Magic keywords are special commands you can run in cells that let you control the notebook itself or perform system calls such as changing directories. For example, you can set up matplotlib to work interactively in the notebook with
Magic commands are preceded with one or two percent signs (
%%) for line magics and cell magics, respectively. Line magics apply only to the line the magic command is written on, while cell magics apply to the whole cell.
NOTE: These magic keywords are specific to the normal Python kernel. If you are using other kernels, these most likely won’t work.
At some point, you’ll probably spend some effort optimizing code to run faster. Timing how quickly your code runs is essential for this optimization. You can use the
timeit magic command to time how long it takes for a function to run, like so:
If you want to time how long it takes for a whole cell to run, you’d use
%%timeit like so:
As mentioned before, notebooks let you embed images along with text and code. This is most useful when you’re using
matplotlib or other plotting packages to create visualizations. You can use
%matplotlib to set up
matplotlib for interactive use in the notebook. By default figures will render in their own window. However, you can pass arguments to the command to select a specific "backend", the software that renders the image. To render figures directly in the notebook, you should use the inline backend with the command
Tip: On higher resolution screens such as Retina displays, the default images in notebooks can look blurry. Use
%config InlineBackend.figure_format = 'retina'after
%matplotlib inlineto render higher resolution images.
Example figure in a notebook
With the Python kernel, you can turn on the interactive debugger using the magic command
%pdb. When you cause an error, you'll be able to inspect the variables in the current namespace.
Debugging in a notebook
Above you can see I tried to sum up a string which gives an error. The debugger raises the error and provides a prompt for inspecting your code.
Read more about
pdb in the documentation. To quit the debugger, simply enter
q in the prompt.
There are a whole bunch of other magic commands, I just touched on a few of the ones you’ll use the most often. To learn more about them, here’s the list of all available magic commands.
You should consider installing Notebook Conda to help manage your environments. Run the following command:
conda install nb_conda
Then if you run the notebook server from a conda environment, you’ll also have access to the “Conda” tab shown below. Here you can manage your environments from within Jupyter. You can create new environments, install packages, update packages, export environments and more.
conda tab in Jupyter
nb_conda installed you will be able to access any of your conda environments when choosing a kernel. For example, the image below shows an example of creating a new notebook on a machine with several different conda environments:
conda environments in Jupyter
Create slideshows from notebooks is one of my favorite features. You can see an example of a slideshow here introducing pandas for working with data.
The slides are created in notebooks like normal, but you’ll need to designate which cells are slides and the type of slide the cell will be. In the menu bar, click View > Cell Toolbar > Slideshow to bring up the slide cell menu on each cell.
Turning on Slideshow toolbars for cells
This will show a menu dropdown on each cell that lets you choose how the cell shows up in the slideshow.
Choose slide type
Slides are full slides that you move through left to right. Sub-slides show up in the slideshow by pressing up or down. Fragments are hidden at first, then appear with a button press. You can skip cells in the slideshow with Skip and Notes leaves the cell as speaker notes.
To create the slideshow from the notebook file, you’ll need to use
jupyter nbconvert notebook.ipynb --to slides
This just converts the notebook to the necessary files for the slideshow, but you need to serve it with an HTTP server to actually see the presentation.
To convert it and immediately see it, use
jupyter nbconvert notebook.ipynb --to slides --post serve
This will open up the slideshow in your browser so you can present it.