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Intro to Seaborn

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Challenge: Bar and Count Plots

1:46 with AJ Tran

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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