Sankey Chart using Python

Sankey diagrams are a useful tool for mapping out manufacturing processes, helping to identify losses and inefficiencies, visualising material and energy movements, and providing a feeling of scale throughout a system. It can be necessary to use new visualisation techniques when available data and models get more intricate and sophisticated. For instance, it is not sufficient to look at only the flows of steel when searching for chances to reduce steel trash through supply chain integration; the alloy, thickness, coating, and forming history of the metal can all be crucial factors. In order to create a new kind of “hybrid” Sankey diagram that is more effective in visualising these many features of flows, this article mixes data-visualization techniques with the standard Sankey diagram.A Sankey diagram can be created in a number of ways, and different approaches are suitable in various contexts. In order to make this easier, an open-source Python implementation and a methodical approach for creating various hybrid Sankey diagrams from a dataset are provided. This method can be applied to construct Sankey diagrams from many data sources, including material flow analysis, life-cycle inventories, or directly measured data, by defining a standard data format for flow data. A number of graphic examples are used to demonstrate the technique, which is then applied to a genuine database of global steel flows.Sankey charts offer a dynamic and understandable depiction of intricate systems, flows, or processes. They are frequently recognised as an effective visualisation technique. These diagrams gracefully illustrate how energy, resources, and information are distributed inside a system, providing a comprehensive perspective that makes in-depth examination easier. Sankey charts are recognisable by their unique flow diagrams, which use different widths of connected routes to represent the amount or size of the objects being tracked. The intricate layout of these diagrams en