| # | census_year | geography | geography_level | area | sex | age_group | topic | category | value | unit |
|---|---|---|---|---|---|---|---|---|---|---|
| loading... | ||||||||||
| COLUMN | TYPE | DESCRIPTION | UNIT |
|---|---|---|---|
| census_year | int64 | Census reference year. Always 2022 - RPHC4 (2012) gender is PDF-only. | - |
| geography | string | Geographic unit name. Rwanda (national), one of five provinces (City of Kigali, Southern Province, Western Province, Northern Province, Eastern Province), or one of 30 districts. topic=sex_ratio is national + province; topic=gpi_education and female_headship are national only; topic=epr_gender_gap covers all 36 geographies. | - |
| geography_level | string | Geographic level - national, province, or district | - |
| area | string | Urban/rural breakdown - total, urban, or rural. topic=sex_ratio and gpi_education are always area=total; female_headship and epr_gender_gap publish total/urban/rural. | - |
| sex | string | Sex breakdown - female, male, or both_sexes. Null for topic=sex_ratio and gpi_education, where the value column itself is the male/female comparison metric. | - |
| age_group | string | 5-year age group (e.g. "20-24") or "total". Populated only for topic=sex_ratio and gpi_education. Null for female_headship and epr_gender_gap. | - |
| topic | string | Indicator topic. Values: sex_ratio (males per 100 females, by age group and province), gpi_education (Gender Parity Index for primary and secondary attainment, by age group), female_headship (share of household heads who are female/male, by area), epr_gender_gap (employment-to-population ratio by sex and area, by geography). | - |
| category | string | For topic=gpi_education, the education level (primary or secondary). Null for all other topics. | - |
| value | float64 | Indicator value, scale depends on unit. sex_ratio: males per 100 females (around 100 = parity). gpi_education: women/men ratio (1.0 = parity, less than 1 favors men, greater than 1 favors women). female_headship and epr_gender_gap: percentage (0-100). | - |
| unit | string | Unit of the value column. Values: ratio_per_100_females (sex_ratio), ratio (gpi_education), percent (female_headship, epr_gender_gap). | - |
import imibare as imi
df = imi.load("rw.nisr.phc-gender.irregular")
print(df.head())import duckdb
duckdb.sql("""
ATTACH 'https://catalog.cloudflarestorage.com/imibare-data'
AS imi (TYPE ICEBERG, READ_ONLY)
""")
duckdb.sql("SELECT * FROM imi.rw_nisr_phc-gender_irregular LIMIT 10").show()curl -L "https://api.imibare.org/v1/data/rw.nisr.phc-gender.irregular/download" \
-o "rw.nisr.phc-gender.irregular.parquet"# List all datasets
curl "https://api.imibare.org/v1/datasets"
# Get metadata for this dataset
curl "https://api.imibare.org/v1/datasets/rw.nisr.phc-gender.irregular"Every version is a change in the data, and every one is retained; snapshots are never overwritten. Each entry is a dated Parquet snapshot in the dataset's versions/ archive.
- 2026-06-11 Jun 11, 2026 LATEST476 rows · 8.3 KB · initial version
imibare contributors. (2026). PHC Gender [Data set]. imibare. v2026-06-11. https://imibare.org/datasets/rw.nisr.phc-gender.irregular
@dataset{imibare_rw_nisr_phc-gender_irregular, author = {imibare contributors}, title = {PHC Gender}, year = {2026}, publisher = {imibare}, version = {v2026-06-11}, url = {https://imibare.org/datasets/rw.nisr.phc-gender.irregular} }
imibare contributors. PHC Gender. v2026-06-11. imibare, 2026. https://imibare.org/datasets/rw.nisr.phc-gender.irregular.
imibare contributors. PHC Gender. v2026-06-11, imibare, 2026, https://imibare.org/datasets/rw.nisr.phc-gender.irregular.