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09 - Introduction to Data Visualization with Seaborn

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

1 hidden cell

Introduction to Seaborn

Making a scatter plot with lists

# Import Matplotlib and Seaborn
import matplotlib.pyplot as plt
import seaborn as sns

# Change this scatter plot to have percent literate on the y-axis
sns.scatterplot(x=gdp, y=phones)

# Show plot
plt.show()

# Change this scatter plot to have percent literate on the y-axis
sns.scatterplot(x=gdp, y=percent_literate)

Making a count plot with a list

# Import Matplotlib and Seaborn
import matplotlib.pyplot as plt
import seaborn as sns

# Create count plot with region on the y-axis
sns.countplot(y=region)

# Show plot
plt.show()

"Tidy" vs. "untidy" data

# Import pandas
import pandas as pd

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

# Print the head of df
print(df.head())

Making a count plot with a DataFrame

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

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

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

# Display the plot
plt.show()

Hue and scatter plots

# Import Matplotlib and Seaborn
import matplotlib.pyplot as plt
import seaborn as sns

# Create a scatter plot of absences vs. final grade
sns.scatterplot(x='absences', y='G3', data=student_data, hue='location')

# Show plot
plt.show()

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

Hue and count plots

# 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(x='school',
                data=student_data,
                hue='location',
                palette=palette_colors)

# Display plot
plt.show()
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