Random thoughts about random subjects… From science to literature and between manga and watercolours, passing by data science and rugby; including film, physics and fiction, programming, pictures and puns.
I know there are a ton of posts out there covering this very topic. I am writing this post more for my out benefit, so that I have a reliable place to check the commands I need to add a new conda environment to my Jupyter and nteract IDEs.
First to create an environment that contains, say TensorFlow, Pillow, Keras and pandas we need to type the following in the command line:
JupyterLab is an interactive development environment for working with notebooks, code, and data.
The Evolution of the Jupyter Notebook
Project Jupyter exists to develop open-source software, open standards, and services for interactive and reproducible computing.
Since 2011, the Jupyter Notebook has been our flagship project for creating reproducible computational narratives. The Jupyter Notebook enables users to create and share documents that combine live code with narrative text, mathematical equations, visualizations, interactive controls, and other rich output. It also provides building blocks for interactive computing with data: a file browser, terminals, and a text editor.
The Jupyter Notebook has become ubiquitous with the rapid growth of data science and machine learning and the rising popularity of open-source software in industry and academia:
Today there are millions of users of the Jupyter Notebook in many domains, from data science and machine learning to music and education. Our international community comes from almost every country on earth.¹
There are over 1.7 million public Jupyter notebooks hosted on GitHub. Authors are publishing Jupyter notebooks in conjunction with scientific research, academic journals, data journalism, educational courses, and books.
At the same time, the community has faced challenges in using various software workflows with the notebook alone, such as running code from text files interactively. The classic Jupyter Notebook, built on web technologies from 2011, is also difficult to customize and extend.
JupyterLab: Ready for Users
JupyterLab is an interactive development environment for working with notebooks, code and data. Most importantly, JupyterLab has full support for Jupyter notebooks. Additionally, JupyterLab 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 enables you to arrange your work area with notebooks, text files, terminals, and notebook outputs.JupyterLab provides a high level of integration between notebooks, documents, and activities:
Drag-and-drop to reorder notebook cells and copy them between notebooks.
Run code blocks interactively from text files (.py, .R, .md, .tex, etc.).
Link a code console to a notebook kernel to explore code interactively without cluttering up the notebook with temporary scratch work.
Edit popular file formats with live preview, such as Markdown, JSON, CSV, Vega, VegaLite, and more.
JupyterLab has been over three years in the making, with over 11,000 commits and 2,000 releases of npm and Python packages. Over 100 contributors from the broader community have helped build JupyterLab in addition to our core JupyterLab developers.
JupyterLab is built on top of an extension system that enables you to customize and enhance JupyterLab by installing additional extensions. In fact, the builtin functionality of JupyterLab itself (notebooks, terminals, file browser, menu system, etc.) is provided by a set of core extensions.
JupyterLab extensions enable you to work with diverse data formats such as GeoJSON, JSON and CSV.²Among other things, extensions can:
Provide new themes, file editors and viewers, or renderers for rich outputs in notebooks;
Add menu items, keyboard shortcuts, or advanced settings options;
Provide an API for other extensions to use.
Community-developed extensions on GitHub are tagged with the jupyterlab-extension topic, and currently include file viewers (GeoJSON, FASTA, etc.), Google Drive integration, GitHub browsing, and ipywidgets support.
Develop JupyterLab Extensions
JupyterLab 1.0 and Beyond
We plan to release JupyterLab 1.0 later in 2018. The beta releases leading up to 1.0 will focus on stabilizing the extension development API, user interface improvements, and additional core features. All releases in the beta series will be stable enough for daily usage.
JupyterLab 1.0 will eventually replace the classic Jupyter Notebook. Throughout this transition, the same notebook document format will be supported by both the classic Notebook and JupyterLab.
There are many ways you can participate in the JupyterLab effort. We welcome contributions from all members of the Jupyter community:
Use our extension development API to make your own JupyterLab extensions. Please add the jupyterlab-extension topic if your extension is hosted on GitHub. We appreciate feedback as we evolve toward a stable API for JupyterLab 1.0.
I am a supporter of using Jupyter Notebooks for data exploration and code prototyping. It is a great way to start writing code and immediately get interactive feedback. Not only can you document your code there using markdown, but also you can embed images, plots, links and bring your work to life.
Nonetheless, there are some little annoyances that I have, for instance the fact that I need to launch a Kernel to open a file and having to do that “the long way” – i.e. I cannot double-click on the file that I am interested in seeing. Some ways to overcome this include looking at Gihub versions of my code as the notebooks are rendered automatically, or even saving HTML or PDF versions of the notebooks. I am sure some of you may have similar solutions for this.
Last week, while looking for entries on something completely different, I stumbled upon a post that suggested using nteract. It sounded promising and I took a look. It turned out to be related to the Hydrogen package available for Atom, something I have used in the past and loved it. nteract was different though as it offered a desktop version and other goodies such as in-app support for publishing, a terminal-free experience sticky cells, input and output hiding… Bring it on!