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Using Jupyter Notebooks, let's start with an exploration of setting up and getting started with matplotlib.
Update
Installing matplotlib has changed slightly since this video was recorded. Use these instructions in your first cell instead:
import sys
!{sys.executable} -m pip install --upgrade pip
!{sys.executable} -m pip install msgpack
!{sys.executable} -m pip install matplotlib
%matplotlib inline
Jupyter Notebook Information and Keyboard Shortcuts
- Jupyter Notebooks
- Control (CTRL) -Enter will run the code in the selected cell
- CTRL-S will save and checkpoint
Matplotlib Resources
- Matplotlib color options
- Matplotlib linestyles
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Let's start our journey by installing
matplotlib in a Jupyter Notebook.
0:00
Fortunately, that is relatively
straightforward by using pip.
0:04
Let's kick off jupyter notebooks.
0:08
If you aren't familiar
with Jupyter Notebooks,
0:15
check out the teacher's notes for
more information.
0:17
We'll start a new notebook here in
the matplotlib directory I created for
0:20
this course.
0:23
And we'll install our matplotlib package.
0:29
The syntax is slightly different here.
0:32
We import sys,
0:34
Pip install matplotlib.
0:44
And then we can run our cell.
0:48
Just make sure in notebooks that you are
running the pip version associated with
0:52
the current Python kernel and
can be used in the current notebook.
0:57
I already had matplotlib
installed in this environment.
1:01
Now that we have the matplot
library installed,
1:04
we want to import the pyplot module, which
allows us to quickly generate our plots.
1:06
It’s convention to import this as plt.
1:12
Now, we can pass in a list of matplotlib.
1:26
It'll assume that it's
a sequence of y values and
1:30
will auto generate the x values for us.
1:32
We wanted to show some output, right?
1:35
For that, we can use the show method.
1:37
So, plt.plot([1, 2, 3, 4])
1:40
And then, plt.show and run our cell.
1:47
Matplotlib generates our plot and
shows it below our notebook work space.
1:56
It provides a simple line graph
with a default blue solid line.
2:01
But wait a moment,
why does our x-range end at 3?
2:05
Recall that Python ranges start at 0.
2:09
So our x data here is 0, 1, 2, 3.
2:11
We can pass in an equal length argument
and have an x and y pairing as well.
2:15
Let's also add a label to our
y-axis here to get used to
2:20
adding labels to our charts.
2:25
So we'll add in our x-values 1, 4, 9,
2:27
16, plt.ylabel.
2:31
These are very important figures.
2:40
And run our cell, again.
2:46
Now we see a line with our x and
y pairings.
2:48
There are additional
parameters we can pass or
2:50
plot method in addition
to our xy pair values.
2:52
Some of which allow for the customization
of the color and style of the plot.
2:56
Let's turn the line green.
3:03
And the linestyle,
let's do dashdot and run our cell.
3:08
The color argument accepts HTML
color names and code values,
3:16
a tuple of RGB values, grey scale values,
and shortcode values.
3:20
I've included a link in
the teacher's notes for
3:26
complete list of color options as
well as linestyle possibilities.
3:28
What if we want to display multiple
pieces of data on the same plot?
3:32
We can just add another plot
method to our PLT object and
3:36
it will display both data series.
3:40
Let's do 2, 3, 4, 5,
very creative here, and 2, 3, 4, 5.
3:47
Let's make this one hex value 2B5B84.
3:56
And we'll do a linestyle of dashed.
4:05
And run our cell.
4:11
As you might imagine, having multiple data
series on same plot can get confusing.
4:14
Let's see if we can add some
clarity in our next video.
4:19
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