How To Install Plotly on Debian 13

Plotly stands as one of the most powerful interactive data visualization libraries available for Python developers and data scientists today. This open-source graphing library enables users to create publication-quality charts, dashboards, and web applications with minimal code. Debian 13 “Trixie,” released in August 2025, provides a stable foundation for running Plotly with its native Python 3.13 support and long-term maintenance commitment through 2030. This comprehensive guide walks through multiple installation methods, from basic pip commands to advanced virtual environment configurations, ensuring every user can successfully deploy Plotly on their Debian 13 system. Whether building data analysis pipelines, creating interactive dashboards, or developing scientific visualizations, mastering Plotly installation represents the essential first step toward leveraging this remarkable library’s full potential.

What is Plotly?

Plotly functions as a comprehensive graphing library that transforms data into stunning, interactive visualizations with just a few lines of code. The library consists of three primary components that work seamlessly together. First, plotly.py serves as the core Python library providing low-level control over chart creation. Second, Plotly Express offers a high-level interface that simplifies common visualization tasks, making it ideal for rapid prototyping and exploratory data analysis. Third, the Dash framework extends Plotly’s capabilities to full-fledged web applications, enabling developers to build production-ready analytical dashboards.

Data scientists employ Plotly across numerous domains including financial analysis, scientific research, business intelligence, and machine learning visualization. The library excels in creating scatter plots, line charts, bar graphs, heatmaps, 3D surfaces, and geographical maps, all featuring built-in interactivity such as zooming, panning, and hover tooltips. Unlike static visualization libraries, Plotly generates HTML-based graphics that users can explore dynamically within web browsers.

The library operates in both online and offline modes, providing flexibility for various deployment scenarios. Online mode connects to Plotly’s cloud services for chart hosting, while offline mode generates standalone HTML files containing all necessary JavaScript dependencies. This versatility makes Plotly suitable for everything from Jupyter notebook explorations to enterprise web applications.

Understanding Debian 13 (Trixie)

Debian 13 “Trixie” emerged as the latest stable release from the Debian Project in August 2025, marking a significant milestone in Linux distribution evolution. This release ships with Python 3.13 as its default Python interpreter, ensuring compatibility with modern Python packages and libraries. The Debian development team has committed to supporting Trixie through 2030, providing security updates and stability patches for the next five years.

Debian’s renowned package management system, built around APT (Advanced Package Tool), distinguishes it from other Linux distributions. The repository structure separates packages into main, contrib, and non-free sections, maintaining strict adherence to free software principles while accommodating proprietary needs. This architecture ensures system stability and security through rigorous testing before packages enter the stable repository.

Python 3.13 compatibility matters significantly for Plotly installation because newer Python versions introduce performance improvements, enhanced syntax features, and security enhancements. Debian 13 users benefit from these advancements immediately upon system installation, eliminating the need for manual Python upgrades. The distribution’s commitment to stability means users can confidently build production systems knowing their foundation remains solid and well-supported.

Prerequisites and System Requirements

Installing Plotly on Debian 13 requires minimal system resources, making it accessible even on modest hardware configurations. The primary requirement involves Python 3, which comes pre-installed with Debian 13 Trixie. Users need pip3, Python’s package installer, to download and install Plotly from the Python Package Index (PyPI). For projects requiring isolated environments, the python3-venv package enables virtual environment creation.

Administrative privileges become necessary when installing system-wide packages through APT. However, pip installations can proceed without root access using the –user flag, which installs packages in the user’s home directory. This approach prevents potential conflicts with system packages and maintains cleaner permission structures.

Internet connectivity stands as an essential prerequisite for downloading Plotly and its dependencies from remote repositories. The installation process downloads several megabytes of data, including the core Plotly library, JavaScript visualization engines, and supporting Python packages. Basic familiarity with the command-line interface enhances the installation experience, though the straightforward commands presented here accommodate users at all skill levels.

Method 1: Installing Plotly Using pip (Recommended)

Step 1: Update System Packages

Begin by refreshing the package lists and upgrading existing software to ensure system security and compatibility. Open a terminal and execute:

sudo apt update && sudo apt upgrade

This command performs two critical operations. The apt update portion retrieves the latest package information from Debian repositories, while apt upgrade installs newer versions of currently installed software. Users may see several packages listed for upgrade depending on how recently the system was updated. Confirm the upgrades by typing ‘y’ when prompted. This maintenance step prevents conflicts arising from outdated system libraries that might interfere with Plotly installation.

Step 2: Install pip3

Python’s package installer, pip3, may not be present on minimal Debian installations despite Python itself being available. Install it with:

sudo apt install python3-pip

The installation completes within seconds, adding the pip3 command to the system. Verify successful installation by checking the version:

pip3 --version

The output displays the pip version number alongside the Python version it targets, typically showing “pip 23.x.x from /usr/lib/python3/dist-packages/pip (python 3.13)”. This confirmation ensures pip3 correctly associates with Python 3.13 on Debian 13. The apt-based installation method integrates pip with Debian’s package management system, providing better stability than alternative installation methods like get-pip.py.

Step 3: Install Plotly with pip

With pip3 operational, installing Plotly becomes straightforward. Execute:

pip3 install plotly

Alternatively, for installation without administrative privileges:

pip3 install --user plotly

The –user flag installs Plotly in the home directory’s .local folder, bypassing system-wide installation requirements. This approach proves particularly valuable on shared systems or when lacking sudo access. For users requiring Plotly Express with all optional dependencies, use:

pip3 install plotly[express]

The installation process downloads Plotly along with its dependencies, displaying progress bars and completion messages. Typical installations complete within 30-60 seconds depending on internet speed. The package manager automatically resolves and installs required libraries, ensuring all components work harmoniously.

Step 4: Verify Installation

Confirmation of successful installation prevents future troubleshooting headaches. Check installed package details with:

pip3 show plotly

This command reveals comprehensive information including version number, installation location, required dependencies, and package description. For immediate functionality verification, test the import mechanism:

python3 -c "import plotly; print(plotly.__version__)"

A successful installation prints the version number, such as “5.22.0”. Any error messages at this stage indicate installation problems requiring investigation. Users can also launch Python’s interactive shell and manually import Plotly to ensure proper integration:

python3
>>> import plotly
>>> import plotly.express as px
>>> exit()

These commands should execute without errors, confirming Plotly’s availability for use.

Step 5: Install Additional Dependencies

While Plotly functions independently, several complementary libraries enhance its capabilities significantly. Install pandas for powerful data manipulation:

pip3 install pandas

Add numpy for numerical computing operations:

pip3 install numpy

For Jupyter notebook integration and enhanced plotting capabilities, consider cufflinks:

pip3 install cufflinks

These packages work synergistically with Plotly, enabling seamless data pipeline creation from raw data through visualization. Pandas DataFrames integrate directly with Plotly Express, allowing single-line chart creation from structured datasets. Numpy arrays provide efficient numerical data structures for scientific computing applications. Installing these dependencies upfront creates a complete data science environment ready for immediate productivity.

Method 2: Installing Plotly in a Virtual Environment (Best Practice)

Understanding Virtual Environments

Python virtual environments represent isolated Python installations that prevent package conflicts between different projects. Each virtual environment maintains its own Python interpreter, pip, and installed packages, completely separate from the system Python installation. This isolation proves invaluable when working on multiple projects requiring different package versions.

System-wide installations risk creating dependency conflicts when different applications require incompatible library versions. Virtual environments eliminate this problem by compartmentalizing each project’s dependencies. Development best practices strongly favor virtual environments for all but the simplest one-off scripts. They facilitate reproducible environments, simplify dependency management, and prevent accidental system-level changes.

The venv module, included in Python 3’s standard library, provides built-in virtual environment functionality without requiring additional tools. This makes virtual environment creation straightforward and universally accessible across Python installations.

Step 1: Install python3-venv

Debian systems require explicit installation of the venv module despite Python 3 being pre-installed. Install it with:

sudo apt install python3-venv

This package provides the necessary components for creating and managing virtual environments on Debian-based systems. The installation completes quickly, adding venv functionality to Python 3.

Step 2: Create Virtual Environment

Navigate to your project directory or create a new one for Plotly work:

mkdir plotly-project && cd plotly-project

Create a virtual environment named “myenv” (or any preferred name):

python3 -m venv myenv

This command generates a directory structure containing a complete Python environment. The myenv folder includes bin/ (containing the Python interpreter and pip), lib/ (holding installed packages), and configuration files. Developers commonly name virtual environments “venv,” “env,” or project-specific names like “plotly_env”.

Step 3: Activate Virtual Environment

Activation makes the virtual environment’s Python interpreter the default for the current shell session:

source myenv/bin/activate

The command prompt changes to indicate activation, typically showing “(myenv)” at the beginning of the line. This visual indicator confirms that subsequent Python and pip commands affect only the virtual environment. All package installations now remain isolated from the system Python. Deactivation later requires simply typing deactivate.

Step 4: Upgrade pip in Virtual Environment

Virtual environments often contain slightly outdated pip versions. Upgrade to the latest:

pip install --upgrade pip

Note that sudo is unnecessary within active virtual environments since all operations occur in user-owned directories. This upgrade ensures access to the newest pip features and bug fixes.

Step 5: Install Plotly in Virtual Environment

With an activated virtual environment and updated pip, install Plotly:

pip install plotly

Or with Express support:

pip install plotly[express]

The installation proceeds identically to system-wide installation, but packages install exclusively within the virtual environment. This isolation guarantees no interference with other Python projects or system packages.

Step 6: Install Supporting Packages

Add complementary data science libraries as needed:

pip install pandas numpy jupyter

The isolated environment maintains all dependencies together, preventing version conflicts with other projects. For reproducibility, generate a requirements file capturing all installed packages:

pip freeze > requirements.txt

This file enables identical environment recreation on different machines using pip install -r requirements.txt. Such practices prove essential for collaborative projects and production deployments.

Method 3: Installing via Debian Package Manager (APT)

Checking Package Availability

Debian repositories may contain pre-packaged versions of Plotly, though these typically lag behind the latest PyPI releases. Search for available packages:

apt search python3-plotly

This command queries Debian’s package database for Plotly-related packages. The python3-plotly package, when available, integrates with Debian’s dependency management system. Understanding Debian’s package naming conventions helps locate Python libraries—most Python 3 packages use the “python3-” prefix followed by the library name.

Significant differences exist between Debian packages and pip packages. Debian packages undergo extensive testing for system integration but may contain older versions. Some Debian-packaged Python libraries lack certain features present in pip versions, particularly optional components like minified JavaScript files required for offline Plotly operation.

Installing from Debian Repositories

If python3-plotly exists in repositories, install it with:

sudo apt install python3-plotly

The apt installation method offers advantages including automatic dependency resolution, integration with system updates, and stability guarantees from Debian’s testing process. However, disadvantages include potentially outdated versions and missing optional components. The apt method suits users prioritizing system stability over having the absolute latest features.

Verification and Limitations

Verify the apt-installed version using the same methods described earlier. Historical issues with Debian’s python3-plotly package included missing minified JavaScript files, preventing full offline functionality. Users requiring complete offline capability may need to supplement apt installation with pip-installed components. Mixing apt and pip for the same package generally works but requires care to avoid conflicts. Best practice suggests choosing one installation method per package and maintaining consistency across the project.

Installing Plotly with Conda (Alternative Method)

Conda, particularly through Anaconda or Miniconda distributions, provides another robust installation pathway for Plotly. This method appeals especially to data scientists already using conda for environment management. Conda combines package management with environment management, offering capabilities beyond pip’s scope.

Install Plotly via conda using:

conda install -c conda-forge plotly

The -c conda-forge flag specifies the conda-forge channel, a community-maintained repository containing up-to-date Python packages. Conda excels in managing complex dependencies, particularly for data science workflows involving packages with C extensions. It resolves dependencies across multiple languages including Python, R, and system libraries.

Setting up conda on Debian 13 requires downloading and installing Miniconda or Anaconda from their respective websites. Miniconda provides a minimal conda installation, while Anaconda includes numerous pre-installed data science packages. Once installed, conda operates independently from system Python, maintaining completely separate environments. This isolation level even exceeds venv, as conda can install different Python versions within each environment.

Creating Your First Plotly Visualization

Validating installation through practical use confirms everything works correctly. Create a simple test script to generate an interactive chart. Using a text editor, create test_plotly.py:

import plotly.express as px
import pandas as pd

# Create sample dataset
data = {
    'x': [1, 2, 3, 4, 5],
    'y': [2, 4, 6, 8, 10],
    'category': ['A', 'B', 'A', 'B', 'A']
}
df = pd.DataFrame(data)

# Generate interactive scatter plot
fig = px.scatter(df, x='x', y='y', color='category', 
                 title='First Plotly Visualization')

# Save as HTML file
fig.write_html('first_plot.html')
print("Chart saved as first_plot.html")

Run the script with:

python3 test_plotly.py

This generates an HTML file containing the interactive visualization. Open first_plot.html in any web browser to view the chart. The visualization features interactive elements including hover tooltips showing exact data values, zoom capabilities, and pan functionality.

The code demonstrates Plotly Express’s high-level interface, which requires minimal code to produce sophisticated visualizations. The px.scatter() function creates scatter plots, while variations like px.line(), px.bar(), and px.histogram() generate other chart types. The write_html() method exports self-contained HTML files perfect for sharing or embedding in web pages.

Integration with Jupyter Notebook

Jupyter notebooks provide interactive computational environments ideal for exploratory data analysis with Plotly. Install Jupyter using pip:

pip3 install jupyter

Or for the next-generation interface:

pip3 install jupyterlab

JupyterLab requires additional extensions for optimal Plotly integration. Install necessary components:

pip3 install jupyterlab-plotly plotlywidget

These extensions enable Plotly charts to render directly within notebook cells rather than opening external browser windows. Launch Jupyter with:

jupyter notebook

Or for JupyterLab:

jupyter lab

Within notebooks, display Plotly charts using the fig.show() method:

import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x='sepal_width', y='sepal_length', color='species')
fig.show()

The chart appears inline below the code cell with full interactivity preserved. Troubleshooting display issues often involves verifying extension installation and restarting the Jupyter kernel. Some older Jupyter versions require using plotly.offline.iplot() instead of fig.show(). Certain JupyterLab features depend on Node.js for building extensions, though this requirement has decreased in recent versions.

Common Issues and Troubleshooting

“No module named plotly” Error

This error indicates Python cannot locate the Plotly package, typically due to environment mismatches. Verify installation in the current environment with pip3 list | grep plotly. Check that the Python executable matches the pip used for installation—using python instead of python3 sometimes accesses Python 2 on systems with both versions installed.

Virtual environment users must ensure activation before running scripts. An inactive virtual environment causes Python to search system packages rather than environment-specific installations. Verify activation by checking for the environment name in the command prompt.

Permission Denied Errors

Permission errors during installation stem from attempting system-wide package installation without administrative privileges. Solutions include using the --user flag with pip: pip3 install --user plotly. This installs packages in the user’s home directory, avoiding system directories.

Alternatively, virtual environments eliminate permission issues entirely since all operations occur in user-owned directories. Running pip with sudo, while possible, creates potential security risks and ownership complications. Best practice recommends virtual environments over sudo-based installations.

Dependency Conflicts

Conflicting package versions occasionally arise, particularly with pandas and numpy. Error messages typically indicate which packages conflict. Resolving conflicts often requires updating all related packages: pip3 install --upgrade plotly pandas numpy. Virtual environments prevent conflicts by isolating project dependencies, allowing different projects to use incompatible package versions simultaneously.

Display Issues in Jupyter

Charts failing to display in Jupyter notebooks usually indicate missing extensions. Verify installation of jupyterlab-plotly and plotlywidget. Restart the Jupyter kernel after installing extensions. Browser compatibility rarely causes issues with modern browsers, but updating to recent versions of Chrome, Firefox, or Edge eliminates potential incompatibilities.

Best Practices for Managing Plotly on Debian 13

Adopting professional workflows enhances productivity and prevents future complications. Always use virtual environments for project work, reserving system-wide installations for truly universal tools. This isolation prevents dependency conflicts and enables clean project handoffs.

Keep packages current with periodic updates. Check for Plotly updates using:

pip3 list --outdated | grep plotly

Update when newer versions appear:

pip3 install --upgrade plotly

Regular updates provide bug fixes, security patches, and new features. Create requirements.txt files documenting exact package versions for every project:

pip freeze > requirements.txt

This practice ensures reproducible environments and smooth collaboration. Version control these files alongside code using Git or similar tools.

Choose between apt and pip based on use case. System administrators managing multiple users benefit from apt’s centralized control. Developers needing latest features should use pip. Avoid mixing both methods for the same package unless absolutely necessary.

Maintain regular system backups before major updates. Document custom configurations and environment setups for disaster recovery. Monitor Debian security announcements for critical updates requiring immediate attention.

Uninstalling Plotly

Removing Plotly becomes necessary when troubleshooting issues or switching installation methods. Uninstall pip-installed Plotly with:

pip3 uninstall plotly

Confirm removal when prompted. For virtual environment installations, activate the environment first, then uninstall. Remove apt-installed versions using:

sudo apt remove python3-plotly

Completely purge configuration files with:

sudo apt purge python3-plotly

Remove related packages simultaneously:

pip3 uninstall plotly cufflinks pandas

Deactivate and delete entire virtual environments by first deactivating:

deactivate

Then removing the directory:

rm -rf myenv/

Verify complete removal by attempting import, which should fail with “ModuleNotFoundError”. Clean installations sometimes resolve persistent issues better than troubleshooting corrupted packages.

Congratulations! You have successfully installed Plotly. Thanks for using this tutorial for installing Plotly graphing library for Python on Debian 13 “Trixie” system. For additional or useful information, we recommend you check the official Plotly website.