Supply Chain Analysis with Python 47 Automate Conditional Formatting

Hi Everyone !

How This Works:
Data Preparation:

The df DataFrame includes columns for Revenue, Quantity_Sold, and Stock_Quantity.
Logical conditions are applied to categorize metrics as Low, High, or Normal.
Seaborn Heatmap:

Visualizes metrics (Revenue, Quantity_Sold, Stock_Quantity) in a heatmap.
Color intensity represents the magnitude of values using the "coolwarm" color map.
Annotates cells with numeric values.
Pandas DataFrame Styling:

The highlight_conditions function applies conditional styles (e.g., background color) to each row based on the conditions.
Output Options:

Heatmap: Displays visually appealing metrics with intensity-based colors.
Styled DataFrame: Provides a table with highlighted cells directly in a Jupyter Notebook or output.

📦 Visualize Conditional Formatting with Seaborn in Python

Why is Conditional Formatting Important?
Conditional formatting highlights important patterns in your data, helping you analyze trends, identify outliers, and make informed decisions. This Python-based approach combines Seaborn and pandas to create heatmaps and dynamically styled tables for supply chain datasets.

📊 Key Features:
1️⃣ Heatmap Visualization:
Visualize metrics like revenue, sales, and stock levels with intensity-based color coding.

2️⃣ Dynamic Styling:
Highlight low revenue, high sales, and excess stock in a formatted table.

3️⃣ Comprehensive Analysis:
Combine visual patterns with dynamic table styling for actionable insights.

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