Python is the preferred choice of developers, engineers, data scientists, and hobbyists everywhere. It is a great scripting language that can power your applications and provide great speed, safety, and scalability. By exposing Python as a series of simple recipes, you can gain insight into specific language features in a particular context. This video will arm you with the knowledge of creating applications with flexible logging, powerful configuration, and command-line options, automated unit tests, and good documentation. You will learn to use the Flask framework for Restful APIs. You will end the course equipped with the knowledge of testing, web services, and configuration and application integration tips and tricks.
About the Authors
Steven F. Lott has been programming since the 70s, when computers were large, expensive, and rare. As a contract software developer and architect, he has worked on hundreds of projects, from very small to very large. He's been using Python to solve business problems for over 10 years. He’s currently leveraging Python to implement microservices and ETL pipelines. His other titles with Packt Publishing include Python Essentials, Mastering Object-Oriented Python, Functional Python Programming, and Python for Secret Agents. Steven is currently a technomad who lives in various places on the east coast of the U.S.
Good Python includes docstrings inside every module, class, function, and method. Many tools can create useful, informative documentation from the docstrings. How can we turn examples into proper test cases? Let's explore this question.
This video will show you how to turn exception processing and the resulting traceback messages into proper test cases.
Doctest examples require an exact match with the text. How can we write doctest examples that handle hash randomization or floating-point implementation details appropriately? This video will enable you to answer this question.
This video will show you how you could create more sophisticated tests.
This video shows how we can combine all of the various tests into one tidy package.
Many applications rely on datetime.datetime.now() to create a timestamp. When we use this with a unit test, the results are essentially impossible to predict. How can we work with datetime stamps? Let's look into this.
Many times we create random values or put values into random order. In many statistical tests, repeated random shuffling or random subset calculations are done. Let's see how we can unit test algorithms that involve randomness.
How can we create more sophisticated mock objects that have internal state and make their own internal state changes? Let's dive into these questions.
Let's create applications that support layered composition following the WSGI standard.
How can we simplify all of the common web application programming and eliminate the boilerplate code? Let's explore these questions.
This video will show you a better way to handle a query string and have a more sophisticated structure that behaves like a dictionary with single values for the common case, and a more complex object for the rare cases where a field key is duplicated and has multiple values.
There are several user stories that involve RESTful API clients written in Python. How can we create a Python program that is a client of RESTful web services? Let's see this.
The path to a resource can be quite complex. It's common in RESTful web services to use the path information to identify groups of resources, individual resources, and even relationships among resources. How can we handle complex path parsing? Let's answer this question.
This video will teach you, how you can parse JSON inputs from web clients and what is an easy way to validate the input.
This video will show a self-service application in which there is no defined set of users. This means that there must be a web service to create new users that doesn't require any authentication. All other services will require a properly authenticated user.
Let's see how we can support a rich hierarchy of locations for configuration files.
Python offers a variety of ways to package application inputs and configuration files. Let's look at writing files in YAML notation because it's elegant and simple. This video will show you how you can represent configuration details in YAML notation.
How can we represent configuration details in Python module notation? Let's find the solution to this problem.
Python offers the logging package, which can be used to direct the ancillary output to a separate file. It can also be used to format and filter that additional output. How can we use logging properly? Let's see this.
This video will walk you through a design pattern that would allow several Python language components to be combined into a larger application. You will learn to combine applications to create a composite.
This video will enable you to create families of closely related commands.
How can we combine these techniques effectively? Can we create longer, more complex sequences of operations using Python? This video will answer all these questions.
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