Nepal Fishery Survey 2015

Nepal, 2015 - 2016
Agriculture, Forestry and Fisheries

Central Bureau of Statistics

CSV JSON

DIST (DIST)

Data file: F_PRODUCTION final

Overview

Valid: 12732

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	12	0.1%
2	Panchthar	13	0.1%
3	Ilam	47	0.4%
4	Jhapa	514	4%
5	Morang	900	7.1%
6	Sunsari	646	5.1%
7	Dhankuta	36	0.3%
8	Terhathum	40	0.3%
9	Sankhuwasabha	32	0.3%
10	Bhojpur	48	0.4%
11	Solukhumbu	1	0%
12	Okhaldhunga	4	0%
13	Khotang	38	0.3%
14	Udayapur	214	1.7%
15	Saptari	613	4.8%
16	Siraha	620	4.9%
17	Dhanusa	753	5.9%
18	Mahottari	370	2.9%
19	Sarlahi	339	2.7%
20	Sindhuli	53	0.4%
21	Ramechhap	62	0.5%
22	Dolakha	12	0.1%
23	Sindhupalchok	51	0.4%
24	Kavre	63	0.5%
25	Lalitpur	64	0.5%
26	Bhaktapur	49	0.4%
27	Kathmandu	49	0.4%
28	Nuwakot	39	0.3%
29	Rasuwa	5	0%
30	Dhading	149	1.2%
31	Makwanpur	165	1.3%
32	Rautahat	558	4.4%
33	Bara	779	6.1%
34	Parsa	574	4.5%
35	Chitawan	855	6.7%
36	Gorkha	100	0.8%
37	Lamjung	20	0.2%
38	Tanahu	120	0.9%
39	Syangja	102	0.8%
40	Kaski	118	0.9%
41	Manang	0	0%
42	Mustang	0	0%
43	Myagdi	31	0.2%
44	Parbat	40	0.3%
45	Baglung	40	0.3%
46	Gulmi	33	0.3%
47	Palpa	61	0.5%
48	Nawalparasi	695	5.5%
49	Rupandehi	653	5.1%
50	Kapilbastu	269	2.1%
51	Arghakhanchi	41	0.3%
52	Pyuthan	37	0.3%
53	Rolpa	26	0.2%
54	Rukum	20	0.2%
55	Salyan	38	0.3%
56	Dang	181	1.4%
57	Banke	209	1.6%
58	Bardiya	375	2.9%
59	Surkhet	45	0.4%
60	Dailekh	30	0.2%
61	Jajarkot	6	0%
62	Dolpa	0	0%
63	Jumla	4	0%
64	Kalikot	0	0%
65	Mugu	0	0%
66	Humla	0	0%
67	Bajura	2	0%
68	Bajhang	0	0%
69	Achham	2	0%
70	Doti	0	0%
71	Kailali	430	3.4%
72	Kanchanpur	232	1.8%
73	Dadeldhura	5	0%
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.