Nepal Fishery Survey 2015

Nepal, 2015 - 2016
Agriculture, Forestry and Fisheries

Central Bureau of Statistics

CSV JSON

DIST (DIST)

Data file: F_PROBLEMS

Overview

Valid: 7794

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	15	0.2%
2	Panchthar	33	0.4%
3	Ilam	81	1%
4	Jhapa	249	3.2%
5	Morang	426	5.5%
6	Sunsari	342	4.4%
7	Dhankuta	69	0.9%
8	Terhathum	66	0.8%
9	Sankhuwasabha	63	0.8%
10	Bhojpur	75	1%
11	Solukhumbu	3	0%
12	Okhaldhunga	6	0.1%
13	Khotang	63	0.8%
14	Udayapur	120	1.5%
15	Saptari	312	4%
16	Siraha	325	4.2%
17	Dhanusa	354	4.5%
18	Mahottari	210	2.7%
19	Sarlahi	228	2.9%
20	Sindhuli	30	0.4%
21	Ramechhap	39	0.5%
22	Dolakha	21	0.3%
23	Sindhupalchok	44	0.6%
24	Kavre	54	0.7%
25	Lalitpur	62	0.8%
26	Bhaktapur	29	0.4%
27	Kathmandu	49	0.6%
28	Nuwakot	129	1.7%
29	Rasuwa	39	0.5%
30	Dhading	117	1.5%
31	Makwanpur	138	1.8%
32	Rautahat	234	3%
33	Bara	341	4.4%
34	Parsa	246	3.2%
35	Chitawan	421	5.4%
36	Gorkha	78	1%
37	Lamjung	27	0.3%
38	Tanahu	105	1.3%
39	Syangja	96	1.2%
40	Kaski	126	1.6%
41	Manang	0	0%
42	Mustang	0	0%
43	Myagdi	54	0.7%
44	Parbat	60	0.8%
45	Baglung	66	0.8%
46	Gulmi	21	0.3%
47	Palpa	54	0.7%
48	Nawalparasi	357	4.6%
49	Rupandehi	345	4.4%
50	Kapilbastu	147	1.9%
51	Arghakhanchi	33	0.4%
52	Pyuthan	36	0.5%
53	Rolpa	30	0.4%
54	Rukum	21	0.3%
55	Salyan	36	0.5%
56	Dang	123	1.6%
57	Banke	165	2.1%
58	Bardiya	274	3.5%
59	Surkhet	51	0.7%
60	Dailekh	36	0.5%
61	Jajarkot	9	0.1%
62	Dolpa	0	0%
63	Jumla	12	0.2%
64	Kalikot	0	0%
65	Mugu	0	0%
66	Humla	0	0%
67	Bajura	6	0.1%
68	Bajhang	0	0%
69	Achham	6	0.1%
70	Doti	0	0%
71	Kailali	231	3%
72	Kanchanpur	150	1.9%
73	Dadeldhura	6	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.