Running Multiple Functions Synchronously in Jupyter Notebook

Learn how to run multiple functions `synchronously` in Jupyter Notebook using threading and Selenium for web scraping and data processing.
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How to Run Multiple Functions Synchronously in Jupyter Notebook

If you're working on a project in Jupyter Notebook that involves web scraping or real-time data processing, you may find yourself needing to run multiple functions simultaneously. This can be especially challenging if the functions are designed to execute indefinitely, as is often the case with web scraping. In this guide, we'll dive into the scenario where you need to run two separate web scraping functions and another function that performs calculations on the collected data. We will explore how to achieve synchronous execution using Python's threading capabilities.

Problem Overview

Imagine you have two functions that scrape data from websites using Selenium, running in an infinite loop and updating a DataFrame every few seconds. Additionally, you have a third function that merges these DataFrames and performs calculations based on the updated data. The goal is to ensure that all functions execute simultaneously to maintain the accuracy of your calculations, especially since the DataFrames update every 5 seconds. Traditional multiprocessing may not work well within Jupyter Notebook, leaving many users frustrated. Let’s break down how to effectively run these functions concurrently.

Solution: Using Threading

Using Python's threading module provides a practical solution to our problem. While threading may not offer the same benefits as multiprocessing for CPU-bound tasks, it can be more than sufficient for I/O-bound operations, such as web scraping with Selenium. Here’s how to implement it:

Step-by-Step Implementation

Import the Required Module
To utilize threading, you'll need to import the Thread class from the threading module.

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Define Your Functions
Ensure your scraping and calculation functions are defined. For example:

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Run Your Functions Concurrently
After defining your functions, you can create threads for each function and start them as follows:

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Important Considerations

Running in Jupyter Notebook: Since Jupyter can behave differently than traditional Python scripts, ensure that your script is placed within an if __name__ == '__main__': block to prevent any potential issues with the threading mechanism.

Infinite Loops: Make sure that your infinite loops within the functions are intended and include appropriate sleep intervals to prevent excessive CPU usage.

Data Synchronization: Since the functions are running concurrently, you may need to handle potential race conditions, especially if the Calculations function accesses shared data that may be modified by the web scraping functions.

Conclusion

Running multiple functions synchronously in a Jupyter Notebook can significantly enhance the efficiency of your data scraping and processing tasks. By using Python’s threading capabilities, you can create responsive scripts that keep your data up to date and ready for analysis. Remember that while threading is often suitable for I/O-bound tasks, always consider your specific use case for the best results. With the above steps, you'll be ready to implement concurrent execution in your own projects!