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Graphs and Charts
4:32Jupyter Notebooks have an awesome feature where they can render charts and graphs from packages like Bokeh and Matplotlib.
Now before I can do anything
with charts and graphs,
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I need to get a couple
of things installed.
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Luckily, we can do this even
inside of our notebook.
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So I'm gonna add a new cell and
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I'm gonna type !pip3 install numpy and
bokeh.
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Now if you have Python 3 installed
to where you can just do pip,
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then you want to go ahead and
do that as well.
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I'm gonna go ahead and
execute that cell, and
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l can see up here that
this is marked as busy.
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And l can see output showing up
in here in this little star here.
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All of these things tell
me that it's busy, and
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what's cool is if you
hover over this thing,
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it'll tell you whether or not the kernel
is idle or whether the kernel is busy.
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If the kernel was still busy,
it would say that it was busy.
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So right now it's installing NumPy and
Bokeh, or it was.
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NumPy is a package for working with
numerical data, for data science.
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And Bokeh is one for
generating interactive charts.
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Now, once this all wraps up,
we can add our next block of code, or
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I could change this one.
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I'm gonna add a new one though,
just to keep that bit of history in there.
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So I've got a block of code,
you can check the teacher's notes for it.
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I'm gonna copy and
paste it from my notes, though.
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So copy that, and
then I'm going to come over here and
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paste that in, all right.
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So it's not super important
what all's going on here, but
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this is using NumPy and
some widgets to generate a sine wave.
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So I'm gonna go ahead and I'm gonna
execute this with control and enter.
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And I don't get any interesting feedback,
I just get this function main update.
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Now the reason that I get that is
because I haven't enabled JavaScript
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inside of our notebook.
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You have to tell Jupyter notebooks that
you're okay with it executing JavaScript
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inside the notebook.
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Now I can't turn this feature on
from inside the notebook itself.
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So I'm gonna hop over to my terminal,
I'm going to stop the notebook server.
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And then I'm going to run the code that
I need to do so that I can execute this.
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So I stopped the server by
pressing Control and C.
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On some systems you may have to do
Control and D or Control and Z.
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Just play with it, you'll find
which one works for your system.
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So now I need to enable the extension,
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the extension that I want to
enable is the widgets extension.
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And we want it to be enable for Python.
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So we're gonna type the following
command into the terminal.
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So here's the command,
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it is jupyter nbextension enable ==py
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==sys-prefix widgetsnbextension.
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So nbextension enable --py
--sys-prefix widgetsnbextension.
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This is in the teacher's notes,
as well, if you just wanna copy and
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paste it out of there.
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You'll see that it's validated and
it's okay.
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And then we're going to run
Jupyter notebook again.
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And when we do, it will again open up,
yeah go ahead and leave.
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>> It'll open up our thing and
we will open up our notebook.
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And then, it looks like I'm gonna
need to paste that code back in.
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And then I will run it one more time,
control Enter.
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And down below now,
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you will see that we have a wonderful
little thing here where we have waves.
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And if we were to change the values,
it will change the wave right away.
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And we can change which wave type it
is whether it's a sine or cos or a tan.
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And we can just kinda play
around with this stuff.
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And then what's cool is we have
over here tools for panning and
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zooming an stuff like that as well.
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So it's pretty cool, we have a bunch
of widgets and we have a wave, and
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if we change the values
we get a different wave.
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And it's all right here in the notebook,
we don't have to use any extra programs or
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anything like that to see what's going on.
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It's really neat, you can do a lot more
with extensions that what I've shown you.
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There are others available online and
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with them you can do things like use
Python's turtle module to draw graphics.
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Explore other data visualization
libraries and even use other languages.
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We'll explore the other languages
a tiny bit in the next video.
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But know that there's a lot more out
there for you to explore and discover.
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If you'd like to share your
notebooks with friends and
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coworkers, check the teacher's notes for
ways to do that.
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It can be as easy as putting
the notebook on GitHub or
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Azure, using Project Jupyter's
official notebook viewer.
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Or you can export the notebook to HTML and
host it on your own server or
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another service that'll
host static websites.
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Again, check the teacher's notes for
more information.
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All right, let's see how they use Jupyter
notebooks with more than just Python.
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I'm gonna have to take
some notes here myself.
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