Hands-On Data Visualization with Bokeh : Interactive Web Plotting for Python Using Bokeh

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. Table of Contents Bokeh installation and key concepts Plotting using glyphs Plotting with different data structures Using layouts for effective presentation Using annotations, widgets and visual attributes for visual enhancement Building and hosting applications using the Bokeh Server Advanced Plotting with Networks, Geo data, WebGL and Exporting plots The Bokeh Workflow: A case study Leverage the power of Matplotlib to visualize and understand your data more effectively Key FeaturesPerform effective data visualization with Matplotlib and get actionable insights from your dataDesign attractive graphs, charts, and 2D plots, and deploy them to the webGet the most out of Matplotlib in this practical guide with updated code and examplesBook DescriptionPython is a general-purpose programming language increasingly being used for data analysis and visualization. Matplotlib is a popular data visualization package in Python used to design effective plots and graphs. This is a practical, hands-on resource to help you visualize data with Python using the Matplotlib library. Matplotlib for Python Developers, Second Edition shows you how to create attractive graphs, charts, and plots using Matplotlib. You will also get a quick introduction to third-party packages, Seaborn, Pandas, Basemap, and Geopandas, and learn how to use them with Matplotlib. After that, you'll embed and customize your plots in third-party tools such as GTK+3, Qt 5, and wxWidgets. You'll also be able to tweak the look and feel of your visualization with the help of practical examples provided in this book. Further on, you'll explore Matplotlib 2.1.x on the web, from a cloud-based platform using third-party packages such as Django. Finally, you will integrate interactive, real-time visualization techniques into your current workflow with the help of practical real-world examples. By the end of this book, you'll be thoroughly comfortable with using the popular Python data visualization library Matplotlib 2.1.x and leveraging its power to build attractive, insightful, and powerful visualizations. What you will learnCreate 2D and 3D static plots such as bar charts, heat maps, and scatter plotsGet acquainted with GTK+3, Qt5, and wxWidgets to understand the UI backend of MatplotlibDevelop advanced static plots with third-party packages such as Pandas, GeoPandas, and SeabornCreate interactive plots with real-time updatesDevelop web-based, Matplotlib-powered graph visualizations with third-party packages such as DjangoWrite data visualization code that is readily expandable on the cloud platformWho this book is forThis book is essentially for anyone who wants to create intuitive data visualizations using the Matplotlib library. If you're a data scientist or analyst and wish to create attractive visualizations using Python, you'll find this book useful. Some knowledge of Python programming is all you need to get started. Leverage the power of Matplotlib to visualize and understand your data more effectively About This Book Perform effective data visualization with Matplotlib and get actionable insights from your data Design attractive graphs, charts, and 2D plots, and deploy them to the web Get the most out of Matplotlib in this practical guide with updated code and examples Who This Book Is For This book is essentially for anyone who wants to create intuitive data visualizations using the Matplotlib library. If you're a data scientist or analyst and wish to create attractive visualizations using Python, you'll find this book useful. Some knowledge of Python programming is all you need to get started. What You Will Learn Create 2D and 3D static plots such as bar charts, heat maps, and scatter plots Get acquainted with GTK+3, Qt5, and wxWidgets to understand the UI backend of Matplotlib Develop advanced static plots with third-party packages such as Pandas, GeoPandas, and Seaborn Create interactive plots with real-time updates Develop web-based, Matplotlib-powered graph visualizations with third-party packages such as Django Write data visualization code that is readily expandable on the cloud platform In Detail Python is a general-purpose programming language increasingly being used for data analysis and visualization. Matplotlib is a popular data visualization package in Python used to design effective plots and graphs. This is a practical, hands-on resource to help you visualize data with Python using the Matplotlib library. Matplotlib for Python Developers, Second Edition shows you how to create attractive graphs, charts, and plots using Matplotlib. You will also get a quick introduction to third-party packages, Seaborn, Pandas, Basemap, and Geopandas, and learn how to use them with Matplotlib. After that, you'll embed and customize your plots in third-party tools such as GTK+3, Qt 5, and wxWidgets. You'll also be able to tweak the look and feel of your visualization with the help of practical examples provided in this book. Further on, you'll explore Matplotlib 2.1.x on the web, from a cloud-based platform using third-party packages such as Django. Finally, you will integrate interactive, real-time visualization techniques into your current workflow with the help of practical real-world examples. By the end of this book, you'll be thoroughly comfortable with using the popular Python data visualization library Matplotlib 2.1.x and leveraging its power to bu.. 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 Installation 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.

In Detail

matplotlib is part of the Scientific Python modules collection. matplotlib provides a large library of customizable plots and a comprehensive set of backends. It tries to make easy things easy and hard things possible. You can generate plots, add dimensions to the plots, and also make the plots interactive with just a few lines of code with matplotlib. Also, matplotlib integrates well with all common GUI modules.

This book is a head-first, hands-on journey into matplotlib, the complete and definite plotting package for Python. You will learn about the basic plots, how to customize them, and combine them to make sophisticated figures. Along with basic plots, you will also learn to make professional scientific plots.

In this book, you will start with the common figures that are offered by most plotting packages. You will learn how to add annotations, and play with styles, colors, scales, and shapes so that you can add personality and visual punch to your graphics. You will also see how to combine several graphics. With this book you will learn how to create sophisticated visualizations with simple code. Finally, you can make your plots interactive.

After reading "matplotlib Plotting Cookbook", you will be able to create the highest quality plots.

Approach

This book follows a cookbook style approach that puts orthogonal and non-redundant recipes in your hands. Rather than rehashing the user manual, the explanations expose the underlying logic behind matplotlib.

Who this book is for

If you are an engineer or scientist who wants to create great visualizations with Python, rather than yet another specialized language, this is the book for you. While there are several very competent plotting packages, matplotlib is just a Python module. Thus, if you know some Python already, you will feel at home from the first steps on. In case you are an application writer, you won't be left out since the integration of matplotlib is covered.

Learn how to create professional scientific plots using matplotlib, with more than 60 recipes that cover common use cases If you are an engineer or scientist who wants to create great visualizations with Python, rather than yet another specialized language, this is the book for you. While there are several very competent plotting packages, matplotlib is just a Python module. Thus, if you know some Python already, you will feel at home from the first steps on. In case you are an application writer, you won't be left out since the integration of matplotlib is covered. matplotlib is part of the Scientific Python modules collection. matplotlib provides a large library of customizable plots and a comprehensive set of backends. It tries to make easy things easy and hard things possible. You can generate plots, add dimensions to the plots, and also make the plots interactive with just a few lines of code with matplotlib. Also, matplotlib integrates well with all common GUI modules. This book is a head-first, hands-on journey into matplotlib, the complete and definite plotting package for Python. You will learn about the basic plots, how to customize them, and combine them to make sophisticated figures. Along with basic plots, you will also learn to make professional scientific plots. In this book, you will start with the common figures that are offered by most plotting packages. You will learn how to add annotations, and play with styles, colors, scales, and shapes so that you can add personality and visual punch to your graphics. You will also see how to combine several graphics. With this book you will learn how to create sophisticated visualizations with simple code. Finally, you can make your plots interactive. After reading "matplotlib Plotting Cookbook", you will be able to create the highest quality plots. Chapter 2: Getting Started with Matplotlib; Loading data; List; NumPy array; pandas DataFrame; Our first plots with Matplotlib; Importing the pyplot; Line plot; Scatter plot; Overlaying multiple data series in a plot; Multiline plots; Scatter plot to show clusters; Adding a trendline over a scatter plot; Adjusting axes, grids, labels, titles, and legends; Adjusting axis limits; Adding axis labels; Adding a grid; Titles and legends; Adding a title; Adding a legend; A complete example; Saving plots to a file; Setting the output format; Setting the figure resolution; Jupyter support 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. COM089000 - COMPUTERS / Data Visualization,COM051360 - COMPUTERS / Programming Languages / Python,COM018000 - COMPUTERS / Data Processing This book is a useful resource to perform data visualization with Python using the latest version of Matplotlib (2.1.x). You will create a variety of graphs and charts, and embed your plots within different third party tools. By the end of the book, you will build attractive, insightful and powerful visualizations to make better sense of your data.