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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.

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

### 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.
``````# Import Matplotlib, pandas, and Seaborn
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Create a DataFrame from csv file

# Create a count plot with "Spiders" on the x-axis
sns.countplot( data = df, x = "Spiders")

# Display the plot
plt.show()``````
``````# Import Matplotlib and Seaborn
import matplotlib.pyplot as plt
import seaborn as sns

# Change the legend order in the scatter plot
sns.scatterplot(x="absences", y="G3",
data=student_data,
hue="location", hue_order = ["Rural", "Urban"])

# Show plot
plt.show()``````
``````# Import Matplotlib and Seaborn
import matplotlib.pyplot as plt
import seaborn as sns

# Create a dictionary mapping subgroup values to colors
palette_colors = {"Rural": "green", "Urban": "blue"}

# Create a count plot of school with location subgroups

sns.countplot(data = student_data, x = "school" , hue = "location", palette = palette_colors)

# Display plot
plt.show()``````