Building Robust Object-Oriented Python Applications and Libraries/

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Solution: Build a Sales Report Generator

Solution: Build a Sales Report Generator

Learn how to implement the coded solution of a sales report generator system using functional programming intersection.

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Read sales data

First create a function named read_sales_data() that will assist us in reading the data written in a CSV file. The path of the file will be given as a parameter to the function.

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import csv
# Read Sales Data
def read_sales_data(file_path):
  sales_data = []
  with open(file_path, 'r') as file:
    csv_reader = csv.DictReader(file)
    for row in csv_reader:
      sales_data.append(row)
  return sales_data

Code explanation

  • Line 4–6: Passed the file_path as input and opened the file using open() method.
  • Line 7: Created a csv.DictReader object to read the contents of the file as a dictionary.
  • Line 8–9: Iterated each row in the CSV file and added it to the sales_data list.
  • Line 10: Finally, returned the sales_data list.

Calculate total sales

Now implement a calculate_total_sales() function to accept the sales_data as a parameter. This function calculates the total sales of each country and returns a dictionary containing the required data.

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import csv
# Read Sales Data
def read_sales_data(file_path):
  sales_data = []
  with open(file_path, 'r') as file:
    csv_reader = csv.DictReader(file)
    for row in csv_reader:
      sales_data.append(row)
  return sales_data
# Calculate Total Sales
def calculate_total_sales(sales_data):
  total_sales = {}
  for transaction in sales_data:
    country = transaction['country']
    quantity = int(transaction['quantity'])
    price = float(transaction['price'])
    sales = quantity * price
    total_sales[country] = total_sales.get(country, 0) + sales
  return total_sales

Code explanation

  • Line 13: Passed the sales_data as input, which is a list of dictionaries representing the sales data.
  • Line 15–18: Iterated over each transaction in the sales_data. For each transaction, it extracted the country, quantity, and price values.
  • Line 19: Calculated the sales by multiplying the
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