Environmental Sensor Network

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

np.random.seed(321)

n = 170
zones = np.random.choice(['Urban', 'Suburban', 'Rural'], n)
data = {
    'PM2.5': np.where(zones == 'Urban', np.random.normal(45, 15, n),
                     np.where(zones == 'Suburban', np.random.normal(25, 8, n), np.random.normal(12, 4, n))),
    'NO2': np.where(zones == 'Urban', np.random.normal(40, 12, n),
                   np.where(zones == 'Suburban', np.random.normal(20, 6, n), np.random.normal(8, 3, n))),
    'O3': np.where(zones == 'Rural', np.random.normal(50, 10, n),
                  np.where(zones == 'Suburban', np.random.normal(40, 8, n), np.random.normal(30, 10, n))),
    'CO': np.random.normal(0.8, 0.3, n) + np.random.uniform(0, 0.5, n),
    'Zone': zones
}
df = pd.DataFrame(data)

plt.style.use('dark_background')
sns.set_style("darkgrid", {'axes.facecolor': '#0a0a0f', 'figure.facecolor': '#0a0a0f', 'grid.color': '#333333'})

palette = {'Urban': '#F54927', 'Suburban': '#F5B027', 'Rural': '#27F5B0'}

g = sns.pairplot(
    df,
    hue='Zone',
    palette=palette,
    height=2,
    kind='scatter',
    diag_kind='kde',
    markers=['o', 's', '^'],
    plot_kws={'alpha': 0.7, 's': 50, 'edgecolor': 'white', 'linewidths': 0.4},
    diag_kws={'alpha': 0.5, 'linewidth': 2.5, 'fill': True}
)

g.fig.set_facecolor('#0a0a0f')
for ax in g.axes.flat:
    if ax:
        ax.set_facecolor('#0a0a0f')
        for spine in ax.spines.values():
            spine.set_color('#333333')
        ax.tick_params(colors='#888888')
        ax.xaxis.label.set_color('white')
        ax.yaxis.label.set_color('white')

g.fig.suptitle('Air Quality Monitoring Network', fontsize=14, fontweight='bold', color='white', y=1.02)
plt.setp(g._legend.get_title(), color='white')
plt.setp(g._legend.get_texts(), color='white')

plt.tight_layout()
plt.show()