精通Python第20篇—数据之美：用Pyecharts打造引人入胜的多维度仪表盘与图表联动

引言

在数据可视化领域，仪表盘图是一种直观而强大的工具，用于展示关键指标的实时状态。Pyecharts是一个基于Echarts的Python图表库，提供了丰富的图表类型，其中包括了仪表盘图。本文将介绍如何使用Pyecharts绘制多种炫酷的仪表盘图，并详细说明相关参数，同时附上实际的代码实例。

安装Pyecharts

首先，确保你已经安装了Pyecharts。如果尚未安装，可以使用以下命令进行安装：

pip install pyecharts

仪表盘图参数说明

在绘制仪表盘图时，我们需要了解一些关键的参数，以便定制化图表外观和功能。以下是一些常见的仪表盘图参数：

1. radius：设置仪表盘的半径大小。
2. title：设置仪表盘的标题。
3. detail_text_color：设置仪表盘数值文字的颜色。
4. min_和max_：设置仪表盘的最小和最大值。
5. split_number：设置仪表盘的刻度数量。
6. start_angle和end_angle：设置仪表盘的起始和结束角度。
7. axis_label_formatter：自定义坐标轴标签的显示格式。
8. range_color：设置不同范围区间的颜色。

代码实战：绘制多种仪表盘图

示例1：基础仪表盘

from pyecharts import options as opts
from pyecharts.charts import Gauge

# 数据
value = 65.5

# 绘制基础仪表盘
gauge_basic = (
    Gauge()
    .add("", [("基础仪表盘", value)])
    .set_global_opts(
        title_opts=opts.TitleOpts(title="基础仪表盘"),
        legend_opts=opts.LegendOpts(is_show=False),
    )
    .set_series_opts(
        axisline_opts=opts.AxisLineOpts(
            linestyle_opts=opts.LineStyleOpts(
                color=[[0.2, "#91c7ae"], [0.8, "#63869e"], [1, "#c23531"]]
            )
        )
    )
)

# 保存图表
gauge_basic.render("gauge_basic.html")

示例2：自定义样式仪表盘

from pyecharts import options as opts
from pyecharts.charts import Gauge

# 数据
value = 75.8

# 绘制自定义样式仪表盘
gauge_custom = (
    Gauge()
    .add("", [("自定义样式仪表盘", value)])
    .set_global_opts(
        title_opts=opts.TitleOpts(title="自定义样式仪表盘"),
        legend_opts=opts.LegendOpts(is_show=False),
    )
    .set_series_opts(
        axisline_opts=opts.AxisLineOpts(
            linestyle_opts=opts.LineStyleOpts(
                color=[[0.2, "#91c7ae"], [0.8, "#63869e"], [1, "#c23531"]],
                width=8,
            )
        ),
        pointer_opts=opts.PointerOpts(width=5),
    )
)

# 保存图表
gauge_custom.render("gauge_custom.html")

示例3：多系列仪表盘

from pyecharts import options as opts
from pyecharts.charts import Gauge

# 数据
value_series = [68.2, 52.6, 80.5]

# 绘制多系列仪表盘
gauge_multi_series = (
    Gauge()
    .add("", [("Series 1", value_series[0]), ("Series 2", value_series[1]), ("Series 3", value_series[2])])
    .set_global_opts(
        title_opts=opts.TitleOpts(title="多系列仪表盘"),
        legend_opts=opts.LegendOpts(is_show=True, pos_top="5%"),
    )
    .set_series_opts(
        axisline_opts=opts.AxisLineOpts(
            linestyle_opts=opts.LineStyleOpts(
                color=[[0.2, "#91c7ae"], [0.8, "#63869e"], [1, "#c23531"]],
                width=8,
            )
        ),
        pointer_opts=opts.PointerOpts(width=5),
    )
)

# 保存图表
gauge_multi_series.render("gauge_multi_series.html")

示例4：自定义刻度仪表盘

from pyecharts import options as opts
from pyecharts.charts import Gauge

# 数据
value = 90.3

# 绘制自定义刻度仪表盘
gauge_custom_scale = (
    Gauge()
    .add("", [("自定义刻度仪表盘", value)])
    .set_global_opts(
        title_opts=opts.TitleOpts(title="自定义刻度仪表盘"),
        legend_opts=opts.LegendOpts(is_show=False),
    )
    .set_series_opts(
        axisline_opts=opts.AxisLineOpts(
            linestyle_opts=opts.LineStyleOpts(
                color=[[0.2, "#91c7ae"], [0.8, "#63869e"], [1, "#c23531"]],
                width=12,
            )
        ),
        split_line_opts=opts.SplitLineOpts(length=20),
        axislabel_opts=opts.LabelOpts(font_size=12),
    )
)

# 保存图表
gauge_custom_scale.render("gauge_custom_scale.html")

示例5：动态仪表盘

import random
import time
from pyecharts import options as opts
from pyecharts.charts import Gauge

# 数据生成函数
def generate_random_value():
    return round(random.uniform(60, 90), 2)

# 实时更新数据并绘制动态仪表盘
def update_dynamic_gauge():
    gauge_dynamic = (
        Gauge()
        .add("", [("动态仪表盘", generate_random_value())])
        .set_global_opts(
            title_opts=opts.TitleOpts(title="动态仪表盘"),
            legend_opts=opts.LegendOpts(is_show=False),
        )
        .set_series_opts(
            axisline_opts=opts.AxisLineOpts(
                linestyle_opts=opts.LineStyleOpts(
                    color=[[0.2, "#91c7ae"], [0.8, "#63869e"], [1, "#c23531"]],
                    width=12,
                )
            ),
            split_line_opts=opts.SplitLineOpts(length=20),
            axislabel_opts=opts.LabelOpts(font_size=12),
        )
    )

    while True:
        # 更新数据
        value = generate_random_value()
        gauge_dynamic.set_series_opts(data=[("动态仪表盘", value)])
        
        # 渲染图表
        gauge_dynamic.render("gauge_dynamic.html")
        
        # 暂停一段时间再更新
        time.sleep(2)

# 运行动态仪表盘更新函数
update_dynamic_gauge()

示例6：仪表盘与其他图表的组合

from pyecharts import options as opts
from pyecharts.charts import Gauge, Line
from pyecharts.commons.utils import JsCode

# 数据
value_gauge = 75.2
data_line = [random.randint(60, 90) for _ in range(10)]

# 绘制仪表盘与折线图的组合
gauge_line_combination = (
    Gauge()
    .add("", [("仪表盘", value_gauge)])
    .set_global_opts(
        title_opts=opts.TitleOpts(title="仪表盘与折线图组合"),
        legend_opts=opts.LegendOpts(is_show=False),
    )
    .set_series_opts(
        axisline_opts=opts.AxisLineOpts(
            linestyle_opts=opts.LineStyleOpts(
                color=[[0.2, "#91c7ae"], [0.8, "#63869e"], [1, "#c23531"]],
                width=12,
            )
        ),
        split_line_opts=opts.SplitLineOpts(length=20),
        axislabel_opts=opts.LabelOpts(font_size=12),
    )
)

line_chart = (
    Line()
    .add_xaxis(list(range(1, 11)))
    .add_yaxis("折线图", data_line)
    .set_global_opts(title_opts=opts.TitleOpts(title="折线图"))
)

# 将仪表盘与折线图组合到同一个页面
gauge_line_page = (
    Page()
    .add(gauge_line_combination, line_chart)
)

# 保存图表
gauge_line_page.render("gauge_line_combination.html")

示例7：自定义仪表盘指针样式

from pyecharts import options as opts
from pyecharts.charts import Gauge

# 数据
value = 80.7

# 绘制自定义指针样式的仪表盘
gauge_custom_pointer = (
    Gauge()
    .add("", [("自定义指针仪表盘", value)])
    .set_global_opts(
        title_opts=opts.TitleOpts(title="自定义指针仪表盘"),
        legend_opts=opts.LegendOpts(is_show=False),
    )
    .set_series_opts(
        axisline_opts=opts.AxisLineOpts(
            linestyle_opts=opts.LineStyleOpts(
                color=[[0.2, "#91c7ae"], [0.8, "#63869e"], [1, "#c23531"]],
                width=12,
            )
        ),
        pointer_opts=opts.PointerOpts(
            width=6, 
            length="80%",
            shadow_color="#fff",
            shadow_offset_y=5,
            itemstyle_opts={"color": "auto", "borderColor": "auto"},
        ),
    )
)

# 保存图表
gauge_custom_pointer.render("gauge_custom_pointer.html")

示例8：仪表盘与饼图的联动

from pyecharts import options as opts
from pyecharts.charts import Gauge, Pie
from pyecharts.faker import Faker

# 数据
value_gauge = 65.8
data_pie = list(zip(Faker.choose(), Faker.values()))

# 绘制仪表盘与饼图的联动
gauge_pie_interaction = (
    Gauge()
    .add("", [("仪表盘", value_gauge)])
    .set_global_opts(
        title_opts=opts.TitleOpts(title="仪表盘与饼图联动"),
        legend_opts=opts.LegendOpts(is_show=False),
    )
    .set_series_opts(
        axisline_opts=opts.AxisLineOpts(
            linestyle_opts=opts.LineStyleOpts(
                color=[[0.2, "#91c7ae"], [0.8, "#63869e"], [1, "#c23531"]],
                width=12,
            )
        ),
        split_line_opts=opts.SplitLineOpts(length=20),
        axislabel_opts=opts.LabelOpts(font_size=12),
    )
)

pie_chart = (
    Pie()
    .add("", data_pie, radius=["30%", "55%"])
    .set_global_opts(title_opts=opts.TitleOpts(title="饼图"))
)

# 将仪表盘与饼图联动到同一个页面
gauge_pie_page = (
    Page()
    .add(gauge_pie_interaction, pie_chart)
)

# 保存图表
gauge_pie_page.render("gauge_pie_interaction.html")

示例9：仪表盘与柱状图的联动

from pyecharts import options as opts
from pyecharts.charts import Gauge, Bar
from pyecharts.faker import Faker

# 数据
value_gauge = 75.4
data_bar = list(zip(Faker.choose(), Faker.values()))

# 绘制仪表盘与柱状图的联动
gauge_bar_interaction = (
    Gauge()
    .add("", [("仪表盘", value_gauge)])
    .set_global_opts(
        title_opts=opts.TitleOpts(title="仪表盘与柱状图联动"),
        legend_opts=opts.LegendOpts(is_show=False),
    )
    .set_series_opts(
        axisline_opts=opts.AxisLineOpts(
            linestyle_opts=opts.LineStyleOpts(
                color=[[0.2, "#91c7ae"], [0.8, "#63869e"], [1, "#c23531"]],
                width=12,
            )
        ),
        split_line_opts=opts.SplitLineOpts(length=20),
        axislabel_opts=opts.LabelOpts(font_size=12),
    )
)

bar_chart = (
    Bar()
    .add_xaxis(Faker.choose())
    .add_yaxis("柱状图", Faker.values())
    .set_global_opts(title_opts=opts.TitleOpts(title="柱状图"))
)

# 将仪表盘与柱状图联动到同一个页面
gauge_bar_page = (
    Page()
    .add(gauge_bar_interaction, bar_chart)
)

# 保存图表
gauge_bar_page.render("gauge_bar_interaction.html")

示例10：仪表盘与散点图的联动

from pyecharts import options as opts
from pyecharts.charts import Gauge, Scatter
from pyecharts.faker import Faker

# 数据
value_gauge = 85.1
data_scatter = [(i, random.randint(60, 90)) for i in range(1, 11)]

# 绘制仪表盘与散点图的联动
gauge_scatter_interaction = (
    Gauge()
    .add("", [("仪表盘", value_gauge)])
    .set_global_opts(
        title_opts=opts.TitleOpts(title="仪表盘与散点图联动"),
        legend_opts=opts.LegendOpts(is_show=False),
    )
    .set_series_opts(
        axisline_opts=opts.AxisLineOpts(
            linestyle_opts=opts.LineStyleOpts(
                color=[[0.2, "#91c7ae"], [0.8, "#63869e"], [1, "#c23531"]],
                width=12,
            )
        ),
        split_line_opts=opts.SplitLineOpts(length=20),
        axislabel_opts=opts.LabelOpts(font_size=12),
    )
)

scatter_chart = (
    Scatter()
    .add_xaxis(list(range(1, 11)))
    .add_yaxis("散点图", data_scatter)
    .set_global_opts(title_opts=opts.TitleOpts(title="散点图"))
)

# 将仪表盘与散点图联动到同一个页面
gauge_scatter_page = (
    Page()
    .add(gauge_scatter_interaction, scatter_chart)
)

# 保存图表
gauge_scatter_page.render("gauge_scatter_interaction.html")

示例11：仪表盘与面积图的联动

from pyecharts import options as opts
from pyecharts.charts import Gauge, Area
from pyecharts.faker import Faker

# 数据
value_gauge = 78.6
data_area = [(i, random.randint(60, 90)) for i in range(1, 11)]

# 绘制仪表盘与面积图的联动
gauge_area_interaction = (
    Gauge()
    .add("", [("仪表盘", value_gauge)])
    .set_global_opts(
        title_opts=opts.TitleOpts(title="仪表盘与面积图联动"),
        legend_opts=opts.LegendOpts(is_show=False),
    )
    .set_series_opts(
        axisline_opts=opts.AxisLineOpts(
            linestyle_opts=opts.LineStyleOpts(
                color=[[0.2, "#91c7ae"], [0.8, "#63869e"], [1, "#c23531"]],
                width=12,
            )
        ),
        split_line_opts=opts.SplitLineOpts(length=20),
        axislabel_opts=opts.LabelOpts(font_size=12),
    )
)

area_chart = (
    Area()
    .add_xaxis(list(range(1, 11)))
    .add_yaxis("面积图", data_area)
    .set_global_opts(title_opts=opts.TitleOpts(title="面积图"))
)

# 将仪表盘与面积图联动到同一个页面
gauge_area_page = (
    Page()
    .add(gauge_area_interaction, area_chart)
)

# 保存图表
gauge_area_page.render("gauge_area_interaction.html")

结语

通过以上示例，我们展示了如何实现仪表盘与散点图、面积图的联动。这样的联动可以帮助我们更全面地呈现数据的分布和趋势，提供更深入的数据洞察。在实际项目中，根据需求和数据类型，选择合适的联动图表，将数据可视化得更为生动和清晰。

希望这些示例对你在使用Pyecharts绘制仪表盘图与其他图表的联动时提供一些灵感。在实践中，可以根据具体场景和数据进行更多的定制化，以满足项目的实际需求。