Academic Performance by Class Format

import numpy as np
import matplotlib.pyplot as plt

np.random.seed(316)
BG_COLOR = '#ffffff'
TEXT_COLOR = '#1f2937'
COLORS = ['#F5276C', '#F5B027', '#4927F5']

def simple_beeswarm(y, nbins=None, width=1.):
    y = np.asarray(y)
    if nbins is None:
        nbins = np.ceil(len(y) / 6).astype(int)
    nn, ybins = np.histogram(y, bins=nbins)
    nmax = nn.max()
    x = np.zeros(len(y))
    ibs = []
    for ymin, ymax in zip(ybins[:-1], ybins[1:]):
        i = np.nonzero((y > ymin) * (y <= ymax))[0]
        ibs.append(i)
    dx = width / (nmax // 2) if nmax > 1 else width
    for i in ibs:
        yy = y[i]
        if len(i) > 1:
            j = len(i) % 2
            i = i[np.argsort(yy)]
            a = i[j::2]
            b = i[j+1::2]
            x[a] = (0.5 + j / 3 + np.arange(len(b))) * dx
            x[b] = (0.5 + j / 3 + np.arange(len(b))) * -dx
    return x

formats = ['In-Person', 'Online', 'Hybrid']
data = {
    'In-Person': np.random.normal(85, 8, 50),
    'Online': np.random.normal(78, 12, 55),
    'Hybrid': np.random.normal(82, 10, 48)
}

fig, ax = plt.subplots(figsize=(10, 6), facecolor=BG_COLOR)
ax.set_facecolor(BG_COLOR)

boxplot_data = []
for i, (fmt, values) in enumerate(data.items()):
    values = np.clip(values, 40, 100)
    x = simple_beeswarm(values, width=0.35)
    ax.scatter(x + i + 1, values, c=COLORS[i], alpha=0.7, s=50, edgecolors='white', linewidth=0.5)
    boxplot_data.append(values)

bp = ax.boxplot(boxplot_data, positions=range(1, len(formats)+1), widths=0.5, patch_artist=True)
for patch in bp['boxes']:
    patch.set_facecolor('none')
    patch.set_edgecolor('#9ca3af')
for element in ['whiskers', 'caps', 'medians']:
    for item in bp[element]:
        item.set_color('#9ca3af')

ax.set_xticks(range(1, len(formats)+1))
ax.set_xticklabels(formats, color=TEXT_COLOR)
ax.set_xlabel('Class Format', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_ylabel('Grade', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_title('Academic Performance by Format', fontsize=14, color=TEXT_COLOR, fontweight='bold', pad=15)

ax.tick_params(colors='#374151', labelsize=10)
for spine in ax.spines.values():
    spine.set_color('#e5e7eb')

plt.tight_layout()
plt.show()