Python----数据可视化（Pyecharts三：绘图二：涟漪散点图，K线图，漏斗图，雷达图，词云图，地图，柱状图折线图组合，时间线轮廓图）

1、涟漪特效散点图

from pyecharts.globals import SymbolType
from pyecharts.charts import EffectScatter
from pyecharts.faker import Faker
from pyecharts import options as opts
from pyecharts.globals import ThemeType
# 绘制图表
es = (EffectScatter(init_opts=opts.InitOpts(theme=ThemeType.VINTAGE)).add_xaxis(Faker.choose()).add_yaxis('',Faker.values(),symbol=SymbolType.DIAMOND,symbol_size=20)
)
# 渲染图表
es.render_notebook()

2、K线图

from pyecharts.charts import Kline
from pyecharts import options as opts
data = [[2320.26, 2320.26, 2287.3, 2362.94],[2300, 2291.3, 2288.26, 2308.38],[2295.35, 2346.5, 2295.35, 2345.92],[2347.22, 2358.98, 2337.35, 2363.8],[2360.75, 2382.48, 2347.89, 2383.76],[2383.43, 2385.42, 2371.23, 2391.82],[2377.41, 2419.02, 2369.57, 2421.15],[2425.92, 2428.15, 2417.58, 2440.38],[2411, 2433.13, 2403.3, 2437.42],[2432.68, 2334.48, 2427.7, 2441.73],[2430.69, 2418.53, 2394.22, 2433.89],[2416.62, 2432.4, 2414.4, 2443.03],[2441.91, 2421.56, 2418.43, 2444.8],[2420.26, 2382.91, 2373.53, 2427.07],[2383.49, 2397.18, 2370.61, 2397.94],[2378.82, 2325.95, 2309.17, 2378.82],[2322.94, 2314.16, 2308.76, 2330.88],[2320.62, 2325.82, 2315.01, 2338.78],[2313.74, 2293.34, 2289.89, 2340.71],[2297.77, 2313.22, 2292.03, 2324.63],[2322.32, 2365.59, 2308.92, 2366.16],[2364.54, 2359.51, 2330.86, 2369.65],[2332.08, 2273.4, 2259.25, 2333.54],[2274.81, 2326.31, 2270.1, 2328.14],[2333.61, 2347.18, 2321.6, 2351.44],[2340.44, 2324.29, 2304.27, 2352.02],[2326.42, 2318.61, 2314.59, 2333.67],[2314.68, 2310.59, 2296.58, 2320.96],[2309.16, 2286.6, 2264.83, 2333.29],[2282.17, 2263.97, 2253.25, 2286.33],[2255.77, 2270.28, 2253.31, 2276.22],
]
# 绘制图表
kl = (Kline().add_xaxis([f'2030/6/{i}' for i in range(1,len(data)+1)]).add_yaxis('',data)
)
# 渲染图表
kl.render_notebook()

3、漏斗图 

from pyecharts.charts import Funnel
from pyecharts.faker import Faker
from pyecharts import options as opts
fun = (Funnel().add('',data_pair=[(k,v) for k,v in zip(Faker.choose(),Faker.values())],label_opts= opts.LabelOpts(formatter='{b}:{c}',position='inside'),sort_='ascending')
)
fun.render_notebook()

4、雷达图 

from pyecharts.charts import Radar
from pyecharts import options as opts
#添加数据项
data1=[[98,100,89,89,97]]
data2 = [[100,89,79,67,61]]
#绘制雷达图
radar = (Radar().add_schema([opts.RadarIndicatorItem(name="语文",max_=100),opts.RadarIndicatorItem(name="数学",max_=100),opts.RadarIndicatorItem(name="化学",max_=100),opts.RadarIndicatorItem(name="历史",max_=100),opts.RadarIndicatorItem(name="地理",max_=100),]).add('小明',data1,color=Faker.rand_color()).add('大虎',data2,color=Faker.rand_color()).set_series_opts(label_opts=opts.LabelOpts(is_show=False),linestyle_opts=opts.LineStyleOpts(width=3))
)
radar.render_notebook()

5、词云图 

from pyecharts.charts import WordCloud
from pyecharts import options as opts
data = [ ['ThinkPad','15.7'], ['联想','14.5'],['惠普','14.4'], ['华为','11.7'], ['华硕','8.2'], ['戴尔','8.1'], ['Acer 宏碁','4.5'], ['苹果','3.5'], ['神舟','3.2'], ['ROG','3.1'], ['机械革命','2.4'], ['msi 微星','1.8'], ['外星人','1.5'], ['微软','1.4'], ['荣耀','1.2'], ['雷神','1'], ['三星','0.7'], ['红米','0.6'], ['机械师','0.5'], ['小米','0.5'], ['炫龙','0.4'], ['雷蛇','0.2'], ['壹号本','0.1'], ['a 豆','0.1'],['未来人类','0.1'], ['技嘉','0.1'], ['中柏','0.1'], ['VAIO','0.1'], ['火影','0.1'], ['LG','0.1'], ['松下','0'], ['麦本本','0'], ['吾空','0'], ['长城','0'], ['GPD','0'], ['清华同方','0'], ['神基','0'], ['爱尔轩','0'], ['酷比魔方','0'], ['海尔','0'], ['谷歌','0'], ['台电','0'], ['iru','0'], ['攀升 IPASON','0'], ['NEC','0'], ['夏普','0'],['京东京造','0'], ['锡恩帝','0'], ['皓勤','0'], ['Intel','0']]
# 绘制指定图形
wd = (WordCloud()     # 初始化词云图表.add('',data,shape='star') 
)
# 渲染图表
wd.render_notebook()

6、地图

from pyecharts.charts import Map
from pyecharts import options as opts
from pyecharts.faker import Faker
map = (Map() # 初始化地图对象.add('商家A',[['北京市',100],['上海市',150],['太原市',99]],'china')  # 1. 数据的key一定要和地图上的名称相符# 2. 地图上没有的key尽量不要去用，很有可能显示不出来
)
# 渲染图表
map.render_notebook()

地图坐标图

from pyecharts.charts import Geo
from pyecharts.faker import Faker
from pyecharts import options as opts
from pyecharts.globals import ChartType
from pyecharts.globals import SymbolType
geo = (Geo().add_schema().add('',data_pair=[i for i in zip(Faker.provinces,Faker.values())],label_opts= opts.LabelOpts(is_show=False),type_=ChartType.EFFECT_SCATTER) # 填充数据.add('',[('北京','上海1'),('北京','广州'),('广州','上海')],type_=ChartType.LINES,      # 绘制成线linestyle_opts=opts.LineStyleOpts(curve=0.2),  # 设置线的平滑度effect_opts=opts.EffectOpts(symbol_size=6,symbol=SymbolType.ARROW,color='green'),color='green')
)
# 渲染图表
geo.render_notebook()

7、柱状图折线图组合

from pyecharts.charts import Bar,Line
from pyecharts.globals import ThemeType
from pyecharts import options as opts
# 设置数据
x_value = [f'{i}月' for i in range(1,13)]
# 蒸发
evaporation = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]
# 降水
precipitation = [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3]
# 温度
temperature = [2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2]
# 柱状图
bar = (Bar().add_xaxis(xaxis_data=x_value) # 设置x轴数据.add_yaxis('蒸发',evaporation).add_yaxis('降水',precipitation).set_global_opts(title_opts=opts.TitleOpts('组合图'),yaxis_opts=opts.AxisOpts(name='水量',min_=0,max_=250,axislabel_opts=opts.LabelOpts(formatter='{value}ml'))).extend_axis(yaxis=opts.AxisOpts(name='温度',min_=0,max_=25,axislabel_opts=opts.LabelOpts(formatter='{value}℃'))) # 追加y轴数据
)
# 折线图
line = (Line().add_xaxis(xaxis_data=x_value).add_yaxis('温度',temperature,yaxis_index=1) # yaxis_index指定第几个y轴内容
)
# 混合2个图表
bar.overlap(line)
# 渲染图表
bar.render_notebook()

8、时间线轮播图

from pyecharts.charts import Timeline,Bar
from pyecharts.faker import Faker
# 绘制时间线图表
timeline = Timeline()
# 设置x轴数据
x_value = Faker.choose()
for year in range(2020,2031):bar = (Bar().add_xaxis(x_value).add_yaxis('商家A',Faker.values()).add_yaxis('商家B',Faker.values()))# 将图表增加到时间线中timeline.add(bar,f'{year}年')
# 渲染图表
timeline.render_notebook()