Developing various analytical projects requires not only the right data and knowledge of programming languages, but also a secure environment in which the created models and algorithms can be tested and improved. All analysts and data scientists are familiar with Jupyter Notebook and JupyterLab, which have a lot to offer in this regard. What precisely accounts for their popularity?
What is Jupyter Notebook and how does it work?
Jupyter Notebook is just an open-source web application that allows users not only to embed the code they create, but also to add notes, equations or even visualizations of the analyzed data sets. For this reason, among others, it is a very popular choice for data analysts, data scientists or specialists who deal with building machine learning models.
Jupyter Notebook supports multiple programming languages, allows you to narrate and thus tell stories about the analyzed data and the steps taken, as well as to visualize them. Additionally, it allows you to embed code used in a specific step of the analytical process into the content of the document.
The biggest advantages of Jupyter Notebook
When writing about the advantages of Jupyter Notebook we should mention first of all that it supports up to 40 different programming languages — including languages of first choice for data specialists: Python, R or even Scala. Moreover, it works perfectly with Big Data tools and libraries commonly used in ML projects (Apache Spark, TensorFlow, ggplot2, scikit-learn or pandas).
The wide range of possibilities for the use of Jupyter Notebook translates directly into the effects that can be achieved with it — output can be highly interactive and can be supplemented with a wide variety of files that include graphics, videos as well as automatically LaTeX-formatted mathematical formulas, diagrams and drawings.
Another great advantage is that the created document can be easily shared with others. This can be done by email, GitHub, Dropbox and many other such platforms. Reading a document is as easy as sending it — you just need to use Jupyter Notebook Viewer.
Where is the Jupyter Notebook used?
Of course, the ability to create interactive and easy-to-read documentation is not the only advantage of Jupyter Notebook. The application is also widely used in the process of data cleaning and transformation, works great for statistical modeling, creating clear and transparent data visualizations, as well as building machine learning models.
At this point, it is also worth mentioning the interactive development environment — JupyterLab, which offers a wide range of possibilities for model testing, refinement and deployment to production. However, when using it for complex analytical processes, you may encounter some problems related to lack of memory. How to solve them? You can easily increase memory in GCP AI Notebook JupyterLab settings before you start using AI Notebook.
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