Programming Effectively in Python

Packt Publishing

Python is an easy to learn, powerful programming language. If you're a developer who wishes to build a strong programming foundation with this simple yet powerful programming language Python, then this course is for you.

This learning path is your step-by-step guide to exploring the possibilities in the field of Go. With this course, you'll start with understanding the principles of refactoring, & spot opportunities by identifying code that requires refactoring. Also, you will be shown how to remove Python anti-patterns from your programs in simple steps. Next, you will learn how you can increase the speed & performance of your code with quick tips, tricks, and techniques for loops, data structures, object-oriented programming, functions, and more. Finally, after all this, its time to troubleshoot Python Application Development Quickly detect which lines of code are causing problems, and fix them quickly without going through lakhs of pages.

Contents and Overview

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Refactoring Python Code starts with teaching you to resolve Python anti-patterns with techniques and methods to improve the design of your existing code. Tackle bugs by understanding the principles of refactoring, and learn to spot opportunities by identifying code that requires refactoring. We will also show you how to build test-driven processes to make refactoring easier. This course will show you how to remove Python anti-patterns from your programs in simple steps. We cover specific techniques for refactoring and improving the sloppy Python code. Take this course if you want to have a legacy Python code base with a lot of issues. Apply real-world refactoring techniques, and turn your code into clean, efficient, and maintainable projects.

The second course, Python Tips, Tricks, and Techniques will take you from a Python outsider to an insider. You will benefit from insights from the Python documentation, PEPs, and online developer communities to learn the ultimate Pythonic ways to tackle common programming patterns. This course covers tips, tricks, and techniques for loops, data structures, object-oriented programming, functions, and more, helping you work on ordered collections and key-value stores for dictionaries. You will be able to increase the speed and performance of your code while making it easier to debug. Start writing cleaner code for your applications and learn to organize it better in just 3 hours. No other course can transform every corner of your Python code. Take this course NOW and become an overnight Python rockstar developer.

The third course, Troubleshooting Python Application Development takes you through a structured journey of performance problems that your application is likely to encounter, and presents both the intuition and the solution to these issues. You'll get things done, without a lengthy detour into how Python is implemented or computational theory. Quickly detect which lines of code are causing problems, and fix them quickly without going through 300 pages of unnecessary detail.

About the Authors:

James Cross is a Big Data Engineer and certified AWS Solutions Architect with a passion for data-driven applications. He's spent the last 3-5 years helping his clients to design and implement huge scale streaming Big Data platforms, Cloud-based analytics stacks, and serverless architectures. He started his professional career in Investment Banking, working with well-established technologies such as Java and SQL Server, before moving into the big data space. Since then he's worked with a huge range of big data tools including most of the Hadoop eco-system, Spark and many No-SQL technologies such as Cassandra, MongoDB, Redis, and DynamoDB. More recently his focus has been on Cloud technologies and how they can be applied to data analytics, culminating in his work at Scout Solutions as CTO, and more recently with Mckinsey. James is an AWS-certified solutions architect with several years' experience designing and implementing solutions on this cloud platform. As CTO of Scout Solutions Ltd, he built a fully serverless set of APIs and an analytics stack based around Lambda and Redshift. He is interested in almost anything that has to do with technology. He has worked with everything from WordPress to Hadoop, from C++ to Java, and from Oracle to DynamoDB. If it's new and solves a problem in an innovative way he's keen to give it a go.
Colibri Ltd is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help its clients navigate the rapidly changing and complex world of emerging technologies, with deep expertise in areas such as big data, data science, machine learning, and cloud computing. Over the past few years, they have worked with some of the world's largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the world's most popular soft drinks companies, helping each of them to make better sense of its data, and process it in more intelligent ways. The company lives by its motto: Data -> Intelligence -> Action.
Rudy Lai is the founder of QuantCopy, a sales acceleration startup using AI to write sales emails to prospects. After taking in leads from your pipelines, QuantCopy researches them online and generates sales emails from that data. It also has a suite of email automation tools to schedule, send, and track email performance—key analytics that all feedback into how our AI generates content. Prior to founding QuantCopy, Rudy ran HighDimension.IO, a machine learning consultancy where he experienced firsthand the frustrations of outbound sales and prospecting. As a founding partner, he helped startups and enterprises with HighDimension.IO's Machine-Learning-as-a-Service, allowing them to scale up data expertise in the blink of an eye. In the first part of his career, Rudy spent 5+ years in quantitative trading at leading investment banks such as Morgan Stanley. This valuable experience allowed him to witness the power of data, but also the pitfalls of automation using data science and machine learning. Quantitative trading was also a great platform from which to learn deeply about reinforcement learning and supervised learning topics in a commercial setting. Rudy holds a Computer Science degree from Imperial College London, where he was part of the Dean's List, and received awards such as the Deutsche Bank Artificial Intelligence prize.

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What's inside

Syllabus

Refactoring Python Code

This video provides an overview of the entire course.

This video is all about refactoring and its benefits.

Learn about clean code
Go through the DRY principle

In this video, we will understand the refactoring principles.

Learn about PEP8
Action DRY: the rule of three
Refactor if it helps you understand

There are a plethora of tools in Python that can help you refactor or write code cleanly the first time.

Learn about IDEs: Visual Studio Code, PyCharm, and Eclipse
Learn about Linters: Pylint, PEP8, and McCabe
Learn about Formatters: autopep8

This video is an overview of Python anti-patterns.

Learn about Python anti-patterns
Learn about the playbook: spot the problem, fix the problem
Go through the types of Python anti-patterns

In this video, we will go through the various types of Python anti-patterns.

Go through the various types of Python anti-patterns
Go through misapplied OOP
Duplicate code

This video is about Playbooks for removing anti-patterns.

Separate the general and the specific
Keep it simple
Move features around

In this video, we will be refactoring through splitting up functions.

Learn about Symptom
Learn why does splitting up the function helps
Refactor by hand

In this video, we will be refactoring through merging functions.

Learn about symptom
Learn why does splitting up the function helps
Refactor by hand

In this video, we will replace complex expressions with inner function calls.

Learn about symptom
Know what fix is needed
Learn why inner function calls help

In this video, we will be refactoring through local variable creation.

Learn about symptom
Know what fix is needed
Learn why local variables help

In this video, we will be assessing the correct class for fields and methods.

Explore the four situations
Understand the results

In this video, we will be moving functions around different classes to group functionality.

Move fields and methods
Create and merge new classes
Extracting common functionality into mixins

In this video, we will refactor delegate classes to remove double dependencies.

Learn about Delegates
Know why to hide delegates
Hide Delegates

In this video we will be removing middlemen classes to reduce needless complexity.

Know what middlemen classes are
Remove middlemen classes

This video is an introduction to pattern based refactoring.

Learn about Design Patterns
Learn about Creational Patterns
Learn about Structural Patterns

In this video, we will be using encapsulation methods for refactoring.

Look at a problem solution pair
Analyze the result

In this video, we will be removing multiple checks for None in your Python code.

Identify the issue
Create the Null object class
Use the Null object class

This video is all about Python refactoring using conditionals.

Combine conditional statements
Replace conditional with subclasses

In this video, we will be using rope, a Python refactoring library.

Know what a rope is
Understand what rope can do
Use features in rope

This video will show how to use codemods.

Learn about codemods
Explore the features and options in codemods

We will explore the tools available for refactoring, in this video.

Learn about PyCharm
Extract methods
Create new modules

Test Your Knowledge

Python Tips, Tricks and Techniques

This video gives an overview of the entire course.

Learn about creating empty lists then filling it with a for loop can be improved.

Create lists with list comprehensions
Modify lists with list comprehensions

In this video, we will see how to reduce for if loops into a simple one-liner.

Collapse conditional statements in for loops with list comprehensions

Learn how to operate on lists of lists with nested list comprehensions.

Operate on lists of lists with nested list comprehensions

Learn How to simplify the way you collapse nested lists.

Simplify the way you collapse nested lists

There are shortcuts to some common list operations that are not trivial to implement from scratch.

Reverse a list with slices
Head and tail a list with slices

In this video, we will learn about avoiding Key Errors.

Learn How to avoid KeyErrors with the defaultdict data structure

Learn about how to simplify the way you collapse nested dictionaries.

Simplify the way you collapse nested dictionaries

In this video, we will explore a surprising application of dictionaries to create Python switch statements.

Create Python switch statements

Learn how to merge two dictionaries with just one line of code.

Merge two dictionaries with just one line of code

Learn how to create a dictionary with list comprehension in Python.

Create a dictionary with list comprehension in Python

This video shows you how to count the occurrences of items quickly with Counter.

Learn how to count occurrences of items quickly with Counter
Explore the results

This video will show you how to create stacks or queues with deque objects.

Learn what Stacks or queues are
Create Stacks and queues with deque
Analyze the results

This video will show you how to group related values with tuples and sequences.

Group related values with tuples and sequences
Analyze the results

This video will show you how to gather unique sets of values with sets and frozensets.

Gather unique sets of values with sets and frozensets
Analyze the results

This video will show you how to power up your dictionaries with Ordered Dictionaries.

Learn how to power up your dictionaries with Ordered Dictionaries
Analyze the results

This video will show you the new ways of calling functions with arguments and kwargs.

Learn the new ways of calling functions with arguments and kwargs
Analyze the results

This video will show the implementation of one line functions with lambdas.

Understand one line functions with lambdas
Analyze the results

In this video we will see how to segment the code with functions within functions.

Segment your code with functions within functions
Analyze the results

This video will show how to create dynamic functions by returning functions.

Create dynamic functions by returning functions
Analyze the results

This video will show how to power up your functions by wrapping them with decorators.

Power up your functions by wrapping them with decorators
Analyze the results

In this video we will see How to copy and clone objects in the right way.

Copy and clone objects in the right way
Analyze the results

In this video, we will focus on the mini-classes of Python: namedtuples.

Understand mini-classes of Python: namedtuples
Analyze the results

Learn how to create smart values with static methods and properties.

Learn how to create smart values with static methods and properties
Analyze the results

This video will show the comparison of two different objects.

Compare two different objects
Analyze the results

Learn how to do real OOP by implementing abstract base classes in Python.

Understand OOP by implementing abstract base classes in Python
Analyze the results

This video will show how to enumerate the indices of your loops with no extra lines.

Enumerate the indices of your loops
Analyze the results

Learn how to underscore useless variables to make your code easier to look at.

Understand underscore useless variables
Make your code easier to look at
Analyze the results

This video will show uncommon “for..else” loop to end your iteration.

Understand the uncommon “for..else” loop to end your iteration
Analyze the results

In this video you will learn about pretty printing any Python Data Structure.

Learn about pretty printing any Python Data Structure
Analyze the results

This video shows how to manage your dynamic resources carefully with Context Managers.

Manage your dynamic resources carefully with Context Managers
Analyze the results

Troubleshooting Python Application Development

This video gives glimpse of the entire course.

Time is of essence and timeit is your tool. In this video we explore timeit.

Understand what is timeit
Set up our timing experiment
Run our timing experiment

In this video, we will look at profile module and understand where time is spent.

Understand what is profiling
Understand how Profiling our Fibonacci sequence generator is done
Profile the sequence generator using the least recently used cache

In this video we will explore and understand tips and tricks which will allow us to track time more precisely using cProfile.

Understand the difference between cProfile vs profile
Explore and create artificially slowed functions
Understand profiling using cProfile

In this video we will understand the memory_profiler.

Explore how to go about profiling a simple function
Explore and understand how profiling manual garbage collection is done

This video is all about reducing the execution time and memory consumption.

Understand what are slots
Create classes that use slots
Inspect the performance of slots vs class dicts

In this video we will use tuples instead of lists when your data does not change.

Understand what are tuples and how to declare them
We will see by example that tuples are fasters than lists
We will understand why are they faster than lists

Save on memory consumption with generators instead of lists.

What are generators?
Why are they memory efficient?
Implementing generators

Generators are designed for a specific purpose, here is when not to use them.

when you need features from a list
when you need to iterate through a list multiple times

Chain together a series of processes so one output is another's output.

Composing two generators
Using parenthesis to define generator comprehensions

Illustrate why we need to move from Python lists to NumPy arrays

Problem 1: Memory consumption of lists for large amounts of data
Problem 2: Dynamic Typing

Show how we can use NumPy and the different ways we can create NumPy arrays

The difference between NumPy arrays and lists
Creating NumPy arrays from Python lists
Creating NumPy arrays from NumPy helpers

Show that for large n, NumPy arrays are much faster than list comprehensions

Setting up our problem with list comprehensions
Setting up its cousin in NumPy
Comparing their performance

Show how to use broadcasting - applying binary functions on arrays of varying length

What is NumPy's broadcasting?
Broadcasting: an example
How broadcasting works - three examples

Show the simple interface of numexpr and where we can use it to optimize

What is numexpr?
How to use numexpr?
Use cases of numexpr

If you have a list of URLs, serially requesting the HTML body of each page is super slow

Using requests to scrape the HTML of a web URL
Timing 10 web scraping requests

What are coroutines and how they help you request all webpages at once

What is gevent?
What are semaphores?
Creating a gevent scraper

What is event driven concurrency, and how that can speed up web scraping

What is tornado?
What is an IO Loop?
Creating a tornado based scraper

Futures and asyncio driven concurrent web scraping

What is asyncio?
await and async
Creating an asyncio based scraper

Introduce the audience to parallel programming concepts.

What is parallel processing?
Embarrassingly parallel and less embarrassingly parallel
The global interpreter lock of Python

Show how the decorate-sort-undecorate pattern can be beaten

The decorate-sort-undecorate pattern
Beating the DSU pattern with a sort key

Show how we can use Pool to achieve multiprocessing

What is Pool?
Our first process Pool
Asynchronous process Pools

In this video we will understand how we can stop processes from interfering with each other using locks.

We will be using multiprocessing.Value to share variables
Why does it not add up?
Using a lock to remove overwrites

This video covers the effects of many processes.

Using the logging module
Creating the right logger format for multiprocessing
Logging multi-processes

This video is focused on understanding how to copy and clone objects the right way.

Identify the wrong object instance
Use the correct object clone

This video will show us how to mini-classes of Python: namedtuples

Learn about mini-classes of Python

In this video we will understand how to create smart values with static methods and properties.

Create smart values with static methods and properties

In this video we will be understanding how to compare two different objects.

Understanding how to compare two different objects

In this video we will explore how to do real OOP by implementing abstract base classes in Python.

Do real OOP by implementing abstract base classes in Python

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Programming Effectively in Python with these activities:

Review Python Data Structures

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Reinforce your understanding of fundamental Python data structures before diving into more advanced topics.

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Review the documentation for lists, dictionaries, tuples, and sets.
Practice creating and manipulating these data structures.
Work through basic exercises involving data structure selection and usage.

Review 'Effective Python' by Brett Slatkin

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Learn practical tips and tricks for writing better Python code, complementing the course material.

View Effective Python: 125 Specific Ways to Write... on Amazon

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Read selected chapters focusing on code style, data structures, and functions.
Implement the recommendations in your own code.
Reflect on how these tips can improve your coding practices.

Create a Python Tips Blog Post

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Solidify your understanding of Python tips and tricks by explaining them in a blog post.

Show steps

Choose a specific Python tip or trick covered in the course.
Write a clear and concise explanation of the tip, including code examples.
Format the blog post for readability and publish it online.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Refactoring Exercises on CodeWars

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Sharpen your refactoring skills by tackling coding challenges that require improving existing code.

Show steps

Find Python refactoring katas on CodeWars or similar platforms.
Analyze the existing code and identify areas for improvement.
Apply refactoring techniques to make the code cleaner and more efficient.
Submit your solution and compare it with others.

Build a Simple Web Scraper

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Apply your knowledge of Python and web scraping libraries to extract data from websites.

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Choose a website to scrape and identify the data you want to extract.
Use libraries like Beautiful Soup and Requests to fetch and parse the HTML.
Write code to extract the desired data and store it in a structured format.
Handle potential errors and edge cases.

Create a Python Refactoring Cheat Sheet

Show steps

Consolidate your knowledge of refactoring techniques into a handy reference guide.

Show steps

Review the refactoring techniques covered in the course.
Create a cheat sheet summarizing each technique, including examples.
Organize the cheat sheet for easy reference.

Review 'High Performance Python' by Micha Gorelick and Ian Ozsvald

Show steps

Deepen your knowledge of Python performance optimization techniques.

View High Performance Python on Amazon

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Read chapters related to profiling, NumPy, and concurrency.
Experiment with the optimization techniques discussed in the book.
Apply these techniques to your own Python projects.

Career center

Learners who complete Programming Effectively in Python will develop knowledge and skills that may be useful to these careers:

Python Developer

A Python Developer builds and maintains software using the Python programming language. This role often involves writing efficient and reliable code, debugging applications, and collaborating with other developers to create innovative solutions. This course helps current and aspiring Python Developers improve skills in refactoring code to improve design. The course provides tips, tricks, and techniques to improve the speed and performance of Python code. The course also helps developers troubleshoot Python applications by detecting which lines of code are causing problems. By learning how to refactor, optimize, and troubleshoot code, one becomes proficient in Python development.

See salaries and explore the career path for Python Developer

Software Engineer

A Software Engineer designs, develops, and tests software systems and applications. They are responsible for the entire software development lifecycle, from initial planning to deployment and maintenance. The content of this course improves the skills of Software Engineers who use Python. By focusing on refactoring, the course helps improve the maintainability and readability of codebases. The course also provides guidance on spotting opportunities to improve code, as well as techniques to remove Python anti-patterns, which helps one write cleaner code. Additionally, the troubleshooting module aids in rapidly identifying and resolving issues in Python applications.

See salaries and explore the career path for Software Engineer

Data Engineer

A Data Engineer builds and maintains the infrastructure required for data storage, processing, and analysis. They are responsible for ensuring that data is accessible, reliable, and secure. This course can be useful for Data Engineers who use Python for data processing and automation tasks. The course teaches techniques to increase the speed and performance of Python code, which is crucial for handling large datasets. The ability to troubleshoot Python application development quickly helps resolve issues in data pipelines. The refactoring techniques taught in this course improves the quality and maintainability of data-related code.

See salaries and explore the career path for Data Engineer

Automation Engineer

An Automation Engineer designs, develops, and implements automated processes for software testing, deployment, and infrastructure management. They help to improve efficiency, reduce errors, and ensure consistent performance. This course helps current and future Automation Engineers who use Python for scripting and automation tasks. The course teaches strategies to refactor Python code, remove anti-patterns, and enhance code quality. The tips and tricks to increase code speed and the troubleshooting techniques provided in this course are helpful in creating robust and efficient automation scripts.

See salaries and explore the career path for Automation Engineer

Backend Developer

A Backend Developer focuses on the server side logic and databases that power web and mobile applications. They are responsible for building and maintaining the application's architecture, ensuring scalability, and handling data processing. Backend developers working with Python may find this course helpful. The refactoring techniques taught in this course improve the robustness and maintainability of backend codebases. It covers specific techniques for improving sloppy code. Also, the troubleshooting module helps quickly resolve performance bottlenecks and other issues in Python-based backend systems. Efficiency allows for improved scaling and performance for applications.

See salaries and explore the career path for Backend Developer

DevOps Engineer

A DevOps Engineer is responsible for bridging the gap between development and operations, focusing on automating and streamlining the software development lifecycle. This role involves continuous integration, continuous delivery, and infrastructure management. For a DevOps Engineer using Python for scripting and automation, this course helps improve code quality and efficiency. The refactoring guidance enables cleaner, more maintainable code, while the performance tips and troubleshooting skills help optimize deployment processes. The course is particularly helpful in identifying and fixing issues, such as identifying and fixing bugs.

See salaries and explore the career path for DevOps Engineer

Software Architect

A Software Architect designs and oversees the implementation of large scale software systems. They are responsible for making high level design choices, ensuring scalability, and maintaining the overall integrity of the architecture. This course may benefit Software Architects who need to understand and improve the quality of Python code within their systems. The refactoring techniques teach the principles of good design. The course also provides tools and techniques for identifying and resolving code issues, which helps architects maintain a clean and efficient codebase.

See salaries and explore the career path for Software Architect

Data Scientist

A Data Scientist analyzes large datasets to extract insights, develop models, and make data-driven decisions. They use programming languages like Python to manipulate data, build machine learning algorithms, and create visualizations. The course may be useful to Data Scientists who want to improve their Python coding skills. By learning how to refactor existing code and avoid anti-patterns, one will produce cleaner and more maintainable code. Tips and tricks for optimizing Python code for speed and performance are beneficial when working with large datasets. The troubleshooting techniques also help resolve issues that may arise during model development and deployment.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A Machine Learning Engineer designs, develops, and deploys machine learning models and systems. They work on tasks such as feature engineering, model training, and performance optimization. This course may be helpful for Machine Learning Engineers who use Python for building and deploying models. The course provides techniques to improve the efficiency and performance of Python codebases. The course presents ways to find and remove anti patterns that can negatively impact model training and deployment. By learning to write cleaner and more efficient code Machine Learning Engineers improve the reliability and scalability of models.

See salaries and explore the career path for Machine Learning Engineer

Site Reliability Engineer

A Site Reliability Engineer focuses on ensuring the reliability, scalability, and performance of software systems in production. They work on monitoring, incident response, and automation to keep systems running smoothly. This course may provide some value to Site Reliability Engineers who use Python for automation and scripting tasks. The troubleshooting section of the course helps in quickly identifying and resolving issues in Python-based systems. Performance tips help write efficient scripts for monitoring and automation.

See salaries and explore the career path for Site Reliability Engineer

Quality Assurance Engineer

A Quality Assurance Engineer is responsible for ensuring the quality of software products through testing, defect tracking, and process improvement. They help identify and resolve issues before the software is released to users. Those in this role who use Python to create test scripts will get value from this course. By understanding refactoring techniques, a Quality Assurance Engineer develops maintainable testing code. By removing anti-patterns, code becomes efficient. Using the code troubleshooting portion of the course, automation scripts are more easily maintained.

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Full-Stack Developer

A Full Stack Developer works on both the front end and back end of web applications, handling everything from user interfaces to server side logic and databases. This course may be helpful to Full Stack Developers who use Python for back end development. The refactoring techniques taught in the course improve the maintainability and scalability of back end code. The debugging tools are very useful. Knowing how to remove Python anti-patterns from programs is useful.

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Technical Lead

A Technical Lead manages a team of developers and is responsible for guiding the technical direction of a software project. They provide mentorship, make architectural decisions, and ensure code quality. This course may be valuable for Technical Leads who want to improve their understanding of Python code quality and best practices. The course provides comprehensive information about refactoring and eliminating anti-patterns, which are vital for maintaining a healthy codebase. It also offers advice on problem-solving which is helpful in guiding technical teams. By improving their skills in Python development, a Technical Lead is more equipped to guide a team.

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Consultant

A Consultant advises organizations on how to improve their performance and efficiency. They analyze problems, develop solutions, and implement changes to help clients achieve their goals. This course may benefit Consultants who work with organizations that use Python. By studying the refactoring principles in the course, a Consultant improves existing code. By using the troubleshooting skills in the course, performance issues are solved more quickly. Application development is also more efficient.

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Educator

An Educator (for example, a teacher, instructor, or professor) imparts knowledge and skills to students through instruction, demonstrations, and assessments. This role can be beneficial for Educators, especially those who teach programming or computer science. Educators will be well-served by the Python code refactoring content of this course. They can use this to show their students how to improve designs. They can also use the problem-solving content of this course to teach debugging.

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Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Programming Effectively in Python.

Effective Python

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Provides practical advice on writing clean, efficient, and Pythonic code. It covers a wide range of topics, including best practices for using data structures, functions, classes, and concurrency. It valuable resource for intermediate to advanced Python programmers looking to improve their skills. This book adds depth to the course by providing real-world examples and actionable advice.

Effective Python: 125 Specific Ways to Write Better...

Kindle Edition

High Performance Python

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Delves into techniques for optimizing Python code for speed and efficiency. It covers topics such as profiling, benchmarking, and using specialized libraries like NumPy and Cython. It is particularly useful for understanding the 'Troubleshooting Python Application Development' section of the course. This book provides a deeper understanding of performance optimization strategies.

High Performance Python

Paperback

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High Performance Python

Kindle Edition

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