Now you've learned about Jupyter notebook and pie charm, and this one was, of course, best to use
for Jupyter notebook files.
And this one was best to use for a native Python files.
And now I want to show you a program that can deal with both of them and the program that I have been
using in the majority of my time when I'm coding in Python.
And this is called B.S. Coat.
So once again, in this version of an excellent navigator, it's already installed.
If not, then you just have to Google for the code.
And you can download a free version.
So here it's already installed.
So I click on launch and it runs this.
Yeah, a visual studio code.
So, yeah, it recognizes that my PC is German, so it asks me if I want to install a German package,
which I don't want to do.
I want to stay in English.
Then here opens up file with the release notes of the newest version because I have just installed the
program.
We don't need this.
We don't need the settings.
And here is some file that I've been using previously already.
So let me close this.
And let me also get rid of this folder, which we don't need.
And what I'm going to do now is I'm going to open the two files that we have created so far.
So here I'm going to open up the our first notebook, a Python file.
So this didn't work.
Let's try again.
Yeah.
So you have to drag and drop the Python file in the larger window here.
So I trust the authors, of course, because I am the author, so I open this.
And here we go.
We have now ourselves from a python.
And you can just run these so you can, for example, click on this play button here, or you can click
on Shift Enter.
And this will automatically open up such a selector here where you can use your Python version that
you want to use.
So as you can see on my PC, I have also other installations of Python.
For example, I had previously installed a version, Anaconda, that was using three point eight, and
also I downloaded a version of Python from the Windows App Store.
But we are going to use this one that is selected and that's suggested.
And now it's connecting to the kernel.
Takes a bit, and now it has successfully run this cell.
So here you see the result.
And what I also like is it gives us this take that it worked and also the time that it took to calculate
this.
And then, of course, we can click Shift Enter.
And yeah, it's really the same thing as previously.
The only thing that's not working here is this image, but I don't think that is too so bad.
So you see the AI Python files, they worked.
Let's delete this or not delete.
But let's close it, and let's open up the other file that we have just created.
This time, let's open up the other file that we have created.
I go to poach on projects where we have created this test project folder, and I drag and drop the main
file where you recognize the three lines of code that we have written previously.
So here in this visual studio code, I think this is really nicely configured configurator because you
can have this nice color highlighting, for example, if you select the AIV and it will select all the
other apps as well.
If you hover over it, it tells you it's a variable pay.
And if you have our over print, it gives you the syntax of the print command in Python.
So really, it's a nice thing.
And also you see that this interface looks different compared to the case where we had opened the Python
file and I just click here on run.
And what happens now is it opens up a terminal, which in my case is to Windows PowerShell.
For you, this can be a different shell, doesn't really matter that much.
So it gives when Microsoft copy because it's two Windows PowerShell, some stuff in German, which is
not important.
And it's just a typical command line, just a shell where I'm currently in this directory for whatever
reason.
I don't know.
And what we have done by clicking play is we have told it to run this file and I have done this already,
previously, once.
So this is why you have it twice here and the output is just trough, as we had in the previous cases.
So it just gives us the print command off five times OnePlus seven, which is 12.
So summarizing this lecture, we have explored the capabilities of Visual Studio Code, and I really
like it because it can deal with native Python files as well as Jupyter notebooks.
It has a nice color highlighting, and it just looks nice in general, which is why I'm using it personally
a lot.
However, for the majority of the course, I will stick to Jupiter Notebook because it's the most simple
thing and everybody can use it very easily and you just have to install Anaconda and then you're ready
to go.