Pyecharts 数据可视化教程

一份培养想象力和实操力的规范化指南，轻松实现美观、运动、主题风格的图表创建！

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📦 安装依赖

pip install pyecharts snapshot-selenium

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📊 基础图表配置

柱状图 (Bar Chart)

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

bar = (
    Bar(init_opts=opts.InitOpts(
        theme="vintage",
        width="1000px",
        height="600px",
        page_title="柱状图示例"
    ))
    .add_xaxis(["类别A", "类别B", "类别C", "类别D"])
    .add_yaxis("系列1", [120, 200, 150, 80])
    .add_yaxis("系列2", [80, 150, 100, 120])
    .set_global_opts(
        title_opts=opts.TitleOpts(
            title="主标题",
            subtitle="副标题",
            pos_left="center"
        ),
        toolbox_opts=opts.ToolboxOpts(),
        datazoom_opts=opts.DataZoomOpts()
    )
)
bar.render("bar_chart.html")

折线图 (Line Chart)

from pyecharts.charts import Line

line = (
    Line()
    .add_xaxis(["1月", "2月", "3月", "4月", "5月"])
    .add_yaxis("销售额", [620, 832, 750, 934, 1290])
    .add_yaxis("利润", [120, 232, 301, 434, 590])
    .set_global_opts(
        title_opts=opts.TitleOpts(title="月度趋势分析"),
        tooltip_opts=opts.TooltipOpts(trigger="axis"),
        yaxis_opts=opts.AxisOpts(
            name="金额(万元)",
            name_location="end"
        )
    )
    .set_series_opts(
        markpoint_opts=opts.MarkPointOpts(
            data=[opts.MarkPointItem(type_="max")]
        ),
        linestyle_opts=opts.LineStyleOpts(width=3)
    )
)
line.render("line_chart.html")

---

🌸 高级图表类型

饼图 & 玫瑰图

from pyecharts.charts import Pie

# 标准饼图
pie = (
    Pie()
    .add("", [("类别A", 40), ("类别B", 25), ("类别C", 35)])
    .set_colors(["#5470c6", "#91cc75", "#fac858"])
    .set_global_opts(title_opts=opts.TitleOpts(title="标准饼图"))
)
pie.render("pie_chart.html")

# 玫瑰图
rose = (
    Pie()
    .add("",
         [("类别A", 40), ("类别B", 25), ("类别C", 35), ("类别D", 20)],
         radius=["30%", "75%"],
         center=["25%", "50%"],
         rosetype="radius")
    .add("",
         [("类别E", 30), ("类别F", 45), ("类别G", 25)],
         radius=["30%", "75%"],
         center=["75%", "50%"],
         rosetype="area")
    .set_global_opts(title_opts=opts.TitleOpts(title="玫瑰图示例"))
)
rose.render("rose_chart.html")

堆叠柱状图

stacked_bar = (
    Bar()
    .add_xaxis(["Q1", "Q2", "Q3", "Q4"])
    .add_yaxis("产品A", [120, 132, 101, 134], stack="stack1")
    .add_yaxis("产品B", [220, 182, 191, 234], stack="stack1")
    .add_yaxis("产品C", [150, 232, 201, 154], stack="stack2")
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    .set_global_opts(
        title_opts=opts.TitleOpts(title="季度销售堆叠图"),
        yaxis_opts=opts.AxisOpts(name="销售额(万元)")
    )
)
stacked_bar.render("stacked_bar.html")

---

🌍 地图可视化

中国地图

from pyecharts.charts import Map

china_map = (
    Map()
    .add("销售额",
         [("广东", 125), ("江苏", 89), ("浙江", 102), ("山东", 78)],
         "china")
    .set_global_opts(
        title_opts=opts.TitleOpts(title="各省销售额分布"),
        visualmap_opts=opts.VisualMapOpts(
            max_=150,
            is_piecewise=True,
            range_color=["#d94e5d", "#eac736", "#50a3ba"]
        )
    )
)
china_map.render("china_map.html")

世界地图

world_map = (
    Map()
    .add("GDP",
         [("United States", 21000), ("China", 14000), ("Japan", 5000)],
         "world")
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    .set_global_opts(
        visualmap_opts=opts.VisualMapOpts(
            max_=25000,
            is_piecewise=True,
            pieces=[
                {"min": 15000, "label": "高经济", "color": "#c23531"},
                {"min": 5000, "max": 15000, "label": "中等经济", "color": "#d48265"},
                {"min": 0, "max": 5000, "label": "低经济", "color": "#91c7ae"}
            ]
        )
    )
)
world_map.render("world_map.html")

---

⏱️ 动态图表

时间轴图

from pyecharts.charts import Timeline

timeline = Timeline()
for year in range(2018, 2023):
    bar = (
        Bar()
        .add_xaxis(["产品A", "产品B", "产品C"])
        .add_yaxis("销售额", [year*10, year*15, year*8])
        .set_global_opts(title_opts=opts.TitleOpts(title=f"{year}年销售情况"))
    )
    timeline.add(bar, f"{year}年")

timeline.render("sales_timeline.html")

热力图

import random
from pyecharts.charts import HeatMap

hours = [str(i) + ":00" for i in range(24)]
days = ["周一", "周二", "周三", "周四", "周五", "周六", "周日"]
data = [[i, j, random.randint(0, 100)] for i in range(7) for j in range(24)]

heatmap = (
    HeatMap()
    .add_xaxis(hours)
    .add_yaxis(
        "活跃度",
        days,
        data,
        label_opts=opts.LabelOpts(is_show=False)
    )
    .set_global_opts(
        title_opts=opts.TitleOpts(title="用户活跃热力图"),
        visualmap_opts=opts.VisualMapOpts(
            min_=0,
            max_=100,
            orient="horizontal",
            pos_left="center"
        )
    )
)
heatmap.render("heatmap.html")

---

⚙️ 实用功能

保存成图片

from snapshot_selenium import snapshot
from pyecharts.render import make_snapshot

make_snapshot(snapshot, bar.render(), "bar_chart.png")