In SQL, moving average over the sample table is calculated with the query below. On the sample data it returns 36.57.
7-day moving average on day t = Average of days t − 6 to t
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.
CREATE TABLE daily (
day INTEGER,
ad_spend INTEGER,
website_orders INTEGER,
app_orders INTEGER,
avg_basket INTEGER
);The query
SELECT ROUND(ma7, 2) AS ma7_day_30
FROM (
SELECT day, AVG(website_orders) OVER (ORDER BY day ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7
FROM daily
) t
WHERE day = 30;Expected result on the sample data: 36.57.
What the result means
The figure here is the 7-day average on day 30 (days 24 to 30). A moving average lags behind turning points: it is late by about half its window.
Notes for SQL
- Tested on SQLite and DuckDB; it uses only standard SQL, so it also runs on PostgreSQL, MySQL and BigQuery.
- Statistical aggregates ignore NULL rows. Decide whether a missing day should be NULL (left out) or 0 (counted as a zero day).
Where moving average misleads
- The first window − 1 days have no full average. Leave them blank rather than averaging fewer days.
- One extreme day sits inside the average for the whole window, so day 17 lifts days 17 to 23.
- Use a window that matches the cycle you want to remove: 7 for weekdays, 12 for months in a year.
The plain-language explanation is on the Moving average page.
The data
| 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
Learn more about SQL
- 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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