x̄ - > Multi-panel data visualization

 import numpy as np

import matplotlib.pyplot as plt

from matplotlib.gridspec import GridSpec

np.random.seed(7)

months = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct']

vals = np.array([38,44,49,57,61,68,74,79,86,92])

line = np.cumsum(np.random.normal(0, 1, 60)) + 20

x = np.linspace(0, 10, 90)

y = 0.6 * x + np.random.normal(0, 0.35, 90) + 2

sizes = np.random.randint(20, 180, 90)

heat = np.random.randint(0, 100, (8, 12))

fig = plt.figure(figsize=(14, 7), dpi=200, facecolor='#f5f1ea')

gs = GridSpec(3, 4, figure=fig, wspace=0.35, hspace=0.35)

# 1. Horizontal bar chart

ax1 = fig.add_subplot(gs[0:2, 0])

ax1.barh(months[::-1], vals[::-1], color='#4d89c7')

ax1.set_title('Keywords per month', fontsize=12)

ax1.tick_params(labelsize=8)

ax1.grid(axis='x', alpha=0.2)

# 2. Scatter cluster view

ax2 = fig.add_subplot(gs[0, 1:3])

ax2.scatter(

    x, y,

    c=np.linspace(0, 1, 90),

    s=sizes,

    cmap='tab20',

    alpha=0.85,

    edgecolor='white',

    linewidth=0.4

)

ax2.set_title('Scatter cluster view', fontsize=12)

ax2.tick_params(labelsize=8)

ax2.grid(alpha=0.15)

# 3. Bubble cluster

ax3 = fig.add_subplot(gs[0:2, 3])

cols = np.random.choice(

    ['#f28e2b', '#59a14f', '#e15759', '#4e79a7', '#b07aa1'],

    120

)

ax3.scatter(

    np.random.randn(120),

    np.random.randn(120),

    s=np.random.randint(20, 300, 120),

    c=cols,

    alpha=0.7

)

ax3.set_title('Bubble cluster', fontsize=12)

ax3.tick_params(labelsize=8)

ax3.grid(alpha=0.15)

# 4. Line chart

ax4 = fig.add_subplot(gs[1, 1:3])

ax4.plot(line, color='#2a6f97', lw=1.6)

ax4.set_title('Aggregated trend over time', fontsize=12)

ax4.tick_params(labelsize=8)

ax4.grid(alpha=0.2)

# 5. Weekday scatter

ax5 = fig.add_subplot(gs[2, 0:2])

ax5.scatter(

    np.repeat(range(7), 20),

    np.random.rand(140),

    s=6,

    alpha=0.45,

    color='#4878cf'

)

ax5.set_title('Keywords per weekday', fontsize=12)

ax5.tick_params(labelsize=8)

ax5.grid(alpha=0.15)

# 6. Heatmap

ax6 = fig.add_subplot(gs[2, 2:4])

ax6.imshow(heat, cmap='RdYlGn', aspect='auto')

ax6.set_title('Heatmap summary', fontsize=12)

ax6.tick_params(labelsize=8)

# remove spines for a clean dashboard style

for ax in [ax1, ax2, ax3, ax4, ax5, ax6]:

    for spine in ax.spines.values():

        spine.set_visible(False)

plt.show()