Dual histogram + boxplot with KDE for univariate analysis + Mean & Median Lines

dual_plot.py

# import libs
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

# this function will create a boxplot + histogram plot using Seaborn's Jointgrid
# we'll also provide Type annotations to provide hints to future users
def dual_plot(series: pd.Series, figsize: tuple = (16,8),
              bins: int = None, return_plot: bool = False,
              color: str = "pastel", xlim: tuple = None, tick_m: int = None,
              add_lbl: str = None) -> None:
    '''
    Parameters:
        series: the feature to plot,
        figsize: the size of the plot,
        bins: interval size for data points to be contained in. aka. classes, buckets,
        return_plot: Return the plot if true, otherwise just show the plot using `Matplotlib.pyplot.show()`,
        color: seaborn color palette to use for the plot,
        xlim: right-left limits of the x axis,
        tick_m: show more ticks in specific multiples,
        add_lbl: additional text to append to the x-label
    Returns:
        Returns plot if `return_plot` is true, otherwise just show the plot using `Matplotlib.pyplot.show()`
    '''
    # set the color palette
    sns.set_palette(color)
    # create the JointGrid object
    dual_plot = sns.JointGrid(x=series,height=figsize[1])
    # add the seaborn histplot
    dual_plot.plot_joint(sns.histplot, kde=True)
    # get ax_joint for joint plot so we can 'do things' with it
    ax_joint = dual_plot.ax_joint
    # set the seaborn plot title with some padding
    ax_joint.set_title(label=series.name, fontdict={'fontsize': 20}, pad=(16))
    # add to x-label if necessary
    if add_lbl:
        ax_joint.set_xlabel(ax_joint.get_xlabel() + ' ' + add_lbl)
    # set limit of x-axis
    ax_joint.set_xlim(xlim)
    # show more ticks - useful for integers
    if tick_m:
        x_ticks = np.unique([(tick_m*round(i/5)) for i in sorted(series.unique())])
        ax_joint.set_xticks(x_ticks)

    # obtain the individual axes to add our mean and median lines for
    # central tendency visualization
    for axis in (dual_plot.ax_joint, dual_plot.ax_marg_x):
        axis.axvline(np.mean(series), color='r', linestyle='-')
        axis.axvline(np.median(series), color='b', linestyle='--')

    # add the boxplot
    dual_plot.plot_marginals(sns.boxplot)
    return dual_plot if return_plot else plt.show()

dual_plot(series=data['Income'],color='colorblind',add_lbl="(in thousands - USD)")

plot.png