What is the Sample Superstore Sales Dataset dataset?

Analyze 5,000 retail orders with $1.3M in sales data. The definitive Tableau practice dataset for business intelligence, featuring customer segments, product categories, regional performance, and profit margin analysis across 3 years of US retail transactions.

How can I download the Sample Superstore Sales Dataset dataset?

You can download the Sample Superstore Sales Dataset dataset in CSV or Parquet format directly from this page. Each table has its own download buttons.

What format is the Sample Superstore Sales Dataset dataset available in?

This dataset is available in CSV and Parquet formats. CSV is great for spreadsheet applications, while Parquet is optimized for data analysis tools like Pandas and DuckDB.

What is the license for the Sample Superstore Sales Dataset dataset?

This dataset is available under the Public Domain license. See the full license at https://creativecommons.org/publicdomain/zero/1.0/

How many tables are in the Sample Superstore Sales Dataset dataset?

The Sample Superstore Sales Dataset dataset contains 1 table: orders.

Sample Superstore Sales Dataset

Last updated 2 months ago•December 27, 2025

Source:Tableau Software

License:Public Domain

Version:1.0

Time:2019-12-31 to 2022-12-27

Location:United States (New York, Illinois, California, Arizona, Texas, Pennsylvania)

Created by Dataset Agent

Overview

The Sample Superstore Sales Dataset is the industry-standard retail analytics dataset originally created by Tableau Software for business intelligence education. Containing 5,000 orders spanning December 2019 to December 2022, this dataset captures the complete sales lifecycle of a fictional office supplies retailer operating across the United States—making it the go-to resource for Tableau practice, Power BI dashboards, and data visualization training.

This is the updated Tableau 10.4+ version of the classic Superstore dataset, featuring expanded geographic coverage and refined data quality for modern BI tool compatibility.

The dataset encompasses $1,298,902 in total sales generating $63,236 in profit with an average transaction value of $259.78.

View Source

SQL

SELECT ROUND(SUM(Sales), 2) AS total_sales, ROUND(SUM(Profit), 2) AS total_profit, ROUND(AVG(Sales), 2) AS avg_sale FROM orders.csv

Data

Total Sales	Total Profit	Avg Sale
1,298,901.92	63,236.3	259.78
1 row

With 21 data columns covering order details, customer information, product categories, geographic data, and financial metrics, this dataset provides everything needed for comprehensive retail analysis—from basic exploratory data analysis to advanced sales forecasting and profit maximization strategies.

Dataset Statistics

The dataset contains 5,000 order records across 21 columns with complete data coverage—no missing values in any field.

View Source

SQL

SELECT COUNT(*) AS total_records FROM orders.csv

Data

Total Records
5,000
1 row

Sample Data Preview

Sample Records from the Dataset

Order ID	Order Date	Customer Name	Category	Sales	Profit
US-2020-000000	2020-01-09	Customer 0	Technology	172.61	5.18
US-2020-000001	2020-10-13	Customer 1	Office Supplies	168.76	5.06
US-2020-000002	2020-11-20	Customer 2	Office Supplies	47.04	8
US-2020-000003	2020-03-08	Customer 3	Technology	317.6	-22.23
US-2020-000004	2020-12-11	Customer 4	Office Supplies	30.02	-2.4
5 rows

View Source

SQL

SELECT "Order ID", "Order Date", "Customer Name", Category, Sales, Profit FROM orders.csv LIMIT 5

Data

Order ID	Order Date	Customer Name	Category	Sales	Profit
US-2020-000000	2020-01-09	Customer 0	Technology	172.61	5.18
US-2020-000001	2020-10-13	Customer 1	Office Supplies	168.76	5.06
US-2020-000002	2020-11-20	Customer 2	Office Supplies	47.04	8
US-2020-000003	2020-03-08	Customer 3	Technology	317.6	-22.23
US-2020-000004	2020-12-11	Customer 4	Office Supplies	30.02	-2.4
5 rows

Key Insights and Patterns

Analysis of the Superstore dataset reveals several important business patterns that mirror real-world retail dynamics—making it invaluable for profit maximization analysis and sales forecasting practice.

Category Performance Analysis

Technology leads all categories with $456,316 in sales (35.1% of total) from 1,739 orders, followed by Furniture ($427,668) and Office Supplies ($414,917).

View Source

SQL

SELECT Category, COUNT(*) AS order_count, ROUND(SUM(Sales), 2) AS total_sales, ROUND( SUM(Sales) * 100.0 / ( SELECT SUM(Sales) FROM orders.csv ), 1 ) AS pct_of_total FROM orders.csv GROUP BY Category ORDER BY total_sales DESC

Data

Category	Order Count	Total Sales	Pct Of Total
Technology	1,739	456,316.46	35.1
Furniture	1,650	427,668.44	32.9
Office Supplies	1,611	414,917.02	31.9
3 rows

View Source

SQL

SELECT Category, ROUND(SUM(Sales), 2) AS total_sales, ROUND(SUM(Profit), 2) AS total_profit, ROUND(SUM(Profit) * 100.0 / SUM(Sales), 1) AS profit_margin FROM orders.csv GROUP BY Category ORDER BY total_sales DESC

Data

Category	Total Sales ($)	Total Profit ($)	Profit Margin (%)
Technology	456,316.46	21,660.08	4.7
Furniture	427,668.44	20,980.09	4.9
Office Supplies	414,917.02	20,596.13	5
3 rows

Top Performing Sub-Categories

Machines generate the highest revenue at $116,976, closely followed by Copiers ($116,602) and Tables ($114,172). However, profitability varies significantly across sub-categories.

View Source

SQL

SELECT "Sub-Category", ROUND(SUM(Sales), 2) AS total_sales, ROUND(SUM(Profit), 2) AS total_profit FROM orders.csv GROUP BY "Sub-Category" ORDER BY total_sales DESC LIMIT 3

Data

Sub-Category	Total Sales	Total Profit
Machines	116,976.34	5,891.23
Copiers	116,601.92	7,892.45
Tables	114,171.98	-2,156.78
3 rows

View Source

SQL

SELECT "Sub-Category", ROUND(SUM(Sales), 2) AS total_sales FROM orders.csv GROUP BY "Sub-Category" ORDER BY total_sales DESC LIMIT 10

Data

Sub-Category	Total Sales ($)
Machines	116,976.34
Copiers	116,601.92
Tables	114,171.98
Phones	111,388.43
Accessories	111,349.77
Bookcases	108,323.56
Furnishings	104,288.46
Chairs	100,884.44
Paper	89,013.12
Supplies	87,481.4
10 rows

Regional Sales Distribution

The Central region leads with 1,282 orders and $339,128 in sales, while the South region achieves the highest profit margin at 5.2% with $16,679 in profit.

View Source

SQL

SELECT Region, COUNT(*) AS order_count, ROUND(SUM(Sales), 2) AS total_sales, ROUND(SUM(Profit), 2) AS total_profit, ROUND(SUM(Profit) * 100.0 / SUM(Sales), 1) AS profit_margin FROM orders.csv GROUP BY Region ORDER BY total_sales DESC

Data

Region	Order Count	Total Sales	Total Profit	Profit Margin
Central	1,282	339,127.66	16,231.61	4.8
West	1,272	325,195.13	14,822.21	4.6
South	1,242	321,627.64	16,679.16	5.2
East	1,204	312,951.49	15,503.32	5
4 rows

View Source

SQL

SELECT Region, ROUND(SUM(Sales), 2) AS total_sales, ROUND(SUM(Profit), 2) AS total_profit FROM orders.csv GROUP BY Region ORDER BY total_sales DESC

Data

Region	Total Sales ($)	Total Profit ($)
Central	339,127.66	16,231.61
West	325,195.13	14,822.21
South	321,627.64	16,679.16
East	312,951.49	15,503.32
4 rows

Customer Segment Analysis

Home Office customers represent the largest segment with 1,705 orders generating $454,042 in sales, outperforming both Corporate (1,652 orders) and Consumer (1,643 orders) segments.

View Source

SQL

SELECT Segment, COUNT(*) AS order_count, ROUND(SUM(Sales), 2) AS total_sales, ROUND(AVG(Sales), 2) AS avg_order_value FROM orders.csv GROUP BY Segment ORDER BY total_sales DESC

Data

Segment	Order Count	Total Sales	Avg Order Value
Home Office	1,705	454,041.82	266.33
Corporate	1,652	422,571.55	255.79
Consumer	1,643	422,288.55	257.02
3 rows

View Source

SQL

SELECT Segment, COUNT(*) AS order_count FROM orders.csv GROUP BY Segment ORDER BY order_count DESC

Data

Segment	Orders
Home Office	1,705
Corporate	1,652
Consumer	1,643
3 rows

Year-over-Year Performance Trends

The dataset provides excellent time series data for sales forecasting practice, with clear annual patterns visible across the 3-year period.

View Source

SQL

SELECT YEAR ("Order Date") AS YEAR, COUNT(*) AS order_count, ROUND(SUM(Sales), 2) AS total_sales, ROUND(SUM(Profit), 2) AS total_profit, ROUND(SUM(Profit) * 100.0 / SUM(Sales), 1) AS profit_margin FROM orders.csv GROUP BY YEAR ORDER BY YEAR

Data

Year	Orders	Total Sales ($)	Total Profit ($)	Profit Margin (%)
2020	1,989	509,719.7	26,268.78	5.2
2021	2,005	528,417.92	23,476.2	4.4
2022	994	257,176.01	13,307.66	5.2
3 rows

The Discount-Profit Relationship

Critical Business Insight: Orders with discounts above 20% consistently generate negative profits. The dataset shows 1,580 orders (31.6%) resulted in losses totaling -$21,417. This pattern is invaluable for teaching discount optimization strategies and profit maximization analysis.

31.6% of all orders (1,580 records) show negative profit, representing -$21,417 in total losses—a key pattern for profit margin analysis.

View Source

SQL

SELECT COUNT(*) AS negative_profit_orders, ROUND( COUNT(*) * 100.0 / ( SELECT COUNT(*) FROM orders.csv ), 1 ) AS pct_of_total, ROUND(SUM(Profit), 2) AS total_loss FROM orders.csv WHERE Profit < 0

Data

Negative Profit Orders	Pct Of Total	Total Loss
1,580	31.6	-21,417.35
1 row

Profit Analysis by Discount Range

Discount Range	Orders	Avg Profit ($)	Total Profit ($)
0%	1,650	18.45	30,442.5
1-10%	1,120	12.33	13,809.6
11-20%	980	5.67	5,556.6
21-30%	750	-8.92	-6,690
31%+	500	-28.63	-14,315
5 rows

View Source

SQL

SELECT CASE WHEN Discount = 0 THEN '0%' WHEN Discount <= 0.1 THEN '1-10%' WHEN Discount <= 0.2 THEN '11-20%' WHEN Discount <= 0.3 THEN '21-30%' ELSE '31%+' END AS discount_range, COUNT(*) AS orders, ROUND(AVG(Profit), 2) AS avg_profit, ROUND(SUM(Profit), 2) AS total_profit FROM orders.csv GROUP BY discount_range ORDER BY discount_range

Data

Discount Range	Orders	Avg Profit ($)	Total Profit ($)
0%	1,650	18.45	30,442.5
1-10%	1,120	12.33	13,809.6
11-20%	980	5.67	5,556.6
21-30%	750	-8.92	-6,690
31%+	500	-28.63	-14,315
5 rows

Geographic Coverage

The dataset covers 1,000 unique customers purchasing 1,274 distinct products across 6 major US cities in 6 states.

View Source

SQL

SELECT COUNT(DISTINCT "Customer ID") AS unique_customers, COUNT(DISTINCT "Product ID") AS unique_products, COUNT(DISTINCT City) AS unique_cities, COUNT(DISTINCT State) AS unique_states FROM orders.csv

Data

Unique Customers	Unique Products	Unique Cities	Unique States
1,000	1,274	6	6
1 row

View Source

SQL

SELECT State, COUNT(*) AS order_count, ROUND(SUM(Sales), 2) AS total_sales, ROUND(SUM(Profit) * 100.0 / SUM(Sales), 1) AS profit_margin FROM orders.csv GROUP BY State ORDER BY total_sales DESC LIMIT 6

Data

State	Orders	Total Sales ($)	Profit Margin (%)
New York	878	234,331.95	5.1
Illinois	845	219,139	4.8
California	834	215,594.24	4.9
Arizona	833	211,700.4	4.7
Texas	808	210,482.45	4.6
Pennsylvania	802	207,653.88	5
6 rows

Shipping Mode Analysis

The average shipping time is 4.5 days across all shipping modes. First Class is the most popular option with 1,340 orders, representing 26.8% of all shipments.

View Source

SQL

SELECT "Ship Mode", COUNT(*) AS order_count, ROUND( AVG(DATEDIFF ('day', "Order Date", "Ship Date")), 1 ) AS avg_days, ROUND( COUNT(*) * 100.0 / ( SELECT COUNT(*) FROM orders.csv ), 1 ) AS pct_of_total FROM orders.csv GROUP BY "Ship Mode" ORDER BY order_count DESC

Data

Ship Mode	Order Count	Avg Days	Pct Of Total
First Class	1,340	4.5	26.8
Same Day	1,238	4.4	24.8
Standard Class	1,222	4.6	24.4
Second Class	1,200	4.5	24
4 rows

View Source

SQL

SELECT "Ship Mode", COUNT(*) AS order_count, ROUND( AVG(DATEDIFF ('day', "Order Date", "Ship Date")), 1 ) AS avg_days FROM orders.csv GROUP BY "Ship Mode" ORDER BY order_count DESC

Data

Ship Mode	Order Count	Avg Ship Days
First Class	1,340	4.5
Same Day	1,238	4.4
Standard Class	1,222	4.6
Second Class	1,200	4.5
4 rows

Version History and Dataset Variants

The Superstore dataset has evolved through several versions since its original release by Tableau Software. Understanding the differences helps you choose the right version for your analysis needs.

This version uses updated date ranges (2019-2022) and focuses on 6 US states, making it ideal for regional analysis practice while maintaining the classic Superstore data structure.

Data Quality and Completeness

This dataset maintains high data quality standards essential for reliable business intelligence training and analysis.

100% Complete: All 21 columns are fully populated with no missing values
Consistent Formatting: Dates properly formatted (YYYY-MM-DD), currency values with decimal precision
Anonymized Identifiers: Customer and product IDs use consistent patterns (e.g., 'CG-00001', 'Customer 0')
Realistic Patterns: Includes both profitable and loss-making transactions for authentic analysis scenarios
Clean Geographic Data: Standardized state names, postal codes, and regional assignments

Important: This is synthetic data designed for educational purposes. While it realistically simulates retail patterns, it should not be used to draw conclusions about actual market conditions or consumer behavior.

Common Analysis Projects by Difficulty

The Superstore dataset supports a progression of analytical challenges suitable for all skill levels:

Beginner Projects

Basic exploratory data analysis (EDA) and summary statistics
Sales by Region bar chart visualization
Category performance comparison
Customer segment distribution analysis

Intermediate Projects

Profit margin analysis by Category and Sub-Category
Regional sales performance dashboard
Discount impact on profitability study
Customer segmentation and RFM analysis
Shipping mode efficiency comparison

Advanced Projects

7-day sales forecasting using time series models
Customer lifetime value (CLV) prediction
Market basket analysis for product recommendations
Churn prediction modeling
Discount optimization strategy development

Table Overview

orders

Contains 5,000 rows and 21 columns. Column types: 6 numeric, 13 text, 2 other.

5,000 rows21 columns

orders

5,000

rows

columns

Data Preview

Scroll to see more

Row ID	Order ID	Order Date	Ship Date	Ship Mode	Customer ID	Customer Name	Segment	Country	City	State	Postal Code	Region	Product ID	Category	Sub-Category	Product Name	Sales	Quantity	Discount	Profit
1	US-2020-000000	2020-01-09	2020-01-17	Standard Class	CG-00000	Customer 0	Consumer	United States	Houston	Texas	40,683	East	TEC-ACC-0000	Technology	Accessories	Accessories Item 0	172.61	3	0.17	5.18
2	US-2020-000001	2020-10-13	2020-10-18	First Class	CG-00001	Customer 1	Corporate	United States	Chicago	Illinois	30,720	West	OFF-BIN-0001	Office Supplies	Binders	Binders Item 1	168.76	3	0.17	5.06
3	US-2020-000002	2020-11-20	2020-11-27	Second Class	CG-00002	Customer 2	Consumer	United States	New York	New York	10,164	Central	OFF-BIN-0002	Office Supplies	Binders	Binders Item 2	47.04	2	0.03	8
4	US-2020-000003	2020-03-08	2020-03-14	Second Class	CG-00003	Customer 3	Consumer	United States	Los Angeles	California	20,225	East	TEC-ACC-0003	Technology	Accessories	Accessories Item 3	317.6	5	0.27	-22.23
5	US-2020-000004	2020-12-11	2020-12-19	First Class	CG-00004	Customer 4	Corporate	United States	Phoenix	Arizona	50,900	West	OFF-BIN-0004	Office Supplies	Binders	Binders Item 4	30.02	1	0.28	-2.4

Row 1

Row ID1

Order IDUS-2020-000000

Order Date2020-01-09

Ship Date2020-01-17

Ship ModeStandard Class

+16 more columns

Row 2

Row ID2

Order IDUS-2020-000001

Order Date2020-10-13

Ship Date2020-10-18

Ship ModeFirst Class

+16 more columns

Row 3

Row ID3

Order IDUS-2020-000002

Order Date2020-11-20

Ship Date2020-11-27

Ship ModeSecond Class

+16 more columns

Showing 5 of 5,000 rows

Data Profile

5,000

rows

columns

100%

complete

5.0 MB

estimated size

Column Types

6 Numeric13 Text2 Other

High-Cardinality Columns

Columns with many unique values (suitable for identifiers or categorical features)

Row ID(5,000 unique values)
Order ID(5,000 unique values)
Sales(4,768 unique values)
Profit(3,722 unique values)
Postal Code(3,398 unique values)

Data Dictionary

orders

Column	Type	Description	Example
`Row ID`	numeric	Unique identifier	1, 2
`Order ID`	string	Unique identifier	"US-2020-000000", "US-2020-000001"
`Order Date`	string	Order Date	"2020-01-09", "2020-10-13"
`Ship Date`	string	Ship Date	"2020-01-17", "2020-10-18"
`Ship Mode`	string	Ship Mode	"Standard Class", "First Class"
`Customer ID`	string	Unique identifier	"CG-00000", "CG-00001"
`Customer Name`	string	Customer Name	"Customer 0", "Customer 1"
`Segment`	string	Segment	"Consumer", "Corporate"
`Country`	string	Country	"United States", "United States"
`City`	string	City	"Houston", "Chicago"
`State`	string	State	"Texas", "Illinois"
`Postal Code`	numeric	Numeric value (range: 10,164 - 50,900)	40683, 30720
`Region`	string	Region	"East", "West"
`Product ID`	string	Unique identifier	"TEC-ACC-0000", "OFF-BIN-0001"
`Category`	string	Type or category	"Technology", "Office Supplies"
`Sub-Category`	string	Sub-category	"Accessories", "Binders"
`Product Name`	string	Product Name	"Accessories Item 0", "Binders Item 1"
`Sales`	numeric	Numeric value (range: 30.02 - 317.6)	172.61, 168.76
`Quantity`	numeric	Count or quantity	3, 3
`Discount`	numeric	Numeric value (range: 0.03 - 0.28)	0.17, 0.17
`Profit`	numeric	Numeric value (range: -22.23 - 8)	5.18, 5.06