The Jupyter Notebook is an open-source internet application which helps you to create documents with live code, equations, visualizations, and narrative text which can be shared. Jupyter Notebook is basically the descendant of the IPython Project, which offered IPython Notebook. The name Jupyter is derived from the programming languages that it supports which is Julia, Python, and R. Jupyter Notebook is used for learning and trying Python, data processing or transformation, numeric simulation, statistical modeling, and machine learning. Jupyter notebook is a standard interactive environment for the data scientists. Jupyter helps you to edit and run notebooks via a browser. Jupyter Notebook can be executed without the help of an internet connection. Jupyter Notebook consists of two main components, they are:
A kernel is a program that introspects the user’s code. The Jupyter Notebook consists of a kernel for Python code. On the other hand, the dashboard serves as multi-tasker, it helps to manage the kernels and at the same time can be used to re-open the notebook documents that you have created.
Uses of Jupyter Notebook
The Jupyter Notebook is an online notebook which lets the faculty of a college and it’s students weave together computational information (code, data, statistics) along with the references of narrative, multimedia, and graphs. Teaching staff can use it to set up interactive textbooks with explanations and examples which students can access and test themselves right from their browsers. Students can use it to enhance their reasoning, showcase their work, and can draw connections between their classwork and the outside world. Even Scientists, journalists, and researchers use it to open up their huge loads of data and share the stories behind their computations which will help them to collaborate with other’s work and be a part of exceptional innovations.
The innovation of Jupyter Notebook is a boon for the data science industry. One of the significant ways which data scientists and engineers interact with their data is through the usage of Jupyter notebooks. Notebooks anchorages the use of collaborative, extensible, and scalable data science. Jupyter Notebooks is considered the de facto platform in quick prototyping and exploratory analysis. A lot of Jupyter functionalities lies under the hood and is not effectively explored. Below are the uses of Jupyter Notebook:
- Executing Shell Commands
Shell is one of the ways used to interact textually with the computer. Bash / Bourne Again Shell is the most popular Unix Shell. Bash is found as the default shell on almost every implementation of Unix. When you work with any one of the Python interpreters, you may need to regularly switch between the shell and the IDLE, if in case you need to use the command line tools. That’s not the case with Jupyter Notebook. Jupyter Notebook provides the ease of executing shell commands from within the notebook by placing an extra ‘!’ right before the commands. Any command at the command-line can be used in IPython by prefixing a ‘!’ character. This helps to ease up the process of execution as the need to frequently switch is eradicated.
Jupyter Notebook extensions are the real time-savers and are of great use. The extensions are Java Scripts modules which can be loaded at any given time. Some of the useful extensions of Jupyter Notebook are Hinterland, Snippets, and Autopep8.
- Hinterland: It enables the option of code autocompletion menu for every keypress in a code cell. This comes in hand as it saves time and autocompletes the code without any error
- Snippets: Snippets adds a drop-down menu to the toolbar of Jupyter Notebook. This allows the easy insertion of code snippet cells in the notebook.
- Autopep8: It helps to reformat the content of the code cells with a click. It acts as a replacement of using spacebar repeatedly for reformatting code errors by helping to reformat the codes with just a click.
Widgets work similarly like a control such as a slider or a textbox. These are used to build interactive GUIs for the notebooks.
- Play Widget: This widget is used to perform animations by reciting a sequence of integers at a certain speed. It iterates a cluster of integers at a constant speed.
Qgrid makes data frames instinctive. It is also a Jupyter notebook widget but it mainly focusses on data frames. QGrid uses SlickGrid to render pandas DataFrames within the Jupyter notebook application. This will allow you to explore the DataFrames with innate scrolling and filtering controls. You can also edit your DataFrames by double-clicking on the cells.
Jupyter notebook is an exceptional innovation which has a huge contribution to the development of the data science industry. The immense contribution of Jupyter Notebook for the development of data science has created a smooth-flow of the data process. These were some of the features of the Jupyter Notebooks that are time-saving and effective. Hopefully, they will help you to save some time and give you a better UI experience.