Hi, and welcome to our fifth module where we will explore a Jupyter Next
Generation interface called Jupyter Lab
to introduce this module. Uh, it'll be not so long like the previous one, uh,
because most of the things that you have learned in the previous module will
work the same way. But in addition,
the Jupyter Lab provides you more flexibility and extended functionality,
which will simplify your work. We'll make some, uh, small interaction.
We'll learn how to organize our workspace in Jupyter Lab.
We'll check the differences, uh,
between classic Notebook interface and Jupyter Lab.
Uh, we'll learn how to change, uh, settings,
how to manage extensions. We will try collaborative editing,
Jupyter Lab debugger. And during all of this,
we will make a small introduction to classification problem, uh,
using logistic regression and the random forest.
In this video, we will learn how to organize Jupyter Lab, uh, workspace.
Okay, now let's go to our Jupyter Lab.
Okay, now we see the Jupyter Lab and it's ui. Uh,
one of the biggest advantages of Jupyter Lab is that you can manage everything
in one tap. Uh, this is a big advantage, um,
but might be also a disadvantage in some cases, uh,
because it uses more JavaScript. And when you have, uh,
big notebooks with a load of j script included like plotly charts or printed
a lot of other elements like charts, data frames,
maybe also other elements from some other extensions,
uh, then working with the notebook might be quite slow. When this happens,
I prefer to switch to classic notebooks interface. So what you can do
now, we are in the landing page, we see the launcher. Uh,
we can start uh, new notebook,
we can open new notebooks,
we can launch, uh, new terminals or open new files
and we can organize it, uh, but is best for us.
We just drag and drop, uh, the window with the terminal, for example.
And now we have it at the bottom.
What is new and v i frequently use is the console,
which can be attached to a notebook.
And you can execute code and access all of the objects of the notebook,
print data frames, or plot data. For example,
when you right click on the notebook, click new console for the notebook
and we can arrange it here to the bottom.
What I like sometimes also is the contextual help with Ctrl I.
You'll see now here very quickly the documentation of some classes or
objects or methods.
What I like also is the lock console.
When we go to the view tab, show log console
here, you'll be able to see all of the outputs, uh, what is in that,
in the notebook as well.
I run this notebook in the module five folder. For example,
I have to set it this way to the back and we can run it now
and we see all the outputs.
We can save this workspace. Go to file,
save current workspace,
and next time you'll load the Jupyter app. Uh,
you can directly open this workspace and continue very left off. In
our next video, we will continue exploring the Jupyter App ui.