E-Commerce Data
This dataset consists of orders made in different countries from December 2010 to December 2011. The company is a UK-based online retailer that mainly sells unique all-occasion gifts. Many of its customers are wholesalers.
Not sure where to begin? Scroll to the bottom to find challenges!
import pandas as pd
import numpy as np
retail = pd.read_csv("online_retail.csv")
retail.head(100)Data Dictionary
| Variable | Explanation |
|---|---|
| InvoiceNo | A 6-digit integral number uniquely assigned to each transaction. If this code starts with letter 'c' it indicates a cancellation. |
| StockCode | A 5-digit integral number uniquely assigned to each distinct product. |
| Description | Product (item) name |
| Quantity | The quantities of each product (item) per transaction |
| InvoiceDate | The day and time when each transaction was generated |
| UnitPrice | Product price per unit in sterling (pound) |
| CustomerID | A 5-digit integral number uniquely assigned to each customer |
| Country | The name of the country where each customer resides |
Source of dataset.
Citation: Daqing Chen, Sai Liang Sain, and Kun Guo, Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining, Journal of Database Marketing and Customer Strategy Management, Vol. 19, No. 3, pp. 197-208, 2012 (Published online before print: 27 August 2012. doi: 10.1057/dbm.2012.17).
Don't know where to start?
Challenges are brief tasks designed to help you practice specific skills:
- πΊοΈ Explore: Negative order quantities indicate returns. Which products have been returned the most?
- π Visualize: Create a plot visualizing the profits earned from UK customers weekly, monthly, and quarterly.
- π Analyze: Are order sizes from countries outside the United Kingdom significantly larger than orders from inside the United Kingdom?
Scenarios are broader questions to help you develop an end-to-end project for your portfolio:
You are working for an online retailer. Currently, the retailer sells over 4000 unique products. To take inventory of the items, your manager has asked you whether you can group the products into a small number of categories. The categories should be similar in terms of price and quantity sold and any other characteristics you can extract from the data.
You will need to prepare a report that is accessible to a broad audience. It should outline your motivation, steps, findings, and conclusions.
TotalPrice Calculated
# Assuming 'retail' DataFrame has columns 'Quantity' and 'UnitPrice'
retail['TotalPrice'] = retail['Quantity'] * retail['UnitPrice']
retail.head()Most returned products value
returned = retail[retail["Quantity"] < 0]
returned
# returned_agg = returned.agg([np.sum])Returns by Country
returned.pivot_table(values=["TotalPrice"], index=["CustomerID", "Country"], aggfunc=np.sum)Worth to check what is happening on the United Kingdom Market
Most Returns
To check:
- Income in period
- Share of return in total income per customer
- Total return value per customer
Top ten best customers