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You have completed Introduction to Data Visualization with Matplotlib!
You have completed Introduction to Data Visualization with Matplotlib!
Preview
Let's see how easy matplotlib makes it to save our charts for publication to the web and other publication formats.
Further Reading
- Matplotlib figure() method
- Matplotlib backend
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So far we've just been displaying
our charts to the screen.
0:00
As I mentioned earlier in the course,
0:03
one of the Matplotlib's strengths is
producing the publication ready images.
0:05
Let's see how we can save some
visualizations of our Iris data, so
0:09
we can share our results with others.
0:13
Let's use our box plot code here as
an example to generate our files.
0:15
To save files, we need to utilize matplotlib's
figure object and save fig method.
0:19
Like I mentioned, we assigned a
variable, fig, to plt.figure.
0:26
We come in here, plt.figure.
0:31
And then down here at the bottom.
0:39
savefig, and
we pass in a file name for our chart.
0:45
We'll call it petal_length_boxplot.png.
0:50
We can run this out.
0:58
This is pretty cool.
1:08
We can see if we go over here
into our folder, and there it is.
1:10
Our petal_length_boxplot.png
file was created, now,
1:15
in this example, we generated the PNG
file which is great for websites.
1:19
Let's jump back over to our notebook and
see what other file types are supported.
1:23
Let's do a new cell.
1:30
So we import matplotlib.pyplot as plt.
1:33
Send our figure object, and
1:46
then if we print out
canvas.get_supported_filetypes.
1:50
And run our cell.
2:00
As you can see, there's support for
a lot of different file types or
2:04
in matplotlib language hard copy or
non-interactive back ends.
2:08
These handle most printed or
static display needs.
2:13
While it is beyond the scope of this
course, there are also user interface or
2:17
interactive back end options too.
2:22
I'll put a link in the teacher's notes for
resources on those.
2:25
This approach for
2:28
finding supported file types works
outside of jupyter notebooks too.
2:29
In case you want to use matplotlib's
power in a different environment.
2:32
We've covered a bunch
of stuff in this stage.
2:37
We explored our Iris Dataset
in several ways,
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used several different visualization
tools and techniques, and
2:42
learned how to leverage matplotlib
to find patterns in data.
2:46
Data visualization is a fundamental
aspect of many job roles today.
2:49
For both data experts and
non-experts alike.
2:54
You're well on your way to
communicating information and
2:57
generating useful visualizations.
2:59
When we get back together,
let's move on to a different data set and
3:03
answer some specific business decision
questions with our new data viz skills.
3:06
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