Full-time equivalent enrolments in government schools by school type, South Australia
liveEnrolments by School Type from the Department for Education. Each row is one type of government school with its full-time equivalent enrolments in each year from 2012 to 2023, collected in the annual Term 3 enrolment census. The publisher leaves the years blank for a school type with no enrolments, such as rural schools after 2013.
Also called: Enrolments by School Type SA, SA public school enrolment numbers by type, South Australian government school FTE enrolments.
- What is in each row?
- One type of government school, such as primary, area or special schools, with its full-time equivalent enrolments in each year's Term 3 census from 2012 to 2023, one column per year.
- What is a full-time equivalent enrolment?
- A student enrolled full time counts as one and a part-time student as the fraction of full time they attend, so the figures can carry decimals.
- Why do primary enrolments fall and high school enrolments rise in 2022?
- South Australian government schools moved Year 7 from primary school to high school from 2022, so about 12,000 FTE enrolments left primary schools and about 10,000 joined high schools that year.
- Why are junior primary enrolments so high in 2012?
- The publisher gives 4,900.4 for junior primary schools in 2012 and 488.0 in 2013, about a tenth as many. The file gives no reason, and the figure is kept as published.
Made for agents
Connect your AI agent
Any agent that connects to publicdata.au/mcp can find this dataset, read its 13 fields, count and filter across all 12 rows and diff its versions. Every answer names the version and carries Education's attribution. No key.
Then ask it: What does Full-time equivalent enrolments in government schools by school type, South Australia hold, and what changed in the newest version?
claude mcp add --transport http publicdata https://publicdata.au/mcpOne command. No key, no account, no sign-up.
Settings, then Connectors, then Add custom connector, and paste https://publicdata.au/mcp.
On the web and in the desktop app.
https://publicdata.au/mcpStreamable HTTP. Cursor, VS Code or any client that connects to a remote MCP server.
Using https://publicdata.au/llms.txt, how many people died on Queensland roads in 2025, by month?For an assistant that reads the web and has no connector.
A dashboard over every row. Click to filter, add panels, share a link or embed a frame.
Query APIFilter and count from a URLaggregate?group=school_type&metric=sum.fte_2023
FTE enrolments, 2023 by school type: 84,181 Primary Schools, 57,062 High/Secondary Schools, 16,700 Primary/Secondary Schools, 9,496 Area Schools.
Download it as Excel, CSV, JSON and 6 more formats
Pick a format and a version. Excel is picked first because it opens in the tools most offices have. The URL is yours to keep. A dated version never changes.
In your own tools
Use it in Excel, R, Python and more
Excel and Power BI read the CSV from its address and refresh from it. The R and Python packages take any dataset on this site by its slug, so a new dataset needs no new release. The DuckDB file attaches read-only over HTTPS, and a query reads only the blocks it touches.
The dated URL in the code never changes. https://publicdata.au/d/sa-school-enrolments-by-type/latest/ redirects to the newest version.
https://publicdata.au/d/sa-school-enrolments-by-type/latest/data.csvIn Excel choose Data, then From Web, and paste this address. Excel keeps it, so Refresh All reads the newest version. A sheet holds about a million rows; past that, load the query to the Data Model.
https://publicdata.au/d/sa-school-enrolments-by-type/latest/data.csvIn Power BI Desktop choose Get data, then Web, paste this address and choose Anonymous when asked how to sign in. A scheduled refresh reads the same address, so the report follows each new version.
library(publicdataau)
df <- pd_read("sa-school-enrolments-by-type")
pd_attribution(df)install.packages("publicdataau"). pd_read() fetches the version's Parquet file; pd_rows() and pd_aggregate() ask the query API instead, and pd_connect() attaches the DuckDB file.
import publicdata_au as pd_au
df = pd_au.read("sa-school-enrolments-by-type")
df.attrs["publicdata"]["attribution"]pip install "publicdata-au[pandas]". read() fetches the version's Parquet file; rows() and aggregate() ask the query API, and connect() attaches the DuckDB file.
INSTALL httpfs; LOAD httpfs;
ATTACH 'https://publicdata.au/d/sa-school-enrolments-by-type/v/2023-11-30/data.duckdb' AS sa_school_enrolments_by_type (READ_ONLY);
SELECT school_type, count(*) FROM sa_school_enrolments_by_type.records GROUP BY 1 ORDER BY 2 DESC;The DuckDB file attaches read-only over HTTPS and only the blocks a query touches are read. Parquet works the same way: FROM read_parquet(url).
const res = await fetch("https://publicdata.au/api/v1/datasets/sa-school-enrolments-by-type/aggregate?group=school_type&metric=sum.fte_2023");
const { rows, publicdata } = await res.json();
console.log(rows, publicdata.attribution);The query API answers a page on any site as well as Node, with no key. It returns the rows and the attribution the licence asks for.
What is in it
Column names are made snake_case and the publisher's original header is kept beside each one. Blank cells are null. Nothing is added, removed, ranked or summarised.
| Field | Type | Publisher's header | Note |
|---|---|---|---|
| school_type | string | Type_of_School | |
| fte_2012 | number | 2012 | |
| fte_2013 | number | 2013 | |
| fte_2014 | number | 2014 | |
| fte_2015 | number | 2015 | |
| fte_2016 | number | 2016 | |
| fte_2017 | number | 2017 | |
| fte_2018 | number | 2018 | |
| fte_2019 | number | 2019 | |
| fte_2020 | number | 2020 | |
| fte_2021 | number | 2021 | |
| fte_2022 | number | 2022 | |
| fte_2023 | number | 2023 |
Download this table as a data dictionary (Excel), or read it as a Frictionless Table Schema at schema.json. The key is school_type.
The first 10 rows
From the latest version, in the publisher's order, with every field. A blank cell is shown as null.
| school_type | fte_2012 | fte_2013 | fte_2014 | fte_2015 | fte_2016 | fte_2017 | fte_2018 | fte_2019 | fte_2020 | fte_2021 | fte_2022 | fte_2023 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Aboriginal Schools | 273.8 | 285.5 | 267.4 | 317.5 | 295 | 287.2 | 257.1 | 243.2 | 255.3 | 241.6 | 236.1 | 278.7 |
| Alternative Schools | 217.4 | 215.4 | 186.2 | 171 | 187 | 178.8 | 182.6 | 174.7 | 205.6 | 200.6 | 206.6 | 230.2 |
| Anangu Schools | 607.5 | 616.4 | 625.8 | 604.5 | 623.5 | 664.2 | 630 | 660 | 609 | 634.9 | 678.8 | 651.4 |
| Area Schools | 10873.4 | 10725.6 | 10428.7 | 10244.3 | 10119 | 9958 | 9768.7 | 9640.8 | 9663.8 | 9474.9 | 9537.4 | 9496.5 |
| High/Secondary Schools | 47965.3 | 46575.5 | 46259.6 | 46678.3 | 46832.8 | 46297.6 | 46193 | 47060.8 | 47510 | 47667.1 | 57413.5 | 57062.4 |
| Junior Primary Schools | 4900.4 | 488 | 473 | 312 | 329 | 319 | 308 | 287 | 258 | 272 | 252 | 259 |
| Language Schools | 401 | 379 | 510 | 419 | 442 | 484 | 383 | 374 | 318 | 167 | 260 | 383.6 |
| Open Access College | 929.5 | 859.9 | 779.7 | 801.8 | 786.7 | 848.9 | 875.4 | 994.6 | 1058.9 | 957.1 | 1220.7 | 1343.8 |
| Primary Schools | 87958.8 | 92844.8 | 91934.2 | 93805.4 | 95817.9 | 97589 | 99427.5 | 100461.4 | 99695.8 | 97197.5 | 84985.8 | 84180.6 |
| Primary/Secondary Schools | 11392.4 | 13662.1 | 13759.5 | 14087 | 14361.9 | 14916.4 | 15123 | 15488.9 | 15610.4 | 15662 | 16217.7 | 16700.2 |
The first row as JSON
As it appears in data.json.
GET https://publicdata.au/d/sa-school-enrolments-by-type/latest/data.json
{
"school_type": "Aboriginal Schools",
"fte_2012": 273.8,
"fte_2013": 285.5,
"fte_2014": 267.4,
"fte_2015": 317.5,
"fte_2016": 295.0,
"fte_2017": 287.2,
"fte_2018": 257.1,
"fte_2019": 243.2,
"fte_2020": 255.3,
"fte_2021": 241.6,
"fte_2022": 236.1,
"fte_2023": 278.7
}
Query it without downloading
The query API returns only the rows you ask for, as JSON, NDJSON or CSV, from the same typed rows as the files. It needs no key. Build a query here and run it, then copy the URL or the code into your own work.
How filters work
Each filter is field=operator.value in the query string, and every filter must match. A number or date field compares as a number or date, and a text field compares as text. Values are case-sensitive except with ilike. Prefix not. to negate a filter, as not.eq.value.
| Operator | What it matches |
|---|---|
| eq.value | Equal to the value. |
| neq.value | Not equal to the value. A blank cell does not match. |
| gt.value | Greater than the value. |
| gte.value | Greater than or equal to the value. |
| lt.value | Less than the value. |
| lte.value | Less than or equal to the value. |
| like.*text* | Matches a pattern where * stands for any run of characters. Case-sensitive. |
| ilike.*text* | The same as like, ignoring case. |
| in.(a,b,c) | Equal to any value in the list. A value cannot contain a comma. |
| is.null | Blank in the source, or suppressed by the publisher. |
| Parameter | What it does |
|---|---|
| select | Fields to return, comma-separated. Every field when absent. |
| order | field.asc or field.desc, comma-separated. The publisher's row order when absent. |
| limit | Rows per page, 1 to 10,000. 100 when absent. |
| offset | Rows to skip. The next URL in each answer sets it for you. |
| group | On aggregate, fields to group by, comma-separated. |
| metric | On aggregate, count, sum.field, avg.field, min.field or max.field, comma-separated. count when absent. |
| format | json, ndjson or csv. JSON carries the provenance header, and the others carry it in response headers. |
Without a version the API answers from the newest loaded version, and that answer changes when the publisher releases again. Put versions/<date>/ before rows or aggregate for an answer that never changes. /api/v1/datasets/<slug>/versions lists the loaded versions. The 1 newest version is loaded; versions lists them.
The API allows 60 requests in 10 seconds from one address. Above that it answers 429 for 10 seconds with a Retry-After header, a RateLimit-Policy header and a JSON body that gives the limit. Every API answer carries the same RateLimit-Policy. A client should wait the Retry-After seconds, or read the files, which have no limit. openapi.json describes this dataset's query paths for client generators and agents.
https://publicdata.au/mcp is a remote MCP server over Streamable HTTP with the same tools, defined in the same place, so a tool added to the pages is added here too. It needs no key and no account. The row tools are held to the query API's limit of 60 queries in 10 seconds from one address, and each call is two queries because it also counts the matching rows. Add it to Claude Code with claude mcp add --transport http publicdata https://publicdata.au/mcp, add the URL in Claude as a custom connector, or give it to any client that connects to remote MCP servers. Each queryable dataset is also a resource at https://publicdata.au/d/<slug>/fields.json, which lists its fields with their types, the publisher's descriptions, their ranges and the values they hold, so an agent can read a dataset's shape before it writes a query.
What changed
One version for every release the publisher has made since this site started following the dataset. The date is the day the file changed on the portal.
- 2023-11-30 12 rows13 fieldsutf-8-sig577a49248051
versions.json · changes.json · history.tar.zst (5.0 KB, every version's Parquet and manifest)
Questions
How do I download Full-time equivalent enrolments in government schools by school type as a CSV file?
Open https://publicdata.au/d/sa-school-enrolments-by-type/latest/data.csv. It redirects to the newest dated version, which is https://publicdata.au/d/sa-school-enrolments-by-type/v/2023-11-30/data.csv today. The same path serves Excel, JSON, Parquet, SQLite, DuckDB, NDJSON, Arrow. A dated URL never changes, so use it when the file must stay the same.
Can I open Full-time equivalent enrolments in government schools by school type in Excel?
Yes. https://publicdata.au/d/sa-school-enrolments-by-type/v/2023-11-30/data.xlsx is a workbook with the 12 rows on a records sheet, the field list on a second sheet and the provenance on a third. The CSV also opens in Excel. https://publicdata.au/d/sa-school-enrolments-by-type/v/2023-11-30/data.csv.gz is the CSV at about a tenth of the size.
What years does Full-time equivalent enrolments in government schools by school type cover?
The current version covers the years since 2012. Each release from Education becomes a new dated version here, and earlier versions stay online.
How often is Full-time equivalent enrolments in government schools by school type updated?
Education releases it yearly. This site checks the portal every week and adds a dated version when the file changes.
Can I use Full-time equivalent enrolments in government schools by school type commercially?
Yes. CC BY 4.0 allows commercial use, redistribution and derived works as long as the attribution is kept. The attribution string is in this page's side column and inside every file.
Is this the official source for Full-time equivalent enrolments in government schools by school type?
No. The publisher is Department for Education, and its page is https://data.sa.gov.au/data/dataset/enrolments-in-sa-government-by-school-type. This site republishes the publisher's file without changing its content. The original sits beside every version as source.csv with its SHA-256, so the two can be compared.
What this site did to the data. Cells were typed, headers were renamed and the encoding was made UTF-8. Rows were left alone. The publisher's file sits beside every version as source.csv so the change can be checked.