Introduction
Sankey charts are a visual representation of hierarchical relationships and complex systems. These charts are popular in fields such as economics, sociology, and public policy where it is important to show the interactions between different entities. Today, many applications are using these charts more and more to make their data more understandable. This article will discuss the basics of Sankey chart creation and applications, and how to use this chart for visualizing complex systems and relationships in a flash.
Creating a Sankey Chart
Before creating a Sankey chart, it is important to understand the data you need to visualize. The chart should be created from a graph that includes nodes and links. The nodes represent different categories or entities, and the links represent the relationships between these categories. Each link can be assigned a value, indicating the amount of influence or effect of the relationship.
Once you have the graph, you can use a Sankey chart software such as Matplotlib, Pandas, or Seaborn. These libraries provide functions to create the Sankey chart, which is a diagram with rectangles for the nodes on one axis, and a smaller rectangle for the links on the other axis.
The following steps will help you create a simple Sankey chart. Firstly, you need to import the necessary libraries. For example, let’s say we have a dataset with categories like location, population, and GDP. Then, we generate the graph by creating nodes with the category labels, and links that represent the relationship between the categories. Finally, we plot the graph to create the Sankey chart.
Let’s take a look at the code behind a Sankey chart example:
“`python
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
Generate a dataset
categories = [‘location’, ‘population’, ‘GDP’]
data = {‘location’: [‘A’, ‘B’, ‘C’, ‘A’, ‘B’, ‘C’, ‘A’, ‘B’],
‘population’: [500000, 550000, 600000, 500000, 550000, 520000, 580000, 520000],
‘GDP’: [80000000, 90000000, 100000000, 90000000, 95000000, 92000000, 97000000, 92000000]}
df = pd.DataFrame(data)
Create an adjacency matrix from a node and its associated labels
adjmatrix = pd.cramermatrix_category(categories)
Generate the graph
G = nx.fromnumpyarray(adj_matrix.astype(np.int64))
Plot the graph with a custom layout
pos = nx.springlayout(G, k=0.01)
nx.drawnetworkx(G, pos, arrows=True, nodecolor=’lightblue’, nodesize=5000, fontweight=’bold’, fontsize=12)
Highlight the nodes and links with their values
nx.drawnetworkxedgelabels(G, pos=pos, edgelabels=nx.getedgeattributes(G, ‘value’).items())
Save the figure
plt.show()
“`
Applications of Sankey Chart
Sankey charts can be used in various fields to visualize complex systems and relationships. Here are some examples of the applications:
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Social network analysis: Sankey charts can be used to visualize the relationships between different people in a social network, such as friends, colleagues, or classmates.
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Finance: Sankey charts can be used to visualize the relationships between different financial instruments and their economic effects. For example, a company’s financial health can be visualized by including the relationships between its stock prices and its dividends.
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Public policy: Sankey charts can be used to visualize the relationships between different policies and their effects on various sectors of society. For example, the impact of different tax rates on different income groups can be visualized through a Sankey chart.
Conclusion
Sankey charts offer a powerful way to visualize complex systems and relationships by showing the hierarchical relationships between different categories. These charts can be used in various fields, such as economics, finance, and public policy. In summary, Sankey charts can be used to make data more understandable and visualized, making it easier to analyze complex systems and relationships.
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