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Python Programming and Visualization for Scientists

Python Programming and Visualization for Scientists PDF Author: Alex Joseph DeCaria
Publisher:
ISBN: 9780972903370
Category :
Languages : en
Pages : 270

Book Description
Python has rapidly become a dominant language in the scientific community for analyzing and visualizing data, in part due to its concise, intuitive syntax and free availability without the purchase of an expensive license. The syntax of the language itself is easy to pick up, but learning how to plot and visualize data has required more effort in the past, since the relevant resources are spread across the Internet. Hence, the idea for this book. Specifically, the author wrote it from the perspective of "What book would I want to have had when I was transitioning to Python?"This book will be useful not only as a classroom text but also as a guide and reference for students, educators, and researchers who have some programming experience already and want to start creating plots and analyzing data using Python. It is not meant for the person who is completely new to programming, nor is it an introductory computer science textbook. The author's assumption is that the reader has some experience programming, though not necessarily with Python.Although the new Python programmer may wish to read the book cover-to-cover, the book is organized such that the experienced Python programmer who wants to get started in plotting data can readily jump to the appropriate chapter. The last few chapters include topics that are more advanced, such as using regular expressions for matching text patterns, performing spectral analysis of data, or solving systems of linear equations.

Python Programming and Visualization for Scientists

Python Programming and Visualization for Scientists PDF Author: Alex Joseph DeCaria
Publisher:
ISBN: 9780972903370
Category :
Languages : en
Pages : 270

Book Description
Python has rapidly become a dominant language in the scientific community for analyzing and visualizing data, in part due to its concise, intuitive syntax and free availability without the purchase of an expensive license. The syntax of the language itself is easy to pick up, but learning how to plot and visualize data has required more effort in the past, since the relevant resources are spread across the Internet. Hence, the idea for this book. Specifically, the author wrote it from the perspective of "What book would I want to have had when I was transitioning to Python?"This book will be useful not only as a classroom text but also as a guide and reference for students, educators, and researchers who have some programming experience already and want to start creating plots and analyzing data using Python. It is not meant for the person who is completely new to programming, nor is it an introductory computer science textbook. The author's assumption is that the reader has some experience programming, though not necessarily with Python.Although the new Python programmer may wish to read the book cover-to-cover, the book is organized such that the experienced Python programmer who wants to get started in plotting data can readily jump to the appropriate chapter. The last few chapters include topics that are more advanced, such as using regular expressions for matching text patterns, performing spectral analysis of data, or solving systems of linear equations.

Scientific Visualization

Scientific Visualization PDF Author: Nicolas Rougier
Publisher:
ISBN: 9782957990108
Category :
Languages : en
Pages : 208

Book Description


Python for Scientists

Python for Scientists PDF Author: John M. Stewart
Publisher: Cambridge University Press
ISBN: 1316641236
Category : Computers
Languages : en
Pages : 272

Book Description
Scientific Python is taught from scratch in this book via copious, downloadable, useful and adaptable code snippets. Everything the working scientist needs to know is covered, quickly providing researchers and research students with the skills to start using Python effectively.

Practical Python Data Visualization

Practical Python Data Visualization PDF Author: Ashwin Pajankar
Publisher: Apress
ISBN: 9781484264546
Category : Computers
Languages : en
Pages : 160

Book Description
Quickly start programming with Python 3 for data visualization with this step-by-step, detailed guide. This book’s programming-friendly approach using libraries such as leather, NumPy, Matplotlib, and Pandas will serve as a template for business and scientific visualizations. You’ll begin by installing Python 3, see how to work in Jupyter notebook, and explore Leather, Python’s popular data visualization charting library. You’ll also be introduced to the scientific Python 3 ecosystem and work with the basics of NumPy, an integral part of that ecosystem. Later chapters are focused on various NumPy routines along with getting started with Scientific Data visualization using matplotlib. You’ll review the visualization of 3D data using graphs and networks and finish up by looking at data visualization with Pandas, including the visualization of COVID-19 data sets. The code examples are tested on popular platforms like Ubuntu, Windows, and Raspberry Pi OS. With Practical Python Data Visualization you’ll master the core concepts of data visualization with Pandas and the Jupyter notebook interface. What You'll Learn Review practical aspects of Python Data Visualization with programming-friendly abstractions Install Python 3 and Jupyter on multiple platforms including Windows, Raspberry Pi, and Ubuntu Visualize COVID-19 data sets with Pandas Who This Book Is For Data Science enthusiasts and professionals, Business analysts and managers, software engineers, data engineers.

Interactive Data Visualization with Python

Interactive Data Visualization with Python PDF Author: Abha Belorkar
Publisher: Packt Publishing Ltd
ISBN: 1800201060
Category : Computers
Languages : en
Pages : 362

Book Description
Create your own clear and impactful interactive data visualizations with the powerful data visualization libraries of Python Key FeaturesStudy and use Python interactive libraries, such as Bokeh and PlotlyExplore different visualization principles and understand when to use which oneCreate interactive data visualizations with real-world dataBook Description With so much data being continuously generated, developers, who can present data as impactful and interesting visualizations, are always in demand. Interactive Data Visualization with Python sharpens your data exploration skills, tells you everything there is to know about interactive data visualization in Python. You'll begin by learning how to draw various plots with Matplotlib and Seaborn, the non-interactive data visualization libraries. You'll study different types of visualizations, compare them, and find out how to select a particular type of visualization to suit your requirements. After you get a hang of the various non-interactive visualization libraries, you'll learn the principles of intuitive and persuasive data visualization, and use Bokeh and Plotly to transform your visuals into strong stories. You'll also gain insight into how interactive data and model visualization can optimize the performance of a regression model. By the end of the course, you'll have a new skill set that'll make you the go-to person for transforming data visualizations into engaging and interesting stories. What you will learnExplore and apply different interactive data visualization techniquesManipulate plotting parameters and styles to create appealing plotsCustomize data visualization for different audiencesDesign data visualizations using interactive librariesUse Matplotlib, Seaborn, Altair and Bokeh for drawing appealing plotsCustomize data visualization for different scenariosWho this book is for This book intends to provide a solid training ground for Python developers, data analysts and data scientists to enable them to present critical data insights in a way that best captures the user's attention and imagination. It serves as a simple step-by-step guide that demonstrates the different types and components of visualization, the principles, and techniques of effective interactivity, as well as common pitfalls to avoid when creating interactive data visualizations. Students should have an intermediate level of competency in writing Python code, as well as some familiarity with using libraries such as pandas.

Python Data Science Handbook

Python Data Science Handbook PDF Author: Jake VanderPlas
Publisher: "O'Reilly Media, Inc."
ISBN: 1491912138
Category : Computers
Languages : en
Pages : 609

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

An Introduction to Python Programming for Scientists and Engineers

An Introduction to Python Programming for Scientists and Engineers PDF Author: Johnny Wei-Bing Lin
Publisher: Cambridge University Press
ISBN: 1108701124
Category : Computers
Languages : en
Pages : 767

Book Description
Textbook that uses examples and Jupyter notebooks from across the sciences and engineering to teach Python programming.

Introduction to Python Programming for Business and Social Science Applications

Introduction to Python Programming for Business and Social Science Applications PDF Author: Frederick Kaefer
Publisher: SAGE Publications
ISBN: 1544377487
Category : Business & Economics
Languages : en
Pages : 553

Book Description
Would you like to gather big datasets, analyze them, and visualize the results, all in one program? If this describes you, then Introduction to Python Programming for Business and Social Science Applications is the book for you. Authors Frederick Kaefer and Paul Kaefer walk you through each step of the Python package installation and analysis process, with frequent exercises throughout so you can immediately try out the functions you’ve learned. Written in straightforward language for those with no programming background, this book will teach you how to use Python for your research and data analysis. Instead of teaching you the principles and practices of programming as a whole, this application-oriented text focuses on only what you need to know to research and answer social science questions. The text features two types of examples, one set from the General Social Survey and one set from a large taxi trip dataset from a major metropolitan area, to help readers understand the possibilities of working with Python. Chapters on installing and working within a programming environment, basic skills, and necessary commands will get you up and running quickly, while chapters on programming logic, data input and output, and data frames help you establish the basic framework for conducting analyses. Further chapters on web scraping, statistical analysis, machine learning, and data visualization help you apply your skills to your research. More advanced information on developing graphical user interfaces (GUIs) help you create functional data products using Python to inform general users of data who don’t work within Python. First there was IBM® SPSS®, then there was R, and now there′s Python. Statistical software is getting more aggressive - let authors Frederick Kaefer and Paul Kaefer help you tame it with Introduction to Python Programming for Business and Social Science Applications.

Data Analysis and Visualization Using Python

Data Analysis and Visualization Using Python PDF Author: Dr. Ossama Embarak
Publisher: Apress
ISBN: 1484241096
Category : Computers
Languages : en
Pages : 390

Book Description
Look at Python from a data science point of view and learn proven techniques for data visualization as used in making critical business decisions. Starting with an introduction to data science with Python, you will take a closer look at the Python environment and get acquainted with editors such as Jupyter Notebook and Spyder. After going through a primer on Python programming, you will grasp fundamental Python programming techniques used in data science. Moving on to data visualization, you will see how it caters to modern business needs and forms a key factor in decision-making. You will also take a look at some popular data visualization libraries in Python. Shifting focus to data structures, you will learn the various aspects of data structures from a data science perspective. You will then work with file I/O and regular expressions in Python, followed by gathering and cleaning data. Moving on to exploring and analyzing data, you will look at advanced data structures in Python. Then, you will take a deep dive into data visualization techniques, going through a number of plotting systems in Python. In conclusion, you will complete a detailed case study, where you’ll get a chance to revisit the concepts you’ve covered so far. What You Will LearnUse Python programming techniques for data science Master data collections in Python Create engaging visualizations for BI systems Deploy effective strategies for gathering and cleaning data Integrate the Seaborn and Matplotlib plotting systems Who This Book Is For Developers with basic Python programming knowledge looking to adopt key strategies for data analysis and visualizations using Python.

Introduction to Computation and Programming Using Python, second edition

Introduction to Computation and Programming Using Python, second edition PDF Author: John V. Guttag
Publisher: MIT Press
ISBN: 0262529629
Category : Computers
Languages : en
Pages : 466

Book Description
The new edition of an introductory text that teaches students the art of computational problem solving, covering topics ranging from simple algorithms to information visualization. This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of data science for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (MOOC). This new edition has been updated for Python 3, reorganized to make it easier to use for courses that cover only a subset of the material, and offers additional material including five new chapters. Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming. This edition offers expanded material on statistics and machine learning and new chapters on Frequentist and Bayesian statistics.