Implementation_ITSA/Reference Data/python_project/loaders/backup/Customer_Stock_Locations.csv

1	name	type	lat	lon
2	Adelaide	customer	0	0
3	BAHODOPI-POSO	customer	0	0
4	Bandirma	customer	40.369697136983	27.9607200622559
5	Bangkok	customer	13.75398	100.50144
6	Barquito	customer	-26.35	-70.65
7	Beaumont	customer	30.077338673234	-94.0840816497804
8	Budge Budge	customer	22.47514	88.17767
9	Fangcheng	customer	21.6140258994842	108.322792053223
10	Gladstone	customer	-23.81603	151.26279
11	Go Dau	customer	15.9742225	105.806431
12	Gresik	customer	-7.14994933032079	112.60986328125
13	Hai Phong	customer	20.86774	106.69179
14	Hibi	customer	34.39572	133.78713
15	Huelva	customer	37.26638	-6.94004
16	Isabel	customer	6.70748678037316	121.965022087097
17	Japan	customer	37.3002752813444	138.515625
18	Jorf Lasfar	customer	-18.7771925	46.854328
19	Kakinada	customer	16.9957254352666	82.4434661865236
20	Kandla	customer	22.80078	69.70705
21	Kemaman	customer	4.23333	103.45
22	LHOKSEUMAWE	customer	0	0
23	Limas	customer	40.7389332411361	29.6246337890626
24	Map Ta Phut	customer	12.648713210033	101.143569946289
25	Mejillones	customer	-22.8215665806022	-70.5869030289666
26	Naoshima	customer	34.4612772884371	134.009170532227
27	New Mangalore	customer	0	0
28	New Orleans	customer	29.95465	-90.07507
29	Onahama	customer	36.9268411923102	140.907897949219
30	Onsan	customer	35.4601106672107	129.37671661377
31	Paradip	customer	20.2644516477765	86.7077064514162
32	Pori	customer	61.6450645681561	21.3828277587891
33	Port Hedland	customer	0	0
34	Ras Al Khair	customer	0	0
35	Rio Grande	customer	-32.082574559546	-52.0916748046876
36	Saganoseki	customer	33.2411282959313	131.885418891907
37	Samut Prakan	customer	13.5051524908849	100.599746704102
38	San Juan	customer	18.46633	-66.10572
39	Stockton	customer	37.9527255601159	-121.316356658936
40	Tampa	customer	27.897652653541	-82.4285316467287
41	Tema	customer	-0.828097	11.598909
42	Tuticorin	customer	8.75436	78.20429
43	Visakhapatnam	customer	17.4764321971955	83.5180664062501