If you are interested in changing how your Jupyter notebook looks, it is possible to install a package with a collection of different themes. This same trick can also be applied to install Python packages in Jupyter notebook.
Running a cell with !pwd will instead print out the current directory file-path. For example, running a cell with !ls will return all the items in the current working directory. Not many users are aware of this, but it is possible to run shell commands in a Jupyter notebook cell by adding an exclamation mark at the beginning of the cell.
Ctrl + End: move cursor to the end of the cell.In order to enter Jupyter edit mode instead, we need to press Enter and successively any of the following commands: Shift + Up/Down Arrow: to select multiple notebook cells at the same time (pressing enter after selecting multiple cells will make all of them run!).H: show all the shortcuts available in Jupyter Notebook.In order to enter Jupyter command mode, we need to press Esc and then any of the following commands: Shift + Enter: run the current cell and move the next one.Ctrl + Enter: to run all the selected cells.Some shortcuts which are common in both modes are: Some shortcuts work only on one mode or another while others are common to both modes. There are two possible way to interact with Jupyter Notebook: Command Mode and Edit Mode. I will now walk you through some of the shortcuts I found most useful to use in Jupyter. Shortcuts can be really useful to speed up writing your code. We will start from useful shortcuts and we will end up adding themes, automatically generated table of contents, and more. In this article, I will walk you through some simple tricks on how to improve your experience with Jupyter Notebook. Notebook documents are documents able to contain both code and rich text elements such as paragraphs, equations, and so on. Jupyter Notebook is a client-server application used for running notebook documents in the browser. Jupyter Notebook is nowadays probably the most used environment for solving Machine Learning/Data Science tasks in Python.
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