salesanalyzer_mds.sales_summary_statistics

Functions

sales_summary_statistics(→ pandas.DataFrame)

Generate summary statistics for sales data, including total revenue, unique customers,

Module Contents

salesanalyzer_mds.sales_summary_statistics.sales_summary_statistics(sales_data: pandas.DataFrame, quantity_col: str = 'Quantity', price_col: str = 'UnitPrice', customer_col: str = 'CustomerID', invoice_col: str = 'InvoiceNo', description_col: str = 'Description') → pandas.DataFrame[source]

Generate summary statistics for sales data, including total revenue, unique customers, average order value, top-selling products by quantity and revenue, and average revenue per customer.

Args:

sales_data (pandas.DataFrame): A DataFrame containing sales data with at least
the following columns: quantity_col, price_col, customer_col, invoice_col, and description_col.

quantity_col (str): The name of the column containing the quantity sold. price_col (str): The name of the column containing the unit price of the product. customer_col (str): The name of the column containing the customer ID. invoice_col (str): The name of the column containing the invoice number. description_col (str): The name of the column containing the product description.

Returns:

pandas.DataFrame: A DataFrame containing the calculated summary statistics. If
no sales data is provided, returns an empty DataFrame.

The function computes the following statistics: - ‘total_revenue’: The total revenue generated by all sales. - ‘unique_customers’: The number of unique customers. - ‘average_order_value’: The average value of an order (sum of revenue per invoice). - ‘top_selling_product_quantity’: The product with the highest quantity sold. - ‘top_selling_product_revenue’: The product with the highest total revenue. - ‘average_revenue_per_customer’: The average revenue generated by each customer.

Example:

>>> df = pd.DataFrame({
>>>     'Quantity': [10, 5, 3, 15],
>>>     'UnitPrice': [100, 200, 150, 100],
>>>     'CustomerID': [1, 2, 1, 3],
>>>     'InvoiceNo': ['INV001', 'INV002', 'INV003', 'INV004'],
>>>     'Description': ['Product A', 'Product B', 'Product A', 'Product C']
>>> })
>>> summary_df = sales_summary_statistics(df)
>>> print(summary_df)
total_revenue  unique_customers  average_order_value      0          3950.0                 3               987.5

top_selling_product_quantity top_selling_product_revenue 0 Product C Product C

average_revenue_per_customer

0 1316.666667