Author: Alex Joseph DeCaria
Release Date: 2016-05-01
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: Zanab Hussain
Publisher: John Wiley & Sons
Release Date: 2015-07-07
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.
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.
Author: Hans Petter Langtangen
Publisher: Springer Science & Business Media
Release Date: 2013-03-14
Scripting with Python makes you productive and increases the reliability of your scientific work. Here, the author teaches you how to develop tailored, flexible, and efficient working environments built from small programs (scripts) written in Python. The focus is on examples and applications of relevance to computational science: gluing existing applications and tools, e.g. for automating simulation, data analysis, and visualization; steering simulations and computational experiments; equipping programs with graphical user interfaces; making computational Web services; creating interactive interfaces with a Maple/Matlab-like syntax to numerical applications in C/C++ or Fortran; and building flexible object-oriented programming interfaces to existing C/C++ or Fortran libraries.
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 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.
Author: Ajay Ohri
Publisher: John Wiley & Sons
Release Date: 2017-11-13
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.
Learn how to create interactive and visually aesthetic plots using the Bokeh package in Python Key Features A step by step approach to creating interactive plots with Bokeh Go from nstallation all the way to deploying your very own Bokeh application Work with a real time datasets to practice and create your very own plots and applications Book Description Adding a layer of interactivity to your plots and converting these plots into applications hold immense value in the field of data science. The standard approach to adding interactivity would be to use paid software such as Tableau, but the Bokeh package in Python offers users a way to create both interactive and visually aesthetic plots for free. This book gets you up to speed with Bokeh - a popular Python library for interactive data visualization. The book starts out by helping you understand how Bokeh works internally and how you can set up and install the package in your local machine. You then use a real world data set which uses stock data from Kaggle to create interactive and visually stunning plots. You will also learn how to leverage Bokeh using some advanced concepts such as plotting with spatial and geo data. Finally you will use all the concepts that you have learned in the previous chapters to create your very own Bokeh application from scratch. By the end of the book you will be able to create your very own Bokeh application. You will have gone through a step by step process that starts with understanding what Bokeh actually is and ends with building your very own Bokeh application filled with interactive and visually aesthetic plots. What you will learn Installing Bokeh and understanding its key concepts Creating plots using glyphs, the fundamental building blocks of Bokeh Creating plots using different data structures like NumPy and Pandas Using layouts and widgets to visually enhance your plots and add a layer of interactivity Building and hosting applications on the Bokeh server Creating advanced plots using spatial data Who this book is for This book is well suited for data scientists and data analysts who want to perform interactive data visualization on their web browsers using Bokeh. Some exposure to Python programming will be helpful, but prior experience with Bokeh is not required.
Author: Matt A Wood
Publisher: Morgan & Claypool Publishers
Release Date: 2015-06-01
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.
Understand, evaluate, and visualize data About This Book Learn basic steps of data analysis and how to use Python and its packages A step-by-step guide to predictive modeling including tips, tricks, and best practices Effectively visualize a broad set of analyzed data and generate effective results Who This Book Is For This book is for Python Developers who are keen to get into data analysis and wish to visualize their analyzed data in a more efficient and insightful manner. What You Will Learn Get acquainted with NumPy and use arrays and array-oriented computing in data analysis Process and analyze data using the time-series capabilities of Pandas Understand the statistical and mathematical concepts behind predictive analytics algorithms Data visualization with Matplotlib Interactive plotting with NumPy, Scipy, and MKL functions Build financial models using Monte-Carlo simulations Create directed graphs and multi-graphs Advanced visualization with D3 In Detail You will start the course with an introduction to the principles of data analysis and supported libraries, along with NumPy basics for statistics and data processing. Next, you will overview the Pandas package and use its powerful features to solve data-processing problems. Moving on, you will get a brief overview of the Matplotlib API .Next, you will learn to manipulate time and data structures, and load and store data in a file or database using Python packages. You will learn how to apply powerful packages in Python to process raw data into pure and helpful data using examples. You will also get a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or building helpful products such as recommendations and predictions using Scikit-learn. After this, you will move on to a data analytics specialization—predictive analytics. Social media and IOT have resulted in an avalanche of data. You will get started with predictive analytics using Python. You will see how to create predictive models from data. You will get balanced information on statistical and mathematical concepts, and implement them in Python using libraries such as Pandas, scikit-learn, and NumPy. You'll learn more about the best predictive modeling algorithms such as Linear Regression, Decision Tree, and Logistic Regression. Finally, you will master best practices in predictive modeling. After this, you will get all the practical guidance you need to help you on the journey to effective data visualization. Starting with a chapter on data frameworks, which explains the transformation of data into information and eventually knowledge, this path subsequently cover the complete visualization process using the most popular Python libraries with working examples This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Getting Started with Python Data Analysis, Phuong Vo.T.H &Martin Czygan Learning Predictive Analytics with Python, Ashish Kumar Mastering Python Data Visualization, Kirthi Raman Style and approach The course acts as a step-by-step guide to get you familiar with data analysis and the libraries supported by Python with the help of real-world examples and datasets. It also helps you gain practical insights into predictive modeling by implementing predictive-analytics algorithms on public datasets with Python. The course offers a wealth of practical guidance to help you on this journey to data visualization
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.
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
Over 60 practical recipes to help you explore Python and its robust data science capabilities About This Book The book is packed with simple and concise Python code examples to effectively demonstrate advanced concepts in action Explore concepts such as programming, data mining, data analysis, data visualization, and machine learning using Python Get up to speed on machine learning algorithms with the help of easy-to-follow, insightful recipes Who This Book Is For This book is intended for all levels of Data Science professionals, both students and practitioners, starting from novice to experts. Novices can spend their time in the first five chapters getting themselves acquainted with Data Science. Experts can refer to the chapters starting from 6 to understand how advanced techniques are implemented using Python. People from non-Python backgrounds can also effectively use this book, but it would be helpful if you have some prior basic programming experience. What You Will Learn Explore the complete range of Data Science algorithms Get to know the tricks used by industry engineers to create the most accurate data science models Manage and use Python libraries such as numpy, scipy, scikit learn, and matplotlib effectively Create meaningful features to solve real-world problems Take a look at Advanced Regression methods for model building and variable selection Get a thorough understanding of the underlying concepts and implementation of Ensemble methods Solve real-world problems using a variety of different datasets from numerical and text data modalities Get accustomed to modern state-of-the art algorithms such as Gradient Boosting, Random Forest, Rotation Forest, and so on In Detail Python is increasingly becoming the language for data science. It is overtaking R in terms of adoption, it is widely known by many developers, and has a strong set of libraries such as Numpy, Pandas, scikit-learn, Matplotlib, Ipython and Scipy, to support its usage in this field. Data Science is the emerging new hot tech field, which is an amalgamation of different disciplines including statistics, machine learning, and computer science. It's a disruptive technology changing the face of today's business and altering the economy of various verticals including retail, manufacturing, online ventures, and hospitality, to name a few, in a big way. This book will walk you through the various steps, starting from simple to the most complex algorithms available in the Data Science arsenal, to effectively mine data and derive intelligence from it. At every step, we provide simple and efficient Python recipes that will not only show you how to implement these algorithms, but also clarify the underlying concept thoroughly. The book begins by introducing you to using Python for Data Science, followed by working with Python environments. You will then learn how to analyse your data with Python. The book then teaches you the concepts of data mining followed by an extensive coverage of machine learning methods. It introduces you to a number of Python libraries available to help implement machine learning and data mining routines effectively. It also covers the principles of shrinkage, ensemble methods, random forest, rotation forest, and extreme trees, which are a must-have for any successful Data Science Professional. Style and approach This is a step-by-step recipe-based approach to Data Science algorithms, introducing the math philosophy behind these algorithms.