Seaborn Plot Two Data Sets on the Same Scatter Plot

In this article, we’ll explore how to visualize two different datasets on the same scatter plot using the popular data visualization library, Seaborn. We’ll discuss the limitations of the default approach and provide a solution that allows for a single scatter plot with shared legends and varying marker colors.

Introduction to Data Visualization

Data visualization is a powerful tool for communicating insights and trends in data. It enables us to represent complex information in a concise and meaningful way, making it easier to understand and analyze. In this article, we’ll focus on Seaborn, a Python library built on top of Matplotlib that provides a high-level interface for creating attractive and informative statistical graphics.

Setting Up the Environment

To follow along with this tutorial, you’ll need to have the following libraries installed:

• pandas (for data manipulation and analysis)
• seaborn (for data visualization)
• matplotlib (for displaying plots)

You can install these libraries using pip:

pip install pandas seaborn matplotlib

Understanding Seaborn’s Pairplot Function

The pairplot() function in Seaborn is a convenient way to visualize multiple variables within a dataset. By default, it creates a separate subplot for each pair of variables, which can result in a large number of subplots if there are many variables in the data.

In our case, we want to create a single scatter plot with both datasets on the same plot. The pairplot() function is not designed for this purpose, and we’ll need to use a different approach.

Merging Datasets

To visualize two separate datasets on the same plot, we first need to merge them into a single dataset that contains all the variables from both datasets.

import pandas as pd

# Create two sample datasets
set1 = pd.DataFrame({
    'Std': [12.5, 13.2, 11.8],
    'ATR': [0.75, 1.08, 0.65]
})

set2 = pd.DataFrame({
    'Std': [15.3, 14.2, 16.5],
    'ATR': [1.02, 1.25, 0.95]
})

# Merge the datasets into a single dataset
concatenated = pd.concat([set1.assign(dataset='set1'), set2.assign(dataset='set2')])

In this example, we use pd.concat() to merge the two datasets into a single DataFrame. We also add an additional column called dataset that contains the original name of each dataset.

Creating a Shared Legend with Varying Marker Colors

To create a scatter plot with shared legends and varying marker colors for both datasets, we’ll use the scatterplot() function from Seaborn.

import seaborn as sns

# Create a scatter plot with shared legend and varying marker colors
sns.scatterplot(x='Std', y='ATR', data=concatenated,
                hue='dataset', style='dataset')
plt.show()

In this code, we use the scatterplot() function to create a scatter plot with x-axis values on Std and y-axis values on ATR. We also specify two color variables: hue='dataset', which uses the dataset column to determine the marker color for each point; and style='dataset', which uses the same dataset column to create a shared legend.

Explanation of Key Concepts

• Hue: In Seaborn, the hue parameter is used to specify a categorical variable that defines the marker colors.
• Style: The style parameter in Seaborn’s scatterplot() function is used to create a shared legend for both datasets.
• Concatenation: When merging two datasets into a single dataset, we use concatenation (e.g., pd.concat()) to combine the data while preserving the original information.

Additional Considerations

When working with multiple datasets in Seaborn, it’s essential to consider the following:

• Data types: Ensure that both datasets have the same data type for the x-axis and y-axis variables.
• Scale: Be aware of the scaling factor between the two datasets to avoid misleading visualizations.
• Overlapping points: If there are overlapping points in either dataset, it may be challenging to distinguish between them visually.

Conclusion

In this article, we’ve explored how to create a shared scatter plot with varying marker colors for two separate datasets using Seaborn. By understanding the limitations of Seaborn’s pairplot() function and leveraging the power of concatenation and color variables, we can effectively visualize complex data in a clear and meaningful way.

Step-by-Step Solution

1. Create two sample datasets with x-axis values on Std and y-axis values on ATR.
2. Merge the two datasets into a single dataset using pd.concat().
3. Use Seaborn’s scatterplot() function to create a scatter plot with shared legend and varying marker colors.

Here is an example of how to implement this step-by-step solution:

# Import necessary libraries
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Create two sample datasets
set1 = pd.DataFrame({
    'Std': [12.5, 13.2, 11.8],
    'ATR': [0.75, 1.08, 0.65]
})

set2 = pd.DataFrame({
    'Std': [15.3, 14.2, 16.5],
    'ATR': [1.02, 1.25, 0.95]
})

# Merge the datasets into a single dataset
concatenated = pd.concat([set1.assign(dataset='set1'), set2.assign(dataset='set2')])

# Create a scatter plot with shared legend and varying marker colors
sns.scatterplot(x='Std', y='ATR', data=concatenated,
                hue='dataset', style='dataset')
plt.show()

By following these steps, you can effectively create a shared scatter plot with varying marker colors for two separate datasets using Seaborn.

Last modified on 2024-12-18