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# Introduction to Data Visualization with Seaborn

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## .mfe-app-workspace-kj242g{position:absolute;top:-8px;}.mfe-app-workspace-11ezf91{display:inline-block;}.mfe-app-workspace-11ezf91:hover .Anchor__copyLink{visibility:visible;}Introduction to Data Visualization with Seaborn

Run the hidden code cell below to import the data used in this course.

### Take Notes

Add notes about the concepts you've learned and code cells with code you want to keep.

Add your notes here

```.mfe-app-workspace-qcdhrn{font-size:13px;line-height:1.5384615384615385;font-family:JetBrainsMonoNL,Menlo,Monaco,'Courier New',monospace;}```# Add your code snippets here

when assigning a plot to a variable, typical documentation uses "g". example:
g= sns.lineplot()

type(g) --> would tell you this is an axes subplot

FacetGrids are objects that contain one or more axes subplots
relplot() and catplot() support subplots because they are FacetGrids, while scatterplot and countplot are axessuplots
``````

### Explore Datasets

Use the DataFrames imported in the first cell to explore the data and practice your skills!

• From `country_data`, create a scatter plot to look at the relationship between GDP and Literacy. Use color to segment the data points by region.
• Use `mpg` to create a line plot with `model_year` on the x-axis and `weight` on the y-axis. Create differentiating lines for each country of origin (`origin`).
• Create a box plot from `student_data` to explore the relationship between the number of failures (`failures`) and the average final grade (`G3`).
• Create a bar plot from `survey` to compare how `Loneliness` differs across values for `Internet usage`. Format it to have two subplots for gender.
• Make sure to add titles and labels to your plots and adjust their format for readability!