Summary  
This chapter covers performing data aggregation and analysis with DataFrame operations: computing derived metrics by summing columns, filtering rows by condition, and sorting results.

General domain of usage  
Logistics and supply chain management.

Understanding **cost analysis** in logistics is essential for managing supply chain efficiency and profitability. Logistics costs are typically divided into several major components:

- **Transportation costs** are incurred when moving goods between locations;
- **Warehousing costs** arise from storing products;
- **Handling costs** cover the movement and processing of goods within facilities;
- **Delay penalties** are charged when shipments do not arrive on time.

Each of these components can significantly impact the total cost of logistics operations.

import pandas as pd

# Sample data for shipments
data = {
    "shipment_id": [101, 102, 103, 104],
    "base_cost": [1200, 1500, 1100, 1800],
    "warehousing_cost": [200, 250, 180, 300],
    "handling_cost": [75, 80, 70, 90],
    "delay_penalty": [0, 150, 0, 400]
}

df = pd.DataFrame(data)
print(df)

With a DataFrame like the one above, you can analyze the **total logistics cost** for each shipment by combining all relevant cost columns. The `base_cost` column represents the primary transportation expense, while `warehousing_cost` and `handling_cost` account for storage and internal movement, respectively. The `delay_penalty` column highlights additional costs incurred when shipments are late. Calculating the **total logistics cost** for each shipment involves summing these columns, which helps you identify cost drivers and areas for improvement.

# Calculate total logistics cost per shipment
df["total_cost"] = df["base_cost"] + df["warehousing_cost"] + df["handling_cost"] + df["delay_penalty"]

# Find shipments with the highest penalties
high_penalty_shipments = df[df["delay_penalty"] > 0].sort_values(by="delay_penalty", ascending=False)

print("Total logistics cost per shipment:")
print(df[["shipment_id", "total_cost"]])

print("\nShipments with highest delay penalties:")
print(high_penalty_shipments[["shipment_id", "delay_penalty"]])

Why is cost analysis important in logistics?

What factors can increase logistics costs unexpectedly?

Learn how to leverage Python to solve real-world supply chain problems, from inventory management to demand forecasting and logistics optimization. This course blends practical coding with supply chain theory, using hands-on examples and engaging challenges.

Explore the fundamentals of supply chain data, learn how to represent and analyze it in Python, and gain insights using data visualization.

Delve into inventory control techniques and demand forecasting methods, applying Python to model and predict supply chain needs.

Apply Python to optimize logistics operations, including route planning, shipment scheduling, and cost analysis.

Cost Analysis in Logistics

1. Why is cost analysis important in logistics?

2. What factors can increase logistics costs unexpectedly?

3. Fill in the blank: To sum a column in a pandas DataFrame, use `df['column'].____()`.

Cost Analysis in Logistics

1. Why is cost analysis important in logistics?

2. What factors can increase logistics costs unexpectedly?

3. Fill in the blank: To sum a column in a pandas DataFrame, use df['column'].____().

3. Fill in the blank: To sum a column in a pandas DataFrame, use `df['column'].____()`.