Oliver Wants To Know How Much Money He Earns On The Product ✓ Solved

11 Oliver Would Like To Know How Much Money He Earns On Products That

Oliver would like to know how much money he earns on products that cost less than 5 dollars per unit. Write a Python function that can take as input a table of sales like the one above, and return the total amount he earned on products that cost less than 5 dollars per unit. Note: You don’t need to import a spreadsheet or CSV at this stage, just write a function that can take as input any table that has the same model as the one above.

Sample Paper For Above instruction

The goal of this academic exercise is to develop a Python function capable of processing sales data stored in a tabular format and calculating the total earnings generated from products priced below a specific threshold. Specifically, the function should accept a table representing sales transactions—comprising at least product prices and quantities—and compute the total profit from items costing less than five dollars per unit. This task emphasizes understanding data structures, conditional filtering, and aggregation operations in Python, which are fundamental skills in data analysis and programming.

To accomplish this, we will first define an appropriate data structure to simulate the sales data table. For flexibility and simplicity, a list of dictionaries is ideal, where each dictionary represents a sales record with keys such as ‘product_name,’ ‘unit_price,’ and ‘quantity_sold.’ This structure allows easy access to individual data points and facilitates filtering operations. Here’s an example of such a data table:

sales_data = [


{'product_name': 'Product A', 'unit_price': 3.50, 'quantity_sold': 10},


{'product_name': 'Product B', 'unit_price': 7.20, 'quantity_sold': 5},


{'product_name': 'Product C', 'unit_price': 2.99, 'quantity_sold': 20},


{'product_name': 'Product D', 'unit_price': 4.50, 'quantity_sold': 15},


]

The core of the solution involves defining a function, say calculate_earnings_below_five, which iterates through this data, filters out products with unit_price less than 5, and sums the earnings obtained from these products. The earnings for each product are calculated by multiplying the unit_price by quantity_sold. The function can be implemented as follows:

def calculate_earnings_below_five(sales_table):


total_earnings = 0


for record in sales_table:


if record['unit_price'] 

total_earnings += record['unit_price'] * record['quantity_sold']


return total_earnings

This approach ensures that the function dynamically evaluates any input table that adheres to the expected format. It’s important to validate the structure of the input data during implementation or to handle potential errors or inconsistencies for more robust code. Furthermore, incorporating comments and descriptive variable names enhances readability and maintainability of the code.

In a real-world context, such a function could be extended to process data from external sources like CSV files or databases. But for the current scope, this implementation perfectly demonstrates fundamental data processing and filtering skills in Python. The next step would be to test this function with sample datasets to verify correctness and to explore additional functionality such as including products priced exactly at 5 dollars or handling missing data gracefully.

Conclusion

By carefully defining input data structures and implementing conditional filtering and aggregation, Python developers can efficiently analyze sales data for specific criteria—such as products costing less than five dollars. This task exemplifies core programming concepts relevant for data analysis, including iteration, conditional logic, and summation operations, forming a foundation for more complex data processing workflows.

References

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