Introduction
Data science fans often call Jupyter Notebook "Jupyter." It is an open-source, interactive coding program that lets the user/programmer test code, equations, data visualizations, text, and many other things using the Python programming language. Jupiter is flexible and works with the most popular programming languages, such as R, JavaScript, HTML/CSS, Node.js, etc. It is mainly used for data visualization. The IPython Notebook project grew into the Jupyter project.
Here are some alternatives to Jupyter notebooks with better features, more flexibility, and a different take on coding and how to use it. Also, Python Online Training may teach you more about this topic.
• PyCharm
People who use Jupyter all the time might want to try this similar notebook interface with more features. PyCharm supports not only the programming language Python but also code written in SQL and other identical database languages. Users of Jupyter will agree that the packages are great. Python libraries like Numpy and Matplotlib can be used with Pycharm to do numerical computing and plot data. In addition to being able to do the math, it also supports Web Development languages like HTML/CSS, JavaScript, Angular.js, and many others. One cool thing about PyCharm is that it has a sharp debugger with a graphical interface that can also be customized.
• Apache Zeppelin
Zeppelin, a stand-alone product from Apache for analyzing data, is making news and competing with tools like Jupyter. Apache is better than Jupyter in many ways, such as its ability to support multiple users and better built-in data visualization tools. It also lets the user combine various lines of Python code into one line, which is helpful when there are a lot of loops in the code. But Zeppelin has a few problems, such as not being able to add new features through extensions. In contrast, Jupyter has more than 80 extensions made by the Jupyter development community. Even so, the Zeppelin community is growing and developing faster.
• RStudio
RStudio is a notebook made just for R. This is because R is becoming the standard statistical programming language. It has a console with a syntax editor that shows errors and lets you run code directly. There are many data visualization tools in RStudio that help the user plot. It has many excellent features, like a customizable console, a code history that can be searched, keyboard shortcuts, cross-platform compatibility (RStudio can be used on any computer with a web browser), a help section, and many more. It can also run on a server. The RStudio community also wants to add support for languages like Julia and Haskell. Rstudio also includes the program RMarkdown. RMarkdown lets you take data analysis reports and turn them into documents and presentations that look good.
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Conclusion
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