Nepal Fishery Survey 2015

Nepal, 2015 - 2016
Agriculture, Forestry and Fisheries

Central Bureau of Statistics

CSV JSON

DIST (DIST)

Data file: INSTITUTIONAL

Overview

Valid: 2632

Invalid: 0

Minimum: 1

Maximum: 73

Type: Discrete

Decimal: 0

Width: 2

Range: 1 - 75

Format: Numeric

Questions and instructions

Categories

Value	Category	Cases
1	Taplejung	5	0.2%
2	Panchthar	11	0.4%
3	Ilam	27	1%
4	Jhapa	83	3.2%
5	Morang	142	5.4%
6	Sunsari	114	4.3%
7	Dhankuta	23	0.9%
8	Terhathum	22	0.8%
9	Sankhuwasabha	21	0.8%
10	Bhojpur	25	0.9%
11	Solukhumbu	1	0%
12	Okhaldhunga	2	0.1%
13	Khotang	21	0.8%
14	Udayapur	40	1.5%
15	Saptari	104	4%
16	Siraha	109	4.1%
17	Dhanusa	118	4.5%
18	Mahottari	70	2.7%
19	Sarlahi	76	2.9%
20	Sindhuli	10	0.4%
21	Ramechhap	13	0.5%
22	Dolakha	7	0.3%
23	Sindhupalchok	16	0.6%
24	Kavre	18	0.7%
25	Lalitpur	32	1.2%
26	Bhaktapur	15	0.6%
27	Kathmandu	22	0.8%
28	Nuwakot	43	1.6%
29	Rasuwa	13	0.5%
30	Dhading	39	1.5%
31	Makwanpur	46	1.7%
32	Rautahat	78	3%
33	Bara	115	4.4%
34	Parsa	82	3.1%
35	Chitawan	148	5.6%
36	Gorkha	26	1%
37	Lamjung	9	0.3%
38	Tanahu	35	1.3%
39	Syangja	32	1.2%
40	Kaski	42	1.6%
41	Manang	0	0%
42	Mustang	0	0%
43	Myagdi	18	0.7%
44	Parbat	20	0.8%
45	Baglung	22	0.8%
46	Gulmi	7	0.3%
47	Palpa	18	0.7%
48	Nawalparasi	119	4.5%
49	Rupandehi	115	4.4%
50	Kapilbastu	49	1.9%
51	Arghakhanchi	11	0.4%
52	Pyuthan	12	0.5%
53	Rolpa	10	0.4%
54	Rukum	7	0.3%
55	Salyan	12	0.5%
56	Dang	41	1.6%
57	Banke	55	2.1%
58	Bardiya	92	3.5%
59	Surkhet	17	0.6%
60	Dailekh	12	0.5%
61	Jajarkot	3	0.1%
62	Dolpa	0	0%
63	Jumla	4	0.2%
64	Kalikot	0	0%
65	Mugu	0	0%
66	Humla	0	0%
67	Bajura	2	0.1%
68	Bajhang	0	0%
69	Achham	2	0.1%
70	Doti	0	0%
71	Kailali	77	2.9%
72	Kanchanpur	50	1.9%
73	Dadeldhura	2	0.1%
74	Baitadi	0	0%
75	Darchula	0	0%

Warning: these figures indicate the number of cases found in the data file. They cannot be interpreted as summary statistics of the population of interest.