对于日常的python可视化过去用的都是Matplotlib可视化图表，但是这都是静态的图表，无法对图表进行交互。
今天介绍的pyecharts是经过网页渲染的可视化可交互的web页面图表，可以更好的时间选择或者维度选择进行交互，得到不同的动态图表。
汇总最常见的图表及代码：
1、柱状图
代码如下：
frompyechartsimportoptionsasoptsfrompyecharts.chartsimportBarfrompyecharts.commons.utilsimportJsCodefrompyecharts.globalsimportThemeTypelist2 = [{"value":12,"percent":12/ (12+3)},{"value":23,"percent":23/ (23+21)},{"value":33,"percent":33/ (33+5)},{"value":3,"percent":3/ (3+52)},{"value":33,"percent":33/ (33+43)},]list3 = [{"value":3,"percent":3/ (12+3)},{"value":21,"percent":21/ (23+21)},{"value":5,"percent":5/ (33+5)},{"value":52,"percent":52/ (3+52)},{"value":43,"percent":43/ (33+43)},]c = (Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT)).add_xaxis([1,2,3,4,5]).add_yaxis("product1", list2, stack="stack1", category_gap="50%").add_yaxis("product2", list3, stack="stack1", category_gap="50%").set_series_opts(label_opts=opts.LabelOpts(position="right",formatter=JsCode("function(x){return Number(x.data.percent * 100).toFixed() + '%';}"),)).render("stack_bar_percent.html"))
2、折线图
代码如下：
import pyecharts.options as optsfrom pyecharts.charts import Lineweek_name_list = ["周一","周二","周三","周四","周五","周六","周日"]high_temperature = [11, 11, 15, 13, 12, 13, 10]low_temperature = [1, -2, 2, 5, 3, 2, 0](Line(init_opts=opts.InitOpts(width="1600px", height="800px")).add_xaxis(xaxis_data=week_name_list).add_yaxis(series_name="最高气温",y_axis=high_temperature,markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max", name="最大值"),opts.MarkPointItem(type_="min", name="最小值"),]),markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average", name="平均值")]),).add_yaxis(series_name="最低气温",y_axis=low_temperature,markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(value=-2, name="周最低", x=1, y=-1.5)]),markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average", name="平均值"),opts.MarkLineItem(symbol="none", x="90%", y="max"),opts.MarkLineItem(symbol="circle", type_="max", name="最高点"),]),).set_global_opts(title_opts=opts.TitleOpts(title="未来一周气温变化", subtitle="纯属虚构"),tooltip_opts=opts.TooltipOpts(trigger="axis"),toolbox_opts=opts.ToolboxOpts(is_show=True),xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),).render("temperature_change_line_chart.html"))
3、漏斗图
代码如下：
from pyechartsimportoptions as optsfrom pyecharts.chartsimportFunnelfrom pyecharts.fakerimportFakerc= (Funnel().add("商品", [list(z)forzinzip(Faker.choose(),Faker.values())]).set_global_opts(title_opts=opts.TitleOpts(title="Funnel-基本示例")).render("funnel_base.html"))
4、仪表盘
代码如下：
frompyechartsimportoptionsasoptsfrompyecharts.chartsimportGaugec = (Gauge().add("", [("完成率",66.6)]).set_global_opts(title_opts=opts.TitleOpts(title="Gauge-基本示例")).render("gauge_base.html"))
5、地理坐标
代码如下：
from pyecharts import options as optsfrom pyecharts.charts import Geofrom pyecharts.globals import ChartType, SymbolTypec = (Geo().add_schema(maptype="china",itemstyle_opts=opts.ItemStyleOpts(color="#323c48", border_color="#111"),).add("",[("广州", 55), ("北京", 66), ("杭州", 77), ("重庆", 88)],type_=ChartType.EFFECT_SCATTER,color="white",).add("geo",[("广州","上海"), ("广州","北京"), ("广州","杭州"), ("广州","重庆")],type_=ChartType.LINES,effect_opts=opts.EffectOpts(symbol=SymbolType.ARROW, symbol_size=6, color="blue"),linestyle_opts=opts.LineStyleOpts(curve=0.2),).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(title_opts=opts.TitleOpts(title="Geo-Lines-background")).render("geo_lines_background.html"))
6、关系图
代码如下：
from pyechartsimportoptions as optsfrom pyecharts.chartsimportGraphnodes = [{"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 = []foriinnodes:forjinnodes:links.append({"source": i.get("name"),"target": j.get("name")})c= (Graph().add("", nodes, links, repulsion=8000).set_global_opts(title_opts=opts.TitleOpts(title="Graph-基本示例")).render("graph_base.html"))
7、地理坐标
代码如下：
frompyechartsimportoptionsasoptsfrompyecharts.chartsimportMapfrompyecharts.fakerimportFakerc = (Map().add("商家A", [list(z)forzinzip(Faker.provinces, Faker.values())],"china").set_global_opts(title_opts=opts.TitleOpts(title="Map-基本示例")).render("map_base.html"))
8、饼状图
代码如下：
from pyecharts import options as optsfrom pyecharts.charts import Piefrom pyecharts.faker import Fakerc = (Pie().add("", [list(z) for z in zip(Faker.choose(), Faker.values())]).set_global_opts(title_opts=opts.TitleOpts(title="Pie-基本示例")).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}")).render("pie_base.html"))
9、表格图
代码如下：
frompyecharts.components import Tablefrompyecharts.options import ComponentTitleOptstable = Table()headers = ["City name","Area","Population","Annual Rainfall"]rows = [["Brisbane", 5905, 1857594, 1146.4],["Adelaide", 1295, 1158259, 600.5],["Darwin", 112, 120900, 1714.7],["Hobart", 1357, 205556, 619.5],["Sydney", 2058, 4336374, 1214.8],["Melbourne", 1566, 3806092, 646.9],["Perth", 5386, 1554769, 869.4],]table.add(headers, rows)table.set_global_opts(title_opts=ComponentTitleOpts(title="Table-基本示例", subtitle="我是副标题支持换行哦"))table.render("table_base.html")
10、时间轴图表
代码如下：
frompyechartsimportoptionsasoptsfrompyecharts.chartsimportBar, Timelinefrompyecharts.fakerimportFakerx = Faker.choose()tl = Timeline()foriinrange(2015,2020):bar = (Bar().add_xaxis(x).add_yaxis("商家A", Faker.values()).add_yaxis("商家B", Faker.values()).set_global_opts(title_opts=opts.TitleOpts("某商店{}年营业额".format(i))))tl.add(bar,"{}年".format(i))tl.render("timeline_bar.html")
11、树图
代码如下：
frompyechartsimportoptionsasoptsfrompyecharts.chartsimportTreedata = [{"children": [{"name":"B"},{"children": [{"children": [{"name":"I"}],"name":"E"}, {"name":"F"}],"name":"C",},{"children": [{"children": [{"name":"J"}, {"name":"K"}],"name":"G"},{"name":"H"},],"name":"D",},],"name":"A",}]c = (Tree().add("", data).set_global_opts(title_opts=opts.TitleOpts(title="Tree-基本示例")).render("tree_base.html"))
12、矩形树图
代码如下：
frompyechartsimportoptionsasoptsfrompyecharts.chartsimportTreeMapdata = [{"value":40,"name":"我是A"},{"value":180,"name":"我是B","children": [{"value":76,"name":"我是B.children","children": [{"value":12,"name":"我是B.children.a"},{"value":28,"name":"我是B.children.b"},{"value":20,"name":"我是B.children.c"},{"value":16,"name":"我是B.children.d"},],}],},]c = (TreeMap().add("演示数据", data).set_global_opts(title_opts=opts.TitleOpts(title="TreeMap-基本示例")).render("treemap_base.html"))
13、雷达图
代码如下：
import pyecharts.options as optsfrom pyecharts.charts import Radarv1 = [[4300, 10000, 28000, 35000, 50000, 19000]]v2 = [[5000, 14000, 28000, 31000, 42000, 21000]](Radar(init_opts=opts.InitOpts(width="1280px", height="720px", bg_color="#CCCCCC")).add_schema(schema=[opts.RadarIndicatorItem(name="销售（sales）", max_=6500),opts.RadarIndicatorItem(name="管理（Administration）", max_=16000),opts.RadarIndicatorItem(name="信息技术（Information Technology）", max_=30000),opts.RadarIndicatorItem(name="客服（Customer Support）", max_=38000),opts.RadarIndicatorItem(name="研发（Development）", max_=52000),opts.RadarIndicatorItem(name="市场（Marketing）", max_=25000),],splitarea_opt=opts.SplitAreaOpts(is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),textstyle_opts=opts.TextStyleOpts(color="#fff"),).add(series_name="预算分配（Allocated Budget）",data=v1,linestyle_opts=opts.LineStyleOpts(color="#CD0000"),).add(series_name="实际开销（Actual Spending）",data=v2,linestyle_opts=opts.LineStyleOpts(color="#5CACEE"),).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(title_opts=opts.TitleOpts(title="基础雷达图"), legend_opts=opts.LegendOpts()).render("basic_radar_chart.html"))
14、散点图
代码如下：
import pyecharts.options as optsfrom pyecharts.charts import Scatterdata = [[10.0, 8.04],[8.0, 6.95],[13.0, 7.58],[9.0, 8.81],[11.0, 8.33],[14.0, 9.96],[6.0, 7.24],[4.0, 4.26],[12.0, 10.84],[7.0, 4.82],[5.0, 5.68],]data.sort(key=lambda x: x[0])x_data = [d[0] for d in data]y_data = [d[1] for d in data](Scatter(init_opts=opts.InitOpts(width="1600px", height="1000px")).add_xaxis(xaxis_data=x_data).add_yaxis(series_name="",y_axis=y_data,symbol_size=20,label_opts=opts.LabelOpts(is_show=False),).set_series_opts().set_global_opts(xaxis_opts=opts.AxisOpts(type_="value", splitline_opts=opts.SplitLineOpts(is_show=True)),yaxis_opts=opts.AxisOpts(type_="value",axistick_opts=opts.AxisTickOpts(is_show=True),splitline_opts=opts.SplitLineOpts(is_show=True),),tooltip_opts=opts.TooltipOpts(is_show=False),).render("basic_scatter_chart.html"))
除此之外还有桑基图、旭日图、河流图、词云图、极坐标系、象型柱图、水球图等等常用图形。
以上的图形都可以进行组合，可以通过python代码自己根据需要设计自己的动态监控大屏。
以下是我自己爬取汽车数据，设计的汽车营销BI数据监控动态大屏。
以上类型的动态大屏还可以补充更多不同类型且炫丽的动态大屏，可以根据不同的行业和分析目的设计更多酷炫的动态大屏。
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