Python可视化神器pyecharts绘制折线图详解
发布时间：2026-10-15| 5人浏览过
本文详解Python中pyecharts库绘制各类折线图的方法，涵盖双折线、面积图、简单图、缺失数据连接、对数轴及堆叠图等模板。通过具体代码示例，展示如何配置参数以清晰展现数据趋势、速率及峰值特征，适用于不同场景的可视化需求。
折线图介绍
折线图与柱状图同为日常可视化中最常用的图表类型，其优势与适用场景广为人知。若需快速捕捉并呈现数据趋势，折线图是理想选择。该图表通过直线按特定顺序连接数据点，适用于展示有序因变量上的变化。其核心特点在于反映事物随类别变化的趋势，能清晰呈现数据的增减趋势、变化速率、规律及峰值等关键特征。
优点：
- 能很好地展现沿某个维度的变化趋势
- 能比较多组数据在同一个维度上的趋势
- 适合展现较大数据集
缺点：每张图上不适合展示太多折线
折线图模板系列
双折线图（气温最高最低温度趋势显示）
双折线图在同一图表中展示，必然存在一个共同维度，并包含两个不同的数据集。例如，展示一天中的最高和最低气温，即可使用此模板进行可视化演示。
import pyecharts.options as opts
from pyecharts.charts import Line
week_name_list = ["周一", "周二", "周三", "周四", "周五", "周六", "周日"]
high_temperature = [11, 11, 15, 13, 12, 13, 10]
low_temperature = [1, -2, 2, 5, 3, 2, 0]
(
Line(init_opts=opts.InitOpts(width="1000px", height="600px"))
.add_xaxis(xaxis_data=week_name_list)
.add_yaxis(
series_name="最高气温",
y_axis=high_temperature,
# 显示最大值和最小值
# markpoint_opts=opts.MarkPointOpts(
# data=[
# opts.MarkPointItem(type_="max", name="最大值"),
# opts.MarkPointItem(type_="min", name="最小值"),
# ]
# ),
# 显示平均值
# markline_opts=opts.MarkLineOpts(
# data=[opts.MarkLineItem(type_="average", name="平均值")]
# ),
)
.add_yaxis(
series_name="最低气温",
y_axis=low_temperature,
# 设置刻度标签
# markpoint_opts=opts.MarkPointOpts(
# data=[opts.MarkPointItem(value=-2, name="周最低", x=1, y=-1.5)]
# ),
# markline_opts=opts.MarkLineOpts(
# data=[
# opts.MarkLineItem(type_="average", name="平均值"),
# opts.MarkLineItem(symbol="none", x="90%", y="max"),
# opts.MarkLineItem(symbol="circle", type_="max", name="最高点"),
# ]
# ),
)
.set_global_opts(
title_opts=opts.TitleOpts(title="未来一周气温变化", subtitle="副标题"),
# tooltip_opts=opts.TooltipOpts(trigger="axis"),
# toolbox_opts=opts.ToolboxOpts(is_show=True),
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),
)
.render("最低最高温度折线图.html")
)
print("图表已生成！请查收！")
面积折线图（紧贴Y轴）
是否还记得二重积分中面积的含义？有时我们需要比较不同数据围成的面积大小，这在物理应用中较为常见。下面将展示具体效果。
import pyecharts.options as opts
from pyecharts.charts import Line
from pyecharts.faker import Faker
from pyecharts.globals import ThemeType
c = (
Line({"theme": ThemeType.MACARONS})
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values(), is_smooth=True)
.add_yaxis("商家B", Faker.values(), is_smooth=True)
.set_series_opts(
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
title_opts=opts.TitleOpts(title="标题"),
xaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
name='类别',
name_location='middle',
name_gap=30, # 标签与轴线之间的距离，默认为20，最好不要设置20
name_textstyle_opts=opts.TextStyleOpts(
font_family='Times New Roman',
font_size=16 # 标签字体大小
)),
yaxis_opts=opts.AxisOpts(
name='数量',
name_location='middle',
name_gap=30,
name_textstyle_opts=opts.TextStyleOpts(
font_family='Times New Roman',
font_size=16
# font_weight='bolder',
)),
# toolbox_opts=opts.ToolboxOpts() # 工具选项
)
.render("面积折线图-紧贴Y轴.html")
)
print("请查收！")
简单折线图（无动态和数据标签）
此模板效果与Excel中的可视化相似，不包含任何动态功能元素。尽管它是最简洁的形式，但我们可以基于此进行二次创作和修改。
import pyecharts.options as opts
from pyecharts.charts import Line
from pyecharts.globals import ThemeType
x_data = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
y_data = [820, 932, 901, 934, 1290, 1330, 1320]
(
Line({"theme": ThemeType.MACARONS})
.set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=False),
xaxis_opts=opts.AxisOpts(
name='类别',
name_location='middle',
name_gap=30, # 标签与轴线之间的距离，默认为20，最好不要设置20
name_textstyle_opts=opts.TextStyleOpts(
font_family='Times New Roman',
font_size=16 # 标签字体大小
)),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
name='数量',
name_location='middle',
name_gap=30,
name_textstyle_opts=opts.TextStyleOpts(
font_family='Times New Roman',
font_size=16
# font_weight='bolder',
)),
)
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="",
y_axis=y_data,
symbol="emptyCircle",
is_symbol_show=True,
label_opts=opts.LabelOpts(is_show=False),
)
.render("简单折线图.html")
)
连接空白数据折线图
在处理数据时，若发现某些类别的数据缺失，希望系统能自动连接这些断点，此模板即可满足需求。
import pyecharts.options as opts
from pyecharts.charts import Line
from pyecharts.faker import Faker
from pyecharts.globals import ThemeType
y = Faker.values()
y[3], y[5] = None, None
c = (
Line({"theme": ThemeType.WONDERLAND})
.add_xaxis(Faker.choose())
.add_yaxis("商家A", y, is_connect_nones=True)
.set_global_opts(title_opts=opts.TitleOpts(title="标题"),
xaxis_opts=opts.AxisOpts(
name='类别',
name_location='middle',
name_gap=30, # 标签与轴线之间的距离，默认为20，最好不要设置20
name_textstyle_opts=opts.TextStyleOpts(
font_family='Times New Roman',
font_size=16 # 标签字体大小
)),
yaxis_opts=opts.AxisOpts(
name='数量',
name_location='middle',
name_gap=30,
name_textstyle_opts=opts.TextStyleOpts(
font_family='Times New Roman',
font_size=16
# font_weight='bolder',
)), )
# toolbox_opts=opts.ToolboxOpts() # 工具选项)
.render("数据缺失折线图.html")
)
对数轴折线图示例
此图表类型未必经常使用，但可作为模板分享参考。
import pyecharts.options as opts
from pyecharts.charts import Line
x_data = ["一", "二", "三", "四", "五", "六", "七", "八", "九"]
y_data_3 = [1, 3, 9, 27, 81, 247, 741, 2223, 6669]
y_data_2 = [1, 2, 4, 8, 16, 32, 64, 128, 256]
y_data_05 = [1 / 2, 1 / 4, 1 / 8, 1 / 16, 1 / 32, 1 / 64, 1 / 128, 1 / 256, 1 / 512]
(
Line(init_opts=opts.InitOpts(width="1200px", height="600px"))
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="1/2的指数",
y_axis=y_data_05,
linestyle_opts=opts.LineStyleOpts(width=2),
)
.add_yaxis(
series_name="2的指数", y_axis=y_data_2, linestyle_opts=opts.LineStyleOpts(width=2)
)
.add_yaxis(
series_name="3的指数", y_axis=y_data_3, linestyle_opts=opts.LineStyleOpts(width=2)
)
.set_global_opts(
title_opts=opts.TitleOpts(title="对数轴示例", pos_left="center"),
tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b} : {c}"),
legend_opts=opts.LegendOpts(pos_left="left"),
xaxis_opts=opts.AxisOpts(type_="category", name="x"),
yaxis_opts=opts.AxisOpts(
type_="log",
name="y",
splitline_opts=opts.SplitLineOpts(is_show=True),
is_scale=True,
),
)
.render("对数轴折线图.html")
)
折线图堆叠（适合多个折线图展示）
展示多个折线图时需注意，数据量不宜过于接近，否则密集的折线会令人视觉不适。
import pyecharts.options as opts
from pyecharts.charts import Line
from pyecharts.globals import ThemeType
x_data = ["周一", "周二", "周三", "周四", "周五", "周六", "周日"]
y_data = [820, 932, 901, 934, 1290, 1330, 1320]
(
Line({"theme": ThemeType.MACARONS})
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="邮件营销",
stack="总量",
y_axis=[120, 132, 101, 134, 90, 230, 210],
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="联盟广告",
stack="总量",
y_axis=[220, 182, 191, 234, 290, 330, 310],
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="视频广告",
stack="总量",
y_axis=[150, 232, 201, 154, 190, 330, 410],
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="直接访问",
stack="总量",
y_axis=[320, 332, 301, 334, 390, 330, 320],
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="搜索引擎",
stack="总量",
y_axis=[820, 932, 901, 934, 1290, 1330, 1320],
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
title_opts=opts.TitleOpts(title="折线图堆叠"),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
name='数量',
name_location='middle',
name_gap=40,
name_textstyle_opts=opts.TextStyleOpts(
font_family='Times New Roman',
font_size=16
# font_weight='bolder',
)),
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False,
name='类别',
name_location='middle',
name_gap=30, # 标签与轴线之间的距离，默认为20，最好不要设置20
name_textstyle_opts=opts.TextStyleOpts(
font_family='Times New Roman',
font_size=16 # 标签字体大小
)),
)
.render("折线图堆叠.html")
)
二维曲线折线图（两个数据）
若需在同一图表中进行对比，如何呈现如丝般顺滑的曲线折线图？请参考以下示例。
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(), is_smooth=True) # 如果不想变成曲线就删除即可
.add_yaxis("商家B", Faker.values(), is_smooth=True)
.set_global_opts(title_opts=opts.TitleOpts(title="标题"),
xaxis_opts=opts.AxisOpts(
name='类别',
name_location='middle',
name_gap=30, # 标签与轴线之间的距离，默认为20，最好不要设置20
name_textstyle_opts=opts.TextStyleOpts(
font_family='Times New Roman',
font_size=16 # 标签字体大小
)),
yaxis_opts=opts.AxisOpts(
name='数量',
name_location='middle',
name_gap=30,
name_textstyle_opts=opts.TextStyleOpts(
font_family='Times New Roman',
font_size=16
# font_weight='bolder',
)),
# toolbox_opts=opts.ToolboxOpts() # 工具选项
)
.render("二维折线图.html")
)
多维度折线图（颜色对比）
此模板的最大优势在于支持鼠标悬停时智能显示数据详情。
import pyecharts.options as opts
from pyecharts.charts import Line
# 将在 v1.1.0 中更改
from pyecharts.commons.utils import JsCode
js_formatter = """function (params) {
console.log(params);
return '降水量 ' + params.value + (params.seriesData.length ? '：' + params.seriesData[0].data : '');
}"""
(
Line(init_opts=opts.InitOpts(width="1200px", height="600px"))
.add_xaxis(
xaxis_data=[
"2016-1",
"2016-2",
"2016-3",
"2016-4",
"2016-5",
"2016-6",
"2016-7",
"2016-8",
"2016-9",
"2016-10",
"2016-11",
"2016-12",
]
)
.extend_axis(
xaxis_data=[
"2015-1",
"2015-2",
"2015-3",
"2015-4",
"2015-5",
"2015-6",
"2015-7",
"2015-8",
"2015-9",
"2015-10",
"2015-11",
"2015-12",
],
xaxis=opts.AxisOpts(
type_="category",
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
axisline_opts=opts.AxisLineOpts(
is_on_zero=False, linestyle_opts=opts.LineStyleOpts(color="#6e9ef1")
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True, label=opts.LabelOpts(formatter=JsCode(js_formatter))
),
),
)
.add_yaxis(
series_name="2015 降水量",
is_smooth=True,
symbol="emptyCircle",
is_symbol_show=False,
# xaxis_index=1,
color="#d14a61",
y_axis=[2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3],
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=2),
)
.add_yaxis(
series_name="2016 降水量",
is_smooth=True,
symbol="emptyCircle",
is_symbol_show=False,
color="#6e9ef1",
y_axis=[3.9, 5.9, 11.1, 18.7, 48.3, 69.2, 231.6, 46.6, 55.4, 18.4, 10.3, 0.7],
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=2),
)
.set_global_opts(
legend_opts=opts.LegendOpts(),
tooltip_opts=opts.TooltipOpts(trigger="none", axis_pointer_type="cross"),
xaxis_opts=opts.AxisOpts(
type_="category",
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
axisline_opts=opts.AxisLineOpts(
is_on_zero=False, linestyle_opts=opts.LineStyleOpts(color="#d14a61")
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True, label=opts.LabelOpts(formatter=JsCode(js_formatter))
),
),
yaxis_opts=opts.AxisOpts(
type_="value",
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(opacity=1)
),
),
)
.render("多维颜色多维折线图.html")
)
阶梯折线图
import pyecharts.options as opts
from pyecharts.charts import Line
from pyecharts.faker import Faker
from pyecharts.globals import ThemeType
c = (
Line({"theme": ThemeType.MACARONS})
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values(), is_step=True)
.set_global_opts(title_opts=opts.TitleOpts(title="标题"),
xaxis_opts=opts.AxisOpts(
name='类别',
name_location='middle',
name_gap=30, # 标签与轴线之间的距离，默认为20，最好不要设置20
name_textstyle_opts=opts.TextStyleOpts(
font_family='Times New Roman',
font_size=16 # 标签字体大小
)),
yaxis_opts=opts.AxisOpts(
name='数量',
name_location='middle',
name_gap=30,
name_textstyle_opts=opts.TextStyleOpts(
font_family='Times New Roman',
font_size=16
# font_weight='bolder',
)),
# toolbox_opts=opts.ToolboxOpts() # 工具选项
)
.render("阶梯折线图.html")
)
js高渲染折线图
其中的渲染效果非常美观，适用于炫酷的展示场景。数据集可选是否展示，只需在相应位置更改参数即可。
import pyecharts.options as opts
from pyecharts.charts import Line
from pyecharts.commons.utils import JsCode
x_data = ["14", "15", "16", "17", "18", "19", "20", "21", "22", "23","24","25","26","27","28","29","30","31","32","33","34","35","36","37","38","39","40"]
y_data = [393, 438, 485, 631, 689, 824, 987, 1000, 1100, 1200,1500,1000,1700,1900,2000,500,1200,1300,1500,1800,1500,1900,1700,1000,1900,1800,2100,1600,2200,2300]
background_color_js = (
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
"[{offset: 0, color: '#c86589'}, {offset: 1, color: '#06a7ff'}], false)"
)
area_color_js = (
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
"[{offset: 0, color: '#eb64fb'}, {offset: 1, color: '#3fbbff0d'}], false)"
)
c = (
Line(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js)))
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="注册总量",
y_axis=y_data,
is_smooth=True,
is_symbol_show=True,
symbol="circle",
symbol_size=6,
linestyle_opts=opts.LineStyleOpts(color="#fff"),
label_opts=opts.LabelOpts(is_show=True, position="top", color="white"),
itemstyle_opts=opts.ItemStyleOpts(
color="red", border_color="#fff", border_width=3
),
tooltip_opts=opts.TooltipOpts(is_show=False),
areastyle_opts=opts.AreaStyleOpts(color=JsCode(area_color_js), opacity=1),
)
.set_global_opts(
title_opts=opts.TitleOpts(
title="OCTOBER 2015",
pos_bottom="5%",
pos_left="center",
title_textstyle_opts=opts.TextStyleOpts(color="#fff", font_size=16),
),
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=False,
axislabel_opts=opts.LabelOpts(margin=30, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=25,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")
),
),
yaxis_opts=opts.AxisOpts(
type_="value",
position="right",
axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(width=2, color="#fff")
),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=15,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")
),
),
legend_opts=opts.LegendOpts(is_show=False),
)
.render("高渲染.html")
)
所有图表均可自定义配置，包括字体大小、颜色及背景等。下期文章我们将继续探索折线图的魅力。
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