Share Your App - Explore Page - 2026

Dash Python
1

Thank you for your interest in submitting your Dash or Plotly Studio app to the Plotly Explore Page platform, visited by thousands of users daily.

To submit your app, please reply to this thread directly.

Please refer to the following suggestions when building and submitting your app. The more suggestions your app adheres to, the more likely it is to be added to the Explore Page.

  • Apps in the following categories are encouraged: Energy & Utilities, Business, Predictive Analytics & Forecasting, NLP, Connecting to APIs
  • App should look as good or better than the current apps on the platform
  • App should use different data than the other apps and try to cover a unique story
  • Content/story should be neutral or positive
  • App with live data that updates itself is encouraged
  • App that goes beyond exploratory analysis – app that perform advanced analytics
  • App that uses 3rd-party libraries (e.g., SciPy, spaCy, TensorFlow, Scikit-learn)
  • App that solves real-life problems, app that could have practical use cases
  • App content and results should be easy to access (we discourage requiring log-ins or uploading data as a precursor to seeing the full app).

The Plotly Example Apps team will review the apps submitted and update this post with the name of the app that has been selected.

Happy Plotly-app building!

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Hi Adam,

Here’s another app I built: Earthquake Monitoring Dashboard. It’s an interactive earthquake analytics system built with Dash for seismic monitoring, historical earthquake exploration, spatial analysis, machine learning-based clustering, and automated earthquake data processing.

You can find the complete documentation and source code in the GitHub repository for the Earthquake Monitoring Dashboard.

Best regards,

4

Hi Adam,

Here is my proposed submission to the gallery: https://erp-gmm2.plotly.app

It is a Plotly Dash app in Finance that illustrates how well Gaussian mixture models fit so-called option smiles. It also serves as supplementary material for my recently released book “The Equity Risk Premium: An Options-based Approach”. (link)

The option smiles are computed from live (although slightly delayed) data from Yahoo Finance, using the options underlying a user-chosen ticker symbol. There are explanatory drop-downs on the upper right.

Regards,

Alan Lewis

5

Hi Adam,

My app can be found at https://spdcalldashboard.onrender.com. It’s deployed on render, I hope that’s not a dealbreaker. The dashboard condenses and visualizes information from 3 different datasets from the City of Seattle that are regularly updated (most are updated daily). Put simply the dashboard gives users access to the latest information on crime, police response times, and police use of force in the City of Seattle.

Concise information on the app:

End of TLDR

Full Background:

The public perception around policing in Seattle is generally categorized by distrust and disapproval. The SPD is considered to be both ineffective and over-reaching by most people I have spoken to who are residents. And while the City of Seattle maintains basic dashboards showing the counts of crimes, information on use of force, and some other resources most people do not have the time to do a rigorous and meaningful analysis of these dashboards.

My dashboard features: Citywide Crime Count, Overall Citywide Crime Rate / 100k Residents, Crime Counts by Category of Offense, a Choropleth/Point Map (with controls to switch between crime rates and raw counts), a Line Chart for Count of Offenses over time, Median Qualified Response time (with controls to filter for specific priority of calls), Count of Use of Force Incidents, Count of Officer Involved Shooting Incidents, and finally a table that displays neighborhoods ranked by response time, call volume, or crime volume (controlled by the user).

All figures (excluding the Use of Force and Officer Involved Shooting figures) can be controlled by the global controls at the top of the dashboard. The global controls feature an explicit date range selector, a crime type selector, crime subcategory selector, and a neighborhood selector. In addition to these options, users can also use the plotly figures themselves (the map and time series figures) to manipulate the global control state. The time series range selector is tied to the global controls date range, and neighborhoods in the map are tied to the global control’s neighborhood selector.

One interesting insight is the clear anomalous downturn in reported crimes. In the past 2 months the City of Seattle has faced an uptick in homicide, since July 21, 2026 there have been 9 reported homicides (up 125% from the preceding 2 months). This pattern coincided with the Bite of Seattle shooting which left 3 dead, 4 injured. In the fallout of the shooting the Seattle Police Department failed to inform the public about the situation for several hours and public backlash led to the resignation of the SPD Police Chief. Since this period there has been a stark decline in reported crimes in the City of Seattle, a 15.1% decrease that is unlike any seasonal effects seen in historical data. This pattern points to severe disfunction in the Seattle Police Department during a period of time where we have seen elevated risks to public safety.

If you’ve made it this far in the message thank you for reading.

Cheers,

Ben

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These guidelines are helpful, especially the focus on apps that solve a real problem and use live or unique data. I also like that the examples should be easy to explore without requiring a login or data upload first. That makes the Explore Page much more useful for people discovering new Dash apps.

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Hi Adam,

I’ve just (re)launched a dash app on ClimateRadarDIY . It’s a publicly available climate analysis platform that makes it easy for anyone to analyse historical and future weather events and trends (roughly 800gb of data, updated daily). The focus is DIY – the tool allows you to do full scale climate analysis all by yourself. No technical background needed. The main expected user groups are education / climate activism / anyone interested in climate and weather. It will answers questions such as 'Was the heatwave / drought / forest fire / extreme rainfall that I’ve just experienced influenced by climate change, and how will this change towards the future?".

This is generally a rather difficult topic, but Dash helps to make such an analysis relatively easy to do and understand the final outcome (always textual summaries of figures), and avoid any of the typical climate lingo. Also, (1) Multiple languages supported through partly pre-translated text and on the fly translations, (2) AI assisted impact analysis (what was the economical / societal impact of a specific event), (3) AI assisted figure summary to help explain difficult figures and (4) walkthroughs that help you with certain analysis can make this topic more easy / less difficult to DIY.

Regarding the criteria you listed above:

  1. Categories — Fits Predictive Analytics & Forecasting (CMIP6 future risk ratios) and Connecting to APIs (Anthropic/Claude calls) well

  2. Looks good? — Looks good if I can say so myself :wink: A lot of effort went into making it a ‘simple’ app, despite the many analysis / figure options you have. Also a lot of specific mobile UI code in order to make these analysis possible as well on a small screen.

  3. Unique data/story — I didn’t see a specific app for climate data yet, so I guess this is a strong fit there. Also the advanced analysis options (extreme values analysis) make it more than a normal generic dashboard.

  4. Neutral/positive tone — I made this in order to let the climate data itself tell the story, so it depends on the analysis that you do if its a positive or negative tone :wink:

  5. Live data: Daily updates of reanalysis data (ERA5) and weather forecasts (GFS).

  6. Advanced analytics — On the fly climate attribution analysis, so non-stationary GEV fitting, delta-method uncertainty, multi-model bias correction. So real statistical methodology, not just exploratory plotting.

  7. 3rd-party libraries — scipy, xarray, geopandas, xclim/xsdba, Anthropic SDK.

  8. Real-world use case — Lets someone see how warming has actually shifted the odds of an extreme event they experienced, for their own location (global coverage)

  9. No login/upload wall — The apps on climateradar (ClimateRadarDIY) are publicly available (with a non-commerical license)

Would be nice to see it being used more so would be very happy to see this on the explore page.

Cheers, Folmer