本篇内容主要讲解“怎么使用Python的pyecharts绘制折线图”，感兴趣的朋友不妨来看看。本文介绍的方法操作简单快捷，实用性强。下面就让小编来带大家学习“怎么使用Python的pyecharts绘制折线图”吧!
折线图介绍
折线图和柱状图一样是我们日常可视化最多的一个图例，当然它的优势和适用场景相信大家肯定不陌生，要想快速的得出趋势，抓住趋势二字，就会很快的想到要用折线图来表示了。折线图是通过直线将这些点按照某种顺序连接起来形成的图，适用于数据在一个有序的因变量上的变化，它的特点是反应事物随类别而变化的趋势，可以清晰展现数据的增减趋势、增减的速率、增减的规律、峰值等特征。
优点：
能很好的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）
双折线图在一张图里面显示，肯定有一个相同的维度，然后有两个不同的数据集。比如一天的温度有最高的和最低的温度，我们就可以用这个来作为展示了。
import pyecharts.options as optsfrom pyecharts.charts import Lineweek_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）
还记得二重积分吗，面积代表什么？有时候我们就想要看谁围出来的面积大，这个在物理的实际运用中比较常见，下面来看看效果吧。
import pyecharts.options as optsfrom pyecharts.charts import Linefrom pyecharts.faker import Fakerfrom pyecharts.globals import ThemeTypec = (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20name_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）
此模板和Excel里面的可视化差不多，没有一点功能元素，虽然它是最简洁的，但是我们可以通过这个进行改动，在上面创作的画作。
import pyecharts.options as optsfrom pyecharts.charts import Linefrom pyecharts.globals import ThemeTypex_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20name_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
有时候我们在处理数据的时候，发现有些类别的数据缺失了，这个时候我们想要它可以自动连接起来，那么这个模板就可以用到了。
import pyecharts.options as optsfrom pyecharts.charts import Linefrom pyecharts.faker import Fakerfrom pyecharts.globals import ThemeTypey = Faker.values()y[3], y[5] = None, Nonec = (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20name_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
此图例未必用的上，当然也可以作为一个模板分享于此。
import pyecharts.options as optsfrom pyecharts.charts import Linex_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）
多个折线图展示要注意的是，数据量不能过于的接近，不然密密麻麻的折线，反而让人看起来不舒服。
import pyecharts.options as optsfrom pyecharts.charts import Linefrom pyecharts.globals import ThemeTypex_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20name_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）
有时候需要在一个图里面进行对比，那么我们应该如何呈现一个丝滑般的曲线折线图呢？看看这个
import pyecharts.options as optsfrom pyecharts.charts import Linefrom pyecharts.faker import Fakerc = (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20name_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）
次模板的最大的好处就是可以移动鼠标智能显示数据
import pyecharts.options as optsfrom pyecharts.charts import Line# 将在 v1.1.0 中更改from 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(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 optsfrom pyecharts.charts import Linefrom pyecharts.faker import Fakerfrom pyecharts.globals import ThemeTypec = (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20name_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
里面的渲染效果相当好看，可以适用于炫酷的展示，数据集可以展示也可以不展示，在相应的位置更改参数即可。
import pyecharts.options as optsfrom pyecharts.charts import Linefrom pyecharts.commons.utils import JsCodex_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"))
到此，相信大家对“怎么使用Python的pyecharts绘制折线图”有了更深的了解，不妨来实际操作一番吧！这里是编程网网站，更多相关内容可以进入相关频道进行查询，关注我们，继续学习！
