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.

This book has attempted to expose the Basic Principle on Data Analysis and Visualization efficiently and smoothly. This book covers an overview of Pandas, NumPy, and Seaborn with implementation using Python, and hence, the book will be useful for Engineering students who are preparing either for a Degree, or Diploma or Associate Membership Examination. Throughout the book, the main importance is given to expressing the subject matter by Python coding and neat steps. The data which is used is an assumption in a manner. A furthernote worthy feature of the book is the inclusion of datavisualization using Seaborn Library.

Amazon is an American multinational technology company that is known for its e-commerce, cloud computing, digital streaming, and artificial intelligence services. It was founded by Jeff Bezos in 1994 and is headquartered in Seattle, Washington. Amazon's primary business is its online marketplace, where it offers a wide range of products, including books, electronics, household items, and more. The company has expanded its operations to various countries and is one of the largest online retailers globally. In addition to its e-commerce business, Amazon has ventured into other areas. It provides cloud computing services through Amazon Web Services (AWS), which offers on-demand computing power, storage, and other services to individuals, businesses, and governments. AWS has become a significant revenue source for Amazon. Amazon has also made a significant impact on the entertainment industry. It operates Amazon Prime Video, a streaming platform that offers a wide selection of movies, TV shows, and original content. As for Amazon's stock price, it has experienced substantial growth since the company went public in 1997. The stock has been highly valued by investors due to Amazon's consistent revenue growth, market dominance, and innovation. The stock price has seen both ups and downs over the years, reflecting market trends and investor sentiment. The dataset used in this project starts from 14-May-1997 and is updated till 27-Oct-2021. It contains 6155 rows and 7 columns. The columns in the dataset are Date, Open, High, Low, Close, Adj Close, and Volume. In this project, you will involve technical indicators such as daily returns, Moving Average Convergence-Divergence (MACD), Relative Strength Index (RSI), Simple Moving Average (SMA), lower and upper bands, and standard deviation. To perform forecasting based on regression on Adj Close price of Amazon stock price, you will use: Linear Regression, Random Forest regression, Decision Tree regression, Support Vector Machine regression, Naïve Bayes regression, K-Nearest Neighbor regression, Adaboost regression, Gradient Boosting regression, Extreme Gradient Boosting regression, Light Gradient Boosting regression, Catboost regression, MLP regression, Lasso regression, and Ridge regression. The machine learning models used predict Amazon stock daily returns as target variable are K-Nearest Neighbor classifier, Random Forest classifier, Naive Bayes classifier, Logistic Regression classifier, Decision Tree classifier, Support Vector Machine classifier, LGBM classifier, Gradient Boosting classifier, XGB classifier, MLP classifier, and Extra Trees classifier. Finally, you will develop GUI to plot boundary decision, distribution of features, feature importance, predicted values versus true values, confusion matrix, learning curve, performance of the model, and scalability of the model.

Leverage the power of Python design patterns to solve real-world problems in software architecture and design About This Book Understand the structural, creational, and behavioral Python design patterns Get to know the context and application of design patterns to solve real-world problems in software architecture, design, and application development Get practical exposure through sample implementations in Python v3.5 for the design patterns featured Who This Book Is For This book is for Software architects and Python application developers who are passionate about software design. It will be very useful to engineers with beginner level proficiency in Python and who love to work with Python 3.5 What You Will Learn Enhance your skills to create better software architecture Understand proven solutions to commonly occurring design issues Explore the design principles that form the basis of software design, such as loose coupling, the Hollywood principle and the Open Close principle among others Delve into the object-oriented programming concepts and find out how they are used in software applications Develop an understanding of Creational Design Patterns and the different object creation methods that help you solve issues in software development Use Structural Design Patterns and find out how objects and classes interact to build larger applications Focus on the interaction between objects with the command and observer patterns Improve the productivity and code base of your application using Python design patterns In Detail With the increasing focus on optimized software architecture and design it is important that software architects think about optimizations in object creation, code structure, and interaction between objects at the architecture or design level. This makes sure that the cost of software maintenance is low and code can be easily reused or is adaptable to change. The key to this is reusability and low maintenance in design patterns. Building on the success of the previous edition, Learning Python Design Patterns, Second Edition will help you implement real-world scenarios with Python's latest release, Python v3.5. We start by introducing design patterns from the Python perspective. As you progress through the book, you will learn about Singleton patterns, Factory patterns, and Facade patterns in detail. After this, we'll look at how to control object access with proxy patterns. It also covers observer patterns, command patterns, and compound patterns. By the end of the book, you will have enhanced your professional abilities in software architecture, design, and development. Style and approach This is an easy-to-follow guide to design patterns with hands-on examples of real-world scenarios and their implementation in Python v3.5. Each topic is explained and placed in context, and for the more inquisitive, there are more details on the concepts used.

Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical modules in Python and its Standard Library as well as popular open source numerical Python packages like NumPy, FiPy, matplotlib and more to numerically compute solutions and mathematically model applications in a number of areas like big data, cloud computing, financial engineering, business management and more. After reading and using this book, you'll get some takeaway case study examples of applications that can be found in areas like business management, big data/cloud computing, financial engineering (i.e., options trading investment alternatives), and even games. Up until very recently, Python was mostly regarded as just a web scripting language. Well, computational scientists and engineers have recently discovered the flexibility and power of Python to do more. Big data analytics and cloud computing programmers are seeing Python's immense use. Financial engineers are also now employing Python in their work. Python seems to be evolving as a language that can even rival C++, Fortran, and Pascal/Delphi for numerical and mathematical computations.

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

The representation of abstract data and ideas can be a difficult and tedious task to handle when learning new concepts; however, the advances of emerging technology have allowed for new methods of representing such conceptual data. The Handbook of Research on Maximizing Cognitive Learning through Knowledge Visualization focuses on the use of visualization technologies to assist in the process of better comprehending scientific concepts, data, and applications. Highlighting the utilization of visual power and the roles of sensory perceptions, computer graphics, animation, and digital storytelling, this book is an essential reference source for instructors, engineers, programmers, and software developers interested in the exchange of information through the visual depiction of data.

Transforming data into actionable insights using Python KEY FEATURES ● Gain a comprehensive understanding of data visualization and exploratory data analysis (EDA) using Python. ● Discover valuable insights and patterns in data through visual analysis. ● Master the art of effectively communicating complex concepts by creating compelling and impactful data visualizations. DESCRIPTION Python is a popular programming language for data visualization due to its rich ecosystem of libraries and tools. If you're interested in delving into data visualization in Python, this book is an excellent resource to begin your journey. With Matplotlib, you'll master the art of creating a wide range of charts, plots, and graphs. From basic line plots to complex 3D visualizations, you'll learn how to transform raw data into engaging visuals that tell compelling stories. Dive into Seaborn, a high-level library built on top of Matplotlib, and discover how to effortlessly create beautiful and informative statistical visualizations effortlessly. From heatmaps to distribution plots, you'll unleash the full potential of Seaborn in your data analysis endeavors. Lastly, you will learn how to unleash the true potential of Bokeh and create compelling data visualizations that allow users to explore and interact with data dynamically. By the end of the book, you will have acquired the knowledge and skills necessary to create a diverse range of visualizations proficiently. WHAT YOU WILL LEARN ● Utilize Matplotlib, Seaborn, and Bokeh to produce visually captivating visualizations. ● Gain expertise in various types of charts, plots, and graphs. ● Craft visually appealing and informative statistical visualizations. ● Construct interactive and adaptable plots using Bokeh. ● Explore various techniques for conducting Exploratory Data Analysis (EDA). WHO THIS BOOK IS FOR This book caters to a wide audience, including undergraduate and postgraduate students, researchers, data managers, and data analysts. It presents an all-encompassing exploration of data visualization, equipping you with the essential groundwork to progress as a data-driven professional. TABLE OF CONTENTS 1. Understanding Data 2. Data Visualization – Importance 3. Data Visualization Use Cases 4. Data Visualization Tools and Techniques 5. Data Visualization with Matplotlib 6. Data Visualization with Seaborn 7. Data Visualization with Bokeh 8. Exploratory Data Analysis

In education today, technology alone doesn't always lead to immediate success for students or institutions. In order to gauge the efficacy of educational technology, we need ways to measure the efficacy of educational practices in their own right. Through a better understanding of how learning takes place, we may work toward establishing best practices for students, educators, and institutions. These goals can be accomplished with learning analytics. Learning Analytics: From Research to Practice updates this emerging field with the latest in theories, findings, strategies, and tools from across education and technological disciplines. Guiding readers through preparation, design, and examples of implementation, this pioneering reference clarifies LA methods as not mere data collection but sophisticated, systems-based analysis with practical applicability inside the classroom and in the larger world. Case studies illustrate applications of LA throughout academic settings (e.g., intervention, advisement, technology design), and their resulting impact on pedagogy and learning. The goal is to bring greater efficiency and deeper engagement to individual students, learning communities, and educators, as chapters show diverse uses of learning analytics to: Enhance student and faculty performance. Improve student understanding of course material. Assess and attend to the needs of struggling learners. Improve accuracy in grading. Allow instructors to assess and develop their own strengths. Encourage more efficient use of resources at the institutional level. Researchers and practitioners in educational technology, IT, and the learning sciences will hail the information in Learning Analytics: From Research to Practice as a springboard to new levels of student, instructor, and institutional success.

The next step in the information age is to gain insights from the deluge of data coming our way. Data mining provides a way of finding this insight, and Python is one of the most popular languages for data mining, providing both power and flexibility in analysis. This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. Next, we move on to more complex data types including text, images, and graphs. In every chapter, we create models that solve real-world problems. There is a rich and varied set of libraries available in Python for data mining. This book covers a large number, including the IPython Notebook, pandas, scikit-learn and NLTK. Each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will gain a large insight into using Python for data mining, with a good knowledge and understanding of the algorithms and implementations.