pyecharts在数据可视化中的应用（二）weixin45676887的博客-

pyecharts在数据可视化中的应用

基本的使用：
https://blog.csdn.net/weixin_45676887/article/details/106550116

用示例来解决实际问题：
1.使用以下JSON数据绘制树图、矩形树图。
json数据：

data = [{ "name": "flare", "children": [ { "name": "flex", "children": [ {"name": "FlareVis", "value": 4116} ] }, { "name": "scale", "children": [ {"name": "IScaleMap", "value": 2105}, {"name": "LinearScale", "value": 1316}, {"name": "LogScale", "value": 3151}, {"name": "OrdinalScale", "value": 3770}, {"name": "QuantileScale", "value": 2435}, {"name": "QuantitativeScale", "value": 4839}, {"name": "RootScale", "value": 1756}, {"name": "Scale", "value": 4268}, {"name": "ScaleType", "value": 1821}, {"name": "TimeScale", "value": 5833} ] }, { "name": "display", "children": [ {"name": "DirtySprite", "value": 8833} ] } ] }] 

树图 代码如下：

from pyecharts.charts import Tree from pyecharts import options as opts import json  data = [{ "name": "flare", "children": [ { "name": "flex", "children": [ {"name": "FlareVis", "value": 4116} ] }, { "name": "scale", "children": [ {"name": "IScaleMap", "value": 2105}, {"name": "LinearScale", "value": 1316}, {"name": "LogScale", "value": 3151}, {"name": "OrdinalScale", "value": 3770}, {"name": "QuantileScale", "value": 2435}, {"name": "QuantitativeScale", "value": 4839}, {"name": "RootScale", "value": 1756}, {"name": "Scale", "value": 4268}, {"name": "ScaleType", "value": 1821}, {"name": "TimeScale", "value": 5833} ] }, { "name": "display", "children": [ {"name": "DirtySprite", "value": 8833} ] } ] }]  c = (     Tree() .add("", data) .set_global_opts(title_opts=opts.TitleOpts(title="树图")) .render("树图tree_base.html") ) 

会在同目录下生成一个树图tree_base.html的网页，打开网页则会显示该代码的运行结果：
矩形树图 代码如下：

from pyecharts import options as opts from pyecharts.charts import TreeMap   data = [{ "name": "flare", "children": [ { "name": "flex", "children": [ {"name": "FlareVis", "value": 4116} ] }, { "name": "scale", "children": [ {"name": "IScaleMap", "value": 2105}, {"name": "LinearScale", "value": 1316}, {"name": "LogScale", "value": 3151}, {"name": "OrdinalScale", "value": 3770}, {"name": "QuantileScale", "value": 2435}, {"name": "QuantitativeScale", "value": 4839}, {"name": "RootScale", "value": 1756}, {"name": "Scale", "value": 4268}, {"name": "ScaleType", "value": 1821}, {"name": "TimeScale", "value": 5833} ] }, { "name": "display", "children": [ {"name": "DirtySprite", "value": 8833} ] } ] }]  c = (     TreeMap() .add("演示数据", data) .set_global_opts(title_opts=opts.TitleOpts(title="矩形树图")) .render("矩形树图treemap_base.html") ) 

打开网页则会显示该代码的运行结果：

2.绘制鸢尾花数据的相关性矩阵（数据：iris.csv）。
iris.csv数据为：

"","Sepal.Length","Sepal.Width","Petal.Length","Petal.Width","Species" "1",5.1,3.5,1.4,0.2,"setosa" "2",4.9,3,1.4,0.2,"setosa" "3",4.7,3.2,1.3,0.2,"setosa" "4",4.6,3.1,1.5,0.2,"setosa" "5",5,3.6,1.4,0.2,"setosa" "6",5.4,3.9,1.7,0.4,"setosa" "7",4.6,3.4,1.4,0.3,"setosa" "8",5,3.4,1.5,0.2,"setosa" "9",4.4,2.9,1.4,0.2,"setosa" "10",4.9,3.1,1.5,0.1,"setosa" "11",5.4,3.7,1.5,0.2,"setosa" "12",4.8,3.4,1.6,0.2,"setosa" "13",4.8,3,1.4,0.1,"setosa" "14",4.3,3,1.1,0.1,"setosa" "15",5.8,4,1.2,0.2,"setosa" "16",5.7,4.4,1.5,0.4,"setosa" "17",5.4,3.9,1.3,0.4,"setosa" "18",5.1,3.5,1.4,0.3,"setosa" "19",5.7,3.8,1.7,0.3,"setosa" "20",5.1,3.8,1.5,0.3,"setosa" "21",5.4,3.4,1.7,0.2,"setosa" "22",5.1,3.7,1.5,0.4,"setosa" "23",4.6,3.6,1,0.2,"setosa" "24",5.1,3.3,1.7,0.5,"setosa" "25",4.8,3.4,1.9,0.2,"setosa" "26",5,3,1.6,0.2,"setosa" "27",5,3.4,1.6,0.4,"setosa" "28",5.2,3.5,1.5,0.2,"setosa" "29",5.2,3.4,1.4,0.2,"setosa" "30",4.7,3.2,1.6,0.2,"setosa" "31",4.8,3.1,1.6,0.2,"setosa" "32",5.4,3.4,1.5,0.4,"setosa" "33",5.2,4.1,1.5,0.1,"setosa" "34",5.5,4.2,1.4,0.2,"setosa" "35",4.9,3.1,1.5,0.2,"setosa" "36",5,3.2,1.2,0.2,"setosa" "37",5.5,3.5,1.3,0.2,"setosa" "38",4.9,3.6,1.4,0.1,"setosa" "39",4.4,3,1.3,0.2,"setosa" "40",5.1,3.4,1.5,0.2,"setosa" "41",5,3.5,1.3,0.3,"setosa" "42",4.5,2.3,1.3,0.3,"setosa" "43",4.4,3.2,1.3,0.2,"setosa" "44",5,3.5,1.6,0.6,"setosa" "45",5.1,3.8,1.9,0.4,"setosa" "46",4.8,3,1.4,0.3,"setosa" "47",5.1,3.8,1.6,0.2,"setosa" "48",4.6,3.2,1.4,0.2,"setosa" "49",5.3,3.7,1.5,0.2,"setosa" "50",5,3.3,1.4,0.2,"setosa" "51",7,3.2,4.7,1.4,"versicolor" "52",6.4,3.2,4.5,1.5,"versicolor" "53",6.9,3.1,4.9,1.5,"versicolor" "54",5.5,2.3,4,1.3,"versicolor" "55",6.5,2.8,4.6,1.5,"versicolor" "56",5.7,2.8,4.5,1.3,"versicolor" "57",6.3,3.3,4.7,1.6,"versicolor" "58",4.9,2.4,3.3,1,"versicolor" "59",6.6,2.9,4.6,1.3,"versicolor" "60",5.2,2.7,3.9,1.4,"versicolor" "61",5,2,3.5,1,"versicolor" "62",5.9,3,4.2,1.5,"versicolor" "63",6,2.2,4,1,"versicolor" "64",6.1,2.9,4.7,1.4,"versicolor" "65",5.6,2.9,3.6,1.3,"versicolor" "66",6.7,3.1,4.4,1.4,"versicolor" "67",5.6,3,4.5,1.5,"versicolor" "68",5.8,2.7,4.1,1,"versicolor" "69",6.2,2.2,4.5,1.5,"versicolor" "70",5.6,2.5,3.9,1.1,"versicolor" "71",5.9,3.2,4.8,1.8,"versicolor" "72",6.1,2.8,4,1.3,"versicolor" "73",6.3,2.5,4.9,1.5,"versicolor" "74",6.1,2.8,4.7,1.2,"versicolor" "75",6.4,2.9,4.3,1.3,"versicolor" "76",6.6,3,4.4,1.4,"versicolor" "77",6.8,2.8,4.8,1.4,"versicolor" "78",6.7,3,5,1.7,"versicolor" "79",6,2.9,4.5,1.5,"versicolor" "80",5.7,2.6,3.5,1,"versicolor" "81",5.5,2.4,3.8,1.1,"versicolor" "82",5.5,2.4,3.7,1,"versicolor" "83",5.8,2.7,3.9,1.2,"versicolor" "84",6,2.7,5.1,1.6,"versicolor" "85",5.4,3,4.5,1.5,"versicolor" "86",6,3.4,4.5,1.6,"versicolor" "87",6.7,3.1,4.7,1.5,"versicolor" "88",6.3,2.3,4.4,1.3,"versicolor" "89",5.6,3,4.1,1.3,"versicolor" "90",5.5,2.5,4,1.3,"versicolor" "91",5.5,2.6,4.4,1.2,"versicolor" "92",6.1,3,4.6,1.4,"versicolor" "93",5.8,2.6,4,1.2,"versicolor" "94",5,2.3,3.3,1,"versicolor" "95",5.6,2.7,4.2,1.3,"versicolor" "96",5.7,3,4.2,1.2,"versicolor" "97",5.7,2.9,4.2,1.3,"versicolor" "98",6.2,2.9,4.3,1.3,"versicolor" "99",5.1,2.5,3,1.1,"versicolor" "100",5.7,2.8,4.1,1.3,"versicolor" "101",6.3,3.3,6,2.5,"virginica" "102",5.8,2.7,5.1,1.9,"virginica" "103",7.1,3,5.9,2.1,"virginica" "104",6.3,2.9,5.6,1.8,"virginica" "105",6.5,3,5.8,2.2,"virginica" "106",7.6,3,6.6,2.1,"virginica" "107",4.9,2.5,4.5,1.7,"virginica" "108",7.3,2.9,6.3,1.8,"virginica" "109",6.7,2.5,5.8,1.8,"virginica" "110",7.2,3.6,6.1,2.5,"virginica" "111",6.5,3.2,5.1,2,"virginica" "112",6.4,2.7,5.3,1.9,"virginica" "113",6.8,3,5.5,2.1,"virginica" "114",5.7,2.5,5,2,"virginica" "115",5.8,2.8,5.1,2.4,"virginica" "116",6.4,3.2,5.3,2.3,"virginica" "117",6.5,3,5.5,1.8,"virginica" "118",7.7,3.8,6.7,2.2,"virginica" "119",7.7,2.6,6.9,2.3,"virginica" "120",6,2.2,5,1.5,"virginica" "121",6.9,3.2,5.7,2.3,"virginica" "122",5.6,2.8,4.9,2,"virginica" "123",7.7,2.8,6.7,2,"virginica" "124",6.3,2.7,4.9,1.8,"virginica" "125",6.7,3.3,5.7,2.1,"virginica" "126",7.2,3.2,6,1.8,"virginica" "127",6.2,2.8,4.8,1.8,"virginica" "128",6.1,3,4.9,1.8,"virginica" "129",6.4,2.8,5.6,2.1,"virginica" "130",7.2,3,5.8,1.6,"virginica" "131",7.4,2.8,6.1,1.9,"virginica" "132",7.9,3.8,6.4,2,"virginica" "133",6.4,2.8,5.6,2.2,"virginica" "134",6.3,2.8,5.1,1.5,"virginica" "135",6.1,2.6,5.6,1.4,"virginica" "136",7.7,3,6.1,2.3,"virginica" "137",6.3,3.4,5.6,2.4,"virginica" "138",6.4,3.1,5.5,1.8,"virginica" "139",6,3,4.8,1.8,"virginica" "140",6.9,3.1,5.4,2.1,"virginica" "141",6.7,3.1,5.6,2.4,"virginica" "142",6.9,3.1,5.1,2.3,"virginica" "143",5.8,2.7,5.1,1.9,"virginica" "144",6.8,3.2,5.9,2.3,"virginica" "145",6.7,3.3,5.7,2.5,"virginica" "146",6.7,3,5.2,2.3,"virginica" "147",6.3,2.5,5,1.9,"virginica" "148",6.5,3,5.2,2,"virginica" "149",6.2,3.4,5.4,2.3,"virginica" "150",5.9,3,5.1,1.8,"virginica" 

鸢尾花数据的相关性矩阵图 代码如下（注：这里用的是matplotlib库做的，没有这个库的需要进行下载）：

import pandas as pd import matplotlib.pyplot as plt import seaborn as sns  data = pd.read_csv("iris.csv") sns.set() #使用默认配色 sns.pairplot(data,hue="Species") #hue 选择分类列 plt.show() 

运行的代码截图：

3.在地图上用圆点标出各省的销售额数据。
数据如下：

province = ['广东', '湖北', '湖南', '四川', '重庆', '黑龙江', '浙江', '山西', '河北', '安徽', '河南', '山东', '西藏'] data = [(i, random.randint(50, 150)) for i in province] 

各省的销售额数据

from pyecharts import options as opts from pyecharts.charts import Map import random  province = ['广东', '湖北', '湖南', '四川', '重庆', '黑龙江', '浙江', '山西', '河北', '安徽', '河南', '山东', '西藏'] data = [(i, random.randint(50, 150)) for i in province]  c = (     Map() .add("商家A", [list(z) for z in zip(province, data)], "china") .set_global_opts(         title_opts=opts.TitleOpts(title="Map-VisualMap（连续型）"),         visualmap_opts=opts.VisualMapOpts(max_=200), ) .render("各省的销售额数据.html") ) 

打开网页则会显示该代码的运行结果：

4.绘制地理热点图展示某连锁企业在湖北省各城市的门店数。
数据如下：

province = ['武汉市', '十堰市', '鄂州市', '宜昌市', '荆州市', '孝感市', '黄石市', '咸宁市', '仙桃市'] data = [(i, random.randint(50, 150)) for i in province] 

地理热点图 代码如下：

from pyecharts import options as opts from pyecharts.charts import Map import random  province = ['武汉市', '十堰市', '鄂州市', '宜昌市', '荆州市', '孝感市', '黄石市', '咸宁市', '仙桃市'] data = [(i, random.randint(50, 150)) for i in province]  c = (     Map() .add("商家A", [list(z) for z in zip(province, data)], "湖北") .set_global_opts(         title_opts=opts.TitleOpts(title="Map-湖北地图"), visualmap_opts=opts.VisualMapOpts() ) .render("湖北省各城市的门店数.html") ) 

打开网页则会显示该代码的运行结果：

5.绘制词云图（数据：word_data.csv）。
word_data.csv数据如下：

id,views,comments,category 5019,148896,28,艺术可视化 1416,81374,26,基础可视化 1416,81374,26,特别推荐 3485,80819,37,特别推荐 3485,80819,37,基础可视化 3485,80819,37,数据源 500,76495,10,统计可视化 500,76495,10,基础可视化 500,76495,10,网络可视化 4092,66650,70,引用 4092,66650,70,教程 

词云图 代码如下：

from pyecharts.charts import WordCloud from pyecharts import options as opts import csv  filename="word_data.csv" data_x=[] #打开文件循环读取数据 with open(filename,'r', encoding='UTF-8') as f:     reader = csv.reader(f) for data_row in reader:         data_x.append(data_row) x=[] #读取数据列表集中第一行数据进行赋值 b=[] c=[] d=[] e=[] for index,values in enumerate(data_x): if(index>0):         b.append(values[0])         c.append(values[1])         d.append(values[2])         e.append(values[3]) elif(index==0):         x.append(values) (     WordCloud() .add(series_name="词云图", data_pair=[list(z) for z in zip(e, c)], word_size_range=[6, 66]) .set_global_opts(         title_opts=opts.TitleOpts(             title="词云图", title_textstyle_opts=opts.TextStyleOpts(font_size=23) ),         tooltip_opts=opts.TooltipOpts(is_show=True), ) .render("词云图+basic_wordcloud.html") ) 

打开网页则会显示该代码的运行结果：

6.绘制主题河流图。
数据如下：

datax = ['分支1', '分支2', '分支3', '分支4', '分支5', '分支6'] datay = [ ['2015/11/08', 10, '分支1'], ['2015/11/09', 15, '分支1'], ['2015/11/10', 35, '分支1'], ['2015/11/14', 7, '分支1'], ['2015/11/15', 2, '分支1'], ['2015/11/16', 17, '分支1'], ['2015/11/17', 33, '分支1'], ['2015/11/18', 40, '分支1'], ['2015/11/19', 32, '分支1'], ['2015/11/20', 26, '分支1'], ['2015/11/21', 35, '分支1'], ['2015/11/22', 40, '分支1'], ['2015/11/23', 32, '分支1'], ['2015/11/24', 26, '分支1'], ['2015/11/25', 22, '分支1'], ['2015/11/08', 35, '分支2'], ['2015/11/09', 36, '分支2'], ['2015/11/10', 37, '分支2'], ['2015/11/11', 22, '分支2'], ['2015/11/12', 24, '分支2'], ['2015/11/13', 26, '分支2'], ['2015/11/14', 34, '分支2'], ['2015/11/15', 21, '分支2'], ['2015/11/16', 18, '分支2'], ['2015/11/17', 45, '分支2'], ['2015/11/18', 32, '分支2'], ['2015/11/19', 35, '分支2'], ['2015/11/20', 30, '分支2'], ['2015/11/21', 28, '分支2'], ['2015/11/22', 27, '分支2'], ['2015/11/23', 26, '分支2'], ['2015/11/24', 15, '分支2'], ['2015/11/25', 30, '分支2'], ['2015/11/26', 35, '分支2'], ['2015/11/27', 42, '分支2'], ['2015/11/28', 42, '分支2'], ['2015/11/08', 21, '分支3'], ['2015/11/09', 25, '分支3'], ['2015/11/10', 27, '分支3'], ['2015/11/11', 23, '分支3'], ['2015/11/12', 24, '分支3'], ['2015/11/13', 21, '分支3'], ['2015/11/14', 35, '分支3'], ['2015/11/15', 39, '分支3'], ['2015/11/16', 40, '分支3'], ['2015/11/17', 36, '分支3'], ['2015/11/18', 33, '分支3'], ['2015/11/19', 43, '分支3'], ['2015/11/20', 40, '分支3'], ['2015/11/21', 34, '分支3'], ['2015/11/22', 28, '分支3'], ['2015/11/14', 7, '分支4'], ['2015/11/15', 2, '分支4'], ['2015/11/16', 17, '分支4'], ['2015/11/17', 33, '分支4'], ['2015/11/18', 40, '分支4'], ['2015/11/19', 32, '分支4'], ['2015/11/20', 26, '分支4'], ['2015/11/21', 35, '分支4'], ['2015/11/22', 40, '分支4'], ['2015/11/23', 32, '分支4'], ['2015/11/24', 26, '分支4'], ['2015/11/25', 22, '分支4'], ['2015/11/26', 16, '分支4'], ['2015/11/27', 22, '分支4'], ['2015/11/28', 10, '分支4'], ['2015/11/08', 10, '分支5'], ['2015/11/09', 15, '分支5'], ['2015/11/10', 35, '分支5'], ['2015/11/11', 38, '分支5'], ['2015/11/12', 22, '分支5'], ['2015/11/13', 16, '分支5'], ['2015/11/14', 7, '分支5'], ['2015/11/15', 2, '分支5'], ['2015/11/16', 17, '分支5'], ['2015/11/17', 33, '分支5'], ['2015/11/18', 40, '分支5'], ['2015/11/19', 32, '分支5'], ['2015/11/20', 26, '分支5'], ['2015/11/21', 35, '分支5'], ['2015/11/22', 4, '分支5'], ['2015/11/23', 32, '分支5'], ['2015/11/24', 26, '分支5'], ['2015/11/25', 22, '分支5'], ['2015/11/26', 16, '分支5'], ['2015/11/27', 22, '分支5'], ['2015/11/28', 10, '分支5'], ['2015/11/08', 10, '分支6'], ['2015/11/09', 15, '分支6'], ['2015/11/10', 35, '分支6'], ['2015/11/11', 38, '分支6'], ['2015/11/12', 22, '分支6'], ['2015/11/13', 16, '分支6'], ['2015/11/14', 7, '分支6'], ['2015/11/15', 2, '分支6'], ['2015/11/16', 17, '分支6'], ['2015/11/17', 33, '分支6'], ['2015/11/18', 4, '分支6'], ['2015/11/19', 32, '分支6'], ['2015/11/20', 26, '分支6'], ['2015/11/21', 35, '分支6'], ['2015/11/22', 40, '分支6'], ['2015/11/23', 32, '分支6'], ['2015/11/24', 26, '分支6'], ['2015/11/25', 22, '分支6'] ] 

河流图 代码如下：

import pyecharts.options as opts from pyecharts.charts import ThemeRiver  datax = ['分支1', '分支2', '分支3', '分支4', '分支5', '分支6'] datay = [ ['2015/11/08', 10, '分支1'], ['2015/11/09', 15, '分支1'], ['2015/11/10', 35, '分支1'], ['2015/11/14', 7, '分支1'], ['2015/11/15', 2, '分支1'], ['2015/11/16', 17, '分支1'], ['2015/11/17', 33, '分支1'], ['2015/11/18', 40, '分支1'], ['2015/11/19', 32, '分支1'], ['2015/11/20', 26, '分支1'], ['2015/11/21', 35, '分支1'], ['2015/11/22', 40, '分支1'], ['2015/11/23', 32, '分支1'], ['2015/11/24', 26, '分支1'], ['2015/11/25', 22, '分支1'], ['2015/11/08', 35, '分支2'], ['2015/11/09', 36, '分支2'], ['2015/11/10', 37, '分支2'], ['2015/11/11', 22, '分支2'], ['2015/11/12', 24, '分支2'], ['2015/11/13', 26, '分支2'], ['2015/11/14', 34, '分支2'], ['2015/11/15', 21, '分支2'], ['2015/11/16', 18, '分支2'], ['2015/11/17', 45, '分支2'], ['2015/11/18', 32, '分支2'], ['2015/11/19', 35, '分支2'], ['2015/11/20', 30, '分支2'], ['2015/11/21', 28, '分支2'], ['2015/11/22', 27, '分支2'], ['2015/11/23', 26, '分支2'], ['2015/11/24', 15, '分支2'], ['2015/11/25', 30, '分支2'], ['2015/11/26', 35, '分支2'], ['2015/11/27', 42, '分支2'], ['2015/11/28', 42, '分支2'], ['2015/11/08', 21, '分支3'], ['2015/11/09', 25, '分支3'], ['2015/11/10', 27, '分支3'], ['2015/11/11', 23, '分支3'], ['2015/11/12', 24, '分支3'], ['2015/11/13', 21, '分支3'], ['2015/11/14', 35, '分支3'], ['2015/11/15', 39, '分支3'], ['2015/11/16', 40, '分支3'], ['2015/11/17', 36, '分支3'], ['2015/11/18', 33, '分支3'], ['2015/11/19', 43, '分支3'], ['2015/11/20', 40, '分支3'], ['2015/11/21', 34, '分支3'], ['2015/11/22', 28, '分支3'], ['2015/11/14', 7, '分支4'], ['2015/11/15', 2, '分支4'], ['2015/11/16', 17, '分支4'], ['2015/11/17', 33, '分支4'], ['2015/11/18', 40, '分支4'], ['2015/11/19', 32, '分支4'], ['2015/11/20', 26, '分支4'], ['2015/11/21', 35, '分支4'], ['2015/11/22', 40, '分支4'], ['2015/11/23', 32, '分支4'], ['2015/11/24', 26, '分支4'], ['2015/11/25', 22, '分支4'], ['2015/11/26', 16, '分支4'], ['2015/11/27', 22, '分支4'], ['2015/11/28', 10, '分支4'], ['2015/11/08', 10, '分支5'], ['2015/11/09', 15, '分支5'], ['2015/11/10', 35, '分支5'], ['2015/11/11', 38, '分支5'], ['2015/11/12', 22, '分支5'], ['2015/11/13', 16, '分支5'], ['2015/11/14', 7, '分支5'], ['2015/11/15', 2, '分支5'], ['2015/11/16', 17, '分支5'], ['2015/11/17', 33, '分支5'], ['2015/11/18', 40, '分支5'], ['2015/11/19', 32, '分支5'], ['2015/11/20', 26, '分支5'], ['2015/11/21', 35, '分支5'], ['2015/11/22', 4, '分支5'], ['2015/11/23', 32, '分支5'], ['2015/11/24', 26, '分支5'], ['2015/11/25', 22, '分支5'], ['2015/11/26', 16, '分支5'], ['2015/11/27', 22, '分支5'], ['2015/11/28', 10, '分支5'], ['2015/11/08', 10, '分支6'], ['2015/11/09', 15, '分支6'], ['2015/11/10', 35, '分支6'], ['2015/11/11', 38, '分支6'], ['2015/11/12', 22, '分支6'], ['2015/11/13', 16, '分支6'], ['2015/11/14', 7, '分支6'], ['2015/11/15', 2, '分支6'], ['2015/11/16', 17, '分支6'], ['2015/11/17', 33, '分支6'], ['2015/11/18', 4, '分支6'], ['2015/11/19', 32, '分支6'], ['2015/11/20', 26, '分支6'], ['2015/11/21', 35, '分支6'], ['2015/11/22', 40, '分支6'], ['2015/11/23', 32, '分支6'], ['2015/11/24', 26, '分支6'], ['2015/11/25', 22, '分支6'] ] (     ThemeRiver(init_opts=opts.InitOpts(width="1600px", height="800px")) .add(         series_name=datax,         data=datay,         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("河流图.html") ) 

打开网页则会显示该代码的运行结果：