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- #HTML OPTIMIZER SAME SELECT HOW TO#
- #HTML OPTIMIZER SAME SELECT INSTALL#
- #HTML OPTIMIZER SAME SELECT CODE#
Notebook extensions can be used to enhance the user experience and offer a wide variety of personalization techniques.
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In case you wish anytime to come back to the original Jupyter notebook theme, you can just run the following command and refresh your page. Once we've run this command and refreshed the page, our notebook should look like the one in Figure 2. We can the run the following command to list the names of all the available themes: !jt -lįinally, we can choose a theme using the following command (in this example I decided to use the solarized1 theme): !jt -t solarizedl
#HTML OPTIMIZER SAME SELECT INSTALL#
We can install our package directly in the notebook using the trick I showed you in the previous section: !pip install jupyterthemes In Figure 2 you will see how we will be able to personalise its aspect. The default Jupyter theme looks like the one in Figure 1. 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.
#HTML OPTIMIZER SAME SELECT HOW TO#
In this article, I will walk you through some simple tricks on how to improve your experience with Jupyter Notebook.
#HTML OPTIMIZER SAME SELECT CODE#
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.