Introduction
Sankey charts are a type of diagram that help to visualize the flow of data between two or more entities. These charts are commonly used in fields such as finance, business, and social sciences. In this article, we will provide an overview of sankey chart creation and applications, explain the different types of sankey charts, and demonstrate how to use them in Python.
Create Sankey Charts
A Sankey chart is a type of network diagram that represents the flow of data between two or more components. The components in the network can be either individuals, companies, objects, or events. The flow in the network is represented by the bars and arrows in the diagram.
To create a sankey chart, you will need to gather data from the flow you want to show. This data can be in the form of a table or a collection of data points. The data should then be organized into a series of columns for the variables that describe the flow of data (sources, sinks, or both), and a series of rows for the actual flow of data. It’s a good idea to add labels or legends to the chart to help visualize the data and make it easier to read.
Sankey Chart Applications
Sankey charts have a wide range of applications in the fields of finance, business, and social sciences. These charts are particularly useful for visualizing the data flow between multiple groups or companies.
In finance, sankey charts are commonly used to show the flow of data such as transaction volumes, customer flows, or supply chain flow. They are also used to show the economic relationships between countries and industries, where the flow of goods and services is represented by horizontal arrows between countries.
In business, sankey charts are used to visualize the flow of information between departments or teams. They can help managers identify bottlenecks or inefficiencies in the flow of information, and can help them make more informed decisions.
In social sciences, sankey charts are used to show the flow of information between different groups or populations. For example, in the field of sociology, sankey charts can be used to show how different groups within a society communicate or exchange knowledge.
Creating a Sankey Chart in Python
Creating a sankey chart in Python is a relatively straightforward process. There are several Python libraries available that can help you create sankey charts. One of the most popular libraries is networkx, which provides a wide range of functions for creating and manipulating graphs and networks. With networkx, you can create a sankey chart by specifying the type of flow you want to show and the data points that you want to display.
In the following examples, we will use the networkx library to create a sankey chart for a stock market database. We will create two columns for the variables that describe the flow of data, and a series of rows to represent the actual flow of data. We will also add labels or legends to the chart to help visualize the data and make it easier to read.
Example 1 – Simple Sankey Chart
Here’s an example of a simple sankey chart:
“`python
import networkx as nx
import matplotlib.pyplot as plt
Create a sankey chart for stock market data
data = {
‘stocks’: [‘AAPL’, ‘MSFT’, ‘GOOGL’, ‘AMD’],
‘volume’: [100, 200, 300, 400],
‘price’: [100, 105, 120, 100]
}
g = nx.DiGraph()
for company, volume, price in zip(*[data[col] for col in data.keys()]):
g.addedge(company, f’Volume’, volume=volume)
g.addedge(company, f’Price’, price=price)
Plot the sankey chart
pos = nx.springlayout(g)
edgelabels = {f'{k} – {l}’: v[‘label’] for k, v in g.outedges(data=True)}
labels = {v[‘label’]: k for k, v in g.inedges(data=True)}
edgelabels = nx.relabelnodes(edgelabels, nx.keggcolorgraph(g),
mapping=lambda x: f”{float(x[1])*0.5}”)
layout = nx.circularlayout(g)
plt.figure(figsize=(10, 8))
nx.drawnetworkx(g, pos, withlabels=True, labels=labels, edgecolor=’grey’, nodecolor=’grey’,
nodesize=100, edgelabels=edgelabels)
nx.drawnetworkx_labels(g, pos, labels)
plt.show()
“`
Output:
Example 2 – Sankey Chart with Custom Variables
Here’s an example of a sankey chart with custom variables:
“`python
Create a sankey chart for stock market data with custom variables
data = {
‘stocks’: [‘AAPL’, ‘MSFT’, ‘GOOGL’, ‘AMD’],
‘volume’: [100, 200, 300, 400],
‘price’: [100, 105, 120, 100],
‘stockdividends’: [10, 20, 30, 40],
‘stockyields’: [0.1, 0.2, 0.3, 0.1]
}
g = nx.DiGraph()
for company, volume, price, stock dividends, stockyields in zip(*[data[col] for col in data.keys()]):
g.addedge(company, f’Volume’, volume=volume, dividends=stockdividends, yields=stockyields)
g.add_edge(company, f’Price’, price=price)
Plot the sankey chart
pos = nx.springlayout(g)
edgelabels = {f'{k} Volume Dividend Yields’: v[‘dividends’]+f’ ({v[“yields”]0.5}’) for k, v in g.out_edges(data=True)}
edge_labels = nx.relabel_nodes(edge_labels, nx.kegg_color_graph(g),
mapping=lambda x: f”{float(x[1])0.5}”)
layout = nx.circularlayout(g)
plt.figure(figsize=(10, 8))
nx.drawnetworkx(g, pos, withlabels=True, labels=labels, edgecolor=’grey’, nodecolor=’grey’,
nodesize=100, edgelabels=edgelabels, ax=plt.gca())
nx.drawnetworkxlabels(g, pos, labels)
plt.show()
“`
Output:
Conclusion
Sankey charts are a powerful tool for visualizing the flow of data in various fields, such as finance, business, and social sciences. With the ability to create and manipulate sankey charts in Python, it’s now easier than ever to create complex data visualizations. Whether you’re looking to analyze stock market data, track customer flows, or show the relationships between different groups, sankey charts can help you make sense of the data and make more informed decisions.
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