Hello guys, so I hope you are now comfortable with Google Colab and I hope you have explored the entire
IDE.
Now in this video I'm going to talk about one more very amazing IDE where it is not at all dependable
on your system.
Let it be your laptop or your desktop and I have personally used it.
It is quite amazing.
That is nothing but GitHub code space.
So in this video I will be talking about GitHub code space and how you can get started with GitHub code
space.
Okay, so first of all, when you once you search for GitHub code space.
So here you will be getting your first link which is called as code spaces.
Okay.
Now inside this first of all you need to login.
You can login with your GitHub account.
Okay I already have my GitHub account.
I hope everybody has their own GitHub account.
So first of all just go ahead and create your GitHub account.
So go to github.com and just go ahead and log in.
I have already logged in over here.
You can either do a GitHub login or you can also do Google login okay.
Now what exactly is this GitHub code space?
It is nothing, but it is a secure development mode made simple GitHub Codespace gets you up and coding
faster with fully configured secured cloud development environment.
Native to GitHub.
Okay, so this is quite amazing because the entire VS code feeling you'll be able to get in the cloud.
And the best thing is that this is running in the GitHub cloud itself.
Okay.
So let it be, uh, you know, Node.js, Python or any other programming languages that you really want
to work with.
You can directly work over here.
Okay.
And the best thing will be also that you will be also able to collaborate if you have a paid version
of this particular code space.
Again, I'm not promoting anything because I'm going to show you the free version of this particular
code space.
So first of all, just go ahead and click on Get Started for free.
So here you can see once you click on this here you'll be getting a various uh templates okay.
To start with.
So here you have blank by GitHub.
You have react by GitHub.
You have Jupiter notebook by GitHub.
Or you have dotnet by GitHub.
Okay.
So let's say you want to go ahead and use blank GitHub.
So I'll just go ahead and first of all show you with blank GitHub.
Then we'll also show with Jupyter Notebook, because both this you can specifically use in order to
execute the code or Python or anything that is related to data structures and algorithms with Python
that is given in this course.
So here first of all click on use this template.
So once you click on use this template you'll be able to see that you'll be getting a feeling of entire
VS code that is set up.
Okay.
So this is how a VS code looks like.
Okay this is in my local.
So I just want to show you in the cloud also.
So this is how your entire GitHub vs code will get selected.
Okay.
Then you can go ahead and select a theme.
Let's say that I'm going to select this specific theme.
Uh this looks quite bright so this will be more than sufficient.
Okay so here you can see in the left hand side you can probably create any number of files that you
really want to work with.
In the right hand side you can see uh you have this.
And in here also you have something called as terminal.
Okay.
Now what you really need to do is that first of all, initially to start with, you know, you can also
go ahead and zoom in.
Okay.
So just press control Plus and you will be able to see the screen very much clearly.
Okay.
Perfect.
So till here.
I hope everybody is clear.
Right.
And this is nothing but this is your VS code ID itself in the cloud, which is GitHub code space here.
The best thing is that you don't even have to create an environment or don't have to do anything as
such.
Okay.
Let's say by default I think this provides you Python 3.12.
Okay.
If you really want to go ahead and start with, let's say I go ahead and create my app dot Pi okay.
And let's say I also want to go ahead and create my Jupyter notebook file.
So I'll go ahead and write test dot ipynb.
Okay.
So for Jupyter Notebook we basically write Ipynb.
And for a.py file we specifically use for this okay Python.
We'll be talking more about it as we go ahead.
But I just want to show you these two kinds of files you can basically create over here.
And you can also create a YAML file or any kind of files that you really want to work with.
Okay.
Now after I have created these two files what I will do I will also go ahead and select or I'll just
go ahead and open my terminal.
Okay.
Now once I go into my terminal, let's say that I want to use one of the library.
Okay I will go ahead and write pip install if I want to install any library, I can go ahead and write
like this.
So let's say that I want to install pandas.
Okay, so once I write pip install pandas here you can see that hey, my requirement is already satisfied.
This looks absolutely perfect.
Okay, now let me do one thing.
Let me just go ahead and run my code.
Print one plus one okay.
So once I write, print one plus one.
And if I want to execute this particular file okay, first of all I'll clear my terminal.
Okay.
There are two ways of running this specific file okay.
So first of all I will just go ahead and write Python app.py okay.
So once I write python app.py since I'm printing one plus one.
So this is basically getting printed okay.
And this is so simple right.
You just need to already go ahead and write app.py print one plus one automatically.
This things will get printed.
Okay this looks absolutely easy.
Let's say I want to go ahead and print one more code.
I can go ahead and write print two plus three right.
And let's say if I'm going to also save this, if I go ahead and write Python app.py again I'm getting
two comma five.
Right.
So this becomes very simple to probably run any code in the py file.
In order to execute it.
I just need to call Python and call that particular file name that is app.py.
Automatically this should run okay.
Now this was with respect to any execution.
You will be seeing that most of our executions that we are going to probably do right in the VS code.
Specifically, we will be using either this.py file or we will be using Ipynb.
And the best thing about this will be that this is entirely running in the cloud, not in your local
laptop.
So even though you don't have a very fast system or laptop or a desktop, you can directly go ahead
and execute your code in the cloud itself.
Now let's go to the towards this test dot Ipynb.
Now what I'm actually going to do over here is that I will just go ahead and execute one plus one.
And here you can see that it is saying install or enable suggested extension.
I will go ahead and install that.
Okay.
So here you can see installing Jupyter extension and install Python extension.
It is basically taking place.
So this will probably take some time.
Once this is getting installed, we can go ahead and execute this.
Now installing Python extension is also happening.
You can see in the corner if I hide my face okay, now it is done I will go ahead and click on Python
environment and I will select the environment.
You can also go ahead and create a new environment if you want.
Okay.
But I'm just going to select the environment that is given by this okay.
So here you can see connecting to kernel Python.
So once I execute this it should get executed.
Then again you can go ahead and write print two plus three.
Execute it and all.
It is up to you right.
So any code you can either write it in Ipynb or you can also write it in uh app dot Pi.
Okay.
Now let's say that, uh, after probably creating this particular file, you also want to download this
file.
Okay.
So for downloading it you just need to do right click and click on download.
So automatically this file will get downloaded over here.
It can be app dot pi.
It can be text dot ipy and beyond.
All right.
So this is the most easiest way of probably starting any integrated development environment because
it has almost each and every thing.
So you can probably go ahead and install your libraries that you really want.
Let's say you want to install any new libraries you can specifically install over here and do multiple
things right.
And you can create any number of files.
You can create folders, you know, and we will be also starting in that specific way.
But uh, in my entire course I have specifically used VS code.
Okay.
Uh, so VS code will be quite amazing to do the coding itself, uh, because in many companies also
we specifically use VS code, and from that we try to commit into the repository and do multiple things.
Okay.
But yes, uh, if you have some system configuration issues and all, you can also use code spaces,
okay.
As per your, uh, criteria or whatever things you really want to execute it.
Okay.
So yeah, this was it from my side.
I hope you like this particular video.
I'll see you all in the next video.
Thank you.
And in the next video we are going to install VSCode and Anaconda.
And I'll also show you that how you can probably create your environments, how to manage packages and
many more things.
So yes, this was it.
I'll see you in the next video.
Thank you.