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()
