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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.

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

  • 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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