Python Seaborn Categorization Plots | How to Plot Categorical Data in Seaborn | Data Visualization

In this video, you’ll learn about creating categorical plots using Seaborn. Seaborn is essential to Machine Learning! It allows you to create advanced plots to visualize your data. In this video, you’ll learn how to create several different types of plots including count plots, bar plots, strip plots, swarm plots, and box plots. In additional, you’ll learn to utilize hue() for specificity. After watching this video and learning several Seaborn commands/plots, there’s an activity right below this description for you to try out on your own using the skills you learned here. Enjoy!

SEABORN CATEGORICAL PLOTS ACTIVITY:

Import libraries and load one Seaborn in-built dataset of your choice. Create a strip plot and a swarm plot, compare these two plots, and utilize hue to make the plots even more specific and effective for analysis.

TIMESTAMP:

00:00 Introduction & Recap of Last Video
00:17 Lesson Structure for Seaborn Categorical Plots
00:29 Import libraries/dataset + set up for first plot
01:05 Create countplot()
01:39 Create barplot()
02:35 Create stipplot()
03:36 Customize stripplot()
04:55 Create swarmplot()
05:27 Create boxplot()
06:06 Utilize hue()
06:25 Recap of Current Video
06:55 Call-to-action: Seaborn Categorical Plots

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