Interactive Dashboard

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Learning Objectives

After completing this recipe, you will be able to:

Understand the difference between static and interactive charts
Grasp dashboard layout concepts

Note: This Cookbook is a static build site, so interactive elements are replaced with screenshots or static examples.

0. Setup


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
 
# Synthetic data for dashboard
df = pd.DataFrame({
    'Metric': ['Revenue', 'Cost', 'Profit'],
    'Value': [1000, 600, 400]
})

1. Dashboard KPI Card Example

Visualize the most commonly used KPI card format in dashboards.


fig, axes = plt.subplots(1, 3, figsize=(15, 4))
colors = ['blue', 'red', 'green']
 
for i, (metric, value, color) in enumerate(zip(df['Metric'], df['Value'], colors)):
    ax = axes[i]
    ax.text(0.5, 0.7, metric, ha='center', va='center', fontsize=20, color='gray')
    ax.text(0.5, 0.4, f"${value}", ha='center', va='center', fontsize=30, fontweight='bold', color=color)
    ax.axis('off')
    ax.set_title(f"KPI: {metric}")
    # Add border
    rect = plt.Rectangle((0,0), 1, 1, fill=False, color='gray', lw=2, transform=ax.transAxes)
    ax.add_patch(rect)
 
plt.suptitle('Dashboard KPI Overview')
plt.show()

Dashboard KPI

2. Composite Chart Layout

Implement a dashboard feel by arranging multiple charts.


fig = plt.figure(figsize=(12, 8))
gs = fig.add_gridspec(2, 2)
 
# Top Left: Bar
ax1 = fig.add_subplot(gs[0, 0])
ax1.bar(['A', 'B', 'C'], [10, 20, 15], color='skyblue')
ax1.set_title('Sales by Category')
 
# Top Right: Line
ax2 = fig.add_subplot(gs[0, 1])
ax2.plot([1, 2, 3, 4, 5], [10, 12, 15, 14, 18], 'r-o')
ax2.set_title('Trend')
 
# Bottom: Scatter matches width
ax3 = fig.add_subplot(gs[1, :])
ax3.scatter(np.random.randn(50), np.random.randn(50), c=np.random.randn(50))
ax3.set_title('Correlation Distribution')
 
plt.tight_layout()
plt.show()

Dashboard Layout