This article explains the origins and applications of Sankey diagrams, demonstrates how to create them with Plotly in Python across various scenarios such as website navigation, energy conversion, cost breakdown, financial flows, data migration, and confusion matrix visualization, and provides complete code examples.
The article introduces d3blocks, a Python library that extends D3.js's interactive visualization capabilities to Python, demonstrating how to create interactive scatter, network, Sankey, and image slider charts with just a few lines of code and offering examples with code snippets and GIF illustrations.
This tutorial explains what Sankey diagrams are, shows their main forms, and provides step‑by‑step Python code using Matplotlib and Pyecharts to generate both basic and advanced Sankey visualizations, including tips on data preparation and styling.
This article examines the limitations of traditional Sankey diagrams for visualizing entity state transitions and proposes a card‑based data flow visualization that offers customizable start/end states, improved readability for disparate data scales, and enhanced user experience through interactive layouts and clear styling.
The article details the design and engineering of a high‑performance path analysis model for internet data operations, explaining session handling, Sankey visualizations, adjacency‑table storage, multi‑granular session partitioning, Spark‑to‑ClickHouse pipelines, and optimizations that enable billion‑scale user‑path queries in about one second.
