How to calculate percentile in SQL

SQL query for percentile, run on a downloadable 30-day sample table, with the expected result and which databases support it.

In SQL, percentile over the sample table is calculated with the query below. On the sample data it returns 38.1.

p-th percentile = the value below which p% of the data falls (interpolated between neighbouring values)

Step by step

Load daily.csv into a table called daily (day, ad_spend, website_orders, app_orders, avg_basket).
Run the query and compare with the expected result.

Table

CREATE TABLE daily (
  day INTEGER,
  ad_spend INTEGER,
  website_orders INTEGER,
  app_orders INTEGER,
  avg_basket INTEGER
);

The query

Query

SELECT PERCENTILE_CONT(0.9) WITHIN GROUP (ORDER BY website_orders) AS p90_orders
FROM daily;

Expected result on the sample data: 38.1.

What the result means

This is the 90th percentile of daily website orders over 30 days. With so few days it sits between the 27th and 28th sorted values.

Notes for SQL

Tested on DuckDB. The same functions exist in PostgreSQL. SQLite and MySQL lack most statistical aggregates; compute in Python or load the data into DuckDB, which can query a CSV file directly.
Statistical aggregates ignore NULL rows. Decide whether a missing day should be NULL (left out) or 0 (counted as a zero day).

Where percentile misleads

Several definitions exist. PERCENTILE.INC, pandas quantile() and PERCENTILE_CONT agree; PERCENTILE.EXC does not.
High percentiles need many rows to be stable. With 30 days, the 99th percentile is little more than the maximum.
A percentile of a value (where does 35 rank?) is a different calculation: PERCENTRANK.

The plain-language explanation is on the Percentile page.

The data

daily.csv (made up): 30 days from one store
day	website_orders
1	24
2	27
3	22
4	30
5	28
6	25
7	31
8	29
9	26
10	33
11	30
12	28
13	35
14	32
15	29
16	34
17	61
18	31
19	36
20	33
21	30
22	37
23	35
24	32
25	38
26	36
27	34
28	40
29	37
30	39

Same method, other tools

Related methods

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Python and SQL for data analysis, side by side — 3 min read
Data analysis methods and techniques, and when to use each — 3 min read

About the data on this page

The figures come from a small made-up dataset, printed above in full so you can check every number by hand. Download it as daily.csv. The results are computed from those rows when the page is built, and the Excel formulas, SQL and Python on these pages are run against the same rows before publishing, so the code and the answers agree.

To get these numbers from your own data without writing formulas, try AnalyzeIt: ask in plain words and it shows the working behind every figure.

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