The 25 best Python Books of all time for advanced programmers

Collect and scrape data of varying complexity from the modern web using the latest tools, best practices, and techniques Key Features Learn different scraping techniques using a range of Python libraries such as Scrapy and Beautiful Soup Build scrapers and crawlers to extract relevant information from the web Automate web scraping operations to bridge any gaps in accuracy and manage complex business needs Book Description Web scraping is an essential technique used in many organizations to gather valuable data from web pages. This book will help you get hands-on with different web scraping techniques, tools, and methodologies.

You'll start by learning the fundamental concepts of web scraping techniques and how they can be applied to multiple sets of web pages. You'll use powerful libraries from the Python ecosystem such as Scrapy, lxml, pyquery, and bs4 to carry out web scraping operations.

Next, you'll get up to speed with simple to intermediate scraping operations such as identifying information from web pages and using patterns or attributes to retrieve information. The book will further guide you through a series of use cases and demonstrate how to use the best tools and techniques to efficiently scrape web pages.

Later, you'll even explore the uses of other popular web scraping tools, such as Selenium and Regex, and web-based APIs. By the end of this book, you will have learned how to efficiently scrape the web using different techniques with Python and other popular tools.

What you will learn Analyze data and information from web pages Understand how to use browser-based developer tools for scraping Use XPath and CSS selectors to identify and explore markup elements Discover how to handle and manage cookies Explore advanced concepts in handling HTML forms and processing logins Optimize web securities, data storage, and API use to scrape data Use Regex with Python to extract data Deal with complex web entities by using Selenium to find and extract data Who this book is for This book is for Python programmers, data analysts, web scraping beginners, or anyone who wants to learn how to perform web scraping from scratch. Working knowledge of the Python programming language is expected

Create distributed applications with clever design patterns to solve complex problems Key Features Set up and run distributed algorithms on a cluster using Dask and PySpark Master skills to accurately implement concurrency in your code Gain practical experience of Python design patterns with real-world examples Book Description This Learning Path shows you how to leverage the power of both native and third-party Python libraries for building robust and responsive applications. You will learn about profilers and reactive programming, concurrency and parallelism, as well as tools for making your apps quick and efficient.

You will discover how to write code for parallel architectures using TensorFlow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark. With the knowledge of how Python design patterns work, you will be able to clone objects, secure interfaces, dynamically choose algorithms, and accomplish much more in high performance computing.

By the end of this Learning Path, you will have the skills and confidence to build engaging models that quickly offer efficient solutions to your problems. This Learning Path includes content from the following Packt products: • Python High Performance - Second Edition by Gabriele Lanaro • Mastering Concurrency in Python by Quan Nguyen • Mastering Python Design Patterns by Sakis Kasampalis What you will learn Use NumPy and pandas to import and manipulate datasets Achieve native performance with Cython and Numba Write asynchronous code using asyncio and RxPy Design highly scalable programs with application scaffolding Explore abstract methods to maintain data consistency Clone objects using the prototype pattern Use the adapter pattern to make incompatible interfaces compatible Employ the strategy pattern to dynamically choose an algorithm Who this book is for This Learning Path is specially designed for Python developers who want to build high-performance applications and learn about single core and multi-core programming, distributed concurrency, and Python design patterns.

Some experience with Python programming language will help you get the most out of this Learning Path. Table of Contents Benchmarking and Profiling Pure Python Optimizations Fast Array Operations with NumPy and Pandas C Performance with Cython Exploring Compilers Implementing Concurrency Parallel Processing Advanced Introduction to Concurrent and Parallel Programming Amdahl's Law Working with Threads in Python Using the with Statement in Threads Concurrent Web Requests Working with Processes in Python Reduction Operators in Processes Concurrent Image Processing Introduction to Asynchronous Programming Implementing Asynchronous Programming in Python Building Communication Channels with asyncio Deadlocks Starvation Race Conditions The Global Interpreter Lock The Factory Pattern The Builder Pattern Other Creational Patterns The Adapter Pattern The Decorator Pattern The Bridge Pattern The Facade Pattern Other Structural Patterns The Chain of Responsibility Pattern The Command Pattern The Observer Pattern

Ready for something more complex? An easy-to-follow guide to writing specialized Python scripts and developing tools for spatial data in ArcGIS Pro. Advanced Python Scripting for ArcGIS Pro builds on Python Scripting for ArcGIS Pro (Esri Press, 2020).

Learn how to create a geoprocessing tool out of your script and automate tasks in ArcGIS Pro, how to share your tools with others, as well as master a number of more specialized tasks. Some of the key topics you will learn include: Writing specialized scripts using ArcPy Creating Python script tools Writing Python toolboxes Sharing scripts and tools Managing Python packages and environments Creating custom functions and classes Migrating scripts and tools from ArcGIS Desktop to ArcGIS Pro NumPy, Pandas, and Matplotlib ArcGIS API for Python and Jupyter Notebook Helpful “points to remember,” key terms, and review questions are included at the end of each chapter to reinforce your understanding of Python.

Companion data and exercises are available online. Advanced Python Scripting for ArcGIS Pro is perfect for more experienced developers who are looking to upgrade their skills

Are you brand new to machine learning and Python?Do you want to learn good coding techniques quickly and easily?Then Python Programming is the book for you! Python is one of the best platforms for those new to programming to begin with. The book will introduce you to the basic concepts of Machine Learning, Python programming language, various program libraries, and supporting platforms.

This guide will help you with your journey into the world of Python Machine Learning and help you navigate your way from a newbie to an intermediate level. You’ll learn: • Getting Started with Python • The Basic Principles of Python Machine Learning • Getting Started With Data Visualization • The Use of Predictive Analytics • How to start writing anAlgorithm• Everything about Decision Tree• How to work with Data • Neural Networks,Big Data, the Internet of Things (IoT), andCloud Computing • And more… Even if you’ve never looked at a computer program before and had always thought that learning a computer language would be too difficult, this book can help.With it’s easy to understand and simple language, you could soon be wondering why you never thought about trying computer programming before

Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystem Key Features Get to grips with building faster and more robust deep learning architectures Investigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorch Apply deep neural networks (DNNs) to computer vision problems, NLP, and GANs Book Description In order to build robust deep learning systems, you'll need to understand everything from how neural networks work to training CNN models. In this book, you'll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application.

You'll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation.

Further on, you'll focus on variational autoencoders and GANs. You'll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU.

You'll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you'll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples.

Finally, you'll understand how to apply deep learning to autonomous vehicles. By the end of this book, you'll have mastered key deep learning concepts and the different applications of deep learning models in the real world.

What you will learn Cover advanced and state-of-the-art neural network architectures Understand the theory and math behind neural networks Train DNNs and apply them to modern deep learning problems Use CNNs for object detection and image segmentation Implement generative adversarial networks (GANs) and variational autoencoders to generate new images Solve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence models Understand DL techniques, such as meta-learning and graph neural networks Who this book is for This book is for data scientists, deep learning engineers and researchers, and AI developers who want to further their knowledge of deep learning and build innovative and unique deep learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world examples will also find this book useful.

Basic understanding of deep learning concepts and working knowledge of the Python programming language is assumed. Table of Contents The Nuts and Bolts of Neural Networks Understanding Convolutional Networks Advanced Convolutional Networks Object Detection and Image Segmentation Generative Models Language Modelling Understanding Recurrent Networks Sequence-to-Sequence Models and Attention Emerging Neural Network Designs Meta Learning Deep Learning for Autonomous Vehicles

Learn to implement complex data structures and algorithms using Python Key Features Understand the analysis and design of fundamental Python data structures Explore advanced Python concepts such as Big O notation and dynamic programming Learn functional and reactive implementations of traditional data structures Book Description Data structures allow you to store and organize data efficiently. They are critical to any problem, provide a complete solution, and act like reusable code.

Hands-On Data Structures and Algorithms with Python teaches you the essential Python data structures and the most common algorithms for building easy and maintainable applications. This book helps you to understand the power of linked lists, double linked lists, and circular linked lists.

You will learn to create complex data structures, such as graphs, stacks, and queues. As you make your way through the chapters, you will explore the application of binary searches and binary search trees, along with learning common techniques and structures used in tasks such as preprocessing, modeling, and transforming data.

In the concluding chapters, you will get to grips with organizing your code in a manageable, consistent, and extendable way. You will also study how to bubble sort, selection sort, insertion sort, and merge sort algorithms in detail.

By the end of the book, you will have learned how to build components that are easy to understand, debug, and use in different applications. You will get insights into Python implementation of all the important and relevant algorithms.

What you will learn Understand object representation, attribute binding, and data encapsulation Gain a solid understanding of Python data structures using algorithms Study algorithms using examples with pictorial representation Learn complex algorithms through easy explanation, implementing Python Build sophisticated and efficient data applications in Python Understand common programming algorithms used in Python data science Write efficient and robust code in Python 3.7 Who this book is for This book is for developers who want to learn data structures and algorithms in Python to write complex and flexible programs. Basic Python programming knowledge is expected

Use the power of pandas to solve most complex scientific computing problems with ease. Revised for pandas 1.x.

Key Features This is the first book on pandas 1.x Practical, easy to implement recipes for quick solutions to common problems in data using pandas Master the fundamentals of pandas to quickly begin exploring any dataset Book Description The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis.

This book guides you, as if you were looking over the shoulder of an expert, through situations that you are highly likely to encounter. This new updated and revised edition provides you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas.

Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way.

Many advanced recipes combine several different features across the pandas library to generate results. What you will learn Master data exploration in pandas through dozens of practice problems Group, aggregate, transform, reshape, and filter data Merge data from different sources through pandas SQL-like operations Create visualizations via pandas hooks to matplotlib and seaborn Use pandas, time series functionality to perform powerful analyses Import, clean, and prepare real-world datasets for machine learning Create workflows for processing big data that doesn't fit in memory Who this book is for This book is for Python developers, data scientists, engineers, and analysts.

Pandas is the ideal tool for manipulating structured data with Python and this book provides ample instruction and examples. Not only does it cover the basics required to be proficient, but it goes into the details of idiomatic pandas

Gain comprehensive insights into programming practices, and code portability and reuse to build flexible and maintainable apps using object-oriented principles Key Features Extend core OOP techniques to increase integration of classes created with Python Explore various Python libraries for handling persistence and object serialization Learn alternative approaches for solving programming problems, with different attributes to address your problem domain Book Description Object-oriented programming (OOP) is a relatively complex discipline to master, and it can be difficult to see how general principles apply to each language's unique features. With the help of the latest edition of Mastering Objected-Oriented Python, you'll be shown how to effectively implement OOP in Python, and even explore Python 3.x.

Complete with practical examples, the book guides you through the advanced concepts of OOP in Python, and demonstrates how you can apply them to solve complex problems in OOP. You will learn how to create high-quality Python programs by exploring design alternatives and determining which design offers the best performance.

Next, you'll work through special methods for handling simple object conversions and also learn about hashing and comparison of objects. As you cover later chapters, you'll discover how essential it is to locate the best algorithms and optimal data structures for developing robust solutions to programming problems with minimal computer processing.

Finally, the book will assist you in leveraging various Python features by implementing object-oriented designs in your programs. By the end of this book, you will have learned a number of alternate approaches with different attributes to confidently solve programming problems in Python.

What you will learn Explore a variety of different design patterns for the __init__() method Learn to use Flask to build a RESTful web service Discover SOLID design patterns and principles Use the features of Python 3's abstract base Create classes for your own applications Design testable code using pytest and fixtures Understand how to design context managers that leverage the 'with' statement Create a new type of collection using standard library and design techniques Develop new number types above and beyond the built-in classes of numbers Who this book is for This book is for developers who want to use Python to create efficient programs. A good understanding of Python programming is required to make the most out of this book.

Knowledge of concepts related to object-oriented design patterns will also be useful. Table of Contents Preliminaries, Tools, and Techniques The __init__() Method Integrating Seamlessly - Basic Special Methods Attribute Access, Properties, and Descriptors The ABCs of Consistent Design Using Callables and Contexts Creating Containers and Collections Creating Numbers Decorators and Mixins - Cross-cutting Aspects Serializing and Saving - JSON, YAML, Pickle, CSV, and XML Storing and Retrieving Objects via Shelve Storing and Retrieving Objects via SQLite Transmitting and Sharing Objects Configuration Files and Persistence Design Principles and Patterns The Logging and Warning Modules Designing for Testability Coping With the Command Line Module and Package Design Quality and Documentation

The definitive, easy-to-follow guide to writing Python code with spatial data in ArcGIS Pro, whether you’re new to programming or not. Python Scripting for ArcGIS Pro starts with the fundamentals of Python programming and then dives into how to write useful Python scripts that work with spatial data in ArcGIS Pro.

Learn how to execute geoprocessing tools, describe, create and update data, as well as execute a number of specialized tasks. See how to write simple, custom scripts that will automate your ArcGIS Pro workflows.

Some of the key topics you will learn include: Python fundamentals Setting up a Python editor Automating geoprocessing tasks using ArcPy Exploring and manipulating spatial and tabular data Working with geometries using cursors Working with rasters and map algebra Map scripting Debugging and error handling Helpful “points to remember,” key terms, and review questions are included at the end of each chapter to reinforce your understanding of Python. Corresponding data and exercises are available online.

Whether you want to learn Python or already have some experience, Python Scripting for ArcGIS Pro is the comprehensive, hands-on book for learning the versatility of Python coding as an approach to solving problems and increasing your productivity in ArcGIS Pro. Follow the step-by-step instruction and common workflow guidance for automating tasks and scripting with Python. Don't forget to also check out Esri Press's other Python title: Advanced Python Scripting for ArcGIS Pro (9781589486188)

How to become a computer programming master without the hassle of taking a course, all while in the comfort of your own home. In today’s technologically-dominated world, it’s only natural to become interested in the workings of computers and ponder on the idea of developing your very own app or game.

Who knows, maybe you have had a million-dollar idea planned out in your mind for a while now, but every time you think about acting on it, reality hits you with the fact that there’s one huge obstacle in your way: You don’t know how to code. Well actually, it may feel like a huge obstacle, but with the right expert guidance, it´s like following the Lego building instructions for 6-year-olds.

By exploring all that computer programming has to offer, it opens a whole new world of possibilities you may not have known existed. Knowing how to code is the magical key to: Developing apps Creating softwares Constructing websites Designing fun, addicting games Or just as a new hobby Did you know that the top 200 apps generate on average $82,500 daily? Whether your incentive to learn about computer programming stems from interest, or it’s because you want more work freedom and higher qualifications for a better paying job, starting with the basics and working your way up is the most promising approach to take.

In Computer Programing Bible, you will discover: A step by step guidance from beginner to advanced level to never get lost in signs and numbers The major programming languages essential to mastering code Why everyone in today’s technology-driven world needs to know how to code if they don´t want to be left behind 7 reasons why diving into the world of programming will change your life path in less than 6 months The key to writing your very first program even if you are a technophobe How to choose the best programming language to reach optimal results The all-too-common programming mistakes to avoid The unspoken challenges to be aware of that each programming language may present and how to overcome them And much more. Don’t worry.

This may seem like too much to handle right now, but rest assured, the sections are broken up into bite-size nuggets of knowledge designed to make understanding computer programming a breeze. Even if you’ve never stepped foot into the how-to’s of coding before and have absolutely no idea where to begin, you will be able to follow along step by step and soon become comfortable exploring it all on your own.

Programming must be a piece of cake if even 7-year-old Zora Ball managed to code her own full version of a video game. The best is you can go at your own pace without the stress of a course where you waste money on something you can easily discover on your own.

But be aware: The Computer Programing Bible is only for people who know how to take action instead of joining the everlasting queue of procrastinators. If you want to discover the unlimited potential computer programming has to offer and become your own digital boss, then scroll up and click the “Add to Cart” button right now.

Python programmers will improve their computer science skills with these useful one-liners. Python One-Liners will teach you how to read and write "one-liners": concise statements of useful functionality packed into a single line of code.

You'll learn how to systematically unpack and understand any line of Python code, and write eloquent, powerfully compressed Python like an expert. The book's five chapters cover tips and tricks, regular expressions, machine learning, core data science topics, and useful algorithms.

Detailed explanations of one-liners introduce key computer science concepts and boost your coding and analytical skills. You'll learn about advanced Python features such as list comprehension, slicing, lambda functions, regular expressions, map and reduce functions, and slice assignments

Uncover modern Python with this guide to Python data structures, design patterns, and effective object-oriented techniques Key Features In-depth analysis of many common object-oriented design patterns that are more suitable to Python's unique style Learn the latest Python syntax and libraries Explore abstract design patterns and implement them in Python 3.8 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. This third edition of Python 3 Object-Oriented Programming fully explains classes, data encapsulation, and exceptions with an emphasis on when you can use each principle to develop well-designed software.

Starting with a detailed analysis of object-oriented programming, you will use the Python programming language to clearly grasp key concepts from the object-oriented paradigm. You will learn how to create maintainable applications by studying higher level design patterns.

The book will show you the complexities of string and file manipulation, and how Python distinguishes between binary and textual data. Not one, but two very powerful automated testing systems, unittest and pytest, will be introduced in this book.

You'll get a comprehensive introduction to Python's concurrent programming ecosystem. By the end of the book, you will have thoroughly learned object-oriented principles using Python syntax and be able to create robust and reliable programs confidently.

What you will learn Implement objects in Python by creating classes and defining methods Grasp common concurrency techniques and pitfalls in Python 3 Extend class functionality using inheritance Understand when to use object-oriented features, and more importantly when not to use them Discover what design patterns are and why they are different in Python Uncover the simplicity of unit testing and why it's so important in Python Explore concurrent object-oriented programming Who this book is for If you're new to object-oriented programming techniques, or if you have basic Python skills and wish to learn in depth how and when to correctly apply OOP in Python, this is the book for you. If you are an object-oriented programmer for other languages or seeking a leg up in the new world of Python 3.8, you too will find this book a useful introduction to Python.

Previous experience with Python 3 is not necessary. Table of Contents Object-oriented Design Objects in Python When Objects Are Alike Expecting the Unexpected When to Use Object-oriented Programming Python Data Structures Python Object-oriented Shortcuts Strings and Serialization The Iterator Pattern Python Design Patterns I Python Design Patterns II Testing Object-oriented Programs Concurrency

As Python continues to grow in popularity, projects are becoming larger and more complex. Many Python developers are taking an interest in high-level software design patterns such as hexagonal/clean architecture, event-driven architecture, and the strategic patterns prescribed by domain-driven design (DDD).

But translating those patterns into Python isn’t always straightforward. With this hands-on guide, Harry Percival and Bob Gregory from MADE.com introduce proven architectural design patterns to help Python developers manage application complexity—and get the most value out of their test suites.

Each pattern is illustrated with concrete examples in beautiful, idiomatic Python, avoiding some of the verbosity of Java and C# syntax. Patterns include: Dependency inversion and its links to ports and adapters (hexagonal/clean architecture) Domain-driven design’s distinction between Entities, Value Objects, and Aggregates Repository and Unit of Work patterns for persistent storage Events, commands, and the message bus Command-query responsibility segregation (CQRS) Event-driven architecture and reactive microservices

For introductory-level Python programming and/or data-science courses. A groundbreaking, flexible approach to computer science and data science The Deitels’ Introduction to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud offers a unique approach to teaching introductory Python programming, appropriate for both computer-science and data-science audiences.

Providing the most current coverage of topics and applications, the book is paired with extensive traditional supplements as well as Jupyter Notebooks supplements. Real-world datasets and artificial-intelligence technologies allow students to work on projects making a difference in business, industry, government and academia.

Hundreds of examples, exercises, projects (EEPs), and implementation case studies give students an engaging, challenging and entertaining introduction to Python programming and hands-on data science. The book's modular architecture enables instructors to conveniently adapt the text to a wide range of computer-science and data-science courses offered to audiences drawn from many majors.

Computer-science instructors can integrate as much or as little data-science and artificial-intelligence topics as they'd like, and data-science instructors can integrate as much or as little Python as they'd like. The book aligns with the latest ACM/IEEE CS-and-related computing curriculum initiatives and with the Data Science Undergraduate Curriculum Proposal sponsored by the National Science Foundation.

Impractical Python Projects is a collection of fun and educational projects designed to entertain programmers while enhancing their Python skills. It picks up where the complete beginner books leave off, expanding on existing concepts and introducing new tools that you'll use every day.

And to keep things interesting, each project includes a zany twist featuring historical incidents, pop culture references, and literary allusions. You'll flex your problem-solving skills and employ Python's many useful libraries to do things like: - Help James Bond crack a high-tech safe with a hill-climbing algorithm - Write haiku poems using Markov Chain Analysis - Use genetic algorithms to breed a race of gigantic rats - Crack the world's most successful military cipher using cryptanalysis - Derive the anagram, "I am Lord Voldemort" using linguistical sieves - Plan your parents' secure retirement with Monte Carlo simulation - Save the sorceress Zatanna from a stabby death using palingrams - Model the Milky Way and calculate our odds of detecting alien civilizations - Help the world's smartest woman win the Monty Hall problem argument - Reveal Jupiter's Great Red Spot using optical stacking - Save the head of Mary, Queen of Scots with steganography - Foil corporate security with invisible electronic ink Simulate volcanoes, map Mars, and more, all while gaining valuable experience using free modules like Tkinter, matplotlib, Cprofile, Pylint, Pygame, Pillow, and Python-Docx

Updated and Expanded for Python 3 It’s easy to start developing programs with Python, which is why the language is so popular. However, Python’s unique strengths, charms, and expressiveness can be hard to grasp, and there are hidden pitfalls that can easily trip you up.

This second edition of Effective Python will help you master a truly “Pythonic” approach to programming, harnessing Python’s full power to write exceptionally robust and well-performing code. Using the concise, scenario-driven style pioneered in Scott Meyers’ best-selling Effective C++, Brett Slatkin brings together 90 Python best practices, tips, and shortcuts, and explains them with realistic code examples so that you can embrace Python with confidence.

Drawing on years of experience building Python infrastructure at Google, Slatkin uncovers little-known quirks and idioms that powerfully impact code behavior and performance. You’ll understand the best way to accomplish key tasks so you can write code that’s easier to understand, maintain, and improve.

In addition to even more advice, this new edition substantially revises all items from the first edition to reflect how best practices have evolved. Key features include 30 new actionable guidelines for all major areas of Python Detailed explanations and examples of statements, expressions, and built-in types Best practices for writing functions that clarify intention, promote reuse, and avoid bugs Better techniques and idioms for using comprehensions and generator functions Coverage of how to accurately express behaviors with classes and interfaces Guidance on how to avoid pitfalls with metaclasses and dynamic attributes More efficient and clear approaches to concurrency and parallelism Solutions for optimizing and hardening to maximize performance and quality Techniques and built-in modules that aid in debugging and testing Tools and best practices for collaborative development Effective Python will prepare growing programmers to make a big impact using Python.

Completely updated for Django 3.1. Django for Beginners is a project-based introduction to Django, the popular Python-based web framework.

Suitable for total beginners who have never built a website before as well as professional programmers looking for a fast-paced guide to modern web development and Django fundamentals. In the book you’ll learn how to: Build 5 websites from scratch, including a Blog and Newspaper website Deploy online using security best practices Customize the look and feel of your sites Write tests and run them for all your code Integrate user authentication, email, and custom user models Add permissions and authorizations to make your app more secure Identify common mistakes and errors so you can build your own websites If you’re curious about Python-based web development, Django for Beginners is a best-practices guide to writing and deploying your own websites quickly.

Reviews "When readers interested in web development ask me what to read next after Python Crash Course, I refer them to Will's books: Django for Beginners, Django for APIs, and Django for Professionals. I highly recommend you check out his work." —ERIC MATTHES, author of Python Crash Course "If you’re looking for a guide into the world of Django, then the three-step of Django for Beginners, Django for APIs, and Django for Professionals is ideal: get up and running, get into APIs, which are a cornerstone of modern app development, and then add the bits you need to your fledging app into production, from databases and static files, to user accounts and security.

It’s a long road. Will’s books are an awesome companion." —CARLTON GIBSON, Django Fellow and Django REST Framework core contributor "Will's books are a fantastic resource for web development with Django and Python

To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, and toolkits—but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch.

If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with New material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today’s messy glut of data.

Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and how and when they’re used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases. .

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.

Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform.

As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives.

The book addresses real life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting

If you need help writing programs in Python 3, or want to update older Python 2 code, this book is just the ticket. Packed with practical recipes written and tested with Python 3.3, this unique cookbook is for experienced Python programmers who want to focus on modern tools and idioms.

Inside, you’ll find complete recipes for more than a dozen topics, covering the core Python language as well as tasks common to a wide variety of application domains. Each recipe contains code samples you can use in your projects right away, along with a discussion about how and why the solution works

Python’s simplicity lets you become productive quickly, but this often means you aren’t using everything it has to offer. With this hands-on guide, you’ll learn how to write effective, idiomatic Python code by leveraging its best—and possibly most neglected—features.

Author Luciano Ramalho takes you through Python’s core language features and libraries, and shows you how to make your code shorter, faster, and more readable at the same time. Many experienced programmers try to bend Python to fit patterns they learned from other languages, and never discover Python features outside of their experience.

With this book, those Python programmers will thoroughly learn how to become proficient in Python 3. This book covers: Python data model: understand how special methods are the key to the consistent behavior of objects Data structures: take full advantage of built-in types, and understand the text vs bytes duality in the Unicode age Functions as objects: view Python functions as first-class objects, and understand how this affects popular design patterns Object-oriented idioms: build classes by learning about references, mutability, interfaces, operator overloading, and multiple inheritance Control flow: leverage context managers, generators, coroutines, and concurrency with the concurrent.futures and asyncio packages Metaprogramming: understand how properties, attribute descriptors, class decorators, and metaclasses work

Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz’s popular training course, this updated fifth edition will help you quickly write efficient, high-quality code with Python.

It’s an ideal way to begin, whether you’re new to programming or a professional developer versed in other languages. Complete with quizzes, exercises, and helpful illustrations, this easy-to-follow, self-paced tutorial gets you started with both Python 2.7 and 3.3— the latest releases in the 3.X and 2.X lines—plus all other releases in common use today.

You’ll also learn some advanced language features that recently have become more common in Python code. Explore Python’s major built-in object types such as numbers, lists, and dictionaries Create and process objects with Python statements, and learn Python’s general syntax model Use functions to avoid code redundancy and package code for reuse Organize statements, functions, and other tools into larger components with modules Dive into classes: Python’s object-oriented programming tool for structuring code Write large programs with Python’s exception-handling model and development tools Learn advanced Python tools, including decorators, descriptors, metaclasses, and Unicode processing

The second edition of this best-selling Python book (100,000+ copies sold in print alone) uses Python 3 to teach even the technically uninclined how to write programs that do in minutes what would take hours to do by hand. There is no prior programming experience required and the book is loved by liberal arts majors and geeks alike.

If you've ever spent hours renaming files or updating hundreds of spreadsheet cells, you know how tedious tasks like these can be. But what if you could have your computer do them for you? In this fully revised second edition of the best-selling classic Automate the Boring Stuff with Python, you'll learn how to use Python to write programs that do in minutes what would take you hours to do by hand--no prior programming experience required.

You'll learn the basics of Python and explore Python's rich library of modules for performing specific tasks, like scraping data off websites, reading PDF and Word documents, and automating clicking and typing tasks. The second edition of this international fan favorite includes a brand-new chapter on input validation, as well as tutorials on automating Gmail and Google Sheets, plus tips on automatically updating CSV files.

You'll learn how to create programs that effortlessly perform useful feats of automation to: • Search for text in a file or across multiple files • Create, update, move, and rename files and folders • Search the Web and download online content • Update and format data in Excel spreadsheets of any size • Split, merge, watermark, and encrypt PDFs • Send email responses and text notifications • Fill out online forms Step-by-step instructions walk you through each program, and updated practice projects at the end of each chapter challenge you to improve those programs and use your newfound skills to automate similar tasks. Don't spend your time doing work a well-trained monkey could do.

Even if you've never written a line of code, you can make your computer do the grunt work. Learn how in Automate the Boring Stuff with Python, 2nd Edition.