This document provides a comprehensive, step-by-step walkthrough for creating a Python Dash application from scratch within the Positron IDE, managing the project with Git and GitHub, and deploying it as a publicly accessible web application using Posit Connect Cloud—all for free. To illustrate the process, we will build a simple, interactive dashboard that visualizes the popular Palmer Penguins dataset.

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1.0 Foundational Setup: Initializing Your Project in Positron

A clean and well-structured project is the bedrock of any successful application. The initial steps of creating a dedicated project, initializing version control, and isolating dependencies are not just administrative tasks; they are foundational practices that ensure reproducible development, simplify collaboration, and enable seamless deployment to a production environment.

1.1 Create the Project Structure

Begin by creating a new, dedicated project folder within the Positron IDE. This contains all your application’s files in a single, organized location.

1. Navigate to File > New Folder from Template.
2. Select Python project from the available templates.
3. Name the project app-penguin.
4. Specify a location on your local machine to save the project (e.g., your Desktop).

1.2 Initialize Version Control with Git

Initializing a Git repository from the very beginning is a critical best practice. It allows you to track every change, revert to previous versions if needed, and is essential for connecting your local work to a cloud deployment platform. Positron allows you to initialize a Git repository automatically by selecting the corresponding checkbox during the project creation process.

1.3 Establish the Python Virtual Environment

A virtual environment is critical for creating reproducible applications. It isolates your project’s specific dependencies to prevent the "it works on my machine" problem and ensures that the package versions you develop with are the exact same ones used during deployment. This guide uses uv for its speed and modern feature set, but the built-in venv or conda are also excellent alternatives.

For this project, we will use Python version 3.13. After the project is created, perform this crucial check: confirm that the correct environment is active by looking at the project folder and interpreter selectors in the top-right corner of the Positron interface. The interpreter should point to the virtual environment located inside the app-penguin folder you just created.

1.4 Install Core Dependencies

With the virtual environment active, you can now install the core Python packages required for the application. Open the terminal within Positron and run the following command:

uv add dash plotly palmerpenguins

This command installs Dash (the web framework), Plotly (for creating interactive visualizations), and Palmer Penguins (the sample dataset). You can confirm that the packages were installed successfully by inspecting the pyproject.toml file, which should now list these packages under its dependencies.

With your project structure and environment now fully configured, you are ready to begin developing the application’s core logic.

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2.0 Prototyping Logic: Building the Core Visualization

Before building the dashboard’s user interface, it’s a strategic best practice to prototype the data processing and visualization logic in a separate script. This approach allows you to debug the core Python and visualization code in isolation, ensuring it works correctly before you have to troubleshoot potential issues within the dashboard framework itself. This separation of concerns greatly simplifies troubleshooting.

2.1 Load Data and Define User Interaction Variables

We will begin by creating a new prototyping script named code.py. The first step is to load the palmerpenguins dataset. Here’s a pro-tip for working in Positron: once the data is loaded, you can click the small data table icon in the variable viewer to inspect the dataframe in a new tab.

Next, identify the key points of user interaction that will drive the dashboard. In this case, the user will select a penguin species to highlight in the visualization. To prepare for this, we create a variable that holds the unique penguin species from the dataset. Saving user-driven inputs as variables during the prototyping phase makes them easier to integrate into the final dashboard components, such as the radio buttons we will build later.

2.2 Construct the Plotly Histograms

The core of our application is a set of layered histograms showing the distribution of penguin bill lengths. The construction can be broken down into the following steps:

1. Overall Histogram: First, create a base Plotly histogram that visualizes the ‘bill length’ for all penguin species combined. This will serve as a background reference.
2. Species-Specific Histogram: Next, filter the dataset for a single, selected species and create a second histogram of its ‘bill length’. This will be the foreground layer that updates based on user selection.
3. Standardize Bins: It is critical to set a consistent bin_width (e.g., to 1) for both histograms. Different subsets of data (like a single species versus all species) have different value ranges. Without a standard bin width, Plotly would create different default bins for each trace, making them visually incomparable.
4. Combine Visuals: Finally, use Plotly’s update_layout method with barmode=‘overlay’ to layer the species-specific histogram on top of the overall histogram. Differentiate the two layers by assigning distinct colors, such as grey for the background and blue for the foreground.

With a functional Plotly figure that can be dynamically updated by changing a single variable, we are now ready to integrate this logic into a fully interactive Dash application.

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3.0 Building the Interactive Dash Application

Dash is a powerful Python framework for building analytical web applications without needing to write JavaScript. In this section, we will wrap our visualization logic within a Dash application structure, create a simple user interface for interaction, and connect the user’s input to the plot to make it dynamic.

3.1 Restart the Python Session

Before creating the app.py file, it’s a vital best practice to restart your Python session. In the Positron console, click the restart button. This clears all variables and objects created by our code.py prototyping script, ensuring our application starts with a clean slate and preventing any accidental state leakage.

3.2 Initialize the Dash App

Create a new file named app.py. This file will contain the complete Dash application. The first step is to add the boilerplate code to initialize the app. This involves importing the necessary components and instantiating the Dash application object.

import dash from dash import dcc, html, Input, Output import plotly.express as px from palmerpenguins import load_penguins

app = dash.Dash(name)

Application code will go here...

3.3 Define the Application Layout

The app.layout attribute defines the visual structure of the application. It is composed of a tree of components that are analogous to HTML elements. For our dashboard, the layout will consist of:

• A main html.Div that acts as the primary container for all other elements.

• A nested html.Div to hold the user controls, which includes:

• An html.Label with the text ‘Species’ to describe the control.

• A dcc.RadioItems component to allow user selection. Its key properties are:

• id: A unique identifier (‘species-radio’) used to reference this component in callbacks.

• options: The list of selectable species.

• value: The default species to be selected when the app loads.

• inline: Set to True to display the radio buttons horizontally.

• A dcc.Graph component that serves as a placeholder for our Plotly figure. It also needs a unique id (‘histogram-dash-plot’) so it can be targeted by the callback.

3.4 Run and Test the Static Layout

A key principle of iterative development is to build and test in small increments. With only the static layout defined, let’s run the application. Click the play button located just below the file name in the Positron editor. Positron will recognize that you are running a Dash application and launch it on a local web server.

You should see a page with the "Species" label and the radio buttons, but with an empty space where the graph will be. This confirms your layout is structured correctly before you add the complexity of interactive callbacks.

3.5 Implement Reactivity with Callbacks

Callbacks are the core of Dash’s interactivity. The @app.callback decorator is a powerful mechanism that connects user inputs, like selecting a radio button, to application outputs, like updating a graph.

The complete callback function ties our layout to our visualization logic:

Load data

penguins = load_penguins()

Define the layout

app.layout = html.Div([ html.Div([ html.Label(‘Species’), dcc.RadioItems( id=‘species-radio’, options=[{‘label’: i, ‘value’: i} for i in penguins[‘species’].unique()], value=‘Adelie’, inline=True ) ]), dcc.Graph(id=‘histogram-dash-plot’) ])

Define the callback

@app.callback( Output(‘histogram-dash-plot’, ‘figure’), Input(‘species-radio’, ‘value’) ) def update_figure(selected_species): # Filter data for the selected species filtered_penguins = penguins[penguins[‘species’] == selected_species]

# Create the layered histogram
fig = px.histogram(penguins, x="bill_length_mm", nbins=40,
color_discrete_sequence=['grey'])

fig.add_histogram(x=filtered_penguins["bill_length_mm"], nbins=40,
name=selected_species)

fig.update_layout(barmode='overlay')
fig.update_traces(marker=dict(line=dict(width=1, color='black')))

return fig # Return the updated figure object

Add server execution block

if name == ‘main’: app.run_server(debug=True)

Let’s break down this structure:

• Decorator Signature: The @app.callback decorator defines the relationship. Output(‘histogram-dash-plot’, ‘figure’) specifies that the function’s return value will update the figure property of our graph component. Input(‘species-radio’, ‘value’) tells Dash to listen for changes to the value of our radio buttons.
• Data Flow: When a user clicks a new radio button, the value of the Input component changes. Dash automatically calls the update_figure function, passing the new value as the selected_species argument.
• Function Body: The function executes the plotting logic from our prototype, creating a new Plotly figure based on the user’s selection.
• Return Value: The return fig statement sends this newly created figure back to the figure property of the Output component, updating the visualization in the user’s browser.

3.6 Run and Test the Application Locally

With the layout and callback now fully defined, run the application again using the play button in Positron.

The output will be a locally hosted web page displaying the "Species" label, the three radio buttons, and the interactive histogram. When you click a different species, the callback will fire, and the foreground of the histogram will update accordingly.

Now that you have a fully functional local application, the next step is to prepare it for public deployment.

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4.0 Version Control: Pushing the Project to GitHub

Git and GitHub are central to any modern CI/CD (Continuous Integration/Continuous Deployment) workflow. GitHub serves not just as a backup for your code, but also as the essential bridge connecting your local development environment to the cloud platform where your application will be hosted.

4.1 Commit Local Changes

Before pushing your code, you must commit your work to the local Git repository. This creates a snapshot of your project’s current state. Ensure that all the necessary files, particularly app.py, are included in the commit.

4.2 Create a New Remote Repository on GitHub

Next, you need a corresponding repository on GitHub to host your code.

1. Navigate to github.com/new in your web browser.
2. Assign a name to your repository (e.g., Penguin-app).
3. Crucially, you must create a completely empty repository. Your local project already has a commit history. If you initialize the GitHub repository with a README or license, GitHub creates its own, separate initial commit. These divergent histories cannot be easily merged and will cause push errors. Starting with a completely empty remote repository ensures it can cleanly accept the history you push from your local machine. Ensure the following options are not selected:

• Do not add a README file.
• Do not add a .gitignore file.
• Do not add a license.

1. Click "Create repository".

4.3 Link Local and Remote Repositories and Push

With the empty GitHub repository created, you can now link your local project to it and push your code using commands in the Positron terminal.

1. Add the GitHub repository as a "remote" named origin. Use the SSH URL provided by GitHub.
2. Verify that the remote was added correctly.
3. Push your local main branch to the origin remote on GitHub.
4. Refresh your repository page on GitHub. You should now see all your project files, including app.py.

With your application’s source code now securely hosted on GitHub, you are ready to deploy it.

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5.0 Deployment: Publishing with Posit Connect Cloud

Posit Connect Cloud is a platform designed to deploy data science applications, including Dash dashboards, directly from GitHub repositories. This final section covers the configuration and publishing process to make your application publicly accessible on the web.

5.1 Configure the Deployment from GitHub

Follow these steps to initiate the deployment process on Posit Connect Cloud:

1. Log into your Posit Connect Cloud account. You can create an account and link it directly to your GitHub profile for easy access.
2. Click the Publish button and select Deploy from GitHub. You may need to authorize Posit Connect to access your repositories.
3. Select the application framework: Dash.
4. Choose the correct GitHub repository (Penguin-app), the main branch, and the primary application file (app.py).

5.2 Resolve Dependencies with requirements.txt

The initial deployment configuration will indicate a missing file. Posit Connect needs a manifest of your project’s dependencies to build the correct environment in the cloud. This is accomplished with a requirements.txt file.

1. Purpose: This file tells the cloud environment exactly which Python packages and versions to install for your application to run correctly.
2. Generation: Return to the terminal in Positron and run the following command to generate the file from your uv virtual environment:
3. Commit and Push: A new requirements.txt file will appear in your project folder. You must commit this file to your local Git repository and then push the change to your remote GitHub repository using git push origin main.

5.3 Finalize and Publish

With the dependency manifest now on GitHub, you can complete the deployment.

1. Return to the Posit Connect Cloud deployment configuration page and refresh it.
2. Verify that the platform now recognizes the requirements.txt file and indicates that it has everything it needs.
3. Select the option to make the application public.
4. Click Publish.

Posit Connect Cloud will now pull your code from GitHub, build the environment specified in requirements.txt, and deploy your application. Once the process is complete, your application will be live. You can use the Share button to get the public URL, which can be accessed by anyone in a web browser.

Conclusion

You’ve just gone from a blank slate to a live, public web application. You have now completed the entire end-to-end workflow for bringing a Python data application to life, covering local project setup in Positron, iterative development and testing, version control with GitHub, and seamless deployment to Posit Connect Cloud. You can apply this same workflow to your own data science projects. Thanks for following along!