The new collaborative editing feature enables collaboration in real-time between multiple clients without user roles, when sharing the URL of a document to other users, they will have access to the same environment you are working on they can write and execute the cells.
Something you need to be aware of is that not all editors in JupyterLab support sync. Additionally, opening the same underlying document using different editor types may result in a lack of synchronization. Clicking on the notebook name brings up a dialog which allows you to rename it. Menu bar : The menu bar presents different options that may be used tomanipulate the way the notebook functions.
Toolbar : The tool bar gives a quick way of performing the most-usedoperations within the notebook, by clicking on an icon. The notebook consists of a sequence of cells. There are threetypes of cells: code cells , markdown cells , and raw cells. For more information on the different things you can do in a notebook,see the collection of examples. A code cell allows you to edit and write new code, with full syntaxhighlighting and tab completion.
The programming language you use dependson the kernel , and the default kernel IPython runs Python code. When a code cell is executed, code that it contains is sent to the kernelassociated with the notebook. Theoutput is not limited to text, with many other possible forms of output arealso possible, including matplotlib figures and HTML tables as used, forexample, in the pandas data analysis package. You can document the computational process in a literate way, alternatingdescriptive text with code, using rich text.
In IPython this is accomplishedby marking up text with the Markdown language. The corresponding cells arecalled Markdown cells. The Markdown language provides a simple way toperform this text markup, that is, to specify which parts of the text shouldbe emphasized italics , bold, form lists, etc. If you want to provide structure for your document, you can use markdownheadings. Markdown headings consist of 1 to 6 hash signs followed by aspace and the title of your section.
The markdown heading will be convertedto a clickable link for a section of the notebook. It is also used as a hintwhen exporting to other document formats, like PDF.
When a Markdown cell is executed, the Markdown code is converted intothe corresponding formatted rich text. Markdown allows arbitrary HTML code forformatting. New LaTeX macros may be defined using standard methods,such as newcommand , by placing them anywhere between math delimiters ina Markdown cell. These definitions are then available throughout the rest ofthe IPython session.
Raw cells provide a place in which you can write output directly. Raw cells are not evaluated by the notebook. When passed through nbconvert, raw cells arrive in thedestination format unmodified. For example, you can type full LaTeXinto a raw cell, which will only be rendered by LaTeX after conversion bynbconvert. Typically, you will work on a computational problem in pieces, organizingrelated ideas into cells and moving forward once previous parts workcorrectly.
This is much more convenient for interactive exploration thanbreaking up a computation into scripts that must be executed together, as waspreviously necessary, especially if parts of them take a long time to run. To interrupt a calculation which is taking too long, use the Kernel , Interrupt menu option, or the i,i keyboard shortcut.
Similarly, to restart the whole computational process,use the Kernel , Restart menu option or 0,0 shortcut. A notebook may be downloaded as a. All actions in the notebook can be performed with the mouse, but keyboardshortcuts are also available for the most common ones. The essential shortcutsto remember are the following:.
For the full list of available shortcuts, click Help , Keyboard Shortcuts in the notebook menus. One major feature of the Jupyter notebook is the ability to display plots thatare the output of running code cells. No exposed passwords in your notebooks.
Connect to a Snowflake warehouse and query the data with dedicated SQL cells. Attach a bucket to your project to read, edit and upload files to the bucket. Connect to a Postgres instance and use SQL directly from a notebook inerface.
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Our community is the best place to give us feedback on new features you would like to see. Awesome service for working on Jupyter Notebooks collaboratively. Really cool is the data management. No mounting required, it's just drag and drop. DeepnoteHQ Amazing tool. Have been using for just couple of days.
Feels great to just start working rather than cracking my head on getting packages installed. Delightful user experience reminds me of Superhuman with the command palette and constant reminders of how to use hotkeys to work more efficiently.
Very impressed with the new DeepnoteHQ! Managing SQL in a notebook's a nightmare! Got early access to DeepnoteHQ. This stuff is insane! Super intuitive and amazing UX. DeepnoteHQ is amazing. You name the feature, it has all covered. Great UI, real-time collaboration, variable explorer, keyboard shortcuts, and many more make it a perfect Jupyter environment for the data scientist.
Machine learning is a very empirical discipline so iteration speed is everything - working in Deepnote is like code-review and rapid prototyping at the same time, saving valuable time in the iteration cycles. A bit blown away tbh.
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