Let's talk about the very last one.
The Jupiter Notebooks.
Now, this one is really, really interesting, and it's very popular with data scientists and machine
learning people because.
Well, let me show you, instead of explaining it, in order for us to use Jupiter notebooks, we can
actually go to Anaconda dot com.
No, this isn't a snake website.
This is a distribution, a platform that allows us to download a ton of tools that Python engineers
use.
And like I said before, a lot of data scientists, a lot of people who are working with machine learning
would download Anaconda because it comes with all these tools that we need to be productive instead
of downloading them one by one.
And here's the thing.
You can actually just download Python with Anaconda.
So if we didn't go to Python dot org initially and just went to Anaconda and clicked Download, well,
that was going to install Python for us as well.
But I wanted us to go through the official way of downloading it instead of Anaconda because we want
to understand how things work.
So in here all we have to do is click download and you'll be able to download the Anaconda distribution
and you can see that it's the most popular python in our data science platform.
And you can see that when you download Anaconda, it comes with a lot of things that we're going to
use throughout the course, such as Jupyter Notebooks.
It comes with different plug ins like NumPy or Boeke or pandas or even TensorFlow.
A lot of things that data scientists use.
Now, if you scroll down, it will detect whatever machine you're on so you can download based on your
version.
So if you're on a windows, you might have to click Windows and make sure you download version 3.7 or
higher.
And based on whatever computer you have, most of you will probably have 64 bit.
That's something dependent on your machine.
If you're on Linux again, you'll have different options as well.
Now.
You can click on download here or you can even click on documentation, read more and go to installation.
Here.
We'll have a step by step guide on how to install Anaconda.
Now I want to give you a warning.
You don't have to do this.
Optional number two step, which is to verify data integrity.
This is something that you do for security reasons to make sure that you're downloading the right package.
So that is completely optional.
You can skip it over and you can walk through the steps to make sure that you install it properly.
Now, one of the things that you'll get is this option, especially on Windows to add Anaconda to my
path environment variable, I recommend and they do as well to not click here because remember we've
installed Python initially already.
So by clicking that and adding it to the path, it's going to conflict with an older version of Python
that you may have installed a couple of lessons ago.
So make sure you follow this this guide.
And you see that Anaconda recently announced that they're going to include pie charm for Anaconda as
well.
So again, if in the previous video or you've already downloaded Pie Charm, you can skip over this
step as well.
So that's the thing with the windows with Mac.
Again, it's very self-explanatory.
You just click through the process install and everything will be done for you.
And if you're on a Linux machine, you will have to use your terminal and essentially have to run this
command that they'll give you in order to run Anaconda.
And remember, we want to use Anaconda three because we want to use Python version three.
Now, once you have installed Anaconda, I'm going to give you a warning.
It's a pretty big distribution.
It is quite big.
So if your computer doesn't have enough memory, it doesn't have enough storage.
There is another option, which is Mini Conda, which is a smaller version of Anaconda that doesn't
come with all these packages.
And most likely in this course you can get away with just running Mini Conda as an option.
Now let's say you've installed Anaconda.
You should have it in your applications.
Right here.
All you need to do is simply click on it and it's going to launch.
Anaconda for you.
Now, it might take a little while just to get everything set up.
But once it's on, look at that.
You have the Anaconda Navigator.
And you have a few things here that you might recognize such as VS Code.
You can actually install it through here.
You can use things like Spider, another id like picture.
You can also have things like our studio if you want to get into our programming and really focus on
data science.
But the one we're really interested in is the Jupyter Notebook.
Jupiter notebooks are extremely popular notebooks that if you're doing any sort of data science or machine
learning with Python, you're for sure using I love them.
And you'll see why in just a second.
Now, you might have to install this if it wasn't installed, but otherwise once installed you can just
click on launch and let's see what happens.
Did you see that?
It opened up my browser, my default browser, and it took me to local host eight, eight, eight,
eight.
Now, if this doesn't happen for you automatically, you might have to just type in manually into the
browser.
Local Host 888, eight.
But otherwise it's going to open up this for me, which right now if you look at it, we're in our desktop.
I can open my python files if I want to.
If I click on them, look at that.
I get my python files that I can edit and type things into.
But this isn't really why we want to use Jupyter Notebooks.
Instead, we want to create a new Jupyter Notebook.
I'm going to click on New and make sure that we select Python three.
Once I click on Python three.
We get a new Jupyter Notebook.
And this notebook, which is untitled, can well, we can just name it my first notebook.
I'm going to rename.
And check this out.
If I go to my desktop, I have some folders here.
I have a hidden folder, which is the check points folder, which is Jupyter Notebooks, keeping track
of my files.
And then I also have my first notebook Dot, I.P., Y and B.
This stands for I Python Notebooks, which is what Jupyter Notebooks used to be called.
As you notice, they are not that pi python files.
There's a special type of file, the Python notebook file, which can be opened with Jupyter notebooks.
Now, the reason we have a browser here is that Jupyter Notebooks needs a gooey or graphical user interface
for us to write this.
It's not a code editor where we have the code editor screens.
Instead, it's using the browser to allow us to run this program.
Even though it's using the browser, though, you're able to run this offline.
So if you're on an airplane, you can still run your Jupyter notebooks and practice coding.
Now, the way Jupyter Notebook works is using these cells.
So these cells I can print or I can write different code in Python.
So if I do this, I can click run and look at that right away.
It gives me the output and now I have a second cell where I can say, let's say four plus five.
If I run this, look at that.
I run a different cell with a different output.
Now, instead of us always constantly running run, we can also just use the shortcut shift and enter
to actually run the code.
So let's say for example, we're doing is five greater than four.
I can run, shift, enter and it gives me the answer right away.
Now the beauty here is that it's almost like creating notes for yourself, right?
I'm able to run different cells, see different outputs, get immediate response to my code instead
of in a code editor or an ID where I had to write everything in one file and everything ran out at once.
And as we get into machine learning and data science and data visualization, we'll actually see that
we use Jupyter Notebooks because they're so easy to visualize these results.
Now, one other thing with Jupyter Notebooks that is really, really nice is this idea of the notebook
where instead of just always having code, which you can see over here, if I click on the dropdown
menu and click markdown, I can now type text like, this is my first python or Jupyter Notebook.
And I can write different sort of notes here.
And once done I can do shift, enter and look at that.
I have my notes in this book so that maybe as you're learning for the rest of the course, you can have
different Jupyter notebooks to take notes, run code, verify it, and well be extremely organized.
And you can even post this on GitHub and share them with other community members.
And all we have to do now is, well, it automatically saves.
So you can see over here that any time we open Jupyter notebooks, let's actually do that.
I'm going to close this.
Close this as well.
Yes.
Let's quit.
I'm going to open Jupyter notebooks one more time.
By clicking on Anaconda.
Its initializing takes a bit of time, but eventually.
I can launch my Jupyter Notebook again.
It's going to open up my Jupiter Notebook on local host.
You'll see over here that the local host has changed to eight, eight, eight, nine.
So that may happen if, for example, local host eight, eight, eight is already taken.
But if I click here on the Jupyter Notebook, remember the Python notebook I go back to?
My Jupyter Notebook with all my texts, all my files.
How cool is that?
And by the way, going back to this local host, the reason that this is actually still taken is, well,
if you look over here, I have two terminal windows because I technically didn't close the session or
the server from last time.
So because I did that Anaconda automatically said, Oh yeah.
Local host eight, eight, eight, eight is already taken.
So let's just increase the number to eight, eight, eight, nine.
So you want to make sure that you close these once you are done.
And when I do that, it tells me connection failed because we've stopped the server.
It no longer works.
So if I type something here.
You see that?
I get a star and it's not doing anything.
If I do four plus five, again, it's not running because the server is no longer running.
All right.
Let's take a break and I'll see you in the next one.