Aprende Cost Estimation and Budget Tracking | Project Management and Optimization

Cost estimation and budget tracking are critical components of any civil engineering project. Accurate cost estimation at the planning stage helps ensure that a project is financially feasible and can be delivered within budget. As a project progresses, tracking actual expenditures against the estimated costs allows for early identification of potential overruns and supports informed decision-making. Python can automate many aspects of these processes, making calculations faster, reducing manual errors, and providing clear, up-to-date information for project managers. By using Python, you can efficiently manage lists of project items, calculate total costs, and generate summary reports that highlight discrepancies between planned and actual spending.


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# Define a list of project items with their estimated quantities and unit costs
project_items = [
    {"name": "Concrete", "quantity": 120, "unit_cost": 100},
    {"name": "Steel", "quantity": 50, "unit_cost": 400},
    {"name": "Bricks", "quantity": 10000, "unit_cost": 0.5},
    {"name": "Labor", "quantity": 800, "unit_cost": 15},
]

# Calculate estimated total cost for each item and overall total
for item in project_items:
    item["estimated_total"] = item["quantity"] * item["unit_cost"]

total_estimated_cost = sum(item["estimated_total"] for item in project_items)

print("Estimated Costs:")
for item in project_items:
    print(f"{item['name']}: ${item['estimated_total']:.2f}")

print(f"\nTotal Estimated Project Cost: ${total_estimated_cost:.2f}")

As the project advances, it is essential to update your cost records to reflect actual purchases and usage. This allows you to compare actual costs with your original estimates, providing insight into where the project is staying on track or deviating. By recording actual quantities and costs, you can create a running comparison that highlights areas of concern, such as overruns in materials or labor. Python makes it easy to automate these updates and produce clear summaries, so you can respond quickly to budget issues and keep your project under control.


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import pandas as pd

# Extend item data with actuals
project_items = [
    {"name": "Concrete", "estimated_total": 12000, "actual_total": 13500},
    {"name": "Steel", "estimated_total": 20000, "actual_total": 21000},
    {"name": "Bricks", "estimated_total": 5000, "actual_total": 4800},
    {"name": "Labor", "estimated_total": 12000, "actual_total": 13000},
]

# Create a DataFrame for summary
df = pd.DataFrame(project_items)
df["overrun"] = df["actual_total"] - df["estimated_total"]

# Highlight items with cost overruns
print("Cost Summary Table:")
print(df[["name", "estimated_total", "actual_total", "overrun"]])

overrun_items = df[df["overrun"] > 0]
if not overrun_items.empty:
    print("\nItems with budget overruns:")
    print(overrun_items[["name", "overrun"]])
else:
    print("\nNo budget overruns detected.")

1. Why is it important to track both estimated and actual costs in a project?

2. How does Python help identify budget overruns early?

3. Fill in the blank: The total project cost is calculated by summing the ______ of all items.

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Suggested prompts:

How can I add more project items to the cost tracking?

Can you explain how the overrun calculation works?

How can I visualize the cost summary data?

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