pyecharts绘制热点图系线图

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),
    ("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)##默认的'scatter'类型
geo.render("热力图.html")
#%%cell2
## 'effectScatter'类型
geo.add("", attr, value, type="effectScatter", is_random=True, effect_scale=5)
geo.render("effect.html")
#%%cell3
##'headmap'类型
geo.add("", attr, value, type="heatmap", is_visualmap=True, visual_range=[0, 300],
        visual_text_color='#fff')
geo.render("headmap.html")
#%%cell4
##绘制地理坐标系线图
from pyecharts import GeoLines, Style

style = Style(title_top="#fff",title_pos = "center",width=1200,height=600,background_color="#404a59")
style_geo = style.add(is_label_show=True,line_curve=0.2,line_opacity=0.6,legend_text_color="#eee",
   legend_pos="right",geo_effect_symbol="plane",geo_effect_symbolsize=15,label_color=['#a6c84c', '#ffa022', '#46bee9'],
   label_pos="right",label_formatter="{b}",label_text_color="#eee",)
data_guangzhou = [
   ["昆明", "上海"],
   ["昆明", "北京"],
   ["昆明", "南京"],
   ["昆明", "重庆"],
   ["北京", "兰州"],
   ["上海", "杭州"]
]
geolines = GeoLines("GeoLines 示例", **style.init_style)
geolines.add("从广州出发", data_guangzhou, **style_geo)
geolines.render("系线图1.html")
#%%cell5
##绘制网络关系图
from pyecharts import Graph

nodes = [{"name": "结点1", "symbolSize": 10},
         {"name": "结点2", "symbolSize": 20},
         {"name": "结点3", "symbolSize": 30},
         {"name": "结点4", "symbolSize": 40},
         {"name": "结点5", "symbolSize": 50},
         {"name": "结点6", "symbolSize": 40},
         {"name": "结点7", "symbolSize": 30},
         {"name": "结点8", "symbolSize": 20}]
links = []
for i in nodes:
    for j in nodes:
        links.append({"source": i.get('name'), "target": j.get('name')})
print(links)
graph = Graph("关系图-环形布局示例")
graph.add("", nodes, links, is_label_show=True,
          graph_repulsion=8000, graph_layout='circular',
          label_text_color=None)
graph.render("graph.html")
#%%cell 6
##绘制雷达图
from pyecharts import Radar

schema = [ 
    ("销售", 6500), ("管理", 16000), ("信息技术", 30000),
    ("客服", 38000), ("研发", 52000), ("市场", 25000)
]
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
radar = Radar()
radar.config(schema)
radar.add("预算分配", v1, is_splitline=True, is_axisline_show=True)
radar.add("实际开销", v2, label_color=["#4e79a7"], is_area_show=False,
          legend_selectedmode='single')
radar.render("雷达图.html")
#%%cell 
##绘制词云图
from pyecharts import WordCloud

name = [
    'Sam S Club', 'Macys', 'Amy Schumer', 'Jurassic World', 'Charter Communications',
    'Chick Fil A', 'Planet Fitness', 'Pitch Perfect', 'Express', 'Home', 'Johnny Depp',
    'Lena Dunham', 'Lewis Hamilton', 'KXAN', 'Mary Ellen Mark', 'Farrah Abraham',
    'Rita Ora', 'Serena Williams', 'NCAA baseball tournament', 'Point Break']
value = [
    10000, 6181, 4386, 4055, 2467, 2244, 1898, 1484, 1112,
    965, 847, 582, 555, 550, 462, 366, 360, 282, 273, 265]
wordcloud = WordCloud(width=1300, height=620)
wordcloud.add("", name, value, word_size_range=[20, 100])
wordcloud.render("词云图.html")
墨染半纸，清心煮字

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