Skip to content

Intermediate 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!

  • Use lmplot() to look at the relationship between temp and total_rentals from bike_share. Plot two regression lines for working and non-working days (workingday).
  • Create a heat map from daily_show to see how the types of guests (Group) have changed yearly.
  • Explore the variables from insurance and their relationship by creating pairwise plots and experimenting with different variables and types of plots. Additionally, you can use color to segment visually for region.
  • Make sure to add titles and labels to your plots and adjust their format for readability!
# import all modules
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Read in the DataFrame
df = pd.read_csv(grant_file)

# Display pandas histogram
df['Award_Amount'].plot.hist()
plt.show()

# Clear out the pandas histogram
plt.clf()


# Display a Seaborn displot
sns.displot(df['Award_Amount'])
plt.show()

# Clear the displot
plt.clf()

# Create a displot of the Award Amount
sns.displot(df['Award_Amount'],
             kind='kde',
             rug=True,
             fill=True)

# Plot the results
plt.show()

# Create a regression plot of premiums vs. insurance_losses
sns.regplot(data=df, x='insurance_losses', y= 'premiums')



# Display the plot
plt.show()

# Create an lmplot of premiums vs. insurance_losses
sns.lmplot(data=df, x='insurance_losses', y='premiums')



# Display the second plot
plt.show()

# Create a regression plot using hue
sns.lmplot(data=df,
           x="insurance_losses",
           y="premiums",
           hue="Region")

# Show the results
plt.show()

# Create a regression plot with multiple rows
sns.lmplot(data=df,
           x="insurance_losses",
           y="premiums",
           row="Region")

# Show the plot
plt.show()