Abstract
This chapter demonstrates various plottings for data visualization. Various Python libraries can be used for data visualization, such as Pandas, Seaborn, Bokeh, Pygal, and Ploty. Python Pandas is the simplest method for basic plotting. Python Seaborn is great for creating visually appealing statistical charts that include color. Python Bokeh works great for more complicated visualizations, especially for web-based interactive presentations. Python Pygal works well for generating vector and interactive files. However, it does not have the flexibility that other methods do. Python Plotly is the most useful and easiest option for creating highly interactive web-based visualizations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2018 Dr. Ossama Embarak
About this chapter
Cite this chapter
Embarak, O. (2018). Data Visualization. In: Data Analysis and Visualization Using Python. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-4109-7_7
Download citation
DOI: https://doi.org/10.1007/978-1-4842-4109-7_7
Published:
Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-4108-0
Online ISBN: 978-1-4842-4109-7
eBook Packages: Professional and Applied ComputingApress Access BooksProfessional and Applied Computing (R0)