Search Shortcut cmd + k | ctrl + k
- Installation
- Guides
- Overview
- SQL Features
- Data Import & Export
- CSV Import
- CSV Export
- Parquet Import
- Parquet Export
- Query Parquet
- HTTP Parquet Import
- S3 Parquet Import
- S3 Parquet Export
- JSON Import
- JSON Export
- Excel Import
- Excel Export
- SQLite Import
- Postgres Import
- Meta Queries
- Python
- Install
- Execute SQL
- Jupyter Notebooks
- SQL on Pandas
- Import From Pandas
- Export To Pandas
- SQL on Arrow
- Import From Arrow
- Export To Arrow
- Relational API on Pandas
- Multiple Python Threads
- DuckDB with Ibis
- DuckDB with Fugue
- DuckDB with Polars
- DuckDB with Vaex
- DuckDB with DataFusion
- DuckDB with fsspec filesystems
- SQL Editors
- Data Viewers
- Documentation
- Connect
- Data Import
- Overview
- CSV Files
- JSON Files
- Multiple Files
- Parquet Files
- Partitioning
- Appender
- Insert Statements
- Client APIs
- Overview
- C
- Overview
- Startup
- Configure
- Query
- Data Chunks
- Values
- Types
- Prepared Statements
- Appender
- Table Functions
- Replacement Scans
- API Reference
- C++
- CLI
- Java
- Julia
- Node.js
- ODBC
- Python
- Overview
- Data Ingestion
- Result Conversion
- DB API
- Relational API
- Function API
- Types API
- API Reference
- R
- Rust
- Scala
- Swift
- Wasm
- SQL
- Introduction
- Statements
- Overview
- Alter Table
- Attach/Detach
- Call
- Checkpoint
- Copy
- Create Macro
- Create Schema
- Create Sequence
- Create Table
- Create View
- Delete
- Drop
- Export
- Insert
- Pivot
- Select
- Set/Reset
- Unpivot
- Update
- Use
- Vacuum
- Query Syntax
- SELECT
- FROM & JOIN
- WHERE
- GROUP BY
- GROUPING SETS
- HAVING
- ORDER BY
- LIMIT
- SAMPLE
- UNNEST
- WITH
- WINDOW
- QUALIFY
- VALUES
- FILTER
- Set Operations
- Data Types
- Overview
- Bitstring
- Blob
- Boolean
- Date
- Enum
- Interval
- List
- Map
- NULL Values
- Numeric
- Struct
- Text
- Timestamp
- Union
- Expressions
- Functions
- Overview
- Bitstring Functions
- Blob Functions
- Date Format Functions
- Date Functions
- Date Part Functions
- Enum Functions
- Interval Functions
- Nested Functions
- Numeric Functions
- Pattern Matching
- Text Functions
- Time Functions
- Timestamp Functions
- Timestamp With Time Zone Functions
- Utility Functions
- Aggregates
- Configuration
- Constraints
- Indexes
- Information Schema
- Metadata Functions
- Pragmas
- Samples
- Window Functions
- Extensions
- Sitemap
- Why DuckDB
- Media
- FAQ
- Code of Conduct
- Live Demo
Documentation
/ SQL
/ Query Syntax
GROUP BY Clause
The GROUP BY clause specifies which grouping columns should be used to perform any aggregations in the SELECT clause. If the GROUP BY clause is specified, the query is always an aggregate query, even if no aggregations are present in the SELECT clause.
When a GROUP BY clause is specified, all tuples that have matching data in the grouping columns (i.e. all tuples that belong to the same group) will be combined. The values of the grouping columns themselves are unchanged, and any other columns can be combined using an aggregate function (such as COUNT, SUM, AVG, etc).
Normally, the GROUP BY clause groups along a single dimension. Using the GROUPING SETS, CUBE or ROLLUP clauses it is possible to group along multiple dimensions. See the GROUPING SETS page for more information.
Examples
-- count the number of entries in the "addresses" table that belong to each different city
SELECT city, COUNT(*)
FROM addresses
GROUP BY city;
-- compute the average income per city per street_name
SELECT city, street_name, AVG(income)
FROM addresses
GROUP BY city, street_name;