guidelines
Installing from the local source code using pip
Publishing a package to Test PyPI
Installing the package from PyPI
Summary
Questions
Further reading
Answers
Chapter 3: Advanced Object-Oriented Python Programming
Technical requirements
Introducing classes and objects
Distinguishing between class attributes and instance attributes
Using constructors and destructors with classes
Distinguishing between class methods and instance methods
Special methods
Understanding OOP principles
Encapsulation of data
Encompassing data and actions
Hiding information
Protecting the data
Using traditional getters and setters
Using property decorators
Extending classes with inheritance
Simple inheritance
Multiple inheritance
Polymorphism
Method overloading
Method overriding
Abstraction
Using composition as an alternative design approach
Introducing duck typing in Python
Learning when not to use OOP in Python
Summary
Questions
Further reading
Answers
Section 2: Advanced Programming Concepts
Chapter 4: Python Libraries for Advanced Programming
Technical requirements
Introducing Python data containers
Strings
Lists
Tuples
Dictionaries
Sets
Using iterators and generators for data processing
Iterators
Generators
Handling files in Python
File operations
Using a context manager
Operating on multiple files
Handling errors and exceptions
Working with exceptions in Python
Raising exceptions
Defining custom exceptions
Using the Python logging module
Introducing core logging components
Working with the logging module
What to log and what not to log
Summary
Questions
Further reading
Answers
Chapter 5: Testing and Automation with Python
Technical requirements
Understanding various levels of testing
Unit testing
Integration testing
System testing
Acceptance testing
Working with Python test frameworks
Working with the unittest framework
Working with the pytest framework
Executing TDD
Red
Green
Refactor
Introducing automated CI
Summary
Questions
Further reading
Answers
Chapter 6: Advanced Tips and Tricks in Python
Technical requirements
Learning advanced tricks for using functions
Introducing the counter, itertools, and zip functions for iterative tasks
Using filters, mappers, and reducers for data transformations
Learning how to build lambda functions
Embedding a function within another function
Modifying function behavior using decorators
Understanding advanced concepts with data structures
Embedding a dictionary inside a dictionary
Using comprehension
Introducing advanced tricks with pandas DataFrame
Learning DataFrame operations
Learning advanced tricks for a DataFrame object
Summary
Questions
Further reading
Answers
Section 3: Scaling beyond a Single Thread
Chapter 7: Multiprocessing, Multithreading, and Asynchronous Programming
Technical requirements
Understanding multithreading in Python and its limitations
What is a Python blind spot?
Learning the key components of multithreaded programming in Python
Case study – a multithreaded application to download files from Google Drive
Going beyond a single CPU – implementing multiprocessing
Creating multiple processes
Sharing data between processes
Exchanging objects between processes
Synchronization between processes
Case study – a multiprocessor application to download files from Google Drive
Using asynchronous programming for responsive systems
Understanding the asyncio module
Distributing tasks using queues
Case study – asyncio application to download files from Google Drive
Summary
Questions
Further reading
Answers
Chapter 8: Scaling out Python Using Clusters
Technical requirements
Learning about the cluster options for parallel processing
Hadoop MapReduce
Apache Spark
Introducing RDDs
Learning RDD operations
Creating RDD objects
Using PySpark for parallel data processing
Creating SparkSession and SparkContext programs
Exploring PySpark for RDD operations
Learning about PySpark DataFrames
Introducing PySpark SQL
Case studies of using Apache Spark and PySpark
Case study 1 – Pi (π) calculator on Apache Spark
Case study 2 – Word cloud using PySpark
Summary
Questions
Further reading
Answers
Chapter 9: Python Programming for the Cloud
Technical requirements
Learning about the cloud options for Python applications
Introducing Python development environments for the cloud
Introducing cloud runtime options for Python
Building Python web services for cloud deployment
Using Google Cloud SDK
Using the GCP web console
Using Google Cloud Platform for data processing
Learning the fundamentals of Apache Beam
Introducing Apache Beam pipelines
Building pipelines for Cloud Dataflow
Summary
Questions
Further reading
Answers
Section 4: Using Python for Web, Cloud, and Network Use Cases
Chapter 10: Using Python for Web Development and REST API
Technical requirements
Learning requirements for web development
Web frameworks
User interface
Web server/application server
Database
Security
API
Documentation
Introducing the Flask framework
Building a basic application with routing
Handling requests with different HTTP method types
Rendering static and dynamic contents
Extracting parameters from an HTTP request
Interacting with database systems
Handling errors and exceptions in web applications
Building a REST API
Using Flask for a REST API
Developing a REST API for database access
Case study– Building a web application using the REST API
Summary
Questions
Further reading
Answers
Chapter 11: Using Python for Microservices Development
Technical requirements
Introducing microservices
Learning best practices for microservices
Building microservices-based applications
Learning microservice development options in Python
Introducing deployment options for microservices
Developing a sample microservices-based application
Summary
Questions
Further reading
Answers
Chapter 12: Building Serverless Functions Using Python
Technical requirements
Introducing serverless functions
Benefits
Use cases
Understanding the deployment options for serverless functions
Learning how to build serverless functions
Building an HTTP-based Cloud Function using the GCP Console
Case study – building a notification app for cloud storage events
Summary
Questions
Further reading
Answers
Chapter 13: Python and Machine Learning
Technical requirements
Introducing machine learning
Using Python for machine learning
Introducing machine learning libraries in Python
Best practices of training data with Python
Building and evaluating a machine learning model
Learning about an ML model building process
Building a sample ML model
Evaluating a model using cross-validation and fine tuning hyperparameters
Saving an ML model to a file
Deploying and predicting an ML model on GCP Cloud
Summary
Questions
Further reading
Answers
Chapter 14: Using Python for Network Automation
Technical requirements
Introducing network automation
Merits and challenges of network automation
Use cases
Interacting with network devices
Protocols for interacting with network devices
Interacting with network devices using SSH-based Python libraries
Interacting with network devices using NETCONF
Integrating with network management systems
Using location services endpoints
Getting an authentication token
Getting network devices and an interface inventory
Updating the network device port
Integrating with event-driven systems
Creating subscriptions for Apache Kafka
Processing events from Apache Kafka
Renewing and deleting a subscription
Summary
Questions
Further reading
Answers
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