StratumRetail sales dataset
Two years of retail orders you can group, total, and join to a store lookup table — for time-series and reshaping practice.
↓ Download retail_sales.csv All datasets
At a glance
- Rows
- 18,818 orders
- Columns
- 9 (5 numeric, 3 categorical, 1 date)
- Format
- CSV (UTF-8, comma-delimited, header row)
- Size
- ~1.1 MB
- License
- CC0 1.0 — public domain, synthetic data
- Best for
- Time series · grouping · joins · aggregation
Columns
| Column | Type | Description |
|---|---|---|
OrderID | ID | Unique order id (e.g. O002551). |
StoreID | Categorical | Store key — joins to the Stores table. |
Date | Date | Order date (YYYY-MM-DD). |
ProductCategory | Categorical | e.g. Electronics, Apparel. |
Channel | Categorical | In-Store or Online. |
Units | Integer | Units sold in the order. |
UnitPrice | Numeric | Price per unit, dollars. |
Discount | Numeric | Fractional discount (0–1). |
Revenue | Numeric | Net revenue for the order, dollars. |
Things to try
- Plot revenue over time. A trend chart of
Revenueby month traces 24 months of seasonal pattern. → Trend & time series lesson - Join to the store table. Merge on
StoreIDto enrich each order with its region and type. → Reshaping data lesson · Stores dataset - Aggregate by category and channel. A bar chart of revenue grouped by
ProductCategoryorChannelshows where the money comes from. → Pie, bar & mosaic lesson
How to open it. Download the CSV, then drag it onto Stratum (or use File ▸ Import). Columns are typed automatically — numbers, the date, and the categories all detected — so you're charting within seconds. Get Stratum →
See also
- More data
- Stores · Housing Market · Survey Responses
- Learn
- The 25-lesson tutorial · Statistics glossary