Dash-by-Plotly, a folder of runnable Dash examples rather than a library

One folder per topic, so you can find the example you need

Dash-by-Plotly is a repository dedicated to teaching Dash and Plotly, and its organising principle is plain folders named after the feature they demonstrate.

The top level is the index. There is AI/, a folder for the artificial intelligence examples, Ag-Grid/ for the grid component, Analytic_Web_Apps/ for the wider application patterns, Bootstrap/ for the styling framework, Callbacks/ for the reactive core of Dash, Cytoscape/ for the graph visualisation component, and a folder with a space in the name, Dash Components/, holding the building blocks.

The rest follows the same logic: Dash_Interactive_Graphs/, Dash_and_Databases/ for connecting to a real data store, DataTable/, Deploy_App_to_Web/, Good_to_Know/ for the background material, LangChain/, Other/ and Plotly_Graphs/.

That structure is the repository's main practical value. A tutorial repository that mixes twenty topics into one notebook is a poor reference, whereas a folder per topic means the file you need is one level deep and the rest of the folder is a set of variations on the same idea. It is also the structure that survives a course ending, which is the real test of example code.

The audience is stated as anyone interested in Dash, with the description reaching further to analytical consultants, data analysts, professors, business owners, financial analysts and people in marketing, social media or medicine, on the reasoning that if you work with data the tool is worth knowing.

Installing Dash is two pip commands and a version decision

The installation advice in the project description is short, and the recommendation is to install pandas first, on the grounds that you will most likely use it.

pip install pandas

Then Dash itself, and the note that Plotly comes with Dash.

pip install dash

There is a third option for the case where you want to control the Plotly version yourself, and the example given pins an old release.

pip install plotly==5.7.0

That pinned example is worth reading as dated information rather than as advice. A version from that era of Plotly is many years old, and pinning to it in a new project would lock you to an old rendering and old APIs. The command is instructive about the mechanism, not a recommendation to copy.

The dependency picture is otherwise remarkably small, which is a genuine advantage of the platform. Two pip commands and you have the data layer and the application framework, and there is no separate front end build, no package.json and no bundler to configure. For someone learning, that is the difference between a working example in an afternoon and a project that stalls on toolchain setup.

The repository also ships a data file, vgsales.csv, at the top level, which is the kind of small sample dataset that lets a dashboard example run without a database.

A browser workspace for running the examples without a local setup

There is a third way to run the code, and it is the one that makes the repository usable as a course rather than as a code archive.

The description says that if you prefer to run the code directly online instead of on your computer, you can paste a workspace link into your browser and follow the accompanying demonstration. The link is a Gitpod workspace snapshot, and it points at a specific snapshot of the environment rather than a live shared instance.

That is a good fit for the material in this repository, because most of it is Python scripts whose value is what they draw rather than how they are structured. Being able to open a browser, see a chart render, and change one line of the callback is the fastest possible feedback loop, and for a beginner the cost of setting up a local Python environment is often where a course stalls.

It is also the option that scales worst. A snapshot is a frozen copy of an environment, so any package version it contains is the version it will keep serving, and an example that depends on a newer release will not run there. The snapshot identifier in the link is the version marker for that reason.

The sensible order is the one the project implies: run the code locally once you have a working environment, and use the workspace link to get past the first hour.

What the example folders imply about the platform

The folder names describe the feature surface of Dash, and reading them together says more about the platform than a summary would.

Callbacks/ is the heart of it. Dash applications are reactive, and a callback is the function that turns an input change into an output update. A repository that gives callbacks a top level folder of their own is telling you that this is where the difficulty lives.

Then there are the component folders. Dash Components/ holds the building blocks, DataTable/ covers the tabular component which is what most business dashboards are built around, Ag-Grid/ covers a more capable grid, Cytoscape/ covers the network graph visualisation, and Plotly_Graphs/ covers the charting itself. Dash_Interactive_Graphs/ is the bridge between a chart and a user clicking on it.

Analytic_Web_Apps/ and Dash_and_Databases/ are the folders that show what a real application needs beyond a chart. The first is about assembling an application rather than a single figure, the second about the database connection that a real deployment needs.

LangChain/ and AI/ are the two most likely to be out of step with the rest of the material, since they cover areas that move quickly, and Deploy_App_to_Web/ is the folder that answers the question every dashboard author asks next.

Deployment is a folder, and the user guide link is a legacy address

Deploy_App_to_Web/ exists as its own folder, which tells you the author treats deployment as a topic worth its own material rather than a footnote.

That is the right call, because a Dash application is a Python web application and putting one on the public internet is a separate problem from building it. A chart that runs on localhost on a laptop is a different problem from an app that has to start on boot, survive a restart and serve other people.

The linked user guide, though, deserves a warning. The reference given for Dash is a dash-docs.herokuapp.com address, and that naming pattern belongs to an earlier era of Heroku hosted applications rather than to the current documentation site. A link of that shape is a sign of material that predates the platform's current documentation arrangement, and the examples in the repository are likely to be of a similar age.

The same age shows up in the pinned Plotly version. The combination of an old documentation host, an old version pin and a code base whose last push was on 2026-06-14 means the examples are best read as a snapshot of how the platform was used, not as a description of its current recommended setup.

That is a normal thing for a teaching repository to be, and it does not make the examples wrong. It does mean you should check the current official documentation against any pattern you intend to copy.

Support comes from the Plotly community forum, not from the repository

The help section points at the Plotly Community Forum, described as a community of people dedicated to supporting others learning Dash, and the author says they can be found there under the name CharmingData.

That is a sensible routing for a teaching repository. Questions about why a callback does not fire or how a particular component behaves are platform questions, and the platform community is where the accumulated answers live. An issue tracker for a collection of course examples would attract questions the repository is not positioned to answer.

The repository also has a supporter structure, with a Supporters/ directory at the top level and a My-Dear-Supporters.md file, and the project description opens by asking for support, stating that the goal is a sustainable project for the author and the viewers of the Charming Data channel, and that the channel is fully member supported. GitHub sponsors and Patreon are both listed.

Two things follow from that. First, the repository is a personal teaching effort with a funding model attached, which tells you how decisions about it are made. Second, and more practically for an adopter, the licence field for the repository is unknown, with no licence identifier recorded, and that is a fact to resolve with the author before reusing example code in something you publish.

How this compares with reading the user guide yourself

The real alternative to this repository is to skip it and work from the platform's own documentation.

That alternative has a genuine advantage: the official user guide is current, it is written by the people who build the platform, and it covers the features in the order the platform intends them to be learned. A repository of examples, however well arranged, is a snapshot, and the snapshot age is visible in the version pin and the documentation address.

The advantage of the examples is a different kind of concrete. A documentation page tells you what a data table accepts. A folder of data table examples shows you the working calls, the imports, the layout arguments and the callback wiring for the same feature, in code you can run and change. For someone who knows Python but has never built a Dash application, the gap between reading and building is where most of the time is lost, and running an example is the fastest way across it.

The comparison that matters is therefore between the two ways of learning the same thing. Read the current documentation for the API, and take the examples here for the shape of a working application, especially in the areas the folders flag as involved, callbacks and deployment.

One thing the examples cannot tell you is which patterns the platform has since deprecated, and the example folder named Good_to_Know/ suggests the author knew that too.

A teaching repository with no releases and no recorded licence

The maintenance picture is honest rather than impressive, and it should be assessed as such.

The repository is not archived, and the last push was on 2026-06-14, so it is a live repository rather than an abandoned one. There are no GitHub releases, which is expected for a course repository, since there is no artifact to version. The version information that matters lives in the example code and in the installation section, and the pinned Plotly version is the clearest dated marker of all.

The licence situation is the more serious point. The repository's licence field is unknown, and no licence identifier is recorded against the repository, in contrast with the many projects in this set that name one explicitly. For a repository whose entire content is example code that people are meant to copy, that gap matters more than it would for a library, because copying is the intended use.

None of this makes the examples less useful for learning, which is what they are for. It means the upgrade cost for an adopter is reading rather than running a version manager, and the licensing question needs an answer from the author before any of this code ships inside something you own.

Editorial conclusion

Adopt this repository if you are learning Dash or need a working reference for a specific feature, such as callbacks, a data table, a database connection or a deployment setup, and you intend to read and adapt the code rather than import it. Do not treat it as a dependency or a maintained library, since it publishes no releases and records no licence, so nothing here should be copied into a product without checking the terms with the author. Before you build on it, install the versions yourself with `pip install dash` and `pip install pandas`, run one example from the folder that matches your task, and confirm the Plotly version your project needs, since the documented pinning example uses plotly==5.7.0.

Frequently asked questions

Official sources

1. Coding-with-Adam/Dash-by-Plotly on GitHub
2. Issues
3. README