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Custom Correlogram with Seaborn

The previous post shows how to make a basic correlogram with seaborn. This post aims to explain how to improve it. It is divided in 2 parts: how to custom the correlation observation (for each pair of numeric variable), and how to custom the distribution (diagonal of the matrix).

The seaborn library allows to draw a correlation matrix through the pairplot() function. The parameters to create the example graphs are:

• data : dataframe
• kind : kind of plot to make (possible kinds are ‘scatter’, ‘kde’, ‘hist’, ‘reg’)

# library & dataset
import matplotlib.pyplot as plt
import seaborn as sns
df = sns.load_dataset('iris')
 
# with regression
sns.pairplot(df, kind="reg")
plt.show()
 
# without regression
sns.pairplot(df, kind="scatter")
plt.show()

In a graph drawn by pairplot() function of seaborn, you can control the marker features, colors and data groups by using additional parameters such as:

• hue : variables that define subsets of the data
• markers : a list of marker shapes
• palette : set of colors for mapping the hue variable
• plot_kws : a dictionary of keyword arguments to modificate the plot

# library & dataset
import matplotlib.pyplot as plt
import seaborn as sns
df = sns.load_dataset('iris')
 
# left
sns.pairplot(df, kind="scatter", hue="species", markers=["o", "s", "D"], palette="Set2")
plt.show()
 
# right: you can give other arguments with plot_kws.
sns.pairplot(df, kind="scatter", hue="species", plot_kws=dict(s=80, edgecolor="white", linewidth=2.5))
plt.show()

As you can select the kind of plot to make in pairplot() function, you can also select the kind of plot for the diagonal subplots.

• diag_kind : the kind of plot for the diagonal subplots (possible kinds are ‘auto’, ‘hist’, ‘kde’, None)

Note that you can use bw_adjust to increase or decrease the amount of smoothing.

# library & dataset
import seaborn as sns
import matplotlib.pyplot as plt
df = sns.load_dataset('iris')
 
# Density
sns.pairplot(df, diag_kind="kde")
 
# Histogram
sns.pairplot(df, diag_kind="hist")
 
# You can custom it as a density plot or histogram so see the related sections
sns.pairplot(df, diag_kind="kde", diag_kws=dict(shade=True, bw_adjust=.05, vertical=False) )

plt.show()

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