Altair Python
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Altair in Python Applications
"Altair in Python Applications" "Altair in Python Applications" is a comprehensive guide for practitioners, researchers, and analysts seeking to master declarative data visualization in the Python ecosystem. The book begins by establishing a strong foundation in the principles of declarative visualization and the grammar of graphics, showcasing Altair's philosophy and technical underpinnings in relation to the broader Vega-Lite ecosystem. Readers are introduced to best practices for installation and environment management, as well as a thoughtful comparison with other major visualization libraries, empowering users to make informed choices for diverse analytical tasks. Building on these fundamentals, the book delves into advanced visualization techniques, including complex data transformations, encoding strategies, and interactive data exploration. Readers learn to construct multilayered, custom, and high-performance visualizations, apply responsive analytics, and design robust dashboards. Special emphasis is placed on interactivity—guiding users through selections, parameterization, cross-filtering, and the integration of user controls and widgets for a truly dynamic analytic experience. Integration with Python's broader data science and web application patterns is explored in detail, covering workflows within Jupyter, deployment in modern frameworks, and strategies for collaboration and reproducibility. The later chapters take the reader further, demonstrating how to extend and customize Altair with plugins, renderers, and schema modifications. Real-world case studies—spanning scientific research, business intelligence, financial analytics, machine learning, and geospatial domains—illustrate Altair’s practical applications and communicative power. The book concludes by addressing crucial topics such as performance, scalability, and security, as well as exploring emerging trends in declarative visualization, interoperability, AI integration, and open-source extensibility. Altogether, "Altair in Python Applications" is an indispensable resource for creating insightful, interactive, and scalable visual analytics with confidence and precision.
Data Visualization in R and Python
Communicate the data that is powering our changing world with this essential text The advent of machine learning and neural networks in recent years, along with other technologies under the broader umbrella of ‘artificial intelligence,’ has produced an explosion in Data Science research and applications. Data Visualization, which combines the technical knowledge of how to work with data and the visual and communication skills required to present it, is an integral part of this subject. The expansion of Data Science is already leading to greater demand for new approaches to Data Visualization, a process that promises only to grow. Data Visualization in R and Python offers a thorough overview of the key dimensions of this subject. Beginning with the fundamentals of data visualization with Python and R, two key environments for data science, the book proceeds to lay out a range of tools for data visualization and their applications in web dashboards, data science environments, graphics, maps, and more. With an eye towards remarkable recent progress in open-source systems and tools, this book offers a cutting-edge introduction to this rapidly growing area of research and technological development. Data Visualization in R and Python readers will also find: Coverage suitable for anyone with a foundational knowledge of R and Python Detailed treatment of tools including the Ggplot2, Seaborn, and Altair libraries, Plotly/Dash, Shiny, and others Case studies accompanying each chapter, with full explanations for data operations and logic for each, based on Open Data from many different sources and of different formats Data Visualization in R and Python is ideal for any student or professional looking to understand the working principles of this key field.
Introduction to Python Programming
Introduction to Python Programming is written for students who are beginners in the field of computer programming. This book presents an intuitive approach to the concepts of Python Programming for students. This book differs from traditional texts not only in its philosophy but also in its overall focus, level of activities, development of topics, and attention to programming details. The contents of the book are chosen with utmost care after analyzing the syllabus for Python course prescribed by various top universities in USA, Europe, and Asia. Since the prerequisite know-how varies significantly from student to student, the book’s overall overture addresses the challenges of teaching and learning of students which is fine-tuned by the authors’ experience with large sections of students. This book uses natural language expressions instead of the traditional shortened words of the programming world. This book has been written with the goal to provide students with a textbook that can be easily understood and to make a connection between what students are learning and how they may apply that knowledge. Features of this book This book does not assume any previous programming experience, although of course, any exposure to other programming languages is useful This book introduces all of the key concepts of Python programming language with helpful illustrations Programming examples are presented in a clear and consistent manner Each line of code is numbered and explained in detail Use of f-strings throughout the book Hundreds of real-world examples are included and they come from fields such as entertainment, sports, music and environmental studies Students can periodically check their progress with in-chapter quizzes that appear in all chapters