Employee Count vs Revenue Per Employee

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

np.random.seed(420)
BG_COLOR = '#ffffff'
TEXT_COLOR = '#1f2937'

n = 60
employees = np.random.uniform(10, 500, n)
rev_per_emp = 300 - 0.3 * employees + np.random.normal(0, 40, n)
rev_per_emp = np.clip(rev_per_emp, 50, 400)

df = pd.DataFrame({'Employees': employees, 'Revenue/Employee ($K)': rev_per_emp})

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

sns.regplot(
    data=df,
    x='Employees',
    y='Revenue/Employee ($K)',
    scatter_kws={'color': '#F5B027', 'alpha': 0.6, 's': 55, 'edgecolor': 'white', 'linewidths': 0.5},
    line_kws={'color': '#5314E6', 'linewidth': 2.5},
    ci=95,
    ax=ax
)

for collection in ax.collections[1:]:
    collection.set_facecolor('#5314E6')
    collection.set_alpha(0.15)

ax.axhline(200, color='#22c55e', linestyle='--', alpha=0.5, linewidth=1)
ax.text(480, 205, 'Target', color='#22c55e', fontsize=9, ha='right')

corr = np.corrcoef(employees, rev_per_emp)[0, 1]
ax.text(0.05, 0.05, 'r = %.3f' % corr, transform=ax.transAxes, fontsize=12,
        color='#5314E6', fontweight='bold', va='bottom',
        bbox=dict(boxstyle='round,pad=0.3', facecolor='#f8fafc', edgecolor='#e5e7eb'))

ax.set_xlabel('Number of Employees', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_ylabel('Revenue per Employee ($K)', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_title('Company Size vs Efficiency', 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()