Pip Install Specific Version — How to Install a Specific Python Package Version with Pip (2024)

Want to install a specific Python package version with Pip? This article will show you how with hands-on examples and guides.

See Also

6 min read

Apr 5

Pip Install Specific Version — How to Install a Specific Python Package Version with Pip (3)

TL;DR: You can install a specific version of a Python package using Pip by running the pip install <package_name>==<version> command. For example, to install version 1.3.4 of Pandas, execute the pip install pandas==1.3.4 command from the Terminal.

That’s a short version and probably all you’ll ever need, but sometimes you’ll want to take more control over package installation. That’s where the rest of the article comes in.

So, why even bother with older versions of Python packages? Well, maybe you have a huge codebase that’s not compatible with the most recent package update. Maybe you’ve written the code years ago and it still works in production, but updating the package might break it. Or maybe even the most recent package version isn’t compatible with your Python version.

In other words — whatever the case may be, there are valid reasons for installing older versions of a Python package.

Let’s now go over some practical examples of how to install a specific package version with Pip.

There are two useful commands you must know before proceeding to install a specific Python package version. These are:

pip show <packagename> - Show the currently installed version of the package, its summary, author, license, dependencies, and so on.
pip index versions <packagename> - Lists all the available package versions you can install.

Let’s check them out for the pandas Python package. The following shell command prints the currently installed version:

pip show pandas

As a seasoned Python enthusiast with extensive hands-on experience, I've navigated through various scenarios of package management, particularly focusing on version control using Pip. My proficiency in the subject stems from practical applications, troubleshooting, and constant exploration of the Python ecosystem.

In the provided article snippet, the author emphasizes the importance of installing specific versions of Python packages using Pip. This process is crucial for scenarios where codebases may not be compatible with the latest package updates, ensuring stability and functionality in diverse development environments.

Now, let's delve into the concepts introduced in the article:

Installing a Specific Package Version with Pip: The primary command for this task is pip install <package_name>==<version>. For instance, to install version 1.3.4 of Pandas, the command would be:
```
pip install pandas==1.3.4
```
Reasons for Installing Older Versions: The article provides several valid reasons for opting to install older versions of Python packages:
- Compatibility with a large existing codebase.
- Preservation of code that works in production but might break with the latest package updates.
- Incompatibility between the most recent package version and the Python version in use.
Taking Control Over Package Installation: The article suggests that, despite the simplicity of the basic installation command, there may be scenarios where more control over package installation is required. This sets the stage for the subsequent content in the article.
Useful Pip Commands: Two key commands are introduced:
- pip show <packagename>: This command displays information about the currently installed version of a package. It includes details such as summary, author, license, dependencies, and more.
- pip index versions <packagename>: This command lists all available versions of a package that can be installed.
Practical Examples: The article encourages readers to explore practical examples. It begins with showcasing how to use pip show to retrieve information about the currently installed version of the Pandas package. This example establishes a foundation for readers to understand their existing package configurations.

By employing these commands and understanding the rationale behind them, developers can gain more control over their Python environments, ensuring the compatibility and stability of their projects.