Python中怎么绘制各种折线图

Python中怎么绘制各种折线图，很多新手对此不是很清楚，为了帮助大家解决这个难题，下面小编将为大家详细讲解，有这方面需求的人可以来学习下，希望你能有所收获。

1.基本折线图

import pyecharts.options as optsfrom pyecharts.charts import Linex=['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 optsfrom pyecharts.charts import Linex=['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 optsfrom pyecharts.charts import Linex=['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 optsfrom pyecharts.charts import Linex=['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 optsfrom pyecharts.charts import Linex=['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 optsfrom pyecharts.charts import Linefrom pyecharts.faker import Fakerx=['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 optsfrom pyecharts.charts import Linex=['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 optsfrom pyecharts.charts import Linefrom pyecharts.commons.utils import JsCodejs_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 optsfrom pyecharts.charts import Linex_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。

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