Fixing Streamlit Plotly Chart `kwargs` Deprecation Warnings

by Alex Johnson 60 views

Understanding the Streamlit st.plotly_chart Deprecation Warning

When you're building interactive data applications with Streamlit, you often turn to powerful visualization libraries like Plotly to bring your data to life. The st.plotly_chart function is Streamlit's fantastic way to embed these dynamic, interactive Plotly figures directly into your apps. It’s designed to be straightforward, allowing you to pass a Plotly figure object and display it beautifully. However, many developers, including you perhaps, have recently encountered a puzzling issue: a persistent st.plotly_chart kwargs deprecation warning popping up, even when seemingly using perfectly valid, documented parameters like width="content". This warning can be quite frustrating because it suggests that you're using deprecated keyword arguments, implying your code might break in a future update, even though you’re adhering to the current best practices laid out in the official Streamlit documentation for controlling chart dimensions. This particular scenario highlights a regression where a previously functional and encouraged parameter now triggers a deprecation notice, creating unnecessary confusion and concern among the developer community. The core of the issue stems from how Streamlit's internal logic processes kwargs (keyword arguments) passed to st.plotly_chart, especially when these arguments aren't strictly Plotly configuration options but rather Streamlit's own parameters for rendering the component. The expectation is that Streamlit-specific parameters, such as width and height, should be handled internally and not flagged as deprecated kwargs intended for Plotly's underlying configuration. This discrepancy means that even when you're simply trying to make your Plotly chart responsively fit its container using width="content", a feature highly valued for creating flexible UI/UX, you’re greeted with a warning that makes you second-guess your implementation and potentially waste time searching for a non-existent bug in your own code. It’s a classic case where the tool's message conflicts with its documented usage, and understanding why this happens is the first step towards resolving it and keeping your Streamlit applications running smoothly and warning-free. We’ll dive deep into what this warning means, why it appears, and how you can gracefully manage it in your projects to ensure your interactive dashboards remain robust and performant.

The Root Cause: kwargs and Parameter Evolution

At its heart, the kwargs deprecation in st.plotly_chart points to a refinement in how Streamlit manages parameters for embedded components. In Python, **kwargs allows functions to accept an arbitrary number of keyword arguments, which is incredibly flexible. Historically, st.plotly_chart leveraged this flexibility to pass any unrecognized keyword arguments directly down to Plotly's underlying configuration options. This meant you could customize various aspects of the Plotly rendering engine (like enabling or disabling the mode bar, setting locale, etc.) by just passing them as kwargs to st.plotly_chart. While convenient, this approach blurred the lines between Streamlit's own display parameters and Plotly's intrinsic configuration settings.

However, Streamlit is evolving, aiming for clearer separation and more explicit control. The new approach is to use a dedicated config argument for passing Plotly-specific configuration options, making the API more predictable and easier to maintain. The problem arises because the deprecation logic, as seen in the provided snippet (if kwargs: show_deprecation_warning(...)), currently flags any keyword argument that isn't explicitly defined as a direct parameter of st.plotly_chart as a deprecated kwargs usage. This includes parameters like width and height, which are indeed documented Streamlit parameters for controlling the chart's display, but appear to be caught in the kwargs net. So, when you write st.plotly_chart(fig, width="content"), the width parameter, despite being a legitimate Streamlit argument, is momentarily treated as part of the kwargs collection by the internal check, triggering the deprecation warning. This is a classic regression where a previously working and documented feature now incorrectly raises a warning, causing confusion and frustration for developers. The intention behind the deprecation is good – to streamline how Plotly configurations are handled – but its current implementation inadvertently impacts valid Streamlit-specific parameters, leading to unexpected warnings and a less-than-ideal developer experience.

Reproducing the st.plotly_chart Warning (and Why it Matters)

Let's look at the reproducible code example provided, which perfectly illustrates this confusing st.plotly_chart warning behavior. It’s a very simple setup, yet it reliably triggers the deprecation message:

import plotly.graph_objects as go
import streamlit as st

# Create a basic Plotly figure
fig = go.Figure()
fig.add_trace(go.Barpolar(r=[1], theta=[0], width=[120]))
fig.update_layout(width=500, height=360) # Note: Plotly's internal layout width/height

# Display the Plotly figure in Streamlit with a specific Streamlit width parameter
st.plotly_chart(fig, width="content")

In this snippet:

  1. We import plotly.graph_objects as go and import streamlit as st to get our tools ready.
  2. A very basic Plotly figure, fig, is created using go.Figure(), with a simple Barpolar trace added for demonstration. This isn't strictly necessary for the warning, but it makes for a complete example.
  3. The fig.update_layout(width=500, height=360) line sets the internal dimensions for the Plotly chart itself. This is distinct from how Streamlit will render the component.
  4. Finally, st.plotly_chart(fig, width="content") is called. Here's the crucial part: width="content" is a documented Streamlit-specific parameter that tells st.plotly_chart to render the Plotly figure using the full width available in its container. It’s a super handy feature for responsive designs, letting your charts naturally adapt to different screen sizes.

Despite width="content" being a legitimate and widely used parameter for st.plotly_chart, running this code in a Streamlit app (especially with recent versions like 1.50.0, as specified in the debug info) will throw the deprecation warning. This happens because, as we discussed, the internal warning mechanism incorrectly categorizes width (when passed this way) as a generic kwargs that should now be handled via the config dictionary. Why does this matter? Warnings, while not immediately breaking your app, are signals from the library authors. They indicate that a piece of code might be removed or changed in future versions. When a valid, non-deprecated parameter triggers such a warning, it creates unnecessary anxiety, causes developers to second-guess their code, and can lead to wasted time trying to "fix" something that isn't actually broken from a usage perspective. It dampens the developer experience and makes it harder to distinguish between genuine deprecations that require action and false positives like this regression. Recognizing and addressing these false positives is essential for maintaining a smooth and reliable development workflow.

Strategies to Resolve the st.plotly_chart Deprecation

Encountering deprecation warnings can be a bit like getting a "check engine" light in your car – it's annoying, but addressing it ensures smoother operation down the road. For the st.plotly_chart deprecation, especially concerning kwargs, there are specific strategies to apply. While the width="content" issue seems to be a regression that Streamlit will likely fix, understanding the intended changes will prepare you for future-proof development and allow you to handle other Plotly configuration needs effectively. Streamlit is actively working to make its API more explicit and robust, and adapting to these changes, even when they come with temporary hiccups, is a part of building high-quality, maintainable applications. The key is to distinguish between parameters that genuinely fall under the new config argument's domain and those that are Streamlit's own direct parameters. By consciously separating these concerns, you can ensure that your charts are displayed exactly as intended, without unnecessary console clutter or the risk of future breakage. This section will walk you through the correct ways to manage Plotly configurations and Streamlit layout parameters, helping you navigate these changes like a seasoned pro and keep your dashboards sparkling clean and fully functional. We'll explore how to properly pass Plotly-specific settings, discuss current best practices for Streamlit's display options, and offer advice on how to stay ahead of the curve as the library evolves.

Option 1: Migrating to the config Argument for Plotly Settings

The primary reason for the kwargs deprecation is to encourage developers to use the config argument for passing Plotly configuration options. This is a much cleaner and more explicit way to customize the behavior of your Plotly charts, like disabling zoom, hiding the mode bar, or controlling how images are saved. Instead of scattering these settings as generic keyword arguments, Streamlit now expects them to be bundled into a dictionary and passed via config.

Here's how you'd typically use the config argument:

import plotly.graph_objects as go
import streamlit as st

fig = go.Figure(data=[go.Scatter(y=[2, 1, 4])])

# Define Plotly-specific configuration options in a dictionary
plotly_config = {
    'displayModeBar': False,  # Hides the Plotly mode bar (zoom, pan, etc.)
    'staticPlot': False,      # Allows interactivity (e.g., zoom, pan)
    'responsive': True        # Makes the chart responsive to container size
}

st.plotly_chart(fig, config=plotly_config)

In this example, displayModeBar, staticPlot, and responsive are specific Plotly configuration parameters. By passing them within the plotly_config dictionary to the config argument of st.plotly_chart, you explicitly tell Streamlit how to render the Plotly chart, avoiding any kwargs related deprecation warnings for these types of settings. This approach makes your code more readable, as it clearly separates Streamlit's own parameters from the underlying Plotly configurations. It also prepares your application for future Streamlit updates, as this is the intended and supported method for these types of customizations.

Option 2: Understanding and Adapting width and height

Now, for the parameters that caused the initial confusion: width and height. When you use st.plotly_chart(fig, width="content"), you are trying to use a Streamlit-specific display parameter, not a Plotly configuration option. The value "content" tells Streamlit to automatically adjust the chart's width to fit its container. This is incredibly useful for creating responsive layouts in your Streamlit apps.

The fact that width="content" triggers the kwargs deprecation warning is, as identified in the problem statement, a regression. It means Streamlit's internal logic is incorrectly classifying this legitimate parameter as a generic, deprecated kwargs. For now, while waiting for an official fix from the Streamlit team, you have a few ways to manage this:

  1. Directly Setting Plotly Layout Dimensions: If the warning is particularly bothersome and width="content" isn't critical for dynamic resizing in your specific context, you can set the width and height directly within your Plotly figure's layout, rather than passing it to st.plotly_chart:

    import plotly.graph_objects as go
import streamlit as st

fig = go.Figure()
fig.add_trace(go.Barpolar(r=[1], theta=[0], width=[120]))
fig.update_layout(width=700, height=400) # Set fixed dimensions in Plotly layout

st.plotly_chart(fig) # No width/height passed to st.plotly_chart

    This will render the chart with the fixed dimensions specified in fig.update_layout. It bypasses the Streamlit width parameter entirely, thus avoiding the warning. However, you lose the "fit to content" responsiveness that width="content" provides.

  2. Using Streamlit Layout Containers: For more control over responsiveness without relying on width="content" directly on st.plotly_chart, you can use Streamlit's layout primitives like st.columns or st.container. This allows Streamlit to manage the overall space, and Plotly charts will generally adapt well within these structures.

    import plotly.graph_objects as go
import streamlit as st

fig = go.Figure()
fig.add_trace(go.Barpolar(r=[1], theta=[0], width=[120]))

# Use Streamlit columns for responsive layout
col1, col2 = st.columns(2)
with col1:
    st.header("Chart in Column 1")
    st.plotly_chart(fig, use_container_width=True) # use_container_width is an alternative, but also causes deprecation.

with col2:
    st.header("Chart in Column 2")
    st.plotly_chart(fig) # Let it default or use Plotly's internal sizing

    Important Note: While use_container_width=True was the predecessor to width="content" and also handles responsiveness, it too is now deprecated for similar reasons, often replaced by width="container". The issue here is specifically width="content" (or "container" or "auto"), which are the intended new ways to achieve this. The deprecation warning indicates a temporary oversight in Streamlit's current release where these intended parameters are mistakenly caught by the kwargs deprecation check.

The best long-term solution is to await a fix from the Streamlit team, which will correctly differentiate between Streamlit's own layout parameters (width, height) and Plotly's configuration options (which should go into config). In the meantime, if the warning is impacting your development flow, using direct Plotly layout sizing or carefully structuring with Streamlit containers can provide temporary relief, though they might not fully replicate the intended width="content" behavior without the warning. Keep an eye on Streamlit's release notes and GitHub issues for updates on this specific regression.

Best Practices for Using Streamlit and Plotly

Integrating Streamlit and Plotly effectively can elevate your data applications from simple scripts to powerful, interactive dashboards. To avoid common pitfalls and ensure a smooth development experience, here are some best practices:

  • Keep Your Libraries Updated: Always run the latest stable versions of Streamlit and Plotly. This ensures you benefit from bug fixes, performance improvements, and the most up-to-date API features. Deprecation warnings, while sometimes annoying, are also a signal to update and adapt.
  • Consult the Documentation Regularly: Both Streamlit and Plotly have excellent, comprehensive documentation. When you encounter unexpected behavior or warnings, the first place to check should always be the official docs. They often contain examples and explanations for common use cases and API changes.
  • Separate Concerns: Try to keep your data processing, Plotly figure creation, and Streamlit display logic distinct.
    • Data Processing: Clean, transform, and prepare your data independently.
    • Plotly Figure Creation: Design your go.Figure() objects and their traces/layouts in a way that's reusable and not tightly coupled to Streamlit.
    • Streamlit Display: Use st.plotly_chart() solely for embedding the prepared Plotly figure into your app, along with any Streamlit-specific layout adjustments (st.columns, st.container).
  • Leverage Streamlit's Layout Features: Instead of fighting with Plotly's layout for overall app structure, embrace Streamlit's own layout primitives. Use st.sidebar, st.columns, st.container, and st.expander to create a visually appealing and organized application. These features are designed to work seamlessly with various components, including Plotly charts, helping them adapt gracefully to different screen sizes.
  • Optimize for Performance: For complex Plotly charts with many data points, consider strategies to optimize performance. This might involve:
    • Data Aggregation: Only display aggregated data initially, allowing users to drill down for more detail.
    • Caching: Use st.cache_data or st.cache_resource for functions that generate Plotly figures if they are computationally expensive and their inputs don't change frequently.
    • Lazy Loading: Load and display charts only when they are visible or specifically requested by the user.
  • Handle Interactivity Mindfully: Plotly charts are highly interactive by default. Think about how this interactivity aligns with your Streamlit app's overall user experience. If certain Plotly interactions conflict with your app's logic (e.g., zoom resetting filters), consider using the config argument to disable specific Plotly UI elements (like displayModeBar: False).
  • Error Handling and User Feedback: Implement clear error handling for your data and chart generation. If a chart can't be rendered due to bad data, provide informative feedback to the user using st.error or st.warning.

By following these best practices, you can build robust, performant, and user-friendly data applications that harness the full power of both Streamlit and Plotly, minimizing deprecation headaches and maximizing impact.

Conclusion: Navigating Deprecation Warnings for Smoother Development

We've delved into the intricacies of the st.plotly_chart kwargs deprecation warning, particularly its unexpected appearance when using parameters like width="content". While deprecation warnings are a natural part of software evolution, designed to guide developers toward more robust and future-proof code, this specific instance highlights a regression where a legitimate Streamlit-specific parameter is mistakenly flagged. This creates unnecessary confusion and can disrupt your development workflow.

The key takeaway is to understand the intent behind the deprecation: Streamlit is moving towards a clearer separation between its own component display parameters and the underlying Plotly configuration options. The config argument is now the designated pathway for passing Plotly-specific settings, leading to cleaner and more maintainable code. For Streamlit's own display parameters like width and height, a fix is expected from the Streamlit team to correctly handle these without triggering the kwargs warning. In the interim, you can use workarounds such as setting Plotly layout dimensions directly or leveraging Streamlit's robust layout containers to manage your chart's appearance.

Staying informed about library updates, regularly consulting documentation, and adopting best practices for integrating Streamlit and Plotly are crucial for navigating these changes effectively. By doing so, you ensure your applications remain stable, performant, and free from perplexing warnings, allowing you to focus on delivering valuable insights through stunning visualizations. Happy Streamlit-ing!

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