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

Create your own clear and impactful interactive data visualizations with the powerful data visualization libraries of Python Key FeaturesStudy and use Python interactive libraries, such as Bokeh and PlotlyExplore different visualization principles and understand when to use which oneCreate interactive data visualizations with real-world dataBook Description With so much data being continuously generated, developers, who can present data as impactful and interesting visualizations, are always in demand. Interactive Data Visualization with Python sharpens your data exploration skills, tells you everything there is to know about interactive data visualization in Python. You'll begin by learning how to draw various plots with Matplotlib and Seaborn, the non-interactive data visualization libraries. You'll study different types of visualizations, compare them, and find out how to select a particular type of visualization to suit your requirements. After you get a hang of the various non-interactive visualization libraries, you'll learn the principles of intuitive and persuasive data visualization, and use Bokeh and Plotly to transform your visuals into strong stories. You'll also gain insight into how interactive data and model visualization can optimize the performance of a regression model. By the end of the course, you'll have a new skill set that'll make you the go-to person for transforming data visualizations into engaging and interesting stories. What you will learnExplore and apply different interactive data visualization techniquesManipulate plotting parameters and styles to create appealing plotsCustomize data visualization for different audiencesDesign data visualizations using interactive librariesUse Matplotlib, Seaborn, Altair and Bokeh for drawing appealing plotsCustomize data visualization for different scenariosWho this book is for This book intends to provide a solid training ground for Python developers, data analysts and data scientists to enable them to present critical data insights in a way that best captures the user's attention and imagination. It serves as a simple step-by-step guide that demonstrates the different types and components of visualization, the principles, and techniques of effective interactivity, as well as common pitfalls to avoid when creating interactive data visualizations. Students should have an intermediate level of competency in writing Python code, as well as some familiarity with using libraries such as pandas.

Understand, explore, and effectively present data using the powerful data visualization techniques of Python programming. Key Features Study key visualization tools and techniques with real-world data Explore industry-standard plotting libraries, including Matplotlib and Seaborn Breathe life into your visuals with exciting widgets and animations using Bokeh Book Description Data Visualization with Python reviews the spectrum of data visualization and its importance. Designed for beginners, it'll help you learn about statistics by computing mean, median, and variance for certain numbers. In the first few chapters, you'll be able to take a quick tour of key NumPy and Pandas techniques, which include indexing, slicing, iterating, filtering, and grouping. The book keeps pace with your learning needs, introducing you to various visualization libraries. As you work through chapters on Matplotlib and Seaborn, you'll discover how to create visualizations in an easier way. After a lesson on these concepts, you can then brush up on advanced visualization techniques like geoplots and interactive plots. You'll learn how to make sense of geospatial data, create interactive visualizations that can be integrated into any webpage, and take any dataset to build beautiful visualizations. What's more? You'll study how to plot geospatial data on a map using Choropleth plot and understand the basics of Bokeh, extending plots by adding widgets and animating the display of information. By the end of this book, you'll be able to put your learning into practice with an engaging activity, where you can work with a new dataset to create an insightful capstone visualization. What you will learn Understand and use various plot types with Python Explore and work with different plotting libraries Learn to create effective visualizations Improve your Python data wrangling skills Hone your skill set by using tools like Matplotlib, Seaborn, and Bokeh Reinforce your knowledge of various data formats and representations Who this book is for Data Visualization with Python is designed for developers and scientists, who want to get into data science or want to use data visualizations to enrich their personal and professional projects. You do not need any prior experience in data analytics and visualization, however, it'll help you to have some knowledge of Python and familiarity with high school level mathematics. Even though this is a beginner level course on data visualization, experienced developers will be able to improve their Python skills by working with real-world data.

Build your data science skills. Start data visualization Using Python. Right away. Become a good data analyst by creating quality data visualizations using Python. KEY FEATURES ● Exciting coverage on loads of Python libraries, including Matplotlib, Seaborn, Pandas, and Plotly. ● Tons of examples, illustrations, and use-cases to demonstrate visual storytelling of varied datasets. ● Covers a strong fundamental understanding of exploratory data analysis (EDA), statistical modeling, and data mining. DESCRIPTION Data visualization plays a major role in solving data science challenges with various capabilities it offers. This book aims to equip you with a sound knowledge of Python in conjunction with the concepts you need to master to succeed as a data visualization expert. The book starts with a brief introduction to the world of data visualization and talks about why it is important, the history of visualization, and the capabilities it offers. You will learn how to do simple Python-based visualization with examples with progressive complexity of key features. The book starts with Matplotlib and explores the power of data visualization with over 50 examples. It then explores the power of data visualization using one of the popular exploratory data analysis-oriented libraries, Pandas. The book talks about statistically inclined data visualization libraries such as Seaborn. The book also teaches how we can leverage bokeh and Plotly for interactive data visualization. Each chapter is enriched and loaded with 30+ examples that will guide you in learning everything about data visualization and storytelling of mixed datasets. WHAT YOU WILL LEARN ● Learn to work with popular Python libraries and frameworks, including Seaborn, Bokeh, and Plotly. ● Practice your data visualization understanding across numerous datasets and real examples. ● Learn to visualize geospatial and time-series datasets. ● Perform correlation and EDA analysis using Pandas and Matplotlib. ● Get to know storytelling of complex and unstructured data using Bokeh and Pandas. ● Learn best practices in writing clean and short python scripts for a quicker visual summary of datasets. WHO THIS BOOK IS FOR This book is for all data analytics professionals, data scientists, and data mining hobbyists who want to be strong data visualizers by learning all the popular Python data visualization libraries. Prior working knowledge of Python is assumed. TABLE OF CONTENTS 1. Introduction to Data Visualization 2. Why Data Visualization 3. Various Data Visualization Elements and Tools 4. Using Matplotlib with Python 5. Using NumPy and Pandas for Plotting 6. Using Seaborn for Visualization 7. Using Bokeh with Python 8. Using Plotly, Folium, and Other Tools for Data Visualization 9. Hands-on Examples and Exercises, Case Studies, and Further Resources

Look at Python from a data science point of view and learn proven techniques for data visualization as used in making critical business decisions. Starting with an introduction to data science with Python, you will take a closer look at the Python environment and get acquainted with editors such as Jupyter Notebook and Spyder. After going through a primer on Python programming, you will grasp fundamental Python programming techniques used in data science. Moving on to data visualization, you will see how it caters to modern business needs and forms a key factor in decision-making. You will also take a look at some popular data visualization libraries in Python. Shifting focus to data structures, you will learn the various aspects of data structures from a data science perspective. You will then work with file I/O and regular expressions in Python, followed by gathering and cleaning data. Moving on to exploring and analyzing data, you will look at advanced data structures in Python. Then, you will take a deep dive into data visualization techniques, going through a number of plotting systems in Python. In conclusion, you will complete a detailed case study, where you’ll get a chance to revisit the concepts you’ve covered so far. What You Will LearnUse Python programming techniques for data science Master data collections in Python Create engaging visualizations for BI systems Deploy effective strategies for gathering and cleaning data Integrate the Seaborn and Matplotlib plotting systems Who This Book Is For Developers with basic Python programming knowledge looking to adopt key strategies for data analysis and visualizations using Python.

Quickly start programming with Python 3 for data visualization with this step-by-step, detailed guide. This book’s programming-friendly approach using libraries such as leather, NumPy, Matplotlib, and Pandas will serve as a template for business and scientific visualizations. You’ll begin by installing Python 3, see how to work in Jupyter notebook, and explore Leather, Python’s popular data visualization charting library. You’ll also be introduced to the scientific Python 3 ecosystem and work with the basics of NumPy, an integral part of that ecosystem. Later chapters are focused on various NumPy routines along with getting started with Scientific Data visualization using matplotlib. You’ll review the visualization of 3D data using graphs and networks and finish up by looking at data visualization with Pandas, including the visualization of COVID-19 data sets. The code examples are tested on popular platforms like Ubuntu, Windows, and Raspberry Pi OS. With Practical Python Data Visualization you’ll master the core concepts of data visualization with Pandas and the Jupyter notebook interface. What You'll Learn Review practical aspects of Python Data Visualization with programming-friendly abstractions Install Python 3 and Jupyter on multiple platforms including Windows, Raspberry Pi, and Ubuntu Visualize COVID-19 data sets with Pandas Who This Book Is For Data Science enthusiasts and professionals, Business analysts and managers, software engineers, data engineers.

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 Visualization is the presentation of data in graphical format.In this tutorial for beginners, you will learn how to present data graphically with Python, Matplotlib, and Seaborn. If you need a short book to master data vizualisation from scratch, this guide is for you. Get your copy now!!!Book ObjectivesThis book is an exploration of data visualization in Python programming language. Here are the objectives of the book: To help you understand the need for data visualization and appreciate its power in data analysis. To help you learn the various types of plots that you can create to visualize your data. To help you know the various tools that you can use for data visualization, including basic, specialized and advanced tools. To help you make the right decision in choosing the tool and the kind of plot to use to visualize your data. To help you learn the power of Python in data visualization. To equip you with data visualization skills in Python programming language. To help you learn the various Python libraries that you can use for data visualization. Who this Book is for? The author targets the following groups of people: Anyone who needs to know the need for data visualization in an organization. Any individual who needs to know the various tools they can use for data visualization. Any individual who needs to know the various types of graphics they can use to represent their data and how to interpret the graphics. Anybody who needs to learn data visualization in Python using various libraries such as Pandas, Matplotlib, Seaborn and Folium. Anyone who needs to learn how to visualize different types of data including textual, numerical and geospatial data. RequirementsThe author expects you to have a computer installed with an operating system such as Linux, Windows or Mac OS X. What is inside the book? BASICS OF DATA VISUALIZATION BASIC AND SPECIALIZED DATA VISUALIZATION TOOLS ADVANCED VISUALIZATIONS TOOLSEXPLORING THE LIBRARIES DATA VISUALIZATION WITH MATPLOTLIBDATA VISUALIZATION WITH PANDAS DATA VISUALIZATION WITH SEABORN CREATING MAPS AND VISUALIZING GEOSPATIAL DATA The author has discussed everything related to data visualization. You are first familiarized with the fundamentals of data visualization to help you know what it is and why it is of importance to any organization. The author has then discussed the various types of tools that can be used for data visualization. These tools include the basic, specialized and advanced ones. Practically, the author focuses on how to visualize data in the Python programming language. The process of plotting different types of data using different types of plots has been discussed. You will learn how to plot textual, numerical and geospatial data in Python using different libraries such as Pandas, Matplotlib, Seaborn and Folium. Python codes have been provided alongside images of the expected outputs and the corresponding code descriptions.

If you are a Python novice or an experienced developer and want to explore data visualization libraries, then this is the book for you. No prior charting or graphics experience is needed.