Features Python best practices, patterns, shortcuts, and "pythonic" idioms you won't find anywhere else Covers Python algorithms, objects, concurrency, collaboration, built-in modules, and much more Guides you to a far deeper understanding of the Python language, so you know why its unique idioms and rules of thumb make sense Follows the enormously popular "Effective" format proven in Scott Meyers' classic Effective C++ 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. Praise For Effective Python: 90 Specific Ways to Write Better Python, 2nd Edition “I have been recommending this book enthusiastically since the first edition appeared in 2015. This new edition, updated and expanded for Python 3, is a treasure trove of practical Python programming wisdom that can benefit programmers of all experience levels.” —Wes McKinney, Creator of Python Pandas project, Director of Ursa Labs “If you’re coming from another language, this is your definitive guide to taking full advantage of the unique features Python has to offer. I’ve been working with Python for nearly twenty years and I still learned a bunch of useful tricks, especially around newer features introduced by Python 3. Effective Python is crammed with actionable advice, and really helps define what our community means when they talk about Pythonic code.” —Simon Willison, Co-creator of Django “I’ve been programming in Python for years and thought I knew it pretty well. Thanks to this treasure trove of tips and techniques, I’ve discovered many ways to improve my Python code to make it faster (e.g., using bisect to search sorted lists), easier to read (e.g., enforcing keyword-only arguments), less prone to error (e.g., unpacking with starred expressions), and more Pythonic (e.g., using zip to iterate over lists in parallel). Plus, the second edition is a great way to quickly get up to speed on Python 3 features, such as the walrus operator, f-strings, and the typing module.” —Pamela Fox, Creator of Khan Academy programming courses “Now that Python 3 has finally become the standard version of Python, it’s already gone through eight minor releases and a lot of new features have been added throughout. Brett Slatkin returns with a second edition of Effective Python with a huge new list of Python idioms and straightforward recommendations, catching up with everything that’s introduced in version 3 all the way through 3.8 that we’ll all want to use as we finally leave Python 2 behind. Early sections lay out an enormous list of tips regarding new Python 3 syntaxes and concepts like string and byte objects, f-strings, assignment expressions (and their special nickname you might not know), and catch-all unpacking of tuples. Later sections take on bigger subjects, all of which are packed with things I either didn’t know or which I’m always trying to teach to others, including ‘Metaclasses and Attributes’ (good advice includes ‘Prefer Class Decorators over Metaclasses’ and also introduces a new magic method ‘__init_subclass__()’ I wasn’t familiar with), ‘Concurrency’ (favorite advice: ‘Use Threads for Blocking I/O, but not Parallelism,’ but it also covers asyncio and coroutines correctly) and ‘Robustness and Performance’ (advice given: ‘Profile before Optimizing’). It’s a joy to go through each section as everything I read is terrific best practice information smartly stated, and I’m considering quoting from this book in the future as it has such great advice all throughout. This is the definite winner for the ‘if you only read one Python book this year...’ contest.” —Mike Bayer, Creator of SQLAlchemy “This is a great book for both novice and experienced programmers. The code examples and explanations are well thought out and explained concisely and thoroughly. The second edition updates the advice for Python 3, and it’s fantastic! I’ve been using Python for almost 20 years, and I learned something new every few pages. The advice given in this book will serve anyone well.” —Titus Brown, Associate Professor at UC Davis “Once again, Brett Slatkin has managed to condense a wide range of solid practices from the community into a single volume. From exotic topics like metaclasses and concurrency to crucial basics like robustness, testing, and collaboration, the updated Effective Python makes a consensus view of what’s ‘Pythonic’ available to a wide audience.” —Brandon Rhodes, Author of python-patterns.guide

Django 3 kroz primere (treće izdanje) Izrada veb aplikacija u realnom svetu Naučite Django osnove, uključujući modele, prikaze, ORM, šablone, URL-ove, obrasce i autentifikaciju. Implementirajte napredne funkcije, kao što su prilagođena polja modela, oznake prilagođenih šablona, keš memorija, posrednički softver, lokalizacija i još mnogo štošta. Kreirajte složene funkcionalnosti, kao što su AJAX interakcija, društvena autentifikacija, pretraga punog teksta, sistem plaćanja, CMS, RESTful API-i i još mnogo štošta. Integrišite u svoje projekte druge tehnologije, uključujući Redis, Celery, RabbitMQ, PostgreSQL i Channels. Uključite Django projekte u izradu veb strana, koristeći NGINX, uWSGI i Daphne. Ako želite da naučite ceo proces razvoja profesionalnih veb aplikacija, koristeći Python i Django, onda je ova knjiga za vas. U procesu izrade četiri profesionalna Django projekta naučićete Django 3 funkcije, kako da rešite uobičajene probleme u razvoju veb strana, kako da implementirate najbolju praksu i kako da uspešno implementirate svoje aplikacije. U ovoj knjizi izradićete aplikaciju za blog, veb sajt za merenje popularnosti veb strana, internet prodavnicu i platformu za elektronsko učenje. Pomoću uputstva „korak po korak“ ćete naučiti kako da integrišete popularne tehnologije, da poboljšate aplikacije pomoću AJAX-a, da kreirate RESTful API-e i da podesite proizvodno okruženje za vaše Django projekte. Kada pročitate ovu knjigu u celosti, savladaćete Django 3, pa ćete moći da izrađujete napredne veb aplikacije. Naučite Django 3, tako što ćete izraditi stvarne veb aplikacije „od „nule“ u Pythonu, koristeći najbolju praksu kodiranja. Integrišite druge tehnologije u svoju aplikaciju pomoću jasnih i detaljnih objašnjenja i opsežnih primera koda. Implementirajte napredne funkcije, kao što su pretraga punog teksta, tok aktivnosti korisnika ili mehanizam za preporuke. Dodajte funkcije u realnom vremenu pomoću Django Channelsa i WebSocketsa. Sadržaj 1. Izrada aplikacije za blog Instaliranje Djangoa Kreiranje vašeg prvog projekta Dizajniranje šeme podataka za blog Izrada administratorskog sajta za modele Upotreba QuerySetsa i menadžera Izrada lista i prikaza detalja Izrada obrazaca za prikaze Dodavanje numerisanja strana Korišćenje prikaza zasnovanih na klasama Rezime 2. Poboljšanje bloga pomoću naprednih funkcija Deljenje postova pomoću e-pošte Kreiranje sistema za komentare Dodavanje funkcije označavanja Učitavanje postova prema sličnosti Rezime 3. Proširenje aplikacije za blog Kreiranje prilagođenih oznaka i filtera Dodavanje mape veb sajta na sajt Kreiranje feedova za postove na blogu Dodavanje pretrage punog teksta na blog Rezime 4. Izrada društvenog veb sajta Izrada projekta društvenog veb sajta Korišćenje Django radnog okvira za autentifikaciju Registracija korisnika i korisnički profili Izrada prilagođenog pozadinskog mehanizma za autentifikaciju Dodavanje društvene autentifikacije na veb sajt Rezime 5. Deljenje sadržaja na veb sajtu Izrada veb sajta za merenje popularnosti veb strana Objavljivanje sadržaja sa drugih veb sajtova Kreiranje prikaza detalja za slike Izrada umanjenog prikaza slika pomoću alatke easy-thumbnails Dodavanje AJAX akcija pomoću jQueryja Kreiranje prilagođenih dekoratora za prikaze Dodavanje AJAX numerisanja strana u prikaze lista Rezime 6. Praćenje korisničkih radnji Izrada sistema za praćenje Izrada aplikacije za generički tok aktivnosti Upotreba signala za denormalizaciju brojeva Korišćenje Redisa za skladištenje prikaza stavki Rezime 7. Izrada internet prodavnice Izrada projekta internet prodavnice Izrada korpe za kupovinu Registracija narudžbenica kupaca Pokretanje asinhronih zadataka pomoću Celeryja Rezime 8. Upravljanje plaćanjem i narudžbenicama Integrisanje platnog mrežnog prolaza Izvoz narudžbenica u CSV datoteke Proširenje administratorskog sajta pomoću prilagođenih prikaza Dinamično generisanje PDF faktura Rezime 9. Proširenje prodavnice Kreiranje sistema za kupone Dodavanje internacionalizacije i lokalizacije Izrada mehanizma za preporuke Rezime 10. Izrada platforme za elektronsko učenje Postavljanje projekta elektronskog učenja Izrada modela kursa Kreiranje modela za raznovrstan sadržaj Kreiranje CMS-a Upravljanje modulima kursa i njihovim sadržajem Rezime 11. Renderovanje i keširanje sadržaja Prikazivanje kurseva Dodavanje registracije učenika Pristup sadržaju kursa Korišćenje radnog okvira keša Rezime 12. Izrada API-a Izrada RESTful API-a Rezime 13. Izrada servera za ćaskanje Kreiranje aplikacije za ćaskanje Django u realnom vremenu sa kanalima Instaliranje kanala Pisanje potrošača Usmeravanje Implementacija WebSocket klijenta Omogučavanje sloja kanala Promena potrošača radi potpune asinhronosti Integrisanje aplikacije za ćaskanje sa postojećim prikazima Rezime 14. Akcija Kreiranje proizvodnog okruženja Implementacija prilagođenih komandi za upravljanje Rezime 15. Indeks

Python is the preferred choice of developers, engineers, data scientists, and hobbyists everywhere. It is a great language that can power your applications and provide great speed, safety, and scalability. It can be used for simple scripting or sophisticated web applications. By exposing Python as a series of simple recipes, this book gives you insight into specific language features in a particular context. Having a tangible context helps make the language or a given standard library feature easier to understand. This book comes with 133 recipes on the latest version of Python 3.8. The recipes will benefit everyone, from beginners just starting out with Python to experts. You'll not only learn Python programming concepts but also how to build complex applications. The recipes will touch upon all necessary Python concepts related to data structures, object oriented programming, functional programming, and statistical programming. You will get acquainted with the nuances of Python syntax and how to effectively take advantage of it. By the end of this Python book, you will be equipped with knowledge of testing, web services, configuration, and application integration tips and tricks. You will be armed with the knowledge of how to create applications with flexible logging, powerful configuration, command-line options, automated unit tests, and good documentation. Table of contents Numbers, Strings, and Tuples Statements and Syntax Function Definitions Built-In Data Structures Part 1: Lists and Sets Built-In Data Structures Part 2: Dictionaries User Inputs and Outputs Basics of Classes and Objects More Advanced Class Design Functional Programming Features Input/Output, Physical Format, and Logical Layout Testing Web Services Application Integration: Configuration Application Integration: Combination Statistical Programming and Linear Regression Other Books You May Enjoy Index

What You Will Learn Use Django to access user-submitted form data, validate it, and work with it Get to know advanced URLconf tips and tricks Extend Django’s template system with custom code Define models and use the database API to create, retrieve, update, and delete records Fully extend and customize the default implementation as per your project’s needs Test and deploy your Django application Get to know more about Django’s session, cache Framework, and middleware Book Description Mastering Django: Core is a completely revised and updated version of the original Django Book, written by Adrian Holovaty and Jacob Kaplan-Moss - the creators of Django. The main goal of this book is to make you a Django expert. By reading this book, you’ll learn the skills needed to develop powerful websites quickly, with code that is clean and easy to maintain. This book is also a programmer’s manual that provides complete coverage of the current Long Term Support (LTS) version of Django. For developers creating applications for commercial and business critical deployments, Mastering Django: Core provides a complete, up-to-date resource for Django 1.8LTS with a stable code-base, security fixes and support out to 2018. Authors Nigel George Nigel George is a business systems developer who specializes in the application of open source technologies to solve common business problems. He has a broad range of experience in software development—from writing database apps for small business to developing the back end and UI for a distributed sensor network at the University of Newcastle, Australia. Nigel also has over 15 years experience in technical writing for business. He has written several training manuals and hundreds of technical procedures for corporations and Australian government departments. He has been using Django since version 0.96 and has written applications in C, C#, C++, VB, VBA, HTML, JavaScript, Python and PHP. He has another book on Django—Beginning Django CMS—published by Apress in December 2015. Nigel lives in Newcastle, NSW, Australia. Table of Contents Chapter 1: Introduction to Django and Getting Started Chapter 2: Views and URLconfs Chapter 3: Templates Chapter 4: Models Chapter 5: The Django Admin Site Chapter 6: Forms Chapter 7: Advanced Views and URLconfs Chapter 8: Advanced Templates Chapter 9: Advanced Models Chapter 10: Generic Views Chapter 11: User Authentication in Django Chapter 12: Testing in Django Chapter 13: Deploying Django Chapter 14: Generating Non-HTML Content Chapter 15: Django Sessions Chapter 16: Djangos Cache Framework Chapter 17: Django Middleware Chapter 18: Internationalization Chapter 19: Security in Django Chapter 20: More on Installing Django Chapter 21: Advanced Database Management

About This Book Understand how PyCharm works and how you can leverage its strength to develop applications quickly Master PyCharm’s editor to get a fast workflow Full of examples and illustrations that focus on the practical aspects of using PyCharm Who This Book Is For If you know PyCharm but want to understand it better and leverage its more powerful but less obvious tool set, this is the book for you. Serving as a launch pad for those who want to master PyCharm and completely harness its best features, it would be helpful if you were familiar with some of Python’s most prominent tools such as virtualenv and Python’s popular docstring formats such as reStructuredText and EpyType. What You Will Learn Understand the internal workings of the IntelliJ Platform Leverage PyCharm’s powerful search tools, and learn which ones are the best for you and your workflow Customize PyCharm’s enhanced Python interpreter and its inbuilt terminal Develop web applications quickly and easily with different frameworks such as Flask and Django Understand how code completion works in PyCharm for Python and JavaScript In Detail PyCharm is addictive, with powerful and configurable code completion, superb editing tools, top-notch support, diverse plugins, and a vibrant ecosystem to boot. Learning how PyCharm works and maximising the synergy of its powerful tools will help you to rapidly develop applications. From leveraging the power of the editor to understanding PyCharm's internals, this book will give you a comprehensive view of PyCharm and allow you to make your own choices about which workflow and tools are best for you. You will start by getting comfortable with PyCharm and making it look exactly like you want. You can customize the tools and taskbars to suit individual developers' coding styles. You also learn how to assign keyboard shortcuts. You will master debugging by inserting breakpoints, collecting runtime data, and debugging from the console. You will understand how PyCharm works underneath and how plugins such as Codemap, Vim, Bitbucket, Assets compressor, markdown, bash file, shortcut translator, and .gitignore leverage the power of the IntelliJ platform. You will become comfortable using the VCS interface in PyCharm and see the benefits of using it for some simple tasks as well as some more complex tasks such as partial commits using changelists. You will take an in-depth look at the various tools in PyCharm, improving your workflow drastically. Finally, you will deploy powerful PyCharm tools for Django, Flask, GAE, and Pyramid Development, becoming well acquainted with PyCharm’s toolset for web development with popular platforms. Packed with insider tricks, this book will help you boost productivity with PyCharm. Authors Quazi Nafiul Islam Quazi Nafiul Islam is a consultant and an occasional speaker, and has worked professionally with Python for 3 years while completing his bachelor's degree in computer science. He blogs regularly on his website, nafiulis.me. He struggled to find the right tools that could aid his workflow when working on large Python projects until he was introduced to PyCharm. He loved it so much that he wrote a book on it, his very first one. Table of Contents Chapter 1: Getting the Right Look Chapter 2: Understanding the Keymap Chapter 3: Getting Places Chapter 4: Editing Chapter 5: Interpreters and Consoles Chapter 6: Debugging Chapter 7: The PyCharm Ecosystem Chapter 8: File Templates and Snippets Chapter 9: Version Control Integration Chapter 10: HTML and JavaScript Tools Chapter 11: Web Development with PyCharm

About This Book Use object-oriented programming to develop amazing GUIs in Python Create a working GUI project as a central resource for developing your Python GUIs Packed with easy-to-follow recipes to help you develop code using the latest released version of Python Who This Book Is For If you are a Python programmer with intermediate level knowledge of GUI programming and want to learn how to create beautiful, effective, and responsive GUIs using the freely available Python GUI frameworks, this book is for you. What You Will Learn Create amazing GUIs with Python’s built-in Tkinter module Customize the GUIs by using layout managers to arrange the GUI widgets Advance to an object-oriented programming style using Python Develop beautiful charts using the free Matplotlib Python module Use threading in a networked environment to make the GUIs responsive Discover ways to connect the GUIs to a database Understand how unit tests can be created and internationalize the GUI Extend the GUIs with free Python frameworks using best practices In Detail Python is a multi-domain, interpreted programming language. It is a widely used general-purpose, high-level programming language. It is often used as a scripting language because of its forgiving syntax and compatibility with a wide variety of different eco-systems. Its flexible syntax enables developers to write short scripts while at the same time, they can use object-oriented concepts to develop very large projects. Python GUI Programming Cookbook follows a task-based approach to help you create beautiful and very effective GUIs with the least amount of code necessary. This book uses the simplest programming style, using the fewest lines of code to create a GUI in Python, and then advances to using object-oriented programming in later chapters. If you are new to object-oriented programming (OOP), this book will teach you how to take advantage of the OOP coding style in the context of creating GUIs written in Python. Throughout the book, you will develop an entire GUI application, building recipe upon recipe, connecting the GUI to a database. In the later chapters, you will explore additional Python GUI frameworks, using best practices. You will also learn how to use threading to ensure your GUI doesn’t go unresponsive. By the end of the book, you will be an expert in Python GUI programming to develop a common set of GUI applications. Authors Burkhard A. Meier Burkhard A. Meier has more than 15 years of professional experience working in the software industry as a software tester and developer, specializing in software test automation development, execution, and analysis. He has a very strong background in SQL relational database administration, the development of stored procedures, and debugging code. While experienced in Visual Studio .NET C#, Visual Test, TestComplete, and other testing languages (such as C/C++), the main focus of the author over the past two years has been developing test automation written in Python 3 to test the leading edge of FLIR ONE infrared cameras for iPhone and Android smart phones as well as handheld tablets. Being highly appreciative of art, beauty, and programming, the author developed GUIs in C# and Python to streamline everyday test automation tasks, enabling these automated tests to run unattended for weeks, collecting very useful data to be analyzed and automatically plotted into graphs and e-mailed to upper management upon completion of nightly automated test runs. His previous jobs include working as a senior test automation engineer and designer for InfoGenesis (now Agilysys), QAD, InTouch Health, and presently, FLIR Systems. You can get in touch with him through his LinkedIn account, https://www.linkedin.com/pub/burkhard-meier/5/246/296. Table of Contents

About This Book Gain in-depth knowledge of Probabilistic Graphical Models Model time-series problems using Dynamic Bayesian Networks A practical guide to help you apply PGMs to real-world problems Who This Book Is For If you are a researcher or a machine learning enthusiast, or are working in the data science field and have a basic idea of Bayesian learning or probabilistic graphical models, this book will help you to understand the details of graphical models and use them in your data science problems. What You Will Learn Get to know the basics of probability theory and graph theory Work with Markov networks Implement Bayesian networks Exact inference techniques in graphical models such as the variable elimination algorithm Understand approximate inference techniques in graphical models such as message passing algorithms Sampling algorithms in graphical models Grasp details of Naive Bayes with real-world examples Deploy probabilistic graphical models using various libraries in Python Gain working details of Hidden Markov models with real-world examples In Detail Probabilistic graphical models is a technique in machine learning that uses the concepts of graph theory to concisely represent and optimally predict values in our data problems. Graphical models gives us techniques to find complex patterns in the data and are widely used in the field of speech recognition, information extraction, image segmentation, and modeling gene regulatory networks. This book starts with the basics of probability theory and graph theory, then goes on to discuss various models and inference algorithms. All the different types of models are discussed along with code examples to create and modify them, and also run different inference algorithms on them. There is an entire chapter that goes on to cover Naive Bayes model and Hidden Markov models. These models have been thoroughly discussed using real-world examples. Authors Ankur Ankan Ankur Ankan is a BTech graduate from IIT (BHU), Varanasi. He is currently working in the field of data science. He is an open source enthusiast and his major work includes starting pgmpy with four other members. In his free time, he likes to participate in Kaggle competitions. Abinash Panda Abinash Panda is an undergraduate from IIT (BHU), Varanasi, and is currently working as a data scientist. He has been a contributor to open source libraries such as the Shogun machine learning toolbox and pgmpy, which he started writing along with four other members. He spends most of his free time on improving pgmpy and helping new contributors. Table of Contents Chapter 1: Bayesian Network Fundamentals Chapter 2: Markov Network Fundamentals Chapter 3: Inference – Asking Questions to Models Chapter 4: Approximate Inference Chapter 5: Model Learning – Parameter Estimation in Bayesian Networks Chapter 6: Model Learning – Parameter Estimation in Markov Networks Chapter 7: Specialized Models

About This Book Explore the process of using geospatial analysis to solve simple to complex problems with fast, reusable recipes Concise step-by-step instructions to teach you all about vector, overlay, raster, routing, and topology analysis Discover performance enhancing tools for your daily work Who This Book Is For If you are a student, teacher, programmer, geospatial or IT administrator, GIS analyst, researcher, or scientist looking to do spatial analysis, then this book is for you. Anyone trying to answer simple to complex spatial analysis questions will get a working demonstration of the power of Python with real-world data. Some of you may be beginners with GIS, but most of you will probably have a basic understanding of geospatial analysis and programming. What You Will Learn Discover the projection and coordinate system information of your data and learn how to transform that data into different projections Import or export your data into different data formats to prepare it for your application or spatial analysis Use the power of PostGIS with Python to take advantage of the powerful analysis functions Execute spatial analysis functions on vector data including clipping, spatial joins, measuring distances, areas, and combining data to new results Perform and ensure quality assurance checks with topology rules in Python Find the shortest path with network analysis functions in easy, extensible recipes revolving around all kinds of network analysis problems Visualize your data on a map using the visualization tools and methods available to create visually stunning results Build a web application with GeoDjango to include your spatial analysis tools built from the previous vector, raster, and overlay analysis In Detail Geospatial development links your data to places on the Earth’s surface. Its analysis is used in almost every industry to answer location type questions. Combined with the power of the Python programming language, which is becoming the de facto spatial scripting choice for developers and analysts worldwide, this technology will help you to solve real-world spatial problems. This book begins by tackling the installation of the necessary software dependencies and libraries needed to perform spatial analysis with Python. From there, the next logical step is to prepare our data for analysis; we will do this by building up our tool box to deal with data preparation, transformations, and projections. Now that our data is ready for analysis, we will tackle the most common analysis methods for vector and raster data. To check or validate our results, we will explore how to use topology checks to ensure top-quality results. Finally, we put it all together in a GeoDjango web application that demonstrates a final working spatial analysis application. The round trip will provide you all the pieces you need to accomplish your own spatial analysis application to suit your requirements. Authors Michael Diener Michael Diener graduated from Simon Fraser University, British Columbia, Canada, in 2001 with a Bachelor of Science degree in Geography. He began working in 1995 with Environment Canada as a GIS (Geographic Information Systems) Analyst and has continued to work with GIS ever since. Beginning in 2008, he founded a company called GOMOGI focused on performing GIS for mobile and web applications with open source software. Michael holds seminars for organizations wanting to explore or discover the possibilities of how GIS can increase productivity and better answer spatial questions. He is also the creative head of new product development and a Python developer working with a wide range of spatial software on a daily basis. Developing spatial applications through the years, he has always used Python to get the job done. He is also lecturer of GIS at the Alpen Adria University, Klagenfurt, where he enjoys teaching students the wonderful powers of GIS and explaining how to solve spatial problems with open source GIS and Python. Table of Contents

What You Will Learn Use the key frameworks of data science, machine learning, and deep learning Ask new questions of your data through machine learning models and neural networks Work with the most powerful Python open-source libraries in machine learning Build deep learning applications using Keras and TensorFlow Embed your machine learning model in accessible web applications Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Analyze images using deep learning techniques Use sentiment analysis to delve deeper into textual and social media data Book Description Machine learning is eating the software world, and now deep learning is extending machine learning. This book is for developers and data scientists who want to master the world of artificial intelligence, with a practical approach to understanding and implementing machine learning, and how to apply the power of deep learning with Python. This Second Edition of Sebastian Raschka’s Python Machine Learning is thoroughly updated to use the most powerful and modern Python open-source libraries, so that you can understand and work at the cutting-edge of machine learning, neural networks, and deep learning. Written for developers and data scientists who want to create practical machine learning code, the authors have extended and modernized this best-selling book, to now include the influential TensorFlow library, and the Keras Python neural network library. The Scikit-learn code has also been fully updated to include recent innovations. The result is a new edition of this classic book at the cutting edge of machine learning. Readers new to machine learning will find this classic book offers the practical knowledge and rich techniques they need to create and contribute to machine learning, deep learning, and modern data analysis. Raschka and Mirjalili introduce you to machine learning and deep learning algorithms, and show you how to apply them to practical industry challenges. By the end of the book, you’ll be ready to meet the new data analysis opportunities in today’s world . Readers of the first edition will be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. Readers can learn and work with TensorFlow more deeply than ever before, and essential coverage of the Keras neural network library has been added, along with the most recent updates to Scikit-learn. Raschka and Mirjalili have updated this book to meet the most modern areas of machine learning, to give developers and data scientists a fresh and practical Python journey into machine learning. Authors Sebastian Raschka Sebastian Raschka, author of the best selling Python Machine Learning, has many years of experience with coding in Python and has given several seminars on the practical applications of data science and machine learning, including a machine learning tutorial at SciPy, the leading conference for scientific computing in Python. Sebastian loves to write and talk about data science, machine learning, and Python, and he's motivated to help people developing data-driven solutions without necessarily requiring a machine learning background. His work and contributions have recently been recognized by the departmental outstanding graduate student award 2016-2017. In his free time, Sebastian loves to contribute to open source projects, and methods that he implemented are now successfully used in machine learning competitions such as Kaggle. Vahid Mirjalili Vahid Mirjalili obtained his Ph.D. in mechanical engineering working on novel methods for large-scale, computational simulations of molecular structures. Currently, Vahid is focusing his research efforts on applications of machine learning in various computer vision projects in the Department of Computer Science and Engineering at Michigan State University. He picked Python as his number one choice of programming language, and throughout his academic and research career has gained tremendous experience with coding in Python. He further taught the Python Programming for Engineering class at Michigan State University, giving him a chance to help students understanding different data structures and developing efficient code in Python. While Vahid's broad research interests are focused on deep learning and computer vision applications, he is especially interested in the fusion of neural networks for pedestrian detection in multispectral images. Table of Contents

What You Will Learn Create the GUI Form and add widgets Arrange the widgets using layout managers Use object-oriented programming to create GUIs Create Matplotlib charts Use threads and talking to networks Talk to a MySQL database via the GUI Perform unit-testing and internationalizing the GUI Extend the GUI with third-party graphical libraries Get to know the best practices to create GUIs Book Description Python is a multi-domain, interpreted programming language. It is a widely used general-purpose, high-level programming language. It is often used as a scripting language because of its forgiving syntax and compatibility with a wide variety of different eco-systems. Python GUI Programming Cookbook follows a task-based approach to help you create beautiful and very effective GUIs with the least amount of code necessary. This book will guide you through the very basics of creating a fully functional GUI in Python with only a few lines of code. Each and every recipe adds more widgets to the GUIs we are creating. While the cookbook recipes all stand on their own, there is a common theme running through all of them. As our GUIs keep expanding, using more and more widgets, we start to talk to networks, databases, and graphical libraries that greatly enhance our GUI’s functionality. This book is what you need to expand your knowledge on the subject of GUIs, and make sure you’re not missing out in the long run. Authors Burkhard A. Meier Burkhard A. Meier has more than 17 years of professional experience working in the software industry as a software tester and developer, specializing in software test automation development, execution, and analysis. He has a very strong background in Python 3 software test automation development, as well as in SQL relational database administration, the development of stored procedures, and debugging code. While experienced in Visual Studio .NET C#, Visual Test, TestComplete, and other testing languages (such as C/C++), the main focus of the author over the past five years has been developing test automation written in Python 3 to test the leading edge of FLIR ONE (now in its third generation) infrared cameras for iPhone and Android smart phones and handheld tablets, as well as assuring the quality of FLIR bolometer IR camera platforms. Being highly appreciative of art, beauty, and programming, the author developed GUIs in C# and Python to streamline everyday test automation tasks, enabling these automated tests to run unattended for weeks, collecting very useful data to be analyzed and automatically plotted into graphs and e-mailed to upper management upon completion of nightly automated test runs. His previous jobs include working as a senior test automation engineer and designer for InfoGenesis (now Agilysys), QAD, InTouch Health, and FLIR Systems. Table of Contents Chapter 1: Creating the GUI Form and Adding Widgets Chapter 2: Layout Management Chapter 3: Look and Feel Customization Chapter 4: Data and Classes Chapter 5: Matplotlib Charts Chapter 6: Threads and Networking Chapter 7: Storing Data in our MySQL Database via our GUI Chapter 8: Internationalization and Testing Chapter 9: Extending Our GUI with the wxPython Library Chapter 10: Creating Amazing 3D GUIs with PyOpenGL and PyGLet Chapter 11: Best Practices

What You Will Learn See the intricate details of the Python syntax and how to use it to your advantage Improve your code readability through functions in Python Manipulate data effectively using built-in data structures Get acquainted with advanced programming techniques in Python Equip yourself with functional and statistical programming features Write proper tests to be sure a program works as advertised Integrate application software using Python Book Description Python is the preferred choice of developers, engineers, data scientists, and hobbyists everywhere. It is a great scripting language that can power your applications and provide great speed, safety, and scalability. By exposing Python as a series of simple recipes, you can gain insight into specific language features in a particular context. Having a tangible context helps make the language or standard library feature easier to understand. This book comes with over 100 recipes on the latest version of Python. The recipes will benefit everyone ranging from beginner to an expert. The book is broken down into 13 chapters that build from simple language concepts to more complex applications of the language. The recipes will touch upon all the necessary Python concepts related to data structures, OOP, functional programming, as well as statistical programming. You will get acquainted with the nuances of Python syntax and how to effectively use the advantages that it offers. You will end the book equipped with the knowledge of testing, web services, and configuration and application integration tips and tricks. The recipes take a problem-solution approach to resolve issues commonly faced by Python programmers across the globe. You will be armed with the knowledge of creating applications with flexible logging, powerful configuration, and command-line options, automated unit tests, and good documentation. Authors Steven F. Lott Steven F. Lott has been programming since the 70s, when computers were large, expensive, and rare. As a contract software developer and architect, he has worked on hundreds of projects, from very small to very large. He's been using Python to solve business problems for over 10 years. He’s currently leveraging Python to implement microservices and ETL pipelines. His other titles with Packt Publishing include Python Essentials, Mastering Object-Oriented Python, Functional Python Programming, and Python for Secret Agents. Steven is currently a technomad who lives in various places on the east coast of the U.S. His technology blog is http://slott-softwarearchitect.blogspot.com and his LinkedIn address is https://www.linkedin.com/in/steven-lott-029835. Table of Contents Chapter 1: Numbers, Strings, and Tuples Chapter 2: Statements and Syntax Chapter 3: Function Definitions Chapter 4: Built-in Data Structures – list, set, dict Chapter 5: User Inputs and Outputs Chapter 6: Basics of Classes and Objects Chapter 7: More Advanced Class Design Chapter 8: Functional and Reactive Programming Features Chapter 9: Input/Output, Physical Format, and Logical Layout Chapter 10: Statistical Programming and Linear Regression Chapter 11: Testing Chapter 12: Web Services Chapter 13: Application Integration

What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it Book Description Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you’ll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that’s based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Authors Prateek Joshi Prateek Joshi is an artificial intelligence researcher, published author of five books, and TEDx speaker. He is the founder of Pluto AI, a venture-funded Silicon Valley startup building an analytics platform for smart water management powered by deep learning. His work in this field has led to patents, tech demos, and research papers at major IEEE conferences. He has been an invited speaker at technology and entrepreneurship conferences including TEDx, AT&T Foundry, Silicon Valley Deep Learning, and Open Silicon Valley. Prateek has also been featured as a guest author in prominent tech magazines. His tech blog (www.prateekjoshi.com) has received more than 1.2 million page views from 200 over countries and has over 6,600+ followers. He frequently writes on topics such as artificial intelligence, Python programming, and abstract mathematics. He is an avid coder and has won many hackathons utilizing a wide variety of technologies. He graduated from University of Southern California with a master’s degree specializing in artificial intelligence. He has worked at companies such as Nvidia and Microsoft Research. You can learn more about him on his personal website at www.prateekj.com. Table of Contents Chapter 1: Introduction to Artificial Intelligence Chapter 2: Classification and Regression Using Supervised Learning Chapter 3: Predictive Analytics with Ensemble Learning Chapter 4: Detecting Patterns with Unsupervised Learning Chapter 5: Building Recommender Systems Chapter 6: Logic Programming Chapter 7: Heuristic Search Techniques Chapter 8: Genetic Algorithms Chapter 9: Building Games With Artificial Intelligence Chapter 10: Natural Language Processing Chapter 11: Probabilistic Reasoning for Sequential Data Chapter 12: Building A Speech Recognizer Chapter 13: Object Detection and Tracking Chapter 14: Artificial Neural Networks Chapter 15: Reinforcement Learning Chapter 16: Deep Learning with Convolutional Neural Networks

What You Will Learn Implement the tools provided by Tkinter to design beautiful GUIs Discover cross-platform development through minor customizations in your existing application Visualize graphs in real time as data comes in using Tkinter's animation capabilities Use PostgreSQL authentication to ensure data security for your application Write unit tests to avoid regressions when updating code Book Description Tkinter is a lightweight, portable, and easy-to-use graphical toolkit available in the Python Standard Library, widely used to build Python GUIs due to its simplicity and availability. This book teaches you to design and build graphical user interfaces that are functional, appealing, and user-friendly using the powerful combination of Python and Tkinter. After being introduced to Tkinter, you will be guided step-by-step through the application development process. Over the course of the book, your application will evolve from a simple data-entry form to a complex data management and visualization tool while maintaining a clean and robust design. In addition to building the GUI, you'll learn how to connect to external databases and network resources, test your code to avoid errors, and maximize performance using asynchronous programming. You'll make the most of Tkinter's cross-platform availability by learning how to maintain compatibility, mimic platform-native look and feel, and build executables for deployment across popular computing platforms. By the end of this book, you will have the skills and confidence to design and build powerful high-end GUI applications to solve real-world problems. Authors Alan D. Moore Alan D. Moore is a data analyst and software developer who has been solving problems with Python since 2006. He's developed both open source and private code using frameworks like Django, Flask, Qt, and, of course, Tkinter, and contributes to various open source Python and Javascript projects. Alan maintains a blog by the name alandmoore, where he writes mainly about Python, Linux, free software, and his home studio recordings. Alan lives in Franklin, Tennessee, where he works for the county government, and with his wife, Cara, raises a crew of children who are just as geeky as their dad. Table of Contents Chapter 1: Introduction to Tkinter Chapter 2: Designing GUI Applications with Tkinter Chapter 3: Creating Basic Forms with Tkinter and ttk Widgets Chapter 4: Reducing User Error with Validation and Automation Chapter 5: Planning for the Expansion of Our Application Chapter 6: Creating Menus with Menu and Tkinter Dialogs Chapter 7: Navigating Records with Treeview Chapter 8: Improving the Look with Styles and Themes Chapter 9: Maintaining Cross-Platform Compatibility Chapter 10: Creating Automated Tests with unittest Chapter 11: Improving Data Storage with SQL Chapter 12: Connecting to the Cloud Chapter 13: Asynchronous Programming with Thread and Queue Chapter 14: Visualizing Data Using the Canvas Widget Chapter 15: Packaging with setuptools and cx_Freeze

What You Will Learn Install open source Python modules such NumPy, SciPy, Pandas, stasmodels, scikit-learn,theano, keras, and tensorflow on various platforms Prepare and clean your data, and use it for exploratory analysis Manipulate your data with Pandas Retrieve and store your data from RDBMS, NoSQL, and distributed filesystems such as HDFS and HDF5 Visualize your data with open source libraries such as matplotlib, bokeh, and plotly Learn about various machine learning methods such as supervised, unsupervised, probabilistic, and Bayesian Understand signal processing and time series data analysis Get to grips with graph processing and social network analysis Book Description Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks. With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis. The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries. Authors Armando Fandango Armando Fandango is Chief Data Scientist at Epic Engineering and Consulting Group, and works on confidential projects related to defense and government agencies. Armando is an accomplished technologist with hands-on capabilities and senior executive-level experience with startups and large companies globally. His work spans diverse industries including FinTech, stock exchanges, banking, bioinformatics, genomics, AdTech, infrastructure, transportation, energy, human resources, and entertainment. Armando has worked for more than ten years in projects involving predictive analytics, data science, machine learning, big data, product engineering, high performance computing, and cloud infrastructures. His research interests spans machine learning, deep learning, and scientific computing. Table of Contents Chapter 1: Getting Started with Python Libraries Chapter 2: NumPy Arrays Chapter 3: The Pandas Primer Chapter 4: Statistics and Linear Algebra Chapter 5: Retrieving, Processing, and Storing Data Chapter 6: Data Visualization Chapter 7: Signal Processing and Time Series Chapter 8: Working with Databases Chapter 9: Analyzing Textual Data and Social Media Chapter 10: Predictive Analytics and Machine Learning Chapter 11: Environments Outside the Python Ecosystem and Cloud Computing Chapter 12: Performance Tuning, Profiling, and Concurrency

What You Will Learn Get to know the basics of robotics and its functions Walk through Interface components with microcontrollers Integrate robotics with the IoT environment Build projects using machine learning Implement path planning and vision processing Interface your robots with Bluetooth Book Description Robotics is a fast-growing industry. Multiple surveys state that investment in the field has increased tenfold in the last 6 years, and is set to become a $100-billion sector by 2020. Robots are prevalent throughout all industries, and they are all set to be a part of our domestic lives. This book starts with the installation and basic steps in configuring a robotic controller. You'll then move on to setting up your environment to use Python with the robotic controller. You'll dive deep into building simple robotic projects, such as a pet-feeding robot, and more complicated projects, such as machine learning enabled home automation system (Jarvis), vision processing based robots and a self-driven robotic vehicle using Python. By the end of this book, you'll know how to build smart robots using Python. Authors Prof. Diwakar Vaish Prof. Diwakar Vaish is a robotics scientist and the inventor of Manav (India's first indigenous humanoid robot), the world's first mind-controlled wheelchair, brain cloning, and the world's cheapest ventilator. He has also been a guest lecturer at over 13 IITs and various other institutions. He is the founder of A-SET Robotics, a leading robotics research company based in New Delhi. Table of Contents Chapter 1: Robotics 101 Chapter 2: Using GPIOs as Input Chapter 3: Making a Gardener Robot Chapter 4: Basics of Motors Chapter 5: Making a Pet Feeding Robot Chapter 6: Bluetooth-Controlled Robotic Car Chapter 7: Sensor Interface for Obstacle Avoidance Chapter 8: Making Your Own Area Scanner Chapter 9: Vision Processing Chapter 10: Making a Guard Robot Chapter 11: Basic Switching Chapter 12: Recognizing Humans with Jarvis Chapter 13: Making Jarvis IoT Enabled Chapter 14: Giving Voice to Jarvis Chapter 15: Gesture Recognition Chapter 16: Machine Learning Chapter 17: Gesture-Controlled Robotic Vehicle Chapter 18: Making a Robotic Arm

What You Will Learn Know the differences between .py and .pyc files Explore the different ways to install and upgrade Python packages Understand the working of the PyPI module that enhances built-in decorators See how coroutines are different from generators and how they can simulate multithreading Grasp how the decimal module improves floating point numbers and their operations Standardize sub interpreters to improve concurrency Discover Python’s built-in docstring analyzer Book Description This book covers the unexplored secrets of Python, delve into its depths, and uncover its mysteries. You’ll unearth secrets related to the implementation of the standard library, by looking at how modules actually work. You’ll understand the implementation of collections, decimals, and fraction modules. If you haven’t used decorators, coroutines, and generator functions much before, as you make your way through the recipes, you’ll learn what you’ve been missing out on. We’ll cover internal special methods in detail, so you understand what they are and how they can be used to improve the engineering decisions you make. Next, you’ll explore the CPython interpreter, which is a treasure trove of secret hacks that not many programmers are aware of. We’ll take you through the depths of the PyPy project, where you’ll come across several exciting ways that you can improve speed and concurrency. Finally, we’ll take time to explore the PEPs of the latest versions to discover some interesting hacks. Authors Cody Jackson Cody Jackson is a military veteran and the founder of Socius Consulting, an IT and business management consulting company in San Antonio, Texas. He also works at CACI International as a constructive modeler. He has been involved in the tech industry since 1994. He worked at Gateway Computers as a lab technician prior to joining the Navy. He worked at ECPI University as a computer information systems adjunct professor. He is a self-taught Python programmer and the author of the book series Learning to Program Using Python. Table of Contents Chapter 1: Working with Python Modules Chapter 2: Utilizing the Python Interpreter Chapter 3: Working with Decorators Chapter 4: Using Python Collections Chapter 5: Generators, Coroutines, and Parallel Processing Chapter 6: Working with Python's Math Module Chapter 7: Improving Python Performance with PyPy Chapter 8: Python Enhancement Proposals Chapter 9: Documenting with LyX

What You Will Learn Conventions and best practices that are widely adopted in the python community Package python code effectively for community and production use Easy and lightweight ways to automate code deployment on remote systems Improve your code’s quality, reliability, and performance Write concurrent code in python Extend python with code written in different languages Book Description Python is a dynamic programming language, used in a wide range of domains by programmers who find it simple, yet powerful. Even if you find writing Python code easy, writing code that is efficient and easy to maintain and reuse is a challenge. The focus of the book is to familiarize you with common conventions, best practices, useful tools and standards used by python professionals on a daily basis when working with code. You will begin with knowing new features in Python 3.5 and quick tricks for improving productivity. Next, you will learn advanced and useful python syntax elements brought to this new version. Using advanced object-oriented concepts and mechanisms available in python, you will learn different approaches to implement metaprogramming. You will learn to choose good names, write packages, and create standalone executables easily. You will also be using some powerful tools such as buildout and vitualenv to release and deploy the code on remote servers for production use. Moving on, you will learn to effectively create Python extensions with C, C++, cython, and pyrex. The important factors while writing code such as code management tools, writing clear documentation, and test-driven development are also covered. You will now dive deeper to make your code efficient with general rules of optimization, strategies for finding bottlenecks, and selected tools for application optimization. By the end of the book, you will be an expert in writing efficient and maintainable code. Authors Michał Jaworski Michał Jaworski has 7 years of experience in Python. He is also the creator of graceful, which is a REST framework built on top of falcon. He has been in various roles at different companies: from an ordinary full-stack developer through software architect to VP of engineering in a fast-paced start-up company. He is currently a lead backend engineer in TV Store team at Opera Software. He is highly experienced in designing high-performance distributed services. He is also an active contributor to some of the popular Python open source projects. Tarek Ziadé Tarek Ziadé is an engineering manager at Mozilla, working with a team specialized in building web services in Python at scale for Firefox. He's contributed to the Python packaging effort and has worked with a lot of different Python web frameworks since Zope in the early days. Tarek has also created Afpy, the French Python User Group, and has written two books on Python in French. He has delivered numerous talks and tutorials in French at international events such as Solutions Linux, PyCon, OSCON, and EuroPython. Table of Contents Chapter 1: Current Status of Python Chapter 2: Syntax Best Practices – below the Class Level Chapter 3: Syntax Best Practices – above the Class Level Chapter 4: Choosing Good Names Chapter 5: Writing a Package Chapter 6: Deploying Code Chapter 7: Python Extensions in Other Languages Chapter 8: Managing Code Chapter 9: Documenting Your Project Chapter 10: Test-Driven Development Chapter 11: Optimization – General Principles and Profiling Techniques Chapter 12: Optimization – Some Powerful Techniques Chapter 13: Concurrency Chapter 14: Useful Design Patterns

About This Book Implement common functional programming design patterns and techniques in Python Learn how to choose between imperative and functional approaches based on expressiveness, clarity, and performance Apply functional Python to common Exploratory Data Analysis (EDA) programming problems Who This Book Is For This book is for developers who want to use Python to write programs that lean heavily on functional programming design patterns. You should be comfortable with Python programming, but no knowledge of functional programming paradigms is needed. What You Will Learn Use Python's generator functions and generator expressions to work with collections in a non-strict (or lazy) manner Utilize Python library modules including itertools, functools, multiprocessing, and concurrent.futures for efficient functional programs Use Python strings using object-oriented suffix notation and prefix notation Avoid stateful classes with families of tuples Design and implement decorators to create composite functions Use functions like max(), min(), map(), filter(), and sorted() Write higher-order functions In Detail Python is an easy-to-learn and extensible programming language that offers a number of functional programming features. It's ideally suited to a number of applications in the broad space of data science. This practical guide demonstrates the Python implementation of a number of functional programming techniques and design patterns. Starting with a general overview of functional programming concepts, you will explore common functional features such as first-class and higher-order functions, pure functions and more, and how these are accomplished in Python. Additionally, you will cover how common functional optimizations can be handled in Python. You'll also explore data preparation techniques and data exploration in depth. Moving on, you will learn how the Python standard library fits the functional programming model. The book concludes with a look at the PyMonad project and some larger examples. By the end of this book, you will be able to understand what functional programming is all about, its impact on the programming workflow, why it's important, and how to implement it in Python. Authors Steven F. Lott Steven F. Lott has been programming since the 70s, when computers were large, expensive, and rare. As a contract software developer and architect, he has worked on hundreds of projects, from very small to very large. He's been using Python to solve business problems for over 10 years. He's particularly adept at struggling with thorny data representation problems. He has also authored Mastering Object-oriented Python by Packt Publishing. He is currently a technomad who lives in various places on the east coast of the US. His technology blog can be found at http://slott-softwarearchitect.blogspot.com. Table of Contents Chapter 1: Introducing Functional Programming Chapter 2: Introducing Some Functional Features Chapter 3: Functions, Iterators, and Generators Chapter 4: Working with Collections Chapter 5: Higher-order Functions Chapter 6: Recursions and Reductions Chapter 7: Additional Tuple Techniques Chapter 8: The Itertools Module Chapter 9: More Itertools Techniques Chapter 10: The Functools Module Chapter 11: Decorator Design Techniques Chapter 12: The Multiprocessing and Threading Modules Chapter 13: Conditional Expressions and the Operator Module Chapter 14: The PyMonad Library Chapter 15: A Functional Approach to Web Services Chapter 16: Optimizations and Improvements

About This Book Get useful guidance on writing Python scripts and using libraries to put websites and web apps through their paces Find the script you need to deal with any stage of the web testing process Develop your Python knowledge to get ahead of the game for web testing and expand your skillset to other testing areas Who This Book Is For This book is for testers looking for quick access to powerful, modern tools and customizable scripts to kick-start the creation of their own Python web penetration testing toolbox. What You Will Learn Enumerate users on web apps through Python Develop complicated header-based attacks through Python Deliver multiple XSS strings and check their execution success Handle outputs from multiple tools and create attractive reports Create PHP pages that test scripts and tools Identify parameters and URLs vulnerable to Directory Traversal Replicate existing tool functionality in Python Create basic dial-back Python scripts using reverse shells and basic Python PoC malware In Detail This book gives you an arsenal of Python scripts perfect to use or to customize your needs for each stage of the testing process. Each chapter takes you step by step through the methods of designing and modifying scripts to attack web apps. You will learn how to collect both open and hidden information from websites to further your attacks, identify vulnerabilities, perform SQL Injections, exploit cookies, and enumerate poorly configured systems. You will also discover how to crack encryption, create payloads to mimic malware, and create tools to output your findings into presentable formats for reporting to your employers. Authors Cameron Buchanan Cameron Buchanan is a penetration tester by trade and a writer in his spare time. He has performed penetration tests around the world for a variety of clients across many industries. Previously, he was a member of the RAF. In his spare time, he enjoys doing stupid things, such as trying to make things fly, getting electrocuted, and dunking himself in freezing cold water. He is married and lives in London. Terry Ip Terry Ip is a security consultant. After nearly a decade of learning how to support IT infrastructure, he decided that it would be much more fun learning how to break it instead. He is married and lives in Buckinghamshire, where he tends to his chickens. Continue reading Andrew Mabbitt Andrew Mabbitt is a penetration tester living in London, UK. He spends his time beating down networks, mentoring, and helping newbies break into the industry. In his free time, he loves to travel, break things, and master the art of sarcasm. Benjamin May Benjamin May is a security test engineer from Cambridge. He studied computing for business at Aston University. With a background in software testing, he recently combined this with his passion for security to create a new role in his current company. He has a broad interest in security across all aspects of the technology field, from reverse engineering embedded devices to hacking with Python and participating in CTFs. He is a husband and a father. Dave Mound Dave Mound is a security consultant. He is a Microsoft Certified Application Developer but spends more time developing Python programs these days. He has been studying information security since 1994 and holds the following qualifications: C|EH, SSCP, and MCAD. He recently studied for OSCP certification but is still to appear for the exam. He enjoys talking and presenting and is keen to pass on his skills to other members of the cyber security community. When not attached to a keyboard, he can be found tinkering with his 1978 Chevrolet Camaro. He once wrestled a bear and was declared the winner by omoplata. Table of Contents Chapter 1: Gathering Open Source Intelligence Chapter 2: Enumeration Chapter 3: Vulnerability Identification Chapter 4: SQL Injection Chapter 5: Web Header Manipulation Chapter 6: Image Analysis and Manipulation Chapter 7: Encryption and Encoding Chapter 8: Payloads and Shells Chapter 9: Reporting

About This Book Leverage your Python programming skills to build powerful network applications Explore steps to interact with a wide range of network services Design multithreaded and event-driven architectures for echo and chat servers Who This Book Is For If you're a Python developer or a system administrator with Python experience and you're looking to take your first steps in network programming, then this book is for you. Basic knowledge of Python is assumed. What You Will Learn Develop an understanding of network stacks and the power of encapsulation Design high-performance network server applications Implement socket-based network applications using asynchronous models Build client applications for major web APIs, including Amazon S3 and Twitter Interact with e-mail servers using SMTP, POP3, and IMAP protocols Deal with remote network servers using SSH, FTP, SNMP, SMB/CIFS, and LDAP protocols Work with IP addresses including Geo-IP lookups Download objects from the Web and craft custom HTTP requests with urllib and the Requests library In Detail Network programming has always been a demanding task. With full-featured and well documented libraries all the way up the stack, Python makes network programming the enjoyable experience it should be. Starting with a walkthrough of today's major networking protocols, with this book you'll learn how to employ Python for network programming, how to request and retrieve web resources, and how to extract data in major formats over the Web. You'll utilize Python for e-mailing using different protocols and you'll interact with remote systems and IP and DNS networking. As the book progresses, socket programming will be covered, followed by how to design servers and the pros and cons of multithreaded and event-driven architectures. You'll develop practical client-side applications, including web API clients, e-mail clients, SSH, and FTP. These applications will also be implemented through existing web application frameworks. Authors Dr. M. O. Faruque Sarker Dr. M. O. Faruque Sarker is a software architect based in London, UK, where he has been shaping various Linux and open source software solutions, mainly on cloud computing platforms, for commercial companies, educational institutions, and multinational consultancies. Over the past 10 years, he has been leading a number of Python software development and cloud infrastructure automation projects. In 2009, he started using Python, where he was responsible for shepherding a fleet of miniature E-puck robots at the University of South Wales, Newport, UK. Later, he honed his Python skills, and he was invited to work on the Google Summer of Code (2009/2010) programs for contributing to the BlueZ and Tahoe-LAFS open source projects. He is the author of Python Network Programming Cookbook, Packt Publishing. He received his PhD in multirobot systems from the University of South Wales. He is currently working at University College London. He takes an active interest in cloud computing, software security, intelligent systems, and child-centric education. He lives in East London with his wife, Shahinur, and daughter, Ayesha. Sam Washington Sam Washington currently works at University College London as a member of its Learning and Teaching Applications team, developing and supporting the University's Moodle virtual learning environment, its wikis and blogs, and its online media services. Prior to this, he was a system administrator for UCL's several museums. He has working experience of managing the demands of varied web applications, and deploying and supporting Windows, Linux, and TCP/IP networks. He has been using Python for professional and personal projects for over 7 years. Table of Contents Chapter 1: Network Programming and Python Chapter 2: HTTP and Working with the Web Chapter 3: APIs in Action Chapter 4: Engaging with E-mails Chapter 5: Interacting with Remote Systems Chapter 6: IP and DNS Chapter 7: Programming with Sockets Chapter 8: Client and Server Applications Chapter 9: Applications for the Web

About This Book Learn Python development best practices from an expert, with detailed coverage of naming and coding conventions Apply object-oriented principles, design patterns, and advanced syntax tricks Manage your code with distributed version control Profile and optimize your code Proactive test-driven development and continuous integration Who This Book Is For This book is for Python developers who are already building applications, but want to build better ones by applying best practices and new development techniques to their projects. The reader is expected to have a sound background in Python programming. What You Will Learn Set up a productive development environment Customize the Python prompt and deploy setuptools Write efficient syntax: iterators, generators, and decorators Build arguments by design and follow the best practices for working on API Build, release, and distribute your applications Write an application based on several eggs Distribute and deploy your application with zc.buildout Build and release your packages and set up a development cycle Manage your code with distributed version control and continuous integration Use an iterative and incremental approach to write software Practice Test-Driven Development Profile and optimize your code to speed up your programs Apply design patterns to your applications Chapter 1, Getting Started, explains how to install Python and makes sure all readers have the closest, standardized environment. Chapter 2, Syntax Best Practices—Below the Class Level, presents iterators, generators, descriptors and so on, in an advanced way. Chapter 3, Syntax Best Practices—Above the Class Level, is also about syntax best practices, but focuses on above the class level. Chapter 4, Choosing Good Names, is an extension to PEP 8 with naming best practices, but also gives tips on designing good APIs. Chapter 5, Writing a Package, explains how to write a package and how to use code templates, then focuses on how to release and distribute your code. Chapter 6, Writing an Application, extends Chapter 5 by describing how a full application can be written. It demonstrates it through a small case study called Atomisator. Chapter 7, Using zc.buildout, is about zc.buildout, a system for managing a development environment and releasing applications, which is widely used in the Zope and Plone community and is starting to be used outside the Zope world. Chapter 8, Managing Code, shows how your project code base can be managed with distributed instead of centralized version control and explains how to set up continuous integration. Chapter 9, Managing Life Cycle, presents how to manage software life cycle through an iterative and incremental approach. Chapter 10, Documenting Your Project, is about documentation and gives tips on technical writing and how Python projects should be documented. Chapter 11, Test-Driven Development, explains Test-Driven Development and the tools that can be used to do it. Chapter 12, Optimization—General Principle and Profiling Techniques, gives profiling techniques and an optimization strategy guideline. Chapter 13, Optimization—Solutions, extends Chapter 12 by providing some solutions to speed up your programs. Chapter 14, Useful Design Patterns, ends the book with a set of design patterns and when to use them. In Detail Python is a dynamic programming language, used in a wide range of domains by programmers who find it simple, yet powerful. From the earliest version 15 years ago to the current one, it has constantly evolved with productivity and code readability in mind. Even if you find writing Python code easy, writing code that is efficient and easy to maintain and reuse is not so straightforward. This book will show you how to do just that: it will show you how Python development should be done. Python expert Tarek Ziadé takes you on a practical tour of Python application development, beginning with setting up the best development environment, and along the way looking at agile methodologies in Python, and applying proven object-oriented principles to your design. Authors Tarek Ziadé Tarek Ziadé is CTO at Ingeniweb in Paris, working on Python, Zope, and Plone technology and on Quality Assurance. He has been involved for 5 years in the Zope community and has contributed to the Zope code itself. Tarek has also created Afpy, the French Python User Group and has written two books in French about Python. He has gave numerous talks and tutorials in French and international events like Solutions Linux, Pycon, OSCON, and EuroPython. Table of Contents Chapter 1: Getting started Chapter 2: Syntax Best Practices—Below the Class Level Chapter 3: Syntax Best Practices—Above the Class Level Chapter 4: Choosing Good Names Chapter 5: Writing a Package Chapter 6: Writing an Application Chapter 7: Working with zc.buildout Chapter 8: Managing Code Chapter 9: Managing Life Cycle Chapter 10: Documenting Your Project Chapter 11: Test-Driven Development Chapter 12: Optimization: General Principles and Profiling Techniques Chapter 13: Optimization: Solutions Chapter 14: Useful Design Patterns

What You Will Learn Get to know “the way of the cloud”, including why developing good cloud software is fundamentally about mindset and discipline Know what microservices are and how to design them Create reactive applications in the cloud with third-party messaging providers Build massive-scale, user-friendly GUIs with React and Flux Secure cloud-based web applications: the do’s, don’ts, and options Plan cloud apps that support continuous delivery and deployment Book Description Businesses today are evolving so rapidly that having their own infrastructure to support their expansion is not feasible. As a result, they have been resorting to the elasticity of the cloud to provide a platform to build and deploy their highly scalable applications. This book will be the one stop for you to learn all about building cloud-native architectures in Python. It will begin by introducing you to cloud-native architecture and will help break it down for you. Then you’ll learn how to build microservices in Python using REST APIs in an event driven approach and you will build the web layer. Next, you’ll learn about Interacting data services and building Web views with React, after which we will take a detailed look at application security and performance. Then, you’ll also learn how to Dockerize your services. And finally, you’ll learn how to deploy the application on the AWS and Azure platforms. We will end the book by discussing some concepts and techniques around troubleshooting problems that might occur with your applications after you’ve deployed them. This book will teach you how to craft applications that are built as small standard units, using all the proven best practices and avoiding the usual traps. It's a practical book: we're going to build everything using Python 3 and its amazing tooling ecosystem. The book will take you on a journey, the destination of which, is the creation of a complete Python application based on microservices over the cloud platform Authors Manish Sethi Manish Sethi works as an engineer in Bangalore, India. Over the course of his career, he has worked for startups and Fortune 10 companies, helping organizations adopt a cloud native approach to architecting massively scalable products. He regularly spends time learning and implementing new technology paradigms and actively finds himself solving practical problems using serverless architecture, machine and deep learning, and so on. He contributes to Bangalore DevOps and the Docker community by writing blog posts, giving talks in meetups, and so on. Table of Contents

What You Will Learn Run test cases from the command line with increased verbosity Write a Nose extension to pick tests based on regular expressions Create testable documentation using doctest Use Selenium to test the Web User Interface Write a testable story with Voidspace Mock and Nose Configure TeamCity to run Python tests on commit Update project-level scripts to provide coverage reports Book Description Automated testing is the best way to increase efficiency while reducing the defects of software testing. It helps find bugs in code easily and at an early stage so that they can be tackled efficiently. This book delves into essential testing concepts used in Python to help you build robust and maintainable code. Python Testing Cookbook begins with a brief introduction to Python's unit testing framework to help you write automated test cases. You will learn how to write suitable test sets for your software and run automated test suites with Nose. You will then work with the unittest.mock library, which allows you to replace the parts of your system that are being tested with mock objects and make assertions about how they have been used. You will also see how to apply Test-driven Development (TDD) and Behavior-driven Development (BDD) and how to eliminate issues caused by TDD. The book explains how to integrate automated tests using Continuous Integration and perform smoke/load testing. It also covers best practices and will help you solve persistent testing issues in Python. The book concludes by helping you understand how doctest works and how Selenium can be used to test code efficiently. Authors Greg L. Turnquist Greg L. Turnquist has worked in the software industry since 1997. He is an active participant in the open source community and has contributed patches to several projects, including MythTV, Spring Security, MediaWiki, and the TestNG Eclipse plugin. As a test-obsessed script junky, he has always sought the right tool for the job. He is a firm believer in agile practices and automated testing. He has developed distributed systems and LAMP-based setups, and he has supported mission-critical systems hosted on various platforms. After graduating from Auburn University with a master's in computer engineering, Greg started working with the Harris Corporation. He worked on many contracts utilizing many types of technology. In 2006, he created the Spring Python project and went on to write Spring Python 1.1 in 2010. He joined SpringSource, a division of VMware in 2010, as part of its international software development team. Bhaskar N. Das Bhaskar N. Das has 8 years' experience in various projects involving application development, maintenance, and support with IBM. He has worked in various technologies and domains including Java, Python, application servers, the cloud, and various database technologies. His domain expertise includes finance and asset management (IT and finance assets). His areas of interest include big data, business finance optimization and scaling, data science, and Machine Learning. Table of Contents Chapter 1: Using Unittest to Develop Basic Tests Chapter 2: Running Automated Test Suites with Nose Chapter 3: Creating Testable Documentation with doctest Chapter 4: Testing Customer Stories with Behavior-Driven Development Chapter 5: High-Level Customer Scenarios with Acceptance Testing Chapter 6: Integrating Automated Tests with Continuous Integration Chapter 7: Measuring Your Success with Test Coverage Chapter 8: Smoke/Load Testing – Testing Major Parts Chapter 9: Good Test Habits for New and Legacy Systems

What You Will Learn Interact with a social media platform via their public API with Python Store social data in a convenient format for data analysis Slice and dice social data using Python tools for data science Apply text analytics techniques to understand what people are talking about on social media Apply advanced statistical and analytical techniques to produce useful insights from data Build beautiful visualizations with web technologies to explore data and present data products Book Description Your social media is filled with a wealth of hidden data – unlock it with the power of Python. Transform your understanding of your clients and customers when you use Python to solve the problems of understanding consumer behavior and turning raw data into actionable customer insights. This book will help you acquire and analyze data from leading social media sites. It will show you how to employ scientific Python tools to mine popular social websites such as Facebook, Twitter, Quora, and more. Explore the Python libraries used for social media mining, and get the tips, tricks, and insider insight you need to make the most of them. Discover how to develop data mining tools that use a social media API, and how to create your own data analysis projects using Python for clear insight from your social data. Authors Marco Bonzanini Marco Bonzanini is a data scientist based in London, United Kingdom. He holds a PhD in information retrieval from Queen Mary University of London. He specializes in text analytics and search applications, and over the years, he has enjoyed working on a variety of information management and data science problems. He maintains a personal blog at http://marcobonzanini.com, where he discusses different technical topics, mainly around Python, text analytics, and data science. When not working on Python projects, he likes to engage with the community at PyData conferences and meet-ups, and he also enjoys brewing homemade beer. Table of Contents Chapter 1: Social Media, Social Data, and Python Chapter 2: #MiningTwitter – Hashtags, Topics, and Time Series Chapter 3: Users, Followers, and Communities on Twitter Chapter 4: Posts, Pages, and User Interactions on Facebook Chapter 5: Topic Analysis on Google+ Chapter 6: Questions and Answers on Stack Exchange Chapter 7: Blogs, RSS, Wikipedia, and Natural Language Processing Chapter 8: Mining All the Data! Chapter 9: Linked Data and the Semantic Web

What You Will Learn See the intricate details of the Python syntax and how to use it to your advantage Improve your code readability through functions in Python Manipulate data effectively using built-in data structures Get acquainted with advanced programming techniques in Python Equip yourself with functional and statistical programming features Write proper tests to be sure a program works as advertised Integrate application software using Python Book Description Python isthe preferred choice of developers, engineers, data scientists, and hobbyists. It is a great scripting language that can power your applications and provide great speed, safety, and scalability. By exposing Python as a series of simple recipes, you can gain insight into specific language features in a particular context. Having a tangible context helps make the language or standard library feature easier to understand. This book comes with over 100 recipes on the latest version of Python. The recipes will benefit everyone ranging from beginner to an expert. The book is broken down into 13 chapters that build from simple language concepts to more complex applications of the language. The recipes will touch upon all the necessary Python concepts related to data structures, OOP, functional programming, as well as statistical programming. You will get acquainted with the nuances of Python syntax and how to effectively use the advantages that it offers. You will end the book equipped with the knowledge of testing, web services, and configuration and application integration tips and tricks. The recipes take a problem-solution approach to resolve issues commonly faced by Python programmers across the globe. You will be armed with the knowledge of creating applications with flexible logging, powerful configuration, and command-line options, automated unit tests, and good documentation. Authors Steven F. Lott Steven F. Lott has been programming since the 70s, when computers were large, expensive, and rare. As a contract software developer and architect, he has worked on hundreds of projects from very small to very large. He's been using Python to solve business problems for over 10 years. He’s currently leveraging Python to implement microservices and ETL pipelines. His other titles with Packt include Python Essentials, Mastering Object-Oriented Python, Functional Python Programming, and Python for Secret Agents. Steven is currently a technomad who lives in various places on the east coast of the U.S. His technology blog is: http://slott-softwarearchitect.blogspot.com and his LinkedIn address is https://www.linkedin.com/in/steven-lott-029835. Table of Contents