Databricks Notebooks: Import Python Functions Easily

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Databricks Notebooks: Import Python Functions Easily

Hey everyone! So, you're diving into Databricks, and you've got a bunch of Python functions scattered across different notebooks. Maybe you're like, "Ugh, how do I avoid rewriting the same code everywhere?" Well, importing Python functions from one Databricks notebook to another is super common, and it's a lifesaver for keeping your code organized and avoiding duplication. Let's break down how to do this, step by step, so you can become a Databricks import pro! Trust me, it's easier than you think, and it'll make your life a whole lot easier when you're working on bigger projects. We're going to explore the different methods available, including using %run, dbutils.notebook.run, and the recommended approach of creating a Python module. By the end, you'll be importing functions like a boss, no sweat!

Method 1: Using the %run Magic Command (Quick but Limited)

Okay, let's start with the basics. The %run magic command is like a quick and dirty way to execute another notebook within your current one. Think of it as a way to say, "Hey Databricks, run this other notebook real quick and bring everything in." It's super simple, but it has some limitations, so keep that in mind. The %run command is great for quick prototyping or when you just want to grab a few functions without getting too fancy. However, it's not the best choice for larger projects or when you need a more robust import mechanism. It can lead to some confusion and potential issues with variable scope, so use it with caution, guys!

So, how does it work? In your current notebook, you simply use the %run command followed by the path to the notebook you want to execute. The path should be relative to your current workspace. Keep in mind that when you use %run, the entire notebook gets executed, not just specific functions. This means any code in the imported notebook will also run, which might not always be what you want. It's like inviting the whole party, not just the people you need! Plus, %run doesn't provide a way to directly import only certain functions. You're effectively running the whole notebook and hoping the functions you want are defined in the global scope. This lack of control can make debugging tricky and may introduce unwanted side effects. If the other notebook has any print statements or other commands that produce output, that will also show up in your current notebook, which can clutter things up. It's a quick fix, but it's not the most elegant solution. For basic stuff, though, it can save you some time. You can use this method for a smaller project but as the project become larger, this method might cause some issues in the long run.

Here is an example, let's say you have a notebook called My_Functions with a function called add_numbers():

def add_numbers(a, b):
    return a + b

In your current notebook, you would run:

%run ./My_Functions

result = add_numbers(5, 3)
print(result)  # Output: 8

In this example, the entire My_Functions notebook is executed, and since add_numbers() is defined, you can then call it in your current notebook. Pretty straightforward, right? But remember, this method is best for simple scenarios and small notebooks. When things get more complex, you'll want a more structured approach. You can also pass parameter to the %run command, just like this %run ./My_Functions $a=1 $b=2

Method 2: Using dbutils.notebook.run() (More Control, Still Imperfect)

Alright, let's move on to the next method, which gives you a bit more control than %run: dbutils.notebook.run(). This Databricks utility lets you execute another notebook and optionally retrieve the results. It's like calling a friend to help with a task and getting their output back, instead of just them showing up and doing their thing. The dbutils.notebook.run() method provides a way to run a notebook and capture its results. The output from dbutils.notebook.run() is stored as a dictionary. It allows you to pass parameters and retrieve results from the executed notebook, which provides more flexibility compared to the %run command. Still, it has its quirks, so listen up.

With dbutils.notebook.run(), you can execute another notebook and get back the values of any widgets defined in that notebook. The values are returned as a dictionary, making it easy to access the outputs. However, similar to %run, dbutils.notebook.run() also executes the entire target notebook. This can lead to the same problem of running extra code you don't need, which might be a performance concern if the target notebook is long or computationally expensive. It's useful, but it still has some limitations when it comes to true modularity.

Let's get into the code. The basic structure looks like this:

result = dbutils.notebook.run("/path/to/your/notebook", timeout_seconds=120, arguments = {"param1": "value1", "param2": "value2"})

Here's what's happening: you're specifying the path to the notebook you want to run. You can set a timeout, which is the maximum time the notebook is allowed to run before it's killed. And, importantly, you can pass arguments to the target notebook using the arguments parameter. The arguments parameter is super handy for passing input to your function when the other notebook requires arguments.

In the notebook you're calling, you can access these arguments using the dbutils.widgets.get() function. For example, if you passed a parameter named "my_param", you could get its value with dbutils.widgets.get("my_param"). The result is a dictionary-like object that holds the widget values. This method allows you to execute another notebook, pass parameters, and retrieve results, providing greater control compared to the %run command. However, since the whole notebook gets executed, you still have the same problem of unintended code execution if the target notebook is not well-designed to only expose functions. It's a step up, but not the ultimate solution for code organization and reusability.

Method 3: Creating a Python Module (The Recommended Way)

Okay, guys, here it is: the gold standard. The best way to import Python functions in Databricks is by creating a proper Python module. This approach is cleaner, more organized, and will save you headaches down the road. Trust me on this one. When you create a Python module, you essentially create a separate file containing the functions you want to import. This file can be in the same workspace or uploaded to a shared location, making it accessible across your Databricks environment. By importing a module, you have fine-grained control over what you bring into your current notebook, which can drastically improve the structure and readability of your code. It's more work upfront but pays off big time in the long run.

To make a Python module, first, you need to create a .py file. This file will contain all the Python functions that you want to be able to import into your other notebooks. This Python file can live in your Databricks workspace or in a shared location, like DBFS or a connected cloud storage, depending on how you want to share your module.

Inside this .py file, you put all your functions. For example:

# my_functions.py
def add(a, b):
    return a + b

def multiply(a, b):
    return a * b

Next, save this file. You can save it in your Databricks workspace (under a specific folder) or upload it to DBFS (Databricks File System), which is recommended if you want to share this module with multiple users or clusters. DBFS is like a distributed file system designed for Databricks.

Now, in your notebook where you want to use these functions, you can import them using the standard Python import statement. You'll need to know the path where you saved your .py file. If the file is in your workspace, you can use a relative path. If it's in DBFS, you'll use the DBFS path.

Here’s how to do the import:

# In your Databricks notebook
import sys

sys.path.append('/Workspace/Repos/your_repo/your_folder')  # Or the path to your module in DBFS or wherever you stored it

from my_functions import add, multiply

result = add(5, 3)
print(result)  # Output: 8

In this code:

  • sys.path.append(): You add the directory containing your .py file to the Python path. This tells Python where to look for modules. The path depends on where you saved your .py file.
  • from my_functions import add, multiply: This line imports the add and multiply functions from the my_functions.py file. Now you can use those functions in your current notebook.

The most important thing about using this method is that it keeps your code organized, reusable, and easy to maintain. You can create multiple Python files, each containing different functions. This approach is highly scalable and best suited for professional development. If you want to share the module with others, it's easier to maintain the different versions. Another advantage is that it enables code reuse across multiple notebooks. This helps in avoiding code duplication and improves maintainability.

Tips and Best Practices

  • Organize your code: Create separate .py files for different functionalities. For example, have one file for data processing functions, another for model training, and so on. This will improve code readability. Consider creating a package if the module grows too big.
  • Use relative paths: When possible, use relative paths to make your code more portable. This will save you time when moving your notebooks around. Make sure your paths are set up correctly.
  • Test your code: Before importing the functions into another notebook, test them thoroughly in the module. This will help you identify and fix any errors quickly.
  • Version control: Use version control (like Git) to manage your module files. This will make it easier to track changes and collaborate with others. This can also help you revert to previous versions if needed.
  • Comments and documentation: Always include comments in your code to explain what each function does, how it works, and what the parameters are. Include docstrings in your Python module to document your functions thoroughly. This makes it easier for other developers to understand and use your code.
  • Consider using Databricks Repos: For more advanced projects, explore using Databricks Repos, which integrates with Git repositories. It will help manage the modules more effectively and make collaboration a breeze.

Conclusion

Alright, you've got the lowdown on how to import Python functions from another notebook in Databricks! Whether you go with the quick %run, the more controlled dbutils.notebook.run(), or the recommended module approach, you're now equipped to organize your code, prevent redundancy, and collaborate effectively. Remember, creating Python modules is the best way to keep your code clean, manageable, and ready to scale. So, get out there, start importing, and happy coding! Don't forget to implement these methods in your next Databricks project to level up your efficiency and make your code a lot easier to read and maintain! Keep coding, and keep learning, guys!