Interactive Data Visalization with Python¶
Welcome to Interactive Data Visalization with Python. This is a short sequence of notes on Data Visualization for scientific work, specially in the field of data science written by Jubayer Hossian. This site is available online at github
Note
These notes were built using the new Sphinx-based Jupyter Book 2.0 tool set, as part of the ExecutableBookProject. They are intended mainly as a demonstration of these tools. Instructions for how to build them from source can be found in the Jupyter Book documentation.
“A picture is worth a thousand words” Because of the way the human brain processes information, using charts or graphs to visualize large amounts of complex data is easier than poring over spreadsheets or reports. Data visualization is a quick, easy way to convey concepts in a universal manner – and you can experiment with different scenarios by making slight adjustments.
In statistics, we generally have two kinds of visualization:
Exploratory data visualization: Exploring the data visually to find patterns among the data entities.
Explanatory data visualization: Showcasing the identified patterns using simple graphs.
In this book, I’ll use Plotly data visualization library. Plotly is built with Python
Plotly, also known by its URL, Plot.ly, is a technical computing company headquartered in Montreal, Quebec, that develops online data analytics and visualization tools.
Plotly.py Plotly’s Python graphing library makes interactive, publication-quality graphs online.
Data visualization toolbox
Create interactive plots
Online collaboration and sharing
Export to variety of formats
Under the hood, each plotly graph is a JSON object
Python API interacts with the plotly.js library
Each object is based on the plotly.js library
This object supports visualizations and interactivity
Python API interacts with the plotly.js library