利用Pyecharts绘制15个超实用精美图表~

女爷i 2022-08-28 04:45 262阅读 0赞

什么是pyecharts？pyecharts是Python与ECharts的结合，Python是我们所熟知的语言，而ECharts是百度开源的数据可视化图表设计，这两者的有效结合，使得图表可视化更加绚丽多彩。

本文使用Python语言，借助pyecharts库，绘制常用的柱形图、折线图、饼图、散点图等，使用pyecharts库的交互功能实现动态可视化功能，对于里面的代码都可以修改进行复用，下面一起学习。

##  ##

条形图

from pyecharts import options as opts
    from pyecharts.charts import Bar
    from pyecharts.faker import Faker
    
    
    c = (
        Bar()
        .add_xaxis(Faker.choose())
        .add_yaxis("商家A", Faker.values())
        .add_yaxis("商家B", Faker.values())
        .set_global_opts(title_opts=opts.TitleOpts(title="Bar-MarkLine（自定义）"))
        .set_series_opts(
            label_opts=opts.LabelOpts(is_show=False),
            markline_opts=opts.MarkLineOpts(
                data=[opts.MarkLineItem(y=50, name="yAxis=50")]
            ),
        )
    )
    c.render_notebook()

![3db56eb528fcc31508c03c1f0b6a1569.png][]

##  ##

水平条形图动图

from pyecharts import options as opts
    from pyecharts.charts import Bar
    from pyecharts.commons.utils import JsCode
    from pyecharts.faker import Faker
    
    
    c = (
            Bar()
            .add_xaxis(Faker.days_attrs)
            .add_yaxis("商家A", Faker.days_values,itemstyle_opts=opts.ItemStyleOpts(color="#d14a61"))
            .set_global_opts(
                title_opts=opts.TitleOpts(title="Bar-DataZoom（slider-水平）"),
                datazoom_opts=[opts.DataZoomOpts()],
            )
        )
    c.render_notebook()

![00178f05fb0e66450e1854936f076add.gif][]

##  ##

折线图

from pyecharts.charts import Line
    from pyecharts import options as opts
    
    
    # 示例数据
    cate = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
    data1 = [123, 153, 89, 107, 98, 23]
    data2 = [56, 77, 93, 68, 45, 67]
    
    
    line = (Line()
           .add_xaxis(cate)
           .add_yaxis('电商渠道', data1, 
                      markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average")]))
           .add_yaxis('门店', data2, 
                      is_smooth=True, 
                      markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(name="自定义标记点", 
                                                                                 coord=[cate[2], data2[2]], value=data2[2])]))
           .set_global_opts(title_opts=opts.TitleOpts(title="Line-基本示例", subtitle="我是副标题"))
          )
    
    
    line.render_notebook()

![84f5ab68aa05035b893d76f68909344a.png][]

##  ##

面积图

import pyecharts.options as opts
    from pyecharts.charts import Line
    from pyecharts.faker import Faker
    
    
    c = (
        Line()
        .add_xaxis(Faker.choose())
        .add_yaxis("商家A", Faker.values(), areastyle_opts=opts.AreaStyleOpts(opacity=0.5))
        .add_yaxis("商家B", Faker.values(), areastyle_opts=opts.AreaStyleOpts(opacity=0.5))
        .set_global_opts(title_opts=opts.TitleOpts(title="Line-面积图"))
    )
    c.render_notebook()

![b4a8ece4f9bcb37cd120283b8694db5c.png][]

##  ##

饼图

from pyecharts.charts import Pie
    from pyecharts import options as opts
    
    
    # 示例数据
    cate = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
    data = [153, 124, 107, 99, 89, 46]
    
    
    pie = (Pie()
           .add('', [list(z) for z in zip(cate, data)],
                radius=["30%", "75%"],
                rosetype="radius"
                )
           .set_global_opts(title_opts=opts.TitleOpts(title="Pie-基本示例", subtitle="我是副标题"))
           .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {d}%"))
          )
    
    
    pie.render_notebook()

![a36c40627b0a6b73052a7e106572838e.png][]

##  ##

玫瑰图

from pyecharts import options as opts
    from pyecharts.charts import Pie
    from pyecharts.faker import Faker
    
    
    v = Faker.choose()
    c = (
        Pie()
        .add(
            "",
            [list(z) for z in zip(v, Faker.values())],
            radius=["30%", "75%"],
            center=["25%", "50%"],
            rosetype="radius",
            label_opts=opts.LabelOpts(is_show=False),
        )
        .add(
            "",
            [list(z) for z in zip(v, Faker.values())],
            radius=["30%", "75%"],
            center=["75%", "50%"],
            rosetype="area",
        )
        .set_global_opts(title_opts=opts.TitleOpts(title="Pie-玫瑰图示例"))
    )
    c.render_notebook()

![6f10c4855e373c01e46d6d3509b642e6.png][]

##  ##

漏斗图

import pyecharts.options as opts
    from pyecharts.charts import Funnel
    
    
    x_data = ["展现", "点击", "访问", "咨询", "订单"]
    y_data = [100, 80, 60, 40, 20]
    
    
    data = [[x_data[i], y_data[i]] for i in range(len(x_data))]
    
    
    (
        Funnel()
        .add(
            series_name="",
            sort_='ascending',
            data_pair=data,
            gap=2,
            tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b} : {c}%"),
            label_opts=opts.LabelOpts(is_show=True, position="inside"),
            itemstyle_opts=opts.ItemStyleOpts(border_color="#fff", border_width=1),
        )
        .set_global_opts(title_opts=opts.TitleOpts(title="漏斗图", subtitle="纯属虚构"))
    ).render_notebook()

![17873e778272342ac6b643a972c430a2.png][]

##  ##

热力图

from pyecharts.charts import HeatMap
    from pyecharts import options as opts
    from pyecharts.faker import Faker
    import random
    
    
    # 示例数据
    data = [[i, j, random.randint(0, 100)] for i in range(24) for j in range(7)]
    
    
    heat = (HeatMap()
            .add_xaxis(Faker.clock)
            .add_yaxis("访客数", 
                       Faker.week, 
                       data,
                       label_opts=opts.LabelOpts(is_show=True, position="inside"))
            .set_global_opts(
                title_opts=opts.TitleOpts(title="HeatMap-基本示例", subtitle="我是副标题"),
                visualmap_opts=opts.VisualMapOpts(),
                legend_opts=opts.LegendOpts(is_show=False))
           )
    
    
    heat.render_notebook()

![3da9d51c53bbb75c77984a5ba3a1b2c5.png][]

##  ##

散点图

from pyecharts import options as opts
    from pyecharts.charts import Scatter
    from pyecharts.faker import Faker
    
    
    c = (
        Scatter()
        .add_xaxis(Faker.choose())
        .add_yaxis("商家A", Faker.values())
        .add_yaxis("商家B", Faker.values())
        .set_global_opts(
            title_opts=opts.TitleOpts(title="Scatter-VisualMap(Size)"),
            visualmap_opts=opts.VisualMapOpts(type_="size", max_=150, min_=20),
        )
    )
    c.render_notebook()

![662d39a9e12021b4c4f31cf32cf95067.png][]

##  ##

三维散点图

from pyecharts import options as opts
    from pyecharts.charts import Scatter3D
    import random
    
    
    
    
    data = [[random.randint(0, 150), random.randint(0, 100), random.randint(0, 100)]
            for _ in range(1000)]
    
    
    scatter3D = (Scatter3D()
                 .add("", data)
                 .set_global_opts(
                     title_opts=opts.TitleOpts("Scatter3D-基本示例"),
                     visualmap_opts=opts.VisualMapOpts(range_color=Faker.visual_color))
                )
    
    
    scatter3D.render_notebook()

![b3df96168dd6edccad74b9ff6b8a0633.png][]

##  ##

箱线图

from pyecharts import options as opts
    from pyecharts.charts import Boxplot
    
    
    v1 = [
        [850, 740, 900, 1070, 930, 850, 950, 980, 980, 880, 1000, 980],
        [960, 940, 960, 940, 880, 800, 850, 880, 900, 840, 830, 790],
    ]
    v2 = [
        [890, 810, 810, 820, 800, 770, 760, 740, 750, 760, 910, 920],
        [890, 840, 780, 810, 760, 810, 790, 810, 820, 850, 870, 870],
    ]
    c = Boxplot()
    c.add_xaxis(["expr1", "expr2"])
    c.add_yaxis("A", c.prepare_data(v1))
    c.add_yaxis("B", c.prepare_data(v2))
    c.set_global_opts(title_opts=opts.TitleOpts(title="BoxPlot-基本示例"))
    c.render_notebook()

![25f9d0c3f06c3e1f0ff243cfeb293db9.png][]

##  ##

地图

from pyecharts import options as opts
    from pyecharts.charts import Map
    import random
    
    
    province = ['广东', '湖北', '湖南', '四川', '重庆', '黑龙江', '浙江', '山西', '河北', '安徽', '河南', '山东', '西藏']
    data = [(i, random.randint(50, 150)) for i in province]
    
    
    _map = (
            Map()
            .add("销售额", data, "china")
            .set_global_opts(
                title_opts=opts.TitleOpts(title="Map-基本示例"),
                legend_opts=opts.LegendOpts(is_show=False),
                visualmap_opts=opts.VisualMapOpts(max_=200, is_piecewise=True),
            )
        )
    
    
    _map.render_notebook()

![1521592792754f7bccfd013343af8c84.png][]

##  ##

区域热力图

from pyecharts import options as opts
    from pyecharts.charts import Geo
    from pyecharts.globals import ChartType
    import random
    
    
    province = ['杭州', '宁波', '温州', '嘉兴', '丽水', '衢州', '绍兴', '台州', '湖州','金华','舟山']
    data = [(i, random.randint(50, 150)) for i in province]
    
    
    geo = (Geo()
            .add_schema(maptype="浙江")
            .add("门店数", 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-浙江热力地图"))
          )
    
    
    geo.render_notebook()

![cb8a16d6b404fe1da552b8a7c9f4e424.png][]

##  ##

世界地图

from pyecharts.charts import Map  # 注意这里与老版本pyecharts调用的区别
    from pyecharts import options as opts
    import random
    country = ['China', 'Canada', 'France', 'Japan', 'Russia', 'USA']
    data_world = [(i, random.randint(100, 200)) for i in country]
    world = (
        Map()
        .add('', # 此处没取名，所以空着
          data_world, # 数据
          'world') # 地图类型
        .set_global_opts(
            title_opts=opts.TitleOpts(title='World Map'),
            visualmap_opts=opts.VisualMapOpts(
                max_=200,
                min_=100,
                is_piecewise=True)  # 定义图例为分段型，默认为连续的图例
        )
        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    )
    world.render_notebook()

![f0f7ac6cb90d1c8470190a7724d2b699.png][]

##  ##

词云图

import pyecharts.options as opts
    from pyecharts.charts import WordCloud
    
    
    data = [
        ("生活资源", "999"),
        ("供热管理", "888"),
        ("供气质量", "777"),
        ("生活用水管理", "688"),
        ("一次供水问题", "588"),
        ("交通运输", "516"),
        ("城市交通", "515"),
        ("环境保护", "683"),
        ("房地产管理", "662"),
        ("城乡建设", "649"),
        ("社会保障与福利", "429"),
        ("社会保障", "707"),
        ("文体与教育管理", "506"),
        ("公共安全", "506"),
        ("公交运输管理", "586"),
        ("出租车运营管理", "585"),
    ]
    
    
    (
        WordCloud()
        .add(series_name="热点分析", data_pair=data, word_size_range=[6, 66])
        .set_global_opts(
            title_opts=opts.TitleOpts(
                title="热点分析", title_textstyle_opts=opts.TextStyleOpts(font_size=23)
            ),
            tooltip_opts=opts.TooltipOpts(is_show=True),
        )
    ).render_notebook()

![ba8d0f4a660a217fd987abd1d16ee384.png][]

##  ##

组合图

from pyecharts import options as opts
    from pyecharts.charts import Bar,Line,Grid
    from pyecharts.commons.utils import JsCode
    from pyecharts.faker import Faker
    
    
    x_data = ["{}月".format(i) for i in range(1, 13)]
    bar = (
            Bar()
            .add_xaxis(x_data)
            .add_yaxis(
                "蒸发量",
                [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3],
                yaxis_index=0,
                color="#d14a61",
            )
            .add_yaxis(
                "降水量",
                [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3],
                yaxis_index=1,
                color="#5793f3",
            )
            .extend_axis(
                yaxis=opts.AxisOpts(
                    name="蒸发量",
                    type_="value",
                    min_=0,
                    max_=250,
                    position="right",
                    axisline_opts=opts.AxisLineOpts(
                        linestyle_opts=opts.LineStyleOpts(color="#d14a61")
                    ),
                    axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
                )
            )
            .extend_axis(
                yaxis=opts.AxisOpts(
                    type_="value",
                    name="温度",
                    min_=0,
                    max_=25,
                    position="left",
                    axisline_opts=opts.AxisLineOpts(
                        linestyle_opts=opts.LineStyleOpts(color="#675bba")
                    ),
                    axislabel_opts=opts.LabelOpts(formatter="{value} °C"),
                    splitline_opts=opts.SplitLineOpts(
                        is_show=True, linestyle_opts=opts.LineStyleOpts(opacity=1)
                    ),
                )
            )
            .set_global_opts(
                yaxis_opts=opts.AxisOpts(
                    name="降水量",
                    min_=0,
                    max_=250,
                    position="right",
                    offset=50,
                    axisline_opts=opts.AxisLineOpts(
                        linestyle_opts=opts.LineStyleOpts(color="#5793f3")
                    ),
                    axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
                ),
                title_opts=opts.TitleOpts(title="Grid-多 Y 轴示例"),
                tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
            )
        )
    
    
    line = (
            Line()
            .add_xaxis(x_data)
            .add_yaxis(
                "平均温度",
                [2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2],
                yaxis_index=2,
                color="#675bba",
                label_opts=opts.LabelOpts(is_show=False),
            )
        )
    bar.overlap(line)
    bar.render_notebook()

![c98cbdbb2d62fe5537ca029a10ccd6fa.gif][]

![269e6283489742a20533c10c8f68d1d5.gif][]

![ea148671b8d177c1238f60acf944ebcc.png][]

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