Hi,
and welcome to our fourth module where we will start finally to work with the
notebook. To introduce our module,
we will learn how to work with cells and the kernel.
We will learn how to write Latinx equations and how to write markdown.
We'll execute r and Python in same notebook.
We'll explore options, how to execute the notebooks and how to export them.
We'll also write a simple script, uh,
where we will parameterize the notebook.
We'll learn how to debug Jupyter notebooks and how to embed
interactive features into them.
We'll create an interactive dashboard with Walla Library
and we will learn how to share our notebooks.
All of these will be done on a real data set where we will do a simple
linear regression model,
and we will make a slight introduction to libraries like Pandas,
Plotly, sticky Learn, and Seaborn.
In this video we will explore first the main panel and the toolbar.
Now let's go to our EPO folder and to our fourth module.
We see here a lot of files which we will need, uh, throughout this module.
I also created example notebook, which you can explore and which has, uh,
all of the content which we will create step-by-step. This is this one.
During this module,
we will install a couple of packages and we will create a new Anaconda
environment as well.
If you mess something up and you will need to recreate your container.
Here is a best script which you can run to perform all the
steps, which we have done so far in previous modules, and, uh,
which we will do in this module as well.
So we have the same state of the container, like packages, extensions and so on.
We can check it briefly.
We see that these steps we have done in our previous module and
these remaining steps we'll do in this module.
To run this script, use this command be and the file name.
I will not run this script now because we'll perform the
steps, uh, in this module one by one.
Now let's open, uh, this notebook, module four I and B.
Okay, it's opened.
Now let's explore the menu bar.
In the file tab,
we are able to perform all the necessary operations with the notebook. Uh,
like creating the notebook, open notebook,
we can making a copy of the notebook, saving the notebook.
We can rename it.
We can also reword the notebook to specific checkpoint
and what is interesting,
we can download the notebook in a specific format like P D
F or H T M L. For example,
in the edit tab,
we can do all of the operations with cells like cutting them,
copying the cells, fasting or delete the cells.
We can split or merge the cells.
We can move the cells through the notebook.
We can also edit notebook, metadata,
and we can also work with cells attachments
in the view tab.
We can toggle heater or toolbar to have more space or line numbers.
We can also turn on or off cell toolbar.
We will work with the slideshow toolbar.
We can insert the cells above or below current cell.
We can run one cell or multiple cells,
or we can run a whole notebook or we can execute just the parts of
the notebook. We can change the cell type.
We'll work just with two cell types, uh, the code and markdown cell.
And we can also toggle or clear the outputs of current
cell, or we can do the same for whole notebook.
In the kernel tap. We can make all the operations with the kernel,
like stopping the kernel, restart the kernel. We can,
uh, clear output after restarting the kernel,
or we can run the whole notebook. After restarting the kernel.
We can also change the kernel in
navigate up. We see basically the same as in a table of contents.
Uh, but the table of contents has the advantage that it is embedable
into P D F files or H T M L files.
Do not worry about widget tab. Uh,
we will explain it more when we will work with the Jupyter Widgets and
the health tab.
Here you can find the references to most used, uh,
Python libraries in Jupyter.
And also we can edit or see the keyboard shortcuts.
We can see here that the keyboard shortcuts are for comm out mode or
or edit mode. What is the difference
when you edit the cell, you are in the edit mode. You can see it also here.
Now, when you press escape, you are in the command mode.
So now I can, for example, invoke the popup for, uh,
the shortcuts with H key,
or I can invoke the popup for a command palette with P
or in the command mode,
I can basically work with the cell or kernel,
like, uh, copy the cell plastic dimens on. We will see that later.
Here we see a button thrust it. What does it mean?
The button trusted or not trusted, is for security reasons.
And basically when you open some notebook, which was not created by you,
Jupyter by default marks it as not trusted.
That means it'll not execute any JavaScript code in the notebook
because it can contain some malicious code.
When you click to trust the notebook,
JavaScript in the notebook will be executed. When you, for example,
open a notebook from another user, which has plotly charts in it,
you will see blank space. It is because of this,
but when you click to trust the notebook, the charts will be generated normally.
And here we have information about our running kernel.
We see that we are currently using the epic kernel.
This is the kernel from the base environment,
and here is the status of the kernel.
It can be busy or idle. Now it's idle.
Now I will execute this cell
and we will see the kernel is now busy.
Regarding notebook toolbar, this is this one with the buttons.
It basically contains the most used, uh,
commands to work with the notebook.
In our next video, we will introduce the dataset.