univariate_plots.py

```python
from matplotlib.pyplot import figure, subplots, show, rc, rcdefaults
from numpy.random import default_rng
from numpy import linspace, atleast_2d
from pandas import DataFrame
from scipy.stats import gaussian_kde
from seaborn import stripplot, rugplot

rcdefaults()
rc('font', size=14)
rc('axes.spines', left=False, top=False, right=False)

rng = default_rng(0)

df = DataFrame({
    'A': rng.normal(40, 3,  size=300),
    'B': rng.normal(65, 5,  size=300),
    'C': rng.normal(50, 10, size=300),
})

fig = figure(figsize=(12, 8))
gs = fig.add_gridspec(2, 2, hspace=.1, top=0.8)

subfigs = {
    'unnecessarily complex': fig.add_subfigure(gs[0, 0]),
    'classic': fig.add_subfigure(gs[0, 1]),
    'indecisive': fig.add_subfigure(gs[1, 0]),
    'minimalist': fig.add_subfigure(gs[1, 1])
}

for i, col_name in enumerate(df.columns):
    ax = subfigs['unnecessarily complex'].add_subplot(1, len(df.columns), i+1)
    s = df[col_name]

    # histogram
    ax.hist(s, bins='auto', orientation='horizontal', ec='white', fc='tab:blue', alpha=.7, density=True)

    # violin
    kde = gaussian_kde(s)
    grid = linspace(s.min(), s.max())
    art_vio = ax.fill_betweenx(grid, -kde(grid), alpha=.7)

    # point/stick
    mean = s.mean()
    stdev = s.std()
    art_std, = ax.plot([0, 0], [mean - stdev, mean + stdev], color='tab:orange', lw=2)
    art_avg = ax.scatter([0], mean, fc='white', ec='tab:orange', linewidth=2, zorder=99)

    ax = subfigs['classic'].add_subplot(1, len(df.columns), i+1)
    ax.boxplot(s, notch=True)


    ax = subfigs['indecisive'].add_subplot(1, len(df.columns), i+1)
    stripplot(y=s, ax=ax, size=4, jitter=0.25, ec='white', alpha=.7)
    ax.set_ylabel('')
    ax.set_xlabel(col_name)

    ax.axhline(mean - stdev, .2, .8, color='tab:orange', zorder=6, lw=3)
    ax.axhline(mean + stdev, .2, .8, color='tab:orange', zorder=6, lw=3)
    ax.axhline(mean,  .2, .8, color='tab:orange', ls='--', zorder=6, lw=3)

    ax = subfigs['minimalist'].add_subplot(1, len(df.columns), i+1)
    ax.eventplot(s, orientation='vertical', alpha=.3)



for ax in fig.axes:
    ax.set_xticks([])

for title, sfig in subfigs.items():
    sfig.suptitle(title.title())

    for col_name, ax in zip(df.columns, sfig.axes):
        ax.set_xlabel(col_name)

    for ax in sfig.axes[1:]:
        ax.yaxis.set_visible(False)
    sfig.subplots_adjust(wspace=0)

fig.subfigs[0].text(
    0.5, 1, va='bottom', s='Which Univariate Plot Are You?', transform=fig.transFigure, ha='center', fontsize='xx-large'
)

from itertools import islice
for sfig in islice(subfigs.values(), 0, None, 3):
    sfig.set_facecolor('gainsboro')

fig.savefig('univariateplot.png', bbox_inches='tight')
show()
```

univariateplot.png