PERCENTILE_EXC
Updated 2023-10-23 20:50:33.653000
Syntax
SELECT [westclintech].[wct].[PERCENTILE_EXC] (
<@x, float,>
,<@k, float,>)
Description
Use the aggregate function PERCENTILE_EXC to return the kth percentile of values in a dataset, where k is in the range 0 through 1, exclusive. To calculate the kth percentile of a dataset, put all the non-NULL values into an ascending ordered set P. Set N equal to the number of items in P.
n = N + 1
r = \left\lfloor{k*n}\right\rfloor
d=(k*n) - r
P=d(k*n) * (P_r - P_{r-1}) + P_{r-1}
Arguments
@x
the input values for the function. @Known_x is an expression of type float or of a type that can be implicitly converted to float.
@k
is the percentile value in the range 0 through 1, inclusive. @k is an expression of type float or of a type that can be implicitly converted to float.
Return Type
float
Remarks
If @k <0 or @k > 1 one, PERCENTILE_EXC returns an error.
If @k is not a multiple of 1/(n+1), PERCENTILE interpolates to determine the value at the kth percentile.
If @k * (N+1) > N, then null is returned.
This is the equivalent of PERCENTILE.EXC function in EXCEL. For the PERCENTILE.INC function, see PERCENTILE.
@k must remain invariant for a group.
PERCENTILE_EXC is an aggregate function and follows the same conventions as all other aggregate functions in SQL Server.
If there is only one value in the dataset, that value will be returned by the function.
Examples
In this example, we calculate the 90th percentile for selected salary inforrmation collected from 10 cities.
SELECT wct.PERCENTILE_EXC( salary, --@x
.90 --@k
) as PERCENTILE
FROM
(
VALUES
('New York', '429-00-6486', 236503),
('New York', '90-70-2526', 224472),
('New York', '87-85-0404', 139802),
('New York', '716-89-3089', 185287),
('New York', '159-78-5370', 211900),
('New York', '195-97-6820', 186703),
('New York', '95-49-2813', 167451),
('New York', '37-20-7422', 149462),
('New York', '44-48-0076', 214708),
('New York', '514-79-0041', 226485),
('Los Angeles', '526-34-4521', 196402),
('Los Angeles', '800-50-0868', 205359),
('Los Angeles', '41-34-3618', 195679),
('Los Angeles', '854-29-9398', 131925),
('Los Angeles', '673-30-3623', 171091),
('Los Angeles', '537-58-8889', 110217),
('Los Angeles', '808-68-4234', 192836),
('Los Angeles', '359-81-6735', 209346),
('Los Angeles', '731-80-2303', 182186),
('Los Angeles', '214-58-0842', 125355),
('Chicago', '456-79-9682', 183698),
('Chicago', '807-97-4784', 194282),
('Chicago', '981-16-3724', 156083),
('Chicago', '252-34-3054', 226619),
('Chicago', '613-28-9452', 153366),
('Chicago', '785-25-8628', 205709),
('Chicago', '451-26-7350', 206085),
('Chicago', '443-94-2401', 120587),
('Chicago', '696-26-8113', 171185),
('Chicago', '277-31-9760', 211160),
('Dallas', '537-88-7532', 245231),
('Dallas', '393-25-3503', 238733),
('Dallas', '612-17-0712', 103152),
('Dallas', '384-93-7285', 228842),
('Dallas', '745-10-7587', 154749),
('Dallas', '950-20-4045', 102156),
('Dallas', '477-48-7550', 196533),
('Dallas', '427-52-8597', 238970),
('Dallas', '891-19-0810', 245204),
('Dallas', '564-79-7612', 196946),
('Boston', '216-84-7134', 128035),
('Boston', '515-84-4073', 249093),
('Boston', '92-43-5775', 205026),
('Boston', '144-08-1092', 198120),
('Boston', '469-14-5012', 174143),
('Boston', '379-92-8313', 166215),
('Boston', '71-22-5132', 105058),
('Boston', '257-39-0324', 107247),
('Boston', '611-57-4279', 118561),
('Boston', '956-53-2865', 232789),
('Denver', '711-81-0072', 240720),
('Denver', '673-39-5028', 159706),
('Denver', '554-33-3980', 232493),
('Denver', '770-03-5304', 203310),
('Denver', '732-47-5077', 123106),
('Denver', '573-18-3567', 149999),
('Denver', '93-92-0334', 162657),
('Denver', '424-76-1468', 116322),
('Denver', '403-47-0063', 246058),
('Denver', '460-58-1833', 198043),
('Miami', '221-04-4153', 130962),
('Miami', '179-09-9839', 227246),
('Miami', '564-76-9437', 144027),
('Miami', '407-48-4081', 138549),
('Miami', '526-79-1840', 208006),
('Miami', '72-68-4977', 170109),
('Miami', '235-72-3903', 191669),
('Miami', '436-62-0474', 161164),
('Miami', '430-52-3914', 162507),
('Miami', '459-27-5541', 238972),
('Phoenix', '576-38-4531', 238281),
('Phoenix', '65-64-1278', 197678),
('Phoenix', '880-29-1997', 159183),
('Phoenix', '304-72-1881', 194733),
('Phoenix', '61-20-1046', 221045),
('Phoenix', '64-95-5514', 105577),
('Phoenix', '262-63-4021', 186399),
('Phoenix', '661-84-1023', 234974),
('Phoenix', '892-31-4821', 115076),
('Phoenix', '319-91-9463', 239548),
('San Franciso', '136-67-6873', 148829),
('San Franciso', '5-41-7374', 114161),
('San Franciso', '381-26-8852', 232509),
('San Franciso', '620-64-6243', 112686),
('San Franciso', '128-33-5550', 208679),
('San Franciso', '422-00-0156', 107685),
('San Franciso', '370-98-5607', 133224),
('San Franciso', '91-58-9543', 218955),
('San Franciso', '911-35-0448', 187826),
('San Franciso', '734-65-1268', 223683),
('Atlanta', '334-97-0585', 240384),
('Atlanta', '405-12-4222', 124350),
('Atlanta', '43-05-7567', 233836),
('Atlanta', '882-97-7996', 134091),
('Atlanta', '368-91-4292', 173787),
('Atlanta', '408-04-5921', 140769),
('Atlanta', '232-13-5280', 206307),
('Atlanta', '88-41-2584', 118159),
('Atlanta', '539-03-7548', 116718),
('Atlanta', '587-63-6935', 174801)
) p (city, id, salary);
This produces the following result.
{"columns":[{"field":"PERCENTILE","headerClass":"ag-right-aligned-header","cellClass":"ag-right-aligned-cell"}],"rows":[{"PERCENTILE":"238687.8"}]}
In this example we will calculate the 90th percentile in each city.
SELECT city,
wct.PERCENTILE_EXC(salary, .90) as PERCENTILE
FROM
(
VALUES
('New York', '429-00-6486', 236503),
('New York', '90-70-2526', 224472),
('New York', '87-85-0404', 139802),
('New York', '716-89-3089', 185287),
('New York', '159-78-5370', 211900),
('New York', '195-97-6820', 186703),
('New York', '95-49-2813', 167451),
('New York', '37-20-7422', 149462),
('New York', '44-48-0076', 214708),
('New York', '514-79-0041', 226485),
('Los Angeles', '526-34-4521', 196402),
('Los Angeles', '800-50-0868', 205359),
('Los Angeles', '41-34-3618', 195679),
('Los Angeles', '854-29-9398', 131925),
('Los Angeles', '673-30-3623', 171091),
('Los Angeles', '537-58-8889', 110217),
('Los Angeles', '808-68-4234', 192836),
('Los Angeles', '359-81-6735', 209346),
('Los Angeles', '731-80-2303', 182186),
('Los Angeles', '214-58-0842', 125355),
('Chicago', '456-79-9682', 183698),
('Chicago', '807-97-4784', 194282),
('Chicago', '981-16-3724', 156083),
('Chicago', '252-34-3054', 226619),
('Chicago', '613-28-9452', 153366),
('Chicago', '785-25-8628', 205709),
('Chicago', '451-26-7350', 206085),
('Chicago', '443-94-2401', 120587),
('Chicago', '696-26-8113', 171185),
('Chicago', '277-31-9760', 211160),
('Dallas', '537-88-7532', 245231),
('Dallas', '393-25-3503', 238733),
('Dallas', '612-17-0712', 103152),
('Dallas', '384-93-7285', 228842),
('Dallas', '745-10-7587', 154749),
('Dallas', '950-20-4045', 102156),
('Dallas', '477-48-7550', 196533),
('Dallas', '427-52-8597', 238970),
('Dallas', '891-19-0810', 245204),
('Dallas', '564-79-7612', 196946),
('Boston', '216-84-7134', 128035),
('Boston', '515-84-4073', 249093),
('Boston', '92-43-5775', 205026),
('Boston', '144-08-1092', 198120),
('Boston', '469-14-5012', 174143),
('Boston', '379-92-8313', 166215),
('Boston', '71-22-5132', 105058),
('Boston', '257-39-0324', 107247),
('Boston', '611-57-4279', 118561),
('Boston', '956-53-2865', 232789),
('Denver', '711-81-0072', 240720),
('Denver', '673-39-5028', 159706),
('Denver', '554-33-3980', 232493),
('Denver', '770-03-5304', 203310),
('Denver', '732-47-5077', 123106),
('Denver', '573-18-3567', 149999),
('Denver', '93-92-0334', 162657),
('Denver', '424-76-1468', 116322),
('Denver', '403-47-0063', 246058),
('Denver', '460-58-1833', 198043),
('Miami', '221-04-4153', 130962),
('Miami', '179-09-9839', 227246),
('Miami', '564-76-9437', 144027),
('Miami', '407-48-4081', 138549),
('Miami', '526-79-1840', 208006),
('Miami', '72-68-4977', 170109),
('Miami', '235-72-3903', 191669),
('Miami', '436-62-0474', 161164),
('Miami', '430-52-3914', 162507),
('Miami', '459-27-5541', 238972),
('Phoenix', '576-38-4531', 238281),
('Phoenix', '65-64-1278', 197678),
('Phoenix', '880-29-1997', 159183),
('Phoenix', '304-72-1881', 194733),
('Phoenix', '61-20-1046', 221045),
('Phoenix', '64-95-5514', 105577),
('Phoenix', '262-63-4021', 186399),
('Phoenix', '661-84-1023', 234974),
('Phoenix', '892-31-4821', 115076),
('Phoenix', '319-91-9463', 239548),
('San Franciso', '136-67-6873', 148829),
('San Franciso', '5-41-7374', 114161),
('San Franciso', '381-26-8852', 232509),
('San Franciso', '620-64-6243', 112686),
('San Franciso', '128-33-5550', 208679),
('San Franciso', '422-00-0156', 107685),
('San Franciso', '370-98-5607', 133224),
('San Franciso', '91-58-9543', 218955),
('San Franciso', '911-35-0448', 187826),
('San Franciso', '734-65-1268', 223683),
('Atlanta', '334-97-0585', 240384),
('Atlanta', '405-12-4222', 124350),
('Atlanta', '43-05-7567', 233836),
('Atlanta', '882-97-7996', 134091),
('Atlanta', '368-91-4292', 173787),
('Atlanta', '408-04-5921', 140769),
('Atlanta', '232-13-5280', 206307),
('Atlanta', '88-41-2584', 118159),
('Atlanta', '539-03-7548', 116718),
('Atlanta', '587-63-6935', 174801)
) p (city, id, salary)
GROUP BY city;
This produces the following result.
{"columns":[{"field":"city"},{"field":"PERCENTILE","headerClass":"ag-right-aligned-header","cellClass":"ag-right-aligned-cell"}],"rows":[{"city":"Atlanta","PERCENTILE":"239729.2"},{"city":"Boston","PERCENTILE":"247462.6"},{"city":"Chicago","PERCENTILE":"225073.1"},{"city":"Dallas","PERCENTILE":"245228.3"},{"city":"Denver","PERCENTILE":"245524.2"},{"city":"Los Angeles","PERCENTILE":"208947.3"},{"city":"Miami","PERCENTILE":"237799.4"},{"city":"New York","PERCENTILE":"235501.2"},{"city":"Phoenix","PERCENTILE":"239421.3"},{"city":"San Franciso","PERCENTILE":"231626.4"}]}
In this example, we only select those cities where the 90th percentile is less than 225,000.
SELECT city,
wct.PERCENTILE_EXC(salary, .90) as PERCENTILE
FROM
(
VALUES
('New York', '429-00-6486', 236503),
('New York', '90-70-2526', 224472),
('New York', '87-85-0404', 139802),
('New York', '716-89-3089', 185287),
('New York', '159-78-5370', 211900),
('New York', '195-97-6820', 186703),
('New York', '95-49-2813', 167451),
('New York', '37-20-7422', 149462),
('New York', '44-48-0076', 214708),
('New York', '514-79-0041', 226485),
('Los Angeles', '526-34-4521', 196402),
('Los Angeles', '800-50-0868', 205359),
('Los Angeles', '41-34-3618', 195679),
('Los Angeles', '854-29-9398', 131925),
('Los Angeles', '673-30-3623', 171091),
('Los Angeles', '537-58-8889', 110217),
('Los Angeles', '808-68-4234', 192836),
('Los Angeles', '359-81-6735', 209346),
('Los Angeles', '731-80-2303', 182186),
('Los Angeles', '214-58-0842', 125355),
('Chicago', '456-79-9682', 183698),
('Chicago', '807-97-4784', 194282),
('Chicago', '981-16-3724', 156083),
('Chicago', '252-34-3054', 226619),
('Chicago', '613-28-9452', 153366),
('Chicago', '785-25-8628', 205709),
('Chicago', '451-26-7350', 206085),
('Chicago', '443-94-2401', 120587),
('Chicago', '696-26-8113', 171185),
('Chicago', '277-31-9760', 211160),
('Dallas', '537-88-7532', 245231),
('Dallas', '393-25-3503', 238733),
('Dallas', '612-17-0712', 103152),
('Dallas', '384-93-7285', 228842),
('Dallas', '745-10-7587', 154749),
('Dallas', '950-20-4045', 102156),
('Dallas', '477-48-7550', 196533),
('Dallas', '427-52-8597', 238970),
('Dallas', '891-19-0810', 245204),
('Dallas', '564-79-7612', 196946),
('Boston', '216-84-7134', 128035),
('Boston', '515-84-4073', 249093),
('Boston', '92-43-5775', 205026),
('Boston', '144-08-1092', 198120),
('Boston', '469-14-5012', 174143),
('Boston', '379-92-8313', 166215),
('Boston', '71-22-5132', 105058),
('Boston', '257-39-0324', 107247),
('Boston', '611-57-4279', 118561),
('Boston', '956-53-2865', 232789),
('Denver', '711-81-0072', 240720),
('Denver', '673-39-5028', 159706),
('Denver', '554-33-3980', 232493),
('Denver', '770-03-5304', 203310),
('Denver', '732-47-5077', 123106),
('Denver', '573-18-3567', 149999),
('Denver', '93-92-0334', 162657),
('Denver', '424-76-1468', 116322),
('Denver', '403-47-0063', 246058),
('Denver', '460-58-1833', 198043),
('Miami', '221-04-4153', 130962),
('Miami', '179-09-9839', 227246),
('Miami', '564-76-9437', 144027),
('Miami', '407-48-4081', 138549),
('Miami', '526-79-1840', 208006),
('Miami', '72-68-4977', 170109),
('Miami', '235-72-3903', 191669),
('Miami', '436-62-0474', 161164),
('Miami', '430-52-3914', 162507),
('Miami', '459-27-5541', 238972),
('Phoenix', '576-38-4531', 238281),
('Phoenix', '65-64-1278', 197678),
('Phoenix', '880-29-1997', 159183),
('Phoenix', '304-72-1881', 194733),
('Phoenix', '61-20-1046', 221045),
('Phoenix', '64-95-5514', 105577),
('Phoenix', '262-63-4021', 186399),
('Phoenix', '661-84-1023', 234974),
('Phoenix', '892-31-4821', 115076),
('Phoenix', '319-91-9463', 239548),
('San Franciso', '136-67-6873', 148829),
('San Franciso', '5-41-7374', 114161),
('San Franciso', '381-26-8852', 232509),
('San Franciso', '620-64-6243', 112686),
('San Franciso', '128-33-5550', 208679),
('San Franciso', '422-00-0156', 107685),
('San Franciso', '370-98-5607', 133224),
('San Franciso', '91-58-9543', 218955),
('San Franciso', '911-35-0448', 187826),
('San Franciso', '734-65-1268', 223683),
('Atlanta', '334-97-0585', 240384),
('Atlanta', '405-12-4222', 124350),
('Atlanta', '43-05-7567', 233836),
('Atlanta', '882-97-7996', 134091),
('Atlanta', '368-91-4292', 173787),
('Atlanta', '408-04-5921', 140769),
('Atlanta', '232-13-5280', 206307),
('Atlanta', '88-41-2584', 118159),
('Atlanta', '539-03-7548', 116718),
('Atlanta', '587-63-6935', 174801)
) p (city, id, salary)
GROUP BY city
HAVING wct.PERCENTILE_EXC(salary, .90) < 225000;
This produces the following result.
{"columns":[{"field":"city"},{"field":"PERCENTILE","headerClass":"ag-right-aligned-header","cellClass":"ag-right-aligned-cell"}],"rows":[{"city":"Los Angeles","PERCENTILE":"208947.3"}]}
In this example we calculate the 40th percentile for a variety of data groupings, including one NULL value and a GROUP that only contains one member.
SELECT dsc,
wct.PERCENTILE_EXC(x, .40) as PERCENTILE
FROM
(
VALUES
('ABC', 15),
('ABC', 20),
('ABC', 35),
('ABC', 40),
('ABC', 50),
('DEF', 7),
('DEF', 10),
('DEF', 17),
('DEF', 20),
('DEF', 25),
('DEF', 70),
('GHI', 21),
('DEF', 28),
('DEF', NULL),
('DEF', 38),
('DEF', 31),
('DEF', 52),
('JKL', 37)
) p (dsc, x)
GROUP BY dsc;
This produces the following result.
{"columns":[{"field":"dsc"},{"field":"PERCENTILE","headerClass":"ag-right-aligned-header","cellClass":"ag-right-aligned-cell"}],"rows":[{"dsc":"ABC","PERCENTILE":"26"},{"dsc":"DEF","PERCENTILE":"22"},{"dsc":"GHI","PERCENTILE":"21"},{"dsc":"JKL","PERCENTILE":"37"}]}
See Also
PERCENTILES - table-valued function to calculate one or many percentile values from a single dataset
PERCENTILE - Aggregate function to calculate a percentile using the 'INC' method
PERCENTILE - Aggregate function to calculate a percentile using the 'INC' method
PERCENTILE_Q - Calculate percentile.
PERCENTRANK - Aggregate function to calculate a percent rank using the 'INC' method