How To Install Plotly on Rocky Linux 9

In this tutorial, we will show you how to install Plotly on Rocky Linux 9. Data visualization is crucial in today’s data-driven world. Plotly is a powerful, open-source graphing library for Python. Rocky Linux 9 provides a robust and stable platform for data analysis and visualization tasks. Installing Plotly on Rocky Linux 9 allows you to create interactive plots and dashboards directly from your Python environment. This comprehensive guide walks you through the installation process step-by-step, ensuring a smooth setup for both beginners and experienced users.

This article provides detailed instructions for installing Plotly, covering various methods and configurations. We aim to equip you with the knowledge to harness Plotly’s capabilities on Rocky Linux 9 effectively. By following this guide, you’ll be able to create stunning visualizations for your data projects.

Introduction

Plotly is a versatile graphing library that supports various programming languages, including Python. It allows users to create interactive, publication-quality plots and dashboards. These visualizations can be embedded in web applications, Jupyter notebooks, or exported as static images. The library is renowned for its flexibility and extensive customization options.

Rocky Linux 9, derived from Red Hat Enterprise Linux (RHEL), offers a stable, secure, and high-performance environment. It’s ideal for data scientists, analysts, and developers who require a reliable platform. The combination of Plotly and Rocky Linux 9 provides a powerful toolkit for data exploration and presentation.

This guide is tailored for data scientists, analysts, and developers who want to leverage Plotly on Rocky Linux 9. Some familiarity with Linux command-line operations and Python is assumed. Prior experience with Plotly is helpful but not required.

Before proceeding, ensure you have the following:

• A running instance of Rocky Linux 9
• A user account with sudo privileges
• Basic knowledge of Python

System Requirements

Before installing Plotly, it’s essential to ensure that your Rocky Linux 9 system meets the necessary requirements. These include both hardware and software prerequisites that enable a smooth installation and optimal performance.

Hardware Requirements

While Plotly itself doesn’t demand significant hardware resources, the performance of your visualizations can be affected by the system’s capabilities, especially when dealing with large datasets. Below are general hardware recommendations:

• Processor: A multi-core processor (e.g., Intel Core i5 or AMD Ryzen 5) is recommended for handling data processing tasks efficiently.
• Memory: At least 4 GB of RAM is advisable. For more extensive data analysis, consider 8 GB or more.
• Storage: Ensure you have sufficient disk space for your datasets and Python environment. A minimum of 20 GB is a good starting point.

Software Prerequisites

The following software components are necessary to install and run Plotly on Rocky Linux 9:

• Operating System: Rocky Linux 9
• Python: Version 3.7 or higher. Plotly is actively supported on Python 3.x.
• pip: The Python package installer. It simplifies the installation of Plotly and its dependencies.
• Development Tools: Essential for compiling Python packages and handling dependencies.

Python Version Compatibility

Plotly is compatible with Python versions 3.7 and above. To check your Python version, open a terminal and run:

python3 --version

If Python is not installed or the version is older than 3.7, you’ll need to install a compatible version. You can install Python using the following command:

sudo dnf install python3

This command installs the default Python 3 version available in the Rocky Linux 9 repositories.

Preparing the Environment

Before installing Plotly, setting up your Rocky Linux 9 environment is crucial. This involves updating the system, installing Python development tools, and creating a virtual environment to manage dependencies effectively.

Updating Rocky Linux System

Start by updating your Rocky Linux 9 system to ensure all packages are up-to-date. Open a terminal and run the following command:

sudo dnf update -y

This command updates all installed packages to their latest versions. The -y flag automatically answers “yes” to any prompts, ensuring a non-interactive update process. Keeping your system updated helps prevent compatibility issues and ensures you have the latest security patches.

Installing Python Development Tools

Python development tools are essential for compiling packages and managing dependencies. Install these tools using the following command:

sudo dnf groupinstall "Development Tools" -y
sudo dnf install python3-devel -y

The dnf groupinstall "Development Tools" command installs a collection of tools required for software development. The python3-devel package provides the necessary header files and libraries for compiling Python extensions. These tools are vital for a smooth installation of Plotly and its dependencies.

Setting up a Python Virtual Environment

A virtual environment isolates Python projects and their dependencies, preventing conflicts between different projects. It’s highly recommended to create a virtual environment for your Plotly project.

First, install the venv module:

sudo dnf install python3-venv -y

Next, create a virtual environment:

python3 -m venv plotly_env

This command creates a new virtual environment named plotly_env in the current directory.

Activate the virtual environment:

source plotly_env/bin/activate

Once activated, your terminal prompt will change to indicate that you are working within the virtual environment (e.g., (plotly_env) $). All subsequent Python package installations will be isolated to this environment.

Installation Methods

Plotly can be installed using two primary methods: pip and conda. Choose the method that aligns with your existing Python environment and package management preferences.

Method 1: Using pip

pip is the default package installer for Python and is widely used for managing Python packages. This method is straightforward and suitable for most users.

Installing pip Package Manager

If pip is not already installed, you can install it using the following command:

sudo dnf install python3-pip -y

Verify the installation by checking the pip version:

pip3 --version

Installing Plotly via pip

With pip installed, you can now install Plotly:

pip install plotly

This command downloads and installs the latest version of Plotly from the Python Package Index (PyPI). pip automatically handles dependencies, ensuring that all required packages are installed.

Installing Additional Dependencies

For enhanced functionality, such as using Plotly Express, install the required dependencies:

pip install plotly[express]

You’ll also need to install a supported dataframe library like pandas:

pip install pandas

pandas is a powerful data manipulation and analysis library that integrates seamlessly with Plotly, enabling you to create visualizations from data stored in dataframes.

Method 2: Using conda

conda is an open-source package, dependency, and environment management system. It’s often preferred in data science for its ability to manage complex dependencies.

Setting up a conda Environment

If you don’t have conda installed, download and install Anaconda or Miniconda from the official website. After installation, initialize conda by running:

conda init

Restart your terminal for the changes to take effect.

Create a new conda environment for Plotly:

conda create --name plotly_env python=3.9

Activate the conda environment:

conda activate plotly_env

Installing Plotly through conda

Install Plotly using the following command:

conda install -c conda-forge plotly

This command installs Plotly from the conda-forge channel, a community-led collection of packages. The -c conda-forge flag specifies that conda should look for the package in the conda-forge channel.

Managing conda Dependencies

For additional features, such as Plotly Express, install the necessary dependencies:

conda install -c conda-forge plotly-express

You may also want to install pandas for data manipulation:

conda install pandas

Additional Components

To enhance your Plotly experience, consider installing additional components such as Jupyter support, static image export capabilities, and geographic data support.

Installing Jupyter Support

Jupyter Notebook is a popular environment for interactive data analysis and visualization. To use Plotly within Jupyter Notebook, install the notebook and ipywidgets packages:

pip install notebook ipywidgets

or, if you’re using conda:

conda install -c conda-forge notebook ipywidgets

Enable the ipywidgets extension:

jupyter nbextension enable --py widgetsnbextension

This allows Plotly plots to render correctly within Jupyter Notebook.

Setting up Static Image Export Capability

Plotly supports static image export, allowing you to save your plots as PNG, JPEG, SVG, and PDF files. The recommended method for static image export is using the kaleido package.

Install kaleido using pip:

pip install -U kaleido

or, using conda:

conda install -c conda-forge python-kaleido

kaleido is a dependency-free package that provides a reliable and efficient way to export Plotly plots as static images.

Installing Geographic Data Support

Some Plotly features, such as county choropleth maps, rely on geographic shape files. These files are distributed as a separate plotly-geo package.

Install plotly-geo using pip:

pip install plotly-geo==1.0.0

or, using conda:

conda install -c plotly plotly-geo=1.0.0

This package provides the necessary geographic data for creating advanced map-based visualizations.

Verification and Testing

After installing Plotly and its additional components, it’s essential to verify the installation and test basic functionality to ensure everything is working correctly.

Verifying Installation

To verify that Plotly is installed correctly, open a Python interpreter or a Jupyter Notebook and run the following code:

import plotly
  print(plotly.__version__)

This code imports the plotly module and prints its version number. If the installation was successful, the version number will be displayed without any errors.

Testing Basic Plotly Functionality

Create a simple plot to test Plotly’s basic functionality:

import plotly.graph_objects as go
  fig = go.Figure(data=[go.Scatter(x=[1, 2, 3, 4], y=[10, 11, 12, 13])])
  fig.show()

This code creates a basic scatter plot and displays it. If the plot is displayed correctly, it confirms that Plotly is functioning as expected.

Common Troubleshooting Steps

If you encounter any issues during the installation or verification process, consider the following troubleshooting steps:

• Check for Import Errors: If you receive a ModuleNotFoundError when importing plotly, ensure that Plotly is installed in the correct environment and that the environment is activated.
• Verify Dependencies: Ensure that all required dependencies, such as pandas and ipywidgets, are installed.
• Update Packages: Use pip install --upgrade plotly or conda update plotly to update Plotly to the latest version.
• Check for Conflicting Installations: Having multiple installations of Plotly (e.g., one installed with pip and another with conda) can lead to conflicts. Uninstall all versions and reinstall using a single method.
• Consult Plotly Documentation: The official Plotly documentation provides detailed information on installation, usage, and troubleshooting.

Optional Extensions

Plotly offers several optional extensions that can enhance your data visualization capabilities. These include Plotly Express, additional visualization libraries, and integration with the Dash framework.

Installing Plotly Express

Plotly Express is a high-level interface for creating common types of plots quickly and easily. It simplifies the process of creating visualizations with minimal code.

Install Plotly Express using pip:

pip install plotly_express

or, using conda:

conda install -c conda-forge plotly-express

With Plotly Express installed, you can create complex plots with just a few lines of code:

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

Setting up Additional Visualization Libraries

Plotly integrates well with other visualization libraries, such as matplotlib and seaborn. These libraries can be used in conjunction with Plotly to create a wide range of visualizations.

Install matplotlib and seaborn using pip:

pip install matplotlib seaborn

or, using conda:

conda install matplotlib seaborn

These libraries provide additional plotting options and customization capabilities that can complement Plotly’s interactive visualizations.

Integration with Dash Framework

Dash is a Python framework for building web applications with interactive data visualizations. Plotly is a core component of Dash, allowing you to create dashboards and web-based data apps.

Install Dash using pip:

pip install dash

or, using conda:

conda install -c conda-forge dash

With Dash and Plotly, you can create interactive web applications that allow users to explore and analyze data in real-time.

Best Practices

To ensure a smooth and efficient Plotly experience on Rocky Linux 9, follow these best practices for virtual environment management, version control, and security.

Virtual Environment Management

Always use virtual environments to isolate your Plotly projects and their dependencies. This prevents conflicts between different projects and ensures that your environment is reproducible.

• Create a New Environment for Each Project: Use a separate virtual environment for each Plotly project to avoid dependency conflicts.
• Activate the Environment: Before working on a project, activate its virtual environment using source plotly_env/bin/activate or conda activate plotly_env.
• Deactivate the Environment: When you’re finished working on a project, deactivate the virtual environment using deactivate or conda deactivate.

Version Control Considerations

Use version control systems like Git to track changes to your Plotly projects. This allows you to revert to previous versions, collaborate with others, and manage your codebase effectively.

• Initialize a Git Repository: Create a new Git repository for your Plotly project using git init.
• Track Dependencies: Use pip freeze > requirements.txt or conda env export > environment.yml to track your project’s dependencies.
• Commit Changes Regularly: Commit your changes regularly with descriptive commit messages.
• Use Branches: Use branches for developing new features or fixing bugs.

Security Recommendations

Follow these security recommendations to protect your Plotly projects and your Rocky Linux 9 system:

• Keep Packages Updated: Regularly update your Python packages and system packages to patch security vulnerabilities.
• Use Strong Passwords: Use strong, unique passwords for your user accounts and any services you deploy.
• Enable Firewalls: Configure firewalls to restrict access to your system and services.
• Monitor Logs: Monitor system and application logs for suspicious activity.

Congratulations! You have successfully installed Plotly. Thanks for using this tutorial for installing the Plotly on Rocky Linux 9 system. For additional or useful information, we recommend you check the official Plotly website.