Install Python Packages In Databricks: A Quick Guide

Nov 8, 2025 by Admin 53 views

Let's dive into the world of Databricks and Python packages! If you're like me, you've probably found yourself needing to install that one Python package to get your Databricks notebook running smoothly. Fear not, my friends! It's a common task, and I'm here to guide you through the process step by step. This guide covers everything from understanding the package ecosystem in Databricks to actually installing and managing those packages effectively. So, let's get started!

Understanding the Python Package Ecosystem in Databricks

Okay, before we get our hands dirty with installations, let's chat about the Python package ecosystem within Databricks. Databricks clusters come pre-configured with a bunch of commonly used libraries. These are your workhorses like pandas, numpy, and scikit-learn. However, you will often need something that isn't included by default, right? That's where understanding how Databricks handles packages becomes crucial.

Databricks Runtime and Default Packages

So, Databricks Runtime is basically the engine that powers your clusters. It's built on top of Apache Spark and includes optimized versions of many popular Python packages. Think of it as a curated set of tools designed to play nicely together. These default packages save you the hassle of installing the basics every time you spin up a new cluster.

But, and it's a big but, these pre-installed packages might not always be the versions you need. Plus, you'll inevitably run into situations where you need a package that isn't included at all. That's where you'll need to roll up your sleeves and start installing your own packages.

Package Management Options

Databricks gives you a few ways to manage your Python packages. You can install packages at the cluster level, which means they're available to all notebooks running on that cluster. Or, you can install packages at the notebook level, making them available only within that specific notebook. This flexibility is super handy depending on your needs.

Cluster Libraries: These are installed once per cluster and are great for packages that many users or notebooks will rely on. Installing at the cluster level ensures consistency and avoids everyone having to install the same packages repeatedly.
Notebook-Scoped Libraries: These are perfect for experimenting or when you need a specific version of a package for a particular notebook that might conflict with other notebooks. They keep your environments isolated and prevent dependency clashes.

Understanding these options is the first step to effectively managing your Python packages in Databricks. Knowing when to use cluster libraries versus notebook-scoped libraries can save you a lot of headaches down the road. Next up, we will explore the actual installation methods.

Installing Python Packages: Step-by-Step

Alright, let's get down to the nitty-gritty: how to actually install those Python packages in Databricks. Whether you prefer using the Databricks UI or executing commands directly in your notebook, I've got you covered. We'll walk through both methods to ensure you can pick the one that best suits your workflow.

Using the Databricks UI

The Databricks UI provides a user-friendly way to manage cluster libraries. Here's how you can use it to install Python packages:

Navigate to your cluster: First, head over to the Databricks workspace and click on the "Clusters" tab. Find the cluster you want to install the package on and click its name to open the cluster details page.
Go to the Libraries tab: On the cluster details page, you'll see a tab labeled "Libraries." Click on it. This is where you manage all the libraries installed on your cluster.
Install New Library: Click the "Install New" button. A pop-up window will appear, giving you several options for specifying the library you want to install.
Choose your installation method: You can choose to upload a library file (like a .whl or .egg file), specify a PyPI package, or even point to a Maven coordinate. For most common Python packages, the PyPI option is the easiest.
Specify the package: If you chose PyPI, simply enter the name of the package you want to install (e.g., requests) in the "Package" field. You can also specify a version if you need a particular one (e.g., requests==2.26.0).
Install: Click the "Install" button. Databricks will then install the package on your cluster. You'll see the status of the installation in the Libraries tab. It might take a few minutes for the installation to complete, especially for larger packages.
Restart the cluster: After the installation is complete, you'll need to restart your cluster for the changes to take effect. Click the "Restart" button on the cluster details page. This ensures that all the worker nodes in the cluster have the new package available.

That's it! Your package is now installed at the cluster level and available to all notebooks running on that cluster.

Using `pip` in a Notebook

For notebook-scoped installations, you can use pip directly within your notebook cells. This is super convenient for quick experiments or when you need a package only for a specific notebook.

Open a notebook: Open the Databricks notebook where you want to install the package.
Use %pip install: In a cell, use the magic command %pip install followed by the name of the package you want to install. For example:
```
%pip install requests
```
You can also specify a version:
```
%pip install requests==2.26.0
```
Run the cell: Execute the cell by pressing Shift + Enter or clicking the "Run" button. pip will install the package in the notebook's environment.
Verify the installation: After the installation is complete, you can verify that the package is installed by importing it and checking its version:
```
import requests
print(requests.__version__)
```
If the package is installed correctly, you should see its version number printed.

Using %pip is a quick and easy way to manage packages within a notebook. Just remember that these packages are only available within that specific notebook session.

Choosing the Right Method

So, which method should you use? It really depends on your use case.

Use the Databricks UI for cluster-level installations when you need a package to be available to all notebooks on the cluster or when you want to ensure consistency across your team.
Use %pip in a notebook for notebook-scoped installations when you're experimenting, when you need a specific version of a package for a particular notebook, or when you want to avoid affecting other notebooks.

Managing Package Versions and Dependencies

Package management isn't just about installing packages; it's also about managing their versions and dependencies. This is crucial for ensuring that your code runs reliably and consistently over time. Let's explore how to handle these aspects in Databricks.

Specifying Package Versions

When installing packages, it's always a good idea to specify the version you need. This prevents unexpected behavior due to updates or changes in newer versions of the package. You can specify a version in both the Databricks UI and when using %pip.

Databricks UI: When adding a PyPI package, you can enter the package name along with the version number in the "Package" field. For example, requests==2.26.0 will install version 2.26.0 of the requests package.
%pip: Similarly, you can specify the version when using %pip install. For example, %pip install requests==2.26.0 will install the same version.

By explicitly specifying the version, you ensure that your code always uses the version you've tested and verified.

Resolving Dependencies

Python packages often depend on other packages. When you install a package, pip automatically resolves and installs its dependencies. However, sometimes you might run into conflicts or issues with dependencies. Here are a few tips for resolving them:

Use a virtual environment: While Databricks doesn't directly support virtual environments in the traditional sense, notebook-scoped libraries provide a similar level of isolation. By installing packages at the notebook level, you can avoid conflicts with other notebooks or cluster-level packages.
Check for conflicting packages: If you're experiencing issues, check if there are any conflicting packages installed. You can use %pip list to see a list of all installed packages and their versions. Look for any packages that might be causing conflicts.
Upgrade or downgrade packages: Sometimes, upgrading or downgrading a package can resolve dependency issues. Use %pip install --upgrade <package_name> to upgrade a package or %pip install <package_name>==<version> to downgrade it.
Consult the package documentation: The package documentation often provides guidance on resolving dependency issues. Check the documentation for any specific requirements or recommendations.

Using `requirements.txt`

A requirements.txt file is a text file that lists all the packages and their versions that your project depends on. It's a convenient way to manage and share your project's dependencies. You can install packages from a requirements.txt file using %pip:

%pip install -r /path/to/requirements.txt

Replace /path/to/requirements.txt with the actual path to your requirements.txt file. This command will install all the packages listed in the file along with their specified versions.

Best Practices for Package Management

To wrap things up, let's go over some best practices for managing Python packages in Databricks. Following these guidelines will help you keep your environment clean, consistent, and reproducible.

Specify package versions: Always specify the version of the package you want to install. This prevents unexpected behavior due to updates or changes in newer versions.
Use notebook-scoped libraries for experimentation: When experimenting with new packages or versions, use notebook-scoped libraries to avoid affecting other notebooks or cluster-level packages.
Keep your cluster libraries up to date: Regularly update your cluster libraries to the latest stable versions. This ensures that you're using the latest features and bug fixes.
Use a requirements.txt file: Use a requirements.txt file to manage and share your project's dependencies. This makes it easy to reproduce your environment on other clusters or in other projects.
Document your environment: Document the packages and versions you're using in your project's documentation. This helps others understand your project's dependencies and reproduce your environment.
Clean up unused packages: Periodically clean up any unused packages from your cluster. This helps keep your environment clean and reduces the risk of conflicts.

By following these best practices, you can ensure that your Python package management in Databricks is smooth, efficient, and reliable. Happy coding!