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Intro to Seaborn

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Challenge: Strip Plot

3:29 with AJ Tran

Now that we've completed making plots to analyze our numerical values, let's move on to explore how Attribute, a categorical variable, is related to certain quantitative variables.

Challenge

  • For each Attribute, what is the relationship between Attribute and:
    • Attack Points?
    • Defense Points?

Hint: use sns.stripplot and sns.swarmplot.

Solution

sns.stripplot(data=monsters, x='Attributes', y='Attack_Points')


sns.catplot(kind='strip', data=monsters, x='Attributes', y='Attack_Points', aspect=1.5)


sns.stripplot(data=monsters, x='Attack_Points', y='Attributes')


sns.swarmplot(data=monsters, x='Attack_Points', y='Attributes')

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    Now that we've completed making plots to analyze our numerical values, 0:00
    let's move on to explore how attribute, a categorical variable, 0:05
    is related to certain quantitative variables. 0:10
    For each attribute, what is the relationship between attribute and 0:14
    attack points, and attribute and defense points? 0:19
    Here's a hint, use sns.stripplot and sns.swarm plot. 0:23
    We haven't used swarm plot in our previous practices yet. 0:30
    So if you need help figuring it out, 0:33
    check the method signature documentation by using the Shift Tab keyboard command. 0:35
    Or reference the seaborn documentation. 0:41
    A swarm plot is very similar to a stripplot, 0:45
    but it plots out individual observations more distinctly. 0:48
    Pause me and try it out. 0:52
    And then I'll catch you on the other side. 0:54
    How did the challenge go? 0:58
    Here's how I did it. 1:00
    For a stripplot, sns.stripplot 1:03
    (data=monsters, x= Attribute, 1:09
    and y=Attack_Points. 1:15
    Whoops, I've got a value error. 1:25
    It says that it could not interpret the input Attribute. 1:27
    Let's make sure I spelled that right. 1:34
    So I'm gonna scroll all the way up. 1:36
    And it's called Attributes not Attribute. 1:40
    Just a little typo, I can fix that by adding an s. 1:44
    There we go. 1:50
    So there's another way to make these plots so 1:52
    that the categorical labels are more readable. 1:55
    In this plot, they are spaced out pretty well. 1:58
    But I can switch the x and y-axis labels around. 2:02
    sns.stripplot (data = monsters and 2:08
    x = Attack_Points. 2:14
    And y = Attributes. 2:24
    So it's up to your personal preference of what you think is more readable. 2:34
    If you like the categorical attributes on the x-axis or 2:38
    on the y-axis, it's up to you. 2:43
    And I can do the same thing with the swarm plot method. 2:47
    sns.swarmplot (data=monsters, 2:51
    for this one, let's plot our attributes on the y-axis. 2:59
    So for the x-axis, we'll say Defense_Points and y= Attributes. 3:04
    Nice, great job making these categorical scatter plots. 3:19
    In the next challenge, we'll talk about categorical distribution plots. 3:24

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