Example output
Certainly! Here's an example of the final output based on the filled prompt:
Step 1: Removing Duplicate Entries
Initial dataset had 10,000 rows and 15 columns.
After removing duplicates based on transaction IDs, we now have 8,500 unique entries.
Step 2: Filtering Irrelevant Columns
Removed columns such as 'Customer Name', 'Phone Number', and 'Address'.
Dataset now contains essential columns: 'Date', 'Product ID', 'Quantity Sold', 'Price', and 'Store ID'.
Step 3: Standardizing Product IDs
Created a mapping dictionary to standardize product IDs across all stores.
For instance, 'ProdID123' and 'PROD-123' now both reference '123' uniformly.
Final Output
Cleaned and standardized dataset with 8,500 entries and 5 relevant columns.
All product IDs are consistent, facilitating seamless analysis across retail outlets.
This optimized dataset is now ready for in-depth retail sales analysis, allowing for accurate insights into product performance, pricing strategies, and store-wise comparisons.
Would you like further assistance or additional details on any particular step?