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Author: Aquiles Carattino
Publisher:
ISBN: 9781716517686
Category :
Languages : en
Pages : 190
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Book Description
Python for the Lab is the first book covering how to develop instrumentation software. It is ideal for researchers willing to automatize their setups and bring their experiments to the next level. The book is the product of countless workshops at different universities, and a carefully design pedagogical strategy. With an easy to follow and task-oriented design, the book uncovers all the best practices in the field. It also shows how to design code for long-term maintainability, opening the doors of fruitful collaboration among researchers from different labs.
Author: Aquiles Carattino
Publisher:
ISBN: 9781716517686
Category :
Languages : en
Pages : 190
Get Book Here
Book Description
Python for the Lab is the first book covering how to develop instrumentation software. It is ideal for researchers willing to automatize their setups and bring their experiments to the next level. The book is the product of countless workshops at different universities, and a carefully design pedagogical strategy. With an easy to follow and task-oriented design, the book uncovers all the best practices in the field. It also shows how to design code for long-term maintainability, opening the doors of fruitful collaboration among researchers from different labs.
Author: Jake VanderPlas
Publisher: "O'Reilly Media, Inc."
ISBN: 1491912138
Category : Computers
Languages : en
Pages : 609
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Book Description
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
Author: Gareth James
Publisher: Springer Nature
ISBN: 3031387473
Category : Mathematics
Languages : en
Pages : 617
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Book Description
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
Author: John Essick
Publisher: Oxford University Press
ISBN: 0198897006
Category : Technology & Engineering
Languages : en
Pages : 433
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Book Description
The Python-Based Laboratory: A Hands-On Guide for Scientists and Engineers provides a learn-by-doing approach to acquiring the Python programming skills needed to implement computer-controlled experimental work. The book leads its readers to mastery of the popular, open-source Python computer language in its role as a powerful laboratory tool by carrying out interesting and relevant projects that explore the acquisition, production, analysis, and presentation of digitized waveforms. Readers, who are assumed to have no prior computer programming or Python background, begin writing meaningful programs in the first few pages. The Python-Based Laboratory can be used as a textbook for science and engineering instructional laboratory students who are being taught up-to-date Python-based experimental skills. The book also works well as a self-study guide for professional laboratory researchers, industrial engineers, hobbyists, and electronics enthusiasts seeking to automate tasks using Python. Topics covered include the control of data acquisition devices (including multifunction data acquisition hardware and IEEE-interfaced stand-alone instruments), data file storage and presentation, digitized data concepts (such as resolution, sampling frequency, and aliasing), and data analysis techniques (curve fitting and fast Fourier transform). As readers work their way through the book, they build several computer-based instruments, including a DC voltmeter, digital oscilloscope, DC voltage source, waveform generator, blinking LED array, digital thermometer, and spectrum analyzer. Each chapter concludes with a Do-It-Yourself project and a Use It! example as well as a healthy selection of homework-style problems, allowing readers to test their understanding and further develop their Python-based experimentation skills.
Author: Jeffrey Humpherys
Publisher: SIAM
ISBN: 1611974895
Category : Mathematics
Languages : en
Pages : 710
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Book Description
This book provides the essential foundations of both linear and nonlinear analysis necessary for understanding and working in twenty-first century applied and computational mathematics. In addition to the standard topics, this text includes several key concepts of modern applied mathematical analysis that should be, but are not typically, included in advanced undergraduate and beginning graduate mathematics curricula. This material is the introductory foundation upon which algorithm analysis, optimization, probability, statistics, differential equations, machine learning, and control theory are built. When used in concert with the free supplemental lab materials, this text teaches students both the theory and the computational practice of modern mathematical analysis. Foundations of Applied Mathematics, Volume 1: Mathematical Analysis includes several key topics not usually treated in courses at this level, such as uniform contraction mappings, the continuous linear extension theorem, Daniell?Lebesgue integration, resolvents, spectral resolution theory, and pseudospectra. Ideas are developed in a mathematically rigorous way and students are provided with powerful tools and beautiful ideas that yield a number of nice proofs, all of which contribute to a deep understanding of advanced analysis and linear algebra. Carefully thought out exercises and examples are built on each other to reinforce and retain concepts and ideas and to achieve greater depth. Associated lab materials are available that expose students to applications and numerical computation and reinforce the theoretical ideas taught in the text. The text and labs combine to make students technically proficient and to answer the age-old question, "When am I going to use this?
Author: Michael Driscoll
Publisher: Lulu.com
ISBN: 0996062815
Category :
Languages : en
Pages : 296
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Book Description
Learn how to program with Python from beginning to end. This book is for beginners who want to get up to speed quickly and become intermediate programmers fast!
Author: Aaron Maxwell
Publisher: "O'Reilly Media, Inc."
ISBN: 1098175670
Category :
Languages : en
Pages : 200
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Book Description
Once you've mastered the basics of Python, how do you skill up to the top 1%? How do you focus your learning time on topics that yield the most benefit for production engineering and data teams—without getting distracted by info of little real-world use? This book answers these questions and more. Based on author Aaron Maxwell's software engineering career in Silicon Valley, this unique book focuses on the Python first principles that act to accelerate everything else: the 5% of programming knowledge that makes the remaining 95% fall like dominos. It's also this knowledge that helps you become an exceptional Python programmer, fast. Learn how to think like a Pythonista: explore advanced Pythonic thinking Create lists, dicts, and other data structures using a high-level, readable, and maintainable syntax Explore higher-order function abstractions that form the basis of Python libraries Examine Python's metaprogramming tool for priceless patterns of code reuse Master Python's error model and learn how to leverage it in your own code Learn the more potent and advanced tools of Python's object system Take a deep dive into Python's automated testing and TDD Learn how Python logging helps you troubleshoot and debug more quickly
Author: John S. Conery
Publisher: CRC Press
ISBN: 1466572450
Category : Computers
Languages : en
Pages : 435
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Book Description
An Active Learning Approach to Teaching the Main Ideas in Computing Explorations in Computing: An Introduction to Computer Science and Python Programming teaches computer science students how to use programming skills to explore fundamental concepts and computational approaches to solving problems. Tbook gives beginning students an introduction to
Author: Brendan Choi
Publisher: Apress
ISBN: 9781484268056
Category : Computers
Languages : en
Pages : 490
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Book Description
Learn and implement network automation within the Enterprise network using Python 3. This introductory book will be your guide to building an integrated virtual networking lab to begin your Network Automation journey and master the basics of Python Network Automation. The book features a review of the practical Python network automation scripting skills and tips learned from the production network, so you can safely test and practice in a lab environment first, various Python modules such as paramiko and netmiko, pandas, re, and much more. You'll also develop essential skills such as Python scripting, regular expressions, Linux and Windows administration, VMware virtualization, and Cisco networking from the comfort of your laptop/PC with no actual networking hardware. Finally, you will learn to write a fully automated and working Cisco IOS XE upgrade application using Python. Introduction to Python Network Automation uses a canonical order, where you begin at the bottom and by the time you have completed this book, you will at least reach the intermediate level of Python coding for enterprise networking automation using native Python tools. What You'll Learn Build a proper GNS3-based networking lab for Python network automation needs. Write the basics of Python codes in both the Windows and Linux environments. Control network devices using telnet, SSH, and SNMP protocols using Python codes. Understand virtualization and how to use VMware workstation Examine virtualization and how to use VMware Workstation Pro Develop a working Cisco IOS upgrade application Who This Book Is For IT Engineers and developers, network managers and students, who would like to learn network automation using Python.
Author: Philipp Kats
Publisher: Packt Publishing Ltd
ISBN: 1789533066
Category : Computers
Languages : en
Pages : 464
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Book Description
Understand the constructs of the Python programming language and use them to build data science projects Key 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 Description Python 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 for If 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.
