Jupyter Notebook, formerly known as IPython, has become the language of choice for data scientists in recent years. This web-based notebook allows interactions between users and the integrated development environment (IDE). When using this notebook, multiple languages are supported, which several tech enthusiasts find rather convenient.
Data scientists, in particular, have taken to this notebook to perform job tasks in languages like Python and R, in countries like New Zealand, where there is a growing data analytics community. This tool has skyrocketed significantly, contributing to 2.5 million notebooks shared in 2015.
With Jupyter Notebook, data science is being utilized by prominent data analysts working for NZ pokies online to evaluate the user experience on their websites. This article will explore the key reasons data scientists utilize Jupyter Notebooks so much.
Benefits of Jupyter Notebook to Data Scientists
Here are the benefits of the Jupyter Notebook to data scientists:
- Exploratory Data Analytics
Unlike other IDEs, every code that has been input to a block can be run to check results. For every cell block, there’s a corresponding output that displays in-line. This is especially useful during exploratory data analytics since details like correlation, descriptive statistics, and group tables need to be examined instantly in languages like Python.
- Ease of Working and Sharing
Jupyter Notebook is easy to work with since it aggregates images, animations, text, plots, and codes into a single document. These documents can also be saved in JSON format, allowing them to get shared easily.
- Ease of Conversion
Jupyter Notebooks can be easily converted into other formats. Sometimes, a device might not be able to view other formats, and Jupyter Notebooks facilitate an environment to bypass this limitation. By allowing notebooks to be shared in HTML and PDF formats, many more people can check the contents of a notebook.
- Explaining the Functions of Codes with In-Line Text
Like the name suggests, the notebooks make it seem like a physical notebook is being viewed by a reader. For one, the user can explain the functions of code with markdown code blocks that highlight subheadings and points.
Also, when a notebook is shared, users can interact with the code. For instance, the interact library from ipywidgets facilitates the ease of interacting with data, just like in Microsoft Excel.
Conclusion
In recent years, Jupyter Notebook has exploded as the language of choice for the emerging data science industry. This notebook offers users several benefits such as ease of conversion, ease of working and sharing, exploratory data analysis, and explaining the functions of code with in-line text.
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