country_name stringclasses 39
values | country_iso3 stringclasses 39
values | year int64 1.99k 2.02k | Armed forces personnel (% of total population) float64 0 7.91 | World region according to OWID stringclasses 2
values |
|---|---|---|---|---|
Afghanistan | AFG | 1,985 | 0.411312 | Asia |
Afghanistan | AFG | 1,989 | 0.463193 | Asia |
Afghanistan | AFG | 1,990 | 0.481501 | Asia |
Afghanistan | AFG | 1,991 | 0.367681 | Asia |
Afghanistan | AFG | 1,992 | 0.338881 | Asia |
Afghanistan | AFG | 1,993 | 0.301141 | Asia |
Afghanistan | AFG | 1,994 | 0.276909 | Asia |
Afghanistan | AFG | 1,995 | 2.24425 | Asia |
Afghanistan | AFG | 1,996 | 2.415097 | Asia |
Afghanistan | AFG | 1,997 | 2.324939 | Asia |
Afghanistan | AFG | 1,998 | 2.087683 | Asia |
Afghanistan | AFG | 1,999 | 2.011284 | Asia |
Afghanistan | AFG | 2,000 | 1.987051 | Asia |
Afghanistan | AFG | 2,002 | 0.561322 | Asia |
Afghanistan | AFG | 2,003 | 0.571855 | Asia |
Afghanistan | AFG | 2,004 | 0.114598 | Asia |
Afghanistan | AFG | 2,005 | 0.110635 | Asia |
Afghanistan | AFG | 2,006 | 0.200597 | Asia |
Afghanistan | AFG | 2,007 | 0.196836 | Asia |
Afghanistan | AFG | 2,008 | 0.35495 | Asia |
Afghanistan | AFG | 2,009 | 0.93113 | Asia |
Afghanistan | AFG | 2,010 | 1.084709 | Asia |
Afghanistan | AFG | 2,011 | 1.159716 | Asia |
Afghanistan | AFG | 2,012 | 1.106511 | Asia |
Afghanistan | AFG | 2,013 | 1.045609 | Asia |
Afghanistan | AFG | 2,014 | 0.941678 | Asia |
Afghanistan | AFG | 2,015 | 0.944083 | Asia |
Afghanistan | AFG | 2,016 | 0.930819 | Asia |
Afghanistan | AFG | 2,017 | 0.905042 | Asia |
Afghanistan | AFG | 2,018 | 0.742726 | Asia |
Afghanistan | AFG | 2,019 | 0.734359 | Asia |
Afghanistan | AFG | 2,020 | 0.42233 | Asia |
Armenia | ARM | 1,992 | 0.559931 | Asia |
Armenia | ARM | 1,993 | 0.608107 | Asia |
Armenia | ARM | 1,994 | 1.337447 | Asia |
Armenia | ARM | 1,995 | 1.844245 | Asia |
Armenia | ARM | 1,996 | 1.781173 | Asia |
Armenia | ARM | 1,997 | 1.879067 | Asia |
Armenia | ARM | 1,998 | 1.69555 | Asia |
Armenia | ARM | 1,999 | 1.717561 | Asia |
Armenia | ARM | 2,000 | 1.353327 | Asia |
Armenia | ARM | 2,001 | 1.394135 | Asia |
Armenia | ARM | 2,002 | 1.492324 | Asia |
Armenia | ARM | 2,003 | 1.500505 | Asia |
Armenia | ARM | 2,004 | 1.621151 | Asia |
Armenia | ARM | 2,005 | 1.629878 | Asia |
Armenia | ARM | 2,006 | 1.571711 | Asia |
Armenia | ARM | 2,007 | 1.411848 | Asia |
Armenia | ARM | 2,008 | 1.418921 | Asia |
Armenia | ARM | 2,009 | 1.886595 | Asia |
Armenia | ARM | 2,010 | 1.894453 | Asia |
Armenia | ARM | 2,011 | 1.900065 | Asia |
Armenia | ARM | 2,012 | 1.680052 | Asia |
Armenia | ARM | 2,013 | 1.680054 | Asia |
Armenia | ARM | 2,014 | 1.680107 | Asia |
Armenia | ARM | 2,015 | 1.680828 | Asia |
Armenia | ARM | 2,016 | 1.6822 | Asia |
Armenia | ARM | 2,017 | 1.68098 | Asia |
Armenia | ARM | 2,018 | 1.676993 | Asia |
Armenia | ARM | 2,019 | 1.687342 | Asia |
Armenia | ARM | 2,020 | 1.625795 | Asia |
Azerbaijan | AZE | 1,992 | 0.580628 | Asia |
Azerbaijan | AZE | 1,993 | 0.599671 | Asia |
Azerbaijan | AZE | 1,994 | 0.657126 | Asia |
Azerbaijan | AZE | 1,995 | 1.641344 | Asia |
Azerbaijan | AZE | 1,996 | 1.413616 | Asia |
Azerbaijan | AZE | 1,997 | 1.344469 | Asia |
Azerbaijan | AZE | 1,998 | 1.085872 | Asia |
Azerbaijan | AZE | 1,999 | 1.048087 | Asia |
Azerbaijan | AZE | 2,000 | 1.065472 | Asia |
Azerbaijan | AZE | 2,001 | 1.05553 | Asia |
Azerbaijan | AZE | 2,002 | 1.046103 | Asia |
Azerbaijan | AZE | 2,003 | 0.970177 | Asia |
Azerbaijan | AZE | 2,004 | 0.954078 | Asia |
Azerbaijan | AZE | 2,005 | 0.953794 | Asia |
Azerbaijan | AZE | 2,006 | 0.941298 | Asia |
Azerbaijan | AZE | 2,007 | 0.92883 | Asia |
Azerbaijan | AZE | 2,008 | 0.916956 | Asia |
Azerbaijan | AZE | 2,009 | 0.905781 | Asia |
Azerbaijan | AZE | 2,010 | 0.895827 | Asia |
Azerbaijan | AZE | 2,011 | 0.885357 | Asia |
Azerbaijan | AZE | 2,012 | 0.874023 | Asia |
Azerbaijan | AZE | 2,013 | 0.862231 | Asia |
Azerbaijan | AZE | 2,014 | 0.850886 | Asia |
Azerbaijan | AZE | 2,015 | 0.840266 | Asia |
Azerbaijan | AZE | 2,016 | 0.830426 | Asia |
Azerbaijan | AZE | 2,017 | 0.82259 | Asia |
Azerbaijan | AZE | 2,018 | 0.816253 | Asia |
Azerbaijan | AZE | 2,019 | 0.811039 | Asia |
Azerbaijan | AZE | 2,020 | 0.805364 | Asia |
Bahrain | BHR | 1,985 | 0.650611 | Asia |
Bahrain | BHR | 1,989 | 1.005371 | Asia |
Bahrain | BHR | 1,990 | 1.554005 | Asia |
Bahrain | BHR | 1,991 | 1.50409 | Asia |
Bahrain | BHR | 1,992 | 1.277948 | Asia |
Bahrain | BHR | 1,993 | 1.243737 | Asia |
Bahrain | BHR | 1,994 | 1.384179 | Asia |
Bahrain | BHR | 1,995 | 3.371374 | Asia |
Bahrain | BHR | 1,996 | 3.335563 | Asia |
Bahrain | BHR | 1,997 | 3.341245 | Asia |
Armed Forces Personnel Percent | Asia (Our World in Data)
🌏 1,453 observations · 48 Asia countries · 1985–2020 · Repackaged by Electric Sheep Asia
TL;DR
This dataset contains 1,453 observations of Armed Forces Personnel Percent data across 48 Asia countries, spanning 1985–2020.
About the source
- Source: Our World in Data
- Publisher: Our World in Data
- License: cc-by-4.0
- Topic: Armed Forces Personnel Percent
Geographic coverage
48 Asia countries · top rows shown below, sorted by row count:
| Country | Rows | First year | Last year |
|---|---|---|---|
ARE |
33 | 1985 | 2020 |
BRN |
33 | 1985 | 2020 |
BHR |
33 | 1985 | 2020 |
BGD |
33 | 1985 | 2020 |
IND |
33 | 1985 | 2020 |
IRN |
33 | 1985 | 2020 |
IDN |
33 | 1985 | 2020 |
CHN |
33 | 1985 | 2020 |
IRQ |
33 | 1985 | 2020 |
ISR |
33 | 1985 | 2020 |
LAO |
33 | 1985 | 2020 |
KWT |
33 | 1985 | 2020 |
KHM |
33 | 1985 | 2020 |
KOR |
33 | 1985 | 2020 |
JPN |
33 | 1985 | 2020 |
| ... | 33 more countries |
Schema
| Column | Type | Description | Example |
|---|---|---|---|
country_name |
string |
— | Afghanistan |
country_iso3 |
string |
— | AFG |
year |
int64 |
— | 1985 |
Armed forces personnel (% of total population) |
float64 |
— | 0.41131178 |
World region according to OWID |
string |
— | Asia |
Usage
from datasets import load_dataset
ds = load_dataset("electricsheepasia/asia-owid-armed-forces-personnel-percent")
df = ds["train"].to_pandas()
print(df.head())
Filter to one country
indonesia = df[df["country_iso3"] == "IDN"]
Time-series for a single indicator
sample = df.sort_values("year")
sample.plot(x="year", y="Armed forces personnel (% of total population)")
Citation
@misc{asia_owid_armed_forces_personnel_percent_2020,
title = {Armed Forces Personnel Percent | Asia (Our World in Data)},
author = {Our World in Data},
year = {2020},
url = {https://ourworldindata.org/grapher/armed-forces-personnel-percent},
publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-owid-armed-forces-personnel-percent}}
}
License
Released under cc-by-4.0.
Original data © Our World in Data. When using this dataset, please cite both the original source above and the Electric Sheep Asia repackaging.
About Electric Sheep
Electric Sheep Asia is part of the Electric Sheep mission: a unified, ML-ready data layer for Asia on HuggingFace. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use load_dataset() to start working in seconds.
Browse the full collection: huggingface.co/electricsheepasia
Provenance: ingested 2026-06-02 via the Electric Sheep pipeline. Source URL: https://ourworldindata.org/grapher/armed-forces-personnel-percent
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