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Hey, in this video

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I'm going to share with
you the Jupyter Notebook.

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The Jupyter notebook is
a great way to get started

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with Python on the
Coursera platform.

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It's a great way to do
data science and other

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more advanced Python aspects
on the platform too.

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So let's dive in and take a look.

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So when you log into
the Jupyter platform

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you'll be greeted with a
screen that looks like this.

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Jupyter is really built around
this notion of code cells.

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So here I have one cell.

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Now there's a full
Python interpreter

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running in the
background behind this.

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So I can do things
like create variables.

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So here I'll just
say x equals 10.

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Then we'll just print x.

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You see there is no output until

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you actually go to run the cell.

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But when you run the cell

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the interpreter returns
you the result.

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You also get a little number

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indicating how many
cells have been run.

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So you can see in
this example I played

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10 practice cells before
actually showing this.

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Now after it runs,

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it's not like the application
is finished running.

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The kernel is still running
in an interpreter mode

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and we can continue to
send queries to it.

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So if we wanted to say x plus x,

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and we wanted to print that,

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you can see that prints
out as we expected.

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If you wanted to, you can
stop the interpreter.

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So if you feel that the state is

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confusing or you just want
a fresh Python instance.

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It's actually pretty
easy to do that.

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You go to kernel and
you say restart.

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Often I use restart and
clear output as a way that

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reminds me that the interpreter

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is actually been restarted.

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You can cut, copy and paste
cells and move them around.

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So you could do
those normal kinds of things.

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The Jupyter notebook
has a special feature,

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it actually has a number of them.

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But one is that if the result of

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your last statement
was an object.

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There were some value returned

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but you don't do
anything with that,

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it then automatically tries
to print that to screen.

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Good example might be this.

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So if we say x equals 10
and then just the value x.

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Then we actually run that.

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We'll actually see the output.

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So you'll often see in some of

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the videos people just leaving
the value towards the end.

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You can individually run cells.

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So for i in range 10.

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So if we want to
have a loop print i.

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We could actually just
not run this yet.

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We can say down here,

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wait what was the value
of i and just run that

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individual cell and i is
not actually defined.

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If we then decide we
want to run that.

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Okay, there's a bunch of i's.

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Now run this cell again.

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We could do that.

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So you can see that we can have

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a non-linear editing format.

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That can be a little tricky
actually because you can do

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things like change
the value in one cell.

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But then you know this seems to

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suggest that i should be nine,

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but then when we run it we

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actually see that it's minus one.

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So you can sometimes get
this irregular state,

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or at least it feels irregular.

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It's actually the interpreter,

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the way that you've run the cells

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in order determines
the interpreter state.

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You can often see that if
you look at the cell number

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here so six is greater than

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five but it's greater than three.

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So you can see that we're running

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some things in a different order.

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One of the benefits of

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the Jupyter platform
is that you can

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adjust text in here as well.

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So let's say I want to describe.

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This is a great
example of a loop.

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Let's say I want this section
to even be called loops.

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So you can change the format
of the cell here,

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and there's a number
of different formats.

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You'll mostly use code

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and then something
called markdown.

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So when you change it to
markdown you'll note that the in

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out goes away because

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it won't be sent to
the interpreter.

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But when you run the cell it'll

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actually run it in
a data format called markdown.

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So, like the pound sign here

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or a double pound sign
means give me a title.

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So when you actually run that
you get a nice bit of text.

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So you can actually mark
up your code execution

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in a way that's a lot
like a textbook might be.

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You could create a whole
textbook in this environment.

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So those are the main features
of the Jupyter notebook.

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There are a bunch
of other options.

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So one that I often use is-
we'll restart and run all.

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So if I want to run
all of the values in

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a notebook I want to just see
the whole execution trace.

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You can do that.
So in this case it

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would- will do that here.

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You'll see that it ran
through the loop and then I

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print it out i and it's
set up our markdown.

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That's a pretty common method.

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If it runs into an error
it will pause execution.

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So that's important, because

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sometimes in a lecture video
you might see we

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intentionally put an error in

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there for a teachable moment.

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Under the view there is
some options as well.

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Line numbers is one
that I will often turn

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on and that will number
our cells as well.

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Each Jupyter notebook has

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a title for it and this
determines the filename.

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So I'll just call this Demo.

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If you click on
the Jupyter logo you'll

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be taken to what's called
the Tree interface

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which is just a
directory interface of

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the files that you
have for this project.

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Within a given course on Coursera

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or a specialization
or a degree on

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Coursera you may
have any number of

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different Jupyter systems with

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different files spaces
mounted on them.

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I also really like to be able to

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download my Jupyter Notebooks.

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So sometimes I
download them as HTML,

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to show them to people
or as a PDF version.

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So that can be quite
helpful as well.

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So that's a quick tour
of the Jupyter notebook.

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Now most of the work you'll
be doing in this course,

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this specialization,
or this degree

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will be in the Jupyter notebook.

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It's a great
environment for doing

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Python and there's ways to create

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assignments and
submit assignments

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directly to auto-graders
from within the platform.

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I hope that we'll be
able to share some of

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our research tools as well
on educational technology.

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They are built into
the Jupyter notebook.

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But we'll see how that goes.

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All right. We'll see
you in the course.