Python for Scientists

Scientific Python is taught from scratch in this book via copious, downloadable, useful and adaptable code snippets.

Python for Scientists

Author: John M. Stewart

Publisher: Cambridge University Press

ISBN: 1316641236

Page: 273

View: 368

Scientific Python is a significant public domain alternative to expensive proprietary software packages. This book teaches from scratch everything the working scientist needs to know using copious, downloadable, useful and adaptable code snippets. Readers will discover how easy it is to implement and test non-trivial mathematical algorithms and will be guided through the many freely available add-on modules. A range of examples, relevant to many different fields, illustrate the language's capabilities. The author also shows how to use pre-existing legacy code (usually in Fortran77) within the Python environment, thus avoiding the need to master the original code. In this new edition, several chapters have been re-written to reflect the IPython notebook style. With an extended index, an entirely new chapter discussing SymPy and a substantial increase in the number of code snippets, researchers and research students will be able to quickly acquire all the skills needed for using Python effectively.

Learning Scientific Programming with Python

This fast-paced introduction to Python moves from the basics to advanced concepts, enabling readers to gain proficiency quickly.

Learning Scientific Programming with Python

Author: Christian Hill

Publisher: Cambridge University Press

ISBN: 1108745911

Page: 568

View: 723

This fast-paced introduction to Python moves from the basics to advanced concepts, enabling readers to gain proficiency quickly.

Introduction to Python for Engineers and Scientists

In this book you will learn by doing, avoiding technical jargon, which makes the concepts easy to learn.

Introduction to Python for Engineers and Scientists

Author: Sandeep Nagar

Publisher: Apress

ISBN: 9781484232033

Page: 254

View: 327

Familiarize yourself with the basics of Python for engineering and scientific computations using this concise, practical tutorial that is focused on writing code to learn concepts. Introduction to Python is useful for industry engineers, researchers, and students who are looking for open-source solutions for numerical computation. In this book you will learn by doing, avoiding technical jargon, which makes the concepts easy to learn. First you’ll see how to run basic calculations, absorbing technical complexities incrementally as you progress toward advanced topics. Throughout, the language is kept simple to ensure that readers at all levels can grasp the concepts. What You'll Learn Understand the fundamentals of the Python programming language Apply Python to numerical computational programming projects in engineering and science Discover the Pythonic way of life Apply data types, operators, and arrays Carry out plotting for visualization Work with functions and loops Who This Book Is For Engineers, scientists, researchers, and students who are new to Python. Some prior programming experience would be helpful but not required.

Python for the Life Sciences

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, ...

Python for the Life Sciences

Author: Alexander Lancaster

Publisher: Apress

ISBN: 9781484245224

Page: 376

View: 992

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 calculations Search for important motifs in genome sequences Use object-oriented programming with Python Study mining interaction network data for patterns Review dynamic modeling of biochemical switches Who This Book Is For Life 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

Python Programming and Visualization for Scientists

A color-illustrated introduction and reference volume for the popular Python 3 language with an emphasis on scientific plotting and data analysis and relevant software modules, including numpy, matplotlib, cartopy, datetime, and pandas.

Python Programming and Visualization for Scientists

Author: Alex DeCaria

Publisher:

ISBN: 9780972903356

Page: 346

View: 884

A color-illustrated introduction and reference volume for the popular Python 3 language with an emphasis on scientific plotting and data analysis and relevant software modules, including numpy, matplotlib, cartopy, datetime, and pandas.

Python Data Science Handbook

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 ...

Python Data Science Handbook

Author: Jake VanderPlas

Publisher: "O'Reilly Media, Inc."

ISBN: 1491912138

Page: 548

View: 345

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

Python Scripting for Computational Science

All the tools and examples in this book are open source codes. This third edition features lots of new material. It is also released after a comprehensive reorganization of the text.

Python Scripting for Computational Science

Author: Hans Petter Langtangen

Publisher: Springer Science & Business Media

ISBN: 3540739157

Page: 756

View: 646

With a primary focus on examples and applications of relevance to computational scientists, this brilliantly useful book shows computational scientists how to develop tailored, flexible, and human-efficient working environments built from small scripts written in the easy-to-learn, high-level Python language. All the tools and examples in this book are open source codes. This third edition features lots of new material. It is also released after a comprehensive reorganization of the text. The author has inserted improved examples and tools and updated information, as well as correcting any errors that crept in to the first imprint.

Introduction to Python

The book introduces basic python for the purpose of scientific computation. The book is useful for students, researchers and students who are looking for a open source software for numerical computation.

Introduction to Python

Author: Sandeep Nagar

Publisher:

ISBN: 9781520153681

Page: 161

View: 971

Python is an all-in-one solution for scientific needs. Its popularity can be judged from the fact that it is now world's most preferred language for coding purposes. The book introduces basic python for the purpose of scientific computation. The book is useful for students, researchers and students who are looking for a open source software for numerical computation. Its designed with minimalistic design which makes a compact source of learning for new learners to start developing quickly. CHAPTERS: 1- Introduction to pythonic way of life, 2- Introduction to basics of python, 3 - Data types, 4- Operators, 5- Arrays, 6- Plotting, 7- File I/O, 8- Functions and Loops, 9- Object oriented programming, 10- Numerical computing formalism

Python and Matplotlib Essentials for Scientists and Engineers

This book provides an introduction to the core features of the Python programming language and Matplotlib plotting routings for scientists and engineers (or students of either discipline) who want to use Python(TM) to analyse data, simulate ...

Python and Matplotlib Essentials for Scientists and Engineers

Author: Matt A. Wood

Publisher: Morgan & Claypool

ISBN: 9781627056199

Page: 135

View: 683

This book provides an introduction to the core features of the Python programming language and Matplotlib plotting routings for scientists and engineers (or students of either discipline) who want to use Python to analyse data, simulate physical processes, and render publication-quality plots. No previous programming experience is needed before reading the first page. Readers will learn the core features of the Python programming language in under a day. They will be able to immediately use Python to implement codes that solve their own problems and make beautiful plots and animations. Python code is extremely fast to prototype, allowing users to achieve results quickly and accurately. The examples within the book are available for download at http: //pythonessentials.com. Python and Matplotlib Essentials for Scientists and Engineers is accessible for motivated high-school students, but will likely be most useful for undergraduate and graduate students as well as working professionals who have some background with the basic mathematical concepts. This book is intended for technical people who want to get things done."

Programming with Python for Social Scientists

Accompanied by numerous code examples, screenshots, sample data sources, this is the textbook for social scientists looking for a complete introduction to programming with Python and incorporating it into their research design and analysis.

Programming with Python for Social Scientists

Author: Phillip D. Brooker

Publisher: SAGE

ISBN: 1526486342

Page: 328

View: 431

As data become ‘big’, fast and complex, the software and computing tools needed to manage and analyse them are rapidly developing. Social scientists need new tools to meet these challenges, tackle big datasets, while also developing a more nuanced understanding of – and control over – how these computing tools and algorithms are implemented. Programming with Python for Social Scientists offers a vital foundation to one of the most popular programming tools in computer science, specifically for social science researchers, assuming no prior coding knowledge. It guides you through the full research process, from question to publication, including: • The fundamentals of why and how to do your own programming in social scientific research • Questions of ethics and research design • A clear, easy to follow ‘how-to’ guide to using Python, with a wide array of applications such as data visualisation, social media data research, social network analysis, and more. Accompanied by numerous code examples, screenshots, sample data sources, this is the textbook for social scientists looking for a complete introduction to programming with Python and incorporating it into their research design and analysis.

Python Programming

This book is written as a reference text for teaching and learning Python as a computer programming course.

Python Programming

Author: Irfan Turk

Publisher: Createspace Independent Publishing Platform

ISBN: 9781543173833

Page: 236

View: 576

This book is written as a reference text for teaching and learning Python as a computer programming course. It has 117 illustrative and instructive examples that include the solutions along with the codes. The book consists of three major parts. The fundamentals of the programming language are explained in the first part. Object-oriented programming and working with databases are discussed in the second part. The third part, which provides the essential topics for engineers and scientists, covers the following topics: - Matrix Algebra - Plotting Graphics - Symbolic Calculations - Introduction to Statistics - Numerical Methods - Digital Image Processing - Graphical User Interfaces.

A Primer on Scientific Programming with Python

F. H. Wild III, Choice, Vol. 47 (8), April 2010 Those of us who have learned scientific programming in Python ‘on the streets’ could be a little jealous of students who have the opportunity to take a course out of Langtangen’s Primer ...

A Primer on Scientific Programming with Python

Author: Hans Petter Langtangen

Publisher: Springer

ISBN: 3662498871

Page: 922

View: 105

The book serves as a first introduction to computer programming of scientific applications, using the high-level Python language. The exposition is example and problem-oriented, where the applications are taken from mathematics, numerical calculus, statistics, physics, biology and finance. The book teaches "Matlab-style" and procedural programming as well as object-oriented programming. High school mathematics is a required background and it is advantageous to study classical and numerical one-variable calculus in parallel with reading this book. Besides learning how to program computers, the reader will also learn how to solve mathematical problems, arising in various branches of science and engineering, with the aid of numerical methods and programming. By blending programming, mathematics and scientific applications, the book lays a solid foundation for practicing computational science. From the reviews: Langtangen ... does an excellent job of introducing programming as a set of skills in problem solving. He guides the reader into thinking properly about producing program logic and data structures for modeling real-world problems using objects and functions and embracing the object-oriented paradigm. ... Summing Up: Highly recommended. F. H. Wild III, Choice, Vol. 47 (8), April 2010 Those of us who have learned scientific programming in Python ‘on the streets’ could be a little jealous of students who have the opportunity to take a course out of Langtangen’s Primer.” John D. Cook, The Mathematical Association of America, September 2011 This book goes through Python in particular, and programming in general, via tasks that scientists will likely perform. It contains valuable information for students new to scientific computing and would be the perfect bridge between an introduction to programming and an advanced course on numerical methods or computational science. Alex Small, IEEE, CiSE Vol. 14 (2), March /April 2012 “This fourth edition is a wonderful, inclusive textbook that covers pretty much everything one needs to know to go from zero to fairly sophisticated scientific programming in Python...” Joan Horvath, Computing Reviews, March 2015

Python Data Science Handbook

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: ...

Python Data Science Handbook

Author: Jacob T. Vanderplas

Publisher: O'Reilly Media

ISBN: 9781491912058

Page: 529

View: 878

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

Python Data Science Essentials

Python Data Science Essentials, Third Edition provides modern insight in setting up and performing data science operations effectively using the latest python tools and libraries.

Python Data Science Essentials

Author: Alberto Boschetti

Publisher: Packt Publishing Ltd

ISBN: 1789531896

Page: 472

View: 396

Gain useful insights from your data using popular data science tools Key Features A one-stop guide to Python libraries such as pandas and NumPy Comprehensive coverage of data science operations such as data cleaning and data manipulation Choose scalable learning algorithms for your data science tasks Book Description Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn. The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost. By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users What you will learn Set up your data science toolbox on Windows, Mac, and Linux Use the core machine learning methods offered by the scikit-learn library Manipulate, fix, and explore data to solve data science problems Learn advanced explorative and manipulative techniques to solve data operations Optimize your machine learning models for optimized performance Explore and cluster graphs, taking advantage of interconnections and links in your data Who this book is for If you’re a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book.

Advanced Data Science and Analytics with Python

Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book.

Advanced Data Science and Analytics with Python

Author: Jesus Rogel-Salazar

Publisher: CRC Press

ISBN: 0429822324

Page: 384

View: 770

Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow-up to the topics discussed in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The model development is supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications. Features: Targets readers with a background in programming, who are interested in the tools used in data analytics and data science Uses Python throughout Presents tools, alongside solved examples, with steps that the reader can easily reproduce and adapt to their needs Focuses on the practical use of the tools rather than on lengthy explanations Provides the reader with the opportunity to use the book whenever needed rather than following a sequential path The book can be read independently from the previous volume and each of the chapters in this volume is sufficiently independent from the others, providing flexibility for the reader. Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book. Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning are comprehensively covered. The book discusses the need to develop data products and addresses the subject of bringing models to their intended audiences – in this case, literally to the users’ fingertips in the form of an iPhone app. About the Author Dr. Jesús Rogel-Salazar is a lead data scientist in the field, working for companies such as Tympa Health Technologies, Barclays, AKQA, IBM Data Science Studio and Dow Jones. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK.

Applied Scientific Computing

Dr. Thomas Arildsen is an Associate Professor in the Department of Electronic Systems at Aalborg University, Denmark. Dr. Kathleen Kavanagh is a Professor in the Department of Mathematics at Clarkson University.

Applied Scientific Computing

Author: Peter R. Turner

Publisher: Springer

ISBN: 9783319895741

Page: 272

View: 845

This easy-to-understand textbook presents a modern approach to learning numerical methods (or scientific computing), with a unique focus on the modeling and applications of the mathematical content. Emphasis is placed on the need for, and methods of, scientific computing for a range of different types of problems, supplying the evidence and justification to motivate the reader. Practical guidance on coding the methods is also provided, through simple-to-follow examples using Python. Topics and features: provides an accessible and applications-oriented approach, supported by working Python code for many of the methods; encourages both problem- and project-based learning through extensive examples, exercises, and projects drawn from practical applications; introduces the main concepts in modeling, python programming, number representation, and errors; explains the essential details of numerical calculus, linear, and nonlinear equations, including the multivariable Newton method; discusses interpolation and the numerical solution of differential equations, covering polynomial interpolation, splines, and the Euler, Runge–Kutta, and shooting methods; presents largely self-contained chapters, arranged in a logical order suitable for an introductory course on scientific computing. Undergraduate students embarking on a first course on numerical methods or scientific computing will find this textbook to be an invaluable guide to the field, and to the application of these methods across such varied disciplines as computer science, engineering, mathematics, economics, the physical sciences, and social science.

Python for Scientists

2.1 Generalities This sounds like software produced by Apple, but it is in fact a
Python interpreter on steroids. It has been designed and written by scientists with
the aim of offering very fast exploration and construction of code with minimal ...

Python for Scientists

Author: John M. Stewart

Publisher: Cambridge University Press

ISBN: 1139952803

Page:

View: 225

Python is a free, open source, easy-to-use software tool that offers a significant alternative to proprietary packages such as MATLAB and Mathematica. This book covers everything the working scientist needs to know to start using Python effectively. The author explains scientific Python from scratch, showing how easy it is to implement and test non-trivial mathematical algorithms and guiding the reader through the many freely available add-on modules. A range of examples, relevant to many different fields, illustrate the program's capabilities. In particular, readers are shown how to use pre-existing legacy code (usually in Fortran77) within the Python environment, thus avoiding the need to master the original code. Instead of exercises the book contains useful snippets of tested code which the reader can adapt to handle problems in their own field, allowing students and researchers with little computer expertise to get up and running as soon as possible.

Python Programming and Numerical Methods

Python Programming and Numerical Methods: A Guide for Engineers and Scientists introduces programming tools and numerical methods to engineering and science students, with the goal of helping the students to develop good computational ...

Python Programming and Numerical Methods

Author: Qingkai Kong

Publisher: Academic Press

ISBN: 0128195495

Page: 480

View: 304

Python Programming and Numerical Methods: A Guide for Engineers and Scientists introduces programming tools and numerical methods to engineering and science students, with the goal of helping the students to develop good computational problem-solving techniques through the use of numerical methods and the Python programming language. Part One introduces fundamental programming concepts, using simple examples to put new concepts quickly into practice. Part Two covers the fundamentals of algorithms and numerical analysis at a level that allows students to quickly apply results in practical settings. Includes tips, warnings and "try this" features within each chapter to help the reader develop good programming practice Summaries at the end of each chapter allow for quick access to important information Includes code in Jupyter notebook format that can be directly run online

Introduction to Machine Learning with Python

With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data ...

Introduction to Machine Learning with Python

Author: Andreas C. Müller

Publisher: "O'Reilly Media, Inc."

ISBN: 1449369898

Page: 400

View: 777

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills