import pandas as pd
#元数据
dict_map= {
'海门':[121.15,31.89],
'鄂尔多斯':[109.781327,39.608266],
'招远':[120.38,37.35],
'舟山':[122.207216,29.985295],
'齐齐哈尔':[123.97,47.33],
'盐城':[120.13,33.38],
'赤峰':[118.87,42.28],
'青岛':[120.33,36.07],
'乳山':[121.52,36.89],
'金昌':[102.188043,38.520089],
'泉州':[118.58,24.93],
'莱西':[120.53,36.86],
'日照':[119.46,35.42],
'胶南':[119.97,35.88],
'南通':[121.05,32.08],
'拉萨':[91.11,29.97],
'云浮':[112.02,22.93],
'梅州':[116.1,24.55],
'文登':[122.05,37.2],
'上海':[121.48,31.22],
'攀枝花':[101.718637,26.582347],
'威海':[122.1,37.5],
'承德':[117.93,40.97],
'厦门':[118.1,24.46],
'汕尾':[115.375279,22.786211],
'潮州':[116.63,23.68],
'丹东':[124.37,40.13],
'太仓':[121.1,31.45],
'曲靖':[103.79,25.51],
'烟台':[121.39,37.52],
'福州':[119.3,26.08],
'瓦房店':[121.979603,39.627114],
'即墨':[120.45,36.38],
'抚顺':[123.97,41.97],
'玉溪':[102.52,24.35],
'张家口':[114.87,40.82],
'阳泉':[113.57,37.85],
'莱州':[119.942327,37.177017],
'湖州':[120.1,30.86],
'汕头':[116.69,23.39],
'昆山':[120.95,31.39],
'宁波':[121.56,29.86],
'湛江':[110.359377,21.270708],
'揭阳':[116.35,23.55],
'荣成':[122.41,37.16],
'连云港':[119.16,34.59],
'葫芦岛':[120.836932,40.711052],
'常熟':[120.74,31.64],
'东莞':[113.75,23.04],
'河源':[114.68,23.73],
'淮安':[119.15,33.5],
'泰州':[119.9,32.49],
'南宁':[108.33,22.84],
'营口':[122.18,40.65],
'惠州':[114.4,23.09],
'江阴':[120.26,31.91],
'蓬莱':[120.75,37.8],
'韶关':[113.62,24.84],
'嘉峪关':[98.289152,39.77313],
'广州':[113.23,23.16],
'延安':[109.47,36.6],
'太原':[112.53,37.87],
'清远':[113.01,23.7],
'中山':[113.38,22.52],
'昆明':[102.73,25.04],
'寿光':[118.73,36.86],
'盘锦':[122.070714,41.119997],
'长治':[113.08,36.18],
'深圳':[114.07,22.62],
'珠海':[113.52,22.3],
'宿迁':[118.3,33.96],
'咸阳':[108.72,34.36],
'铜川':[109.11,35.09],
'平度':[119.97,36.77],
'佛山':[113.11,23.05],
'海口':[110.35,20.02],
'江门':[113.06,22.61],
'章丘':[117.53,36.72],
'肇庆':[112.44,23.05],
'大连':[121.62,38.92],
'临汾':[111.5,36.08],
'吴江':[120.63,31.16],
'石嘴山':[106.39,39.04],
'沈阳':[123.38,41.8],
'苏州':[120.62,31.32],
'茂名':[110.88,21.68],
'嘉兴':[120.76,30.77],
'长春':[125.35,43.88],
'胶州':[120.03336,36.264622],
'银川':[106.27,38.47],
'张家港':[120.555821,31.875428],
'三门峡':[111.19,34.76],
'锦州':[121.15,41.13],
'南昌':[115.89,28.68],
'柳州':[109.4,24.33],
'三亚':[109.511909,18.252847],
'自贡':[104.778442,29.33903],
'吉林':[126.57,43.87],
'阳江':[111.95,21.85],
'泸州':[105.39,28.91],
'西宁':[101.74,36.56],
'宜宾':[104.56,29.77],
'呼和浩特':[111.65,40.82],
'成都':[104.06,30.67],
'大同':[113.3,40.12],
'镇江':[119.44,32.2],
'桂林':[110.28,25.29],
'张家界':[110.479191,29.117096],
'宜兴':[119.82,31.36],
'北海':[109.12,21.49],
'西安':[108.95,34.27],
'金坛':[119.56,31.74],
'东营':[118.49,37.46],
'牡丹江':[129.58,44.6],
'遵义':[106.9,27.7],
'绍兴':[120.58,30.01],
'扬州':[119.42,32.39],
'常州':[119.95,31.79],
'潍坊':[119.1,36.62],
'重庆':[106.54,29.59],
'台州':[121.420757,28.656386],
'南京':[118.78,32.04],
'滨州':[118.03,37.36],
'贵阳':[106.71,26.57],
'无锡':[120.29,31.59],
'本溪':[123.73,41.3],
'克拉玛依':[84.77,45.59],
'渭南':[109.5,34.52],
'马鞍山':[118.48,31.56],
'宝鸡':[107.15,34.38],
'焦作':[113.21,35.24],
'句容':[119.16,31.95],
'北京':[116.46,39.92],
'徐州':[117.2,34.26],
'衡水':[115.72,37.72],
'包头':[110,40.58],
'绵阳':[104.73,31.48],
'乌鲁木齐':[87.68,43.77],
'枣庄':[117.57,34.86],
'杭州':[120.19,30.26],
'淄博':[118.05,36.78],
'鞍山':[122.85,41.12],
'溧阳':[119.48,31.43],
'库尔勒':[86.06,41.68],
'安阳':[114.35,36.1],
'开封':[114.35,34.79],
'济南':[117,36.65],
'德阳':[104.37,31.13],
'温州':[120.65,28.01],
'九江':[115.97,29.71],
'邯郸':[114.47,36.6],
'临安':[119.72,30.23],
'兰州':[103.73,36.03],
'沧州':[116.83,38.33],
'临沂':[118.35,35.05],
'南充':[106.110698,30.837793],
'天津':[117.2,39.13],
'富阳':[119.95,30.07],
'泰安':[117.13,36.18],
'诸暨':[120.23,29.71],
'郑州':[113.65,34.76],
'哈尔滨':[126.63,45.75],
'聊城':[115.97,36.45],
'芜湖':[118.38,31.33],
'唐山':[118.02,39.63],
'平顶山':[113.29,33.75],
'邢台':[114.48,37.05],
'德州':[116.29,37.45],
'济宁':[116.59,35.38],
'荆州':[112.239741,30.335165],
'宜昌':[111.3,30.7],
'义乌':[120.06,29.32],
'丽水':[119.92,28.45],
'洛阳':[112.44,34.7],
'秦皇岛':[119.57,39.95],
'株洲':[113.16,27.83],
'石家庄':[114.48,38.03],
'莱芜':[117.67,36.19],
'常德':[111.69,29.05],
'保定':[115.48,38.85],
'湘潭':[112.91,27.87],
'金华':[119.64,29.12],
'岳阳':[113.09,29.37],
'长沙':[113,28.21],
'衢州':[118.88,28.97],
'廊坊':[116.7,39.53],
'菏泽':[115.480656,35.23375],
'合肥':[117.27,31.86],
'武汉':[114.31,30.52],
'大庆':[125.03,46.58]
}
#经纬度四舍五入
dict_map_round={}
for (key,value) in dict_map.items():
dict_map_round[key]=[round(value[0]),round(value[1])]
#创建datafarme
dict_test={}
for i in range(15,50):
dict_test[i]=[0]*80
#核心 同经纬度字符串连接
for (key,value) in dict_map_round.items():
if dict_test[value[1]][value[0]-80]==0:
dict_test[value[1]][value[0]-80]=key
else:
dict_test[value[1]][value[0]-80]=dict_test[value[1]][value[0]-80]+key
#存储，并且按纬度倒叙
frame = pd.DataFrame.from_dict(dict_test, orient='index')
frame=frame.reindex(index=frame.index[::-1])
#本地存储输出
frame.to_excel('21213.xlsx')
效果图: