def geo_test(a, b): attr = pd.Series(unique(a)).values value = pd.Series(b.groupby(a).count()).values data = [("上海", 47647), ("北京", 21454), ("南京", 849), ("南充", 15745), ("南通", 16352), ("合肥", 11895), ("广州", 43589), ("延安", 3180), ("成都", 19979), ("杭州", 33143), ("武汉", 6465), ("沧州", 13223), ("深圳", 31281), ("湖州", 22433), ("牡丹江", 20526), ("西安", 30548), ("金华", 22799), ("阜阳", 12004)] geo = Geo("全国各个城市的门店分布", "数据来源:香飘飘-饿了么爬虫原始数据", title_color="#fff", title_pos="center", width=1200, height=600, background_color="#404a59") attr_x, value_y = geo.cast(data) geo.add("", attr_x, value_y, visual_range=[0, 200], visual_text_color="#fff", symbol_size=15, is_visualmap=True) geo.show_config() geo.render("E:\\py_data_html\\ele_data_2.html") geo
def demo2(): from pyecharts import Geo data = [("海门", 9), ("鄂尔多斯", 12), ("招远", 12), ("舟山", 12), ("齐齐哈尔", 14), ("盐城", 15)] geo = Geo("全国主要城市空气质量", "data from pm2.5", title_color="#fff", title_pos="center", width=1200, height=600, background_color='#404a59') attr, value = geo.cast(data) geo.add("", attr, value, type="effectScatter", is_random=True, effect_scale=5) geo.show_config() geo.render()
def demo1(): data = [ ("海门", 9),("鄂尔多斯", 12),("招远", 12),("舟山", 12),("齐齐哈尔", 14),("盐城", 15), ("赤峰", 16),("青岛", 18),("乳山", 18),("金昌", 19),("泉州", 21),("莱西", 21), ("日照", 21),("胶南", 22),("南通", 23),("拉萨", 24),("云浮", 24),("梅州", 25)] geo = Geo("全国主要城市空气质量", "data from pm2.5", title_color="#fff", title_pos="center", width=1200, height=600, background_color='#404a59') attr, value = geo.cast(data) geo.add("", attr, value, visual_range=[0, 200], visual_text_color="#fff", symbol_size=15, is_visualmap=True) geo.show_config() geo.render()
def geo_drawing(): """Geo坐标图""" # 读取处理数据 cityname_sql = "select cityname from maoyan_wumingzhibei where movie_name = '无名之辈'" data_tuple = DataBase().create(cityname_sql) data_counter = Counter((i[0] for i in data_tuple)) data = dict(data_counter) # 初始化配置 # 'title_color'文本标题颜色; 'title_pos'标题位置; 'background_color'画布背景颜色 geo = Geo("《毒液》观影人群分布图", "猫眼数据", title_color="#fff", title_pos="center", width=1200, height=800, background_color='#404a59') # 过滤无坐标数据 for n, m in data.items(): list_1 = [] list_2 = [] list_1.append(n) list_2.append(m) try: # 'type'动画效果; 'is_random'随机排列颜色; 'effect_scale'波动大小; 'is_more_utils'实用工具按钮 # 'visual_range' 指定组件的允许的最小值与最大值 'is_visualmap' 是否使用视觉映射组件默认Flase geo.add("", list_1, list_2, type="effectScatter", tooltip_formatter=geo_formatter, is_label_emphasis=False, visual_range=[0, 500], is_visualmap=True, is_random=False, effect_scale=5, is_more_utils=True) except Exception as e: print(e) pass geo.show_config() geo.render("影评1.html")
def draw_geo(): data = [("海门", 9), ("鄂尔多斯", 12), ("招远", 12), ("舟山", 12), ("齐齐哈尔", 14), ("盐城", 15)] geo = Geo("全国主要城市空气质量", "data from pm2.5", title_color="#fff", title_pos="center", width=1200, height=600, background_color='#404a59') attr, value = geo.cast(data) geo.add("", attr, value, type="effectScatter", is_random=True, effect_scale=5) geo.show_config() geo.render()
def render_city(cities): # 对城市数据和坐标文件中的地名进行处理 data = Counter(cities).most_common() # 使用Counter类统计出现的次数,并转换为元组列表 print(data) handle(cities) data = Counter(cities).most_common() # 使用Counter类统计出现的次数,并转换为元组列表 print(data) # 定义样式 # style = Style( # title_color='#fff', # title_pos='center', # width=1200, # height=600, # background_color='#404a59' # ) # # # 根据城市数据生成地理坐标图 # geo = Geo('', **style.init_style) # attr, value = geo.cast(data) # geo.add('', attr, value, visual_range=[0, 3000], maptype='china', # visual_text_color='#fff', symbol_size=15, # is_visualmap=True, is_piecewise=True, visual_split_number=10) geo = Geo("《影》粉丝位置分布", "", title_color="#fff", title_pos="center", width=1200, height=600, background_color='#404a59') attr, value = geo.cast(data) geo.add("", attr, value, visual_range=[0, 3000], maptype='china', visual_text_color="#fff", symbol_size=10, is_visualmap=True) geo.show_config() geo.render(r'geo.html')
def test_geo(): geo = Geo("大蒜价格和地区的关系", "", title_color="#fff", title_pos="center", width=1200, height=600, background_color='#404a59') attr = arealist value = pricelist geo.add("", attr, value, visual_range=[0, 15], visual_split_number=0.5, visual_text_color="#fff", symbol_size=16, is_visualmap=True) geo.show_config() geo.render('大蒜1.html')
def demo1(): data = [("海门", 9), ("鄂尔多斯", 12), ("招远", 12), ("舟山", 12), ("齐齐哈尔", 14), ("盐城", 15), ("赤峰", 16), ("青岛", 18), ("乳山", 18), ("金昌", 19), ("泉州", 21), ("莱西", 21), ("日照", 21), ("胶南", 22), ("南通", 23), ("拉萨", 24), ("云浮", 24), ("梅州", 25)] geo = Geo("全国主要城市空气质量", "data from pm2.5", title_color="#fff", title_pos="center", width=1200, height=600, background_color='#404a59') attr, value = geo.cast(data) geo.add("", attr, value, visual_range=[0, 200], visual_text_color="#fff", symbol_size=15, is_visualmap=True) geo.show_config() geo.render()
#-*- coding:utf-8 -*- from pyecharts import Geo data = [("海门", 9), ("鄂尔多斯", 12), ("招远", 12), ("舟山", 12), ("齐齐哈尔", 14), ("盐城", 15), ("赤峰", 16), ("青岛", 18), ("乳山", 18), ("金昌", 19), ("泉州", 21), ("莱西", 21), ("日照", 21), ("胶南", 22), ("南通", 23), ("拉萨", 24), ("云浮", 24), ("梅州", 25)] geo = Geo("全国主要城市空气质量", "data from pm2.5", title_color="#fff", title_pos="center", width=1200, height=600, background_color='#404a59') attr, value = geo.cast(data) geo.add("", attr, value, visual_range=[0, 200], visual_text_color="#fff", symbol_size=15, is_visualmap=True) geo.show_config() geo.render("kongqi.html")
def test_geo(): # geo_0 data = [ ("海门", 9), ("鄂尔多斯", 12), ("招远", 12), ("舟山", 12), ("齐齐哈尔", 14), ("盐城", 15), ("赤峰", 16), ("青岛", 18), ("乳山", 18), ("金昌", 19), ("泉州", 21), ("莱西", 21), ("日照", 21), ("胶南", 22), ("南通", 23), ("拉萨", 24), ("云浮", 24), ("梅州", 25), ("文登", 25), ("上海", 25), ("攀枝花", 25), ("威海", 25), ("承德", 25), ("厦门", 26), ("汕尾", 26), ("潮州", 26), ("丹东", 27), ("太仓", 27), ("曲靖", 27), ("烟台", 28), ("福州", 29), ("瓦房店", 30), ("即墨", 30), ("抚顺", 31), ("玉溪", 31), ("张家口", 31), ("阳泉", 31), ("莱州", 32), ("湖州", 32), ("汕头", 32), ("昆山", 33), ("宁波", 33), ("湛江", 33), ("揭阳", 34), ("荣成", 34), ("连云港", 35), ("葫芦岛", 35), ("常熟", 36), ("东莞", 36), ("河源", 36), ("淮安", 36), ("泰州", 36), ("南宁", 37), ("营口", 37), ("惠州", 37), ("江阴", 37), ("蓬莱", 37), ("韶关", 38), ("嘉峪关", 38), ("广州", 38), ("延安", 38), ("太原", 39), ("清远", 39), ("中山", 39), ("昆明", 39), ("寿光", 40), ("盘锦", 40), ("长治", 41), ("深圳", 41), ("珠海", 42), ("宿迁", 43), ("咸阳", 43), ("铜川", 44), ("平度", 44), ("佛山", 44), ("海口", 44), ("江门", 45), ("章丘", 45), ("肇庆", 46), ("大连", 47), ("临汾", 47), ("吴江", 47), ("石嘴山", 49), ("沈阳", 50), ("苏州", 50), ("茂名", 50), ("嘉兴", 51), ("长春", 51), ("胶州", 52), ("银川", 52), ("张家港", 52), ("三门峡", 53), ("锦州", 54), ("南昌", 54), ("柳州", 54), ("三亚", 54), ("自贡", 56), ("吉林", 56), ("阳江", 57), ("泸州", 57), ("西宁", 57), ("宜宾", 58), ("呼和浩特", 58), ("成都", 58), ("大同", 58), ("镇江", 59), ("桂林", 59), ("张家界", 59), ("宜兴", 59), ("北海", 60), ("西安", 61), ("金坛", 62), ("东营", 62), ("牡丹江", 63), ("遵义", 63), ("绍兴", 63), ("扬州", 64), ("常州", 64), ("潍坊", 65), ("重庆", 66), ("台州", 67), ("南京", 67), ("滨州", 70), ("贵阳", 71), ("无锡", 71), ("本溪", 71), ("克拉玛依", 72), ("渭南", 72), ("马鞍山", 72), ("宝鸡", 72), ("焦作", 75), ("句容", 75), ("北京", 79), ("徐州", 79), ("衡水", 80), ("包头", 80), ("绵阳", 80), ("乌鲁木齐", 84), ("枣庄", 84), ("杭州", 84), ("淄博", 85), ("鞍山", 86), ("溧阳", 86), ("库尔勒", 86), ("安阳", 90), ("开封", 90), ("济南", 92), ("德阳", 93), ("温州", 95), ("九江", 96), ("邯郸", 98), ("临安", 99), ("兰州", 99), ("沧州", 100), ("临沂", 103), ("南充", 104), ("天津", 105), ("富阳", 106), ("泰安", 112), ("诸暨", 112), ("郑州", 113), ("哈尔滨", 114), ("聊城", 116), ("芜湖", 117), ("唐山", 119), ("平顶山", 119), ("邢台", 119), ("德州", 120), ("济宁", 120), ("荆州", 127), ("宜昌", 130), ("义乌", 132), ("丽水", 133), ("洛阳", 134), ("秦皇岛", 136), ("株洲", 143), ("石家庄", 147), ("莱芜", 148), ("常德", 152), ("保定", 153), ("湘潭", 154), ("金华", 157), ("岳阳", 169), ("长沙", 175), ("衢州", 177), ("廊坊", 193), ("菏泽", 194), ("合肥", 229), ("武汉", 273), ("大庆", 279) ] geo = Geo("全国主要城市空气质量", "data from pm2.5", title_color="#fff", title_pos="center", width=1200, height=600, background_color='#404a59') attr, value = geo.cast(data) geo.add("", attr, value, visual_range=[0, 200], visual_text_color="#fff", symbol_size=15, is_visualmap=True) geo.show_config() geo.render() # geo_0_1 geo = Geo("全国主要城市空气质量", "data from pm2.5", title_color="#fff", title_pos="center", width=1200, height=600, background_color='#404a59') attr, value = geo.cast(data) geo.add("", attr, value, type="heatmap", is_visualmap=True, visual_range=[0, 300], visual_text_color='#fff') geo.show_config() geo.render() # geo_1 data = [("海门", 9), ("鄂尔多斯", 12), ("招远", 12), ("舟山", 12), ("齐齐哈尔", 14), ("盐城", 15)] geo = Geo("全国主要城市空气质量", "data from pm2.5", title_color="#fff", title_pos="center", width=1200, height=600, background_color='#404a59') attr, value = geo.cast(data) geo.add("", attr, value, type="effectScatter", is_random=True, effect_scale=5) geo.show_config() geo.render() # geo_with_noexist_city data = [("海门", 9), ("鄂尔多斯", 12), ("招远", 12), ("舟山", 12), ("齐齐哈尔", 14), ("伦敦", 15)] geo = Geo("全国主要城市空气质量", "data from pm2.5", title_color="#fff", title_pos="center", width=1200, height=600, background_color='#404a59') attr, value = geo.cast(data) geo.add("", attr, value, type="effectScatter", is_random=True, effect_scale=5) geo.show_config() geo.render()
def Maps(): #好友分布图 province_distribution = {'河南': 45, '北京': 97, '河北': 21, '辽宁': 12, '江西': 6, '上海': 20, '安徽': 10, '江苏': 16, '湖南': 9, '浙江': 13, '海南': 2, '广东': 22, '湖北': 8, '黑龙江': 11, '澳门': 1, '陕西': 11, '四川': 7, '内蒙古': 3, '重庆': 3, '云南': 6, '贵州': 2, '吉林': 3, '山西': 12, '山东': 11, '福建': 4, '青海': 1, '舵主科技,质量保证': 1, '天津': 1, '其他': 1} province_keys=province_distribution.keys() province_values=province_distribution.values() map = Map("我的微信好友分布", "@SilenceYaung",width=1200, height=600) map.add("", province_keys, province_values, maptype='china', is_visualmap=True, visual_text_color='#000') map.render() #PM2.5分析 # 空气质量评分 indexs = ['上海', '北京', '合肥', '哈尔滨', '广州', '成都', '无锡', '杭州', '武汉', '深圳', '西安', '郑州', '重庆', '长沙'] values = [4.07, 1.85, 4.38, 2.21, 3.53, 4.37, 1.38, 4.29, 4.1, 1.31, 3.92, 4.47, 2.40, 3.60] geo = Geo("全国主要城市空气质量评分", "data from pm2.5", title_color="#fff", title_pos="center", width=1200, height=600, background_color='#404a59') # type="effectScatter", is_random=True, effect_scale=5 使点具有发散性 geo.add("空气质量评分", indexs, values, type="effectScatter", is_random=True, effect_scale=5, visual_range=[0, 5],visual_text_color="#fff", symbol_size=15, is_visualmap=True, is_roam=False) geo.show_config() geo.render(path="./data/04-05空气质量评分.html") value = [95.1, 23.2, 43.3, 66.4, 88.5] attr= ["China", "Canada", "Brazil", "Russia", "United States"] # 省和直辖市 province_distribution = {'河南': 45.23, '北京': 37.56, '河北': 21, '辽宁': 12, '江西': 6, '上海': 20, '安徽': 10, '江苏': 16, '湖南': 9, '浙江': 13, '海南': 2, '广东': 22, '湖北': 8, '黑龙江': 11, '澳门': 1, '陕西': 11, '四川': 7, '内蒙古': 3, '重庆': 3, '云南': 6, '贵州': 2, '吉林': 3, '山西': 12, '山东': 11, '福建': 4, '青海': 1, '舵主科技,质量保证': 1, '天津': 1, '其他': 1} provice=list(province_distribution.keys()) values=list(province_distribution.values()) # 城市 -- 指定省的城市 xx市 city = ['郑州市', '安阳市', '洛阳市', '濮阳市', '南阳市', '开封市', '商丘市', '信阳市', '新乡市'] values2 = [1.07, 3.85, 6.38, 8.21, 2.53, 4.37, 9.38, 4.29, 6.1] # 区县 -- 具体城市内的区县 xx县 quxian = ['夏邑县', '民权县', '梁园区', '睢阳区', '柘城县', '宁陵县'] values3 = [3, 5, 7, 8, 2, 4] map0 = Map("世界地图示例", width=1200, height=600) map0.add("世界地图", attr, value, maptype="world", is_visualmap=True, visual_text_color='#000') map0.render(path="aa.html") #热力分布图 data = [ ("海门", 9),("鄂尔多斯", 12),("招远", 12),("舟山", 12),("齐齐哈尔", 14),("盐城", 15), ("赤峰", 16),("青岛", 18),("乳山", 18),("金昌", 19),("泉州", 21),("莱西", 21), ("日照", 21),("胶南", 22),("南通", 23),("拉萨", 24),("云浮", 24),("梅州", 25)] attr, value = geo.cast(data) geo = Geo("全国主要城市空气质量热力图", "data from pm2.5", title_color="#fff", title_pos="center", width=1200, height=600, background_color='#404a59') geo.add("空气质量热力图", attr, value, visual_range=[0, 25], type='heatmap',visual_text_color="#fff", symbol_size=15, is_visualmap=True, is_roam=False) geo.show_config() geo.render(path="./data/04-04空气质量热力图.html") # maptype='china' 只显示全国直辖市和省级 # 数据只能是省名和直辖市的名称 map = Map("中国地图",'中国地图', width=1200, height=600) map.add("", provice, values, visual_range=[0, 50], maptype='china', is_visualmap=True, visual_text_color='#000') map.show_config() map.render(path="./data/04-01中国地图.html") # 河南地图 数据必须是省内放入城市名 map2 = Map("河南地图",'河南', width=1200, height=600) map2.add('河南', city, values2, visual_range=[1, 10], maptype='河南', is_visualmap=True, visual_text_color='#000') map2.show_config() map2.render(path="./data/04-02河南地图.html") # # 商丘地图 数据为商丘市下的区县 map3 = Map("商丘地图",'商丘', width=1200, height=600) map3.add("商丘", quxian, values3, visual_range=[1, 10], maptype='商丘', is_visualmap=True, visual_text_color='#000') map3.render(path="./data/04-03商丘地图.html")
def test_geo(): # geo_0 data = [ ("海门", 9), ("鄂尔多斯", 12), ("招远", 12), ("舟山", 12), ("齐齐哈尔", 14), ("盐城", 15), ("赤峰", 16), ("青岛", 18), ("乳山", 18), ("金昌", 19), ("泉州", 21), ("莱西", 21), ("日照", 21), ("胶南", 22), ("南通", 23), ("拉萨", 24), ("云浮", 24), ("梅州", 25), ("文登", 25), ("上海", 25), ("攀枝花", 25), ("威海", 25), ("承德", 25), ("厦门", 26), ("汕尾", 26), ("潮州", 26), ("丹东", 27), ("太仓", 27), ("曲靖", 27), ("烟台", 28), ("福州", 29), ("瓦房店", 30), ("即墨", 30), ("抚顺", 31), ("玉溪", 31), ("张家口", 31), ("阳泉", 31), ("莱州", 32), ("湖州", 32), ("汕头", 32), ("昆山", 33), ("宁波", 33), ("湛江", 33), ("揭阳", 34), ("荣成", 34), ("连云港", 35), ("葫芦岛", 35), ("常熟", 36), ("东莞", 36), ("河源", 36), ("淮安", 36), ("泰州", 36), ("南宁", 37), ("营口", 37), ("惠州", 37), ("江阴", 37), ("蓬莱", 37), ("韶关", 38), ("嘉峪关", 38), ("广州", 38), ("延安", 38), ("太原", 39), ("清远", 39), ("中山", 39), ("昆明", 39), ("寿光", 40), ("盘锦", 40), ("长治", 41), ("深圳", 41), ("珠海", 42), ("宿迁", 43), ("咸阳", 43), ("铜川", 44), ("平度", 44), ("佛山", 44), ("海口", 44), ("江门", 45), ("章丘", 45), ("肇庆", 46), ("大连", 47), ("临汾", 47), ("吴江", 47), ("石嘴山", 49), ("沈阳", 50), ("苏州", 50), ("茂名", 50), ("嘉兴", 51), ("长春", 51), ("胶州", 52), ("银川", 52), ("张家港", 52), ("三门峡", 53), ("锦州", 54), ("南昌", 54), ("柳州", 54), ("三亚", 54), ("自贡", 56), ("吉林", 56), ("阳江", 57), ("泸州", 57), ("西宁", 57), ("宜宾", 58), ("呼和浩特", 58), ("成都", 58), ("大同", 58), ("镇江", 59), ("桂林", 59), ("张家界", 59), ("宜兴", 59), ("北海", 60), ("西安", 61), ("金坛", 62), ("东营", 62), ("牡丹江", 63), ("遵义", 63), ("绍兴", 63), ("扬州", 64), ("常州", 64), ("潍坊", 65), ("重庆", 66), ("台州", 67), ("南京", 67), ("滨州", 70), ("贵阳", 71), ("无锡", 71), ("本溪", 71), ("克拉玛依", 72), ("渭南", 72), ("马鞍山", 72), ("宝鸡", 72), ("焦作", 75), ("句容", 75), ("北京", 79), ("徐州", 79), ("衡水", 80), ("包头", 80), ("绵阳", 80), ("乌鲁木齐", 84), ("枣庄", 84), ("杭州", 84), ("淄博", 85), ("鞍山", 86), ("溧阳", 86), ("库尔勒", 86), ("安阳", 90), ("开封", 90), ("济南", 92), ("德阳", 93), ("温州", 95), ("九江", 96), ("邯郸", 98), ("临安", 99), ("兰州", 99), ("沧州", 100), ("临沂", 103), ("南充", 104), ("天津", 105), ("富阳", 106), ("泰安", 112), ("诸暨", 112), ("郑州", 113), ("哈尔滨", 114), ("聊城", 116), ("芜湖", 117), ("唐山", 119), ("平顶山", 119), ("邢台", 119), ("德州", 120), ("济宁", 120), ("荆州", 127), ("宜昌", 130), ("义乌", 132), ("丽水", 133), ("洛阳", 134), ("秦皇岛", 136), ("株洲", 143), ("石家庄", 147), ("莱芜", 148), ("常德", 152), ("保定", 153), ("湘潭", 154), ("金华", 157), ("岳阳", 169), ("长沙", 175), ("衢州", 177), ("廊坊", 193), ("菏泽", 194), ("合肥", 229), ("武汉", 273), ("大庆", 279) ] geo = Geo("全国主要城市空气质量", "data from pm2.5", title_color="#fff", title_pos="center", width=1200, height=600, background_color='#404a59') attr, value = geo.cast(data) geo.add("", attr, value, visual_range=[0, 200], visual_text_color="#fff", symbol_size=15, is_visualmap=True) geo.show_config() geo.render() # geo_0_1 geo = Geo("全国主要城市空气质量", "data from pm2.5", title_color="#fff", title_pos="center", width=1200, height=600, background_color='#404a59') attr, value = geo.cast(data) geo.add("", attr, value, type="heatmap", is_visualmap=True, visual_range=[0, 300], visual_text_color='#fff') geo.show_config() geo.render() # geo_1 data = [("海门", 9), ("鄂尔多斯", 12), ("招远", 12), ("舟山", 12), ("齐齐哈尔", 14), ("盐城", 15)] geo = Geo("全国主要城市空气质量", "data from pm2.5", title_color="#fff", title_pos="center", width=1200, height=600, background_color='#404a59') attr, value = geo.cast(data) geo.add("", attr, value, type="effectScatter", is_random=True, effect_scale=5) geo.show_config() geo.render()