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Interactive Data Visualization with Python: Present Your Data as an Effective and Compelling Story
Create your own clear and impactful interactive data visualizations with the powerful data visualization libraries of Python
Key Features
Study and use Python interactive libraries, such as Bokeh and Plotly Explore different visualization principles and understand when to use which one Create interactive data visualizations with real-world data Book 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 learn
Explore and apply different interactive data visualization techniques Manipulate plotting parameters and styles to create appealing plots Customize data visualization for different audiences Design data visualizations using interactive libraries Use Matplotlib, Seaborn, Altair and Bokeh for drawing appealing plots Customize data visualization for different scenarios Who 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.
Key Features
Study and use Python interactive libraries, such as Bokeh and Plotly Explore different visualization principles and understand when to use which one Create interactive data visualizations with real-world data Book 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 learn
Explore and apply different interactive data visualization techniques Manipulate plotting parameters and styles to create appealing plots Customize data visualization for different audiences Design data visualizations using interactive libraries Use Matplotlib, Seaborn, Altair and Bokeh for drawing appealing plots Customize data visualization for different scenarios Who 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.
- GenresProgramming
362 pages, Paperback
Published April 14, 2020
2 people are currently reading
23 people want to read
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Displaying 1 of 1 review
May 31, 2024
I found this book difficult to read for a couple of reasons:
1) Too much new information is given in the form of exercises without clearly explaining the conceptual underpinning and functions a-priori. In some cases new formulas are introduced in the exercise code snippets without any discussion, so I had to go elsewhere to understand how the functions work.
2) There is too much repetition in the code snippets. Each time an example is displayed, you have to read the duplicate snippets of code for installing the same packages and loading the same data. Removing these vast sections of duplicate code could have been used to address some of the issues referenced in point 1 above.
3) There are errors in the text too. References to other pages are sometimes incorrect, sometimes new lines of code start halfway through the line (following another section of code on the preceding part of the line) and in one case the table displayed after a code snippet is not consistent with commentary in the text surrounding the table.
Whilst I do think that there is some great content within the book, the structure and lack of sufficient editing and proof-reading make this a difficult book to read.
1) Too much new information is given in the form of exercises without clearly explaining the conceptual underpinning and functions a-priori. In some cases new formulas are introduced in the exercise code snippets without any discussion, so I had to go elsewhere to understand how the functions work.
2) There is too much repetition in the code snippets. Each time an example is displayed, you have to read the duplicate snippets of code for installing the same packages and loading the same data. Removing these vast sections of duplicate code could have been used to address some of the issues referenced in point 1 above.
3) There are errors in the text too. References to other pages are sometimes incorrect, sometimes new lines of code start halfway through the line (following another section of code on the preceding part of the line) and in one case the table displayed after a code snippet is not consistent with commentary in the text surrounding the table.
Whilst I do think that there is some great content within the book, the structure and lack of sufficient editing and proof-reading make this a difficult book to read.
Displaying 1 of 1 review