Developers and data scientists are used to complicated work, and they will take anything that will make life easier. Jupyter Notebook is a prime example of a tool that organizes code and data analysis into a single resource. It's undoubtedly an excellent resource for data science projects. But like any tool, you need to consider some things before choosing Jupyter Notebook or a different resource for your efforts.
What makes Jupyter Notebook Amazing
First, Jupyter solves an important developer experience problem: Iterating on software programs can be cumbersome or tedious. Especially if loading data is involved. A program usually runs to the end or throws an error. Debuggers must be attached when data needs to be inspected halfway through, but they are also complicated to set up. Data science often requires developing your program line by line. Jupyter makes this the primary interface. – And therefore, makes working on data science much more accessible.
As a result: Jupyter has emerged in recent years as a de facto standard for data science. The migration is arguably the fastest into a platform in recent memory. Jupyter Notebook has great features that make it very suited for projects at the intersection of data and development. Some things Jupyter Notebook does well are:
What is less great about Jupyter Notebook?
So, of course, Jupyter Notebook is fantastic, and no use case is beyond this marvelous piece of software. Well… not so fast. Yes, it is fantastic for the things it is supposed to do, but like with every tool, you need to consider some limits. As a tool primarily aimed at data science projects, Jupyter Notebook's limitations manifest mainly on the developer front.
Roughly speaking, data science projects can be divided into three types of activities: Exploratory Data Analysis (EDA), Model Training, and Model and Product Deployment. Jupyter Notebook puts the focus on incremental working. You can write some code, test and correct it, and move to the next part. The price you pay for this is that it is a bit slow compared to scripts or modules. Also, it tends to break after running for an extended time, and the entries to production logs leave much to be desired.
In short: Jupyter Notebook is great for EDA and Model Training but less for Deployment. There are no tools more capable than the Jupyter Notebook for producing prototypes. But better alternatives are available when it comes to putting things into production.
Alternatives and filling the gaps
Choosing the right tool is not at all trivial. Two alternatives that jump out are PyCharm and Spyder. PyCharm because it is the IDE cousin of the shell IPython, which forms the basis of Jupyter Notebook. Spyder because it is aimed at the scientific programming community, just like Jupyter is.
The question of which one is better -the Jupyter Notebook or the alternative- depends on what you need at a given point. Compared to PyCharm, Jupyter Notebook makes it much easier to present and share data visualizations next to code and text. However, PyCharm trumps Jupyter Notebooks on complex projects with multiple scripts that interface with each other. Similarly, Spyder is great for complex data science applications that feed each other but is not as good at presenting data to different audiences.
Jupyter puts data in the best light
The best way to leverage Jupyter Notebook is to use it to its strength in concert with the strengths of other tools. Now we have the obvious part out of the way: in practice what you do is:
Once everything is set, copy the code to a script to run on the complete data set.
No matter what the exact form of your data science project is, there is a place to include Jupyter Notebook in one way or another. The beauty of Jupyter is that it creates a computational narrative. It makes a document that allows researchers to supplement their code and data with analysis, hypothesis, and conjecture. It can be a great driver for data scientists to explore creatively. If you haven't already looked at Jupyter technology, it is high time to do so!
Check out our tutorials to get started and our sequel article with a great example of using Jupyter with an image generation framework (Huggingface).