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).
Correlation
The seaborn library allows to draw a correlation matrix through the pairplot() function. The parameters to create the example graphs are:
data: dataframekind: 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 datamarkers: a list of marker shapespalette: set of colors for mapping the hue variableplot_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()
Distribution
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()
