折线图是排列在工作表的列或行中的数据可以绘制到折线图中。折线图可以显示随时间（根据常用比例设置）而变化的连续数据，因此非常适用于显示在相等时间间隔下数据的趋势。

下面我给大家介绍一下如何用pyecharts画出各种折线图

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 Line

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=(
    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。