Nepal

NPL Global Data Lab NPL-GDL Development Indices

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

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1 Admin 1 — 1 table(s)

NPL GDL - Subnational Indices — 272 rows, 11 columns

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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 UNHCR Refugee Data Finder NPL-UNHCR Migration

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

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0 Admin 0 — 2 table(s)

NPL UNHCR - Hosted Populations — 75 rows, 24 columns

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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 — 75 rows, 24 columns

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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 World Bank Open Data NPL-WORLD_BANK Development Indicators

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

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0 Admin 0 — 2 table(s)

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

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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

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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) Glottolog Language Catalog 0 1 2 3 1 SEL 12 CCL 3 ext ▶

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0 Admin 0 — 1 table(s)

NPL Glottolog - Languages — 129 rows, 16 columns

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

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

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0 Admin 0 — 1 table(s)

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

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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

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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

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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) 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

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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

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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) 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

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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) 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

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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) 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

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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) 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, 22 columns

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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, 155 columns

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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

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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, 45 columns

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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, 9 columns

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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, 53 columns

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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, 119 columns

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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, 7 columns

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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, 9 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, 13 columns

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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, 27 columns

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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, 152 columns

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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, 36 columns

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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

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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, 15 columns

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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 NGA GEOnet Names Server (GNS) NPL-NGA_GNS Language

8 rows · 2 table(s) NGA GEOnet Names Server (GNS) 0 1 2 3 4 SEL 12 CCL 22 ext ▶

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0 Admin 0 — 2 table(s)

NPL Nga Gns - Language — 7 rows, 9 columns

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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

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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 Ethnic Power Relations Dataset NPL-EPR Political Ethnicity

6 rows · 1 table(s) 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

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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) D-PLACE 0 1 2 3 1 SEL 10 CCL 7 ext ▶

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0 Admin 0 — 1 table(s)

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

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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) GI-TOC / ENACT 0 1 2 3 ▶

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0 Admin 0 — 1 table(s)

NPL Organized Crime Scores 2025 — 1 rows, 35 columns

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Field details — 35 fields (type, nulls, unique, samples)

Extension Fields (35) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`oc_anti_money_laundering`	oc_anti_money_laundering	numeric	0%	1	-
`oc_arms_trafficking`	oc_arms_trafficking	numeric	0%	1	-
`oc_cannabis_trade`	oc_cannabis_trade	numeric	0%	1	-
`oc_cocaine_trade`	oc_cocaine_trade	numeric	0%	1	-
`oc_criminal_actors`	oc_criminal_actors	numeric	0%	1	-
`oc_criminal_markets`	oc_criminal_markets	numeric	0%	1	-
`oc_criminal_networks`	oc_criminal_networks	numeric	0%	1	-
`oc_criminality`	oc_criminality	numeric	0%	1	-
`oc_cyber_dependent_crimes`	oc_cyber_dependent_crimes	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_financial_crimes`	oc_financial_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_human_smuggling`	oc_human_smuggling	numeric	0%	1	-
`oc_human_trafficking`	oc_human_trafficking	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_judicial_system_and_detention`	oc_judicial_system_and_detention	numeric	0%	1	-
`oc_law_enforcement`	oc_law_enforcement	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_political_leadership_and_governance`	oc_political_leadership_and_governance	numeric	0%	1	-
`oc_prevention`	oc_prevention	numeric	0%	1	-
`oc_private_sector_actors`	oc_private_sector_actors	numeric	0%	1	-
+5 more extension 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)	92,488	99.6%	Devanagari (Nagari)
English (eng)	350	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