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Jupyter Notebooks have an awesome feature where they can render charts and graphs from packages like Bokeh and Matplotlib.
Update
If you experience an AttributeError stating unexpected attribute 'plot_height' to figure when running the code below, you may have to change plot_height on line 11 to height and change plot_width to width.
Code Snippet
Here's the code snippet from the video:
from ipywidgets import interact
import numpy as np
from bokeh.io import push_notebook, show, output_notebook
from bokeh.plotting import figure
output_notebook()
x = np.linspace(0, 2*np.pi, 2000)
y = np.sin(x)
p = figure(title='simple line example', plot_height=300, plot_width=600, y_range=(-5,5))
r = p.line(x, y, color='#2222aa', line_width=3)
def update(f, w=1, A=1, phi=0):
if f == "sin": func = np.sin
elif f == "cos": func = np.cos
elif f == "tan": func = np.tan
r.data_source.data['y'] = A * func(w * x + phi)
push_notebook()
show(p, notebook_handle=True)
interact(update, f=["sin", "cos", "tan"], w=(0,100), A=(1,5), phi=(0, 20, 0.1))
You'll also need to run jupyter nbextension enable --py --sys-prefix widgetsnbextension in your terminal.
Sharing your notebooks
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Now before I can do anything
with charts and graphs,
0:00
I need to get a couple
of things installed.
0:02
Luckily, we can do this even
inside of our notebook.
0:04
So I'm gonna add a new cell and
0:07
I'm gonna type !pip3 install numpy and
bokeh.
0:10
Now if you have Python 3 installed
to where you can just do pip,
0:16
then you want to go ahead and
do that as well.
0:19
I'm gonna go ahead and
execute that cell, and
0:22
l can see up here that
this is marked as busy.
0:24
And l can see output showing up
in here in this little star here.
0:27
All of these things tell
me that it's busy, and
0:30
what's cool is if you
hover over this thing,
0:33
it'll tell you whether or not the kernel
is idle or whether the kernel is busy.
0:35
If the kernel was still busy,
it would say that it was busy.
0:38
So right now it's installing NumPy and
Bokeh, or it was.
0:42
NumPy is a package for working with
numerical data, for data science.
0:46
And Bokeh is one for
generating interactive charts.
0:50
Now, once this all wraps up,
we can add our next block of code, or
0:52
I could change this one.
0:57
I'm gonna add a new one though,
just to keep that bit of history in there.
0:58
So I've got a block of code,
you can check the teacher's notes for it.
1:02
I'm gonna copy and
paste it from my notes, though.
1:06
So copy that, and
then I'm going to come over here and
1:11
paste that in, all right.
1:16
So it's not super important
what all's going on here, but
1:19
this is using NumPy and
some widgets to generate a sine wave.
1:22
So I'm gonna go ahead and I'm gonna
execute this with control and enter.
1:27
And I don't get any interesting feedback,
I just get this function main update.
1:32
Now the reason that I get that is
because I haven't enabled JavaScript
1:38
inside of our notebook.
1:41
You have to tell Jupyter notebooks that
you're okay with it executing JavaScript
1:43
inside the notebook.
1:47
Now I can't turn this feature on
from inside the notebook itself.
1:48
So I'm gonna hop over to my terminal,
I'm going to stop the notebook server.
1:52
And then I'm going to run the code that
I need to do so that I can execute this.
1:59
So I stopped the server by
pressing Control and C.
2:04
On some systems you may have to do
Control and D or Control and Z.
2:07
Just play with it, you'll find
which one works for your system.
2:11
So now I need to enable the extension,
2:14
the extension that I want to
enable is the widgets extension.
2:16
And we want it to be enable for Python.
2:19
So we're gonna type the following
command into the terminal.
2:20
So here's the command,
2:23
it is jupyter nbextension enable ==py
2:26
==sys-prefix widgetsnbextension.
2:32
So nbextension enable --py
--sys-prefix widgetsnbextension.
2:40
This is in the teacher's notes,
as well, if you just wanna copy and
2:46
paste it out of there.
2:49
You'll see that it's validated and
it's okay.
2:51
And then we're going to run
Jupyter notebook again.
2:53
And when we do, it will again open up,
yeah go ahead and leave.
2:57
>> It'll open up our thing and
we will open up our notebook.
3:00
And then, it looks like I'm gonna
need to paste that code back in.
3:06
And then I will run it one more time,
control Enter.
3:11
And down below now,
3:15
you will see that we have a wonderful
little thing here where we have waves.
3:16
And if we were to change the values,
it will change the wave right away.
3:21
And we can change which wave type it
is whether it's a sine or cos or a tan.
3:28
And we can just kinda play
around with this stuff.
3:33
And then what's cool is we have
over here tools for panning and
3:36
zooming an stuff like that as well.
3:38
So it's pretty cool, we have a bunch
of widgets and we have a wave, and
3:40
if we change the values
we get a different wave.
3:43
And it's all right here in the notebook,
we don't have to use any extra programs or
3:45
anything like that to see what's going on.
3:48
It's really neat, you can do a lot more
with extensions that what I've shown you.
3:50
There are others available online and
3:54
with them you can do things like use
Python's turtle module to draw graphics.
3:56
Explore other data visualization
libraries and even use other languages.
3:59
We'll explore the other languages
a tiny bit in the next video.
4:03
But know that there's a lot more out
there for you to explore and discover.
4:06
If you'd like to share your
notebooks with friends and
4:10
coworkers, check the teacher's notes for
ways to do that.
4:11
It can be as easy as putting
the notebook on GitHub or
4:13
Azure, using Project Jupyter's
official notebook viewer.
4:16
Or you can export the notebook to HTML and
host it on your own server or
4:19
another service that'll
host static websites.
4:22
Again, check the teacher's notes for
more information.
4:24
All right, let's see how they use Jupyter
notebooks with more than just Python.
4:27
I'm gonna have to take
some notes here myself.
4:30
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