<filename>7 - introduction to data visualization with python/seaborn/plotting distributions pairwise (1).py
'''
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()

<filename>7 - introduction to data visualization with python/seaborn/plotting distributions pairwise (1).py
'''
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()

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 # Plot the pairwise joint distributions from the DataFrame # Display the plot