It has full support for Jupyter notebooks and enables you to use text editors, terminals, data file viewers, and other custom components side by side with notebooks in a tabbed work area. JupyterLab is an interactive development environment for working with notebooks, code and data. This eventually led again to the idea of splitting these functionalities and laid the foundation for JupyterLab. Some parts of it rather dealt with managing files, running notebooks and parallel workers. As the code grew bigger, people also started to realise that it actually is more than just a notebook. The name Jupyter itself was chosen to reflect the fact that the three most popular languages in data science are supported among others, thus Jupyter is actually an acronym for Julia, Python, R.īut evolution never stops and the source code of Jupyter notebook built on the web technologies of 2011 started to show its age. Another reason for the split was the fact that Jupyter wanted to support other languages besides Python like R, Julia and more. After the spin-off, IPython concentrated on providing solely an interactive shell for Python while Project Jupyter itself started as an umbrella organisation for several components like Jupyter notebook and QTConsole, which were moved over from IPython, as well as many others. At that time IPython encompassed an interactive shell, the notebook server, the QT console and other parts in a single repository with the obvious organisational downsides. In 2014, Project Jupyter started as a spin-off project from IPython for several reasons. People were psyched about the possibilities IPython notebook provided them and the adoption rose quickly. As the speed of development picked up, IPython 0.12 was released only one year later in December 2011 and included for the first time a browser-based IPython notebook environment. Several years and many failed attempts later, it took until late 2010 for Grain Granger and several others to develop a first graphical console, named QTConsole which was based on QT. IPython quickly became a success as the REPL of choice for many users but it was only a small step towards a graphical interactive notebook environment. In order to improve upon this situation he laid the foundation for a notebook environment by building IPython (Interactive Python), a command shell for interactive computing. In 2001 Fernando Pérez was quite dissatisfied with the capabilities of Python’s interactive prompt compared to the commercial notebook environments of Maple and Mathematica which he really liked. This will also clarify the confusion people sometimes have over IPython, Jupyter and JupyterLab notebooks. In this blog post I will introduce several best practices and techniques that will help you to create notebooks which are focused, easy to comprehend and to work with.īefore we get into the actual subject let’s take some time to understand how Project Jupyter evolved and where it came from. Notebooks that need you to tamper with the PYTHONPATH or to start Jupyter from a certain directory for modules to import correctly. Also sharing these notebooks is quite often an unnecessary pain. In strong contrast to this, and actually more often to find in practise, are notebooks with cells containing pages of incomprehensible source code, distracting you from the actual analysis. Notebooks that are beautifully designed and perfectly convey ideas and concepts by having the perfect balance between text, code and visualisations like in my all time favourite Probabilistic Programming and Bayesian Methods for Hackers. But as Pythagoras already noted “If there be light, then there is darkness.” and with Jupyter notebooks it’s no difference of course.īeing in the data science domain for quite some years, I have seen good but also a lot of ugly. Due to this unique characteristic, Jupyter notebooks have achieved a strong adoption particularly in the data science community. This combination makes it extremely useful for explorative tasks where the source code, documentation and even visualisations of your analysis are strongly intertwined. Python and R, as well as rich text elements like paragraphs, equations, figures, links, etc. In a nutshell, a notebook is an interactive document displayed in your browser which contains source code, e.g. If you have ever done something analytical or anything closely related to data science in Python, there is just no way you have not heard of Jupyter or IPython notebooks.
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