text: '''
  Data sets often contain more than two continuous variables. The function sns.jointplot() is restricted to representing joint variation between only two quantities (i.e., two columns of a DataFrame). Visualizing multivariate relationships is trickier.
  
  The function sns.pairplot() constructs a grid of all joint plots pairwise from all pairs of (non-categorical) columns in a DataFrame. The syntax is very simple: sns.pairplot(df), where df is a DataFrame. The non-categorical columns are identified and the corresponding joint plots are plotted in a square grid of subplots. The diagonal of the subplot grid shows the univariate histograms of the individual columns.
  
  In this exercise, you will use a DataFrame auto comprising only three columns from the original auto-mpg data set.
  '''
  # Print the first 5 rows of the DataFrame
  print(auto.head())
  
  # Plot the pairwise joint distributions from the DataFrame 
  sns.pairplot(auto)
  
  # Display the plot
  plt.show()
  