Multiple plot representations

Seaborn provides several functions that allow you to plot multiple different graphs at the same time.

Let’s start with jointplot(). With it, you can display a two-dimensional plot along with the corresponding one-dimensional plots on the axes. Here is an example using the penguins dataset:

g=sns.jointplot(data=data, x="bill_length_mm", y="bill_depth_mm",
 kind="kde", hue="species")

for species in data["species"].unique():
    subset = data[data["species"] == species]
    g.ax_joint.scatter(
        subset["bill_length_mm"],
        subset["bill_depth_mm"],
        s=subset["body_mass_g"]/500,
        label=species,
        alpha=0.7)

The for loop in the code allows the points to be displayed in addition to the plots. It is Matplotlib code.

g5 = sns.JointGrid(data=data, x="bill_length_mm", y="bill_depth_mm",
 hue="species")
g5.plot_joint(sns.scatterplot)
g5.plot_marginals(sns.kdeplot, fill=True)

plot_joint() controls the type of the bivariate plot, and plot_marginals() controls the univariate ones.

Another type of visualization is pairplot(). It allows you to display, within a single figure, plots for all pairwise combinations of variables around the diagonal, along with a distribution for each variable on the diagonal.

sns.pairplot(data=data, hue="species")

As with jointplot(), pairplot() has a complementary tool that allows for more control and customization: PairGrid(). With it, you can, for example, choose different types of plots above and below the diagonal.

g7 = sns.PairGrid(data, hue="species")
g7.map_upper(sns.histplot)
g7.map_lower(sns.kdeplot, fill=True)
g7.map_diag(sns.histplot, kde=True)

map_upper(), map_lower(), and map_diag() allow you to control the different types of plots in the figure.

Finally, we have FacetGrid(), which does not automatically create different plots within a figure but allows you to do so manually using col and row, and to modify plots individually within the figure. It is a function that is closer to Matplotlib:

tips = sns.load_dataset("tips")
g = sns.FacetGrid(tips,col="sex",row="time",margin_titles=True,
     despine=False,sharex=False)
g.figure.subplots_adjust(wspace=0.05, hspace=0.2)
for (row_val, col_val), ax in g.axes_dict.items():
     subset = tips[(tips["time"] == row_val) & (tips["sex"] == col_val)]
     if row_val == "Lunch" and col_val == "Female":
         subset = subset[subset["tip"] > 2]
         sns.scatterplot(data=subset,x="total_bill",y="tip",hue="smoker",
         ax=ax)
         ax.set_facecolor(".3")
         ax.set_xlim(0,20)
         ax.set_ylim(0,30)
         ax.set_xticks([0, 5, 15])
         ax.set_xlabel("tip>2")
         ax.grid(True, color="gray", linestyle="-", linewidth=0.5)
         ax.spines["top"].set_color("red")
         ax.spines["right"].set_color("red")
         ax.spines["bottom"].set_color("red")
         ax.spines["left"].set_color("red")
         ax.tick_params(axis="x", colors="blue")
     else:
         ax.set_facecolor((0, 0, 0, 0))
         sns.scatterplot(data=subset,x="total_bill",y="tip",hue="smoker",
         ax=ax)