Population by single year of age and sex, annual
Resident population of Norway by single year of age (0-105) and sex, whole country, annual from 1986. Source: StatBank table 07459.
populationdemographicsage-structurenorway
Quality
100
Attribution
Statistics Norway
Schema
| Column | Type | Description |
|---|---|---|
| date | timestamp | Start of the observation period (SSB StatBank dimension Tid: month for monthly tables, year for annual tables). |
| series_id | string | Stable row key: the SSB StatBank dimension codes for this observation, joined by '.'. |
| series_label | string | Human-readable label: the SSB StatBank dimension value texts for this observation, joined by ' — '. |
| region | string | SSB StatBank dimension 'region'. Provider code values include: 0: The whole country; 31: Østfold; 3101: Halden; 3103: Moss; 3105: Sarpsborg; 3107: Fredrikstad. |
| region_name | string | Statistics Norway's value text for the 'region' dimension code. |
| sex | string | SSB StatBank dimension 'sex'. Provider code values include: 2: Females; 1: Males. |
| sex_name | string | Statistics Norway's value text for the 'sex' dimension code. |
| age | string | SSB StatBank dimension 'age'. Provider code values include: 000: 0 years; 001: 1 year; 002: 2 years; 003: 3 years; 004: 4 years; 005: 5 years. |
| age_name | string | Statistics Norway's value text for the 'age' dimension code. |
| contents | string | SSB StatBank dimension 'contents'. Provider code values include: Personer1: Persons. |
| contents_name | string | Statistics Norway's value text for the 'contents' dimension code. |
| value | integer | Measured value. Definition and unit depend on the series — see the 'contents' / 'contents_name' dimension, whose value texts are Statistics Norway's own (e.g. 'Consumer Price Index', '12-month rate (per cent)', 'Unemployment rate (LFS)', 'Persons'). |
Sample rows
| date | series_id | series_label | region | region_name | sex | sex_name | age | age_name | contents | contents_name | value |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1986-01-01T00:00:00 | 0.1.000.Personer1 | The whole country — Males — 0 years — Persons | 0 | The whole country | 1 | Males | 000 | 0 years | Personer1 | Persons | 26175 |
| 1987-01-01T00:00:00 | 0.1.000.Personer1 | The whole country — Males — 0 years — Persons | 0 | The whole country | 1 | Males | 000 | 0 years | Personer1 | Persons | 26923 |
| 1988-01-01T00:00:00 | 0.1.000.Personer1 | The whole country — Males — 0 years — Persons | 0 | The whole country | 1 | Males | 000 | 0 years | Personer1 | Persons | 27394 |
| 1989-01-01T00:00:00 | 0.1.000.Personer1 | The whole country — Males — 0 years — Persons | 0 | The whole country | 1 | Males | 000 | 0 years | Personer1 | Persons | 29332 |
| 1990-01-01T00:00:00 | 0.1.000.Personer1 | The whole country — Males — 0 years — Persons | 0 | The whole country | 1 | Males | 000 | 0 years | Personer1 | Persons | 30325 |
Download sample data
Download the full sample snapshot for this dataset (sample rows, not the complete dataset).
Use with an LLM
Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).
cURL
curl "https://datazimuts.com/v1/datasets/ssb/population_by_age_sex" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/ssb/population_by_age_sex").json()
print(ds["title"], ds["rows"], "rows")
# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
print(row)API endpoint: https://datazimuts.com/v1/datasets/ssb/population_by_age_sex
Tip: fetch /llms.txt for the full machine-readable catalog.