1.基本折线图

import pyecharts.options as opts
from pyecharts.charts import Line
x=['星期一','星期二','星期三','星期四','星期五','星期七','星期日']
y=[100,200,300,400,500,400,300]line=(Line().set_global_opts(tooltip_opts=opts.TooltipOpts(is_show=False),xaxis_opts=opts.AxisOpts(type_="category"),yaxis_opts=opts.AxisOpts(type_="value",axistick_opts=opts.AxisTickOpts(is_show=True),splitline_opts=opts.SplitLineOpts(is_show=True),),).add_xaxis(xaxis_data=x).add_yaxis(series_name="基本折线图",y_axis=y,symbol="emptyCircle",is_symbol_show=True,label_opts=opts.LabelOpts(is_show=False),)
)
line.render_notebook()

series_name：图形名称
y_axis：数据
symbol：标记的图形，pyecharts提供的类型包括’circle’, ‘rect’, ‘roundRect’, ‘triangle’, ‘diamond’, ‘pin’, ‘arrow’, ‘none’，也可以通过 ‘image://url’ 设置为图片，其中 URL 为图片的链接。
is_symbol_show：是否显示 symbol

2.连接空数据（折线图）

有时候我们要分析的数据存在空缺值，需要进行处理才能画出折线图

import pyecharts.options as opts
from pyecharts.charts import Line
x=['星期一','星期二','星期三','星期四','星期五','星期七','星期日']
y=[100,200,300,400,None,400,300]line=(Line().add_xaxis(xaxis_data=x).add_yaxis(series_name="连接空数据（折线图）",y_axis=y,is_connect_nones=True).set_global_opts(title_opts=opts.TitleOpts(title="Line-连接空数据"))
)
line.render_notebook()

3.多条折线重叠

import pyecharts.options as opts
from pyecharts.charts import Line
x=['星期一','星期二','星期三','星期四','星期五','星期七','星期日']
y1=[100,200,300,400,100,400,300]
y2=[200,300,200,100,200,300,400]
line=(Line().add_xaxis(xaxis_data=x).add_yaxis(series_name="y1线",y_axis=y1,symbol="arrow",is_symbol_show=True).add_yaxis(series_name="y2线",y_axis=y2).set_global_opts(title_opts=opts.TitleOpts(title="Line-多折线重叠"))
)
line.render_notebook()

4.平滑曲线折线图

import pyecharts.options as opts
from pyecharts.charts import Line
x=['星期一','星期二','星期三','星期四','星期五','星期七','星期日']
y1=[100,200,300,400,100,400,300]
y2=[200,300,200,100,200,300,400]
line=(Line().add_xaxis(xaxis_data=x).add_yaxis(series_name="y1线",y_axis=y1, is_smooth=True).add_yaxis(series_name="y2线",y_axis=y2, is_smooth=True).set_global_opts(title_opts=opts.TitleOpts(title="Line-多折线重叠"))
)
line.render_notebook()

is_smooth：平滑曲线标志

5.阶梯图

import pyecharts.options as opts
from pyecharts.charts import Line
x=['星期一','星期二','星期三','星期四','星期五','星期七','星期日']
y1=[100,200,300,400,100,400,300]
line=(Line().add_xaxis(xaxis_data=x).add_yaxis(series_name="y1线",y_axis=y1, is_step=True).set_global_opts(title_opts=opts.TitleOpts(title="Line-阶梯图"))
)
line.render_notebook()

is_step：阶梯图参数

6.变换折线的样式

import pyecharts.options as opts
from pyecharts.charts import Line
from pyecharts.faker import Faker
x=['星期一','星期二','星期三','星期四','星期五','星期七','星期日']
y1=[100,200,300,400,100,400,300]
line = (Line().add_xaxis(xaxis_data=x).add_yaxis("y1",y1,symbol="triangle",symbol_size=30,linestyle_opts=opts.LineStyleOpts(color="red", width=4, type_="dashed"),itemstyle_opts=opts.ItemStyleOpts(border_width=3, border_color="yellow", color="blue"),).set_global_opts(title_opts=opts.TitleOpts(title="Line-ItemStyle"))
)
line.render_notebook()

linestyle_opts:折线样式配置，color设置颜色，width设置宽度，type设置类型，有’solid’, ‘dashed’, 'dotted’三种类型
itemstyle_opts：图元样式配置，border_width设置描边宽度，border_color设置描边颜色，color设置纹理填充颜色

7.折线面积图

import pyecharts.options as opts
from pyecharts.charts import Line
x=['星期一','星期二','星期三','星期四','星期五','星期七','星期日']
y1=[100,200,300,400,100,400,300]
y2=[200,300,200,100,200,300,400]
line=(Line().add_xaxis(xaxis_data=x).add_yaxis(series_name="y1线",y_axis=y1,areastyle_opts=opts.AreaStyleOpts(opacity=0.5)).add_yaxis(series_name="y2线",y_axis=y2,areastyle_opts=opts.AreaStyleOpts(opacity=0.5)).set_global_opts(title_opts=opts.TitleOpts(title="Line-多折线重叠"))
)
line.render_notebook()

8.双横坐标折线图

import pyecharts.options as opts
from pyecharts.charts import Linefrom 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=(Line().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,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)),),)
)
line.render_notebook()

9.用电量随时间变化

import pyecharts.options as opts
from pyecharts.charts import Line
x_data = ["00:00","01:15","02:30","03:45","05:00","06:15","07:30","08:45","10:00","11:15","12:30","13:45","15:00","16:15","17:30","18:45","20:00","21:15","22:30","23:45",
]
y_data = [300,280,250,260,270,300,550,500,400,390,380,390,400,500,600,750,800,700,600,400,
]line=(Line().add_xaxis(xaxis_data=x_data).add_yaxis(series_name="用电量",y_axis=y_data,is_smooth=True,label_opts=opts.LabelOpts(is_show=False),linestyle_opts=opts.LineStyleOpts(width=2),).set_global_opts(title_opts=opts.TitleOpts(title="一天用电量分布", subtitle="纯属虚构"),tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),xaxis_opts=opts.AxisOpts(boundary_gap=False),yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} W"),splitline_opts=opts.SplitLineOpts(is_show=True),),visualmap_opts=opts.VisualMapOpts(is_piecewise=True,dimension=0,pieces=[{"lte": 6, "color": "green"},{"gt": 6, "lte": 8, "color": "red"},{"gt": 8, "lte": 14, "color": "yellow"},{"gt": 14, "lte": 17, "color": "red"},{"gt": 17, "color": "green"},],pos_right=0,pos_bottom=100),).set_series_opts(markarea_opts=opts.MarkAreaOpts(data=[opts.MarkAreaItem(name="早高峰", x=("07:30", "10:00")),opts.MarkAreaItem(name="晚高峰", x=("17:30", "21:15")),]))
)
line.render_notebook()

这里给大家介绍几个关键参数：
①visualmap_opts：视觉映射配置项，可以将折线分段并设置标签（is_piecewise），将不同段设置颜色（pieces）；
②markarea_opts：标记区域配置项，data参数可以设置标记区域名称和位置。

来源：python数据分析——pyecharts折线图全解（小白必看）_小bird的博客-CSDN博客_pyecharts折线图

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