Create CaseStudy.md

Author: Pankaj-Str

35 Day Pandas Basic/CaseStudy.md

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+# Pandas Case Study: Analyzing Sales Data
+# This case study introduces beginners to basic Pandas operations using a sample sales dataset
+
+import pandas as pd
+import numpy as np
+
+# Step 1: Create a sample sales dataset
+data = {
+    'order_id': [1, 2, 3, 4, 5, 6],
+    'product': ['Laptop', 'Phone', 'Tablet', 'Laptop', 'Phone', 'Tablet'],
+    'category': ['Electronics', 'Electronics', 'Electronics', 'Electronics', 'Electronics', 'Electronics'],
+    'quantity': [1, 2, 1, 3, 1, 2],
+    'price': [999.99, 499.99, 299.99, 999.99, 499.99, 299.99],
+    'date': ['2025-01-01', '2025-01-02', '2025-01-02', '2025-01-03', '2025-01-03', '2025-01-04']
+}
+df = pd.DataFrame(data)
+
+# Step 2: Display basic information about the dataset
+print("Step 2: Basic Dataset Information")
+print("\nFirst 5 rows of the dataset:")
+print(df.head())
+print("\nDataset Info:")
+print(df.info())
+print("\nBasic Statistics:")
+print(df.describe())
+
+# Step 3: Calculate total revenue (quantity * price)
+df['total_revenue'] = df['quantity'] * df['price']
+print("\nStep 3: Dataset with Total Revenue")
+print(df[['order_id', 'product', 'quantity', 'price', 'total_revenue']])
+
+# Step 4: Group by product and calculate total quantity sold and revenue
+product_summary = df.groupby('product').agg({
+    'quantity': 'sum',
+    'total_revenue': 'sum'
+}).reset_index()
+print("\nStep 4: Product Summary")
+print(product_summary)
+
+# Step 5: Find the best-selling product by quantity
+best_seller = product_summary.loc[product_summary['quantity'].idxmax()]
+print("\nStep 5: Best-Selling Product")
+print(f"Best-selling product: {best_seller['product']} with {best_seller['quantity']} units sold")
+
+# Step 6: Filter orders with total revenue > $1000
+high_value_orders = df[df['total_revenue'] > 1000]
+print("\nStep 6: High-Value Orders (> $1000)")
+print(high_value_orders[['order_id', 'product', 'total_revenue']])
+
+# Step 7: Convert date column to datetime and extract month
+df['date'] = pd.to_datetime(df['date'])
+df['month'] = df['date'].dt.month
+daily_sales = df.groupby('date').agg({
+    'total_revenue': 'sum'
+}).reset_index()
+print("\nStep 7: Daily Sales Summary")
+print(daily_sales)
+
+# Step 8: Save the processed data to a CSV file
+df.to_csv('processed_sales_data.csv', index=False)
+print("\nStep 8: Data saved to 'processed_sales_data.csv'")