hvplot define colorbar for multiple plotted dataframes

Guri Bee

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21-10-2024

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Mastering Colorbars in hvPlot: A Guide for Visualizing Multiple DataFrames

When visualizing multiple dataframes in hvPlot, you might want to use colorbars to represent different values within your data. This can be particularly useful for understanding trends and relationships across your data. However, achieving this with multiple dataframes can pose a challenge.

This article will provide a comprehensive guide on how to effectively define colorbars for multiple plotted dataframes in hvPlot, drawing upon real-world examples and insights from the open-source community on GitHub.

Understanding the Challenge

The key issue lies in how hvPlot handles colorbars when plotting multiple dataframes. By default, it assigns a single colorbar for all dataframes, potentially leading to ambiguity and difficulty in interpreting the visualization.

Let's look at a simplified example from GitHub, highlighting the issue:

# Example from: https://github.com/holoviz/hvplot/issues/726 
import pandas as pd
import hvplot.pandas

df1 = pd.DataFrame({'x': range(10), 'y': range(10), 'z': range(10)})
df2 = pd.DataFrame({'x': range(10), 'y': range(10), 'z': range(10)})

df1.hvplot.scatter(x='x', y='y', c='z', label='df1') * df2.hvplot.scatter(x='x', y='y', c='z', label='df2')

This code will plot two scatter plots, both colored based on the 'z' column. However, the single colorbar will be shared, making it hard to discern the values represented for each dataframe.

The Solution: Leveraging Customizing and Layering

The solution lies in customizing the individual plots and layering them intelligently. Here's how to achieve a separate colorbar for each dataframe:

1. Create Individual hvPlot Objects: Instead of directly combining the plots, create separate hvPlot objects for each dataframe. This allows for independent customization.

2. Use colorbar=True and color_index: For each hvPlot object, set the colorbar=True parameter to enable individual colorbars. Furthermore, use the color_index parameter to explicitly specify the column used for coloring.

3. Combine using + or *: Finally, combine the customized hvPlot objects using the + or * operators, depending on whether you want to overlay or stack the plots.

Let's illustrate this approach with the example code:

# Modified code with individual colorbars
import pandas as pd
import hvplot.pandas

df1 = pd.DataFrame({'x': range(10), 'y': range(10), 'z': range(10)})
df2 = pd.DataFrame({'x': range(10), 'y': range(10), 'z': range(10)})

plot1 = df1.hvplot.scatter(x='x', y='y', c='z', label='df1', colorbar=True, color_index='z')
plot2 = df2.hvplot.scatter(x='x', y='y', c='z', label='df2', colorbar=True, color_index='z')

plot1 * plot2 

This revised code generates two scatter plots with separate colorbars, each representing the 'z' values for its respective dataframe. This approach allows for clearer and more informative visualization.

Additional Insights and Considerations

• Colormap Choice: For greater visual clarity, consider using distinct colormaps for each dataframe. hvPlot provides a range of colormaps; explore them to find the most suitable combination.

• Legend: If you want to differentiate the plotted points further, consider adding a legend to the visualization.

• Axis Labels: Don't forget to properly label the axes and colorbars to ensure accurate and comprehensive interpretation of your visualization.

• Data Types: The color_index parameter works effectively with numerical data. If you're dealing with categorical data, you can still use color to represent categories.

Conclusion

Understanding how to define colorbars for multiple dataframes in hvPlot is crucial for creating effective and informative visualizations. By applying the techniques outlined in this article, you can leverage the power of hvPlot to gain deeper insights from your data. Remember to explore the customization options available in hvPlot and consult resources like the hvPlot documentation and GitHub discussions for advanced techniques and solutions to specific scenarios.