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Pro Python 3
Refine your programming techniques and approaches to become a more productive and creative Python programmer. This book explores the concepts and features that will improve not only your code but also your understanding of the Python community with insights and details about the Python philosophy.Pro Python 3, Third Edition gives you the tools to write clean, innovative code. It starts with a review of some core Python principles, which are illustrated by various concepts and examples later in the book. The first half of the book explores aspects of functions, classes, protocols, and strings, describing techniques which may not be common knowledge, but which together form a solid foundation. Later chapters cover documentation, testing, and app distribution. Along the way, you’ll develop a complex Python framework that incorporates ideas learned throughout the book.Updates in this edition include the role of iterators in Python 3, web scraping with Scrapy and BeautifulSoup, using Requests to call web pages without strings, new tools for distribution and installation, and much more. By the end of the book you'll be ready to deploy uncommon features that can take your skills to the next level in Python.What You’ll LearnImplement programs with various types of Python functionsWork with classes and object-oriented programmingUse strings from the standard library and third-party librariesHarvest web site data with PythonAutomate unit testing by writing a test suiteReview imaging, random number generation, and NumPy scientific extensionsUnderstand The Zen of Python documentation to help you decide the best way to distribute your codeWho This Book Is ForIntermediate programmers familiar with Python who are looking to move to an advanced level. You should have written at least a simple Python application, and be comfortable with a basic object-oriented approach, using the interactive interpreter, and writing control structures.
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Introduction to Python Programming for Scientists and Engineers
Python is one of the most popular programming languages, widely used for data analysis and modelling, and is fast becoming the leading choice for scientists and engineers. Unlike other textbooks introducing Python, typically organised by language syntax, this book uses many examples from across Biology, Chemistry, Physics, Earth science, and Engineering to teach and motivate students in science and engineering. The text is organised by the tasks and workflows students undertake day-to-day, helping them see the connections between programming tools and their disciplines. The pace of study is carefully developed for complete beginners, and a spiral pedagogy is used so concepts are introduced across multiple chapters, allowing readers to engage with topics more than once. “Try This!” exercises and online Jupyter notebooks encourage students to test their new knowledge, and further develop their programming skills. Online solutions are available for instructors, alongside discipline-specific homework problems across the sciences and engineering.
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Python for Engineers and Scientists
This book equips technical professionals with a complete, structured path to mastering Python for real-world engineering and scientific applications. As Python becomes central to research labs, automation, simulation, and data-driven engineering, the learning curve remains steep. This book bridges that gap with a practical roadmap from fundamentals to professional-grade scientific computing.The book begins by building a strong programming foundation—covering Python’s architecture, essential syntax, data structures, modules, functions, control flow, and object-oriented design. Readers learn not only how to write Python, but why the language behaves the way it does, ensuring deep understanding and strong problem-solving skills. From there, the focus shifts to practical engineering tools. Dedicated chapters train readers in file handling, math modules, and key scientific libraries including NumPy, SciPy, Pandas, and Matplotlib, enabling efficient computation, signal processing, data analysis, and high-quality visualization. Advanced chapters then showcase Python’s versatility through GUI development with Kivy, database work with SQLAlchemy, and hands-on control system modeling and analysis. Each topic is rooted in industry and academic scenarios, making the content directly applicable to automation, simulation, data workflows, and engineering computation.Whether you're a student building foundational skills or a professional modernizing engineering workflows, this guide provides the programming depth, scientific tools, and applied knowledge needed to use Python confidently for prototyping, simulation, analysis, and automation. It is the one-stop resource for mastering Python in technical disciplines.What you will learn:Learn core Python programming concepts, including variables, loops, functions, and object-oriented principles in a scientific and engineering context.Build strong skills in key scientific Python libraries such as NumPy, Pandas, Matplotlib, and SciPy for computation, analysis, and visualization.Apply Python to real-world engineering scenarios, including dynamic control systems and automation workflows.Develop interactive engineering applications by creating graphical user interfaces (GUIs) with Kivy. Who this book is for:This book is intended for practicing engineers and scientists across various disciplines of engineering who need to use Python for complex problem-solving, simulation, and data analysis in their work.
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Python for Experimental Psychologists
Python for Experimental Psychologists equips researchers who have no prior programming experience with the essential knowledge to independently script experiments and analyses in the programming language Python. The book offers an excellent introduction, whether you are an undergraduate, a PhD candidate, or an established researcher.This updated edition is on Python 3 (the most current version). It starts by teaching the fundamentals of programming in Python, and then offers several chapters on scripting experiments (displaying stimuli, obtaining and logging user input, precision timing, etc.) using the popular PsychoPy package. The remainder of the book is dedicated to data analysis, and includes chapters on reading/writing to text files, time series, eye tracking, data visualisation, and statistics.Access to a companion website enriches the learning experience with colour figures, example stimuli, datasets, scripts, and a portable Windows installation of Python. The book assumes no prior knowledge, and its informal and accessible tone helps readers with backgrounds in experimental psychology and cognitive neuroscience to quickly understand Python. It serves as a useful resource not only for researchers in these fields, but also for lecturers instructing on methodology and data analysis.Python for Experimental Psychologists demystifies programming complexities, and empowers researchers to proficiently conduct experiments and to analyse their results.
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Mastering Python for Networking and Security
Master Python scripting to build a network and perform security operationsKey FeaturesLearn to handle cyber attacks with modern Python scriptingDiscover various Python libraries for building and securing your networkUnderstand Python packages and libraries to secure your network infrastructureBook DescriptionIt’s becoming more and more apparent that security is a critical aspect of IT infrastructure. A data breach is a major security incident, usually carried out by just hacking a simple network line. Increasing your network’s security helps step up your defenses against cyber attacks. Meanwhile, Python is being used for increasingly advanced tasks, with the latest update introducing many new packages. This book focuses on leveraging these updated packages to build a secure network with the help of Python scripting. This book covers topics from building a network to the different procedures you need to follow to secure it. You’ll first be introduced to different packages and libraries, before moving on to different ways to build a network with the help of Python scripting. Later, you will learn how to check a network’s vulnerability using Python security scripting, and understand how to check vulnerabilities in your network. As you progress through the chapters, you will also learn how to achieve endpoint protection by leveraging Python packages along with writing forensic scripts. By the end of this book, you will be able to get the most out of the Python language to build secure and robust networks that are resilient to attacks.What you will learnDevelop Python scripts for automating security and pentesting tasksDiscover the Python standard library's main modules used for performing security-related tasksAutomate analytical tasks and the extraction of information from serversExplore processes for detecting and exploiting vulnerabilities in serversUse network software for Python programmingPerform server scripting and port scanning with PythonIdentify vulnerabilities in web applications with PythonUse Python to extract metadata and forensicsWho this book is forThis book is ideal for network engineers, system administrators, or any security professional looking at tackling networking and security challenges. Programmers with some prior experience in Python will get the most out of this book. Some basic understanding of general programming structures and Python is required.
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Python for Engineers and Scientists
The text focuses on the basics of Python programming fundamentals and introduction to present-day applications in technology and the upcoming state-of-art trends in a comprehensive manner. The text is based on Python 3.x and it covers the fundamentals of Python with object-oriented concepts having numerous worked-out examples. It provides a learning tool for the students of beginner level as well as for researchers of advanced level. Each chapter contains additional examples that explain the usage of methods/functions discussed in the chapter. It provides numerous programming examples along with their outputs.The book:Includes programming tips to highlight the important concepts and help readers avoid common programming errorsProvides programming examples along with their outputs to ensure the correctness and help readers in mastering the art of writing efficient Python programsContains MCQs with their answers; conceptual questions and programming questions; and solutions to some selected programming questions, for every chapterDiscusses applications like time zone converter and password generators at the endCovers fundamental of Python up to object oriented concepts including regular expressionThe book offers a simple and lucid treatment of concepts supported with illustrations for easy understanding, provides numerous programming examples along with their outputs, and includes programming tips to highlight the important concepts. It will be a valuable resource for senior undergraduate, graduate students, and professionals in the fields of electrical engineering, electronics and communication engineering, and computer engineering.
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Python for Engineers and Scientists
The text focuses on the basics of Python programming fundamentals and introduction to present-day applications in technology and the upcoming state-of-art trends in a comprehensive manner. The text is based on Python 3.x and it covers the fundamentals of Python with object-oriented concepts having numerous worked-out examples. It provides a learning tool for the students of beginner level as well as for researchers of advanced level. Each chapter contains additional examples that explain the usage of methods/functions discussed in the chapter. It provides numerous programming examples along with their outputs.The book:Includes programming tips to highlight the important concepts and help readers avoid common programming errorsProvides programming examples along with their outputs to ensure the correctness and help readers in mastering the art of writing efficient Python programsContains MCQs with their answers; conceptual questions and programming questions; and solutions to some selected programming questions, for every chapterDiscusses applications like time zone converter and password generators at the endCovers fundamental of Python up to object oriented concepts including regular expressionThe book offers a simple and lucid treatment of concepts supported with illustrations for easy understanding, provides numerous programming examples along with their outputs, and includes programming tips to highlight the important concepts. It will be a valuable resource for senior undergraduate, graduate students, and professionals in the fields of electrical engineering, electronics and communication engineering, and computer engineering.
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Parametric and Generative Design with Rhino Python and Grasshopper
Learn how to create simple to complex parametric models with Rhino Python and Grasshopper (GH) in your form finding and design exploration. Tap into the generative power of Rhino via Grasshopper’s intuitive drag-n-drop visual scripting interface and combine GH nodes with Python to generate virtually any geometry.You’ll start by working through a series of bite-sized exercises to get up-to-speed with basics like creating curves and surfaces, utilizing input widgets, working with lists and data trees, and editing geometry with operations like loft, pipe, and extrude. Then you’ll elevate your skills by learning how to generate popular effects like attractors, tessellations, spirals andloops, stacking and voxelizing, cracks and wireframes, plus their variations. After getting comfortable with GH essentials, in the second half of the book you’ll work through five complex application workshops—(1) Generating basket and textile weaved versions of a 3D model; (2) Creating sliceforms from meshes by cutting interlocking cross sections at various angles and intervals; (3) Making tiles, lithophanes, and molds by generating Breps from images, and projecting them onto other objects as decorative elements; (4) Generating fractals and tree-like canopies from L-systems, and (5) Creating city skyline models from building footprints (GIS data). With five applications under your belt, you’ll wrap up the book by learning how to package Rhino Python and Grasshopper scripts into plugins suitable for online publication.Whether you are interested in architecture, landscape, industrial, product, jewelry, fashion, or naval design, Parametric & Generative Design with Rhino Python and Grasshopper will add many tools to your generative repertoire.You Will Learn to· Generate a wide range of parametric models using Grasshopper and Python scripting· Produce models from images and project them onto other objects as decorative elements· Source building footprints (GIS) data and generate city skyline models from themWho Is This Book ForThis book is for Rhino users who want to learn Grasshopper and Python for parametric design and form finding; some basic knowledge of Python is helpful but not required
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Learning IPython for Interactive Computing and Data Visualization -
Get started with Python for data analysis and numerical computing in the Jupyter notebookAbout This Book• Learn the basics of Python in the Jupyter Notebook• Analyze and visualize data with pandas, NumPy, matplotlib, and seaborn• Perform highly-efficient numerical computations with Numba, Cython, and ipyparallelWho This Book Is ForThis book targets students, teachers, researchers, engineers, analysts, journalists, hobbyists, and all data enthusiasts who are interested in analyzing and visualizing real-world datasets. If you are new to programming and data analysis, this book is exactly for you. If you're already familiar with another language or analysis software, you will also appreciate this introduction to the Python data analysis platform. Finally, there are more technical topics for advanced readers. No prior experience is required; this book contains everything you need to know.What You Will Learn• Install Anaconda and code in Python in the Jupyter Notebook• Load and explore datasets interactively• Perform complex data manipulations effectively with pandas• Create engaging data visualizations with matplotlib and seaborn• Simulate mathematical models with NumPy• Visualize and process images interactively in the Jupyter Notebook with scikit-image• Accelerate your code with Numba, Cython, and IPython.parallel• Extend the Notebook interface with HTML, JavaScript, and D3In DetailPython is a user-friendly and powerful programming language. IPython offers a convenient interface to the language and its analysis libraries, while the Jupyter Notebook is a rich environment well-adapted to data science and visualization. Together, these open source tools are widely used by beginners and experts around the world, and in a huge variety of fields and endeavors.This book is a beginner-friendly guide to the Python data analysis platform. After an introduction to the Python language, IPython, and the Jupyter Notebook, you will learn how to analyze and visualize data on real-world examples, how to create graphical user interfaces for image processing in the Notebook, and how to perform fast numerical computations for scientific simulations with NumPy, Numba, Cython, and ipyparallel. By the end of this book, you will be able to perform in-depth analyses of all sorts of data.Style and approachThis is a hands-on beginner-friendly guide to analyze and visualize data on real-world examples with Python and the Jupyter Notebook.
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Python for Experimental Psychologists
Python for Experimental Psychologists equips researchers who have no prior programming experience with the essential knowledge to independently script experiments and analyses in the programming language Python. The book offers an excellent introduction, whether you are an undergraduate, a PhD candidate, or an established researcher.This updated edition is on Python 3 (the most current version). It starts by teaching the fundamentals of programming in Python, and then offers several chapters on scripting experiments (displaying stimuli, obtaining and logging user input, precision timing, etc.) using the popular PsychoPy package. The remainder of the book is dedicated to data analysis, and includes chapters on reading/writing to text files, time series, eye tracking, data visualisation, and statistics.Access to a companion website enriches the learning experience with colour figures, example stimuli, datasets, scripts, and a portable Windows installation of Python. The book assumes no prior knowledge, and its informal and accessible tone helps readers with backgrounds in experimental psychology and cognitive neuroscience to quickly understand Python. It serves as a useful resource not only for researchers in these fields, but also for lecturers instructing on methodology and data analysis.Python for Experimental Psychologists demystifies programming complexities, and empowers researchers to proficiently conduct experiments and to analyse their results.
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Python for the Life Sciences
Treat yourself to a lively, intuitive, and easy-to-follow introduction to computer programming in Python. The book was written specifically for biologists with little or no prior experience of writing code - with the goal of giving them not only a foundation in Python programming, but also the confidence and inspiration to start using Python in their own research. Virtually all of the examples in the book are drawn from across a wide spectrum of life science research, from simple biochemical calculations and sequence analysis, to modeling the dynamic interactions of genes and proteins in cells, or the drift of genes in an evolving population. Best of all, Python for the Life Sciences shows you how to implement all of these projects in Python, one of the most popular programming languages for scientific computing. If you are a life scientist interested in learning Python to jump-start your research, this is the book for you.What You'll Learn Write Python scripts to automate your lab calculationsSearch for important motifs in genome sequencesUse object-oriented programming with PythonStudy mining interaction network data for patternsReview dynamic modeling of biochemical switchesWho This Book Is ForLife scientists with little or no programming experience, including undergraduate and graduate students, postdoctoral researchers in academia and industry, medical professionals, and teachers/lecturers.“A comprehensive introduction to using Python for computational biology... A lovely book with humor and perspective” -- John Novembre, Associate Professor of Human Genetics, University of Chicago and MacArthur Fellow“Fun, entertaining, witty and darn useful. A magical portal to the big data revolution” -- Sandro Santagata, Assistant Professor in Pathology, Harvard Medical School“Alex and Gordon’s enthusiasm for Python is contagious” -- Glenys Thomson Professor of Integrative Biology, University of California, Berkeley
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Python: Penetration Testing for Developers
Unleash the power of Python scripting to execute effective and efficient penetration testsAbout This Book* Sharpen your pentesting skills with Python* Develop your fluency with Python to write sharper scripts for rigorous security testing* Get stuck into some of the most powerful tools in the security worldWho This Book Is ForIf you are a Python programmer or a security researcher who has basic knowledge of Python programming and wants to learn about penetration testing with the help of Python, this course is ideal for you. Even if you are new to the field of ethical hacking, this course can help you find the vulnerabilities in your system so that you are ready to tackle any kind of attack or intrusion.What You Will Learn* Familiarize yourself with the generation of Metasploit resource files and use the Metasploit Remote Procedure Call to automate exploit generation and execution* Exploit the Remote File Inclusion to gain administrative access to systems with Python and other scripting languages* Crack an organization's Internet perimeter and chain exploits to gain deeper access to an organization's resources* Explore wireless traffic with the help of various programs and perform wireless attacks with Python programs* Gather passive information from a website using automated scripts and perform XSS, SQL injection, and parameter tampering attacks* Develop complicated header-based attacks through PythonIn DetailCybercriminals are always one step ahead, when it comes to tools and techniques. This means you need to use the same tools and adopt the same mindset to properly secure your software. This course shows you how to do just that, demonstrating how effective Python can be for powerful pentesting that keeps your software safe. Comprising of three key modules, follow each one to push your Python and security skills to the next level.In the first module, we'll show you how to get to grips with the fundamentals. This means you'll quickly find out how to tackle some of the common challenges facing pentesters using custom Python tools designed specifically for your needs. You'll also learn what tools to use and when, giving you complete confidence when deploying your pentester tools to combat any potential threat.In the next module you'll begin hacking into the application layer. Covering everything from parameter tampering, DDoS, XXS and SQL injection, it will build on the knowledge and skills you learned in the first module to make you an even more fluent security expert.Finally in the third module, you'll find more than 60 Python pentesting recipes. We think this will soon become your trusted resource for any pentesting situation.This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:* Learning Penetration Testing with Python by Christopher Duffy* Python Penetration Testing Essentials by Mohit* Python Web Penetration Testing Cookbook by
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Python Tools for Data Scientists
As part of the best-selling Pocket Primer series, this book is designed to provide an introduction to Python tools which are used by data scientists. It includes coverage of fundamental aspects of NumPy and Pandas, how to write regular expressions, and how to perform data cleaning tasks. The first chapter contains a quick tour of basic Python, followed by a chapter introducing NumPy, and a chapter on Pandas. Chapter 4 provides a high-level view of Sklearn and SciPy. Chapter 5 contains an assortment of data cleaning tasks that are solved via Python and the awk programming language. Chapter 6 delves into data visualization with Matplotlib, Seaborn, and Bokeh. Next, one appendix explores issues that can arise with data, followed by an appendix on awk. Numerous code samples are used to illustrate concepts. Companion files with source code are available for downloading from the publisher. FEATURES:Introduces Python, NumPy, Sklearn, SciPy, and awk.Covers data cleaning tasks and data visualization.Features numerous code samples throughout.Includes companion files with source code .
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Data Visualization with Python and JavaScript 2e
How do you turn raw, unprocessed, or malformed data into dynamic, interactive web visualizations? In this practical book, author Kyran Dale shows data scientists and analysts--as well as Python and JavaScript developers--how to create the ideal toolchain for the job. By providing engaging examples and stressing hard-earned best practices, this guide teaches you how to leverage the power of best-of-breed Python and JavaScript libraries. Python provides accessible, powerful, and mature libraries for scraping, cleaning, and processing data. And while JavaScript is the best language when it comes to programming web visualizations, its data processing abilities can't compare with Python's. Together, these two languages are a perfect complement for creating a modern web-visualization toolchain. This book gets you started. You'll learn how to: Obtain data you need programmatically, using scraping tools or web APIs: Requests, Scrapy, Beautiful Soup Clean and process data using Python's heavyweight data processing libraries within the NumPy ecosystem: Jupyter notebooks with pandas+Matplotlib+Seaborn Deliver the data to a browser with static files or by using Flask, the lightweight Python server, and a RESTful API Pick up enough web development skills (HTML, CSS, JS) to get your visualized data on the web Use the data you've mined and refined to create web charts and visualizations with Plotly, D3, Leaflet, and other libraries
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R 977.00
Mastering Python for Networking and Security
Tackle security and networking issues using Python libraries such as Nmap, requests, asyncio, and scapyKey FeaturesEnhance your Python programming skills in securing systems and executing networking tasksExplore Python scripts to debug and secure complex networksLearn to avoid common cyber events with modern Python scriptingBook DescriptionIt's now more apparent than ever that security is a critical aspect of IT infrastructure, and that devastating data breaches can occur from simple network line hacks. As shown in this book, combining the latest version of Python with an increased focus on network security can help you to level up your defenses against cyber attacks and cyber threats. Python is being used for increasingly advanced tasks, with the latest update introducing new libraries and packages featured in the Python 3.7.4 recommended version. Moreover, most scripts are compatible with the latest versions of Python and can also be executed in a virtual environment. This book will guide you through using these updated packages to build a secure network with the help of Python scripting. You'll cover a range of topics, from building a network to the procedures you need to follow to secure it. Starting by exploring different packages and libraries, you'll learn about various ways to build a network and connect with the Tor network through Python scripting. You will also learn how to assess a network's vulnerabilities using Python security scripting. Later, you'll learn how to achieve endpoint protection by leveraging Python packages, along with writing forensic scripts. By the end of this Python book, you'll be able to use Python to build secure apps using cryptography and steganography techniques.What you will learnCreate scripts in Python to automate security and pentesting tasksExplore Python programming tools that are used in network security processesAutomate tasks such as analyzing and extracting information from serversUnderstand how to detect server vulnerabilities and analyze security modulesDiscover ways to connect to and get information from the Tor networkFocus on how to extract information with Python forensics toolsWho this book is forThis Python network security book is for network engineers, system administrators, or any security professional looking to overcome networking and security challenges. You will also find this book useful if you're a programmer with prior experience in Python. A basic understanding of general programming structures and the Python programming language is required before getting started.
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Learn Python by Building Data Science Applications
Understand the constructs of the Python programming language and use them to build data science projectsKey FeaturesLearn the basics of developing applications with Python and deploy your first data applicationTake your first steps in Python programming by understanding and using data structures, variables, and loopsDelve into Jupyter, NumPy, Pandas, SciPy, and sklearn to explore the data science ecosystem in PythonBook DescriptionPython is the most widely used programming language for building data science applications. Complete with step-by-step instructions, this book contains easy-to-follow tutorials to help you learn Python and develop real-world data science projects. The “secret sauce” of the book is its curated list of topics and solutions, put together using a range of real-world projects, covering initial data collection, data analysis, and production.This Python book starts by taking you through the basics of programming, right from variables and data types to classes and functions. You’ll learn how to write idiomatic code and test and debug it, and discover how you can create packages or use the range of built-in ones. You’ll also be introduced to the extensive ecosystem of Python data science packages, including NumPy, Pandas, scikit-learn, Altair, and Datashader. Furthermore, you’ll be able to perform data analysis, train models, and interpret and communicate the results. Finally, you’ll get to grips with structuring and scheduling scripts using Luigi and sharing your machine learning models with the world as a microservice.By the end of the book, you’ll have learned not only how to implement Python in data science projects, but also how to maintain and design them to meet high programming standards.What you will learnCode in Python using Jupyter and VS CodeExplore the basics of coding – loops, variables, functions, and classesDeploy continuous integration with Git, Bash, and DVCGet to grips with Pandas, NumPy, and scikit-learnPerform data visualization with Matplotlib, Altair, and DatashaderCreate a package out of your code using poetry and test it with PyTestMake your machine learning model accessible to anyone with the web APIWho this book is forIf you want to learn Python or data science in a fun and engaging way, this book is for you. You’ll also find this book useful if you’re a high school student, researcher, analyst, or anyone with little or no coding experience with an interest in the subject and courage to learn, fail, and learn from failing. A basic understanding of how computers work will be useful.
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Python for Scientific Computing and Artificial Intelligence
Python for Scientific Computing and Artificial Intelligence is split into 3 parts: in Section 1, the reader is introduced to the Python programming language and shown how Python can aid in the understanding of advanced High School Mathematics. In Section 2, the reader is shown how Python can be used to solve real-world problems from a broad range of scientific disciplines. Finally, in Section 3, the reader is introduced to neural networks and shown how TensorFlow (written in Python) can be used to solve a large array of problems in Artificial Intelligence (AI).This book was developed from a series of national and international workshops that the author has been delivering for over twenty years. The book is beginner friendly and has a strong practical emphasis on programming and computational modelling.Features:No prior experience of programming is requiredOnline GitHub repository available with codes for readers to practiceCovers applications and examples from biology, chemistry, computer science, data science, electrical and mechanical engineering, economics, mathematics, physics, statistics and binary oscillator computingFull solutions to exercises are available as Jupyter notebooks on the WebSupport MaterialGitHub Repository of Python Files and Notebooks: https://github.com/proflynch/CRC-Press/Solutions to All Exercises:Section 1: An Introduction to Python: https://drstephenlynch.github.io/webpages/SolutionsSection1.htmlSection 2: Python for Scientific Computing: https://drstephenlynch.github.io/webpages/SolutionsSection2.htmlSection 3: Artificial Intelligence: https://drstephenlynch.github.io/webpages/SolutionsSection3.html
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R 916.00
Python for Data Analysis 3e
Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process.Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.Use the Jupyter notebook and IPython shell for exploratory computingLearn basic and advanced features in NumPyGet started with data analysis tools in the pandas libraryUse flexible tools to load, clean, transform, merge, and reshape dataCreate informative visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsAnalyze and manipulate regular and irregular time series dataLearn how to solve real-world data analysis problems with thorough, detailed examples
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Foundations for Analytics with Python
If you're like many of Excel's 750 million users, you want to do more with your data-like repeating similar analyses over hundreds of files, or combining data in many files for analysis at one time. This practical guide shows ambitious non-programmers how to automate and scale the processing and analysis of data in different formats-by using Python. After author Clinton Brownley takes you through Python basics, you'll be able to write simple scripts for processing data in spreadsheets as well as databases. You'll also learn how to use several Python modules for parsing files, grouping data, and producing statistics. No programming experience is necessary.Create and run your own Python scripts by learning basic syntax Use Python's csv module to read and parse CSV files Read multiple Excel worksheets and workbooks with the xlrd module Perform database operations in MySQL or with the mysqlclient module Create Python applications to find specific records, group data, and parse text files Build statistical graphs and plots with matplotlib, pandas, ggplot, and seaborn Produce summary statistics, and estimate regression and classification models Schedule your scripts to run automatically in both Windows and Mac environments
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Introduction to Python for Science and Engineering
Introduction to Python for Science and Engineering offers a quick and incisive introduction to the Python programming language for use in any science or engineering discipline. The approach is pedagogical and “bottom up,” which means starting with examples and extracting more general principles from that experience. No prior programming experience is assumed.Readers will learn the basics of Python syntax, data structures, input and output, conditionals and loops, user-defined functions, plotting, animation, and visualization. They will also learn how to use Python for numerical analysis, including curve fitting, random numbers, linear algebra, solutions to nonlinear equations, numerical integration, solutions to differential equations, and fast Fourier transforms.Readers learn how to interact and program with Python using JupyterLab and Spyder, two simple and widely used integrated development environments.All the major Python libraries for science and engineering are covered, including NumPy, SciPy, Matplotlib, and Pandas. Other packages are also introduced, including Numba, which can render Python numerical calculations as fast as compiled computer languages such as C but without their complex overhead.
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R 550.00
R and Python for Oceanographers
R and Python for Oceanographers: A Practical Guide with Applications describes the uses of scientific Python packages and R in oceanographic data analysis, including both script codes and graphic outputs. Each chapter begins with theoretical background that is followed by step-by-step examples of software applications, including scripts, graphics, tables and practical exercises for better understanding of the subject. Examples include frequently used data analysis approaches in physical and chemical oceanography, but also contain topics on data import/export and GIS mapping. The examples seen in book provide uses of the latest versions of Python and R libraries.
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