1. var data = [
  2. {name: '海门', value: 9},
  3. {name: '鄂尔多斯', value: 12},
  4. {name: '招远', value: 12},
  5. {name: '舟山', value: 12},
  6. {name: '齐齐哈尔', value: 14},
  7. {name: '盐城', value: 15},
  8. {name: '赤峰', value: 16},
  9. {name: '青岛', value: 18},
  10. {name: '乳山', value: 18},
  11. {name: '金昌', value: 19},
  12. {name: '泉州', value: 21},
  13. {name: '莱西', value: 21},
  14. {name: '日照', value: 21},
  15. {name: '胶南', value: 22},
  16. {name: '南通', value: 23},
  17. {name: '拉萨', value: 24},
  18. {name: '云浮', value: 24},
  19. {name: '梅州', value: 25},
  20. {name: '文登', value: 25},
  21. {name: '上海', value: 25},
  22. {name: '攀枝花', value: 25},
  23. {name: '威海', value: 25},
  24. {name: '承德', value: 25},
  25. {name: '厦门', value: 26},
  26. {name: '汕尾', value: 26},
  27. {name: '潮州', value: 26},
  28. {name: '丹东', value: 27},
  29. {name: '太仓', value: 27},
  30. {name: '曲靖', value: 27},
  31. {name: '烟台', value: 28},
  32. {name: '福州', value: 29},
  33. {name: '瓦房店', value: 30},
  34. {name: '即墨', value: 30},
  35. {name: '抚顺', value: 31},
  36. {name: '玉溪', value: 31},
  37. {name: '张家口', value: 31},
  38. {name: '阳泉', value: 31},
  39. {name: '莱州', value: 32},
  40. {name: '湖州', value: 32},
  41. {name: '汕头', value: 32},
  42. {name: '昆山', value: 33},
  43. {name: '宁波', value: 33},
  44. {name: '湛江', value: 33},
  45. {name: '揭阳', value: 34},
  46. {name: '荣成', value: 34},
  47. {name: '连云港', value: 35},
  48. {name: '葫芦岛', value: 35},
  49. {name: '常熟', value: 36},
  50. {name: '东莞', value: 36},
  51. {name: '河源', value: 36},
  52. {name: '淮安', value: 36},
  53. {name: '泰州', value: 36},
  54. {name: '南宁', value: 37},
  55. {name: '营口', value: 37},
  56. {name: '惠州', value: 37},
  57. {name: '江阴', value: 37},
  58. {name: '蓬莱', value: 37},
  59. {name: '韶关', value: 38},
  60. {name: '嘉峪关', value: 38},
  61. {name: '广州', value: 38},
  62. {name: '延安', value: 38},
  63. {name: '太原', value: 39},
  64. {name: '清远', value: 39},
  65. {name: '中山', value: 39},
  66. {name: '昆明', value: 39},
  67. {name: '寿光', value: 40},
  68. {name: '盘锦', value: 40},
  69. {name: '长治', value: 41},
  70. {name: '深圳', value: 41},
  71. {name: '珠海', value: 42},
  72. {name: '宿迁', value: 43},
  73. {name: '咸阳', value: 43},
  74. {name: '铜川', value: 44},
  75. {name: '平度', value: 44},
  76. {name: '佛山', value: 44},
  77. {name: '海口', value: 44},
  78. {name: '江门', value: 45},
  79. {name: '章丘', value: 45},
  80. {name: '肇庆', value: 46},
  81. {name: '大连', value: 47},
  82. {name: '临汾', value: 47},
  83. {name: '吴江', value: 47},
  84. {name: '石嘴山', value: 49},
  85. {name: '沈阳', value: 50},
  86. {name: '苏州', value: 50},
  87. {name: '茂名', value: 50},
  88. {name: '嘉兴', value: 51},
  89. {name: '长春', value: 51},
  90. {name: '胶州', value: 52},
  91. {name: '银川', value: 52},
  92. {name: '张家港', value: 52},
  93. {name: '三门峡', value: 53},
  94. {name: '锦州', value: 54},
  95. {name: '南昌', value: 54},
  96. {name: '柳州', value: 54},
  97. {name: '三亚', value: 54},
  98. {name: '自贡', value: 56},
  99. {name: '吉林', value: 56},
  100. {name: '阳江', value: 57},
  101. {name: '泸州', value: 57},
  102. {name: '西宁', value: 57},
  103. {name: '宜宾', value: 58},
  104. {name: '呼和浩特', value: 58},
  105. {name: '成都', value: 58},
  106. {name: '大同', value: 58},
  107. {name: '镇江', value: 59},
  108. {name: '桂林', value: 59},
  109. {name: '张家界', value: 59},
  110. {name: '宜兴', value: 59},
  111. {name: '北海', value: 60},
  112. {name: '西安', value: 61},
  113. {name: '金坛', value: 62},
  114. {name: '东营', value: 62},
  115. {name: '牡丹江', value: 63},
  116. {name: '遵义', value: 63},
  117. {name: '绍兴', value: 63},
  118. {name: '扬州', value: 64},
  119. {name: '常州', value: 64},
  120. {name: '潍坊', value: 65},
  121. {name: '重庆', value: 66},
  122. {name: '台州', value: 67},
  123. {name: '南京', value: 67},
  124. {name: '滨州', value: 70},
  125. {name: '贵阳', value: 71},
  126. {name: '无锡', value: 71},
  127. {name: '本溪', value: 71},
  128. {name: '克拉玛依', value: 72},
  129. {name: '渭南', value: 72},
  130. {name: '马鞍山', value: 72},
  131. {name: '宝鸡', value: 72},
  132. {name: '焦作', value: 75},
  133. {name: '句容', value: 75},
  134. {name: '北京', value: 79},
  135. {name: '徐州', value: 79},
  136. {name: '衡水', value: 80},
  137. {name: '包头', value: 80},
  138. {name: '绵阳', value: 80},
  139. {name: '乌鲁木齐', value: 84},
  140. {name: '枣庄', value: 84},
  141. {name: '杭州', value: 84},
  142. {name: '淄博', value: 85},
  143. {name: '鞍山', value: 86},
  144. {name: '溧阳', value: 86},
  145. {name: '库尔勒', value: 86},
  146. {name: '安阳', value: 90},
  147. {name: '开封', value: 90},
  148. {name: '济南', value: 92},
  149. {name: '德阳', value: 93},
  150. {name: '温州', value: 95},
  151. {name: '九江', value: 96},
  152. {name: '邯郸', value: 98},
  153. {name: '临安', value: 99},
  154. {name: '兰州', value: 99},
  155. {name: '沧州', value: 100},
  156. {name: '临沂', value: 103},
  157. {name: '南充', value: 104},
  158. {name: '天津', value: 105},
  159. {name: '富阳', value: 106},
  160. {name: '泰安', value: 112},
  161. {name: '诸暨', value: 112},
  162. {name: '郑州', value: 113},
  163. {name: '哈尔滨', value: 114},
  164. {name: '聊城', value: 116},
  165. {name: '芜湖', value: 117},
  166. {name: '唐山', value: 119},
  167. {name: '平顶山', value: 119},
  168. {name: '邢台', value: 119},
  169. {name: '德州', value: 120},
  170. {name: '济宁', value: 120},
  171. {name: '荆州', value: 127},
  172. {name: '宜昌', value: 130},
  173. {name: '义乌', value: 132},
  174. {name: '丽水', value: 133},
  175. {name: '洛阳', value: 134},
  176. {name: '秦皇岛', value: 136},
  177. {name: '株洲', value: 143},
  178. {name: '石家庄', value: 147},
  179. {name: '莱芜', value: 148},
  180. {name: '常德', value: 152},
  181. {name: '保定', value: 153},
  182. {name: '湘潭', value: 154},
  183. {name: '金华', value: 157},
  184. {name: '岳阳', value: 169},
  185. {name: '长沙', value: 175},
  186. {name: '衢州', value: 177},
  187. {name: '廊坊', value: 193},
  188. {name: '菏泽', value: 194},
  189. {name: '合肥', value: 229},
  190. {name: '武汉', value: 273},
  191. {name: '大庆', value: 279}
  192. ];
  193. var geoCoordMap = {
  194. '海门':[121.15,31.89],
  195. '鄂尔多斯':[109.781327,39.608266],
  196. '招远':[120.38,37.35],
  197. '舟山':[122.207216,29.985295],
  198. '齐齐哈尔':[123.97,47.33],
  199. '盐城':[120.13,33.38],
  200. '赤峰':[118.87,42.28],
  201. '青岛':[120.33,36.07],
  202. '乳山':[121.52,36.89],
  203. '金昌':[102.188043,38.520089],
  204. '泉州':[118.58,24.93],
  205. '莱西':[120.53,36.86],
  206. '日照':[119.46,35.42],
  207. '胶南':[119.97,35.88],
  208. '南通':[121.05,32.08],
  209. '拉萨':[91.11,29.97],
  210. '云浮':[112.02,22.93],
  211. '梅州':[116.1,24.55],
  212. '文登':[122.05,37.2],
  213. '上海':[121.48,31.22],
  214. '攀枝花':[101.718637,26.582347],
  215. '威海':[122.1,37.5],
  216. '承德':[117.93,40.97],
  217. '厦门':[118.1,24.46],
  218. '汕尾':[115.375279,22.786211],
  219. '潮州':[116.63,23.68],
  220. '丹东':[124.37,40.13],
  221. '太仓':[121.1,31.45],
  222. '曲靖':[103.79,25.51],
  223. '烟台':[121.39,37.52],
  224. '福州':[119.3,26.08],
  225. '瓦房店':[121.979603,39.627114],
  226. '即墨':[120.45,36.38],
  227. '抚顺':[123.97,41.97],
  228. '玉溪':[102.52,24.35],
  229. '张家口':[114.87,40.82],
  230. '阳泉':[113.57,37.85],
  231. '莱州':[119.942327,37.177017],
  232. '湖州':[120.1,30.86],
  233. '汕头':[116.69,23.39],
  234. '昆山':[120.95,31.39],
  235. '宁波':[121.56,29.86],
  236. '湛江':[110.359377,21.270708],
  237. '揭阳':[116.35,23.55],
  238. '荣成':[122.41,37.16],
  239. '连云港':[119.16,34.59],
  240. '葫芦岛':[120.836932,40.711052],
  241. '常熟':[120.74,31.64],
  242. '东莞':[113.75,23.04],
  243. '河源':[114.68,23.73],
  244. '淮安':[119.15,33.5],
  245. '泰州':[119.9,32.49],
  246. '南宁':[108.33,22.84],
  247. '营口':[122.18,40.65],
  248. '惠州':[114.4,23.09],
  249. '江阴':[120.26,31.91],
  250. '蓬莱':[120.75,37.8],
  251. '韶关':[113.62,24.84],
  252. '嘉峪关':[98.289152,39.77313],
  253. '广州':[113.23,23.16],
  254. '延安':[109.47,36.6],
  255. '太原':[112.53,37.87],
  256. '清远':[113.01,23.7],
  257. '中山':[113.38,22.52],
  258. '昆明':[102.73,25.04],
  259. '寿光':[118.73,36.86],
  260. '盘锦':[122.070714,41.119997],
  261. '长治':[113.08,36.18],
  262. '深圳':[114.07,22.62],
  263. '珠海':[113.52,22.3],
  264. '宿迁':[118.3,33.96],
  265. '咸阳':[108.72,34.36],
  266. '铜川':[109.11,35.09],
  267. '平度':[119.97,36.77],
  268. '佛山':[113.11,23.05],
  269. '海口':[110.35,20.02],
  270. '江门':[113.06,22.61],
  271. '章丘':[117.53,36.72],
  272. '肇庆':[112.44,23.05],
  273. '大连':[121.62,38.92],
  274. '临汾':[111.5,36.08],
  275. '吴江':[120.63,31.16],
  276. '石嘴山':[106.39,39.04],
  277. '沈阳':[123.38,41.8],
  278. '苏州':[120.62,31.32],
  279. '茂名':[110.88,21.68],
  280. '嘉兴':[120.76,30.77],
  281. '长春':[125.35,43.88],
  282. '胶州':[120.03336,36.264622],
  283. '银川':[106.27,38.47],
  284. '张家港':[120.555821,31.875428],
  285. '三门峡':[111.19,34.76],
  286. '锦州':[121.15,41.13],
  287. '南昌':[115.89,28.68],
  288. '柳州':[109.4,24.33],
  289. '三亚':[109.511909,18.252847],
  290. '自贡':[104.778442,29.33903],
  291. '吉林':[126.57,43.87],
  292. '阳江':[111.95,21.85],
  293. '泸州':[105.39,28.91],
  294. '西宁':[101.74,36.56],
  295. '宜宾':[104.56,29.77],
  296. '呼和浩特':[111.65,40.82],
  297. '成都':[104.06,30.67],
  298. '大同':[113.3,40.12],
  299. '镇江':[119.44,32.2],
  300. '桂林':[110.28,25.29],
  301. '张家界':[110.479191,29.117096],
  302. '宜兴':[119.82,31.36],
  303. '北海':[109.12,21.49],
  304. '西安':[108.95,34.27],
  305. '金坛':[119.56,31.74],
  306. '东营':[118.49,37.46],
  307. '牡丹江':[129.58,44.6],
  308. '遵义':[106.9,27.7],
  309. '绍兴':[120.58,30.01],
  310. '扬州':[119.42,32.39],
  311. '常州':[119.95,31.79],
  312. '潍坊':[119.1,36.62],
  313. '重庆':[106.54,29.59],
  314. '台州':[121.420757,28.656386],
  315. '南京':[118.78,32.04],
  316. '滨州':[118.03,37.36],
  317. '贵阳':[106.71,26.57],
  318. '无锡':[120.29,31.59],
  319. '本溪':[123.73,41.3],
  320. '克拉玛依':[84.77,45.59],
  321. '渭南':[109.5,34.52],
  322. '马鞍山':[118.48,31.56],
  323. '宝鸡':[107.15,34.38],
  324. '焦作':[113.21,35.24],
  325. '句容':[119.16,31.95],
  326. '北京':[116.46,39.92],
  327. '徐州':[117.2,34.26],
  328. '衡水':[115.72,37.72],
  329. '包头':[110,40.58],
  330. '绵阳':[104.73,31.48],
  331. '乌鲁木齐':[87.68,43.77],
  332. '枣庄':[117.57,34.86],
  333. '杭州':[120.19,30.26],
  334. '淄博':[118.05,36.78],
  335. '鞍山':[122.85,41.12],
  336. '溧阳':[119.48,31.43],
  337. '库尔勒':[86.06,41.68],
  338. '安阳':[114.35,36.1],
  339. '开封':[114.35,34.79],
  340. '济南':[117,36.65],
  341. '德阳':[104.37,31.13],
  342. '温州':[120.65,28.01],
  343. '九江':[115.97,29.71],
  344. '邯郸':[114.47,36.6],
  345. '临安':[119.72,30.23],
  346. '兰州':[103.73,36.03],
  347. '沧州':[116.83,38.33],
  348. '临沂':[118.35,35.05],
  349. '南充':[106.110698,30.837793],
  350. '天津':[117.2,39.13],
  351. '富阳':[119.95,30.07],
  352. '泰安':[117.13,36.18],
  353. '诸暨':[120.23,29.71],
  354. '郑州':[113.65,34.76],
  355. '哈尔滨':[126.63,45.75],
  356. '聊城':[115.97,36.45],
  357. '芜湖':[118.38,31.33],
  358. '唐山':[118.02,39.63],
  359. '平顶山':[113.29,33.75],
  360. '邢台':[114.48,37.05],
  361. '德州':[116.29,37.45],
  362. '济宁':[116.59,35.38],
  363. '荆州':[112.239741,30.335165],
  364. '宜昌':[111.3,30.7],
  365. '义乌':[120.06,29.32],
  366. '丽水':[119.92,28.45],
  367. '洛阳':[112.44,34.7],
  368. '秦皇岛':[119.57,39.95],
  369. '株洲':[113.16,27.83],
  370. '石家庄':[114.48,38.03],
  371. '莱芜':[117.67,36.19],
  372. '常德':[111.69,29.05],
  373. '保定':[115.48,38.85],
  374. '湘潭':[112.91,27.87],
  375. '金华':[119.64,29.12],
  376. '岳阳':[113.09,29.37],
  377. '长沙':[113,28.21],
  378. '衢州':[118.88,28.97],
  379. '廊坊':[116.7,39.53],
  380. '菏泽':[115.480656,35.23375],
  381. '合肥':[117.27,31.86],
  382. '武汉':[114.31,30.52],
  383. '大庆':[125.03,46.58]
  384. };
  385.  
  386. var convertData = function (data) {
  387. var res = [];
  388. for (var i = 0; i < data.length; i++) {
  389. var geoCoord = geoCoordMap[data[i].name];
  390. if (geoCoord) {
  391. res.push({
  392. name: data[i].name,
  393. value: geoCoord.concat(data[i].value)
  394. });
  395. }
  396. }
  397. return res;
  398. };
  399. console.log(convertData(data))

打印:

(190) [{…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, {…}, …]

  1. [0 … 99]
    1. 0:
      1. name:"海门"
      2. value:Array(3)
        1. 0:121.15
        2. 1:31.89
        3. 2:9
        4. length:3
        5. __proto__:Array(0)
      3. __proto__:Object
    2. 1:
      1. name:"鄂尔多斯"
      2. value:Array(3)
        1. 0:109.781327
        2. 1:39.608266
        3. 2:12
        4. length:3
        5. __proto__:Array(0)
      3. __proto__:Object
    3. 2:
      1. name:"招远"
      2. value:Array(3)
        1. 0:120.38
        2. 1:37.35
        3. 2:12
        4. length:3
        5. __proto__:Array(0)
      3. __proto__:Object
    4. 3:{name: "舟山", value: Array(3)}
    5. 4:{name: "齐齐哈尔", value: Array(3)}
    6. 5:{name: "盐城", value: Array(3)}
    7. 6:{name: "赤峰", value: Array(3)}
    8. 7:{name: "青岛", value: Array(3)}
    9. 8:{name: "乳山", value: Array(3)}
    10. 9:{name: "金昌", value: Array(3)}
    11. 10:{name: "泉州", value: Array(3)}
    12. 11:{name: "莱西", value: Array(3)}
    13. 12:{name: "日照", value: Array(3)}
    14. 13:{name: "胶南", value: Array(3)}
    15. 14:{name: "南通", value: Array(3)}
    16. 15:{name: "拉萨", value: Array(3)}
    17. 16:{name: "云浮", value: Array(3)}
    18. 17:{name: "梅州", value: Array(3)}
    19. 18:{name: "文登", value: Array(3)}
    20. 19:{name: "上海", value: Array(3)}

数据合并处理concat的更多相关文章

  1. teradata 字符串数据合并 在concat()函数无法使用的情况下

    在teradata sql中不存在concat()函数或者stuff()函数,在此情况下,如何实现多条字符串数据合并成一行? 在查找不同方法过程中,在stackflow中找到最简便的方法,使用xml_ ...

  2. pandas中,dataframe 进行数据合并-pd.concat()

    ``# 通过数据框列向(左右)合并 a = pd.DataFrame(X_train) b = pd.DataFrame(y_train) # 合并数据框(合并前需要将数据设置成DataFrame格式 ...

  3. 9-Pandas之数据合并与轴向连接(pd.concat()的详解)

    数据合并:由于数据可能是不同的格式,且来自不同的数据源,为了方便之后的处理与加工,需要将不同的数据转换成一个DataFrame. Numpy中的concatenate().vstack().hstac ...

  4. 初识Javascript.03 -- switch、自增、while循环、for、break、continue、数组、遍历数组、合并数组concat

    除了注意大小写,别的木啥了 Switch语句 Switch(变量){ case 1: 如果变量和1的值相同,执行该处代码 break; case 2: 如果变量和2的值相同,执行该处代码 break; ...

  5. pandas学习(数据分组与分组运算、离散化处理、数据合并)

    pandas学习(数据分组与分组运算.离散化处理.数据合并) 目录 数据分组与分组运算 离散化处理 数据合并 数据分组与分组运算 GroupBy技术:实现数据的分组,和分组运算,作用类似于数据透视表 ...

  6. python 数据合并

    1. 数据合并 前言 一.横向合并 1. 基本合并语句 2. 键值名不一样的合并 3. “两个数据列名字重复了”的合并 二.纵向堆叠 统计师的Python日记[第6天:数据合并] 前言 根据我的Pyt ...

  7. 使用Notepad++将多行数据合并成一行

    1.按Ctrl+F,弹出“替换”的窗口: 2.选择“替换”菜单: 3.“查找目标”内容输入为:\r\n: 4.“替换为”内容为空: 5.“查找模式”选择为正则表达式: 6.设置好之后,点击“全部替换” ...

  8. R语言数据合并使用merge数据追加使用rbind和cbind

    R语言中的横向数据合并merge及纵向数据合并rbind的使用 我们经常会遇到两个数据框拥有相同的时间或观测值,但这些列却不尽相同.处理的办法就是使用merge(x, y ,by.x = ,by.y ...

  9. python 数据清洗之数据合并、转换、过滤、排序

    前面我们用pandas做了一些基本的操作,接下来进一步了解数据的操作, 数据清洗一直是数据分析中极为重要的一个环节. 数据合并 在pandas中可以通过merge对数据进行合并操作. import n ...

随机推荐

  1. [转]Mysql FROM_UNIXTIME as UTC

    本文转自:https://stackoverflow.com/questions/18276768/mysql-from-unixtime-as-utc You would be better off ...

  2. junit 测试报错 java.lang.Exception: No runnable methods

    转自:http://blog.csdn.net/snails_zx/article/details/51275894 在maven 项目中  建立测试类时,基类只用作加载spring配置文件,里面没有 ...

  3. [android] sharedPreference入门

    /********************2016年5月6日 更新**************************************/ 知乎:Android 如何实现判断用户首次使用,比如首 ...

  4. mybatis_ The content of element type association must match (constructor,id,result,ass ociation,collection,discriminator)

    一般遇到这种问题肯定要看一看association中元素编写顺序, <resultMap id="orderRslMap" type="orders"&g ...

  5. SQL语句在数据库中可以执行在mybatis执行不了

    这个问题竟然纠结了半个小时! 就问题而言,肯定是出在mybatis中 终于,找到了答案, 原来是DataSource配置问题, 我将配置连接池的数据写到了文件db.properties中, SqlMa ...

  6. JavaScript机器学习之KNN算法

    译者按: 机器学习原来很简单啊,不妨动手试试! 原文: Machine Learning with JavaScript : Part 2 译者: Fundebug 为了保证可读性,本文采用意译而非直 ...

  7. EJS-初识

    项目中使用了EJS,因此,也开始接触了EJS. EJS官方定义:it's just plain JavaScript. 总的来说,上手较快(毕竟我是个菜鸟). 第一步:安装: 第二部使用: 在html ...

  8. 【linux】如何开放防火墙端口

    linux默认大部分端口的是关闭的.而我们在开发.部署环境时,需要用到大量的服务,如mysql.tomcat.redis.zk等,需要开放指定的端口号. 以mysql端口3306为例 首先编辑服务器的 ...

  9. 51nod1238 最小公倍数之和 V3(莫比乌斯反演)

    题意 题目链接 Sol 不想打公式了,最后就是求一个 \(\sum_{i=1}^n ig(\frac{N}{i})\) \(g(i) = \sum_{i=1}^n \phi(i) i^2\) 拉个\( ...

  10. 关于TensorFlow你需要了解的9件事

    关于TensorFlow你需要了解的9件事 https://mp.weixin.qq.com/s/cEQAdLnueMEj0OQZtYvcuw 摘要:本文对近期在旧金山举办的谷歌 Cloud Next ...