文章目录
引言安装Pyecharts仪表盘图参数说明代码实战：绘制多种仪表盘图示例1：基础仪表盘示例2：自定义样式仪表盘示例3：多系列仪表盘示例4：自定义刻度仪表盘示例5：动态仪表盘示例6：仪表盘与其他图表的组合示例7：自定义仪表盘指针样式示例8：仪表盘与饼图的联动示例9：仪表盘与柱状图的联动示例10：仪表盘与散点图的联动示例11：仪表盘与面积图的联动结语
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：
radius：设置仪表盘的半径大小。title：设置仪表盘的标题。detail_text_color：设置仪表盘数值文字的颜色。min_和max_：设置仪表盘的最小和最大值。split_number：设置仪表盘的刻度数量。start_angle和end_angle：设置仪表盘的起始和结束角度。axis_label_formatter：自定义坐标轴标签的显示格式。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 optsfrom 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 optsfrom 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 optsfrom 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 optsfrom 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 randomimport timefrom pyecharts import options as optsfrom 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 optsfrom pyecharts.charts import Gauge, Linefrom pyecharts.commons.utils import JsCode# 数据value_gauge = 75.2data_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 optsfrom 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 optsfrom pyecharts.charts import Gauge, Piefrom pyecharts.faker import Faker# 数据value_gauge = 65.8data_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 optsfrom pyecharts.charts import Gauge, Barfrom pyecharts.faker import Faker# 数据value_gauge = 75.4data_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 optsfrom pyecharts.charts import Gauge, Scatterfrom pyecharts.faker import Faker# 数据value_gauge = 85.1data_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 optsfrom pyecharts.charts import Gauge, Areafrom pyecharts.faker import Faker# 数据value_gauge = 78.6data_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。