Explore customer product purchase dataset

You have explored the customer by product purchase dataset in the video exercise and are now ready to plot some exploratory charts to understand the distribution of the variables and relationships between them. Here, you will explore the wholesale dataset and plot the pairwise relationships as well as the estimated distributions for each variable with the pairplot function from the seaborn library. It's an important step to explore the distribution types, and the relationships between the variables to inform the need for further data preprocessing.

The pandas library is loaded as pd, seaborn as sns, and matplotlib.pyplot as plt. Also, the wholesale dataset has been loaded as a pandas DataFrame.

This exercise is part of the course

Machine Learning for Marketing in Python

View Course

Exercise instructions

Print the header of the wholesale dataset
Plot the pairwise relationships between the variables
Display the chart.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Print the header of the `wholesale` dataset
print(___.head())

# Plot the pairwise relationships between the variables
sns.pairplot(___, diag_kind='kde')

# Display the chart
plt.___()

Edit and Run Code