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Recall that Seaborn's barplot will aggregate the mean as an estimation of central tendency; whereas the count plot will return a plot of individual observations.
Challenge
- For each Attribute:
- What is the mean Attack Points?
- What is the count of monsters?
Solution
sns.barplot(data=monsters, x='Attributes', y='Attack_Points')
sns.countplot(data=monsters, x='Attributes')
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For our final challenges, we will be
making some categorical estimation plots.
0:01
For each attribute, what is the mean or
average attack points?
0:07
And what is the count of monsters?
0:11
Hint, use sns bar plot and sns catplot.
0:13
Recall that Seaborn's bar
plot will aggregate the mean
0:19
as an estimation of central tendency,
0:22
whereas the count plot will return
a plot of individual observations.
0:25
Pause me and try it out.
0:30
Okay, how did it go?
0:33
Here are my solutions.
0:36
For the bar plot, sns.barplot,
0:37
data=monsters, x=attributes.
0:42
And y=attack points.
0:52
Beautiful.
0:59
And for our counts plot, sns.countplot.
1:03
Data=monsters.
1:07
And x=attributes.
1:10
Nice, great job analysts.
1:18
We're done with all of our challenges.
1:22
If you weren't able to complete all or
certain parts of these challenges,
1:25
that's totally okay.
1:29
Why not start over and try them again
without looking at my solutions.
1:31
And this concludes intro to Seaborn.
1:36
Thank you for
taking the time to complete my course.
1:39
I'm AJ and I'll catch you again next time!
1:42
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