Deep Learning with Python, Second Edition

Unlock the groundbreaking advances of deep learning with this extensively revised edition of the bestselling original. Learn directly from the creator of Keras and master practical Python deep learning techniques that are easy to apply in the real world.

In Deep Learning with Python, Second Edition you will learn:

    Deep learning from first principles

    Image classification & image segmentation

    Timeseries forecasting

    Text classification and machine translation

    Text generation, neural style transfer, and image generation

Deep Learning with Python has taught thousands of readers how to put the full capabilities of deep learning into action. This extensively revised second edition introduces deep learning using Python and Keras, and is loaded with insights for both novice and experienced ML practitioners. You’ll learn practical techniques that are easy to apply in the real world, and important theory for perfecting neural networks.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology

Recent innovations in deep learning unlock exciting new software capabilities like automated language translation, image recognition, and more. Deep learning is becoming essential knowledge for every software developer, and modern tools like Keras and TensorFlow put it within your reach, even if you have no background in mathematics or data science. 

About the book

Deep Learning with Python, Second Edition introduces the field of deep learning using Python and the powerful Keras library. In this new edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners. As you move through this book, you’ll build your understanding through intuitive explanations, crisp illustrations, and clear examples. You’ll pick up the skills to start developing deep-learning applications.

What's inside

    Deep learning from first principles

    Image classification and image segmentation

    Time series forecasting

    Text classification and machine translation

    Text generation, neural style transfer, and image generation

About the reader

For readers with intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required.

About the author

François Chollet is a software engineer at Google and creator of the Keras deep-learning library.

Table of Contents

1  What is deep learning?

2 The mathematical building blocks of neural networks

3 Introduction to Keras and TensorFlow

4 Getting started with neural networks: Classification and regression

5 Fundamentals of machine learning

6 The universal workflow of machine learning

7 Working with Keras: A deep dive

8 Introduction to deep learning for computer vision

9 Advanced deep learning for computer vision

10 Deep learning for timeseries

11 Deep learning for text

12 Generative deep learning

13 Best practices for the real world

14 Conclusions

Hard Copy: Deep Learning with Python, Second Edition

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Python Coding challenge - Day 120 | What is the output of the following Python Code?

 

Code :

def sum(num):

    if num == 1:

        return 1

    return num + sum(num - 1)

print(sum(5))

Solution and Explanation:

Here's how the function works:

The function sum takes a parameter num.
The base case is defined with if num == 1:. If num is 1, the function returns 1.
If the base case is not met, the function returns num + sum(num - 1). This is the recursive step, where the sum of the current num and the sum of the numbers from 1 to num - 1 is calculated.
The print(sum(5)) statement calls the sum function with the argument 5 and prints the result.
Let's trace the function call for sum(5):

sum(5) returns 5 + sum(4)
sum(4) returns 4 + sum(3)
sum(3) returns 3 + sum(2)
sum(2) returns 2 + sum(1)
sum(1) returns 1 (base case)
Now we substitute these values back:

sum(2) returns 2 + 1 = 3
sum(3) returns 3 + 3 = 6
sum(4) returns 4 + 6 = 10
sum(5) returns 5 + 10 = 15
So, the final result printed by print(sum(5)) is 15.

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Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more

 

Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data

Purchase of the print or Kindle book includes a free PDF eBook

Key Features

Examine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and more

Discover modern causal inference techniques for average and heterogenous treatment effect estimation

Explore and leverage traditional and modern causal discovery methods

Book Description

Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality.

You’ll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code.

Next, you’ll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you’ll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You’ll further explore the mechanics of how “causes leave traces” and compare the main families of causal discovery algorithms.

The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more.

What you will learn

Master the fundamental concepts of causal inference

Decipher the mysteries of structural causal models

Unleash the power of the 4-step causal inference process in Python

Explore advanced uplift modeling techniques

Unlock the secrets of modern causal discovery using Python

Use causal inference for social impact and community benefit

Who this book is for

This book is for machine learning engineers, data scientists, and machine learning researchers looking to extend their data science toolkit and explore causal machine learning. It will also help developers familiar with causality who have worked in another technology and want to switch to Python, and data scientists with a history of working with traditional causality who want to learn causal machine learning. It’s also a must-read for tech-savvy entrepreneurs looking to build a competitive edge for their products and go beyond the limitations of traditional machine learning.

Table of Contents

Causality – Hey, We Have Machine Learning, So Why Even Bother?

Judea Pearl and the Ladder of Causation

Regression, Observations, and Interventions

Graphical Models

Forks, Chains, and Immoralities

Nodes, Edges, and Statistical (In)dependence

The Four-Step Process of Causal Inference

Causal Models – Assumptions and Challenges

Causal Inference and Machine Learning – from Matching to Meta-Learners

Causal Inference and Machine Learning – Advanced Estimators, Experiments, Evaluations, and More

Causal Inference and Machine Learning – Deep Learning, NLP, and Beyond

Can I Have a Causal Graph, Please?

Causal Discovery and Machine Learning – from Assumptions to Applications

Causal Discovery and Machine Learning – Advanced Deep Learning and Beyond

Epilogue

Hard Copy: Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more

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Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

 

This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch's simple to code framework.

Purchase of the print or Kindle book includes a free eBook in PDF format.

Key Features

Learn applied machine learning with a solid foundation in theory

Clear, intuitive explanations take you deep into the theory and practice of Python machine learning

Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices

Book Description

Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems.

Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself.

Why PyTorch?

PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric.

You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP).

This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.

What you will learn

Explore frameworks, models, and techniques for machines to 'learn' from data

Use scikit-learn for machine learning and PyTorch for deep learning

Train machine learning classifiers on images, text, and more

Build and train neural networks, transformers, and boosting algorithms

Discover best practices for evaluating and tuning models

Predict continuous target outcomes using regression analysis

Dig deeper into textual and social media data using sentiment analysis

Who this book is for

If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch.

Before you get started with this book, you'll need a good understanding of calculus, as well as linear algebra.

Table of Contents

Giving Computers the Ability to Learn from Data

Training Simple Machine Learning Algorithms for Classification

A Tour of Machine Learning Classifiers Using Scikit-Learn

Building Good Training Datasets – Data Preprocessing

Compressing Data via Dimensionality Reduction

Learning Best Practices for Model Evaluation and Hyperparameter Tuning

Combining Different Models for Ensemble Learning

Applying Machine Learning to Sentiment Analysis

Predicting Continuous Target Variables with Regression Analysis

Working with Unlabeled Data – Clustering Analysis

Hard Copy : Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

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Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition 3rd Edition

 

Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning.

Purchase of the print or Kindle book includes a free eBook in the PDF format.

Key Features

Third edition of the bestselling, widely acclaimed Python machine learning book

Clear and intuitive explanations take you deep into the theory and practice of Python machine learning

Fully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices

Book Description

Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems.

Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.

Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.

This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.

What you will learn

Master the frameworks, models, and techniques that enable machines to 'learn' from data

Use scikit-learn for machine learning and TensorFlow for deep learning

Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more

Build and train neural networks, GANs, and other models

Discover best practices for evaluating and tuning models

Predict continuous target outcomes using regression analysis

Dig deeper into textual and social media data using sentiment analysis

Who this book is for

If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.

Table of Contents

Giving Computers the Ability to Learn from Data

Training Simple Machine Learning Algorithms for Classification

A Tour of Machine Learning Classifiers Using scikit-learn

Building Good Training Datasets – Data Preprocessing

Compressing Data via Dimensionality Reduction

Learning Best Practices for Model Evaluation and Hyperparameter Tuning

Combining Different Models for Ensemble Learning

Applying Machine Learning to Sentiment Analysis

Embedding a Machine Learning Model into a Web Application

Predicting Continuous Target Variables with Regression Analysis

Working with Unlabeled Data – Clustering Analysis

Implementing a Multilayer Artificial Neural Network from Scratch

Parallelizing Neural Network Training with TensorFlow

Hard Copy: Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition 3rd Edition

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Python Coding challenge - Day 119 | What is the output of the following Python Code?

 

Code: 

value = 7 and 8

result = "Even" if value % 2 == 0 else "Odd"

print(result)

Solution and Explanation: 

The variable value is assigned the result of the logical AND operation between 7 and 8. In Python, the and operator returns the last true value or the first false value. In this case, since both 7 and 8 are considered true in a boolean context, value will be assigned the last true value, which is 8.

Then, the code checks if value % 2 == 0 (i.e., if value is even) and assigns "Even" to result if true, otherwise "Odd". Since 8 is even, the output of the code will be:

Even

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Data Analysis Foundations with Python: Master Python and Data Analysis using NumPy, Pandas, Matplotlib, and Seaborn: A Hands-On Guide with Projects and Case Studies.

 

Unlock the Full Potential of Data Analysis with Python—All in One Comprehensive Guide!

Are you an aspiring data scientist or analyst with a passion for exploring the vast possibilities of Python-based data analysis? If so, you're in luck because "Data Analysis Foundations with Python" is the perfect guide for you.

This comprehensive and immersive book will not only provide you with a hands-on approach but also offer a detailed exploration of the fascinating world of Python-based data analysis. Whether you're a beginner or an experienced professional, this book will take you on a journey that will deepen your understanding and expand your skills in the field.

✅ Include a Free Repository Code with all code blocks used in this book.

✅ This free resource allows you to copy and paste the book code for easy manipulation.

✅ Free premium customer support.

From Basics to Mastery: A Structured Learning Journey

This book is not just a mere compilation of Python codes and data sets. It goes beyond that, offering a comprehensive course that will guide you from being a Python beginner to becoming a highly skilled Data Analyst.

Throughout this course, you will not only acquire essential Python skills, but also gain practical experience in data manipulation techniques and learn about the latest advancements in machine learning. With its well-structured content and engaging learning activities, this book ensures that your journey towards becoming a proficient Data Analyst is both seamless and enjoyable.

Three Exceptional Projects and Two In-Depth Case Studies

Project 1: Analyzing Customer Reviews: Learn how to extract, clean, and make sense of textual data from online customer reviews.

Project 2: Predicting House Prices: Delve into the fascinating world of supervised learning, where you'll get to apply complex machine learning models to predict property prices.

Project 3: Building a Recommender System: Uncover the secrets of unsupervised learning as you build and deploy a fully functioning recommender system.

Case Studies for Real-world Insight

Case Study 1: Sales Data Analysis: Unearth the power of Python to transform raw sales data into actionable insights.

Case Study 2: Social Media Sentiment Analysis: Venture into the realm of Natural Language Processing and learn how to analyze public sentiment from social media data.

Additional Features

Practical Exercises: Each chapter concludes with practical exercises, designed to test your understanding and apply what you’ve learned in real-world scenarios.

Best Practices and Tips: The final section of the book is devoted to best practices in the field, including code organization and how to continue learning and growing in your data analysis journey.

Who This Book Is For

Whether you're a student who is eager to expand your knowledge, a professional who is seeking to embark on a new career path, or an experienced analyst who is looking to enhance your skills and stay ahead in the industry—this comprehensive book is specifically tailored to meet your needs and provide valuable insights and guidance.

What Are You Waiting For?

Embark on a transformative journey to unlock Python's potential for data analysis. Gain a deep understanding of Python's capabilities and learn how to extract insights from complex datasets using libraries and tools. Develop skills through real-world case studies and hands-on exercises to confidently tackle analytical challenges.

This book equips you with technical knowledge, practical skills, and a growth mindset for continuous learning. Don't miss this opportunity to become a proficient Python data analyst. Get your copy now for unlimited possibilities in data analysis.

Hard Copy: Data Analysis Foundations with Python: Master Python and Data Analysis using NumPy, Pandas, Matplotlib, and Seaborn: A Hands-On Guide with Projects and Case Studies.

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Python for Data Analysts and Scientists: Jump start your career in Data Analysis and Data Science Field

 

This is an excellent book for those who want to Jumpstart their career in Data Analytics and Data Scientist field.

My interest in learning Python script faced a challenging question - “Where shall I start from?”. I browsed through numerous online videos and training materials but with little success. After I agreed to pay a reasonable amount, a training course from a well-known e-learning platform gave me introductory knowledge on Python script. Learning the basic Python commands is one thing, whereas applying them to real life problems is another. For many months, the question - “Which Python commands are important in solving real-life problems?” bothered me a lot. It took me several sleepless nights, and a frantic lookout for a concise list of Python commands from an ocean of online information. My hands-on experiences designing Machine Learning models, performing root cause analysis by statistical hypothesis, and providing consultation as a Data Scientist, helped me learn the most crucial Python commands. The birth of this book is from the thoughts of my struggle in mastering and applying the Python script for resolving numerous challenging tasks. This book concisely lists the essential commands, the data visualization technics, and the statistical knowledge. I have mindfully placed the contents of this book for the day-to-day activities of a Data Analyst and a Data Scientist. This book aims to provide a quick starting platform for those who want to dive into the vast field of Machine Learning and Data Analytics. Further, this book will be a quick reference for those already in this field. With the hope of helping beginners and practitioners, and with a silent prayer of goodwill, I walk you through the simple steps to the proficiency in Python. Let us dive in and enjoy the journey into the world of Python.

Hard Copy: Python for Data Analysts and Scientists: Jump start your career in Data Analysis and Data Science Field

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Python 3: The Comprehensive Guide to Hands-On Python Programming Paperback – September 26, 2022

 

2023 IBPA Benjamin Franklin Award Gold Winner: Professional and Technical Category

Ready to master Python? Learn to write effective code with this award-winning comprehensive guide, whether you’re a beginner or a professional programmer. Review core Python concepts, including functions, modularization, and object orientation and walk through the available data types. Then dive into more advanced topics, such as using Django and working with GUIs. With plenty of code examples throughout, this hands-on reference guide has everything you need to become proficient in Python!

The complete Python 3 handbook

Learn basic Python principles and work with functions, methods, data types, and more

Walk through GUIs, network programming, debugging, optimization, and other advanced topics

Consult and download practical code examples

Coding with Python

Learn about Python syntax and structure. Follow examples to start developing and testing your own programs using downloadable code.

The Standard Library

Explore Python’s built-in library and see how it can be used for a variety of different tasks, from running your mathematical functions to debugging your code.

Advanced Programming Techniques

Already know the basics? Enhance your professional skills with more advanced concepts, including GUIs, Django, scientific computing, and connecting to other languages.

Hard Copy: Python 3: The Comprehensive Guide to Hands-On Python Programming Paperback – September 26, 2022

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Mastering Python Networking: Utilize Python packages and frameworks for network automation, monitoring, cloud, and management, 4th Edition

 

Get to grips with the latest container examples, Python 3 features, GitLab DevOps, network data analysis, and cloud networking to get the most out of Python for network engineering with the latest edition of this bestselling guide

Purchase of the print or Kindle book includes a free eBook in PDF format.

Key Features

Explore the power of the latest Python libraries and frameworks to tackle common and complex network problems efficiently and effectively

Use Python and other open source tools for Network DevOps, automation, management, and monitoring

Use Python 3 to implement advanced network-related features

Book Description

Networks in your infrastructure set the foundation for how your application can be deployed, maintained, and serviced. Python is the ideal language for network engineers to explore tools that were previously available to systems engineers and application developers. In Mastering Python Networking, Fourth edition, you'll embark on a Python-based journey to transition from a traditional network engineer to a network developer ready for the next generation of networks.

This new edition is completely revised and updated to work with the latest Python features and DevOps frameworks. In addition to new chapters on introducing Docker containers and Python 3 Async IO for network engineers, each chapter is updated with the latest libraries with working examples to ensure compatibility and understanding of the concepts.

Starting with a basic overview of Python, the book teaches you how it can interact with both legacy and API-enabled network devices. You will learn to leverage high-level Python packages and frameworks to perform network automation tasks, monitoring, management, and enhanced network security, followed by AWS and Azure cloud networking. You will use Git for code management, GitLab for continuous integration, and Python-based testing tools to verify your network.

What you will learn

Use Python to interact with network devices

Understand Docker as a tool that you can use for the development and deployment

Use Python and various other tools to obtain information from the network

Learn how to use ELK for network data analysis

Utilize Flask and construct high-level API to interact with in-house applications

Discover the new AsyncIO feature and its concepts in Python 3

Explore test-driven development concepts and use PyTest to drive code test coverage

Understand how GitLab can be used with DevOps practices in networking

Who this book is for

Mastering Python Networking, Fourth edition is for network engineers, developers, and SREs who want to learn Python for network automation, programmability, monitoring, cloud, and data analysis. Network engineers who want to transition from manual to automation-based networks using the latest DevOps tools will also get a lot of useful information from this book.

Basic familiarity with Python programming and networking-related concepts such as Transmission Control Protocol/Internet Protocol (TCP/IP) will be helpful in getting the most out of this book.

Table of Contents

Review of TCP/IP Protocol Suite and Python

Low-Level Network Device Interactions

APIs and Intent-Driven Networking

The Python Automation Framework – Ansible

Docker Containers for Network Engineers

Network Security with Python

Network Monitoring with Python - Part 1

Network Monitoring with Python - Part 2

Building Network Web Services with Python

Introduction to AsyncIO

AWS Cloud Networking

Azure Cloud Networking

Hard Copy: Mastering Python Networking: Utilize Python packages and frameworks for network automation, monitoring, cloud, and management, 4th Edition

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Python for Finance Cookbook: Over 50 recipes for applying modern Python libraries to financial data analysis 1st Edition

 

Solve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandas

Key Features

Use powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial data

Explore unique recipes for financial data analysis and processing with Python

Estimate popular financial models such as CAPM and GARCH using a problem-solution approach

Book Description

Python is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries.

In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks.

By the end of this book, you’ll have learned how to effectively analyze financial data using a recipe-based approach.

What you will learn

Download and preprocess financial data from different sources

Backtest the performance of automatic trading strategies in a real-world setting

Estimate financial econometrics models in Python and interpret their results

Use Monte Carlo simulations for a variety of tasks such as derivatives valuation and risk assessment

Improve the performance of financial models with the latest Python libraries

Apply machine learning and deep learning techniques to solve different financial problems

Understand the different approaches used to model financial time series data

Who this book is for

This book is for financial analysts, data analysts, and Python developers who want to learn how to implement a broad range of tasks in the finance domain. Data scientists looking to devise intelligent financial strategies to perform efficient financial analysis will also find this book useful. Working knowledge of the Python programming language is mandatory to grasp the concepts covered in the book effectively.

Table of Contents

Financial Data and Preprocessing

Technical Analysis in Python

Time Series Modelling

Multi-factor Models

Modeling Volatility with GARCH class models

Monte Carlo Simulations in Finance

Asset Allocation in Python

Identifying Credit Default with Machine Learning

Advanced Machine Learning Models in Finance

Deep Learning in Finance

Hard Copy: Python for Finance Cookbook: Over 50 recipes for applying modern Python libraries to financial data analysis 1st Edition

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Python Data Cleaning Cookbook: Modern techniques and Python tools to detect and remove dirty data and extract key insights

 

Discover how to describe your data in detail, identify data issues, and find out how to solve them using commonly used techniques and tips and tricks

Key Features

Get well-versed with various data cleaning techniques to reveal key insights

Manipulate data of different complexities to shape them into the right form as per your business needs

Clean, monitor, and validate large data volumes to diagnose problems before moving on to data analysis

Book Description

Getting clean data to reveal insights is essential, as directly jumping into data analysis without proper data cleaning may lead to incorrect results. This book shows you tools and techniques that you can apply to clean and handle data with Python. You'll begin by getting familiar with the shape of data by using practices that can be deployed routinely with most data sources. Then, the book teaches you how to manipulate data to get it into a useful form. You'll also learn how to filter and summarize data to gain insights and better understand what makes sense and what does not, along with discovering how to operate on data to address the issues you've identified. Moving on, you'll perform key tasks, such as handling missing values, validating errors, removing duplicate data, monitoring high volumes of data, and handling outliers and invalid dates. Next, you'll cover recipes on using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors, and generate visualizations for exploratory data analysis (EDA) to visualize unexpected values. Finally, you'll build functions and classes that you can reuse without modification when you have new data.

By the end of this Python book, you'll be equipped with all the key skills that you need to clean data and diagnose problems within it.

What you will learn

Find out how to read and analyze data from a variety of sources

Produce summaries of the attributes of data frames, columns, and rows

Filter data and select columns of interest that satisfy given criteria

Address messy data issues, including working with dates and missing values

Improve your productivity in Python pandas by using method chaining

Use visualizations to gain additional insights and identify potential data issues

Enhance your ability to learn what is going on in your data

Build user-defined functions and classes to automate data cleaning

Who this book is for

This book is for anyone looking for ways to handle messy, duplicate, and poor data using different Python tools and techniques. The book takes a recipe-based approach to help you to learn how to clean and manage data. Working knowledge of Python programming is all you need to get the most out of the book.

Table of Contents

Anticipating Data Cleaning Issues when Importing Tabular Data into pandas

Anticipating Data Cleaning Issues when Importing HTML and JSON into Pandas

Taking the Measure of Your Data

Identifying Issues in Subsets of Data

Using Visualizations for Exploratory Data Analysis

Cleaning and Wrangling Data with Pandas Data Series Operations

Fixing Messy Data When Aggregating

Addressing Data Issues When Combining Data Frames

Tidying and Reshaping Data

User Defined Functions and Classes to Automate Data Cleaning

Hard Copy: Python Data Cleaning Cookbook: Modern techniques and Python tools to detect and remove dirty data and extract key insights

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Python Basics: A Practical Introduction to Python 3

 

Make the Leap From Beginner to Intermediate in Python…

Python Basics: A Practical Introduction to Python 3

Your Complete Python Curriculum—With Exercises, Interactive Quizzes, and Sample Projects

What should you learn about Python in the beginning to get a strong foundation? With Python Basics, you’ll not only cover the core concepts you really need to know, but you’ll also learn them in the most efficient order with the help of practical exercises and interactive quizzes. You’ll know enough to be dangerous with Python, fast!

Who Should Read This Book

If you’re new to Python, you’ll get a practical, step-by-step roadmap on developing your foundational skills. You’ll be introduced to each concept and language feature in a logical order. Every step in this curriculum is explained and illustrated with short, clear code samples. Our goal with this book is to educate, not to impress or intimidate.

If you’re familiar with some basic programming concepts, you’ll get a clear and well-tested introduction to Python. This is a practical introduction to Python that jumps right into the meat and potatoes without sacrificing substance. If you have prior experience with languages like VBA, PowerShell, R, Perl, C, C++, C#, Java, or Swift the numerous exercises within each chapter will fast-track your progress.

If you’re a seasoned developer, you’ll get a Python 3 crash course that brings you up to speed with modern Python programming. Mix and match the chapters that interest you the most and use the interactive quizzes and review exercises to check your learning progress as you go along.

If you’re a self-starter completely new to coding, you’ll get practical and motivating examples. You’ll begin by installing Python and setting up a coding environment on your computer from scratch, and then continue from there. We’ll get you coding right away so that you become competent and knowledgeable enough to solve real-world problems, fast. Develop a passion for programming by solving interesting problems with Python every day!

If you’re looking to break into a coding or data-science career, you’ll pick up the practical foundations with this book. We won’t just dump a boat load of theoretical information on you so you can “sink or swim”—instead you’ll learn from hands-on, practical examples one step at a time. Each concept is broken down for you so you’ll always know what you can do with it in practical terms.

If you’re interested in teaching others “how to Python,” this will be your guidebook. If you’re looking to stoke the coding flame in your coworkers, kids, or relatives—use our material to teach them. All the sequencing has been done for you so you’ll always know what to cover next and how to explain it.

What Python Developers Say About The Book:

“Go forth and learn this amazing language using this great book.” — Michael Kennedy, Talk Python

“The wording is casual, easy to understand, and makes the information flow well.” — Thomas Wong, Pythonista

“I floundered for a long time trying to teach myself. I slogged through dozens of incomplete online tutorials. I snoozed through hours of boring screencasts. I gave up on countless crufty books from big-time publishers. And then I found Real Python. The easy-to-follow, step-by-step instructions break the big concepts down into bite-sized chunks written in plain English. The authors never forget their audience and are consistently thorough and detailed in their explanations. I’m up and running now, but I constantly refer to the material for guidance.” — Jared Nielsen, Pythonista

Hard Copy : Python Basics: A Practical Introduction to Python 3

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Artificial Intelligence with Python Cookbook: Proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6

 

Work through practical recipes to learn how to solve complex machine learning and deep learning problems using Python

Key Features

Get up and running with artificial intelligence in no time using hands-on problem-solving recipes

Explore popular Python libraries and tools to build AI solutions for images, text, sounds, and images

Implement NLP, reinforcement learning, deep learning, GANs, Monte-Carlo tree search, and much more

Book Description

Artificial intelligence (AI) plays an integral role in automating problem-solving. This involves predicting and classifying data and training agents to execute tasks successfully. This book will teach you how to solve complex problems with the help of independent and insightful recipes ranging from the essentials to advanced methods that have just come out of research.

Artificial Intelligence with Python Cookbook starts by showing you how to set up your Python environment and taking you through the fundamentals of data exploration. Moving ahead, you’ll be able to implement heuristic search techniques and genetic algorithms. In addition to this, you'll apply probabilistic models, constraint optimization, and reinforcement learning. As you advance through the book, you'll build deep learning models for text, images, video, and audio, and then delve into algorithmic bias, style transfer, music generation, and AI use cases in the healthcare and insurance industries. Throughout the book, you’ll learn about a variety of tools for problem-solving and gain the knowledge needed to effectively approach complex problems.

By the end of this book on AI, you will have the skills you need to write AI and machine learning algorithms, test them, and deploy them for production.

What you will learn

Implement data preprocessing steps and optimize model hyperparameters

Delve into representational learning with adversarial autoencoders

Use active learning, recommenders, knowledge embedding, and SAT solvers

Get to grips with probabilistic modeling with TensorFlow probability

Run object detection, text-to-speech conversion, and text and music generation

Apply swarm algorithms, multi-agent systems, and graph networks

Go from proof of concept to production by deploying models as microservices

Understand how to use modern AI in practice

Who this book is for

This AI machine learning book is for Python developers, data scientists, machine learning engineers, and deep learning practitioners who want to learn how to build artificial intelligence solutions with easy-to-follow recipes. You’ll also find this book useful if you’re looking for state-of-the-art solutions to perform different machine learning tasks in various use cases. Basic working knowledge of the Python programming language and machine learning concepts will help you to work with code effectively in this book.

Table of Contents

Getting Started with Artificial Intelligence in Python

Advanced Topics in Supervised Machine Learning

Patterns, Outliers, and Recommendations

Probabilistic Modeling

Heuristic Search Techniques and Logical Inference

Deep Reinforcement Learning

Advanced Image Applications

Working with Moving Images

Deep Learning in Audio and Speech

Natural Language Processing

Artificial Intelligence in Production

Hard Copy: Artificial Intelligence with Python Cookbook: Proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6

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Distance Measures in Data Science with Algorithms

Distance Measures in data science with algorithms

1. Euclidean Distance:

import numpy as np

def euclidean_distance(p1, p2):

    return np.sqrt(np.sum((p1 - p2) ** 2))

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Euclidean distance:", euclidean_distance(point1, point2))

#clcoding.com

Euclidean distance: 2.8284271247461903

2. Manhattan Distance:

import numpy as np

def manhattan_distance(p1, p2):

    return np.sum(np.abs(p1 - p2))

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Manhattan distance:", manhattan_distance(point1, point2))

#clcoding.com

Manhattan distance: 4

3. Cosine Similarity:

from scipy.spatial import distance

def cosine_similarity(p1, p2):

    return 1 - distance.cosine(p1, p2)

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Cosine similarity:", cosine_similarity(point1, point2))

#clcoding.com

Cosine similarity: 0.9838699100999074

4. Minkowski Distance:

import numpy as np

def minkowski_distance(p1, p2, r):

    return np.power(np.sum(np.power(np.abs(p1 - p2), r)), 1/r)

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Minkowski distance:", minkowski_distance(point1, point2, 3))

#clcoding.com

Minkowski distance: 2.5198420997897464

5. Chebyshev Distance:

import numpy as np

def chebyshev_distance(p1, p2):

    return np.max(np.abs(p1 - p2))

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Chebyshev distance:", chebyshev_distance(point1, point2))

#clcoding.com

Chebyshev distance: 2

6. Hamming Distance:

import jellyfish

def hamming_distance(s1, s2):

    return jellyfish.hamming_distance(s1, s2)

# Example usage

string1 = "hello"

string2 = "hallo"

print("Hamming distance:", hamming_distance(string1, string2))

#clcoding.com

Hamming distance: 1

7. Jaccard Similarity:

def jaccard_similarity(s1, s2):

    set1 = set(s1)

    set2 = set(s2)

    intersection = set1.intersection(set2)

    union = set1.union(set2)

    return len(intersection) / len(union)

# Example usage

string1 = "hello"

string2 = "hallo"

print("Jaccard similarity:", jaccard_similarity(string1, string2))

#clcoding.com

Jaccard similarity: 0.6

8. Sørensen-Dice Index:

def sorensen_dice_index(s1, s2):

    set1 = set(s1)

    set2 = set(s2)

    intersection = set1.intersection(set2)

    return (2 * len(intersection)) / (len(set1) + len(set2))

# Example usage

string1 = "hello"

string2 = "hallo"

print("Sørensen-Dice index:", sorensen_dice_index(string1, string2))

#clcoding.com

Sørensen-Dice index: 0.75

9. Haversine Distance:

def haversine_distance(lat1, lon1, lat2, lon2):

    R = 6371.0  # Radius of the earth in km

    dLat = np.deg2rad(lat2 - lat1)

    dLon = np.deg2rad(lon2 - lon1)

    a = np.sin(dLat / 2)**2 + np.cos(np.deg2rad(lat1)) * np.cos(np.deg2rad(lat2)) * np.sin(dLon / 2)**2

    c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a))

    return R * c

# Example usage

print("Haversine distance:", haversine_distance(51.5074, 0.1278, 40.7128, -74.0060))

#clcoding.com

  Input In [14]

    a = np.sin(dLat / 2)**2 + np.cos(np.deg2rad(lat1)) *

                                                         ^

SyntaxError: invalid syntax

10. Mahalanobis Distance:

from scipy.spatial.distance import cdist

def mahalanobis_distance(X, Y):

    return cdist(X.reshape(1,-1), Y.reshape(1,-1), 'mahalanobis', VI=np.cov(X))

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Mahalanobis distance:", mahalanobis_distance(point1, point2))

#clcoding.com

Mahalanobis distance: [[1.41421356]]

11. Pearson Correlation:

from scipy.stats import pearsonr

def pearson_correlation(X, Y):

    return pearsonr(X, Y)[0]

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Pearson correlation:", pearson_correlation(point1, point2))

#clcoding.com

Pearson correlation: 1.0

12. Squared Euclidean Distance:

def squared_euclidean_distance(X, Y):

    return euclidean_distance(X, Y)**2

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Squared Euclidean distance:", squared_euclidean_distance(point1, point2))

#clcoding.com

Squared Euclidean distance: 8.000000000000002

13. Jensen-Shannon Divergence:

def jensen_shannon_divergence(X, Y):

    M = 0.5 * (X + Y)

    return np.sqrt(0.5 * (rel_entr(X, M).sum() + rel_entr(Y, M).sum()))

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Jensen-Shannon divergence:", jensen_shannon_divergence(point1, point2))

#clcoding.com

Jensen-Shannon divergence: 0.6569041853099059

14. Chi-Square Distance:

def chi_square_distance(X, Y):

    X = X / np.sum(X)

    Y = Y / np.sum(Y)

    return np.sum((X - Y) ** 2 / (X + Y))

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Chi-Square distance:", chi_square_distance(point1, point2))

#clcoding.com

Chi-Square distance: 0.01923076923076923

15. Spearman Correlation:

from scipy.stats import spearmanr

def spearman_correlation(X, Y):

    return spearmanr(X, Y)[0]

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Spearman correlation:", spearman_correlation(point1, point2))

#clcoding.com

Spearman correlation: 0.9999999999999999

16. Canberra Distance:

from scipy.spatial.distance import canberra

def canberra_distance(X, Y):

    return canberra(X, Y)

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Canberra distance:", canberra_distance(point1, point2))

#clcoding.com

Canberra distance: 0.8333333333333333

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Python Automation Cookbook: 75 Python automation ideas for web scraping, data wrangling, and processing Excel, reports, emails, and more, 2nd Edition

 

Get a firm grip on the core processes including browser automation, web scraping, Word, Excel, and GUI automation with Python 3.8 and higher

Key Features

Automate integral business processes such as report generation, email marketing, and lead generation

Explore automated code testing and Python’s growth in data science and AI automation in three new chapters

Understand techniques to extract information and generate appealing graphs, and reports with Matplotlib

Book Description

In this updated and extended version of Python Automation Cookbook, each chapter now comprises the newest recipes and is revised to align with Python 3.8 and higher. The book includes three new chapters that focus on using Python for test automation, machine learning projects, and for working with messy data.

This edition will enable you to develop a sharp understanding of the fundamentals required to automate business processes through real-world tasks, such as developing your first web scraping application, analyzing information to generate spreadsheet reports with graphs, and communicating with automatically generated emails.

Once you grasp the basics, you will acquire the practical knowledge to create stunning graphs and charts using Matplotlib, generate rich graphics with relevant information, automate marketing campaigns, build machine learning projects, and execute debugging techniques.

By the end of this book, you will be proficient in identifying monotonous tasks and resolving process inefficiencies to produce superior and reliable systems.

What you will learn

Learn data wrangling with Python and Pandas for your data science and AI projects

Automate tasks such as text classification, email filtering, and web scraping with Python

Use Matplotlib to generate a variety of stunning graphs, charts, and maps

Automate a range of report generation tasks, from sending SMS and email campaigns to creating templates, adding images in Word, and even encrypting PDFs

Master web scraping and web crawling of popular file formats and directories with tools like Beautiful Soup

Build cool projects such as a Telegram bot for your marketing campaign, a reader from a news RSS feed, and a machine learning model to classify emails to the correct department based on their content

Create fire-and-forget automation tasks by writing cron jobs, log files, and regexes with Python scripting

Who this book is for

Python Automation Cookbook - Second Edition is for developers, data enthusiasts or anyone who wants to automate monotonous manual tasks related to business processes such as finance, sales, and HR, among others. Working knowledge of Python is all you need to get started with this book.

Table of Contents

Let's Begin Our Automation Journey

Automating Tasks Made Easy

Building Your First Web Scraping Application

Searching and Reading Local Files

Generating Fantastic Reports

Fun with Spreadsheets

Cleaning and Processing Data

Developing Stunning Graphs

Dealing with Communication Channels

Why Not Automate Your Marketing Campaign?

Machine Learning for Automation

Automatic Testing Routines

Debugging Techniques

Hard Copy : Python Automation Cookbook: 75 Python automation ideas for web scraping, data wrangling, and processing Excel, reports, emails, and more, 2nd Edition

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Hands-On Data Structures and Algorithms with Python: Store, manipulate, and access data effectively and boost the performance of your applications, 3rd Edition

 

Understand how implementing different data structures and algorithms intelligently can make your Python code and applications more maintainable and efficient

Key Features

Explore functional and reactive implementations of traditional and advanced data structures

Apply a diverse range of algorithms in your Python code

Implement the skills you have learned to maximize the performance of your applications

Book Description

Choosing the right data structure is pivotal to optimizing the performance and scalability of applications. This new edition of Hands-On Data Structures and Algorithms with Python will expand your understanding of key structures, including stacks, queues, and lists, and also show you how to apply priority queues and heaps in applications. You'll learn how to analyze and compare Python algorithms, and understand which algorithms should be used for a problem based on running time and computational complexity. You will also become confident organizing your code in a manageable, consistent, and scalable way, which will boost your productivity as a Python developer.

By the end of this Python book, you'll be able to manipulate the most important data structures and algorithms to more efficiently store, organize, and access data in your applications.

What you will learn

Understand common data structures and algorithms using examples, diagrams, and exercises

Explore how more complex structures, such as priority queues and heaps, can benefit your code

Implement searching, sorting, and selection algorithms on number and string sequences

Become confident with key string-matching algorithms

Understand algorithmic paradigms and apply dynamic programming techniques

Use asymptotic notation to analyze algorithm performance with regard to time and space complexities

Write powerful, robust code using the latest features of Python

Who this book is for

This book is for developers and programmers who are interested in learning about data structures and algorithms in Python to write complex, flexible programs. Basic Python programming knowledge is expected.

Table of Contents

Python Data Types and Structures

Introduction to Algorithm Design

Algorithm Design Techniques and Strategies

Linked Lists

Stacks and Queues

Trees

Heaps and Priority Queues

Hash Tables

Graphs and Algorithms

Searching

Sorting

Selection Algorithms

String Matching Algorithms

Appendix: Answers to the Questions

Hard Copy: Hands-On Data Structures and Algorithms with Python: Store, manipulate, and access data effectively and boost the performance of your applications, 3rd Edition

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Mastering Python: Write powerful and efficient code using the full range of Python's capabilities, 2nd Edition

 

Use advanced features of Python to write high-quality, readable code and packages

Key Features

Extensively updated for Python 3.10 with new chapters on design patterns, scientific programming, machine learning, and interactive Python

Shape your scripts using key concepts like concurrency, performance optimization, asyncio, and multiprocessing

Learn how advanced Python features fit together to produce maintainable code

Book Description

Even if you find writing Python code easy, writing code that is efficient, maintainable, and reusable is not so straightforward. Many of Python's capabilities are underutilized even by more experienced programmers. Mastering Python, Second Edition, is an authoritative guide to understanding advanced Python programming so you can write the highest quality code. This new edition has been extensively revised and updated with exercises, four new chapters and updates up to Python 3.10.

Revisit important basics, including Pythonic style and syntax and functional programming. Avoid common mistakes made by programmers of all experience levels. Make smart decisions about the best testing and debugging tools to use, optimize your code's performance across multiple machines and Python versions, and deploy often-forgotten Python features to your advantage. Get fully up to speed with asyncio and stretch the language even further by accessing C functions with simple Python calls. Finally, turn your new-and-improved code into packages and share them with the wider Python community.

If you are a Python programmer wanting to improve your code quality and readability, this Python book will make you confident in writing high-quality scripts and taking on bigger challenges

What you will learn

Write beautiful Pythonic code and avoid common Python coding mistakes

Apply the power of decorators, generators, coroutines, and metaclasses

Use different testing systems like pytest, unittest, and doctest

Track and optimize application performance for both memory and CPU usage

Debug your applications with PDB, Werkzeug, and faulthandler

Improve your performance through asyncio, multiprocessing, and distributed computing

Explore popular libraries like Dask, NumPy, SciPy, pandas, TensorFlow, and scikit-learn

Extend Python's capabilities with C/C++ libraries and system calls

Who this book is for

This book will benefit more experienced Python programmers who wish to upskill, serving as a reference for best practices and some of the more intricate Python techniques. Even if you have been using Python for years, chances are that you haven't yet encountered every topic discussed in this book. A good understanding of Python programming is necessary

Table of Contents

Getting Started – One Environment per Project

Interactive Python Interpreters

Pythonic Syntax and Common Pitfalls

Pythonic Design Patterns

Functional Programming – Readability Versus Brevity

Decorators – Enabling Code Reuse by Decorating

Generators and Coroutines – Infinity, One Step at a Time

Metaclasses – Making Classes (Not Instances) Smarter

Documentation – How to Use Sphinx and reStructuredText

Testing and Logging – Preparing for Bugs

Debugging – Solving the Bugs

Performance – Tracking and Reducing Your Memory and CPU Usage

asyncio – Multithreading without Threads

Multiprocessing – When a Single CPU Core Is Not Enough

Scientific Python and Plotting

Artificial Intelligence

Extensions in C/C++, System Calls, and C/C++ Libraries

Packaging – Creating Your Own Libraries or Applications

Hard Copy : Mastering Python: Write powerful and efficient code using the full range of Python's capabilities, 2nd Edition

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Learn Python Programming: An in-depth introduction to the fundamentals of Python, 3rd Edition

 

Get up and running with Python 3.9 through concise tutorials and practical projects in this fully updated third edition.

Purchase of the print or Kindle book includes a free eBook in PDF format.

Key Features

Extensively revised with richer examples, Python 3.9 syntax, and new chapters on APIs and packaging and distributing Python code

Discover how to think like a Python programmer

Learn the fundamentals of Python through real-world projects in API development, GUI programming, and data science

Book Description

Learn Python Programming, Third Edition is both a theoretical and practical introduction to Python, an extremely flexible and powerful programming language that can be applied to many disciplines. This book will make learning Python easy and give you a thorough understanding of the language. You'll learn how to write programs, build modern APIs, and work with data by using renowned Python data science libraries.

This revised edition covers the latest updates on API management, packaging applications, and testing. There is also broader coverage of context managers and an updated data science chapter.

The book empowers you to take ownership of writing your software and become independent in fetching the resources you need. You will have a clear idea of where to go and how to build on what you have learned from the book.

Through examples, the book explores a wide range of applications and concludes by building real-world Python projects based on the concepts you have learned.

What you will learn

Get Python up and running on Windows, Mac, and Linux

Write elegant, reusable, and efficient code in any situation

Avoid common pitfalls like duplication, complicated design, and over-engineering

Understand when to use the functional or object-oriented approach to programming

Build a simple API with FastAPI and program GUI applications with Tkinter

Get an initial overview of more complex topics such as data persistence and cryptography

Fetch, clean, and manipulate data, making efficient use of Python’s built-in data structures

Who this book is for

This book is for everyone who wants to learn Python from scratch, as well as experienced programmers looking for a reference book. Prior knowledge of basic programming concepts will help you follow along, but it’s not a prerequisite.

Table of Contents

A Gentle Introduction to Python

Built-In Data Types

Conditionals and Iteration

Functions, the Building Blocks of Code

Comprehensions and Generators

OOP, Decorators, and Iterators

Exceptions and Context Managers

Files and Data Persistence

Cryptography and Tokens

Testing

Debugging and Profiling

GUIs and Scripting

Data Science in Brief

Introduction to API Development

Packaging Python Applications

Hard Copy : Learn Python Programming: An in-depth introduction to the fundamentals of Python, 3rd Edition

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Python Object-Oriented Programming: Build robust and maintainable object-oriented Python applications and libraries, 4th Edition

 

A comprehensive guide to exploring modern Python through data structures, design patterns, and effective object-oriented techniques

Key Features

Build an intuitive understanding of object-oriented design, from introductory to mature programs

Learn the ins and outs of Python syntax, libraries, and best practices

Examine a machine-learning case study at the end of each chapter

Book Description

Object-oriented programming (OOP) is a popular design paradigm in which data and behaviors are encapsulated in such a way that they can be manipulated together. Python Object-Oriented Programming, Fourth Edition dives deep into the various aspects of OOP, Python as an OOP language, common and advanced design patterns, and hands-on data manipulation and testing of more complex OOP systems. These concepts are consolidated by open-ended exercises, as well as a real-world case study at the end of every chapter, newly written for this edition. All example code is now compatible with Python 3.9+ syntax and has been updated with type hints for ease of learning.

Steven and Dusty provide a comprehensive, illustrative tour of important OOP concepts, such as inheritance, composition, and polymorphism, and explain how they work together with Python's classes and data structures to facilitate good design. In addition, the book also features an in-depth look at Python's exception handling and how functional programming intersects with OOP. Two very powerful automated testing systems, unittest and pytest, are introduced. The final chapter provides a detailed discussion of Python's concurrent programming ecosystem.

By the end of the book, you will have a thorough understanding of how to think about and apply object-oriented principles using Python syntax and be able to confidently create robust and reliable programs.

What you will learn

Implement objects in Python by creating classes and defining methods

Extend class functionality using inheritance

Use exceptions to handle unusual situations cleanly

Understand when to use object-oriented features, and more importantly, when not to use them

Discover several widely used design patterns and how they are implemented in Python

Uncover the simplicity of unit and integration testing and understand why they are so important

Learn to statically type check your dynamic code

Understand concurrency with asyncio and how it speeds up programs

Who this book is for

If you are new to object-oriented programming techniques, or if you have basic Python skills and wish to learn how and when to correctly apply OOP principles in Python, this is the book for you. Moreover, if you are an object-oriented programmer coming from other languages or seeking a leg up in the new world of Python, you will find this book a useful introduction to Python. Minimal previous experience with Python is necessary.

Table of Contents

Object-Oriented Design

Objects in Python

When Objects Are Alike

Expecting the Unexpected

When to Use Object-Oriented Programming

Abstract Base Classes and Operator Overloading

Python Data Structures

The Intersection of Object-Oriented and Functional Programming

Strings, Serialization, and File Paths

The Iterator Pattern

Common Design Patterns

Advanced Design Patterns

Testing Object-Oriented Programs

Concurrency

Hard Copy: Python Object-Oriented Programming: Build robust and maintainable object-oriented Python applications and libraries, 4th Edition

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Coding for Kids: Python: Learn to Code with 50 Awesome Games and Activities

 

Games and activities that teach kids ages 10+ to code with Python

Learning to code isn't as hard as it sounds—you just have to get started! Coding for Kids: Python starts kids off right with 50 fun, interactive activities that teach them the basics of the Python programming language. From learning the essential building blocks of programming to creating their very own games, kids will progress through unique lessons packed with helpful examples—and a little silliness!

Kids will follow along by starting to code (and debug their code) step by step, seeing the results of their coding in real time. Activities at the end of each chapter help test their new knowledge by combining multiple concepts. For young programmers who really want to show off their creativity, there are extra tricky challenges to tackle after each chapter. All kids need to get started is a computer and this book.

This beginner's guide to Python for kids includes:

50 Innovative exercises—Coding concepts come to life with game-based exercises for creating code blocks, drawing pictures using a prewritten module, and more.

Easy-to-follow guidance—New coders will be supported by thorough instructions, sample code, and explanations of new programming terms.

Engaging visual lessons—Colorful illustrations and screenshots for reference help capture kids' interest and keep lessons clear and simple.

Encourage kids to think independently and have fun learning an amazing new skill with this coding book for kids.

Hard Copy : Coding for Kids: Python: Learn to Code with 50 Awesome Games and Activities

Sprial Bound : Coding for Kids: Python: Learn to Code with 50 Awesome Games and Activities [Spiral-bound] Adrienne Tacke

PDF : Coding for Kids: Python: Learn to Code with 50 Awesome Games and Activities [Spiral-bound] Adrienne Tacke

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