Verbatim Labels in Legend of Bokeh Plots
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In this article, we’ll explore a common challenge when working with Bokeh plots in Python. Specifically, we’ll examine how to ensure that the labels in the legend of our plot are displayed as column names from our data source, rather than the actual values from those columns.
Introduction to Bokeh and DataFrames
Before diving into the specifics of this issue, let’s quickly review how Bokeh works with Pandas DataFrames. Bokeh is a Python plotting library that provides elegant, concise construction of complex graphics in low code. A key feature of Bokeh is its ability to work seamlessly with Pandas DataFrames.
When working with Pandas DataFrames in Bokeh, we often use the ColumnDataSource class to store our data and pass it to the plot function. This allows us to create interactive plots that respond to user input.
The Problem: Extracting Values from Columns
In this example, we have a Pandas DataFrame with a single column named ‘accuracy’. We want to display the accuracy values in the legend of our Bokeh plot.
However, by default, Bokeh extracts the actual values from the columns instead of using the column names as labels. This can be problematic when working with DataFrames that have multiple columns or complex data structures.
A Simple Solution: Using value Property
Fortunately, there is a simple way to solve this problem using the value property provided by Bokeh.
When we pass a string to the legend parameter of the plot function, Bokeh attempts to interpret it as a column name. However, if we prefix the string with value, Bokeh treats the string as a literal value instead of attempting to extract it from a column.
Here’s an updated version of our code snippet that demonstrates this:
from bokeh.core.properties import value
p.line(x='iteration', y='accuracy', source=source, legend=value('accuracy'))
By adding the value property before 'accuracy', we ensure that Bokeh uses the column name as the label in the legend instead of extracting the actual values.
Additional Context and Edge Cases
While using the value property is a simple solution to this problem, it’s essential to understand when this might not be sufficient or even desirable.
For instance, if we have multiple columns with similar names, using the value property can lead to unexpected behavior. In such cases, additional context or more explicit way of specifying column names may be required.
Additionally, some users might prefer a more explicit approach to labeling their plots, where they specify both the column name and value in the legend. While this is technically possible using Bokeh’s API, it requires careful consideration of how to handle overlapping or conflicting labels.
Conclusion
In conclusion, while working with Bokeh and Pandas DataFrames can be challenging at times, there are often elegant solutions available. By understanding how to use the value property provided by Bokeh, we can ensure that our plots display column names as intended in the legend, rather than extracting actual values.
Remember to always check the official Bokeh documentation for the most up-to-date information on its features and capabilities.
Last modified on 2023-06-03