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Like most people, the first tool I used when started learning data science is Jupyter Notebook. Most of the online data science courses use Jupyter Notebook as a medium to teach. This makes sense because it is easier for beginners to start writing code in Jupyter Notebook’s cells than writing a script with classes and functions.

Another reason why Jupyter Notebook is such a common tool in data science is that Jupyter Notebook makes it easy to explore and plot the data. When we type ‘Shift + Enter’, we will immediately see the results of the code, which makes it easy for us to identify whether our code works or not.

However, I realized several fallbacks of Jupyter Notebook as I work with more data science projects:

Unorganized: As my code gets bigger, it becomes increasingly difficult for me to keep track of what I write. No matter how many markdowns I use to separate the notebook into different sections, the disconnected cells make it difficult for me to concentrate on what the code does.

Difficult to experiment: You may want to test with different methods of processing your data, choose different parameters for your machine learning algorithm to see if the accuracy increases. But every time you experiment with new methods, you need to find and rerun the related cells. This is confusing and time-consuming, especially when the processing procedure or the training takes a long time to run.

Not ideal for reproducibility: If you want to use new data with a slightly different structure, it would be difficult to identify the source of error in your notebook.

Difficult to debug: When you get an error in your code, it is difficult to know whether the reason for the error is the code or the change in data. If the error is in the code, which part of the code is causing the problem?

Not ideal for production: Jupyter Notebook does not play very well with other tools. It is not easy to run the code from Jupyter Notebook while using other tools.

I knew there must be a better way to handle my code so I decided to give scripts a try. To make it less confusing, I refer to .py file when using word “script” in this article. These are the benefits I found when using scripts:

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