Author: Alex Joseph DeCaria

Publisher: N.A

ISBN: 9780972903370

Category:

Page: 270

View: 7194

Author: Alex Joseph DeCaria

Publisher: N.A

ISBN: 9780972903370

Category:

Page: 270

View: 7194

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.

Author: John M. Stewart

Publisher: Cambridge University Press

ISBN: 1316641236

Category: Computers

Page: 273

View: 2430

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.

*Open Source Solutions for Numerical Computation*

Author: Sandeep Nagar

Publisher: Apress

ISBN: 1484232046

Category: Computers

Page: 254

View: 9861

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.

Author: Matt A Wood

Publisher: Morgan & Claypool Publishers

ISBN: 1627056211

Category: Computers

Page: 150

View: 6231

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 PythonTM 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.

Author: John Paul Mueller,Luca Massaron

Publisher: John Wiley & Sons

ISBN: 1118843983

Category: Computers

Page: 432

View: 1431

Unleash the power of Python for your data analysis projects with For Dummies! Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. You’ll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide. Covers the fundamentals of Python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models Explains objects, functions, modules, and libraries and their role in data analysis Walks you through some of the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLib Whether you’re new to data analysis or just new to Python, Python for Data Science For Dummies is your practical guide to getting a grip on data overload and doing interesting things with the oodles of information you uncover.

Author: Kirthi Raman

Publisher: Packt Publishing Ltd

ISBN: 1783988339

Category: Computers

Page: 372

View: 1263

Generate effective results in a variety of visually appealing charts using the plotting packages in Python About This Book Explore various tools and their strengths while building meaningful representations that can make it easier to understand data Packed with computational methods and algorithms in diverse fields of science Written in an easy-to-follow categorical style, this book discusses some niche techniques that will make your code easier to work with and reuse Who This Book Is For If you are a Python developer who performs data visualization and wants to develop existing knowledge about Python to build analytical results and produce some amazing visual display, then this book is for you. A basic knowledge level and understanding of Python libraries is assumed. What You Will Learn Gather, cleanse, access, and map data to a visual framework Recognize which visualization method is applicable and learn best practices for data visualization Get acquainted with reader-driven narratives and author-driven narratives and the principles of perception Understand why Python is an effective tool to be used for numerical computation much like MATLAB, and explore some interesting data structures that come with it Explore with various visualization choices how Python can be very useful in computation in the field of finance and statistics Get to know why Python is the second choice after Java, and is used frequently in the field of machine learning Compare Python with other visualization approaches using Julia and a JavaScript-based framework such as D3.js Discover how Python can be used in conjunction with NoSQL such as Hive to produce results efficiently in a distributed environment In Detail Python has a handful of open source libraries for numerical computations involving optimization, linear algebra, integration, interpolation, and other special functions using array objects, machine learning, data mining, and plotting. Pandas have a productive environment for data analysis. These libraries have a specific purpose and play an important role in the research into diverse domains including economics, finance, biological sciences, social science, health care, and many more. The variety of tools and approaches available within Python community is stunning, and can bolster and enhance visual story experiences. This book offers practical guidance to help you on the journey to effective data visualization. Commencing with a chapter on the data framework, which explains the transformation of data into information and eventually knowledge, this book subsequently covers the complete visualization process using the most popular Python libraries with working examples. You will learn the usage of Numpy, Scipy, IPython, MatPlotLib, Pandas, Patsy, and Scikit-Learn with a focus on generating results that can be visualized in many different ways. Further chapters are aimed at not only showing advanced techniques such as interactive plotting; numerical, graphical linear, and non-linear regression; clustering and classification, but also in helping you understand the aesthetics and best practices of data visualization. The book concludes with interesting examples such as social networks, directed graph examples in real-life, data structures appropriate for these problems, and network analysis. By the end of this book, you will be able to effectively solve a broad set of data analysis problems. Style and approach The approach of this book is not step by step, but rather categorical. The categories are based on fields such as bioinformatics, statistical and machine learning, financial computation, and linear algebra. This approach is beneficial for the community in many different fields of work and also helps you learn how one approach can make sense across many fields

Author: Samir Madhavan

Publisher: Packt Publishing Ltd

ISBN: 1784392626

Category: Computers

Page: 294

View: 7993

Explore the world of data science through Python and learn how to make sense of data About This Book Master data science methods using Python and its libraries Create data visualizations and mine for patterns Advanced techniques for the four fundamentals of Data Science with Python - data mining, data analysis, data visualization, and machine learning Who This Book Is For If you are a Python developer who wants to master the world of data science then this book is for you. Some knowledge of data science is assumed. What You Will Learn Manage data and perform linear algebra in Python Derive inferences from the analysis by performing inferential statistics Solve data science problems in Python Create high-end visualizations using Python Evaluate and apply the linear regression technique to estimate the relationships among variables. Build recommendation engines with the various collaborative filtering algorithms Apply the ensemble methods to improve your predictions Work with big data technologies to handle data at scale In Detail Data science is a relatively new knowledge domain which is used by various organizations to make data driven decisions. Data scientists have to wear various hats to work with data and to derive value from it. The Python programming language, beyond having conquered the scientific community in the last decade, is now an indispensable tool for the data science practitioner and a must-know tool for every aspiring data scientist. Using Python will offer you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving. This comprehensive guide helps you move beyond the hype and transcend the theory by providing you with a hands-on, advanced study of data science. Beginning with the essentials of Python in data science, you will learn to manage data and perform linear algebra in Python. You will move on to deriving inferences from the analysis by performing inferential statistics, and mining data to reveal hidden patterns and trends. You will use the matplot library to create high-end visualizations in Python and uncover the fundamentals of machine learning. Next, you will apply the linear regression technique and also learn to apply the logistic regression technique to your applications, before creating recommendation engines with various collaborative filtering algorithms and improving your predictions by applying the ensemble methods. Finally, you will perform K-means clustering, along with an analysis of unstructured data with different text mining techniques and leveraging the power of Python in big data analytics. Style and approach This book is an easy-to-follow, comprehensive guide on data science using Python. The topics covered in the book can all be used in real world scenarios.

*Scrape, Clean, Explore & Transform Your Data*

Author: Kyran Dale

Publisher: "O'Reilly Media, Inc."

ISBN: 1491920548

Category: Computers

Page: 592

View: 2065

Learn how to turn raw data into rich, interactive web visualizations with the powerful combination of Python and JavaScript. With this hands-on guide, author Kyran Dale teaches you how build a basic dataviz toolchain with best-of-breed Python and JavaScript libraries—including Scrapy, Matplotlib, Pandas, Flask, and D3—for crafting engaging, browser-based visualizations. As a working example, throughout the book Dale walks you through transforming Wikipedia’s table-based list of Nobel Prize winners into an interactive visualization. You’ll examine steps along the entire toolchain, from scraping, cleaning, exploring, and delivering data to building the visualization with JavaScript’s D3 library. If you’re ready to create your own web-based data visualizations—and know either Python or JavaScript— this is the book for you. Learn how to manipulate data with Python Understand the commonalities between Python and JavaScript Extract information from websites by using Python’s web-scraping tools, BeautifulSoup and Scrapy Clean and explore data with Python’s Pandas, Matplotlib, and Numpy libraries Serve data and create RESTful web APIs with Python’s Flask framework Create engaging, interactive web visualizations with JavaScript’s D3 library

*Crafting Visual Transformation Scripts*

Author: Shai Vaingast

Publisher: Apress

ISBN: 1484200527

Category: Computers

Page: 416

View: 7035

We are visual animals. But before we can see the world in its true splendor, our brains, just like our computers, have to sort and organize raw data, and then transform that data to produce new images of the world. Beginning Python Visualization: Crafting Visual Transformation Scripts, Second Edition discusses turning many types of data sources, big and small, into useful visual data. And, you will learn Python as part of the bargain. In this second edition you’ll learn about Spyder, which is a Python IDE with MATLAB® -like features. Here and throughout the book, you’ll get detailed exposure to the growing IPython project for interactive visualization. In addition, you'll learn about the changes in NumPy and Scipy that have occurred since the first edition. Along the way, you'll get many pointers and a few visual examples. As part of this update, you’ll learn about matplotlib in detail; this includes creating 3D graphs and using the basemap package that allows you to render geographical maps. Finally, you'll learn about image processing, annotating, and filtering, as well as how to make movies using Python. This includes learning how to edit/open video files and how to create your own movie, all with Python scripts. Today's big data and computational scientists, financial analysts/engineers and web developers – like you - will find this updated book very relevant.

Author: Isaac D. Cody

Publisher: Createspace Independent Publishing Platform

ISBN: 9781541334571

Category:

Page: 264

View: 5048

This is a 2 book bundle related to data analytics and learning Python Programming from scratch! Two manuscripts for the price of one! What's included in this 2 book bundle manuscript: Data Analytics: Practical Data Analysis and Statistical Guide to Transform and Evolve Any Business, Leveraging the power of Data Analytics, Data Science, and Predictive Analytics for Beginners Hacking University: Junior Edition Learn Python Computer Programming from Scratch. Become a Python Zero to Hero. The Ultimate Beginners Guide in Mastering the Python Language In Data Analytics, you will learn: Why your business should be using data analytics Issues with using big data Effective data management Examples of data management in the real-world The different kinds of data analytics and their definitions How data management, data mining, data integration and data warehousing work together A step-by-step guide for conducting data analysis for your business An organizational guide to data analytics Tools for data visualization (with hyperlinks) In Hacking University Junior Edition, you will learn: The history of Python Language The benefits of learning Python and the job market outlook when learning Python Setting Up a Development Environment Variables, Variable Types, Inputs, String Formatting, Decision Structures, Conditional Operators, Loops Several Programming Examples to make sure you practice what you learn String Formatting and Programming Concepts Classes, Special Methods, and Inheritance Key Modules, and Common Errors And a WHOLE lot more! Get your copy today! Scroll up and hit the buy button to download now!

*Data Wrangling with Pandas, NumPy, and IPython*

Author: Wes McKinney

Publisher: "O'Reilly Media, Inc."

ISBN: 1491957611

Category: Computers

Page: 550

View: 9830

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second 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, IPython, 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 IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples

*Data Analysis and Science using pandas, matplotlib and the Python Programming Language*

Author: Fabio Nelli

Publisher: Apress

ISBN: 1484209583

Category: Computers

Page: 337

View: 4805

Python Data Analytics will help you tackle the world of data acquisition and analysis using the power of the Python language. At the heart of this book lies the coverage of pandas, an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Author Fabio Nelli expertly shows the strength of the Python programming language when applied to processing, managing and retrieving information. Inside, you will see how intuitive and flexible it is to discover and communicate meaningful patterns of data using Python scripts, reporting systems, and data export. This book examines how to go about obtaining, processing, storing, managing and analyzing data using the Python programming language. You will use Python and other open source tools to wrangle data and tease out interesting and important trends in that data that will allow you to predict future patterns. Whether you are dealing with sales data, investment data (stocks, bonds, etc.), medical data, web page usage, or any other type of data set, Python can be used to interpret, analyze, and glean information from a pile of numbers and statistics. This book is an invaluable reference with its examples of storing and accessing data in a database; it walks you through the process of report generation; it provides three real world case studies or examples that you can take with you for your everyday analysis needs.

*Recognize - Construct - Visualize - Analyze - Interpret*

Author: Dmitry Zinoviev

Publisher: Pragmatic Bookshelf

ISBN: 1680505408

Category: Computers

Page: 262

View: 3658

Construct, analyze, and visualize networks with networkx, a Python language module. Network analysis is a powerful tool you can apply to a multitude of datasets and situations. Discover how to work with all kinds of networks, including social, product, temporal, spatial, and semantic networks. Convert almost any real-world data into a complex network--such as recommendations on co-using cosmetic products, muddy hedge fund connections, and online friendships. Analyze and visualize the network, and make business decisions based on your analysis. If you're a curious Python programmer, a data scientist, or a CNA specialist interested in mechanizing mundane tasks, you'll increase your productivity exponentially. Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience. Starting with simple networks, convert real-life and synthetic network graphs into networkx data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. Get familiar with presentation-quality network visualization tools, both programmable and interactive--such as Gephi, a CNA explorer. Adapt the patterns from the case studies to your problems. Explore big networks with NetworKit, a high-performance networkx substitute. Each part in the book gives you an overview of a class of networks, includes a practical study of networkx functions and techniques, and concludes with case studies from various fields, including social networking, anthropology, marketing, and sports analytics. Combine your CNA and Python programming skills to become a better network analyst, a more accomplished data scientist, and a more versatile programmer. What You Need: You will need a Python 3.x installation with the following additional modules: Pandas (>=0.18), NumPy (>=1.10), matplotlib (>=1.5), networkx (>=1.11), python-louvain (>=0.5), NetworKit (>=3.6), and generalizesimilarity. We recommend using the Anaconda distribution that comes with all these modules, except for python-louvain, NetworKit, and generalizedsimilarity, and works on all major modern operating systems.

*Essential Techniques for Predictive Analysis*

Author: Michael Bowles

Publisher: John Wiley & Sons

ISBN: 1118961749

Category: Computers

Page: 336

View: 6911

This book shows readers how they can successfully analyze data using only two core machine learning algorithms---and how to do so using the popular Python programming language. These algorithms deal with common scenarios faced by all data analysts and data scientists. This book focuses on two algorithm families (linear methods and ensemble methods) that effectively predict outcomes. This type of problem covers a multitude of use cases (what ad to place on a web page, predicting prices in securities markets, detecting credit card fraud, etc.). The focus on two families gives enough room for full descriptions of the mechanisms at work in the algorithms. Then the code examples serve to illustrate the workings of the machinery with specific hackable code. The author will explain in simple terms, using no complex math, how these algorithms work, and will then show how to apply them in Python. He will also provide advice on how to select from among these algorithms, and will show how to prepare the data, and how to use the trained models in practice. The author begins with an overview of the two core algorithms, explaining the types of problems solved by each one. He then introduces a core set of Python programming techniques that can be used to apply these algorithms. The author shows various techniques for building predictive models that solve a range of problems, from simple to complex; he also shows how to measure the performance of each model to ensure you use the right one. The following chapters provide a deep dive into each of the two algorithms: penalized linear regression and ensemble methods. Chapters will show how to apply each algorithm in Python. Readers can directly use the sample code to build their own solutions.

Author: Brian R. Kent

Publisher: Morgan & Claypool Publishers

ISBN: 1627056130

Category: Technology & Engineering

Page: 105

View: 5019

This is the first book written on using Blender (an open-source visualization suite widely used in the entertainment and gaming industries) for scientific visualization. It is a practical and interesting introduction to Blender for understanding key parts

*A Data Science Approach*

Author: Ajay Ohri

Publisher: John Wiley & Sons

ISBN: 1119126762

Category: Computers

Page: 368

View: 7261

The definitive guide for statisticians and data scientists who understand the advantages of becoming proficient in both R and Python The first book of its kind, Python for R Users: A Data Science Approach makes it easy for R programmers to code in Python and Python users to program in R. Short on theory and long on actionable analytics, it provides readers with a detailed comparative introduction and overview of both languages and features concise tutorials with command-by-command translations—complete with sample code—of R to Python and Python to R. Following an introduction to both languages, the author cuts to the chase with step-by-step coverage of the full range of pertinent programming features and functions, including data input, data inspection/data quality, data analysis, and data visualization. Statistical modeling, machine learning, and data mining—including supervised and unsupervised data mining methods—are treated in detail, as are time series forecasting, text mining, and natural language processing. • Features a quick-learning format with concise tutorials and actionable analytics • Provides command-by-command translations of R to Python and vice versa • Incorporates Python and R code throughout to make it easier for readers to compare and contrast features in both languages • Offers numerous comparative examples and applications in both programming languages • Designed for use for practitioners and students that know one language and want to learn the other • Supplies slides useful for teaching and learning either software on a companion website Python for R Users: A Data Science Approach is a valuable working resource for computer scientists and data scientists that know R and would like to learn Python or are familiar with Python and want to learn R. It also functions as textbook for students of computer science and statistics. A. Ohri is the founder of Decisionstats.com and currently works as a senior data scientist. He has advised multiple startups in analytics off-shoring, analytics services, and analytics education, as well as using social media to enhance buzz for analytics products. Mr. Ohri's research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces for cloud computing, investigating climate change and knowledge flows. His other books include R for Business Analytics and R for Cloud Computing.

Author: David Paper

Publisher: Apress

ISBN: 1484235975

Category: Computers

Page: 214

View: 7675

Build the foundational data science skills necessary to work with and better understand complex data science algorithms. This example-driven book provides complete Python coding examples to complement and clarify data science concepts, and enrich the learning experience. Coding examples include visualizations whenever appropriate. The book is a necessary precursor to applying and implementing machine learning algorithms. The book is self-contained. All of the math, statistics, stochastic, and programming skills required to master the content are covered. In-depth knowledge of object-oriented programming isn’t required because complete examples are provided and explained. Data Science Fundamentals with Python and MongoDB is an excellent starting point for those interested in pursuing a career in data science. Like any science, the fundamentals of data science are a prerequisite to competency. Without proficiency in mathematics, statistics, data manipulation, and coding, the path to success is “rocky” at best. The coding examples in this book are concise, accurate, and complete, and perfectly complement the data science concepts introduced. What You'll Learn Prepare for a career in data science Work with complex data structures in Python Simulate with Monte Carlo and Stochastic algorithms Apply linear algebra using vectors and matrices Utilize complex algorithms such as gradient descent and principal component analysis Wrangle, cleanse, visualize, and problem solve with data Use MongoDB and JSON to work with data Who This Book Is For The novice yearning to break into the data science world, and the enthusiast looking to enrich, deepen, and develop data science skills through mastering the underlying fundamentals that are sometimes skipped over in the rush to be productive. Some knowledge of object-oriented programming will make learning easier.

*Over 100 hands-on recipes to sharpen your skills in high-performance numerical computing and data science in the Jupyter Notebook*

Author: Cyrille Rossant

Publisher: Packt Publishing Ltd

ISBN: 1785881930

Category: Computers

Page: 548

View: 8773

Learn to use IPython and Jupyter Notebook for your data analysis and visualization work. Key Features Leverage the Jupyter Notebook for interactive data science and visualization Become an expert in high-performance computing and visualization for data analysis and scientific modeling A comprehensive coverage of scientific computing through many hands-on, example-driven recipes with detailed, step-by-step explanations Book Description Python is one of the leading open source platforms for data science and numerical computing. IPython and the associated Jupyter Notebook offer efficient interfaces to Python for data analysis and interactive visualization, and they constitute an ideal gateway to the platform. IPython Interactive Computing and Visualization Cookbook, Second Edition contains many ready-to-use, focused recipes for high-performance scientific computing and data analysis, from the latest IPython/Jupyter features to the most advanced tricks, to help you write better and faster code. You will apply these state-of-the-art methods to various real-world examples, illustrating topics in applied mathematics, scientific modeling, and machine learning. The first part of the book covers programming techniques: code quality and reproducibility, code optimization, high-performance computing through just-in-time compilation, parallel computing, and graphics card programming. The second part tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics. What you will learn Master all features of the Jupyter Notebook Code better: write high-quality, readable, and well-tested programs; profile and optimize your code; and conduct reproducible interactive computing experiments Visualize data and create interactive plots in the Jupyter Notebook Write blazingly fast Python programs with NumPy, ctypes, Numba, Cython, OpenMP, GPU programming (CUDA), parallel IPython, Dask, and more Analyze data with Bayesian or frequentist statistics (Pandas, PyMC, and R), and learn from actual data through machine learning (scikit-learn) Gain valuable insights into signals, images, and sounds with SciPy, scikit-image, and OpenCV Simulate deterministic and stochastic dynamical systems in Python Familiarize yourself with math in Python using SymPy and Sage: algebra, analysis, logic, graphs, geometry, and probability theory Who this book is for This book is intended for anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, and hobbyists. A basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods.

Author: Mark J. Johnson

Publisher: CRC Press

ISBN: 1498759696

Category: Computers

Page: 217

View: 9514

Suitable for newcomers to computer science, A Concise Introduction to Programming in Python provides a succinct, yet complete, first course in computer science using the Python programming language. The book features: Short, modular chapters with brief and precise explanations, intended for one class period Early introduction of basic procedural constructs such as functions, selection, and repetition, allowing them to be used throughout the course Objects are introduced in the middle of the course, and class design comes toward the end Examples, exercises, and projects from a wide range of application domains, including biology, physics, images, sound, mathematics, games, and textual analysis No external libraries are required, simplifying the book’s use in common lab spaces Each chapter introduces a main idea through a concrete example and a series of exercises. Designed to teach programming in a concise, yet comprehensive way, this book provides a timely introduction for students and anyone interested in learning Python.

Author: Igor Milovanovic,Dimitry Foures,Giuseppe Vettigli

Publisher: Packt Publishing Ltd

ISBN: 1784394947

Category: Computers

Page: 302

View: 1670

Over 70 recipes to get you started with popular Python libraries based on the principal concepts of data visualization About This Book Learn how to set up an optimal Python environment for data visualization Understand how to import, clean and organize your data Determine different approaches to data visualization and how to choose the most appropriate for your needs Who This Book Is For If you already know about Python programming and want to understand data, data formats, data visualization, and how to use Python to visualize data then this book is for you. What You Will Learn Introduce yourself to the essential tooling to set up your working environment Explore your data using the capabilities of standard Python Data Library and Panda Library Draw your first chart and customize it Use the most popular data visualization Python libraries Make 3D visualizations mainly using mplot3d Create charts with images and maps Understand the most appropriate charts to describe your data Know the matplotlib hidden gems Use plot.ly to share your visualization online In Detail Python Data Visualization Cookbook will progress the reader from the point of installing and setting up a Python environment for data manipulation and visualization all the way to 3D animations using Python libraries. Readers will benefit from over 60 precise and reproducible recipes that will guide the reader towards a better understanding of data concepts and the building blocks for subsequent and sometimes more advanced concepts. Python Data Visualization Cookbook starts by showing how to set up matplotlib and the related libraries that are required for most parts of the book, before moving on to discuss some of the lesser-used diagrams and charts such as Gantt Charts or Sankey diagrams. Initially it uses simple plots and charts to more advanced ones, to make it easy to understand for readers. As the readers will go through the book, they will get to know about the 3D diagrams and animations. Maps are irreplaceable for displaying geo-spatial data, so this book will also show how to build them. In the last chapter, it includes explanation on how to incorporate matplotlib into different environments, such as a writing system, LaTeX, or how to create Gantt charts using Python. Style and approach A step-by-step recipe based approach to data visualization. The topics are explained sequentially as cookbook recipes consisting of a code snippet and the resulting visualization.