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Scatter_Ternary Plotting: Adjusting Range & Limits in Plotly

Scatter_ternary plots are a unique solution for visualizing three variables in a single plot. They are a powerful tool in the realm of data visualization, especially when working with compositional data where the sum of the three variables is a constant. In this article, we will delve into the intricacies of scatter_ternary plots, focusing on the range and how to adjust it to suit your data.

The scatter_ternary plot, a feature available in Python's Plotly library, offers an alternative to traditional methods like heatmaps, colormaps, and bubble charts. While these methods rely on encoding the third variable in color or size, or introducing a Z-axis for 3-dimensional representations, scatter_ternary plots present a more straightforward approach. They plot three variables on three axes in a triangular format, with each axis representing a variable.

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What is a Scatter_Ternary Plot?

A scatter_ternary plot is a type of plot that allows the visualization of three variables simultaneously. It is especially useful when dealing with compositional data, where the three variables represent parts of a whole and their sum is a constant. The plot is triangular, with each corner representing one of the variables. The position of a point within the triangle indicates the proportions of the three variables.

In Python, the Plotly library provides a function, plotly.express.scatter_ternary, to create scatter_ternary plots. This function takes as input a data frame and the names of the three columns to be plotted. It returns a ternary scatter plot where each point represents an observation, and its position in the triangle reflects the values of the three variables.

How to Create a Scatter_Ternary Plot?

Creating a scatter_ternary plot in Python using Plotly is straightforward. Here is a basic example:

import plotly.express as px
 
# Assuming df is a DataFrame with three columns: 'A', 'B', 'C'
fig = px.scatter_ternary(df, a='A', b='B', c='C')
fig.show()

In this example, 'A', 'B', and 'C' are the three variables to be plotted. The scatter_ternary function creates a ternary scatter plot with each point's position reflecting the values of 'A', 'B', and 'C'.

What is the Range of a Scatter_Ternary Plot?

The range of a scatter_ternary plot refers to the extent of values that the plot covers along each axis. Since the three variables in a scatter_ternary plot represent parts of a whole, the range for each axis is typically from 0 to 1, or 0% to 100%. However, the actual range can be adjusted based on the data.

In Plotly, the range of a scatter_ternary plot can be adjusted using the range attribute of the layout object. This attribute takes a list of two numbers, representing the lower and upper limits of the range. For example, to set the range of the 'A' axis to be from 0.1 to 0.9, you would do:

fig.layout.ternary.aaxis.range = [0.1, 0.9]

This flexibility in adjusting the range allows you to focus on specific parts of the

data, or to zoom out to see the bigger picture.

Use Cases of Scatter_Ternary Plot

Scatter_ternary plots are incredibly versatile and can be used in a variety of fields. Here are a few examples:

  1. Geology and Petrology: In these fields, scatter_ternary plots are often used to represent the composition of rocks. The three variables could represent the proportions of three different minerals in a rock sample.

  2. Chemistry: Chemists use scatter_ternary plots to represent the composition of mixtures. For instance, in a three-component system, the three variables could represent the proportions of the three components.

  3. Economics: In economics, scatter_ternary plots can be used to visualize the distribution of economic activities. For example, the three variables could represent the proportions of agriculture, industry, and services in a country's economy.

  4. Data Science: In data science, scatter_ternary plots can be used to visualize the results of clustering algorithms or to explore the relationships between three variables.

Adjusting the Axes Limits of a Scatter_Ternary Plot

As mentioned earlier, the range of a scatter_ternary plot can be adjusted using the range attribute of the layout object. This allows you to control the axes limits of the plot. Here's how you can do it:

# Adjusting the range of the 'A' axis
fig.layout.ternary.aaxis.range = [0.1, 0.9]
 
# Adjusting the range of the 'B' axis
fig.layout.ternary.baxis.range = [0.2, 0.8]
 
# Adjusting the range of the 'C' axis
fig.layout.ternary.caxis.range = [0.3, 0.7]

In this example, the range of the 'A' axis is set to be from 0.1 to 0.9, the range of the 'B' axis is from 0.2 to 0.8, and the range of the 'C' axis is from 0.3 to 0.7. This allows you to focus on the specific parts of the data that are of interest.

Let's Talk More About Scatter_Ternary Plotting

Scatter_Ternary Plot Python and Plotly Scatter_Ternary Range

Creating a scatter_ternary plot in Python is straightforward with the Plotly library. The plotly.express.scatter_ternary function is specifically designed for this purpose. As for the range, it can be adjusted using the range attribute of the layout object in Plotly, allowing you to focus on specific parts of your data.

Ternary Scatter Plot and Scatter_Ternary Plot Documentation

A ternary scatter plot is a type of scatter plot designed for visualizing three variables simultaneously. It is especially useful for compositional data where the three variables represent parts of a whole. For more detailed information, the scatter_ternary plot documentation in the Plotly library provides comprehensive guidance.

Plotly Ternary Heatmap X Range

The x range in a Plotly ternary heatmap can be adjusted in a similar way to the scatter_ternary plot. The range attribute of the layout object allows you to set the lower and upper limits of the x-axis, enabling you to focus on specific parts of your data.

FAQs

  1. What is a scatter_ternary plot? A scatter_ternary plot is a type of plot that allows the visualization of three variables simultaneously. It is especially useful when dealing with compositional data, where the three variables represent parts of a whole and their sum is a constant.

  2. How do I create a scatter_ternary plot in Python? You can create a scatter_ternary plot in Python using the Plotly library. The plotly.express.scatter_ternary function allows you to create these plots easily.

  3. How do I adjust the range of a scatter_ternary plot? The range of a scatter_ternary plot can be adjusted using the range attribute of the layout object in Plotly. This attribute takes a list of two numbers, representing the lower and upper limits of the range.

Conclusion

In conclusion, the scatter_ternary plot is a powerful tool for visualizing three variables simultaneously. With the ability to adjust the range, you can tailor the plot to suit your data and focus on the areas of interest. Whether you're a geologist, a chemist, an economist, or a data scientist, scatter_ternary plots can be a valuable addition to your data visualization toolkit.