Current State of Styling Dataframes in Streamlit

Oct 13, 2025 by recoveryshake.com 24 views

Introduction

Streamlit is a powerful Python library used for creating web applications. It provides an easy-to-use interface for building interactive dashboards and data visualizations. One of the key features of Streamlit is its ability to style and customize dataframes using the st.dataframe() function. However, there is a limitation in the current implementation of st.dataframe() that prevents users from setting the background CSS property. In this article, we will explore the current state of styling dataframes in Streamlit, the limitations of the current implementation, and propose a solution to support the background CSS property in st.dataframe().

Current State of Styling Dataframes in Streamlit

Streamlit provides a powerful styling system for dataframes using the st.dataframe() function. Users can customize the appearance of their dataframes by setting various CSS properties, such as background-color, color, font-size, and more. However, there is a limitation in the current implementation of st.dataframe() that prevents users from setting the background CSS property.

Limitations of the Current Implementation

The current implementation of st.dataframe() only supports setting the background-color CSS property, but not the background property. This limitation is evident in the following code snippet:

import pandas as pd
import streamlit as st

df = pd.DataFrame({"solid": [0.1, 0.2, 0.3], "gradient": [0.4, 0.5, 0.6], "bar": [0.7, 0.8, 0.9]})
styler = df.style
styler.format(lambda x: f"{x:.0%}")
styler.map(lambda x: f"background-color: green;", subset="solid")
styler.map(lambda x: f"background: linear-gradient(to right, green {x:%}, transparent {x:%});", subset="gradient")
styler.bar(subset="bar", vmin=0, vmax=1, color="green")  # Uses a `background: linear-gradient` under the hood.

st.code("st.table()")
st.table(styler)  # Both the solid color and the gradient work as expected.
st.divider()
st.code("st.dataframe()")
st.dataframe(styler)  # The solid color works as expected, but not the gradient.

As shown in the code snippet above, the background-color property works as expected, but the background property does not. This limitation is also evident in the fact that both background-color and background work in st.table().

Proposed Solution

To support the background CSS property in st.dataframe(), we propose the following solution:

Add support for the background CSS property: The first step is to add support for the background CSS property in the st.dataframe() function. This can be achieved by modifying the underlying CSS code that generates the dataframe.
Implement a fallback for older browsers: To ensure that the background property works in older browsers that do not support it, we can implement a fallback using the background-color property.
Test and validate the solution: Once the solution is implemented, we need to test and validate it to ensure that it works as expected.

Implementation Details

To implement the proposed solution, we need to modify the underlying CSS code that generates the dataframe. We can achieve this by adding a new CSS class that sets the background property and then applying that class to the dataframe.

Here is an example of how we can implement the solution:

/* Add a new CSS class that sets the background property */
.dataframe-background {
    background: linear-gradient(to right, green 50%, transparent 50%);
}

/* Apply the new CSS class to the dataframe */
.dataframe {
    background: linear-gradient(to right, green 50%, transparent 50%);
}

Conclusion

Q&A

Q: What is the current limitation of `st.dataframe()` in terms of styling?

A: The current limitation of st.dataframe() is that it only supports setting the background-color CSS property, but not the background property.

Q: Why is the `background` property not supported in `st.dataframe()`?

A: The background property is not supported in st.dataframe() because of a limitation in the underlying CSS code that generates the dataframe.

Q: How can I style a dataframe in Streamlit?

A: You can style a dataframe in Streamlit using the st.dataframe() function and the styler object. You can set various CSS properties, such as background-color, color, font-size, and more.

Q: What is the difference between `st.dataframe()` and `st.table()`?

A: The main difference between st.dataframe() and st.table() is that st.dataframe() supports more advanced styling options, such as setting the background property, while st.table() only supports basic styling options.

Q: How can I set the `background` property in `st.dataframe()`?

A: To set the background property in st.dataframe(), you need to use the styler object and the map method to apply a CSS style to the dataframe.

Q: What is the proposed solution to support the `background` property in `st.dataframe()`?

A: The proposed solution to support the background property in st.dataframe() is to add a new CSS class that sets the background property and then apply that class to the dataframe.

Q: How can I implement the proposed solution?

A: To implement the proposed solution, you need to modify the underlying CSS code that generates the dataframe and add a new CSS class that sets the background property.

Q: What are the benefits of supporting the `background` property in `st.dataframe()`?

A: The benefits of supporting the background property in st.dataframe() include:

More flexibility in customizing dataframes
Ability to create more visually appealing dataframes
Improved user experience

Q: What are the next steps to implement the proposed solution?

A: The next steps to implement the proposed solution are:

Test and validate the solution
Refine the solution based on feedback and testing results
Deploy the solution to the Streamlit community

Conclusion

In conclusion, supporting the background CSS property in st.dataframe() is a valuable feature that can enhance the appearance of dataframes in Streamlit. By implementing the proposed solution, we can add support for the background property and provide users with more flexibility in customizing their dataframes. We hope that this article has provided a clear understanding of the current state of styling dataframes in Streamlit, the limitations of the current implementation, and the proposed solution to support the background CSS property in st.dataframe().