Holoviews In Scientific Data Visualization

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Holoviews In Scientific Data Visualization

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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-05-29

Holoviews In Scientific Data Visualization written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-29 with Computers categories.

"HoloViews in Scientific Data Visualization" "HoloViews in Scientific Data Visualization" is an expertly crafted guide that navigates the evolving landscape of scientific data visualization with a focus on clarity, reproducibility, and modern Python tooling. Beginning with the foundational role of visualization in scientific computing, the book explores the complexities of handling multidimensional, heterogeneous data and the imperative goals of scientific visual analytics, including exploration, confirmation, and transparent communication. It provides a comprehensive survey of both traditional platforms and emerging Python-based solutions, situating HoloViews within the broader ecosystem and emphasizing the crucial balance between interactivity and reproducibility in research. The heart of the book dives into the architecture and declarative paradigms that distinguish HoloViews from imperative plotting libraries, detailing its object model of elements, overlays, and layouts. Readers are guided through the seamless integration of data from arrays and dataframes, flexible customization, and the extensibility afforded by various plotting backends. Practical chapters dissect the building blocks of visualization—curves, heatmaps, vector fields, linked multi-plot layouts, and annotation strategies—while advanced sections provide actionable techniques for real-time streaming, cross-filtering, performance optimization, and interactive scientific dashboards that scale from desktop to web deployments. Rich in multidisciplinary case studies and best practices, this volume equips scientists, analysts, and developers with the tools to construct robust, reproducible, and publication-quality visualizations across domains. From handling high-density arrays and geospatial datasets to enabling seamless collaboration through version control, CI/CD, and adherence to open science standards, "HoloViews in Scientific Data Visualization" serves as a definitive reference for leveraging HoloViews to unlock deeper insight and foster innovation at the frontier of computational research.

Python Tools For Scientists

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Author : Lee Vaughan
language : en
Publisher: No Starch Press
Release Date : 2023-01-17

Python Tools For Scientists written by Lee Vaughan and has been published by No Starch Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-17 with Computers categories.

An introduction to the Python programming language and its most popular tools for scientists, engineers, students, and anyone who wants to use Python for research, simulations, and collaboration. Python Tools for Scientists will introduce you to Python tools you can use in your scientific research, including Anaconda, Spyder, Jupyter Notebooks, JupyterLab, and numerous Python libraries. You’ll learn to use Python for tasks such as creating visualizations, representing geospatial information, simulating natural events, and manipulating numerical data. Once you’ve built an optimal programming environment with Anaconda, you’ll learn how to organize your projects and use interpreters, text editors, notebooks, and development environments to work with your code. Following the book’s fast-paced Python primer, you’ll tour a range of scientific tools and libraries like scikit-learn and seaborn that you can use to manipulate and visualize your data, or analyze it with machine learning algorithms. You’ll also learn how to: Create isolated projects in virtual environments, build interactive notebooks, test code in the Qt console, and use Spyder’s interactive development features Use Python’s built-in data types, write custom functions and classes, and document your code Represent data with the essential NumPy, Matplotlib, and pandas libraries Use Python plotting libraries like Plotly, HoloViews, and Datashader to handle large datasets and create 3D visualizations Regardless of your scientific field, Python Tools for Scientists will show you how to choose the best tools to meet your research and computational analysis needs.

Efficient Data Science Workflows With Vaex

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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-18

Efficient Data Science Workflows With Vaex written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-18 with Computers categories.

"Efficient Data Science Workflows with Vaex" Efficient Data Science Workflows with Vaex delivers a comprehensive exploration of modern data science challenges and introduces Vaex as an innovative solution for handling and analyzing massive datasets at scale. The book presents a compelling case for the transition from traditional in-memory tools, such as pandas and NumPy, to more advanced, out-of-core solutions that effortlessly process data far exceeding physical memory constraints. Through detailed case studies and foundational principles, readers gain a deep understanding of both the limitations of legacy approaches and the critical requirements for building robust, reproducible, and scalable data pipelines. The book systematically guides practitioners through Vaex’s architecture, emphasizing its memory mapping, lazy evaluation, and columnar data handling capabilities. Practical chapters cover everything from efficient data ingestion and preprocessing, advanced transformation techniques, and high-performance analytics to seamless machine learning workflows and interactive visualization. Special attention is given to challenging aspects such as distributed and cloud-based analysis, incorporating strategies for parallelism, cloud-native deployments, and orchestration, all while maintaining security, scalability, and performance. Featuring real-world case studies and empirical benchmarks comparing Vaex to alternative frameworks, this book is an authoritative reference for data scientists and engineers seeking to maximize efficiency and throughput in their analytics workflows. Best practices, troubleshooting guidance, and insights into the growing Vaex ecosystem ensure that readers are equipped not only to master today’s large-scale data challenges but also to contribute to and shape the future of scalable data science.

Hands On Data Analysis With Pandas

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Author : Stefanie Molin
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-04-29

Hands On Data Analysis With Pandas written by Stefanie Molin and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-29 with Computers categories.

Get to grips with pandas by working with real datasets and master data discovery, data manipulation, data preparation, and handling data for analytical tasks Key Features Perform efficient data analysis and manipulation tasks using pandas 1.x Apply pandas to different real-world domains with the help of step-by-step examples Make the most of pandas as an effective data exploration tool Book DescriptionExtracting valuable business insights is no longer a ‘nice-to-have’, but an essential skill for anyone who handles data in their enterprise. Hands-On Data Analysis with Pandas is here to help beginners and those who are migrating their skills into data science get up to speed in no time. This book will show you how to analyze your data, get started with machine learning, and work effectively with the Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. This updated edition will equip you with the skills you need to use pandas 1.x to efficiently perform various data manipulation tasks, reliably reproduce analyses, and visualize your data for effective decision making – valuable knowledge that can be applied across multiple domains.What you will learn Understand how data analysts and scientists gather and analyze data Perform data analysis and data wrangling using Python Combine, group, and aggregate data from multiple sources Create data visualizations with pandas, matplotlib, and seaborn Apply machine learning algorithms to identify patterns and make predictions Use Python data science libraries to analyze real-world datasets Solve common data representation and analysis problems using pandas Build Python scripts, modules, and packages for reusable analysis code Who this book is for This book is for data science beginners, data analysts, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. Data scientists looking to implement pandas in their machine learning workflow will also find plenty of valuable know-how as they progress. You’ll find it easier to follow along with this book if you have a working knowledge of the Python programming language, but a Python crash-course tutorial is provided in the code bundle for anyone who needs a refresher.

Ipython Interactive Computing And Visualization Cookbook

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Author : Cyrille Rossant
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-01-31

Ipython Interactive Computing And Visualization Cookbook written by Cyrille Rossant and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-31 with Computers categories.

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.

Applied Geospatial Data Science With Python

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Author : David S. Jordan
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-02-28

Applied Geospatial Data Science With Python written by David S. Jordan and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-28 with Computers categories.

Intelligently connect data points and gain a deeper understanding of environmental problems through hands-on Geospatial Data Science case studies written in Python The book includes colored images of important concepts Key Features Learn how to integrate spatial data and spatial thinking into traditional data science workflows Develop a spatial perspective and learn to avoid common pitfalls along the way Gain expertise through practical case studies applicable in a variety of industries with code samples that can be reproduced and expanded Book DescriptionData scientists, when presented with a myriad of data, can often lose sight of how to present geospatial analyses in a meaningful way so that it makes sense to everyone. Using Python to visualize data helps stakeholders in less technical roles to understand the problem and seek solutions. The goal of this book is to help data scientists and GIS professionals learn and implement geospatial data science workflows using Python. Throughout this book, you’ll uncover numerous geospatial Python libraries with which you can develop end-to-end spatial data science workflows. You’ll learn how to read, process, and manipulate spatial data effectively. With data in hand, you’ll move on to crafting spatial data visualizations to better understand and tell the story of your data through static and dynamic mapping applications. As you progress through the book, you’ll find yourself developing geospatial AI and ML models focused on clustering, regression, and optimization. The use cases can be leveraged as building blocks for more advanced work in a variety of industries. By the end of the book, you’ll be able to tackle random data, find meaningful correlations, and make geospatial data models.What you will learn Understand the fundamentals needed to work with geospatial data Transition from tabular to geo-enabled data in your workflows Develop an introductory portfolio of spatial data science work using Python Gain hands-on skills with case studies relevant to different industries Discover best practices focusing on geospatial data to bring a positive change in your environment Explore solving use cases, such as traveling salesperson and vehicle routing problems Who this book is for This book is for you if you are a data scientist seeking to incorporate geospatial thinking into your workflows or a GIS professional seeking to incorporate data science methods into yours. You’ll need to have a foundational knowledge of Python for data analysis and/or data science.

Python Data Science Handbook

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Author : Jake VanderPlas
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-11-21

Python Data Science Handbook written by Jake VanderPlas and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-21 with Computers categories.

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

Data Science

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Author : Prabhu TL
language : en
Publisher: NestFame Creations Pvt Ltd.
Release Date : 2025-04-12

Data Science written by Prabhu TL and has been published by NestFame Creations Pvt Ltd. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-12 with Computers categories.

Data Science: From Basics to Advanced Unlock the Power of Data to Build Intelligent Solutions and Transform Your Career Are you ready to master one of the most in-demand and future-proof skills of the 21st century? Whether you're a beginner, student, working professional, or tech enthusiast—this comprehensive guide is your ultimate roadmap to becoming a data science expert. “Data Science: From Basics to Advanced” takes you on a complete journey through the world of data, starting from foundational concepts and evolving all the way to advanced machine learning, deep learning, and real-world deployment. ✅ What You’ll Learn Inside: 📊 Statistics, Probability & Linear Algebra — The math behind the magic 🐍 Python Programming — Clean and efficient data handling with NumPy and pandas 🔍 Exploratory Data Analysis — Visualize, understand, and tell stories with data 🤖 Machine Learning & Deep Learning — Build, train, and tune powerful models 🧠 Natural Language Processing, Time Series, and Computer Vision ☁️ Cloud Tools, Big Data, and MLOps — Deploy scalable solutions using AWS, GCP, and more 🔐 Bias, Fairness & Data Ethics — Build responsible, human-centered AI 🧳 Career Tools — Portfolio templates, interview prep, certifications, and roadmaps 💡 Who This Book Is For: Beginners looking for a step-by-step introduction to data science Professionals seeking to upskill or transition into AI/ML roles Students preparing for internships and job interviews Entrepreneurs and business leaders leveraging data-driven strategies 🎓 Includes: ✅ Real-world projects and use cases ✅ Sample code and reusable templates ✅ Cheat sheets, glossary, and portfolio guidance ✅ Companion resources and learning roadmap If you've ever wanted to extract insight from raw data, build machine learning models, or launch a data science career, this is the book you've been waiting for. 🔥 Your journey into data starts now. 📘 Get your copy of Data Science: From Basics to Advanced and turn information into impact.

Data Science With Python And Dask

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Author : Jesse Daniel
language : en
Publisher: Simon and Schuster
Release Date : 2019-07-08

Data Science With Python And Dask written by Jesse Daniel and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-08 with Computers categories.

Summary Dask is a native parallel analytics tool designed to integrate seamlessly with the libraries you're already using, including Pandas, NumPy, and Scikit-Learn. With Dask you can crunch and work with huge datasets, using the tools you already have. And Data Science with Python and Dask is your guide to using Dask for your data projects without changing the way you work! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. You'll find registration instructions inside the print book. About the Technology An efficient data pipeline means everything for the success of a data science project. Dask is a flexible library for parallel computing in Python that makes it easy to build intuitive workflows for ingesting and analyzing large, distributed datasets. Dask provides dynamic task scheduling and parallel collections that extend the functionality of NumPy, Pandas, and Scikit-learn, enabling users to scale their code from a single laptop to a cluster of hundreds of machines with ease. About the Book Data Science with Python and Dask teaches you to build scalable projects that can handle massive datasets. After meeting the Dask framework, you'll analyze data in the NYC Parking Ticket database and use DataFrames to streamline your process. Then, you'll create machine learning models using Dask-ML, build interactive visualizations, and build clusters using AWS and Docker. What's inside Working with large, structured and unstructured datasets Visualization with Seaborn and Datashader Implementing your own algorithms Building distributed apps with Dask Distributed Packaging and deploying Dask apps About the Reader For data scientists and developers with experience using Python and the PyData stack. About the Author Jesse Daniel is an experienced Python developer. He taught Python for Data Science at the University of Denver and leads a team of data scientists at a Denver-based media technology company. Table of Contents PART 1 - The Building Blocks of scalable computing Why scalable computing matters Introducing Dask PART 2 - Working with Structured Data using Dask DataFrames Introducing Dask DataFrames Loading data into DataFrames Cleaning and transforming DataFrames Summarizing and analyzing DataFrames Visualizing DataFrames with Seaborn Visualizing location data with Datashader PART 3 - Extending and deploying Dask Working with Bags and Arrays Machine learning with Dask-ML Scaling and deploying Dask

Data Mining With Python

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Author : Di Wu
language : en
Publisher: CRC Press
Release Date : 2024-04-10

Data Mining With Python written by Di Wu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-10 with Computers categories.

Data is everywhere and it’s growing at an unprecedented rate. But making sense of all that data is a challenge. Data Mining is the process of discovering patterns and knowledge from large data sets, and Data Mining with Python focuses on the hands-on approach to learning Data Mining. It showcases how to use Python Packages to fulfill the Data Mining pipeline, which is to collect, integrate, manipulate, clean, process, organize, and analyze data for knowledge. The contents are organized based on the Data Mining pipeline, so readers can naturally progress step by step through the process. Topics, methods, and tools are explained in three aspects: “What it is” as a theoretical background, “why we need it” as an application orientation, and “how we do it” as a case study. This book is designed to give students, data scientists, and business analysts an understanding of Data Mining concepts in an applicable way. Through interactive tutorials that can be run, modified, and used for a more comprehensive learning experience, this book will help its readers to gain practical skills to implement Data Mining techniques in their work.