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Make a histogram to find out the distribution of Attack Points. Then use a kernel density estimation plot to find the distribution of Defense Points as a probability curve.
Challenge
- What is the distribution of:
- Attack Points?
- Defense Points?
Solution
sns.histplot(data=monsters, x='Attack_Points', kde=True)
sns.kdeplot(data=monsters, x='Defense_Points', kde=True)
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In our last challenge,
we explored the relationship between two
0:00
quantitative variables,
level and attack points.
0:04
It would be nice to know the distribution
of some variables with the histogram and
0:09
a kernel density estimation.
0:13
So what is the distribution of
attack points and defense points?
0:16
Here's some hints,
use sns.histplot and sns.kdeplot.
0:21
Make a histogram to find out
the distribution of attack points.
0:29
You can also plot a kde curve over
the histogram by setting the kde keyword
0:33
argument to true.
0:38
Then use the kernel density estimation
plot to find the distribution of
0:39
defense points as a probability curve.
0:44
Pause me and try it out.
0:47
How did it go?
0:51
Here's how I solved these problems.
0:53
For the histogram sns.histplot,
0:55
(data = monsters,
1:07
x= 'Attack_Points',
1:10
and kde = True).
1:15
Nice, for our histogram of attack
points it looks like most monsters
1:18
have attack points distributed
between 750 and 1500.
1:24
There are 10 observations between 750 and
1000 and
1:29
10 observations between 1250 and 1500.
1:35
That's about half of
the monsters in our collection.
1:40
Now for the defense points,
1:46
sns.histplot (data = monsters,
1:50
x = 'Defense_Points',
1:55
And kde = true).
2:04
Nice, for our defense points
distribution it looks like most
2:09
monsters have defense points
distributed between 1000 and
2:14
2000 with 10 observations at 1000 and
2:19
9 observations between 1500 and 2000.
2:24
Awesome work so far, analysts.
2:28
We're done with quantitative analysis.
2:30
In the next challenges,
2:34
we will move on to explore our
categorical variable attribute.
2:36
I'll catch you there.
2:41
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