Challenge: Automate Monthly Expenditure Summaries
Automated expenditure summaries play a crucial role in supporting budget oversight for government agencies. By systematically compiling and aggregating spending data, you can quickly identify departmental spending patterns, ensure that expenditures align with budgetary constraints, and provide timely information for audits or policy decisions. Automating this process reduces manual errors and frees up valuable analyst time for higher-level tasks.
12345678910111213# Example dataset: Each dictionary represents a monthly expenditure record. monthly_expenditures = [ {"department": "Health", "month": "January", "amount": 12000}, {"department": "Transport", "month": "January", "amount": 8000}, {"department": "Education", "month": "January", "amount": 15000}, {"department": "Health", "month": "February", "amount": 11000}, {"department": "Transport", "month": "February", "amount": 8500}, {"department": "Education", "month": "February", "amount": 14500}, {"department": "Health", "month": "March", "amount": 13000}, {"department": "Transport", "month": "March", "amount": 9000}, {"department": "Education", "month": "March", "amount": 14800}, # ... additional months for each department ]
To create a summary of total annual expenditure by department, you need to aggregate values across all months for each department. This involves iterating through the dataset, grouping amounts by the department field, and summing the expenditures for each group. Such aggregation is foundational for producing clear, actionable reports that inform funding decisions and resource allocation.
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Write a function that takes a list of expenditure records and returns a dictionary mapping each department to its total expenditure for the year.
- Iterate through each record in the provided list.
- For each record, add the amount to the running total for the corresponding department.
- Return a dictionary where each key is a department name and the value is the total expenditure for that department.
Ratkaisu
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Automated expenditure summaries play a crucial role in supporting budget oversight for government agencies. By systematically compiling and aggregating spending data, you can quickly identify departmental spending patterns, ensure that expenditures align with budgetary constraints, and provide timely information for audits or policy decisions. Automating this process reduces manual errors and frees up valuable analyst time for higher-level tasks.
12345678910111213# Example dataset: Each dictionary represents a monthly expenditure record. monthly_expenditures = [ {"department": "Health", "month": "January", "amount": 12000}, {"department": "Transport", "month": "January", "amount": 8000}, {"department": "Education", "month": "January", "amount": 15000}, {"department": "Health", "month": "February", "amount": 11000}, {"department": "Transport", "month": "February", "amount": 8500}, {"department": "Education", "month": "February", "amount": 14500}, {"department": "Health", "month": "March", "amount": 13000}, {"department": "Transport", "month": "March", "amount": 9000}, {"department": "Education", "month": "March", "amount": 14800}, # ... additional months for each department ]
To create a summary of total annual expenditure by department, you need to aggregate values across all months for each department. This involves iterating through the dataset, grouping amounts by the department field, and summing the expenditures for each group. Such aggregation is foundational for producing clear, actionable reports that inform funding decisions and resource allocation.
Swipe to start coding
Write a function that takes a list of expenditure records and returns a dictionary mapping each department to its total expenditure for the year.
- Iterate through each record in the provided list.
- For each record, add the amount to the running total for the corresponding department.
- Return a dictionary where each key is a department name and the value is the total expenditure for that department.
Ratkaisu
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