Introduction:
Sankey diagrams are a type of flow visualization that can be used to represent the distribution of quantities across multiple categories or nodes. This type of chart is particularly useful in domains such as economics, renewable energy, data science, and business management, as well as for creating stunning visualizations on websites to provide insights and help decision makers understand complex data sets.

This article aims to delve into the world of Sankey chart creation, understand its applications, and the power it can unleash.

Creating a Sankey Chart:
To create a Sankey Chart, you need three main components:

1. The source(s) of the flow.
2. The flow of quantities between nodes (categories).
3. The destination(s) of the flow.

Essentially, Sankey Charts illustrate the flow of material or energy from one set of values to another using horizontal rectangles or boxes that vary in thickness depending on the quantity of flow.

Tools for creating Sankey charts vary, and you can do this using software packages like Tableau, Microsoft Power BI, Google Charts, or specialized libraries, such as Plotly, Bokeh, and even in statistical software like R and Python libraries like networkX and plotly for Python.

Here is a simple Sankey chart using Python:
“`python
import networkx as nx
import matplotlib.pyplot as plt
import pandas as pd

Example Data:

data = {‘source’: [‘A’, ‘A’, ‘B’, ‘B’, ‘C’],
‘target’: [‘Z’, ‘Z’, ‘Z’, ‘A’, ‘A’],
‘value’: [30, 70, 10, 40, 50]}

df = pd.DataFrame(data)
G = nx.DiGraph()

Adding edges with values

G.addweightededges_from(zip(df[‘source’], df[‘target’], df[‘value’]))

pos = nx.shell_layout(G)

fig, ax = plt.subplots()
Sankey = nx.shelllayout(G)
n = nx.shelllayout(G)

labels = {node: f”{node}” for node in G.nodes()}
nodelabels = labels

nx.drawnetworkxnodes(G, pos, nodesize=3000)
nx.drawnetworkxlabels(G, pos, labels=nodelabels)
nx.drawnetworkx_edges(G, pos, ax=ax)

Sankeylayout = nx.shelllayout(G)
node_labels = {node: text for node, text in nodelabels.items()}

edge_labels = dict([((u, v,), d[‘value’]) for u, v, d in G.edges(data=True)])

nx.drawnetworkxnodes(G, Sankeylayout, nodesize=3000)
nx.drawnetworkxlabels(G, Sankeylayout, labels=nodelabels)
nx.drawnetworkxedges(G, Sankey_layout, ax=ax)
plt.show()

“`

Applications of Sankey Charts:
Sankey charts help visualize the distribution and flow of quantities like energy or goods through systems, enabling understanding of:

1. Energy Systems: These charts can be used to illustrate energy flows in power systems, showing how energy moves from renewable sources to final consumption by industries, households, etc.

2. Supply Chain: In logistics and retail, Sankey Charts allow visualizing the flow of goods and materials from suppliers to consumers, revealing bottlenecks and inefficiencies.

3. Biological Processes: They can represent metabolic pathways in biological systems, showing the flow of molecules like nutrients and hormones.

4. Financial Flows: In the banking and finance sectors, Sankey charts can illustrate the flow of financial transactions, showing money movement between different stakeholders or across various accounts.

5. Website Traffic: To understand where visitors come from and go to on websites, or how data moves through a system, including tracking sessions, page loads, and user interactions.

Conclusion:
The power of Sankey Charts lies not only in their ability to visually represent the complex flow of quantities across interconnected nodes but also in their potential to reveal patterns, correlations, and insights that are not immediately apparent from tabular data. By effectively utilizing these charts, businesses, researchers, and decision-makers can gain a deeper understanding of their data, leading to more informed policy and strategy adjustments. The ease of creation and versatile nature of Sankey Charts make them a valuable tool for both simple and intricate data visualization projects.

SankeyMaster

SankeyMaster is your go-to tool for creating complex Sankey charts . Easily enter data and create Sankey charts that accurately reveal intricate data relationships.