Nepal

NPL Global Data Lab NPL-GDL Development Indices

272 rows · 1 table(s) · 2023 Global Data Lab 0 1 2 3 9 SEL 2 ext ▶

ⓘ Global Data Lab publishes its own subnational regions, which match national admin units in some countries and group several together in others. The level shown is the nearest administrative tier, not a claim that these are that tier.

or use the per-admin-level buttons below. Free downloads return a 10-row sample. Contact us for the full pack.

1 Admin 1 — 1 table(s)

NPL GDL - Subnational Indices — 272 rows, 11 columns

Loading data…

Field details — 11 fields (type, nulls, unique, samples)

Standards-Aligned Fields (9)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	NPL, NPL, NPL, NPL, NPL
`region_code`	Admin code	string	SEL	100%	-	-
`region_name`	Admin name	string	SEL	0%	8	Total, Bagmati province, Gandaki province, Karnali...
`human_development_index`	Human development index	float	SEL	0%	86	0.454, 0.504, 0.491, 0.384, 0.479
`health_index`	Health index	float	SEL	0%	82	0.535, 0.56, 0.598, 0.506, 0.551
`education_index`	Education index	float	SEL	0%	85	0.404, 0.474, 0.435, 0.285, 0.465
`income_index`	Income index	float	SEL	0%	71	0.433, 0.484, 0.456, 0.392, 0.428
`life_expectancy`	Life expectancy	float	SEL	0%	98	54.77, 56.39, 58.87, 52.9, 55.83
`mean_years_schooling`	Mean years schooling	float	SEL	0%	98	6.061, 7.504, 6.003, 3.261, 7.1

Extension Fields (2) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`year`	year	integer	0%	13	1990, 1990, 1990, 1990, 1990
`level`	level	string	0%	2	national, subnational, subnational, subnational, subnational

NPL World Bank Open Data NPL-WORLD_BANK Development Indicators

132 rows · 2 table(s) · 2024 World Bank Open Data 0 1 2 3 19 SEL 4 ext ▶

or use the per-admin-level buttons below. Free downloads return a 10-row sample. Contact us for the full pack.

0 Admin 0 — 2 table(s)

NPL World Bank - Development Indicators — 66 rows, 9 columns

Loading data…

Field details — 9 fields (type, nulls, unique, samples)

Standards-Aligned Fields (7)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	NP, NP, NP, NP, NP
`population_count`	Population count	float	SEL	2%	65	10123658.0, 10318396.0, 10521116.0, 10729818.0, 10946392.0
`life_expectancy`	Life expectancy	float	SEL	3%	64	38.652, 38.966, 39.333, 39.608, 40.229
`gdp_per_capita_usd`	Gdp per capita usd	float	SEL	2%	65	50.2125233749587, 51.5544820747585, 54.565608932967,...
`literacy_rate_pct`	Literacy rate percent	float	SEL	88%	8	20.5699996948242, 32.9799995422363, 48.6100006103516,...
`under5_mortality_per_1000`	Under5 mortality per 1000	float	SEL	3%	64	327.6, 323.6, 318.6, 313.2, 306.9
`poverty_headcount_pct`	Poverty headcount percent	float	SEL	94%	4	41.8, 30.9, 25.2, 20.3

Extension Fields (2) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`country_name`	country_name	string	0%	1	Nepal, Nepal, Nepal, Nepal, Nepal
`year`	year	integer	0%	66	1960, 1961, 1962, 1963, 1964

NPL World Bank - Health Indicators — 66 rows, 14 columns

Loading data…

Field details — 14 fields (type, nulls, unique, samples)

Standards-Aligned Fields (12)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	NP, NP, NP, NP, NP
`life_expectancy`	Life expectancy	float	SEL	3%	64	38.652, 38.966, 39.333, 39.608, 40.229
`infant_mortality_per_1000`	Infant mortality per 1000	float	SEL	3%	64	97.8, 97.2, 96.5, 95.6, 94.6
`under5_mortality_per_1000`	Under5 mortality per 1000	float	SEL	3%	64	327.6, 323.6, 318.6, 313.2, 306.9
`maternal_mortality_per_100k`	Maternal mortality per 100k	float	SEL	41%	39	1186.0, 1143.0, 1088.0, 1038.0, 973.0
`fertility_rate`	Fertility rate	float	SEL	3%	64	6.069, 6.093, 6.082, 6.056, 6.043
`birth_rate_per_1000`	Birth rate per 1000	float	SEL	3%	64	44.806, 44.817, 44.639, 44.387, 44.218
`death_rate_per_1000`	Death rate per 1000	float	SEL	3%	63	24.717, 24.473, 24.169, 23.922, 23.394
`physicians_per_1000`	Physicians per 1000	float	SEL	58%	21	0.013, 0.021, 0.019, 0.019, 0.034
`hospital_beds_per_1000`	Hospital beds per 1000	float	SEL	39%	26	0.115763798356056, 0.135299995541573, 0.137700006365776,...
`vaccination_rate_pct`	Vaccination rate percent	float	SEL	32%	32	8.0, 16.0, 18.0, 23.0, 27.0
`health_expenditure_pct_gdp`	Health expenditure percent gdp	float	SEL	64%	24	3.13292074, 3.81229901, 3.93863344, 3.8560729, 4.04629898

Extension Fields (2) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`country_name`	country_name	string	0%	1	Nepal, Nepal, Nepal, Nepal, Nepal
`year`	year	integer	0%	66	1960, 1961, 1962, 1963, 1964

NPL Glottolog Language Catalog NPL-GLOTTOLOG Language

129 rows · 1 table(s) · 2024 Glottolog Language Catalog 0 1 2 3 1 SEL 12 CCL 3 ext ▶

or use the per-admin-level buttons below. Free downloads return a 10-row sample. Contact us for the full pack.

0 Admin 0 — 1 table(s)

NPL Glottolog - Languages — 129 rows, 16 columns

Loading data…

Field details — 16 fields (type, nulls, unique, samples)

Standards-Aligned Fields (13)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`glottocode`	Glottocode	string	CCL	0%	100	aimo1244, angi1238, athp1241, awad1243, bagh1251
`name`	Name	string	CCL	0%	100	Aimol, Angika, Athpariya, Awadhi, Bagheli
`iso639_3`	Iso639 3	string	CCL	2%	98	aim, anp, aph, awa, bfy
`level`	Level	string	CCL	0%	1	language, language, language, language, language
`family_name`	Family name	string	CCL	100%	-	-
`family_glottocode`	Family glottocode	string	CCL	1%	5	sino1245, indo1319, sino1245, indo1319, indo1319
`parent_name`	Parent name	string	CCL	100%	-	-
`parent_glottocode`	Parent glottocode	string	CCL	1%	64	cent2411, mait1254, athp1240, awad1245, awad1245
`endangerment_status`	Endangerment status	string	CCL	100%	-	-
`country_codes`	Country codes	string	SEL+	0%	9	['IN', 'NP'], ['IN', 'NP'], ['NP'], ['IN', 'NP'], ['IN', 'NP']
`child_family_count`	Child family count	integer	CCL	0%	1	0, 0, 0, 0, 0
`child_language_count`	Child language count	integer	CCL	0%	1	0, 0, 0, 0, 0
`child_dialect_count`	Child dialect count	integer	CCL	0%	12	2, 0, 0, 15, 6

Extension Fields (3) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`latitude`	latitude	float	0%	100	24.6437, 26.0047, 26.8794, 27.5907, 24.6837
`longitude`	longitude	float	0%	99	94.3556, 85.534, 87.3296, 82.4663, 87.4994
`classification`	classification	string	0%	1	[], [], [], [], []

NPL UNHCR Refugee Data Finder NPL-UNHCR Migration

94 rows · 2 table(s) · 2025 UNHCR Refugee Data Finder 0 1 2 3 12 CCL 36 ext ▶

or use the per-admin-level buttons below. Free downloads return a 10-row sample. Contact us for the full pack.

0 Admin 0 — 2 table(s)

NPL UNHCR - Hosted Populations — 62 rows, 24 columns

Loading data…

Field details — 24 fields (type, nulls, unique, samples)

Standards-Aligned Fields (6)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_of_origin_iso`	Country of origin iso	string	CCL	0%	1	-, -, -, -, -
`refugees`	Refugees	integer	CCL	0%	75	2116011, 1952928, 1847304, 1749628, 1717966
`idps`	Idps	integer	CCL	0%	34	0, 0, 0, 0, 0
`stateless`	Stateless	integer	CCL	0%	23	0, 0, 0, 0, 0
`others_of_concern`	Others of concern	integer	CCL	0%	30	0, 0, 0, 0, 0
`total_population`	Total population	string	CCL	100%	-	-

Extension Fields (18) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`year`	year	integer	0%	75	1951, 1952, 1953, 1954, 1955
`country_of_origin`	country_of_origin	string	100%	-	-
`country_of_asylum`	country_of_asylum	string	100%	-	-
`country_of_asylum_iso`	country_of_asylum_iso	string	0%	1	-, -, -, -, -
`population_type`	population_type	string	100%	-	-
`asylum_seekers`	asylum_seekers	integer	0%	34	0, 0, 0, 0, 0
`female_total`	female_total	string	100%	-	-
`male_total`	male_total	string	100%	-	-
`female_0_4`	female_0_4	string	100%	-	-
`female_5_11`	female_5_11	string	100%	-	-
`female_12_17`	female_12_17	string	100%	-	-
`female_18_59`	female_18_59	string	100%	-	-
`female_60_plus`	female_60_plus	string	100%	-	-
`male_0_4`	male_0_4	string	100%	-	-
`male_5_11`	male_5_11	string	100%	-	-
`male_12_17`	male_12_17	string	100%	-	-
`male_18_59`	male_18_59	string	100%	-	-
`male_60_plus`	male_60_plus	string	100%	-	-

NPL UNHCR - Origin Populations — 32 rows, 24 columns

Loading data…

Field details — 24 fields (type, nulls, unique, samples)

Standards-Aligned Fields (6)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_of_origin_iso`	Country of origin iso	string	CCL	0%	1	-, -, -, -, -
`refugees`	Refugees	integer	CCL	0%	75	2116011, 1952928, 1847304, 1749628, 1717966
`idps`	Idps	integer	CCL	0%	34	0, 0, 0, 0, 0
`stateless`	Stateless	integer	CCL	0%	23	0, 0, 0, 0, 0
`others_of_concern`	Others of concern	integer	CCL	0%	30	0, 0, 0, 0, 0
`total_population`	Total population	string	CCL	100%	-	-

Extension Fields (18) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`year`	year	integer	0%	75	1951, 1952, 1953, 1954, 1955
`country_of_origin`	country_of_origin	string	100%	-	-
`country_of_asylum`	country_of_asylum	string	100%	-	-
`country_of_asylum_iso`	country_of_asylum_iso	string	0%	1	-, -, -, -, -
`population_type`	population_type	string	100%	-	-
`asylum_seekers`	asylum_seekers	integer	0%	34	0, 0, 0, 0, 0
`female_total`	female_total	string	100%	-	-
`male_total`	male_total	string	100%	-	-
`female_0_4`	female_0_4	string	100%	-	-
`female_5_11`	female_5_11	string	100%	-	-
`female_12_17`	female_12_17	string	100%	-	-
`female_18_59`	female_18_59	string	100%	-	-
`female_60_plus`	female_60_plus	string	100%	-	-
`male_0_4`	male_0_4	string	100%	-	-
`male_5_11`	male_5_11	string	100%	-	-
`male_12_17`	male_12_17	string	100%	-	-
`male_18_59`	male_18_59	string	100%	-	-
`male_60_plus`	male_60_plus	string	100%	-	-

NPL NGA GEOnet Names Server (GNS) NPL-NGA_GNS Language

92 rows · 3 table(s) · 2026 NGA GEOnet Names Server (GNS) 0 1 2 3 5 SEL 12 CCL 33 ext ▶

or use the per-admin-level buttons below. Free downloads return a 10-row sample. Contact us for the full pack.

0 Admin 0 — 3 table(s)

NPL Nga Gns - Language — 7 rows, 9 columns

Loading data…

Field details — 9 fields (type, nulls, unique, samples)

Standards-Aligned Fields (9)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	country_code	string	SEL	0%	1	NPL, NPL, NPL, NPL, NPL
`gns_language_code`	gns_language_code	string	CCL	0%	7	nep, eng, zho, bod, hin
`gns_language_name`	gns_language_name	string	CCL	0%	7	Nepali, English, Chinese, Tibetan, Hindi
`gns_toponym_count`	gns_toponym_count	integer	CCL	0%	6	92488, 350, 21, 5, 3
`gns_toponym_share_pct`	gns_toponym_share_pct	float	CCL	0%	6	99.5897, 0.3769, 0.0226, 0.0054, 0.0032
`gns_non_roman_toponym_count`	gns_non_roman_toponym_count	integer	CCL	0%	3	7, 0, 6, 0, 0
`gns_dominant_script_code`	gns_dominant_script_code	string	CCL	0%	3	Deva, , Hans, ,
`gns_dominant_script_name`	gns_dominant_script_name	string	CCL	0%	3	Devanagari (Nagari), , Han (Simplified variant), ,
`gns_script_count`	gns_script_count	integer	CCL	0%	3	1, 0, 2, 0, 0

NPL Nga Gns - Summary — 1 rows, 29 columns

Loading data…

Field details — 29 fields (type, nulls, unique, samples)

Standards-Aligned Fields (7)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	country_code	string	SEL	0%	1	NPL
`admin_level`	admin_level	integer	SEL	0%	1	0
`gns_country_name`	gns_country_name	string	SEL	0%	1	Nepal
`gns_language_count`	gns_language_count	integer	CCL	0%	1	7
`gns_script_count`	gns_script_count	integer	CCL	0%	1	3
`gns_endonym_share_pct`	gns_endonym_share_pct	float	CCL	0%	1	99.998
`gns_non_roman_name_count`	gns_non_roman_name_count	integer	CCL	0%	1	13

Extension Fields (22) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`gns_name_count`	gns_name_count	integer	0%	1	101594
`gns_feature_count`	gns_feature_count	integer	0%	1	92508
`gns_endonym_count`	gns_endonym_count	integer	0%	1	101592
`gns_exonym_count`	gns_exonym_count	integer	0%	1	2
`gns_source_build_date`	gns_source_build_date	string	0%	1	Wed, 05 Aug 2026
`gns_source_change_date`	gns_source_change_date	string	0%	1	2026-08-05
`gns_name_count_hypsographic`	gns_name_count_hypsographic	integer	0%	1	3636
`gns_feature_count_hypsographic`	gns_feature_count_hypsographic	integer	0%	1	2915
`gns_name_count_populated_places`	gns_name_count_populated_places	integer	0%	1	77754
`gns_feature_count_populated_places`	gns_feature_count_populated_places	integer	0%	1	71437
`gns_name_count_administrative_regions`	gns_name_count_administrative_regions	integer	0%	1	3378
`gns_feature_count_administrative_regions`	gns_feature_count_administrative_regions	integer	0%	1	2555
`gns_name_count_hydrographic`	gns_name_count_hydrographic	integer	0%	1	14860
`gns_feature_count_hydrographic`	gns_feature_count_hydrographic	integer	0%	1	13770
`gns_name_count_areas_localities`	gns_name_count_areas_localities	integer	0%	1	965
`gns_feature_count_areas_localities`	gns_feature_count_areas_localities	integer	0%	1	935
`gns_name_count_spot_features`	gns_name_count_spot_features	integer	0%	1	517
`gns_feature_count_spot_features`	gns_feature_count_spot_features	integer	0%	1	441
`gns_name_count_vegetation`	gns_name_count_vegetation	integer	0%	1	405
`gns_feature_count_vegetation`	gns_feature_count_vegetation	integer	0%	1	392
`gns_name_count_transportation_networks`	gns_name_count_transportation_networks	integer	0%	1	79
`gns_feature_count_transportation_networks`	gns_feature_count_transportation_networks	integer	0%	1	63

NPL Nga Gns - Admin Crosswalk — 84 rows, 12 columns

Loading data…

Field details — 12 fields (type, nulls, unique, samples)

Standards-Aligned Fields (1)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	country_code	string	SEL	0%	1	NPL, NPL, NPL, NPL, NPL

Extension Fields (11) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`gns_ufi`	gns_ufi	integer	0%	84	14829251, 14829253, 14829264, 14829319, 14829259
`admin_designation`	admin_designation	string	0%	2	ADM1, ADM1, ADM1, ADM1, ADM1
`gns_bgn_name`	gns_bgn_name	string	0%	84	Bāgmatī, Gaṇḍakī, Karṇālī, Koshī, Lumbinī
`gns_local_name`	gns_local_name	string	100%	-	-
`iso_3166_2`	iso_3166_2	string	0%	7	NP-P3, NP-P4, NP-P6, NP-P1, NP-P5
`parent_code`	parent_code	string	0%	7	NP-P3, NP-P4, NP-P6, NP-P1, NP-P5
`gns_prominence_band`	gns_prominence_band	integer	0%	3	9, 9, 9, 9, 9
`latitude`	latitude	float	0%	84	27.733333, 28.416667, 29.2, 27.233333, 28.083333
`longitude`	longitude	float	0%	84	85.45, 84.033333, 82.3, 87.25, 82.766667
`gns_mgrs`	gns_mgrs	string	0%	84	45RUL4721668625, 45RTM0936846943, 44RPT2638130844,...
`name_variant_count`	name_variant_count	integer	0%	7	4, 2, 2, 3, 3

NPL HDX COD — Population Statistics (OCHA/UNFPA) NPL-COD_PS Census Derived Population

85 rows · 3 table(s) · 2023 HDX COD — Population Statistics (OCHA/UNFPA) 0 1 2 3 15 SEL ▶

or use the per-admin-level buttons below. Free downloads return a 10-row sample. Contact us for the full pack.

0 Admin 0 — 1 table(s)

NPL Cod Ps - Npl Codps (admin_0) — 1 rows, 59 columns

Loading data…

Field details — 59 fields (type, nulls, unique, samples)

Standards-Aligned Fields (5)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`pcode`	Admin code	string	SEL	0%	1	NP
`region_name`	Region name	string	SEL	0%	1	Nepal
`F_TL`	Female population	integer	SEL	0%	1	15776443
`M_TL`	Male population	integer	SEL	0%	1	15123000
`T_TL`	Total population	integer	SEL	0%	1	30899443

Pending (54) — awaiting mapping

Field Name	Label	Type	Nulls	Unique	Samples
`admin_level`	Admin level	string	0%	1	admin_0
`year`	Reference year	integer	0%	1	2023
`year`	year	integer	0%	1	2023
`F_00_04`	Female population age 0-4	integer	0%	1	1271243
`F_05_09`	Female population age 5-9	integer	0%	1	1312748
`F_10_14`	Female population age 10-14	integer	0%	1	1372162
`F_15_19`	Female population age 15-19	integer	0%	1	1430764
`F_20_24`	Female population age 20-24	integer	0%	1	1524822
`F_25_29`	Female population age 25-29	integer	0%	1	1560710
`F_30_34`	Female population age 30-34	integer	0%	1	1392470
`F_35_39`	Female population age 35-39	integer	0%	1	1165809
`F_40_44`	Female population age 40-44	integer	0%	1	993165
`F_45_49`	Female population age 45-49	integer	0%	1	885874
`F_50_54`	Female population age 50-54	integer	0%	1	752256
`F_55_59`	Female population age 55-59	integer	0%	1	630435
`F_60_64`	Female population age 60-64	integer	0%	1	509796
`F_65_69`	Female population age 65-69	integer	0%	1	393093
`F_70_74`	Female population age 70-74	integer	0%	1	276466
`F_75_79`	Female population age 75-79	integer	0%	1	169111
`F_80Plus`	F_80Plus	integer	0%	1	135519
`M_00_04`	Male population age 0-4	integer	0%	1	1342697
`M_05_09`	Male population age 5-9	integer	0%	1	1384137
`M_10_14`	Male population age 10-14	integer	0%	1	1448908
`M_15_19`	Male population age 15-19	integer	0%	1	1506793
`M_20_24`	Male population age 20-24	integer	0%	1	1549376
`M_25_29`	Male population age 25-29	integer	0%	1	1558333
`M_30_34`	Male population age 30-34	integer	0%	1	1280638
`M_35_39`	Male population age 35-39	integer	0%	1	957396
`M_40_44`	Male population age 40-44	integer	0%	1	773453
`M_45_49`	Male population age 45-49	integer	0%	1	767709
+24 more pending fields — download the CSV/Parquet to see them all.

1 Admin 1 — 1 table(s)

NPL Cod Ps - Npl Codps (admin_1) — 7 rows, 59 columns

Loading data…

Field details — 59 fields (type, nulls, unique, samples)

Standards-Aligned Fields (5)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`pcode`	Admin code	string	SEL	0%	7	NP03, NP04, NP06, NP01, NP05
`region_name`	Region name	string	SEL	0%	7	Bagmati, Gandaki, Karnali, Koshi, Lumbini
`F_TL`	Female population	integer	SEL	0%	7	3241525, 1358453, 909566, 2694170, 2838479
`M_TL`	Male population	integer	SEL	0%	7	3246231, 1226393, 872561, 2559277, 2617485
`T_TL`	Total population	integer	SEL	0%	7	6487756, 2584846, 1782127, 5253447, 5455964

Pending (54) — awaiting mapping

Field Name	Label	Type	Nulls	Unique	Samples
`admin_level`	Admin level	string	0%	1	admin_1, admin_1, admin_1, admin_1, admin_1
`year`	Reference year	integer	0%	1	2023, 2023, 2023, 2023, 2023
`year`	year	integer	0%	1	2023, 2023, 2023, 2023, 2023
`F_00_04`	Female population age 0-4	integer	0%	7	205566, 86257, 88652, 203726, 232488
`F_05_09`	Female population age 5-9	integer	0%	7	212172, 92927, 87578, 217807, 237422
`F_10_14`	Female population age 10-14	integer	0%	7	229212, 100842, 99592, 216670, 247954
`F_15_19`	Female population age 15-19	integer	0%	7	272407, 112246, 98293, 232211, 268628
`F_20_24`	Female population age 20-24	integer	0%	7	314428, 122745, 95028, 247270, 283490
`F_25_29`	Female population age 25-29	integer	0%	7	344576, 134626, 82195, 263542, 292479
`F_30_34`	Female population age 30-34	integer	0%	7	319128, 122614, 70092, 238140, 257768
`F_35_39`	Female population age 35-39	integer	0%	7	261193, 101016, 56820, 201700, 209678
`F_40_44`	Female population age 40-44	integer	0%	7	234715, 91555, 50145, 177229, 175143
`F_45_49`	Female population age 45-49	integer	0%	7	204894, 83686, 45103, 157879, 150688
`F_50_54`	Female population age 50-54	integer	0%	7	174156, 74448, 37146, 141023, 130569
`F_55_59`	Female population age 55-59	integer	0%	7	141556, 67517, 31159, 121835, 106914
`F_60_64`	Female population age 60-64	integer	0%	7	111194, 55967, 25592, 97230, 85977
`F_65_69`	Female population age 65-69	integer	0%	7	81768, 43363, 17642, 73289, 67865
`F_70_74`	Female population age 70-74	integer	0%	7	55112, 29743, 13441, 47685, 46133
`F_75_79`	Female population age 75-79	integer	0%	7	40930, 20387, 7127, 30661, 26391
`F_80Plus`	F_80Plus	integer	0%	7	38518, 18514, 3961, 26273, 18892
`M_00_04`	Male population age 0-4	integer	0%	7	218306, 92720, 91979, 206377, 241236
`M_05_09`	Male population age 5-9	integer	0%	7	230676, 101810, 89400, 221820, 250393
`M_10_14`	Male population age 10-14	integer	0%	7	249677, 108076, 102259, 224022, 261792
`M_15_19`	Male population age 15-19	integer	0%	7	302434, 119442, 96244, 244332, 269797
`M_20_24`	Male population age 20-24	integer	0%	7	358479, 123268, 93738, 250582, 266714
`M_25_29`	Male population age 25-29	integer	0%	7	379905, 121881, 87218, 261510, 267129
`M_30_34`	Male population age 30-34	integer	0%	7	321635, 103409, 67695, 220651, 219413
`M_35_39`	Male population age 35-39	integer	0%	7	234547, 75672, 47602, 167742, 163960
`M_40_44`	Male population age 40-44	integer	0%	7	190622, 61879, 37023, 139913, 129326
`M_45_49`	Male population age 45-49	integer	0%	7	186258, 64790, 38979, 139496, 126870
+24 more pending fields — download the CSV/Parquet to see them all.

2 Admin 2 — 1 table(s)

NPL Cod Ps - Npl Codps (admin_2) — 77 rows, 59 columns

Loading data…

Field details — 59 fields (type, nulls, unique, samples)

Standards-Aligned Fields (5)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`pcode`	Admin code	string	SEL	0%	77	NP0320, NP0321, NP0322, NP0323, NP0324
`region_name`	Region name	string	SEL	0%	77	Sindhuli, Ramechhap, Dolakha, Sindhupalchok, Kabhrepalanchok
`F_TL`	Female population	integer	SEL	0%	77	159302, 90732, 91534, 136961, 191159
`M_TL`	Male population	integer	SEL	0%	77	154095, 82007, 86404, 132891, 185469
`T_TL`	Total population	integer	SEL	0%	77	313397, 172739, 177938, 269852, 376628

Pending (54) — awaiting mapping

Field Name	Label	Type	Nulls	Unique	Samples
`admin_level`	Admin level	string	0%	1	admin_2, admin_2, admin_2, admin_2, admin_2
`year`	Reference year	integer	0%	1	2023, 2023, 2023, 2023, 2023
`year`	year	integer	0%	1	2023, 2023, 2023, 2023, 2023
`F_00_04`	Female population age 0-4	integer	0%	77	12759, 5720, 6145, 9602, 12445
`F_05_09`	Female population age 5-9	integer	0%	77	13241, 6116, 6538, 10095, 12563
`F_10_14`	Female population age 10-14	integer	0%	77	15176, 7664, 7268, 11118, 14004
`F_15_19`	Female population age 15-19	integer	0%	76	16450, 8091, 7950, 11734, 15858
`F_20_24`	Female population age 20-24	integer	0%	77	15274, 7483, 7637, 11166, 16592
`F_25_29`	Female population age 25-29	integer	0%	77	14375, 7007, 7875, 11860, 18136
`F_30_34`	Female population age 30-34	integer	0%	77	12368, 6236, 6975, 10551, 16582
`F_35_39`	Female population age 35-39	integer	0%	76	10612, 5580, 5767, 9291, 14150
`F_40_44`	Female population age 40-44	integer	0%	77	9475, 5365, 5512, 8988, 13394
`F_45_49`	Female population age 45-49	integer	0%	76	8886, 5796, 5435, 8729, 12806
`F_50_54`	Female population age 50-54	integer	0%	77	8094, 5936, 5668, 8326, 11609
`F_55_59`	Female population age 55-59	integer	0%	77	6835, 5330, 5162, 6916, 9924
`F_60_64`	Female population age 60-64	integer	0%	76	5311, 4531, 4399, 6196, 8125
`F_65_69`	Female population age 65-69	integer	0%	76	4159, 3592, 3512, 4544, 5697
`F_70_74`	Female population age 70-74	integer	0%	76	2743, 2589, 2323, 3448, 3902
`F_75_79`	Female population age 75-79	integer	0%	77	1956, 1879, 1700, 2262, 2885
`F_80Plus`	F_80Plus	integer	0%	76	1588, 1817, 1668, 2135, 2487
`M_00_04`	Male population age 0-4	integer	0%	77	12794, 5691, 6305, 10065, 13203
`M_05_09`	Male population age 5-9	integer	0%	76	13124, 5974, 6549, 9976, 13315
`M_10_14`	Male population age 10-14	integer	0%	77	15333, 7331, 7213, 10977, 14380
`M_15_19`	Male population age 15-19	integer	0%	77	16555, 7868, 7958, 11652, 16110
`M_20_24`	Male population age 20-24	integer	0%	77	16597, 7437, 8319, 12018, 17675
`M_25_29`	Male population age 25-29	integer	0%	77	15572, 7695, 8989, 13485, 20122
`M_30_34`	Male population age 30-34	integer	0%	77	12472, 6345, 7302, 11353, 18114
`M_35_39`	Male population age 35-39	integer	0%	77	9251, 4490, 5055, 8187, 13704
`M_40_44`	Male population age 40-44	integer	0%	77	7136, 3862, 4106, 7018, 10537
`M_45_49`	Male population age 45-49	integer	0%	77	7928, 4407, 4253, 7494, 10678
+24 more pending fields — download the CSV/Parquet to see them all.

NPL USAID DHS Program NPL-DHS Health Surveys

85 rows · 2 table(s) · 2022 USAID DHS Program 0 1 2 3 6 SEL 3 CCL 10 ext ▶

or use the per-admin-level buttons below. Free downloads return a 10-row sample. Contact us for the full pack.

1 Admin 1 — 2 table(s)

NPL DHS - Ethnicity Indicators — 40 rows, 8 columns

Loading data…

Field details — 8 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	100%	-	-
`women_who_are_literate`	Women who are literate	float	CCL	0%	39	82.0, 86.8, 27.9, 50.5, 59.6
`men_who_are_literate`	Men who are literate	float	CCL	0%	38	94.0, 95.4, 67.7, 77.7, 82.6

Extension Fields (5) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`country_name`	country_name	string	0%	1	Nepal, Nepal, Nepal, Nepal, Nepal
`survey_year`	survey_year	integer	0%	5	2016, 2022, 2001, 2006, 2011
`region`	region	string	0%	15	Bagmati province, Bagmati province, Central, Central, Central
`survey_id`	survey_id	string	0%	5	NP2016DHS, NP2022DHS, NP2001DHS, NP2006DHS, NP2011DHS
`survey_type`	survey_type	string	0%	1	DHS, DHS, DHS, DHS, DHS

NPL DHS - Health Indicators — 45 rows, 11 columns

Loading data…

Field details — 11 fields (type, nulls, unique, samples)

Standards-Aligned Fields (6)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	100%	-	-
`contraception_modern_pct`	Contraception modern percent	float	SEL	22%	27	35.0, 32.0, 36.4, 38.4, 35.0
`infant_mortality_per_1000`	Infant mortality per 1000	float	SEL	0%	36	29.0, 21.0, 86.0, 77.0, 52.0
`under5_mortality_per_1000`	Under5 mortality per 1000	float	SEL	0%	36	36.0, 24.0, 138.0, 111.0, 68.0
`vaccination_rate_pct`	Vaccination rate percent	float	SEL	0%	38	85.3, 83.4, 43.2, 60.0, 78.3
`children_underweight`	Children underweight	float	CCL	0%	43	13.3, 10.5, 44.5, 46.7, 38.3

Extension Fields (5) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`country_name`	country_name	string	0%	1	Nepal, Nepal, Nepal, Nepal, Nepal
`survey_year`	survey_year	integer	0%	6	2016, 2022, 1996, 2001, 2006
`region`	region	string	0%	15	Bagmati province, Bagmati province, Central, Central, Central
`survey_id`	survey_id	string	0%	6	NP2016DHS, NP2022DHS, NP1996DHS, NP2001DHS, NP2006DHS
`survey_type`	survey_type	string	0%	1	DHS, DHS, DHS, DHS, DHS

NPL HDX Subnational Population NPL-HDX-SUBNATIONAL-POP Census

54 rows · 1 table(s) · 2024 OCHA HDX Humanitarian Data Exchange 0 1 2 3 ▶

or use the per-admin-level buttons below. Free downloads return a 10-row sample. Contact us for the full pack.

2 Admin 2 — 1 table(s)

NPL Subnational Population — 54 rows, 19 columns

Loading data…

Field details — 19 fields (type, nulls, unique, samples)

Extension Fields (19) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`ISO3`	ISO3	string	0%	1	NPL, NPL, NPL, NPL, NPL
`Country`	Country	string	0%	1	Nepal, Nepal, Nepal, Nepal, Nepal
`ADM1_PCODE`	ADM1 PCODE	string	100%	-	-
`ADM1_NAME`	ADM1 NAME	string	100%	-	-
`ADM2_PCODE`	ADM2 PCODE	string	100%	-	-
`ADM2_NAME`	ADM2 NAME	string	100%	-	-
`ADM3_PCODE`	ADM3 PCODE	string	100%	-	-
`ADM3_NAME`	ADM3 NAME	string	100%	-	-
`ADM4_PCODE`	ADM4 PCODE	string	100%	-	-
`ADM4_NAME`	ADM4 NAME	string	100%	-	-
`Population_group`	Population group	string	0%	54	F_TL, M_TL, T_TL, F_00_04, F_05_09
`Gender`	Gender	string	0%	3	f, m, all, f, f
`Age_range`	Age range	string	0%	18	all, all, all, 0-4, 5-9
`Age_min`	Age min	string	6%	17	0, 5, 10, 15, 20
`Age_max`	Age max	string	11%	16	4, 9, 14, 19, 24
`Population`	Population	string	0%	54	15776443, 15123000, 30899443, 1271243, 1312748
`Reference_year`	Reference year	string	0%	1	2023, 2023, 2023, 2023, 2023
`Source`	Source	string	0%	1	Census of population, Census of population, Census of...
`Contributor`	Contributor	string	0%	1	UNFPA, UNFPA, UNFPA, UNFPA, UNFPA

NPL LandScan Global (ORNL) NPL-LANDSCAN Modeled Population

30 rows · 3 table(s) · 2024 LandScan Global (ORNL) 0 1 2 3 Locality 21 SEL 2 ext ▶

or use the per-admin-level buttons below. Free downloads return a 10-row sample. Contact us for the full pack.

0 Admin 0 — 1 table(s)

NPL LandScan - Population Admin0 (admin_0) — 1 rows, 7 columns

Loading data…

Field details — 7 fields (type, nulls, unique, samples)

Standards-Aligned Fields (7)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	NPL
`admin_level`	Admin level	string	SEL	0%	1	admin_0
`admin_name`	Admin name	string	SEL	0%	1	NEPAL
`admin_code`	Admin code	string	SEL	0%	1	63295607B89032319438996
`area_sqkm`	Area sqkm	float	SEL	0%	1	148039.4494
`pop_2024`	Population count	integer	SEL	0%	1	31126559
`pop_density_2024`	Population density	float	SEL	0%	1	210.26

1 Admin 1 — 1 table(s)

NPL LandScan - Population Admin1 (admin_1) — 7 rows, 7 columns

Loading data…

Field details — 7 fields (type, nulls, unique, samples)

Standards-Aligned Fields (7)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	NPL, NPL, NPL, NPL, NPL
`admin_level`	Admin level	string	SEL	0%	1	admin_1, admin_1, admin_1, admin_1, admin_1
`admin_name`	Admin name	string	SEL	0%	7	Bagmati, Province 2, Province 1, Lumbini, Sudurpaschim
`admin_code`	Admin code	string	SEL	0%	7	38925275B1624743257395, 38925275B32852473043531,...
`area_sqkm`	Area sqkm	float	SEL	0%	7	20295.4453, 9591.4511, 26061.1326, 19261.4903, 19743.9618
`pop_2024`	Population count	integer	SEL	0%	7	6444905, 6428416, 5338711, 5297051, 3008672
`pop_density_2024`	Population density	float	SEL	0%	7	317.55, 670.22, 204.85, 275.01, 152.38

Locality Locality — 1 table(s)

NPL LandScan - Cities (locality) — 22 rows, 9 columns

Loading data…

Field details — 9 fields (type, nulls, unique, samples)

Standards-Aligned Fields (7)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	NPL, NPL, NPL, NPL, NPL
`admin_level`	Admin level	string	SEL	0%	1	locality, locality, locality, locality, locality
`admin_name`	Admin name	string	SEL	0%	22	Kathmandu, Biratnagar, Pokhara, Birgunj, Bharatpur
`admin_code`	Admin code	integer	SEL	0%	22	1822, 2311, 1345, 1733, 1445
`area_sqkm`	Area sqkm	float	SEL	0%	22	348.6639, 194.2171, 44.8315, 28.8857, 40.8437
`pop_2024`	Population count	integer	SEL	0%	22	2717201, 474301, 212059, 159063, 155972
`pop_density_2024`	Population density	float	SEL	0%	22	7793.18, 2442.12, 4730.13, 5506.63, 3818.75

Extension Fields (2) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`ghs_pop_2025`	ghs_pop_2025	integer	0%	22	3862697, 987897, 151399, 90237, 153049
`landscan_vs_ghs_ratio`	landscan_vs_ghs_ratio	float	0%	22	0.703, 0.48, 1.401, 1.763, 1.019

NPL GHS Urban Centre Database (EU JRC) NPL-GHSL_UCDB Modeled Population

22 rows · 1 table(s) · 2025 GHS Urban Centre Database (EU JRC) 0 1 2 3 Locality 3 SEL 7 ext ▶

or use the per-admin-level buttons below. Free downloads return a 10-row sample. Contact us for the full pack.

Locality Locality — 1 table(s)

NPL GHS-UCDB - Cities (locality) — 22 rows, 10 columns

Loading data…

Field details — 10 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`city_name`	Admin name	string	SEL	0%	22	Kathmandu, Biratnagar, Pokhariya, Laksmipur, Inaruwa
`country_code`	Country code	string	SEL	0%	1	NPL, NPL, NPL, NPL, NPL
`population`	Population count	integer	SEL	0%	22	3862697, 987897, 391129, 387185, 270225

Extension Fields (7) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`city_id`	city_id	integer	0%	22	1822, 2311, 1545, 334, 2232
`name_alternates`	name_alternates	string	100%	-	-
`country_name`	country_name	string	0%	1	Nepal, Nepal, Nepal, Nepal, Nepal
`population_year`	population_year	integer	0%	1	2025, 2025, 2025, 2025, 2025
`latitude`	latitude	string	100%	-	-
`longitude`	longitude	string	100%	-	-
`region`	region	string	100%	-	-

NPL OpenFactBook NPL-OPENFACTBOOK Country Profile

15 rows · 15 table(s) · 2024 OpenFactBook 0 1 2 3 96 SEL 4 CCL 584 ext ▶

or use the per-admin-level buttons below. Free downloads return a 10-row sample. Contact us for the full pack.

0 Admin 0 — 15 table(s)

NPL OpenFactBook - Communications — 1 rows, 21 columns

Loading data…

Field details — 22 fields (type, nulls, unique, samples)

Standards-Aligned Fields (8)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`telephones_fixed_lines_subscriptions_per_100_inhabitants_numeric`	Fixed line subscriptions per 100	float	SEL	0%	1	2.0
`telephones_mobile_cellular_subscriptions_per_100_inhabitants_numeric`	Mobile subscriptions per 100	float	SEL	0%	1	100.0
`internet_country_code_text`	Internet country code text	string	SEL+	0%	1	.np
`internet_users_percent_of_population_numeric`	Internet penetration percent	float	SEL	0%	1	56.0
`broadband_fixed_subscriptions_subscriptions_per_100_inhabitants_numeric`	Broadband subscriptions per 100	float	SEL	0%	1	5.0
`country_code`	Country code	string	SEL	0%	1	NPL
`country_name`	Country name	string	SEL	0%	1	Nepal
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (14) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`telephones_fixed_lines_total_subscriptions_text`	telephones_fixed_lines_total_subscriptions_text	string	0%	1	726,000 (2021 est.)
`telephones_fixed_lines_total_subscriptions_numeric`	telephones_fixed_lines_total_subscriptions_numeric	float	0%	1	726000.0
`telephones_fixed_lines_subscriptions_per_100_inhabitants_text`	telephones_fixed_lines_subscriptions_per_100_inhabitants_text	string	0%	1	2 (2022 est.)
`telephones_mobile_cellular_total_subscriptions_text`	telephones_mobile_cellular_total_subscriptions_text	string	0%	1	29.6 million (2024 est.)
`telephones_mobile_cellular_total_subscriptions_numeric`	telephones_mobile_cellular_total_subscriptions_numeric	float	0%	1	29.6
`telephones_mobile_cellular_subscriptions_per_100_inhabitants_text`	telephones_mobile_cellular_subscriptions_per_100_inhabitants_text	string	0%	1	100 (2024 est.)
`broadcast_media_text`	broadcast_media_text	string	0%	1	state operates 3 TV stations, as well as national and...
`broadcast_media_numeric`	broadcast_media_numeric	float	0%	1	3.0
`internet_users_percent_of_population_text`	internet_users_percent_of_population_text	string	0%	1	56% (2023 est.)
`broadband_fixed_subscriptions_total_text`	broadband_fixed_subscriptions_total_text	string	0%	1	1.44 million (2022 est.)
`broadband_fixed_subscriptions_total_numeric`	broadband_fixed_subscriptions_total_numeric	float	0%	1	1.44
`broadband_fixed_subscriptions_subscriptions_per_100_inhabitants_text`	broadband_fixed_subscriptions_subscriptions_per_100_inhabitants_text	string	0%	1	5 (2022 est.)
`source_section`	source_section	string	0%	1	Communications
`source_profile_path`	source_profile_path	string	0%	1	south-asia/np.json

NPL OpenFactBook - Economy — 1 rows, 149 columns

Loading data…

Field details — 155 fields (type, nulls, unique, samples)

Standards-Aligned Fields (6)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`real_gdp_per_capita_real_gdp_per_capita_2024_numeric`	Real gdp per capita 2024 (numeric)	float	SEL	0%	1	5000.0
`gdp_official_exchange_rate_numeric`	Gdp total usd	float	SEL	0%	1	42.914
`population_below_poverty_line_numeric`	Poverty headcount percent	float	SEL	0%	1	20.3
`country_code`	Country code	string	SEL	0%	1	NPL
`country_name`	Country name	string	SEL	0%	1	Nepal
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (149) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`economic_overview_text`	economic_overview_text	string	0%	1	low-income South Asian economy; post-conflict fiscal...
`economic_overview_numeric`	economic_overview_numeric	float	0%	1	-19.0
`real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2024_text`	Real gdp purchasing power parity 2024 (text)	string	0%	1	$149.643 billion (2024 est.)
`real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2024_numeric`	Real gdp purchasing power parity 2024 (numeric)	float	0%	1	149.643
`real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2023_text`	Real gdp purchasing power parity 2023 (text)	string	0%	1	$144.352 billion (2023 est.)
`real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2023_numeric`	Real gdp purchasing power parity 2023 (numeric)	float	0%	1	144.352
`real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2022_text`	Real gdp purchasing power parity 2022 (text)	string	0%	1	$141.546 billion (2022 est.)
`real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2022_numeric`	Real gdp purchasing power parity 2022 (numeric)	float	0%	1	141.546
`real_gdp_purchasing_power_parity_note`	real_gdp_purchasing_power_parity_note	string	0%	1	note: data in 2021 dollars
`real_gdp_growth_rate_real_gdp_growth_rate_2024_text`	Real gdp growth rate 2024 (text)	string	0%	1	3.7% (2024 est.)
`real_gdp_growth_rate_real_gdp_growth_rate_2024_numeric`	Real gdp growth rate 2024 (numeric)	float	0%	1	3.7
`real_gdp_growth_rate_real_gdp_growth_rate_2023_text`	Real gdp growth rate 2023 (text)	string	0%	1	2% (2023 est.)
`real_gdp_growth_rate_real_gdp_growth_rate_2023_numeric`	Real gdp growth rate 2023 (numeric)	float	0%	1	2.0
`real_gdp_growth_rate_real_gdp_growth_rate_2022_text`	Real gdp growth rate 2022 (text)	string	0%	1	5.6% (2022 est.)
`real_gdp_growth_rate_real_gdp_growth_rate_2022_numeric`	Real gdp growth rate 2022 (numeric)	float	0%	1	5.6
`real_gdp_growth_rate_note`	real_gdp_growth_rate_note	string	0%	1	note: annual GDP % growth based on constant local currency
`real_gdp_per_capita_real_gdp_per_capita_2024_text`	Real gdp per capita 2024 (text)	string	0%	1	$5,000 (2024 est.)
`real_gdp_per_capita_real_gdp_per_capita_2023_text`	Real gdp per capita 2023 (text)	string	0%	1	$4,900 (2023 est.)
`real_gdp_per_capita_real_gdp_per_capita_2023_numeric`	Real gdp per capita 2023 (numeric)	float	0%	1	4900.0
`real_gdp_per_capita_real_gdp_per_capita_2022_text`	Real gdp per capita 2022 (text)	string	0%	1	$4,800 (2022 est.)
`real_gdp_per_capita_real_gdp_per_capita_2022_numeric`	Real gdp per capita 2022 (numeric)	float	0%	1	4800.0
`real_gdp_per_capita_note`	real_gdp_per_capita_note	string	0%	1	note: data in 2021 dollars
`gdp_official_exchange_rate_text`	gdp_official_exchange_rate_text	string	0%	1	$42.914 billion (2024 est.)
`inflation_rate_consumer_prices_inflation_rate_consumer_prices_2023_text`	Inflation rate consumer prices 2023 (text)	string	0%	1	7.1% (2023 est.)
`inflation_rate_consumer_prices_inflation_rate_consumer_prices_2023_numeric`	Inflation rate consumer prices 2023 (numeric)	float	0%	1	7.1
`inflation_rate_consumer_prices_inflation_rate_consumer_prices_2022_text`	Inflation rate consumer prices 2022 (text)	string	0%	1	7.7% (2022 est.)
`inflation_rate_consumer_prices_inflation_rate_consumer_prices_2022_numeric`	Inflation rate consumer prices 2022 (numeric)	float	0%	1	7.7
`inflation_rate_consumer_prices_inflation_rate_consumer_prices_2021_text`	Inflation rate consumer prices 2021 (text)	string	0%	1	4.1% (2021 est.)
`inflation_rate_consumer_prices_inflation_rate_consumer_prices_2021_numeric`	Inflation rate consumer prices 2021 (numeric)	float	0%	1	4.1
`inflation_rate_consumer_prices_note`	inflation_rate_consumer_prices_note	string	0%	1	note: annual % change based on consumer prices
+119 more extension fields — download the CSV/Parquet to see them all.

NPL OpenFactBook - Energy — 1 rows, 41 columns

Loading data…

Field details — 41 fields (type, nulls, unique, samples)

Standards-Aligned Fields (4)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`electricity_access_electrification_total_population_numeric`	Electricity access percent	float	SEL	0%	1	91.3
`country_code`	Country code	string	SEL	0%	1	NPL
`country_name`	Country name	string	SEL	0%	1	Nepal
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (37) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`electricity_access_electrification_total_population_text`	electricity_access_electrification_total_population_text	string	0%	1	91.3% (2022 est.)
`electricity_access_electrification_urban_areas_text`	electricity_access_electrification_urban_areas_text	string	0%	1	97.7%
`electricity_access_electrification_urban_areas_numeric`	electricity_access_electrification_urban_areas_numeric	float	0%	1	97.7
`electricity_access_electrification_rural_areas_text`	electricity_access_electrification_rural_areas_text	string	0%	1	93.7%
`electricity_access_electrification_rural_areas_numeric`	electricity_access_electrification_rural_areas_numeric	float	0%	1	93.7
`electricity_installed_generating_capacity_text`	electricity_installed_generating_capacity_text	string	0%	1	2.853 million kW (2023 est.)
`electricity_installed_generating_capacity_numeric`	electricity_installed_generating_capacity_numeric	float	0%	1	2.853
`electricity_consumption_text`	electricity_consumption_text	string	0%	1	9.806 billion kWh (2023 est.)
`electricity_consumption_numeric`	electricity_consumption_numeric	float	0%	1	9.806
`electricity_exports_text`	electricity_exports_text	string	0%	1	1.1 billion kWh (2023 est.)
`electricity_exports_numeric`	electricity_exports_numeric	float	0%	1	1.1
`electricity_imports_text`	electricity_imports_text	string	0%	1	1.846 billion kWh (2023 est.)
`electricity_imports_numeric`	electricity_imports_numeric	float	0%	1	1.846
`electricity_transmission_distribution_losses_text`	electricity_transmission_distribution_losses_text	string	0%	1	1.638 billion kWh (2023 est.)
`electricity_transmission_distribution_losses_numeric`	electricity_transmission_distribution_losses_numeric	float	0%	1	1.638
`electricity_generation_sources_solar_text`	electricity_generation_sources_solar_text	string	0%	1	1% of total installed capacity (2023 est.)
`electricity_generation_sources_solar_numeric`	electricity_generation_sources_solar_numeric	float	0%	1	1.0
`electricity_generation_sources_wind_text`	electricity_generation_sources_wind_text	string	0%	1	0.1% of total installed capacity (2023 est.)
`electricity_generation_sources_wind_numeric`	electricity_generation_sources_wind_numeric	float	0%	1	0.1
`electricity_generation_sources_hydroelectricity_text`	electricity_generation_sources_hydroelectricity_text	string	0%	1	99% of total installed capacity (2023 est.)
`electricity_generation_sources_hydroelectricity_numeric`	electricity_generation_sources_hydroelectricity_numeric	float	0%	1	99.0
`coal_production_text`	coal_production_text	string	0%	1	9,000 metric tons (2023 est.)
`coal_production_numeric`	coal_production_numeric	float	0%	1	9000.0
`coal_consumption_text`	coal_consumption_text	string	0%	1	1.091 million metric tons (2023 est.)
`coal_consumption_numeric`	coal_consumption_numeric	float	0%	1	1.091
`coal_exports_text`	coal_exports_text	string	0%	1	100 metric tons (2023 est.)
`coal_exports_numeric`	coal_exports_numeric	float	0%	1	100.0
`coal_imports_text`	coal_imports_text	string	0%	1	1.076 million metric tons (2023 est.)
`coal_imports_numeric`	coal_imports_numeric	float	0%	1	1.076
`coal_proven_reserves_text`	coal_proven_reserves_text	string	0%	1	8 million metric tons (2023 est.)
+7 more extension fields — download the CSV/Parquet to see them all.

NPL OpenFactBook - Environment — 1 rows, 42 columns

Loading data…

Field details — 45 fields (type, nulls, unique, samples)

Standards-Aligned Fields (8)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`land_use_agricultural_land_numeric`	Agricultural land percent	float	SEL	0%	1	26.1
`land_use_forest_numeric`	Forest area percent	float	SEL	0%	1	43.5
`urbanization_urban_population_numeric`	Urban population percent	float	SEL	0%	1	21.9
`urbanization_rate_of_urbanization_numeric`	Urban growth rate percent	float	SEL	0%	1	3.09
`waste_and_recycling_municipal_solid_waste_generated_annually_numeric`	Municipal waste kg per capita	float	SEL	0%	1	1.769
`country_code`	Country code	string	SEL	0%	1	NPL
`country_name`	Country name	string	SEL	0%	1	Nepal
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (37) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`environmental_issues_text`	environmental_issues_text	string	0%	1	deforestation (overuse of wood for fuel and lack of...
`international_environmental_agreements_party_to_text`	international_environmental_agreements_party_to_text	string	0%	1	Biodiversity, Climate Change, Climate Change-Kyoto...
`international_environmental_agreements_signed_but_not_ratified_text`	international_environmental_agreements_signed_but_not_ratified_text	string	0%	1	Comprehensive Nuclear Test Ban, Marine Life Conservation
`climate_text`	climate_text	string	0%	1	varies from cool summers and severe winters in north to...
`land_use_agricultural_land_text`	land_use_agricultural_land_text	string	0%	1	26.1% (2023 est.)
`land_use_agricultural_land_arable_land_text`	land_use_agricultural_land_arable_land_text	string	0%	1	arable land: 12.6% (2023 est.)
`land_use_agricultural_land_arable_land_numeric`	land_use_agricultural_land_arable_land_numeric	float	0%	1	12.6
`land_use_agricultural_land_permanent_crops_text`	land_use_agricultural_land_permanent_crops_text	string	0%	1	permanent crops: 1% (2023 est.)
`land_use_agricultural_land_permanent_crops_numeric`	land_use_agricultural_land_permanent_crops_numeric	float	0%	1	1.0
`land_use_agricultural_land_permanent_pasture_text`	land_use_agricultural_land_permanent_pasture_text	string	0%	1	permanent pasture: 12.5% (2023 est.)
`land_use_agricultural_land_permanent_pasture_numeric`	land_use_agricultural_land_permanent_pasture_numeric	float	0%	1	12.5
`land_use_forest_text`	land_use_forest_text	string	0%	1	43.5% (2023 est.)
`land_use_other_text`	land_use_other_text	string	0%	1	27.7% (2023 est.)
`land_use_other_numeric`	land_use_other_numeric	float	0%	1	27.7
`urbanization_urban_population_text`	urbanization_urban_population_text	string	0%	1	21.9% of total population (2023)
`urbanization_rate_of_urbanization_text`	urbanization_rate_of_urbanization_text	string	0%	1	3.09% annual rate of change (2020-25 est.)
`carbon_dioxide_emissions_total_emissions_text`	carbon_dioxide_emissions_total_emissions_text	string	0%	1	11.357 million metric tonnes of CO2 (2023 est.)
`carbon_dioxide_emissions_total_emissions_numeric`	carbon_dioxide_emissions_total_emissions_numeric	float	0%	1	11.357
`carbon_dioxide_emissions_from_coal_and_metallurgical_coke_text`	carbon_dioxide_emissions_from_coal_and_metallurgical_coke_text	string	0%	1	2.025 million metric tonnes of CO2 (2023 est.)
`carbon_dioxide_emissions_from_coal_and_metallurgical_coke_numeric`	carbon_dioxide_emissions_from_coal_and_metallurgical_coke_numeric	float	0%	1	2.025
`carbon_dioxide_emissions_from_petroleum_and_other_liquids_text`	carbon_dioxide_emissions_from_petroleum_and_other_liquids_text	string	0%	1	9.332 million metric tonnes of CO2 (2023 est.)
`carbon_dioxide_emissions_from_petroleum_and_other_liquids_numeric`	carbon_dioxide_emissions_from_petroleum_and_other_liquids_numeric	float	0%	1	9.332
`particulate_matter_emissions_text`	particulate_matter_emissions_text	string	0%	1	36.9 micrograms per cubic meter (2019 est.)
`particulate_matter_emissions_numeric`	particulate_matter_emissions_numeric	float	0%	1	36.9
`waste_and_recycling_municipal_solid_waste_generated_annually_text`	waste_and_recycling_municipal_solid_waste_generated_annually_text	string	0%	1	1.769 million tons (2024 est.)
`waste_and_recycling_percent_of_municipal_solid_waste_recycled_text`	waste_and_recycling_percent_of_municipal_solid_waste_recycled_text	string	0%	1	4.6% (2022 est.)
`waste_and_recycling_percent_of_municipal_solid_waste_recycled_numeric`	waste_and_recycling_percent_of_municipal_solid_waste_recycled_numeric	float	0%	1	4.6
`total_water_withdrawal_municipal_text`	total_water_withdrawal_municipal_text	string	0%	1	147.6 million cubic meters (2022 est.)
`total_water_withdrawal_municipal_numeric`	total_water_withdrawal_municipal_numeric	float	0%	1	147.6
`total_water_withdrawal_industrial_text`	total_water_withdrawal_industrial_text	string	0%	1	29.5 million cubic meters (2022 est.)
+7 more extension fields — download the CSV/Parquet to see them all.

NPL OpenFactBook - Ethnic Groups — 1 rows, 8 columns

Loading data…

Field details — 9 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	NPL
`country_name`	Country name	string	SEL	0%	1	Nepal
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (6) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`nationality_noun_text`	nationality_noun_text	string	0%	1	Nepali (singular and plural)
`nationality_adjective_text`	nationality_adjective_text	string	0%	1	Nepali
`ethnic_groups_text`	ethnic_groups_text	string	0%	1	Chhettri 16.5%, Brahman-Hill 11.3%, Magar 6.9%, Tharu...
`ethnic_groups_numeric`	ethnic_groups_numeric	float	0%	1	16.5
`source_section`	source_section	string	0%	1	People and Society:ethnic_groups
`source_profile_path`	source_profile_path	string	0%	1	south-asia/np.json

NPL OpenFactBook - Geography — 1 rows, 48 columns

Loading data…

Field details — 53 fields (type, nulls, unique, samples)

Standards-Aligned Fields (13)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`area_total_numeric`	Area sqkm	float	SEL	0%	1	147181.0
`area_land_numeric`	Area land sqkm	float	SEL	0%	1	143351.0
`area_water_numeric`	Area water sqkm	float	SEL	0%	1	3830.0
`land_boundaries_total_numeric`	Land boundary km	float	SEL	0%	1	3159.0
`coastline_numeric`	Coastline km	float	SEL	0%	1	0.0
`elevation_highest_point_numeric`	Elevation max m	float	SEL	0%	1	8849.0
`elevation_lowest_point_numeric`	Elevation min m	float	SEL	0%	1	70.0
`land_use_agricultural_land_numeric`	Agricultural land percent	float	SEL	0%	1	26.1
`land_use_forest_numeric`	Forest area percent	float	SEL	0%	1	43.5
`irrigated_land_numeric`	Irrigated land sqkm	float	SEL	0%	1	12090.0
`country_code`	Country code	string	SEL	0%	1	NPL
`country_name`	Country name	string	SEL	0%	1	Nepal
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (40) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`location_text`	location_text	string	0%	1	Southern Asia, between China and India
`geographic_coordinates_text`	geographic_coordinates_text	string	0%	1	28 00 N, 84 00 E
`geographic_coordinates_numeric`	geographic_coordinates_numeric	float	0%	1	28.0
`map_references_text`	map_references_text	string	0%	1	Asia
`area_total_text`	area_total_text	string	0%	1	147,181 sq km
`area_land_text`	area_land_text	string	0%	1	143,351 sq km
`area_water_text`	area_water_text	string	0%	1	3,830 sq km
`area_comparative_text`	area_comparative_text	string	0%	1	slightly larger than New York State
`land_boundaries_total_text`	land_boundaries_total_text	string	0%	1	3,159 km
`land_boundaries_border_countries_text`	land_boundaries_border_countries_text	string	0%	1	China 1,389 km; India 1,770 km
`land_boundaries_border_countries_numeric`	land_boundaries_border_countries_numeric	float	0%	1	1389.0
`coastline_text`	coastline_text	string	0%	1	0 km (landlocked)
`maritime_claims_text`	maritime_claims_text	string	0%	1	none (landlocked)
`climate_text`	climate_text	string	0%	1	varies from cool summers and severe winters in north to...
`terrain_text`	terrain_text	string	0%	1	Tarai or flat river plain of the Ganges in south;...
`elevation_highest_point_text`	elevation_highest_point_text	string	0%	1	Mount Everest (highest peak in Asia and highest point on...
`elevation_lowest_point_text`	elevation_lowest_point_text	string	0%	1	Kanchan Kalan 70 m
`elevation_mean_elevation_text`	elevation_mean_elevation_text	string	0%	1	2,565 m
`elevation_mean_elevation_numeric`	elevation_mean_elevation_numeric	float	0%	1	2565.0
`natural_resources_text`	natural_resources_text	string	0%	1	quartz, water, timber, hydropower, scenic beauty, small...
`land_use_agricultural_land_text`	land_use_agricultural_land_text	string	0%	1	26.1% (2023 est.)
`land_use_agricultural_land_arable_land_text`	land_use_agricultural_land_arable_land_text	string	0%	1	arable land: 12.6% (2023 est.)
`land_use_agricultural_land_arable_land_numeric`	land_use_agricultural_land_arable_land_numeric	float	0%	1	12.6
`land_use_agricultural_land_permanent_crops_text`	land_use_agricultural_land_permanent_crops_text	string	0%	1	permanent crops: 1% (2023 est.)
`land_use_agricultural_land_permanent_crops_numeric`	land_use_agricultural_land_permanent_crops_numeric	float	0%	1	1.0
`land_use_agricultural_land_permanent_pasture_text`	land_use_agricultural_land_permanent_pasture_text	string	0%	1	permanent pasture: 12.5% (2023 est.)
`land_use_agricultural_land_permanent_pasture_numeric`	land_use_agricultural_land_permanent_pasture_numeric	float	0%	1	12.5
`land_use_forest_text`	land_use_forest_text	string	0%	1	43.5% (2023 est.)
`land_use_other_text`	land_use_other_text	string	0%	1	27.7% (2023 est.)
`land_use_other_numeric`	land_use_other_numeric	float	0%	1	27.7
+10 more extension fields — download the CSV/Parquet to see them all.

NPL OpenFactBook - Government — 1 rows, 91 columns

Loading data…

Field details — 119 fields (type, nulls, unique, samples)

Standards-Aligned Fields (2)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	NPL
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (117) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`country_name_conventional_long_form_text`	country_name_conventional_long_form_text	string	0%	1	none
`country_name_conventional_short_form_text`	country_name_conventional_short_form_text	string	0%	1	Nepal
`country_name_local_long_form_text`	country_name_local_long_form_text	string	0%	1	none
`country_name_local_short_form_text`	country_name_local_short_form_text	string	0%	1	Nepal
`country_name_etymology_text`	country_name_etymology_text	string	0%	1	the name probably comes from the Sanskrit term nepala,...
`government_type_text`	government_type_text	string	0%	1	federal parliamentary republic
`capital_name_text`	capital_name_text	string	0%	1	Kathmandu
`capital_geographic_coordinates_text`	capital_geographic_coordinates_text	string	0%	1	27 43 N, 85 19 E
`capital_geographic_coordinates_numeric`	capital_geographic_coordinates_numeric	float	0%	1	27.0
`capital_time_difference_text`	capital_time_difference_text	string	0%	1	UTC+5.75 (10.75 hours ahead of Washington, DC, during...
`capital_time_difference_numeric`	capital_time_difference_numeric	float	0%	1	5.75
`capital_etymology_text`	capital_etymology_text	string	0%	1	the name comes from the Nepalese words kath (wooden) and...
`administrative_divisions_text`	administrative_divisions_text	string	0%	1	7 provinces (pradesh, singular - pradesh); Bagmati,...
`administrative_divisions_numeric`	administrative_divisions_numeric	float	0%	1	7.0
`legal_system_text`	legal_system_text	string	0%	1	English common law and Hindu legal concepts
`constitution_history_text`	constitution_history_text	string	0%	1	several previous; latest approved by the Second...
`constitution_history_numeric`	constitution_history_numeric	float	0%	1	16.0
`constitution_amendment_process_text`	constitution_amendment_process_text	string	0%	1	proposed as a bill by either house of the Federal...
`international_law_organization_participation_text`	international_law_organization_participation_text	string	0%	1	has not submitted an ICJ jurisdiction declaration;...
`citizenship_citizenship_by_birth_text`	Citizenship by birth (text)	string	0%	1	yes
`citizenship_citizenship_by_descent_only_text`	Citizenship by descent only (text)	string	0%	1	yes
`citizenship_dual_citizenship_recognized_text`	citizenship_dual_citizenship_recognized_text	string	0%	1	no
`citizenship_residency_requirement_for_naturalization_text`	citizenship_residency_requirement_for_naturalization_text	string	0%	1	15 years
`citizenship_residency_requirement_for_naturalization_numeric`	citizenship_residency_requirement_for_naturalization_numeric	float	0%	1	15.0
`suffrage_text`	suffrage_text	string	0%	1	18 years of age; universal
`suffrage_numeric`	suffrage_numeric	float	0%	1	18.0
`executive_branch_chief_of_state_text`	executive_branch_chief_of_state_text	string	0%	1	President Ram Chandra POUDEL (since 13 March 2023)
`executive_branch_chief_of_state_numeric`	executive_branch_chief_of_state_numeric	float	0%	1	13.0
`executive_branch_head_of_government_text`	executive_branch_head_of_government_text	string	0%	1	Prime Minister Sushila KARKI (since 12 September 2025)
`executive_branch_head_of_government_numeric`	executive_branch_head_of_government_numeric	float	0%	1	12.0
+87 more extension fields — download the CSV/Parquet to see them all.

NPL OpenFactBook - Introduction — 1 rows, 6 columns

Loading data…

Field details — 7 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	NPL
`country_name`	Country name	string	SEL	0%	1	Nepal
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (4) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`background_text`	background_text	string	0%	1	During the late 18th and early 19th centuries, the...
`background_numeric`	background_numeric	float	0%	1	18.0
`source_section`	source_section	string	0%	1	Introduction
`source_profile_path`	source_profile_path	string	0%	1	south-asia/np.json

NPL OpenFactBook - Languages — 1 rows, 8 columns

Loading data…

Field details — 9 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	NPL
`country_name`	Country name	string	SEL	0%	1	Nepal
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (6) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`languages_languages_text`	Languages (text)	string	0%	1	Nepali (official) 44.9%, Maithali 11.1%, Bhojpuri 6.2%,...
`languages_languages_numeric`	Languages (numeric)	float	0%	1	44.9
`languages_major_language_sample_s_text`	languages_major_language_sample_s_text	string	0%	1	विश्व तथ्य पुस्तक,आधारभूत जानकारीको लागि अपरिहार्य स्रोत...
`languages_note`	languages_note	string	0%	1	note: 123 languages reported as mother tongue in 2021...
`source_section`	source_section	string	0%	1	People and Society:languages
`source_profile_path`	source_profile_path	string	0%	1	south-asia/np.json

NPL OpenFactBook - Migration — 1 rows, 12 columns

Loading data…

Field details — 13 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	NPL
`country_name`	Country name	string	SEL	0%	1	Nepal
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (10) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`refugees_and_internally_displaced_persons_refugees_text`	refugees_and_internally_displaced_persons_refugees_text	string	0%	1	19,874 (2024 est.)
`refugees_and_internally_displaced_persons_refugees_numeric`	refugees_and_internally_displaced_persons_refugees_numeric	float	0%	1	19874.0
`refugees_and_internally_displaced_persons_idps_text`	refugees_and_internally_displaced_persons_idps_text	string	0%	1	18,671 (2024 est.)
`refugees_and_internally_displaced_persons_idps_numeric`	refugees_and_internally_displaced_persons_idps_numeric	float	0%	1	18671.0
`refugees_and_internally_displaced_persons_stateless_persons_text`	refugees_and_internally_displaced_persons_stateless_persons_text	string	0%	1	467 (2024 est.)
`refugees_and_internally_displaced_persons_stateless_persons_numeric`	refugees_and_internally_displaced_persons_stateless_persons_numeric	float	0%	1	467.0
`trafficking_in_persons_tier_rating_text`	trafficking_in_persons_tier_rating_text	string	0%	1	Tier 2 Watch List — the government did not demonstrate...
`trafficking_in_persons_tier_rating_numeric`	trafficking_in_persons_tier_rating_numeric	float	0%	1	2.0
`source_section`	source_section	string	0%	1	Transnational Issues:migration
`source_profile_path`	source_profile_path	string	0%	1	south-asia/np.json

NPL OpenFactBook - Military And Security — 1 rows, 22 columns

Loading data…

Field details — 27 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	NPL
`country_name`	Country name	string	SEL	0%	1	Nepal
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (24) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`military_and_security_forces_text`	military_and_security_forces_text	string	0%	1	Nepalese Armed Forces (Ministry of Defense): Nepali Army...
`military_and_security_forces_numeric`	military_and_security_forces_numeric	float	0%	1	2025.0
`military_expenditures_military_expenditures_2024_text`	Military expenditures 2024 (text)	string	0%	1	1% of GDP (2024 est.)
`military_expenditures_military_expenditures_2024_numeric`	Military expenditures 2024 (numeric)	float	0%	1	1.0
`military_expenditures_military_expenditures_2023_text`	Military expenditures 2023 (text)	string	0%	1	1% of GDP (2023 est.)
`military_expenditures_military_expenditures_2023_numeric`	Military expenditures 2023 (numeric)	float	0%	1	1.0
`military_expenditures_military_expenditures_2022_text`	Military expenditures 2022 (text)	string	0%	1	1.1% of GDP (2022 est.)
`military_expenditures_military_expenditures_2022_numeric`	Military expenditures 2022 (numeric)	float	0%	1	1.1
`military_expenditures_military_expenditures_2021_text`	Military expenditures 2021 (text)	string	0%	1	1.3% of GDP (2021 est.)
`military_expenditures_military_expenditures_2021_numeric`	Military expenditures 2021 (numeric)	float	0%	1	1.3
`military_expenditures_military_expenditures_2020_text`	Military expenditures 2020 (text)	string	0%	1	1.3% of GDP (2020 est.)
`military_expenditures_military_expenditures_2020_numeric`	Military expenditures 2020 (numeric)	float	0%	1	1.3
`military_and_security_service_personnel_strengths_text`	military_and_security_service_personnel_strengths_text	string	0%	1	approximately 95,000 active Armed Forces (2025)
`military_and_security_service_personnel_strengths_numeric`	military_and_security_service_personnel_strengths_numeric	float	0%	1	95000.0
`military_equipment_inventories_and_acquisitions_text`	military_equipment_inventories_and_acquisitions_text	string	0%	1	the Army's inventory includes a mix of mostly older...
`military_equipment_inventories_and_acquisitions_numeric`	military_equipment_inventories_and_acquisitions_numeric	float	0%	1	2025.0
`military_service_age_and_obligation_text`	military_service_age_and_obligation_text	string	0%	1	18 years of age for voluntary military service for men...
`military_service_age_and_obligation_numeric`	military_service_age_and_obligation_numeric	float	0%	1	18.0
`military_deployments_text`	military_deployments_text	string	0%	1	1240 Central African Republic (MINUSCA); 1,150...
`military_deployments_numeric`	military_deployments_numeric	float	0%	1	1240.0
`military_note_text`	military_note_text	string	0%	1	the Nepali Army is responsible for territorial defense,...
`military_note_numeric`	military_note_numeric	float	0%	1	10.0
`source_section`	source_section	string	0%	1	Military and Security
`source_profile_path`	source_profile_path	string	0%	1	south-asia/np.json

NPL OpenFactBook - People And Society — 1 rows, 140 columns

Loading data…

Field details — 152 fields (type, nulls, unique, samples)

Standards-Aligned Fields (29)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`population_total_numeric`	Population count	float	SEL	0%	1	31334402.0
`population_male_numeric`	Population male	float	SEL	0%	1	15352706.0
`population_female_numeric`	Population female	float	SEL	0%	1	15981696.0
`age_structure_0_14_years_numeric`	Population 0 14 percent	float	SEL	0%	1	25.8
`age_structure_15_64_years_numeric`	Population 15 64 percent	float	SEL	0%	1	67.8
`age_structure_65_years_and_over_numeric`	Population 65 plus percent	float	SEL	0%	1	6.4
`dependency_ratios_total_dependency_ratio_numeric`	Total dependency ratio	float	SEL	0%	1	46.8
`dependency_ratios_youth_dependency_ratio_numeric`	Youth dependency ratio	float	SEL	0%	1	37.2
`dependency_ratios_elderly_dependency_ratio_numeric`	Elderly dependency ratio	float	SEL	0%	1	9.6
`median_age_total_numeric`	Median age	float	SEL	0%	1	28.1
`population_growth_rate_numeric`	Population growth rate percent	float	SEL	0%	1	0.66
`birth_rate_numeric`	Birth rate per 1000	float	SEL	0%	1	16.66
`death_rate_numeric`	Death rate per 1000	float	SEL	0%	1	5.62
`net_migration_rate_numeric`	Net migration per 1000	float	SEL	0%	1	-4.46
`urbanization_urban_population_numeric`	Urban population percent	float	SEL	0%	1	21.9
`urbanization_rate_of_urbanization_numeric`	Urban growth rate percent	float	SEL	0%	1	3.09
`sex_ratio_at_birth_numeric`	Sex ratio at birth	float	SEL	0%	1	1.06
`sex_ratio_total_population_numeric`	Sex ratio overall	float	SEL	0%	1	0.96
`maternal_mortality_ratio_numeric`	Maternal mortality per 100k	float	SEL	0%	1	142.0
`infant_mortality_rate_total_numeric`	Infant mortality per 1000	float	SEL	0%	1	23.4
`life_expectancy_at_birth_total_population_numeric`	Life expectancy	float	SEL	0%	1	73.0
`total_fertility_rate_numeric`	Fertility rate	float	SEL	0%	1	1.82
`gross_reproduction_rate_numeric`	Gross reproduction rate	float	SEL	0%	1	0.88
`physician_density_numeric`	Physicians per 1000	float	SEL	0%	1	1.01
`hospital_bed_density_numeric`	Hospital beds per 1000	float	SEL	0%	1	0.4
`literacy_total_population_numeric`	Literacy rate percent	float	SEL	0%	1	68.7
`country_code`	Country code	string	SEL	0%	1	NPL
`country_name`	Country name	string	SEL	0%	1	Nepal
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (123) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`population_total_text`	population_total_text	string	0%	1	31,334,402 (2025 est.)
`population_male_text`	population_male_text	string	0%	1	15,352,706
`population_female_text`	population_female_text	string	0%	1	15,981,696
`age_structure_0_14_years_text`	age_structure_0_14_years_text	string	0%	1	25.8% (male 4,125,244/female 3,909,135)
`age_structure_15_64_years_text`	age_structure_15_64_years_text	string	0%	1	67.8% (male 10,153,682/female 10,957,011)
`age_structure_65_years_and_over_text`	age_structure_65_years_and_over_text	string	0%	1	6.4% (2024 est.) (male 961,717/female 1,015,598)
`dependency_ratios_total_dependency_ratio_text`	dependency_ratios_total_dependency_ratio_text	string	0%	1	46.8 (2025 est.)
`dependency_ratios_youth_dependency_ratio_text`	dependency_ratios_youth_dependency_ratio_text	string	0%	1	37.2 (2025 est.)
`dependency_ratios_elderly_dependency_ratio_text`	dependency_ratios_elderly_dependency_ratio_text	string	0%	1	9.6 (2025 est.)
`dependency_ratios_potential_support_ratio_text`	dependency_ratios_potential_support_ratio_text	string	0%	1	10.4 (2025 est.)
`dependency_ratios_potential_support_ratio_numeric`	dependency_ratios_potential_support_ratio_numeric	float	0%	1	10.4
`median_age_total_text`	median_age_total_text	string	0%	1	28.1 years (2025 est.)
`median_age_male_text`	median_age_male_text	string	0%	1	26.5 years
`median_age_male_numeric`	median_age_male_numeric	float	0%	1	26.5
`median_age_female_text`	median_age_female_text	string	0%	1	28.6 years
`median_age_female_numeric`	median_age_female_numeric	float	0%	1	28.6
`population_growth_rate_text`	population_growth_rate_text	string	0%	1	0.66% (2025 est.)
`birth_rate_text`	birth_rate_text	string	0%	1	16.66 births/1,000 population (2025 est.)
`death_rate_text`	death_rate_text	string	0%	1	5.62 deaths/1,000 population (2025 est.)
`net_migration_rate_text`	net_migration_rate_text	string	0%	1	-4.46 migrant(s)/1,000 population (2025 est.)
`population_distribution_text`	population_distribution_text	string	0%	1	most of the population is divided nearly equally between...
`urbanization_urban_population_text`	urbanization_urban_population_text	string	0%	1	21.9% of total population (2023)
`urbanization_rate_of_urbanization_text`	urbanization_rate_of_urbanization_text	string	0%	1	3.09% annual rate of change (2020-25 est.)
`major_urban_areas_population_text`	major_urban_areas_population_text	string	0%	1	1.571 million KATHMANDU (capital) (2023)
`major_urban_areas_population_numeric`	major_urban_areas_population_numeric	float	0%	1	1.571
`sex_ratio_at_birth_text`	sex_ratio_at_birth_text	string	0%	1	1.06 male(s)/female
`sex_ratio_0_14_years_text`	sex_ratio_0_14_years_text	string	0%	1	1.06 male(s)/female
`sex_ratio_0_14_years_numeric`	sex_ratio_0_14_years_numeric	float	0%	1	1.06
`sex_ratio_15_64_years_text`	sex_ratio_15_64_years_text	string	0%	1	0.93 male(s)/female
`sex_ratio_15_64_years_numeric`	sex_ratio_15_64_years_numeric	float	0%	1	0.93
+93 more extension fields — download the CSV/Parquet to see them all.

NPL OpenFactBook - Religions — 1 rows, 6 columns

Loading data…

Field details — 36 fields (type, nulls, unique, samples)

Standards-Aligned Fields (7)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	NPL
`country_name`	Country name	string	SEL	0%	1	Nepal
`admin_level`	Admin level	string	SEL	0%	1	national
`composition_religion_hindu_pct_synth`	Hindu	numeric	CCL	0%	-	81.2
`composition_religion_buddhist_pct_synth`	Buddhist	numeric	CCL	0%	-	8.2
`composition_religion_muslim_pct_synth`	Muslim	numeric	CCL	0%	-	5.1
`composition_ethnicity_primary_label_synth`	other	string	CCL	0%	-	other

Extension Fields (4) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`religions_text`	religions_text	string	0%	1	Hindu 81.2%, Buddhist 8.2%, Muslim 5.1%, Kirat 3.2%,...
`religions_numeric`	religions_numeric	float	0%	1	81.2
`source_section`	source_section	string	0%	1	People and Society:religions
`source_profile_path`	source_profile_path	string	0%	1	south-asia/np.json

Pending (25) — awaiting mapping

Field Name	Label	Type	Nulls	Unique	Samples
`composition_religion_kirat_pct_synth`	Kirat	numeric	0%	-	3.2
`composition_religion_christian_1_8_prakriti_pct_synth`	Christian 1.8%; : Prakriti	numeric	0%	-	0.5
`composition_religion_bon_pct_synth`	Bon	numeric	0%	-	-
`composition_religion_jains_pct_synth`	Jains	numeric	0%	-	-
`composition_religion_sikh_pct_synth`	Sikh	numeric	0%	-	-
`composition_ethnicity_chhettri_pct_synth`	Chhettri	numeric	0%	-	16.5
`composition_ethnicity_brahman_hill_pct_synth`	Brahman-Hill	numeric	0%	-	11.3
`composition_ethnicity_magar_pct_synth`	Magar	numeric	0%	-	6.9
`composition_ethnicity_tharu_pct_synth`	Tharu	numeric	0%	-	6.2
`composition_ethnicity_tamang_pct_synth`	Tamang	numeric	0%	-	5.6
`composition_ethnicity_bishwokarma_pct_synth`	Bishwokarma	numeric	0%	-	5.0
`composition_ethnicity_musalman_pct_synth`	Musalman	numeric	0%	-	4.9
`composition_ethnicity_newar_pct_synth`	Newar	numeric	0%	-	4.6
`composition_ethnicity_yadav_pct_synth`	Yadav	numeric	0%	-	4.2
`composition_ethnicity_rai_pct_synth`	Rai	numeric	0%	-	2.2
`composition_ethnicity_pariyar_pct_synth`	Pariyar	numeric	0%	-	1.9
`composition_ethnicity_gurung_pct_synth`	Gurung	numeric	0%	-	1.9
`composition_ethnicity_thakuri_pct_synth`	Thakuri	numeric	0%	-	1.7
`composition_ethnicity_mijar_pct_synth`	Mijar	numeric	0%	-	1.6
`composition_ethnicity_teli_pct_synth`	Teli	numeric	0%	-	1.5
`composition_ethnicity_yakthung_limbu_pct_synth`	Yakthung/Limbu	numeric	0%	-	1.4
`composition_ethnicity_chamar_harijan_ram_pct_synth`	Chamar/Harijan/Ram	numeric	0%	-	1.4
`composition_ethnicity_koiri_kushwaha_pct_synth`	Koiri/Kushwaha	numeric	0%	-	1.2
`composition_ethnicity_other_pct_synth`	other	numeric	0%	-	20.0
`composition_ethnicity_primary_share_pct_synth`	other	numeric	0%	-	20.0

NPL OpenFactBook - Terrorism — 1 rows, 6 columns

Loading data…

Field details — 6 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	NPL
`country_name`	Country name	string	SEL	0%	1	Nepal
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (3) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`terrorist_group_s_text`	terrorist_group_s_text	string	0%	1	Indian Mujahedeen
`source_section`	source_section	string	0%	1	Terrorism
`source_profile_path`	source_profile_path	string	0%	1	south-asia/np.json

NPL OpenFactBook - Transportation — 1 rows, 14 columns

Loading data…

Field details — 15 fields (type, nulls, unique, samples)

Standards-Aligned Fields (5)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`civil_aircraft_registration_country_code_prefix_text`	Civil aircraft registration country code prefix text	string	SEL+	0%	1	9N
`airports_numeric`	Airports count	float	SEL	0%	1	51.0
`country_code`	Country code	string	SEL	0%	1	NPL
`country_name`	Country name	string	SEL	0%	1	Nepal
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (10) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`civil_aircraft_registration_country_code_prefix_numeric`	civil_aircraft_registration_country_code_prefix_numeric	float	0%	1	9.0
`airports_text`	airports_text	string	0%	1	51 (2025)
`heliports_text`	heliports_text	string	0%	1	14 (2025)
`heliports_numeric`	heliports_numeric	float	0%	1	14.0
`railways_total_text`	railways_total_text	string	0%	1	59 km (2018)
`railways_total_numeric`	railways_total_numeric	float	0%	1	59.0
`railways_narrow_gauge_text`	railways_narrow_gauge_text	string	0%	1	59 km (2018) 0.762-m gauge
`railways_narrow_gauge_numeric`	railways_narrow_gauge_numeric	float	0%	1	59.0
`source_section`	source_section	string	0%	1	Transportation
`source_profile_path`	source_profile_path	string	0%	1	south-asia/np.json

NPL Ethnic Power Relations Dataset NPL-EPR Political Ethnicity

6 rows · 1 table(s) · 2021 Ethnic Power Relations Dataset 0 1 2 3 1 SEL 5 CCL 2 ext ▶

or use the per-admin-level buttons below. Free downloads return a 10-row sample. Contact us for the full pack.

0 Admin 0 — 1 table(s)

NPL EPR - Groups — 6 rows, 8 columns

Loading data…

Field details — 8 fields (type, nulls, unique, samples)

Standards-Aligned Fields (6)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	NPL, NPL, NPL, NPL, NPL
`ethnic_group_name`	Ethnic group name	string	CCL	0%	6	Caste Hill Hindu Elite, Adibasi Janajati, Dalits, Madhesi, Newars
`ethnic_power_status`	Ethnic power status	string	CCL	0%	3	SENIOR PARTNER, POWERLESS, POWERLESS, JUNIOR PARTNER,...
`ethnic_population_share`	Ethnic population share	float	CCL	0%	5	0.31, 0.31, 0.15, 0.12, 0.06
`ethnic_group_id`	Ethnic group id	float	CCL	0%	6	79001000, 79002000, 79004000, 79005000, 79003000
`regional_autonomy`	Regional autonomy	string	CCL	0%	1	false, false, false, false, false

Extension Fields (2) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`year`	year	integer	0%	1	2021, 2021, 2021, 2021, 2021
`group_relevance`	group_relevance	string	0%	1	, , , ,

NPL D-PLACE NPL-DPLACE Ethnographic Societies

1 rows · 1 table(s) · 2024 D-PLACE 0 1 2 3 1 SEL 10 CCL 7 ext ▶

or use the per-admin-level buttons below. Free downloads return a 10-row sample. Contact us for the full pack.

0 Admin 0 — 1 table(s)

NPL D-PLACE - Dplace Ea Societies — 1 rows, 18 columns

Loading data…

Field details — 18 fields (type, nulls, unique, samples)

Standards-Aligned Fields (11)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	NPL
`society_id`	Society id	string	CCL	0%	1	Ee8
`society_name`	Society name	string	CCL	0%	1	Magar
`language_glottocode`	Language glottocode	string	CCL	0%	1	west2418
`language_name`	Language name	string	CCL	0%	1
`kinship_system`	Kinship system	string	CCL	0%	1	EA001:0; EA002:0; EA003:1; EA004:2; EA005:7
`marriage_pattern`	Marriage pattern	string	CCL	0%	1	EA006:7; EA007:8; EA008:2; EA009:2; EA010:8
`subsistence_pattern`	Subsistence pattern	string	CCL	0%	1	EA028:6; EA029:6; EA030:6; EA031:NA; EA032:3
`political_complexity`	Political complexity	string	CCL	0%	1	EA033:4; EA034:1; EA035:NA
`religion_importance`	Religion importance	string	CCL	0%	1	EA034:1; EA112:NA
`residence_pattern`	Residence pattern	string	CCL	0%	1	EA011:1; EA012:8; EA013:2

Extension Fields (7) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`country_name`	country_name	string	0%	1	Nepal
`dataset`	dataset	string	0%	1	EA
`region`	region	string	0%	1
`latitude`	latitude	float	0%	1	28.0
`longitude`	longitude	float	0%	1	84.0
`assignment_method`	assignment_method	string	0%	1	point_in_polygon
`assignment_confidence`	assignment_confidence	string	0%	1	approximate

NPL Global Organized Crime Index NPL-OCINDEX Crime Index

1 rows · 1 table(s) · 2025 GI-TOC / ENACT 0 1 2 3 13 CCL 22 ext ▶

or use the per-admin-level buttons below. Free downloads return a 10-row sample. Contact us for the full pack.

0 Admin 0 — 1 table(s)

NPL Organized Crime Scores 2025 — 1 rows, 35 columns

Loading data…

Field details — 139 fields (type, nulls, unique, samples)

Standards-Aligned Fields (13)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`oc_anti_money_laundering`	oc_anti_money_laundering	numeric	CCL	0%	1	-
`oc_arms_trafficking`	oc_arms_trafficking	numeric	CCL	0%	1	-
`oc_criminal_actors`	oc_criminal_actors	numeric	CCL	0%	1	-
`oc_criminal_markets`	oc_criminal_markets	numeric	CCL	0%	1	-
`oc_criminality`	oc_criminality	numeric	CCL	0%	1	-
`oc_cyber_dependent_crimes`	oc_cyber_dependent_crimes	numeric	CCL	0%	1	-
`oc_financial_crimes`	oc_financial_crimes	numeric	CCL	0%	1	-
`oc_human_smuggling`	oc_human_smuggling	numeric	CCL	0%	1	-
`oc_human_trafficking`	oc_human_trafficking	numeric	CCL	0%	1	-
`oc_judicial_system_and_detention`	oc_judicial_system_and_detention	numeric	CCL	0%	1	-
`oc_law_enforcement`	oc_law_enforcement	numeric	CCL	0%	1	-
`oc_political_leadership_and_governance`	oc_political_leadership_and_governance	numeric	CCL	0%	1	-
`oc_resilience`	oc_resilience	numeric	CCL	0%	1	-

Extension Fields (22) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`oc_cannabis_trade`	oc_cannabis_trade	numeric	0%	1	-
`oc_cocaine_trade`	oc_cocaine_trade	numeric	0%	1	-
`oc_criminal_networks`	oc_criminal_networks	numeric	0%	1	-
`oc_economic_regulatory_capacity`	oc_economic_regulatory_capacity	numeric	0%	1	-
`oc_extortion_and_protection_racketeering`	oc_extortion_and_protection_racketeering	numeric	0%	1	-
`oc_fauna_crimes`	oc_fauna_crimes	numeric	0%	1	-
`oc_flora_crimes`	oc_flora_crimes	numeric	0%	1	-
`oc_foreign_actors`	oc_foreign_actors	numeric	0%	1	-
`oc_government_transparency_and_accountability`	oc_government_transparency_and_accountability	numeric	0%	1	-
`oc_heroin_trade`	oc_heroin_trade	numeric	0%	1	-
`oc_illicit_trade_in_excisable_goods`	oc_illicit_trade_in_excisable_goods	numeric	0%	1	-
`oc_international_cooperation`	oc_international_cooperation	numeric	0%	1	-
`oc_mafia_style_groups`	oc_mafia_style_groups	numeric	0%	1	-
`oc_national_policies_and_laws`	oc_national_policies_and_laws	numeric	0%	1	-
`oc_non_renewable_resource_crimes`	oc_non_renewable_resource_crimes	numeric	0%	1	-
`oc_non_state_actors`	oc_non_state_actors	numeric	0%	1	-
`oc_prevention`	oc_prevention	numeric	0%	1	-
`oc_private_sector_actors`	oc_private_sector_actors	numeric	0%	1	-
`oc_state_embedded_actors`	oc_state_embedded_actors	numeric	0%	1	-
`oc_synthetic_drug_trade`	oc_synthetic_drug_trade	numeric	0%	1	-
`oc_territorial_integrity`	oc_territorial_integrity	numeric	0%	1	-
`oc_trade_in_counterfeit_goods`	oc_trade_in_counterfeit_goods	numeric	0%	1	-

Pending (104) — awaiting mapping

Field Name	Label	Type	Nulls	Unique	Samples
`iso3`	iso3	string	0%	1	NPL
`source`	source	string	0%	1	Global Organized Crime Index
`source_url`	source_url	string	0%	1	https://ocindex.net/
`year`	year	integer	0%	1	2025
`license`	license	string	0%	1	Creative Commons (GI-TOC / ENACT)
`oc_anti_money_laundering_rank`	oc_anti_money_laundering_rank	integer	0%	1	143
`oc_anti_money_laundering_2023`	oc_anti_money_laundering_2023	float	0%	1	3.5
`oc_anti_money_laundering_2021`	oc_anti_money_laundering_2021	integer	0%	1	3
`oc_arms_trafficking_rank`	oc_arms_trafficking_rank	integer	0%	1	79
`oc_arms_trafficking_2023`	oc_arms_trafficking_2023	integer	0%	1	6
`oc_arms_trafficking_2021`	oc_arms_trafficking_2021	integer	0%	1	6
`oc_cannabis_trade_rank`	oc_cannabis_trade_rank	integer	0%	1	51
`oc_cannabis_trade_2023`	oc_cannabis_trade_2023	integer	0%	1	6
`oc_cannabis_trade_2021`	oc_cannabis_trade_2021	integer	0%	1	5
`oc_cocaine_trade_rank`	oc_cocaine_trade_rank	integer	0%	1	126
`oc_cocaine_trade_2023`	oc_cocaine_trade_2023	integer	0%	1	4
`oc_cocaine_trade_2021`	oc_cocaine_trade_2021	float	0%	1	3.5
`oc_criminal_actors_rank`	oc_criminal_actors_rank	integer	0%	1	23
`oc_criminal_actors_2023`	oc_criminal_actors_2023	float	0%	1	7.1
`oc_criminal_actors_2021`	oc_criminal_actors_2021	float	0%	1	6.63
`oc_criminal_markets_rank`	oc_criminal_markets_rank	integer	0%	1	46
`oc_criminal_markets_2023`	oc_criminal_markets_2023	float	0%	1	6.03
`oc_criminal_markets_2021`	oc_criminal_markets_2021	float	0%	1	5.7
`oc_criminal_networks_rank`	oc_criminal_networks_rank	integer	0%	1	21
`oc_criminal_networks_2023`	oc_criminal_networks_2023	float	0%	1	7.5
`oc_criminal_networks_2021`	oc_criminal_networks_2021	float	0%	1	6.5
`oc_criminality_rank`	oc_criminality_rank	integer	0%	1	27
`oc_criminality_2023`	oc_criminality_2023	float	0%	1	6.57
`oc_criminality_2021`	oc_criminality_2021	float	0%	1	6.16
`oc_cyber_dependent_crimes_rank`	oc_cyber_dependent_crimes_rank	integer	0%	1	111
+74 more pending fields — download the CSV/Parquet to see them all.

HERA · Human Entity Reference Atlas by Masego Inc

Build a download

Language	Place names	Share	Script
Nepali (nep)	92,858	99.6%	Devanagari (Nagari)
English (eng)	352	0.4%	—

Source	Type	Access
HDX COD — Population Statistics (OCHA/UNFPA)	international_organization	bulk_download
OCHA HDX Humanitarian Data Exchange (UN Office for the Coordination of Humanitarian Affairs)	international_organization	bulk_download
GI-TOC / ENACT (Global Initiative Against Transnational Organized Crime · ENACT)	academic	bulk_download
Ethnic Power Relations Dataset	academic	bulk_download
D-PLACE (Database of Places, Language, Culture & Environment)	academic	bulk_download
Glottolog Language Catalog	academic	bulk_download
Global Data Lab	academic	api
UNHCR Refugee Data Finder (UN High Commissioner for Refugees)	international_organization	api
USAID DHS Program (US Agency for International Development · Demographic and Health Surveys)	international_organization	api
NGA GEOnet Names Server (GNS)	official_government	bulk_download
World Bank Open Data	international_organization	api
LandScan Global (ORNL — Oak Ridge National Laboratory)	research_institution	earth_engine
GHS Urban Centre Database (Global Human Settlement · EU Joint Research Centre)	international_organization	bulk_download
OpenFactBook	community_compilation	bulk_download