Search results for "python"
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Introduction to Programming Concepts with Case Studies in Python
The current text provides a clear introduction to Computer Science concepts in a programming environment. It is designed as suitable use in freshman- or introductory level coursework in CS and provides the fundamental concepts as well as abstract theorems for solving computational problems. The Python language serves as a medium for illustrating and demonstrating the concepts.
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Python for Security and Networking
Gain a firm, practical understanding of securing your network and utilize Python’s packages to detect vulnerabilities in your applicationKey FeaturesDiscover security techniques to protect your network and systems using PythonCreate scripts in Python to automate security and pentesting tasksAnalyze traffic in a network and extract information using PythonBook DescriptionPython’s latest updates add numerous libraries that can be used to perform critical security-related missions, including detecting vulnerabilities in web applications, taking care of attacks, and helping to build secure and robust networks that are resilient to them. This fully updated third edition will show you how to make the most of them and improve your security posture.The first part of this book will walk you through Python scripts and libraries that you’ll use throughout the book. Next, you’ll dive deep into the core networking tasks where you will learn how to check a network’s vulnerability using Python security scripting and understand how to check for vulnerabilities in your network – including tasks related to packet sniffing. You’ll also learn how to achieve endpoint protection by leveraging Python packages along with writing forensics scripts.The next part of the book will show you a variety of modern techniques, libraries, and frameworks from the Python ecosystem that will help you extract data from servers and analyze the security in web applications. You’ll take your first steps in extracting data from a domain using OSINT tools and using Python tools to perform forensics tasks.By the end of this book, you will be able to make the most of Python to test the security of your network and applications.What you will learnProgram your own tools in Python that can be used in a Network Security processAutomate tasks of analysis and extraction of information from serversDetect server vulnerabilities and analyze security in web applicationsAutomate security and pentesting tasks by creating scripts with PythonUtilize the ssh-audit tool to check the security in SSH serversExplore WriteHat as a pentesting reports tool written in PythonAutomate the process of detecting vulnerabilities in applications with tools like FuxploiderWho this book is forThis Python book is for network engineers, system administrators, and other security professionals looking to overcome common networking and security issues using Python. You will also find this book useful if you're an experienced programmer looking to explore Python’s full range of capabilities. A basic understanding of general programming structures as well as familiarity with the Python programming language is a prerequisite.
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Artificial Intelligence with Python
Entering the field of artificial intelligence and data science can seem daunting to beginners with little to no prior background, especially those with no programming experience. The concepts used in self-driving cars and virtual assistants like Amazon’s Alexa may seem very complex and difficult to grasp. The aim of Artificial Intelligence in Python is to make AI accessible and easy to understand for people with little to no programming experience though practical exercises. Newcomers will gain the necessary knowledge on how to create such systems, which are capable of executing tasks that require some form of human-like intelligence.This book introduces readers to various topics and examples of programming in Python, as well as key concepts in artificial intelligence. Python programming skills will be imparted as we go along. Concepts and code snippets will be covered in a step-by-step manner, to guide and instill confidence in beginners. Complex subjects in deep learning and machine learning will be broken down into easy-to-digest content and examples. Artificial intelligence implementations will also be shared, allowing beginners to generate their own artificial intelligence algorithms for reinforcement learning, style transfer, chatbots, speech, and natural language processing.
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Python Programming for Data Analysis
This textbook grew out of notes for the ECE143 Programming for Data Analysis class that the author has been teaching at University of California, San Diego, which is a requirement for both graduate and undergraduate degrees in Machine Learning and Data Science. This book is ideal for readers with some Python programming experience. The book covers key language concepts that must be understood to program effectively, especially for data analysis applications. Certain low-level language features are discussed in detail, especially Python memory management and data structures. Using Python effectively means taking advantage of its vast ecosystem. The book discusses Python package management and how to use third-party modules as well as how to structure your own Python modules. The section on object-oriented programming explains features of the language that facilitate common programming patterns.After developing the key Python language features, the book moves on to third-party modules that are foundational for effective data analysis, starting with Numpy. The book develops key Numpy concepts and discusses internal Numpy array data structures and memory usage. Then, the author moves onto Pandas and details its many features for data processing and alignment. Because strong visualizations are important for communicating data analysis, key modules such as Matplotlib are developed in detail, along with web-based options such as Bokeh, Holoviews, Altair, and Plotly.The text is sprinkled with many tricks-of-the-trade that help avoid common pitfalls. The author explains the internal logic embodied in the Python language so that readers can get into the Python mindset and make better design choices in their codes, which is especially helpful for newcomers to both Python and data analysis. To get the most out of this book, open a Python interpreter and type along with the many code samples.
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Pythonic Programming
Make your good Python code even better by following proven and effective pythonic programming tips. Avoid logical errors that usually go undetected by Python linters and code formatters, such as frequent data look-ups in long lists, improper use of local and global variables, and mishandled user input. Discover rare language features, like rational numbers, set comprehensions, counters, and pickling, that may boost your productivity. Discover how to apply general programming patterns, including caching, in your Python code. Become a better-than-average Python programmer, and develop self-documented, maintainable, easy-to-understand programs that are fast to run and hard to break.Python is one of the most popular and rapidly growing modern programming languages. With more than 200 standard libraries and even more third-party libraries, it reaches into the software development areas as diverse as artificial intelligence, bioinformatics, natural language processing, and computer vision. Find out how to improve your understanding of the spirit of the language by using one hundred pythonic tips to make your code safer, faster, and better documented.This programming style manual is a quick reference of helpful hints and a random source of inspiration. Choose the suitable data structures for searching and sorting jobs and become aware of how a wrong choice may cause your application to be completely ineffective. Understand global and local variables, class and instance attributes, and information-hiding techniques. Create functions with flexible interfaces. Manage intermediate computation results by caching them in files and memory to improve performance and reliability. Polish your documentation skills to make your code easy for other programmers to understand. As a bonus, discover Easter eggs cleverly planted in the standard library by its developers.Polish, secure, and speed-up your Python applications, and make them easier to maintain by following pythonic programming tips.What You Need:You will need a Python interpreter (ideally, version 3.4 or above) and the standard Python library that usually comes with the interpreter.
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Python in Excel Step-by-Step
An intuitive guide for professionals wanting to prepare for the future of Microsoft Excel by building Python in Excel skills and unleashing the power of their data. A hands-on guide to the foundational Python in Excel skills you’ll need to understand and use this powerful analytics tool, Python in Excel Step-by-Step is for current Excel users interested in expanding their data analysis skillset with Python. Analytics educator and Microsoft Excel MVP David Langer demonstrates how to use Python in Excel, tounlock new analytics capabilities in Excel, and build your foundation for the future of Excel: do-it-yourself (DIY) data science. The book leverages your existing Excel knowledge to learn the Python foundation you can apply right away. This is the same approach David has used to successfully teach more than 1,000 professionals Python – even if you’ve never written code before. David also includes: Targeted coverage of the Python fundamentals required for analytics – learn just what you need fastHow to use the powerful pandas and plotnine libraries to facilitate data manipulation and visualization using Python in ExcelA DIY data science roadmap for you to build the skills you need to unleash the power of your data to have more impact at work Perfect for professionals use Microsoft Excel for data analysis, like marketing managers, financial analysts, and supply chain manager, Python in Excel Step-by-Step is an invaluable new resource for all business professionals who use Excel and want to build skills for Excel’s AI-powered future.
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Classic Computer Science Problems in Python
Classic Computer Science Problems in Python presents dozens of coding challenges, ranging from simple tasks like finding items in a list with a binary sort algorithm to clustering data using k-means. Classic Computer Science Problems in Python deepens your Python language skills by challenging you with time-tested scenarios, exercises, and algorithms. As you work through examples in search, clustering, graphs, and more, you'll remember important things you've forgotten and discover classic solutions to your "new" problems Key Features · Breadth-first and depth-first search algorithms · Constraints satisfaction problems · Common techniques for graphs · Adversarial Search · Neural networks and genetic algorithms · Written for data engineers and scientists with experience using Python. For readers comfortable with the basics of Python About the technology Python is used everywhere for web applications, data munging, and powerful machine learning applications. Even problems that seem new or unique stand on the shoulders of classic algorithms, coding techniques, and engineering principles. Master these core skills, and you’ll be ready to use Python for AI, data-centric programming, deep learning, and the other challenges you’ll face as you grow your skill as a programmer. David Kopec teaches at Champlain College in Burlington, VT and is the author of Manning’s Classic Computer Science Problemsin Swift.
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Python for DevOps
Much has changed in technology over the past decade. Data is hot, the cloud is ubiquitous, and many organizations need some form of automation. Throughout these transformations, Python has become one of the most popular languages in the world. This practical resource shows you how to use Python for everyday Linux systems administration tasks with today’s most useful DevOps tools, including Docker, Kubernetes, and Terraform.Learning how to interact and automate with Linux is essential for millions of professionals. Python makes it much easier. With this book, you’ll learn how to develop software and solve problems using containers, as well as how to monitor, instrument, load-test, and operationalize your software. Looking for effective ways to "get stuff done" in Python? This is your guide.Python foundations, including a brief introduction to the languageHow to automate text, write command-line tools, and automate the filesystemLinux utilities, package management, build systems, monitoring and instrumentation, and automated testingCloud computing, infrastructure as code, Kubernetes, and serverlessMachine learning operations and data engineering from a DevOps perspectiveBuilding, deploying, and operationalizing a machine learning project
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Modular Programming with Python
Introducing modular techniques for building sophisticated programs using PythonAbout This Book• The book would help you develop succinct, expressive programs using modular deign• The book would explain best practices and common idioms through carefully explained and structured examples• It will have broad appeal as far as target audience is concerned and there would be take away for all beginners to PythonWho This Book Is ForThis book is intended for beginner to intermediate level Python programmers who wish to learn how to use modules and packages within their programs. While readers must understand the basics of Python programming, no knowledge of modular programming techniques is required.What You Will Learn• Learn how to use modules and packages to organize your Python code• Understand how to use the import statement to load modules and packages into your program• Use common module patterns such as abstraction and encapsulation to write better programs• Discover how to create self-testing Python packages• Create reusable modules that other programmers can use• Learn how to use GitHub and the Python Package Index to share your code with other people• Make use of modules and packages that others have written• Use modular techniques to build robust systems that can handle complexity and changing requirements over timeIn DetailPython has evolved over the years and has become the primary choice of developers in various fields. The purpose of this book is to help readers develop readable, reliable, and maintainable programs in Python.Starting with an introduction to the concept of modules and packages, this book shows how you can use these building blocks to organize a complex program into logical parts and make sure those parts are working correctly together.Using clearly written, real-world examples, this book demonstrates how you can use modular techniques to build better programs. A number of common modular programming patterns are covered, including divide-and-conquer, abstraction, encapsulation, wrappers and extensibility. You will also learn how to test your modules and packages, how to prepare your code for sharing with other people, and how to publish your modules and packages on GitHub and the Python Package Index so that other people can use them. Finally, you will learn how to use modular design techniques to be a more effective programmer.Style and approachThis book will be simple and straightforward, focusing on imparting learning through a wide array of examples that the readers can put into use as they read through the book. They should not only be able to understand the way modules help in improving development, but they should also be able to improvise on their techniques of writing concise and effective code.
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Python For ArcGIS
This book introduces Python scripting for geographic information science (GIS) workflow optimization using ArcGIS. It builds essential programming skills for automating GIS analysis. Over 200 sample Python scripts and 175 classroom-tested exercises reinforce the learning objectives. Readers will learn to: • Write and run Python in the ArcGIS Python Window, the PythonWin IDE, and the PyScripter IDE • Work with Python syntax and data types • Call ArcToolbox tools, batch process GIS datasets, and manipulate map documents using the arcpy package • Read and modify proprietary and ASCII text GIS data • Parse HTML web pages and KML datasets • Create Web pages and fetch GIS data from Web sources. • Build user-interfaces with the native Python file dialog toolkit or the ArcGIS Script tools and PyToolboxes Python for ArcGIS is designed as a primary textbook for advanced-level students in GIS. Researchers, government specialists and professionals working in GIS will also find this book useful as a reference.
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