一、概述

河流图（stream chart），是堆叠面积图的一个变种，，是堆积面积图的一种变形，通过“流动”的形状来展示不同类别的数据随时间的变化情况。但不同于堆积面积图，河流图并不是将数据描绘在一个固定的、笔直的轴上（堆积图的基准线就是x轴），而是将数据分散到一个变化的中心基准线上（该基准线不一定是笔直的）。

二、案例

在浏览金融时报的信息图表时，注意到一个疫情图很吸引眼球，就是下面这张图：

各国新冠疫情发展状况

冬季奥林匹克运动会奖牌获得者国家分布

以下图为例，作品《音曲繁美》就采用了这种河流图，它每一条的宽窄代表了音乐在特定年代流行的程度，用不同的颜色去区分不同的音乐，图表最后形成水流状的样子。

2008年2月，《纽约时报》发布了一个最典型、最著名的河流图的例子《电影的衰退和流动：过去20年的电影票房收入》，描述了从1986年1月到2008年2月期间，所有电影的上映时间以及期间的周票房变化。在这个河流图中，流形状的宽度代表了某部电影的周票房，流形状的起始是由电影的上映时间决定的。颜色由电影的总票房决定，票房就是电影的“附加定量”，颜色越深代表了电影最终票房越高。

三、pyecharts绘图

import pyecharts.options as optsfrom pyecharts.charts import ThemeRiver

x_data = ["DQ", "TY", "SS", "QG", "SY", "DD"]y_data = [    ["2015/11/08", 10, "DQ"],    ["2015/11/09", 15, "DQ"],    ["2015/11/10", 35, "DQ"],    ["2015/11/11", 38, "DQ"],    ["2015/11/12", 22, "DQ"],    ["2015/11/13", 16, "DQ"],    ["2015/11/14", 7, "DQ"],    ["2015/11/15", 2, "DQ"],    ["2015/11/16", 17, "DQ"],    ["2015/11/17", 33, "DQ"],    ["2015/11/18", 40, "DQ"],    ["2015/11/19", 32, "DQ"],    ["2015/11/20", 26, "DQ"],    ["2015/11/21", 35, "DQ"],    ["2015/11/22", 40, "DQ"],    ["2015/11/23", 32, "DQ"],    ["2015/11/24", 26, "DQ"],    ["2015/11/25", 22, "DQ"],    ["2015/11/26", 16, "DQ"],    ["2015/11/27", 22, "DQ"],    ["2015/11/28", 10, "DQ"],    ["2015/11/08", 35, "TY"],    ["2015/11/09", 36, "TY"],    ["2015/11/10", 37, "TY"],    ["2015/11/11", 22, "TY"],    ["2015/11/12", 24, "TY"],    ["2015/11/13", 26, "TY"],    ["2015/11/14", 34, "TY"],    ["2015/11/15", 21, "TY"],    ["2015/11/16", 18, "TY"],    ["2015/11/17", 45, "TY"],    ["2015/11/18", 32, "TY"],    ["2015/11/19", 35, "TY"],    ["2015/11/20", 30, "TY"],    ["2015/11/21", 28, "TY"],    ["2015/11/22", 27, "TY"],    ["2015/11/23", 26, "TY"],    ["2015/11/24", 15, "TY"],    ["2015/11/25", 30, "TY"],    ["2015/11/26", 35, "TY"],    ["2015/11/27", 42, "TY"],    ["2015/11/28", 42, "TY"],    ["2015/11/08", 21, "SS"],    ["2015/11/09", 25, "SS"],    ["2015/11/10", 27, "SS"],    ["2015/11/11", 23, "SS"],    ["2015/11/12", 24, "SS"],    ["2015/11/13", 21, "SS"],    ["2015/11/14", 35, "SS"],    ["2015/11/15", 39, "SS"],    ["2015/11/16", 40, "SS"],    ["2015/11/17", 36, "SS"],    ["2015/11/18", 33, "SS"],    ["2015/11/19", 43, "SS"],    ["2015/11/20", 40, "SS"],    ["2015/11/21", 34, "SS"],    ["2015/11/22", 28, "SS"],    ["2015/11/23", 26, "SS"],    ["2015/11/24", 37, "SS"],    ["2015/11/25", 41, "SS"],    ["2015/11/26", 46, "SS"],    ["2015/11/27", 47, "SS"],    ["2015/11/28", 41, "SS"],    ["2015/11/08", 10, "QG"],    ["2015/11/09", 15, "QG"],    ["2015/11/10", 35, "QG"],    ["2015/11/11", 38, "QG"],    ["2015/11/12", 22, "QG"],    ["2015/11/13", 16, "QG"],    ["2015/11/14", 7, "QG"],    ["2015/11/15", 2, "QG"],    ["2015/11/16", 17, "QG"],    ["2015/11/17", 33, "QG"],    ["2015/11/18", 40, "QG"],    ["2015/11/19", 32, "QG"],    ["2015/11/20", 26, "QG"],    ["2015/11/21", 35, "QG"],    ["2015/11/22", 40, "QG"],    ["2015/11/23", 32, "QG"],    ["2015/11/24", 26, "QG"],    ["2015/11/25", 22, "QG"],    ["2015/11/26", 16, "QG"],    ["2015/11/27", 22, "QG"],    ["2015/11/28", 10, "QG"],    ["2015/11/08", 10, "SY"],    ["2015/11/09", 15, "SY"],    ["2015/11/10", 35, "SY"],    ["2015/11/11", 38, "SY"],    ["2015/11/12", 22, "SY"],    ["2015/11/13", 16, "SY"],    ["2015/11/14", 7, "SY"],    ["2015/11/15", 2, "SY"],    ["2015/11/16", 17, "SY"],    ["2015/11/17", 33, "SY"],    ["2015/11/18", 40, "SY"],    ["2015/11/19", 32, "SY"],    ["2015/11/20", 26, "SY"],    ["2015/11/21", 35, "SY"],    ["2015/11/22", 4, "SY"],    ["2015/11/23", 32, "SY"],    ["2015/11/24", 26, "SY"],    ["2015/11/25", 22, "SY"],    ["2015/11/26", 16, "SY"],    ["2015/11/27", 22, "SY"],    ["2015/11/28", 10, "SY"],    ["2015/11/08", 10, "DD"],    ["2015/11/09", 15, "DD"],    ["2015/11/10", 35, "DD"],    ["2015/11/11", 38, "DD"],    ["2015/11/12", 22, "DD"],    ["2015/11/13", 16, "DD"],    ["2015/11/14", 7, "DD"],    ["2015/11/15", 2, "DD"],    ["2015/11/16", 17, "DD"],    ["2015/11/17", 33, "DD"],    ["2015/11/18", 4, "DD"],    ["2015/11/19", 32, "DD"],    ["2015/11/20", 26, "DD"],    ["2015/11/21", 35, "DD"],    ["2015/11/22", 40, "DD"],    ["2015/11/23", 32, "DD"],    ["2015/11/24", 26, "DD"],    ["2015/11/25", 22, "DD"],    ["2015/11/26", 16, "DD"],    ["2015/11/27", 22, "DD"],    ["2015/11/28", 10, "DD"],]



(    ThemeRiver(init_opts=opts.InitOpts(width="1600px", height="800px"))    .add(        series_name=x_data,        data=y_data,        singleaxis_opts=opts.SingleAxisOpts(            pos_top="50", pos_bottom="50", type_="time"        ),    )    .set_global_opts(        tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="line")    )    .render("theme_river.html")

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画图神器pyecharts-河流图

一、概述

二、案例

三、pyecharts绘图