Graphs and Charts

4:32 with Kenneth Love

Jupyter Notebooks have an awesome feature where they can render charts and graphs from packages like Bokeh and Matplotlib.

Teacher's Notes
Video Transcript
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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.

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

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