from 

from pyecharts.charts import Geo
# 导入配置项
from pyecharts import options as opts
# ChartType：图标类型，SymbolType：标记点类型
from pyecharts .globals import ChartType, SymbolType
import randomc = (Geo().add_schema(maptype="china") # 地图类型,这里选择china.add("",[("天津", 120000), ("上海", 100000), ("广州", 80000), ("深圳", 90000), ("长沙", 100000), ("郑州", 100000),("杭州", 70000),("乌鲁木齐",90)],type_=ChartType.EFFECT_SCATTER,color="green",)# 添加数据点.add("geo",[("北京", "天津"), ("北京", "上海"),("北京", "广州"),("北京", "深圳"),("北京", "长沙"),("北京", "郑州"),("北京", "杭州"),("北京", "乌鲁木齐")],type_=ChartType.LINES,effect_opts=opts.EffectOpts(symbol=SymbolType.ARROW, symbol_size=6, color="yellow"),# 添加流向，type_设置为LINES，涟漪配置为箭头，提供的标记类型包括 'circle', 'rect', 'roundRect', 'triangle', 'diamond', 'pin', 'arrow', 'none'linestyle_opts=opts.LineStyleOpts(curve=0.2),).set_series_opts(label_opts=opts.LabelOpts(is_show=False))# 不显示标签.set_global_opts(title_opts=opts.TitleOpts(title="北京人口省外输出图")))c.render_notebook()# 直接在notebook里显示图表

c = (Geo().add_schema(maptype="河南") # 地图类型,这里选择郑州.add("",[("安阳", 20000), ("信阳", 10000), ("南阳", 8000), ("洛阳", 9000), ("濮阳", 1000)],type_=ChartType.EFFECT_SCATTER,color="green",)# 添加数据点.add("geo",[("郑州", "安阳"), ("郑州", "信阳"),("郑州", "南阳"),("郑州", "洛阳"),("郑州", "濮阳")],type_=ChartType.LINES,effect_opts=opts.EffectOpts(symbol=SymbolType.ARROW, symbol_size=6, color="yellow"),# 添加流向，type_设置为LINES，涟漪配置为箭头，提供的标记类型包括 'circle', 'rect', 'roundRect', 'triangle', 'diamond', 'pin', 'arrow', 'none'linestyle_opts=opts.LineStyleOpts(curve=0.2),).set_series_opts(label_opts=opts.LabelOpts(is_show=False))# 不显示标签.set_global_opts(title_opts=opts.TitleOpts(title="郑州人口省内输出图")))c.render_notebook()# 直接在notebook里显示图表

province = ['北京', '上海', '广州', '深圳', '河北', '河南', '湖北', '湖南', '黑龙江', '吉林', '内蒙古', '新疆', '西藏']
data = [(i, random.randint(100, 350)) for i in province]geo = (Geo().add_schema(maptype="china").add("人口", data,type_=ChartType.HEATMAP).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(visualmap_opts=opts.VisualMapOpts(),legend_opts=opts.LegendOpts(is_show=False),title_opts=opts.TitleOpts(title="Geo-全国热力地图")))geo.render_notebook()

from pyecharts import options as opts
from pyecharts.charts import Bar, Timeline
from pyecharts.globals import ThemeType
import randomcate = ['Apple', 'Dell', 'Toshiba', 'Samsung', 'Lenovo'] tl = Timeline()
for i in range(2015, 2019): #选择年份范围bar = (Bar().add_xaxis(cate).add_yaxis("线上", [random.randint(10000, 15000) for _ in cate]) #随机赋值.add_yaxis("门店", [random.randint(7000, 12000) for _ in cate]).set_global_opts(title_opts=opts.TitleOpts("电脑品牌{}年营业额".format(i))))tl.add(bar, "{}年".format(i))tl.render_notebook()

import pyecharts.options as opts
from pyecharts.charts import Bar3Dhours = ["0","1","2","3","4","5","6","7","8","9","10","11", "12","13","14","15","16","17","18","19","20","21","22","23",]
days = ["Saturday", "Friday", "Thursday", "Wednesday", "Tuesday", "Monday", "Sunday"]
data = [[random.randint(0, 6), random.randint(0, 23), random.randint(0, 15)]for _ in range(100)]
data = [[d[1], d[0], d[2]] for d in data]Bar3D = (Bar3D(init_opts=opts.InitOpts(width="800px", height="400px")).add(series_name="",data=data,xaxis3d_opts=opts.Axis3DOpts(type_="category", data=hours),yaxis3d_opts=opts.Axis3DOpts(type_="category", data=days),zaxis3d_opts=opts.Axis3DOpts(type_="value"),).set_global_opts(visualmap_opts=opts.VisualMapOpts(max_=20,range_color=["#313695","#4575b4","#74add1","#abd9e9","#e0f3f8","#ffffbf","#fee090","#fdae61","#f46d43","#d73027","#a50026",],)))Bar3D.render_notebook()