Thank you for your interest in submitting your Dash app to the Plotly Explore Page platform, visited by thousands of Dash users daily.
Current submissions cycle will run from November 1 to November 30, 2023.
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 names of the apps that have been selected.
Happy Dash app building!
I created this little app to get some experience of client-side callbacks: https://motion-binding.onrender.com
Though it might be of interest, even if it’s too frivolous for the Explorer Page
@rjenc29 - nice use of Dash (and client-side callbacks) to demonstrate this optical illusion.
What a neat illusion. Thanks for sharing the app, @rjenc29, and also for making the code available.
We’ll share this app at the next publication of the Dash Club newsletter.
Hi @adamschroeder, i experimented with a client-side callback to annotate the time of a audio player on a plotly. time-series-figure.
so interesting and unique, @ChironeX1976
Thank you for sharing.
I git cloned your repo and ran the app. Then, I chose the
GL 22 007_LoggedBB data file but the first date and time (08:56:06) doesn’t work. The annotation is on the left side of the x-axis but the play button is blank. I can’t play it.
Do you know why this is happening?
sorry to hear that.
when the play button is blank, the .wav - file is not in the right place.
the .wav-file must be in the data/Gl22 Annotations folder under the project data folder.
here at home, in Belgium this is:
while the main.py is in c:\werk\dash04 - folder.
Thank you Kris.
Do you plan to deploy this app to the web as well?
I just wanted to share my code (that i could make thanks to some client-side callback lessons.
I do not have deployment goals for the web; I just do not have the right skills to do such things. Furthermore, the app uses local wav-files that were spit out by a sound level meter, i can’t think of a way to fix this in a web environment.
I will try to fix the path - issue. I can conclude I made several mistakes:
- I tried the code in a raspbery pi, and indeed, there is a path issue to the wav-file that needs to be solved. The path ‘calculation’ (line 207 and further in data.py) is not recognized on a linux computer.
- I simply forgot to put the actual corresponding wav-file SR0.wav in my github repositories.
I would like to share my app GreenSpace www.greenspace.city. I know you’ve had trouble with the SSL / security with this domain, but I am unable to replicate the issue on any other machine, so I’m going try and share the app anyway.
This app shows the impact of Urban Heat Island (UHI) and Tree Canopy Cover for major Australian cities. The urban heat island (UHI) effect is a phenomenon that causes cities to have higher temperatures and more intense heat extremes than rural areas. It is caused by several factors, such as the replacement of vegetation with buildings, roads, and other heat-absorbing surfaces, the emissions of heat from human activities, and the shape and height of buildings that affect airflow. The urban heat island effect can have negative impacts on public health, energy use, and air quality. The UHI index values (in °C) in this app are estimates of how much the urban built environment boosts the temperature in your area.
Thanks for sharing, @masands
I wonder if other community members here are able to open the link and see @masands 's app?
My two browsers (Edge, Brave) are blocking it.
Works for me with Firefox.
Works for me in Firefox and Chrome
Works for me in Safari Version 16.4!
ok, it my be due to the firewall installed on my computer. I just got it to work. Very nice!
That’s a helpful explanation on how the UHI index values (in °C) are calculated!
This definitely isn’t as advanced or scientific as others’ projects but I’d still like to submit my app anyways: WhereToLive.LA. It’s using Dash, Dash Leaflet, and Pandas to display a map of available rental & for-sale properties in Los Angeles County.
The data comes from a Google spreadsheet updated weekly by a local realtor at https://www.freelarentals.com/. I essentially just used Dash Leaflet to turn that spreadsheet into a filterable map, so it has callbacks to filter the dataframe & resulting datapoints based on the options the user selects. There are a lot of options, so I added toggle buttons to reduce or increase the number as desired so people aren’t overwhelmed without sacrificing detailed filtering.
Each datapoint/marker contains an HTML popup of that property’s details (rent/list price, pet policy, security deposit, HOA fee, etc.) and a photo too. That popup appears when you click a marker.
The code is up at GitHub - perfectly-preserved-pie/larentals: An interactive map of rental property listings in Los Angeles County, updated weekly.. I’d love some feedback
Hello you can find my App Here:
My app automates the fundamental analysis of Equities. Please have a look.
It is still in beta so sign up with google is not possible but normal signup with email is.
Here you can find my first ever public dash app: yahoo fantasy basketball app!
An app that allows users not only to make multiple player comparisons but also in-depth individual player analysis.
It connects to nba.com API by utilizing the nba_api python package to fetch “live” (the recent) data daily. I daily because it does it half locally - half on cloud. NBA API, like other mainstream APIs, does not allow mainstream cloud server IPs to connect to itself. Thus, I needed to find a solution by writing a daily scheduled script that fetches data from NBA API locally and sends it to an online database to which my app has a connection. (I am open to any suggestions to make it more efficient)
Moreover, it suggests similar players to the selected player according to a calculated similarity metric utilizing 3rd-part libraries.
There is a high chance that the app might not be perfectly responsive for small devices. Hence, sorry for that
Please take a look and share your thoughts with me. Also, if you have any questions, please do not hesitate to ask me; I would be happy to help all of you.
Last but not least, from time to time it may take a little bit longer for the app to get loaded fully. Please try to be patient.
Happy dash app building!
Hey @Berbere - This is a cool app! Thanks for sharing !
This is a very useful app! Nice use of Dash Leaflet!