1 00:00:00,370 --> 00:00:04,997 There are two types of categorical distribution plots that we've 2 00:00:04,997 --> 00:00:07,562 learned: boxplots and violin plots. 3 00:00:07,562 --> 00:00:10,940 In this challenge, let's make some boxplots. 4 00:00:10,940 --> 00:00:12,804 For each attribute, 5 00:00:12,804 --> 00:00:17,353 what is the distribution of the attack points and defense points? 6 00:00:17,353 --> 00:00:21,484 Hint: use sns.boxplot. 7 00:00:21,484 --> 00:00:25,855 Remember that you can adjust the readability of the attributes 8 00:00:25,855 --> 00:00:28,751 labels by plotting them along the y axis. 9 00:00:28,751 --> 00:00:30,823 Pause me and try it out. 10 00:00:30,823 --> 00:00:35,063 And when you're done, I'll catch you on the other side. 11 00:00:35,063 --> 00:00:36,731 How did it go? 12 00:00:36,731 --> 00:00:45,706 Here are my solutions, sns.boxplot(data=monsters, 13 00:00:45,706 --> 00:00:50,373 x='Attack_Points', and 14 00:00:50,373 --> 00:00:54,147 y='attributes'). 15 00:01:00,829 --> 00:01:06,495 And for defense points, I'll copy and paste this cell and 16 00:01:06,495 --> 00:01:10,618 change my x axis label to Defense_Points. 17 00:01:13,006 --> 00:01:13,820 Nice. 18 00:01:15,406 --> 00:01:18,353 We are nearly done with all of our challenges. 19 00:01:18,353 --> 00:01:19,895 Great job. 20 00:01:19,895 --> 00:01:24,382 In the next video, we will go over categorical estimation plots. 21 00:01:24,382 --> 00:01:25,330 I'll catch you there!