How To Install Plotly on openSUSE

Plotly stands as one of the most powerful interactive visualization libraries available for Python developers and data scientists. This comprehensive guide walks you through multiple installation methods for Plotly on openSUSE, ensuring you can leverage its full potential for creating stunning, interactive data visualizations.

Whether you’re a seasoned Linux administrator, data scientist, or developer new to openSUSE, this tutorial provides step-by-step instructions for successful Plotly installation and configuration. You’ll discover the most efficient installation methods, troubleshoot common issues, and optimize your setup for maximum performance.

Understanding Plotly: Features and Capabilities

Plotly revolutionizes data visualization by offering interactive, web-based charts that surpass traditional static plotting libraries. Built on the robust plotly.js foundation, this Python library supports over 40 different chart types, ranging from simple line graphs to complex 3D visualizations.

The library excels in multiple domains. Statistical analysis benefits from Plotly’s extensive chart collection, including box plots, violin plots, and advanced statistical visualizations. Financial professionals appreciate built-in OHLC charts, candlestick plots, and time-series analysis tools. Geographic data visualization becomes effortless with integrated mapping capabilities supporting choropleth maps, scatter plots on maps, and custom geographic projections.

Scientific researchers find Plotly invaluable for 3D plotting, contour maps, and specialized scientific chart types. The library’s integration with Jupyter notebooks creates seamless workflow experiences, while its ability to export standalone HTML files ensures easy sharing and deployment.

Plotly Express, the high-level interface, simplifies complex visualization creation with just a few lines of code. Meanwhile, the low-level plotly.graph_objects module provides granular control for custom visualizations. Integration with Dash framework enables building full-featured web applications with interactive dashboards.

Prerequisites and System Requirements

Before installing Plotly on openSUSE, ensure your system meets essential requirements. OpenSUSE Leap 15.4 or newer versions provide optimal compatibility, while openSUSE Tumbleweed users benefit from cutting-edge package availability.

Python 3.7 or higher is mandatory for Plotly installation. Most openSUSE installations include Python 3 by default, but verification remains crucial. Check your Python version using:

python3 --version

Essential system packages include development tools, pip, and virtual environment support. Update your system packages first:

sudo zypper refresh
sudo zypper update

Install basic development tools if they’re missing:

sudo zypper install -t pattern devel_basis
sudo zypper install python3-pip python3-venv python3-devel

Network connectivity and sufficient disk space (approximately 50MB for basic Plotly installation) are required. Corporate environments may need proxy configuration for package downloads.

Installation Method 1: Using openSUSE Package Manager (Zypper)

The zypper package manager provides the most straightforward installation method for openSUSE users. This system-wide installation approach integrates seamlessly with openSUSE’s package management infrastructure.

Search for available Plotly packages in openSUSE repositories:

zypper search plotly

Install the main Plotly package:

sudo zypper install python3-plotly

Zypper automatically resolves dependencies, installing required packages like NumPy, pandas, and other scientific computing libraries. This method ensures compatibility with your specific openSUSE version and maintains system integrity through proper dependency management.

Advantages of zypper installation:

• Automatic dependency resolution
• System integration and consistency
• Regular security updates through system updates
• No virtual environment management required
• Suitable for system-wide deployment

Disadvantages to consider:

• Potentially older Plotly versions
• System-wide installation affects all users
• Limited control over specific package versions
• May conflict with user-specific Python environments

Verify successful installation by importing Plotly in Python:

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

This method works best for users preferring system-managed packages and those requiring consistent installations across multiple machines.

Installation Method 2: Using Pip (Python Package Installer)

Pip installation offers maximum flexibility and access to the latest Plotly versions. This method supports virtual environments, enabling isolated Python environments for different projects.

Setting up virtual environment:

Create a dedicated directory for your Plotly project:

mkdir ~/plotly-project
cd ~/plotly-project

Create and activate a virtual environment:

python3 -m venv plotly-env
source plotly-env/bin/activate

Your command prompt should change, indicating active virtual environment status. Install Plotly with all optional dependencies:

pip install plotly[express,kaleido]

The express option includes Plotly Express for high-level plotting, while kaleido enables static image export functionality.

Installing additional components:

For Jupyter integration:

pip install jupyter anywidget

For geographic plotting capabilities:

pip install plotly-geo

Managing virtual environments effectively:

List installed packages:

pip list

Create requirements file for reproducible environments:

pip freeze > requirements.txt

Deactivate virtual environment when finished:

deactivate

Best practices for pip installation:

Always use virtual environments for project isolation. Upgrade pip before installing packages to avoid compatibility issues:

pip install --upgrade pip

Pin specific versions in production environments to ensure consistency:

pip install plotly==5.17.0

This installation method provides maximum control and flexibility, making it ideal for development environments and users requiring specific Plotly versions.

Installation Method 3: Using Conda/Miniconda

Conda package management excels in scientific computing environments, providing superior dependency resolution for complex package ecosystems. Install Miniconda first if not already present on your openSUSE system.

Installing Miniconda:

Download the latest Miniconda installer:

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh

Run the installer:

bash Miniconda3-latest-Linux-x86_64.sh

Follow installation prompts and restart your terminal to activate conda.

Setting up Plotly with conda:

Create a dedicated conda environment:

conda create -n plotly-env python=3.11
conda activate plotly-env

Install Plotly from the conda-forge channel for latest versions:

conda install -c conda-forge plotly

Install additional scientific computing packages:

conda install -c conda-forge jupyter pandas numpy scipy scikit-learn

Advantages of conda installation:

Conda’s dependency resolver handles complex scientific package interactions more effectively than pip. Binary packages install faster and avoid compilation issues. Environment management provides robust isolation between projects.

Managing conda environments:

List available environments:

conda env list

Export environment for reproducibility:

conda env export > environment.yml

Create environment from file:

conda env create -f environment.yml

This method suits data scientists and researchers requiring comprehensive scientific computing environments with complex dependency requirements.

Post-Installation Configuration and Setup

Proper configuration enhances Plotly functionality and enables advanced features. Configure API credentials for online Plotly services if needed:

import plotly.tools as tls
tls.set_credentials_file(username='your_username', api_key='your_api_key')

Set up default rendering options for your preferred output format:

import plotly.io as pio
pio.renderers.default = "browser"  # or "notebook", "svg", "png"

Configure static image export with Kaleido:

import plotly.graph_objects as go
import plotly.io as pio

# Test static export
fig = go.Figure(data=go.Bar(x=['A', 'B', 'C'], y=[1, 3, 2]))
pio.write_image(fig, 'test.png')

Optimizing performance settings:

Set memory limits for large datasets:

import plotly.io as pio
pio.kaleido.scope.default_width = 1200
pio.kaleido.scope.default_height = 700

Configure color themes and templates:

import plotly.io as pio
pio.templates.default = "plotly_white"  # or "ggplot2", "seaborn", etc.

These configurations enhance user experience and provide consistent visualization output across different environments.

Jupyter Integration and Widget Support

Jupyter notebook integration transforms Plotly into a powerful interactive analysis tool. Install required Jupyter extensions:

pip install jupyter anywidget ipywidgets

Enable widget support in Jupyter:

jupyter nbextension enable --py widgetsnbextension --sys-prefix

For JupyterLab users:

pip install jupyterlab
jupyter labextension install @jupyter-widgets/jupyterlab-manager

Testing Jupyter integration:

Launch Jupyter notebook:

jupyter notebook

Create a test notebook with this code:

import plotly.graph_objects as go
from plotly.subplots import make_subplots

# Create interactive plot
fig = go.Figure()
fig.add_trace(go.Scatter(x=[1, 2, 3, 4], y=[10, 11, 12, 13]))
fig.update_layout(title="Test Interactive Plot")
fig.show()

Using FigureWidget for enhanced interactivity:

import plotly.graph_objects as go

# Create interactive widget
f = go.FigureWidget([go.Scatter(x=[1, 2, 3], y=[4, 5, 6])])
f.layout.title = 'Interactive Widget'
display(f)

Proper Jupyter integration enables seamless interactive data exploration and presentation within notebook environments.

Verification and Testing Your Installation

Comprehensive testing ensures your Plotly installation functions correctly across different use cases. Start with basic import verification:

import plotly
import plotly.express as px
import plotly.graph_objects as go
import plotly.subplots

print(f"Plotly version: {plotly.__version__}")

Test basic plotting functionality:

import plotly.express as px
import pandas as pd

# Create sample data
df = pd.DataFrame({
    'x': [1, 2, 3, 4, 5],
    'y': [2, 5, 3, 8, 7],
    'category': ['A', 'B', 'A', 'B', 'A']
})

# Create interactive scatter plot
fig = px.scatter(df, x='x', y='y', color='category', title='Test Plot')
fig.show()

Verify static image export:

import plotly.io as pio

# Test image export capabilities
fig = px.bar(x=['A', 'B', 'C'], y=[1, 3, 2])
pio.write_image(fig, 'test_plot.png', width=800, height=600)

Performance testing:

import time
import numpy as np

# Test with larger dataset
data = np.random.randn(10000, 2)
start_time = time.time()

fig = px.scatter(x=data[:, 0], y=data[:, 1])
fig.show()

print(f"Rendering time: {time.time() - start_time:.2f} seconds")

Successful completion of these tests confirms proper installation and optimal performance for your Plotly environment.

Advanced Features and Extensions

Plotly’s ecosystem includes specialized extensions for enhanced functionality. Install geographic visualization support:

pip install plotly-geo

Setting up Dash for web applications:

pip install dash

Create a simple Dash application:

import dash
from dash import dcc, html
import plotly.express as px

app = dash.Dash(__name__)

fig = px.bar(x=['A', 'B', 'C'], y=[1, 3, 2])

app.layout = html.Div([
    dcc.Graph(figure=fig)
])

if __name__ == '__main__':
    app.run_server(debug=True)

Custom themes and templates:

import plotly.io as pio
import plotly.graph_objects as go

# Create custom template
custom_template = {
    'layout': {
        'colorway': ['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4'],
        'font': {'family': 'Arial, sans-serif', 'size': 12},
        'title': {'font': {'size': 16}}
    }
}

pio.templates['custom'] = custom_template
pio.templates.default = 'custom'

These advanced features enable sophisticated data applications and customized visualization experiences.

Troubleshooting Common Issues

Installation and runtime problems require systematic troubleshooting approaches. Address common dependency conflicts by using virtual environments:

# Clean installation in new environment
python3 -m venv fresh-env
source fresh-env/bin/activate
pip install --upgrade pip
pip install plotly

Resolving Jupyter display issues:

If plots don’t display in Jupyter notebooks:

pip install --upgrade nbformat
jupyter nbextension enable --py widgetsnbextension

Restart Jupyter kernel after installing extensions.

Memory and performance optimization:

For large datasets, implement data sampling:

import pandas as pd

# Sample large datasets
large_df = pd.read_csv('large_file.csv')
sample_df = large_df.sample(n=10000)  # Sample 10,000 rows

SSL and proxy configuration:

Corporate environments may require proxy settings:

pip install --trusted-host pypi.org --trusted-host files.pythonhosted.org plotly

Configure pip proxy settings:

pip config set global.proxy https://proxy.company.com:8080

Permission errors:

Use user installation to avoid permission issues:

pip install --user plotly

Systematic troubleshooting resolves most installation and runtime issues, ensuring stable Plotly operation.

Best Practices and Recommendations

Professional development workflows benefit from established best practices. Choose installation methods based on specific requirements: zypper for system-wide deployment, pip for development flexibility, and conda for scientific computing environments.

Environment management strategies:

Maintain separate environments for different projects:

# Project-specific environments
python3 -m venv project1-env
python3 -m venv project2-env

Document dependencies using requirements files:

pip freeze > requirements.txt

Version control integration:

Include environment specifications in version control:

# .gitignore
venv/
*.pyc
__pycache__/

# Include requirements.txt
git add requirements.txt

Performance optimization:

Implement lazy loading for large applications:

import plotly.graph_objects as go

# Lazy loading for better performance
def create_plot():
    return go.Figure(data=go.Scatter(x=[1, 2, 3], y=[4, 5, 6]))

Use appropriate data types and optimize memory usage:

import pandas as pd

# Optimize data types
df = pd.read_csv('data.csv')
df = df.astype({'category_col': 'category'})

Following these practices ensures maintainable, scalable, and efficient Plotly implementations.

Comparing Installation Methods

Selection criteria:

Choose zypper for stable, system-wide installations requiring minimal maintenance. Select pip for development environments needing latest features and version control. Opt for conda when working with complex scientific computing stacks requiring robust dependency resolution.

Congratulations! You have successfully installed Plotly. Thanks for using this tutorial for installing the Plotly on openSUSE Linux system. For additional or useful information, we recommend you check the official Plotly website.