Python for Geeks: Build production-ready applications using advanced Python concepts and industry best practices

Cover
Title Page
Copyright and Credits
Dedication
Contributors
Table of Contents
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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