How to calculate median in SQL

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

In SQL, median over the sample table is calculated with the query below. On the sample data it returns 32.

Median = The middle value when the values are sorted (the average of the two middle values when there is an even number)

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(0.5) WITHIN GROUP (ORDER BY website_orders) AS median_orders
FROM daily;

Expected result on the sample data: 32.

What the result means

With 30 days, the median is the average of the 15th and 16th values after sorting. The one-day sale on day 17 barely moves it, while it lifts the mean.

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 median misleads

The median of several group medians is not the overall median. Compute it from all the rows.
It ignores how far the extremes are. For totals and budgets you still need the mean.
For small samples the median jumps between values; with a handful of rows, show the values themselves.

The plain-language explanation is on the Median 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

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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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