Sankey charts are a type of flowchart that are used to visualize the flow of materials, energy, or information between different systems or entities. They are a popular choice for data visualization because they are easy to read and understand, even for those without a background in engineering or finance.

Here’s a step-by-step guide to creating a Sankey chart in Python:

1. First, install the sankey package by running pip install sankey in your terminal or command prompt.
2. Next, import the necessary modules:
   python
import sankey as sk
import matplotlib.pyplot as plt

3. Define the input data as a dictionary, where the keys represent the source nodes and the values represent the flow rates between each node. For example:
   python
input_data = {
'Source A': {
'Node 1': 10,
'Node 2': 20
},
'Source B': {
'Node 1': 20,
'Node 3': 30
},
'Node 4': {
'Node 5': 5,
'Node 6': 10
}
}

4. Create a Sankey diagram using the sk.Sankey() function and passing in the input data and the desired number of nodes. For example, to create a diagram with six nodes:
   python
sk_diagram = sk.Sankey()(input_data, values=input_data.values())

5. Save the diagram to an image file by calling the save method on the Sankey object:
   python
sk_diagram.save('sankey_chart.png', format='png', ax=plt.gca())

6. Finally, show the plot by calling the show method on the Sankey object:
   python
sk_diagram.show()

   This will display the Sankey chart in your default web browser. You can also customize the appearance of the chart by passing arguments to the sk.Sankey() function or modifying the plot after it has been created using matplotlib.

Some common applications for Sankey charts include visualizing the flow of materials or resources through a supply chain, analyzing the distribution of tax revenue across different government agencies, or examining the flow of ideas or information between different academic disciplines.

Overall, Sankey charts are a powerful and versatile tool for visualizing complex data flows, and they are easy to use and customize in a variety of data visualization libraries.

SankeyMaster

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