Understanding Plotly’s Custom Data and Hover Template Syntax

In the realm of data visualization, Plotly is a popular library used for creating interactive charts and graphs. One of its powerful features is the ability to customize hover text with dynamic data from your dataset. However, when working with multiple columns in custom data, it can be challenging to achieve the desired output. In this article, we will delve into the intricacies of Plotly’s custom data and hover template syntax to overcome common issues and provide a comprehensive guide on how to effectively use these features.

Introduction to Custom Data

Custom data is used to pass additional information from your dataset to the hover text of a plot. It can be useful for displaying more complex or detailed data that may not fit within the default hover text format.

import plotly.express as px

# Sample data
df = pd.DataFrame({
    'Category': ['A', 'B', 'C', 'D'],
    'Values1': [10, 15, 7, 12],
    'Values2': [20, 25, 18, 22]
})

fig = px.bar(df, x='Category', y='Values1')
fig.update_traces(
    hoverdata=['Values1', 'Values2']
)

In this example, we create a bar chart and pass the Values1 and Values2 columns to the hoverdata parameter. This allows us to display both values in the hover text.

Custom Data Syntax

When working with multiple columns in custom data, it is essential to understand the syntax used to access these values in the hover template.

fig.update_traces(
    hovertemplate='Value1: %{customdata[0]}\nValue2: %{customdata[1]}'
)

In this example, we pass a list of column names ['Value1', 'Value2'] to the custom_data parameter. When using these values in the hovertemplate, we access them using the index [0] and [1], respectively.

Issue with Saving Data at First Index

When working with multiple columns, it is common for Plotly to save data at the first index by default. This can be frustrating when trying to access other indexes or format the data using commas.

Solution 1: Specify Custom Data Location

To fix this issue, we need to specify the location of our custom data list in the hovertemplate parameter.

fig.update_traces(
    hoverdata=['Values1', 'Values2'],
    hovertemplate='Value1: %{customdata[0]}\nValue2: %{customdata[1]}'
)

By doing so, we ensure that Plotly accesses our custom data from the correct index.

Solution 2: Use Custom Data Array

Another approach is to use a custom data array with multiple values at each index. This allows us to access any value in the array using the desired index.

fig.update_traces(
    hoverdata=['Values1', 'Values2'],
    hovertemplate='Value1: %{customdata[0][0]}\nValue2: %{customdata[0][1]}'
)

In this example, we create a custom data array with shape (n, 2), where n is the number of rows in our dataset. This allows us to access any value at index [0] using [0][0] and [0][1].

Formatting Data

When formatting data using commas or other characters, we need to ensure that Plotly can handle these operations correctly.

fig.update_traces(
    hovertemplate='Value1: $%,.2f\nValue2: %{customdata[0][1]:,.2f}'
)

In this example, we use the $ symbol to enable formatting in the hovertemplate. We also specify the desired format using commas and two decimal places.

Conclusion

Plotly’s custom data and hover template syntax can be powerful tools for creating interactive charts with dynamic data. However, working with multiple columns requires careful consideration of the custom data location and formatting options. By understanding these intricacies and applying the solutions presented in this article, you can effectively use Plotly to create visually appealing and informative charts that showcase your data in the best possible light.

Last modified on 2023-05-13