Jupyter Notebooks – The Good, The Bad, and Callisto

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Jupyter is currently the most widely used data science notebook with over 10 million notebooks made publically available as of 2021. It has the reputation of being one of the best resources for scientists to use while exploring their data and collaborating with peers. Many data scientists gravitate towards Jupyter Notebooks because it supports several coding languages including Python, R, and Julia.

In this post, we will address the pros and cons of Jupyter Notebooks and show how you can optimize Jupyter with Callisto for seamless data exploration.

Pros – What Jupyter Notebooks Does Well

Overall, Jupyter Notebooks is a beginner-friendly resource once setup is complete. Jupyter makes research easy by allowing you to break up your code into small cells. It can also display code in a side-by-side format, making analysis more efficient. These features will enable you to focus on specific pieces of your code, modify cells to your exact needs, and ensure minimal errors before progressing to the next portion of your code.

Jupyter also offers visualization tools, including the ability to add images, graphs, and links within a cell. Visualization is a crucial aspect of data exploration, as scientists are often required to present their code in a way that non-programmers can easily understand.

Cons – Where Jupyter Needs To Improve

Keep in mind that Jupyter Notebooks require extensive setup. The installation process is complicated, especially for beginners. Depending on the complexity of your goals for Jupyter, you may be required to install several packages and libraries that cater to the programming language you wish to use. While small datasets are not particularly difficult to analyze, large datasets may require you to use significant resources – connecting Jupyter to a larger machine or the cloud can be a complex and difficult process.

Additionally, Jupyter offers limited support for debugging code, and the system tends to run slowly when operating long or complex code. Overall, it could use improvement when it comes to production-level development.

Callisto and Jupyter – The Perfect Pair

We built Callisto to address the pitfalls of Jupyter Notebooks. With no setup required, simply open the app and you’ll be ready to hit the ground running! Callisto also offers Cloud computing and GPU support for you to confidently run data without any DevOps experience. It connects with primary tools that most data scientists already use including DropBox and Google Drive.

In our most recent update, we added a Package Manager which will enable you to add and remove packages via the UI. We also added an automatic notebook check to make sure that your environment is ready for analysis. With this update, you have an easy alternative to manually installing libraries on your device! As developers, our mission is to make everyday tasks simpler. Pairing Jupyter Notebooks with Callisto will save you time and effort while minimizing errors in your research. We believe that anyone can do data science – with the help of Callisto, we have no doubt that your data will reach new heights!


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