Why Jupyter Notebooks Are So Popular Among Data Scientists
Born out of IPython in 2014, Jupyter Notebook has seen an enthusiastic adoption among the data science community, to an extent where it has become a default environment for research. By definition, Jupyter is a free, open-source interactive web-based computational notebook. Computational notebooks have been around for several years; however, Jupyter, in particular, has exploded in popularity over the past couple of years. This nifty tool supports multi-language programming and therefore became the de facto choice for data scientists for practising and sharing various codes, quick prototyping, and exploratory analysis.
Although there is no dearth of language-specific IDEs (Integrated Development Environments), such as PyCharm, Spyder, or Atom, because of its flexibility and interactiveness, Jupyter has exploded in popularity among data scientists. Jupyter Notebook has also gained massive traction within digital humanities as a pedagogical tool. According to an analysis by GitHub, it has been counted that more than 2.5 million public Jupyter notebooks were shared in September 2018, which is up by 200,000 counted in 2015. So before we delve deeper into the features and advantages of Jupyter, and why it is considered to be the best platform for data scientists, we would discuss what a Jupyter Notebook is.
An indirect acronym of three languages — Julia, Python and R — Jupyter Notebook is a client-based interactive web application that allows users to create and share codes, equations, visualisations, as well as text. The notebook is considered as a multi-language interactive computing environment, which supports 40+ programming languages to its users. With Jupyter Notebook, users can bring in data, code and prose in together to create an interactive computational story.
Whether to analyse a collection of written text, creating music or art or to develop engineering concepts, Jupyter Notebook can combine codes and explanations with the interactivity of the application. This makes it a handy tool for data scientists for streamlining end to end data science workflows.
The Jupyter Notebook can be installed using the Python pip command. And, if using Anaconda, then it gets automatically installed as part of the Anaconda installation. It is combined of three components — the notebook application, kernels, and notebook documents. The notebook web application is used for writing and running codes in an interactive way, however kernels controls the system by running and introspecting users’ codes. And thirdly, notebook documents are the self-contained documents of all the contents visible in the notebook. Each document in the notebook has the kernel that controls it.
According to Lorena Barba, a mechanical and aeronautical engineer at George Washington University in Washington DC for — data scientists, Jupyter has emerged as a de-facto standard.
Due to the rising popularity of open-source software in the industry, along with rapid growth of data science and machine learning the Jupyter Notebook has become ubiquitous among data scientists. Apart from supporting multi-language programming, this interactive web-based computing platform also supports Markdown cells, allowing for more detailed write-ups with easy formatting. With Jupyter, the final product can be exported as a PDF or HTML file, which can be presented on a browser or can be shared on sites like GitHub. Jupyter Notebooks are saved in the structured text files — JSON (JavaScript Object Notation) — which makes it extremely easy to share.
