Lære Challenge: Analyze City Budget Data | Data Collection and Cleaning for Journalists

As a journalist, being able to analyze city budget data can reveal important stories about how public funds are allocated and spent. By examining which departments receive the most or least funding, you can uncover patterns, priorities, or disparities that may be newsworthy and relevant to your audience. In this challenge, you will use Python and pandas to break down a city's annual budget and summarize the findings in a clear report.


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import pandas as pd

# Sample data: city budget allocations by department and project
data = {
    "Department": ["Parks", "Parks", "Police", "Fire", "Health", "Health", "Police"],
    "Project": ["Playground", "Gardens", "Patrol", "Fire Engines", "Clinics", "Outreach", "Equipment"],
    "Budget": [50000, 30000, 120000, 90000, 40000, 35000, 80000]
}

df = pd.DataFrame(data)

# Group by department and sum the budgets
department_budgets = df.groupby("Department")["Budget"].sum().reset_index()

print(department_budgets)

Grouping and aggregation are essential tools for journalists working with complex datasets. By grouping data by categories—such as city departments—and aggregating numerical values like budgets, you can quickly summarize large tables into meaningful insights. This approach makes it easier to spot trends, compare allocations, and identify outliers, all of which can lead to compelling stories about government priorities and spending.

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You are given a DataFrame that shows a city's annual budget broken down by department. Your task is to analyze this data and create a summary report.

Calculate the total budget for each department by summing the "Budget" values for each department.
Find the department with the highest total budget.
Find the department with the lowest total budget.
Build a summary report as a string that lists each department and its total budget, and clearly states which department has the highest and lowest budgets.
Print the summary report at the end.

Follow these steps closely to ensure your code produces the correct results and passes all tests.

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              123456789101112131415
            
import pandas as pd

# Sample data: city budget allocations by department and project
data = {
    "Department": ["Parks", "Parks", "Police", "Fire", "Health", "Health", "Police"],
    "Project": ["Playground", "Gardens", "Patrol", "Fire Engines", "Clinics", "Outreach", "Equipment"],
    "Budget": [50000, 30000, 120000, 90000, 40000, 35000, 80000]
}

df = pd.DataFrame(data)

# Group by department and sum the budgets
department_budgets = df.groupby("Department")["Budget"].sum().reset_index()

print(department_budgets)