Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

Amazon Redshift will no longer support the creation of new Python UDFs starting November 1, 2025. If you would like to use Python UDFs, create the UDFs prior to that date. Existing Python UDFs will continue to function as normal. For more information, see the blog post .

PIVOT and UNPIVOT examples

PIVOT and UNPIVOT are parameters in the FROM clause that rotate query output from rows to columns and columns to rows, respectively. They represent tabular query results in a format that's easy to read. The following examples use test data and queries to show how to use them.

For more information about these and other parameters, see FROM clause.

PIVOT examples

Set up the sample table and data and use them to run the subsequent example queries.

CREATE TABLE part (
    partname varchar,
    manufacturer varchar,
    quality int,
    price decimal(12, 2)
);

INSERT INTO part VALUES ('prop', 'local parts co', 2, 10.00);
INSERT INTO part VALUES ('prop', 'big parts co', NULL, 9.00);
INSERT INTO part VALUES ('prop', 'small parts co', 1, 12.00);

INSERT INTO part VALUES ('rudder', 'local parts co', 1, 2.50);
INSERT INTO part VALUES ('rudder', 'big parts co', 2, 3.75);
INSERT INTO part VALUES ('rudder', 'small parts co', NULL, 1.90);

INSERT INTO part VALUES ('wing', 'local parts co', NULL, 7.50);
INSERT INTO part VALUES ('wing', 'big parts co', 1, 15.20);
INSERT INTO part VALUES ('wing', 'small parts co', NULL, 11.80);

PIVOT on partname with an AVG aggregation on price.

SELECT *
FROM (SELECT partname, price FROM part) PIVOT (
    AVG(price) FOR partname IN ('prop', 'rudder', 'wing')
);

The query results in the following output.

  prop   |  rudder  |  wing
---------+----------+---------
 10.33   | 2.71     | 11.50

In the previous example, the results are transformed into columns. The following example shows a GROUP BY query that returns the average prices in rows, rather than in columns.

SELECT partname, avg(price)
FROM (SELECT partname, price FROM part)
WHERE partname IN ('prop', 'rudder', 'wing')
GROUP BY partname;

The query results in the following output.

 partname |  avg
----------+-------
 prop     | 10.33
 rudder   |  2.71
 wing     | 11.50

A PIVOT example with manufacturer as an implicit column.

SELECT *
FROM (SELECT quality, manufacturer FROM part) PIVOT (
    count(*) FOR quality IN (1, 2, NULL)
);

The query results in the following output.

 manufacturer      | 1  | 2  | null
-------------------+----+----+------
 local parts co    | 1  | 1  |  1
 big parts co      | 1  | 1  |  1
 small parts co    | 1  | 0  |  2

Input table columns that are not referenced in the PIVOT definition are added implicitly to the result table. This is the case for the manufacturer column in the previous example. The example also shows that NULL is a valid value for the IN operator.

PIVOT in the above example returns similar information as the following query, which includes GROUP BY. The difference is that PIVOT returns the value 0 for column 2 and the manufacturer small parts co. The GROUP BY query does not contain a corresponding row. In most cases, PIVOT inserts NULL if a row doesn't have input data for a given column. However, the count aggregate doesn't return NULL and 0 is the default value.

SELECT manufacturer, quality, count(*)
FROM (SELECT quality, manufacturer FROM part)
WHERE quality IN (1, 2) OR quality IS NULL
GROUP BY manufacturer, quality
ORDER BY manufacturer;

The query results in the following output.

 manufacturer        | quality | count
---------------------+---------+-------
 big parts co        |         |     1
 big parts co        |       2 |     1
 big parts co        |       1 |     1
 local parts co      |       2 |     1
 local parts co      |       1 |     1
 local parts co      |         |     1
 small parts co      |       1 |     1
 small parts co      |         |     2

The PIVOT operator accepts optional aliases on the aggregate expression and on each value for the IN operator. Use aliases to customize the column names. If there is no aggregate alias, only the IN list aliases are used. Otherwise, the aggregate alias is appended to the column name with an underscore to separate the names.

SELECT *
FROM (SELECT quality, manufacturer FROM part) PIVOT (
    count(*) AS count FOR quality IN (1 AS high, 2 AS low, NULL AS na)
);

The query results in the following output.

 manufacturer      | high_count  | low_count | na_count
-------------------+-------------+-----------+----------
 local parts co    |           1 |         1 |        1
 big parts co      |           1 |         1 |        1
 small parts co    |           1 |         0 |        2

Set up the following sample table and data and use them to run the subsequent example queries. The data represents booking dates for a collection of hotels.

CREATE TABLE bookings (
    booking_id int,
    hotel_code char(8),
    booking_date date,
    price decimal(12, 2)
);

INSERT INTO bookings VALUES (1, 'FOREST_L', '02/01/2023', 75.12);
INSERT INTO bookings VALUES (2, 'FOREST_L', '02/02/2023', 75.00);
INSERT INTO bookings VALUES (3, 'FOREST_L', '02/04/2023', 85.54);

INSERT INTO bookings VALUES (4, 'FOREST_L', '02/08/2023', 75.00);
INSERT INTO bookings VALUES (5, 'FOREST_L', '02/11/2023', 75.00);
INSERT INTO bookings VALUES (6, 'FOREST_L', '02/14/2023', 90.00);

INSERT INTO bookings VALUES (7, 'FOREST_L', '02/21/2023', 60.00);
INSERT INTO bookings VALUES (8, 'FOREST_L', '02/22/2023', 85.00);
INSERT INTO bookings VALUES (9, 'FOREST_L', '02/27/2023', 90.00);

INSERT INTO bookings VALUES (10, 'DESERT_S', '02/01/2023', 98.00);
INSERT INTO bookings VALUES (11, 'DESERT_S', '02/02/2023', 75.00);
INSERT INTO bookings VALUES (12, 'DESERT_S', '02/04/2023', 85.00);

INSERT INTO bookings VALUES (13, 'DESERT_S', '02/05/2023', 75.00);
INSERT INTO bookings VALUES (14, 'DESERT_S', '02/06/2023', 34.00);
INSERT INTO bookings VALUES (15, 'DESERT_S', '02/09/2023', 85.00);

INSERT INTO bookings VALUES (16, 'DESERT_S', '02/12/2023', 23.00);
INSERT INTO bookings VALUES (17, 'DESERT_S', '02/13/2023', 76.00);
INSERT INTO bookings VALUES (18, 'DESERT_S', '02/14/2023', 85.00);

INSERT INTO bookings VALUES (19, 'OCEAN_WV', '02/01/2023', 98.00);
INSERT INTO bookings VALUES (20, 'OCEAN_WV', '02/02/2023', 75.00);
INSERT INTO bookings VALUES (21, 'OCEAN_WV', '02/04/2023', 85.00);

INSERT INTO bookings VALUES (22, 'OCEAN_WV', '02/06/2023', 75.00);
INSERT INTO bookings VALUES (23, 'OCEAN_WV', '02/09/2023', 34.00);
INSERT INTO bookings VALUES (24, 'OCEAN_WV', '02/12/2023', 85.00);

INSERT INTO bookings VALUES (25, 'OCEAN_WV', '02/13/2023', 23.00);
INSERT INTO bookings VALUES (26, 'OCEAN_WV', '02/14/2023', 76.00);
INSERT INTO bookings VALUES (27, 'OCEAN_WV', '02/16/2023', 85.00);

INSERT INTO bookings VALUES (28, 'CITY_BLD', '02/01/2023', 98.00);
INSERT INTO bookings VALUES (29, 'CITY_BLD', '02/02/2023', 75.00);
INSERT INTO bookings VALUES (30, 'CITY_BLD', '02/04/2023', 85.00);

INSERT INTO bookings VALUES (31, 'CITY_BLD', '02/12/2023', 75.00);
INSERT INTO bookings VALUES (32, 'CITY_BLD', '02/13/2023', 34.00);
INSERT INTO bookings VALUES (33, 'CITY_BLD', '02/17/2023', 85.00);

INSERT INTO bookings VALUES (34, 'CITY_BLD', '02/22/2023', 23.00);
INSERT INTO bookings VALUES (35, 'CITY_BLD', '02/23/2023', 76.00);
INSERT INTO bookings VALUES (36, 'CITY_BLD', '02/24/2023', 85.00);

In this sample query, booking records are tallied to give a total for each week. The end date for each week becomes a column name.

SELECT * FROM
    (SELECT
       booking_id,
       (date_trunc('week', booking_date::date) + '5 days'::interval)::date as enddate,
       hotel_code AS "hotel code"
FROM bookings
) PIVOT (
    count(booking_id) FOR enddate IN ('2023-02-04','2023-02-11','2023-02-18') 
);

The query results in the following output.

 hotel code | 2023-02-04  | 2023-02-11 | 2023-02-18
------------+-------------+------------+----------
 FOREST_L   |           3 |          2 |        1
 DESERT_S   |           4 |          3 |        2
 OCEAN_WV   |           3 |          3 |        3
 CITY_BLD   |           3 |          1 |        2

Amazon Redshift doesn't support CROSSTAB to pivot on multiple columns. But you can change row data to columns, in a similar manner to an aggregation with PIVOT, with a query like the following. This uses the same booking sample data as the previous example.

SELECT 
  booking_date,
  MAX(CASE WHEN hotel_code = 'FOREST_L' THEN 'forest is booked' ELSE '' END) AS FOREST_L,
  MAX(CASE WHEN hotel_code = 'DESERT_S' THEN 'desert is booked' ELSE '' END) AS DESERT_S,
  MAX(CASE WHEN hotel_code = 'OCEAN_WV' THEN 'ocean is booked' ELSE '' END)  AS OCEAN_WV
FROM bookings
GROUP BY booking_date
ORDER BY booking_date asc;

The sample query results in booking dates listed next to short phrases that indicate which hotels are booked.

 booking_date  | forest_l         | desert_s         | ocean_wv
---------------+------------------+------------------+--------------------
 2023-02-01    | forest is booked | desert is booked |  ocean is booked
 2023-02-02    | forest is booked | desert is booked |  ocean is booked
 2023-02-04    | forest is booked | desert is booked |  ocean is booked
 2023-02-05    |                  | desert is booked |        
 2023-02-06    |                  | desert is booked |         

The following are usage notes for PIVOT:

UNPIVOT examples

Set up the sample data and use it to run the subsequent examples.

CREATE TABLE count_by_color (quality varchar, red int, green int, blue int);

INSERT INTO count_by_color VALUES ('high', 15, 20, 7);
INSERT INTO count_by_color VALUES ('normal', 35, NULL, 40);
INSERT INTO count_by_color VALUES ('low', 10, 23, NULL);

UNPIVOT on input columns red, green, and blue.

SELECT *
FROM (SELECT red, green, blue FROM count_by_color) UNPIVOT (
    cnt FOR color IN (red, green, blue)
);

The query results in the following output.

 color | cnt
-------+-----
 red   |  15
 red   |  35
 red   |  10
 green |  20
 green |  23
 blue  |   7
 blue  |  40

By default, NULL values in the input column are skipped and do not yield a result row.

The following example shows UNPIVOT with INCLUDE NULLS.

SELECT *
FROM (
    SELECT red, green, blue
    FROM count_by_color
) UNPIVOT INCLUDE NULLS (
    cnt FOR color IN (red, green, blue)
);

The following is the resulting output.

 color | cnt
-------+-----
 red   |  15
 red   |  35
 red   |  10
 green |  20
 green |
 green |  23
 blue  |   7
 blue  |  40
 blue  |  

If the INCLUDING NULLS parameter is set, NULL input values generate result rows.

The following query shows UNPIVOT with quality as an implicit column.

SELECT *
FROM count_by_color UNPIVOT (
    cnt FOR color IN (red, green, blue)
);

The query results in the following output.

 quality | color | cnt
---------+-------+-----
 high    | red   |  15
 normal  | red   |  35
 low     | red   |  10
 high    | green |  20
 low     | green |  23
 high    | blue  |   7
 normal  | blue  |  40

Columns of the input table that are not referenced in the UNPIVOT definition are added implicitly to the result table. In the example, this is the case for the quality column.

The following example shows UNPIVOT with aliases for values in the IN list.

SELECT *
FROM count_by_color UNPIVOT (
    cnt FOR color IN (red AS r, green AS g, blue AS b)
);

The previous query results in the following output.

 quality | color | cnt
---------+-------+-----
 high    | r     |  15
 normal  | r     |  35
 low     | r     |  10
 high    | g     |  20
 low     | g     |  23
 high    | b     |   7
 normal  | b     |  40

The UNPIVOT operator accepts optional aliases on each IN list value. Each alias provides customization of the data in each value column.

The following are usage notes for UNPIVOT.