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Introduction to Data Visualization with Seaborn

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

1 hidden cell

Take Notes

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

Add your notes here

# 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.
  • Make sure to add titles and labels to your plots and adjust their format for readability!
# Import Matplotlib, pandas, and Seaborn
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt



# Create a DataFrame from csv file
df = pd.read_csv(csv_filepath)


# 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()