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pyecharts怎么绘制散点图_Python绘制地图及其它图表

作者：手机用户2502921877 | 来源：互联网 | 2023-09-08 21:18
pyecharts分为v0.5和v1两个大版本，v0.5和v1间不兼容。最早写这篇文章的时候还是0.5版本，里面的代码都运行正常。但是最近大家反馈说代码
80b5c5fe1bf8d21d45afb326807fccbd.png

pyecharts 分为 v0.5 和 v1 两个大版本&＃xff0c;v0.5 和 v1 间不兼容。最早写这篇文章的时候还是0.5版本&＃xff0c;里面的代码都运行正常。但是最近大家反馈说代码不能正常使用&＃xff0c;因为我也有一段时间没有使用pyecharts&＃xff0c;所以今天就特意去官网研究了一下&＃xff0c;顺便也把旧的代码更新掉。

由于0.5.x 版本将不再进行维护&＃xff0c;建议大家都用最新版本。官网和Github地址如下&＃xff0c;大家直接有问题&＃xff0c;可以先上这三个网址看看。第三个网址是所有的案例&＃xff0c;里面有实例代码。

A Python Echarts Plotting Library​pyecharts.orgpyecharts/pyecharts​github.com
92661584ccd4503b302c54e53d573e1f.png
Document​gallery.pyecharts.org

(1)中国人口地图

pyecharts在v1.x之后支持链式调用

from pyecharts.charts import Map,Geo
from pyecharts import options as opts
data&＃61;[("广东",10430.03),("山东",9579.31),("河南",9402.36),("四川",8041.82),("江苏",7865.99),("河北",7185.42),("湖南",6568.37),("安徽",5950.1),("浙江",5442),("湖北",5723.77),("广西",4602.66),("云南",4596.6),("江西",4456.74),("辽宁",4374.63),("黑龙江",3831.22),("陕西",3732.74),("山西",3571.21),("福建",3552),("重庆",2884),("贵州",3476.65),("吉林",2746.22),("甘肃",2557.53),("内蒙古",2470.63),("上海",2301.391),("台湾",2316.2),("新疆",2181.33),("北京",1961.2),("天津",1293.82),("海南",867.15),("香港",709.76),("青海",562.67),("宁夏",630.14),("西藏",300.21),("澳门",55.23)]
map&＃61;(Map().add("",data,"china").set_global_opts(title_opts&＃61;opts.TitleOpts(title&＃61;"各省市人口数",subtitle&＃61;"数据来源&＃xff1a;中国统计年鉴&＃xff08;万人&＃xff09;",pos_right&＃61;"center",pos_top&＃61;"5%"),visualmap_opts&＃61;opts.VisualMapOpts(max_&＃61;12000),)
)
map.render_notebook()

不习惯链式调用的话&＃xff0c;你也可以单独调用

from pyecharts.charts import Map,Geo
from pyecharts import options as opts
data&＃61;[("广东",10430.03),("山东",9579.31),("河南",9402.36),("四川",8041.82),("江苏",7865.99),("河北",7185.42),("湖南",6568.37),("安徽",5950.1),("浙江",5442),("湖北",5723.77),("广西",4602.66),("云南",4596.6),("江西",4456.74),("辽宁",4374.63),("黑龙江",3831.22),("陕西",3732.74),("山西",3571.21),("福建",3552),("重庆",2884),("贵州",3476.65),("吉林",2746.22),("甘肃",2557.53),("内蒙古",2470.63),("上海",2301.391),("台湾",2316.2),("新疆",2181.33),("北京",1961.2),("天津",1293.82),("海南",867.15),("香港",709.76),("青海",562.67),("宁夏",630.14),("西藏",300.21),("澳门",55.23)]
map&＃61;Map()
map.add("",data,"china")
map.set_global_opts(title_opts&＃61;opts.TitleOpts(title&＃61;"各省市人口数",subtitle&＃61;"数据来源&＃xff1a;中国统计年鉴&＃xff08;万人&＃xff09;",pos_right&＃61;"center",pos_top&＃61;"5%"),visualmap_opts&＃61;opts.VisualMapOpts(max_&＃61;12000),)
map.render_notebook()

1b0bc26aaa90f2d2b86f775331c6958c.png

(2)世界地图

from pyecharts import options as opts
from pyecharts.charts import Map
from pyecharts.faker import Faker
map &＃61; (Map().add("", [list(z) for z in zip(Faker.country, Faker.values())], "world").set_series_opts(label_opts&＃61;opts.LabelOpts(is_show&＃61;False)).set_global_opts(title_opts&＃61;opts.TitleOpts(title&＃61;"Map-世界地图",pos_right&＃61;&＃39;center&＃39;),visualmap_opts&＃61;opts.VisualMapOpts(max_&＃61;200),)
)
# 单独调用
# map &＃61; Map()
# map.add("商家A", [list(z) for z in zip(Faker.country, Faker.values())], "world")
# map.set_series_opts(label_opts&＃61;opts.LabelOpts(is_show&＃61;False))
# map.set_global_opts(
# title_opts&＃61;opts.TitleOpts(title&＃61;"Map-世界地图"),
# visualmap_opts&＃61;opts.VisualMapOpts(max_&＃61;200),)map.render_notebook()

33b17836391c192a7c309a0379b4deb4.png

&＃xff08;3&＃xff09;热力图

from pyecharts import options as opts
from pyecharts.charts import Geo
from pyecharts.globals import GeoType #Geo图的类型
data&＃61;[ ("北京","2171"),("天津","1557"),("河北","7520"),("内蒙古","2529"),("辽宁","4369"),("吉林","2717"), ("黑龙江","3789"),("上海","2418"),("江苏","8029"),("浙江","5657"),("安徽","6255"),("福建","3911"), ("江西","4622"),("山东","10006"),("河南","9559"),("湖北","5902"),("湖南","6860"),("广东","11169"), ("广西","4885"),("海南","926"),("重庆","3075"),("四川","8302"),("贵州","3580"),("云南","4801"), ("西藏","337"),("陕西","3835"),("甘肃","2626"),("青海","598"),("宁夏","682"),("新疆","2445"),]
pop_geo &＃61; ( Geo() .add_schema(maptype&＃61;"china") .add("",data,type_&＃61;GeoType.HEATMAP) #热度效果HEATMAP.set_series_opts(label_opts&＃61;opts.LabelOpts(is_show&＃61;False)) .set_global_opts(title_opts&＃61;opts.TitleOpts(title&＃61;"全国年度人口数",subtitle&＃61;"数据来源&＃xff1a;中国统计年鉴&＃xff08;万人&＃xff09;",pos_right&＃61;"center",pos_top&＃61;"5%"), visualmap_opts&＃61;opts.VisualMapOpts(max_&＃61;11169,pos_left&＃61;"8%",is_piecewise&＃61;True))
)
pop_geo.render_notebook()

6415a7399ed22d7b4d546938edff7406.png

&＃xff08;4&＃xff09;散点图

from pyecharts import options as opts
from pyecharts.charts import Geo
data&＃61;[ ("北京","2171"),("天津","1557"),("河北","7520"),("内蒙古","2529"),("辽宁","4369"),("吉林","2717"), ("黑龙江","3789"),("上海","2418"),("江苏","8029"),("浙江","5657"),("安徽","6255"),("福建","3911"), ("江西","4622"),("山东","10006"),("河南","9559"),("湖北","5902"),("湖南","6860"),("广东","11169"), ("广西","4885"),("海南","926"),("重庆","3075"),("四川","8302"),("贵州","3580"),("云南","4801"), ("西藏","337"),("陕西","3835"),("甘肃","2626"),("青海","598"),("宁夏","682"),("新疆","2445"),]
pop_geo &＃61; ( Geo() .add_schema(maptype&＃61;"china") .add("",data) .set_series_opts(label_opts&＃61;opts.LabelOpts(is_show&＃61;False)) #标签显示即为每个地区对应的人口数 .set_global_opts(title_opts&＃61;opts.TitleOpts(title&＃61;"全国年度人口数",subtitle&＃61;"数据来源&＃xff1a;中国统计年鉴&＃xff08;万人&＃xff09;",pos_right&＃61;"center",pos_top&＃61;"5%"), visualmap_opts&＃61;opts.VisualMapOpts(max_&＃61;11169,pos_left&＃61;"8%",is_piecewise&＃61;True))
)
pop_geo.render_notebook()

6c80aeda6195e43f08df96578bcbd876.png

&＃xff08;5&＃xff09;航线动态图

from pyecharts import options as opts
from pyecharts.charts import Geo # 地理坐标系绘制方法
from pyecharts.globals import GeoType,ThemeType,SymbolType #Geo图的类型 主题 涟漪图形符号
data&＃61;[("四川","8302"),("山东","10006"),("河南","9559"),("湖南","6860"),("广东","11169")]
geo &＃61; (Geo(init_opts&＃61;opts.InitOpts(width&＃61;"600px",height&＃61;"500px",theme&＃61;ThemeType.DARK)) .add_schema(maptype&＃61;"china",#itemstyle_opts&＃61;opts.ItemStyleOpts(color&＃61;&＃39;white&＃39;,border_color&＃61;"#EE2C2C")#地图背景色#zoom&＃61;1.5,#is_roam&＃61;True,#center&＃61;Geo().get_coordinate(&＃39;广东&＃39;), # 视角中心emphasis_itemstyle_opts&＃61;opts.ItemStyleOpts(color&＃61;"#31708f"), # 高亮颜色# emphasis_label_opts&＃61;opts.ItemStyleOpts(color&＃61;"white", ) # 字体颜色).add("",data,type_&＃61;GeoType.EFFECT_SCATTER,symbol_size&＃61;6).add("",[("广东","四川"),("广东","山东"),("广东","湖南"),("广东","河南")],type_&＃61;GeoType.LINES, effect_opts&＃61;opts.EffectOpts(symbol&＃61;SymbolType.ARROW,symbol_size&＃61;6,color&＃61;&＃39;#5f99bb&＃39;),linestyle_opts&＃61;opts.LineStyleOpts(curve&＃61;0.2,color&＃61;"#B0E2FF")).set_series_opts(label_opts&＃61;opts.LabelOpts(is_show&＃61;False)) .set_global_opts(title_opts&＃61;opts.TitleOpts(title&＃61;"广东出发的航班",subtitle&＃61;"数据来源&＃xff1a;中国民航局官方网站",pos_right&＃61;"center",pos_top&＃61;"5%"))
)
geo.render_notebook()

a71391b74ed99dde133c2091c9db4f05.png

&＃xff08;6&＃xff09;其他图表比如组合图

pyecharts能实现非常多的图表&＃xff0c;基本上你日常用到的图表&＃xff0c;都能实现。有兴趣的话&＃xff0c;自己去官网下载案例研究。官网的案例非常全面。

from pyecharts import options as opts
from pyecharts.charts import Map, Bar, Grid
from pyecharts.globals import ChartType, ThemeType
import randomprovince &＃61; [&＃39;武汉&＃39;, &＃39;十堰&＃39;, &＃39;鄂州&＃39;, &＃39;宜昌&＃39;, &＃39;荆州&＃39;, &＃39;孝感&＃39;, &＃39;黄石&＃39;, &＃39;咸宁&＃39;, &＃39;仙桃&＃39;]
data &＃61; [100,150 ,123 ,210 ,168 ,234 ,350 ,435 ,342 ]
bar &＃61; (Bar().add_xaxis(province).add_yaxis(&＃39;营业额&＃39;, data).set_series_opts(label_opts&＃61;opts.LabelOpts(is_show&＃61;False)).set_global_opts(title_opts&＃61;opts.TitleOpts(title&＃61;"Grid-Bar")))line &＃61; (Line().add_xaxis(province).add_yaxis(&＃39;营业额&＃39;, data, markline_opts&＃61;opts.MarkLineOpts(data&＃61;[opts.MarkLineItem(type_&＃61;"average")])).set_global_opts(title_opts&＃61;opts.TitleOpts(title&＃61;"Grid-Line", pos_top&＃61;"48%")))grid &＃61; (Grid().add(bar, grid_opts&＃61;opts.GridOpts(pos_bottom&＃61;"60%")).add(line, grid_opts&＃61;opts.GridOpts(pos_top&＃61;"60%")))grid.render_notebook()

1b0c3af4813e7d4981bf1431add84dd4.png



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