How to create a sankey diagram using matplotlib python

A Sankey diagram represents data, energy, or quantities flowing through a system. We can compose it with nodes and links. The nodes are the sources and destinations of the flows, and the links represent the flow volume or value. Sankey diagrams visualize the contributions to a flow by defining the source. It represents the source node and the target for the target node. It represents the value of setting the flow volume and the label showing the node name. Sankey diagrams help visualize processes or flows. Nodes represent each entity or process stage. We can express the flows between nodes in arcs. The flow's numerical size determines this arc's size.  

We can use libraries to create Sankey diagrams, like Plotly, matplotlib, and PySankey. These libraries provide easy-to-use functions for creating both simple and complex Sankey diagrams. For complex Sankey diagrams, you can use libraries like Plotly Express or PySankey. It provides more advanced features for customizing the diagram's appearance and behavior. Additionally, we can use it in various data analysis and visualization tasks. Exploring behavior, energy consumption, or web traffic flow can help explore customer behavior.  

It shows how data or quantities move through a system. It can represent various data types. It can be lists, dictionaries, and sets, and it is a useful tool for comprehending intricate data sets.  

• Lists: Sankey graphs can address the progression of things through a rundown. For instance, you could use it to envision the progression of items through a store network. We can represent the flow of products between each node and the link. We can do it by a stage in the supply chain, such as manufacturing, warehousing, and distribution.  
• Dictionaries: We depict the dictionary's data flow using Sankey diagrams. A Sankey diagram can help you understand how visitors move through a website's pages. We can represent the traffic flow between the various pages on the website. We can do it by the links between each node in the diagram.  
• Sets: We can represent the data flow through a set using Sankey diagrams. We can use it to imagine the client's progression through various showcasing channels. The links would represent the flow of customers between these channels. Each node in the diagram would represent a marketing channel. It can be advertising, email, or social media.  

Data visualizations show how data or quantities move through a system. We can make connections that between data items to help comprehend intricate datasets.  

• Joins: In a Sankey outline, joins address the mix of information from sources into a solitary goal. You could use it to imagine the progression of information between various divisions. The last goal is the organization's income. Joins would represent data consolidation from various departments into a single revenue figure.  
• Filters: We can represent the data removal from a flow by filters. You could use it to imagine the progression of clients through a deals pipe, with the last goal being a buy. In this scenario, we can represent customers who do not buy at the end of the sales funnel by filters.  
• Splits: Split is the division of data into many streams. For instance, you could imagine how energy moves through a power grid. We can see how it reaches homes and businesses. In this instance, the division of energy into various streams. Some go to homes and others to businesses which we represent as splits. 
• Transformations: Data transforms as it moves through the system. Using this, you could imagine how materials move through a manufacturing process. We can also imagine how we arrive at a finished product. We can handle the materials going through a completed item. We can handle the changes that would address the progressions.  

A Sankey diagram represents data flow or quantities through a system. While we cannot use it to create charts, it is possible to create hybrid charts. We can then combine Sankey diagrams with other chart types. Here are some examples:  

• Sankey + Bar Chart: We can combine it with a bar chart to show both the flow of data and the quantity of data.  
• Sankey + Line Chart: We can combine the Sankey diagram with a line chart to show the change in data over time.  
• Sankey + Pie Chart: Combining a Sankey diagram with a pie chart is possible. It will show the proportion of data at each stage in the flow.  

While we use the Sankey diagrams to represent data flows, we can combine them. We can combine them with other chart types to create hybrid charts. It will provide extra insights into complex data sets.  

• Labels should be concise and clear:  

Sankey diagrams can be quite complex and contain a lot of nodes and links. Labeling them is important so the viewer can understand what they represent. Avoid using truncations or abbreviations. It that may not be natural for the viewer and use marks that depict the information addressed at every hub.  

• Organize the data items logically:  

More nodes or links in a Sankey diagram can become overwhelming. To avoid this, arrange the data items in a logical order. It will make comprehending the data's flow easier. You could group nodes representing a particular stage in a process or similar data items.  

• Highlight significant information with color:  

Using color to emphasize important data can be useful in a Sankey diagram. You can do it to highlight the differences in flow between various data groups. You could use a different color to emphasize the main data flow.  

• Keep the graph basic:  

The Sankey diagram can become overwhelming if there is too much detail. It can be tempting to add as much detail as possible. Keep the diagram as straightforward as possible while still conveying the essential details.  

• Try the diagram out on others:  

Test the Sankey diagram with others to ensure it is clear and easy to understand before you finish it. We can communicate and assist you in determining potential improvement areas. It will ensure the information presented in the diagram.  

• Sankey diagrams' readability makes wise use of whitespace:  

Whitespace is the space between nodes and links in the diagram. We can connect the nodes to ensure enough space between links and nodes to clarify. We can improve the diagram's readability by making good use of whitespace. But use a little whitespace. It can make the diagram look cluttered and disconnected.  

• Use a straightforward variety range:

We can use a color scheme; it may be simpler to distinguish. It can be between various nodes and links in the diagram. Select colors that are easy to differentiate from one another. You can use no more than three or four colors in the diagram.  

• Keep your formatting simple:

Although adding a lot of formatting to the diagram. It may be tempting to give it a more interesting appearance, making it harder to read. Stick to simple formattings like bold text or font size to emphasize information. Avoid using visual elements because they could detract from the diagram's main message.  

• Use a simple layout:

The layout may affect the diagram's readability. Attempt to use an unmistakable, legitimate format. It gathers hubs together. It makes it simple to follow the progression of information through the chart. You should play around with a few different ones to find the most effective layout for your data.  

• Use concise labels:

Clear names are fundamental for making the chart straightforward. Ensure that each link and node have a label that is easy to understand and explains the data it represents. Labels can state measurement units or periods. It will do it by providing extra context or information about the data.  

These are methods for depicting the data flow through a system or procedure. We can use the Sankey diagrams to analyze data by showing how data moves through a system. We can use it to point out bottlenecks or areas of inefficiency. We can do it by looking into various scenarios or what-if scenarios. Plotly and Matplotlib are two Python libraries that can create Sankey diagrams. You need data representing a collection of nodes and links to create a Sankey diagram in Python. Making a viable Sankey outline expects regard for a few elements. Labeling nodes and links are important as grouping data items. We can do it by using color to highlight important data. There are various data types, including lists, dictionaries, and sets. We can represent it by Sankey diagrams. We can use it to create charts and connections between data items, such as joins and filters. We can keep it simple and tested with others to guarantee its clarity and efficacy.  

In conclusion, Sankey diagrams are a potent data analysis and visualization tool. It can assist us in comprehending the data flow through a system or procedure. We can identify bottlenecks or areas of inefficiency. We can investigate various scenarios or what-if scenarios. It will make informed decisions based on data-driven insights by creating Sankey diagrams.  

We can identify patterns and trends. It might not be easy to discern by utilizing Sankey diagrams. We can assist us in communicating complex data relationships to others more effectively. We can gather and dissect always expanding information measures. We can see the capacity to make compelling perceptions like outlines becomes basic. By becoming amazing at chart creation, we can open new experiences. It will help pursue better choices given information-driven bits of knowledge.  

Preview of the output that you will get on running this code in your IDE

Code

In this solution we have used "Sankey plot", which is a type of plot used to visualize flows and their quantities

Instructions

1. Download and install VS Code on your desktop.
2. Open VS Code and create a new file in the editor.
3. Copy the code snippet that you want to run, using the "Copy" button or by selecting the text and using the copy command (Ctrl+C on Windows/Linux or Cmd+C on Mac).,
4. Paste the code into your file in VS Code, and save the file with a meaningful name and the appropriate file extension for python use (.py).file extension. use this command in your terminal to download pip install matplotlib.
5. To run the code, open the file in VS Code and click the "Run" button in the top menu, or use the keyboard shortcut Ctrl+Alt+N (on Windows and Linux) or Cmd+Alt+N (on Mac). The output of your code will appear in the VS Code output console.

I hope you found this useful i have added the Dependent libraries , versions in the following sections

I have searched using " Sankey with Matplotlib" in Kandi. you can try any use case

Environment Tested

I have tested this solution with following versions. Be mindful of changes when working with other versions

1. This solution is created and executed in Python 3.7.15 version.
2. This solution is tested on matplotlib 3.5.3 version.
3. This solution is tested on sankey-0.0.2 version.

It can be helpful in decision-making processes, such as optimizing resource allocation or identifying areas for improvement in a process.. This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us edit the text in Python.

Dependent Libraries

If you don't have this matplotlib Library that required to run this code. You can install by clicking the above link and copying the pip install command from the matplotlib page in Kandi. You can search any Library Like matplotlib in kandi

FAQ:-  

1. What is a simple Sankey diagram, and how does it differ from a flow diagram?  

A Sankey diagram represents the energy or material flow through a system. It differs from a flow diagram. It emphasizes the proportion of the total flow rather than the absolute values.  

2. Can we create a Scatter plot as a Sankey diagram?  

No, creating a scatter plot as a Sankey diagram is impossible. They are two types of visualization representing different data types.  

• A scatter plot visualizes the relationship between two continuous variables. It displays individual data points as dots on a two-dimensional coordinate system.  
• But a Sankey diagram shows a system's flow of data or resources. It displays the proportion of the total flow through the width of the lines.  

3. How can I use data analysis techniques to understand more about my Sankey diagrams?  

You can use data analysis techniques to gain insights from your Sankey diagrams. We can do it by exploring the underlying data on the diagram. Here are some techniques that you could use:  

Calculate the flow values:  

Sankey diagrams show the proportion of the total flow through the width of the lines. But it is also important to know the actual values of the flows. You can calculate the flow values by multiplying the proportion shown by the total flow.  

Identify the major and minor flows:  

By examining the flow values, you can identify major and minor flows. This can help you identify areas where changes may impact the system most.  

4. Using the objects module, are there different methods of calculating node flow rate?  

Different methods exist using the objects module to calculate the node flow rate. Here are a few examples:  

Using the nodes attribute:  

You can access the Sankey diagram nodes using the Sankey object's nodes attribute. Each node has a value attribute representing that node's total flow rate.  

Using the flow's attribute:  

Each flow has a value attribute that represents the flow rate for that flow. You can access the diagram's flows attribute using the Sankey object's flows attribute. To calculate the flow rate for a particular node, you can sum up the flow rates of all flows that connect to that node.  

Using the plot function:  

The plot function of the object allows you to plot the Sankey diagram. It returns a dictionary containing the flow rates for each node. You can access the flow rates for a particular node by indexing the dictionary with the node's label.  

5. How can I create a Sankey diagram with Python Code?  

You can create a diagram with code using the matplotlib library. It provides a Sankey class in its Sankey module. Here is a code that demonstrates how to create a basic Sankey diagram:  

import matplotlib.pyplot as plt  

from matplotlib.sankey import Sankey  

# Define the Sankey diagram inputs and outputs  

flows = [100, -20, -30, -50]  

labels = ['Input', 'Output 1', 'Output 2', 'Output 3']  

# Create a Sankey diagram instance and add the flows and labels  

sankey = Sankey()  

sankey.add(flows=flows, labels=labels, orientations= [0, 1, 1, 0])  

# Set the positions of the inputs and outputs on the Sankey diagram  

sankey.finish()  

plt.show()  

This code creates a Sankey diagram with an input flow of 100 and three output flows of -20, -30, and -50. The labels list defines the labels for each input and output. The orientations list specifies the orientation of each input and output. Finally, we can call the finish() method to create the diagram, and the show() method displays the diagram.  

You can customize it by adjusting the color and size. You can start changing the arrows' curvature and adding labels. The documentation provides information on the different customization options available for Sankey diagrams. 

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