table-visualization-seaborn.py

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

# Data for the table (updated with your data)
data = {
    'Customer Segment': ['Consumer', 'Corporate', 'Home Office'],
    'Historical Expected': [5000.0, 3000.0, 2000.0],
    'Market Research Expected': [4000.0, 3500.0, 2500.0],
    'Business Goals Expected': [5000.0, 3000.0, 2000.0]
}

# Create a DataFrame
df = pd.DataFrame(data)

# Set the style
sns.set(style="whitegrid")

# Create a heatmap with only the numeric data
plt.figure(figsize=(8, 4))  # Adjust the size as needed
heatmap = sns.heatmap(df.iloc[:, 1:], annot=True, fmt='g', cmap='coolwarm', cbar=False, linewidths=0.5)

# Customize the plot
heatmap.set_xticklabels(df.columns[1:], fontsize=12, fontweight='bold')
heatmap.set_yticklabels(df['Customer Segment'], fontsize=12, fontweight='bold')
plt.title("Expected Values for Customer Segments", fontsize=16)

# Show the plot
plt.tight_layout()  # Ensure everything fits in the figure
plt.show()