Pyecharts交互式可视化：我用Python做出了高颜值的动态数据大屏

2026-03-01

大家好，我是正在实战各种AI项目的程序员晚枫。

今天学习Pyecharts，这是一个生成ECharts图表的Python库。

用它可以轻松制作高颜值的交互式图表：鼠标悬停显示详情、点击图例筛选、缩放拖拽……非常适合做数据大屏和Web展示。

为什么选择Pyecharts？

优势

✅ 美观：基于百度ECharts，颜值在线
✅ 交互：支持鼠标悬停、缩放、筛选等交互
✅ 丰富：30+种图表类型
✅ Web友好：生成HTML文件，可直接嵌入网页

对比

特性	Matplotlib	Pyecharts
静态/动态	静态	交互式
输出格式	PNG/PDF	HTML
适用场景	论文/报告	Web/大屏
学习曲线	中等	简单

安装与基础用法

1	pip install pyecharts

from pyecharts.charts import Bar, Line, Pie, Map
from pyecharts import options as opts
import pandas as pd
import numpy as np

1. 柱状图（Bar）

# 基础柱状图
bar = Bar()
bar.add_xaxis(["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"])
bar.add_yaxis("商家A", [5, 20, 36, 10, 75, 90])
bar.add_yaxis("商家B", [15, 25, 16, 55, 48, 8])
bar.set_global_opts(title_opts=opts.TitleOpts(title="主标题", subtitle="副标题"))
bar.render("bar_chart.html")  # 保存为HTML文件

链式调用写法（推荐）

from pyecharts.charts import Bar

bar = (
    Bar()
    .add_xaxis(["一月", "二月", "三月", "四月", "五月"])
    .add_yaxis("销售额", [120, 200, 150, 80, 70])
    .add_yaxis("利润", [30, 50, 40, 20, 15])
    .set_global_opts(
        title_opts=opts.TitleOpts(title="月度业绩"),
        toolbox_opts=opts.ToolboxOpts(),  # 工具箱
        datazoom_opts=opts.DataZoomOpts(),  # 缩放
    )
)
bar.render("sales_bar.html")

2. 折线图（Line）

from pyecharts.charts import Line

line = (
    Line()
    .add_xaxis(["周一", "周二", "周三", "周四", "周五", "周六", "周日"])
    .add_yaxis("邮件营销", [120, 132, 101, 134, 90, 230, 210],
               is_smooth=True)  # 平滑曲线
    .add_yaxis("联盟广告", [220, 182, 191, 234, 290, 330, 310],
               markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max")]))
    .set_global_opts(title_opts=opts.TitleOpts(title="周流量趋势"))
)
line.render("traffic_line.html")

3. 饼图（Pie）

from pyecharts.charts import Pie

pie = (
    Pie()
    .add("", [list(z) for z in zip(["直接访问", "邮件营销", "联盟广告", "视频广告", "搜索引擎"],
                                   [335, 310, 274, 235, 400])])
    .set_global_opts(title_opts=opts.TitleOpts(title="流量来源"))
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)"))
)
pie.render("source_pie.html")

环形图

pie = (
    Pie()
    .add("", [list(z) for z in zip(["A", "B", "C", "D"], [30, 25, 25, 20])],
         radius=["40%", "75%"])  # 内半径和外半径
    .set_global_opts(title_opts=opts.TitleOpts(title="环形图"))
)
pie.render("donut.html")

4. 地图（Map）

from pyecharts.charts import Map

# 中国地图
data = [
    ("广东", 10430.03),
    ("山东", 9579.31),
    ("河南", 9402.36),
    ("四川", 8041.82),
    ("江苏", 7865.99),
]

map_chart = (
    Map()
    .add("人口数量", data, "china")
    .set_global_opts(
        title_opts=opts.TitleOpts(title="中国人口分布"),
        visualmap_opts=opts.VisualMapOpts(max_=11000),
    )
)
map_chart.render("population_map.html")

5. 组合图表（Grid/Page）

Grid：多图组合

from pyecharts.charts import Grid

grid = (
    Grid()
    .add(bar, grid_opts=opts.GridOpts(pos_bottom="60%"))
    .add(line, grid_opts=opts.GridOpts(pos_top="60%"))
)
grid.render("grid_chart.html")

Page：多页面

from pyecharts.charts import Page

page = Page(layout=Page.SimplePageLayout)
page.add(bar, line, pie)
page.render("multi_charts.html")

6. 时间轴（Timeline）

from pyecharts.charts import Timeline

timeline = Timeline()
for year in ["2019", "2020", "2021", "2022", "2023"]:
    bar = (
        Bar()
        .add_xaxis(["北京", "上海", "广州", "深圳", "杭州"])
        .add_yaxis("GDP", np.random.randint(1000, 5000, 5).tolist())
        .set_global_opts(title_opts=opts.TitleOpts(title=f"{year}年GDP"))
    )
    timeline.add(bar, year)

timeline.render("gdp_timeline.html")

7. 主题样式

from pyecharts.globals import ThemeType

# 使用主题
bar = (
    Bar(init_opts=opts.InitOpts(theme=ThemeType.DARK))  # 暗黑主题
    .add_xaxis(["A", "B", "C", "D"])
    .add_yaxis("数值", [10, 20, 30, 40])
)

# 其他主题：LIGHT, MACARONS, VINTAGE, SHINE, WALDEN

实战：销售数据大屏

from pyecharts.charts import Bar, Line, Pie, Page
from pyecharts import options as opts
import pandas as pd
import numpy as np

# 模拟数据
months = ["1月", "2月", "3月", "4月", "5月", "6月"]
sales = [120, 150, 180, 140, 200, 220]
profit = [30, 40, 50, 35, 55, 65]
regions = ["华北", "华东", "华南", "西南"]
region_sales = [350, 420, 380, 280]

# 1. 销售额趋势
line = (
    Line()
    .add_xaxis(months)
    .add_yaxis("销售额", sales, is_smooth=True, 
               markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max")]))
    .add_yaxis("利润", profit, is_smooth=True)
    .set_global_opts(
        title_opts=opts.TitleOpts(title="月度业绩趋势"),
        toolbox_opts=opts.ToolboxOpts(),
    )
)

# 2. 地区占比
pie = (
    Pie()
    .add("", [list(z) for z in zip(regions, region_sales)],
         radius=["40%", "70%"])
    .set_global_opts(title_opts=opts.TitleOpts(title="地区销售占比"))
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {d}%"))
)

# 3. 产品销量排行
products = ["产品A", "产品B", "产品C", "产品D", "产品E"]
product_sales = [320, 280, 250, 220, 180]

bar = (
    Bar()
    .add_xaxis(products)
    .add_yaxis("销量", product_sales)
    .reversal_axis()  # 水平条形图
    .set_series_opts(label_opts=opts.LabelOpts(position="right"))
    .set_global_opts(title_opts=opts.TitleOpts(title="产品销量排行"))
)

# 组合成大屏
page = Page(layout=Page.DraggablePageLayout, page_title="销售数据大屏")
page.add(
    line,
    pie,
    bar,
)
page.render("dashboard.html")

print("数据大屏已生成：dashboard.html")
print("请在浏览器中打开查看")

部署到Web

生成的HTML文件可以直接：

双击在浏览器中打开
上传到Web服务器
嵌入到Flask/Django项目中

# Flask示例
from flask import Flask, render_template

app = Flask(__name__)

@app.route('/')
def index():
    return render_template('dashboard.html')

if __name__ == '__main__':
    app.run(debug=True)