5：Echarts数据可视化-多条曲线、多个子图、TreeMap类似盒图、树形图、热力图、词云...
2024-05-22 14:33:26
〇、目标
本次实验主要基于Echarts的Python库实现高维数据、网络和层次化数据、时空数据和文本数据的可视化，掌握可视化的操作流程和相关库的使用。
一、绘制平行坐标系
平行坐标是信息可视化的一种重要技术。为了克服传统的笛卡尔直角坐标系 难以表达三维以上数据的问题， 平行坐标将高维数据的各个变量用一系列相互平行的坐标轴表示， 变量值对应轴上位置。 为了反映变化趋势和各个变量间相互关系，将描述不同变量的各点连接成折线。
平行坐标因形式的紧凑型和表达的高效性，被广泛使用，但是每个点需要多个像素，数据量大，容易产生视觉混淆。
1、启动Python编辑工具IDLE
双击桌面IDLE图标启动编辑器。
2、新建Python文件
在导航栏中选择File->New File 新建Python文件。
3、编写Python程序
在弹出的新窗口中编写代码如下：
from pyecharts import options as opts
from pyecharts.charts import Page, Paralleldef parallel_base() -> Parallel:dataBJ = [[1,55,9,56,0.46,18,6,"4"],[2,25,11,21,0.65,34,9,"5"],[3,56,7,63,0.3,14,5,"4"],[4,33,7,29,0.33,16,6,"5"],[5,42,24,44,0.76,40,16,"5"],[6,82,58,90,1.77,68,33,"4"],[7,74,49,77,1.46,48,27,"4"],[8,78,55,80,1.29,59,29,"4"],[9,267,216,280,4.8,108,64,"1"],[10,185,127,216,2.52,61,27,"2"],[11,39,19,38,0.57,31,15,"5"],[12,41,11,40,0.43,21,7,"5"],[13,64,38,74,1.04,46,22,"4"],[14,108,79,120,1.7,75,41,"3"],[15,108,63,116,1.48,44,26,"3"],[16,33,6,29,0.34,13,5,"5"],[17,94,66,110,1.54,62,31,"4"],[18,186,142,192,3.88,93,79,"2"],[19,57,31,54,0.96,32,14,"4"],[20,22,8,17,0.48,23,10,"5"],[21,39,15,36,0.61,29,13,"5"],[22,94,69,114,2.08,73,39,"4"],[23,99,73,110,2.43,76,48,"4"],[24,31,12,30,0.5,32,16,"5"],[25,42,27,43,1,53,22,"5"],[26,154,117,157,3.05,92,58,"2"],[27,234,185,230,4.09,123,69,"1"],[28,160,120,186,2.77,91,50,"2"],[29,134,96,165,2.76,83,41,"3"],[30,52,24,60,1.03,50,21,"4"],[31,46,5,49,0.28,10,6,"5"]];dataGZ = [[1,26,37,27,1.163,27,13,"5"],[2,85,62,71,1.195,60,8,"4"],[3,78,38,74,1.363,37,7,"4"],[4,21,21,36,0.634,40,9,"5"],[5,41,42,46,0.915,81,13,"5"],[6,56,52,69,1.067,92,16,"4"],[7,64,30,28,0.924,51,2,"4"],[8,55,48,74,1.236,75,26,"4"],[9,76,85,113,1.237,114,27,"4"],[10,91,81,104,1.041,56,40,"4"],[11,84,39,60,0.964,25,11,"4"],[12,64,51,101,0.862,58,23,"4"],[13,70,69,120,1.198,65,36,"4"],[14,77,105,178,2.549,64,16,"4"],[15,109,68,87,0.996,74,29,"3"],[16,73,68,97,0.905,51,34,"4"],[17,54,27,47,0.592,53,12,"4"],[18,51,61,97,0.811,65,19,"4"],[19,91,71,121,1.374,43,18,"4"],[20,73,102,182,2.787,44,19,"4"],[21,73,50,76,0.717,31,20,"4"],[22,84,94,140,2.238,68,18,"4"],[23,93,77,104,1.165,53,7,"4"],[24,99,130,227,3.97,55,15,"4"],[25,146,84,139,1.094,40,17,"3"],[26,113,108,137,1.481,48,15,"3"],[27,81,48,62,1.619,26,3,"4"],[28,56,48,68,1.336,37,9,"4"],[29,82,92,174,3.29,0,13,"4"],[30,106,116,188,3.628,101,16,"3"],[31,118,50,0,1.383,76,11,"3"]];dataSH = [[1,91,45,125,0.82,34,23,"4"],[2,65,27,78,0.86,45,29,"4"],[3,83,60,84,1.09,73,27,"4"],[4,109,81,121,1.28,68,51,"3"],[5,106,77,114,1.07,55,51,"3"],[6,109,81,121,1.28,68,51,"3"],[7,106,77,114,1.07,55,51,"3"],[8,89,65,78,0.86,51,26,"4"],[9,53,33,47,0.64,50,17,"4"],[10,80,55,80,1.01,75,24,"4"],[11,117,81,124,1.03,45,24,"3"],[12,99,71,142,1.1,62,42,"4"],[13,95,69,130,1.28,74,50,"4"],[14,116,87,131,1.47,84,40,"3"],[15,108,80,121,1.3,85,37,"3"],[16,134,83,167,1.16,57,43,"3"],[17,79,43,107,1.05,59,37,"4"],[18,71,46,89,0.86,64,25,"4"],[19,97,71,113,1.17,88,31,"4"],[20,84,57,91,0.85,55,31,"4"],[21,87,63,101,0.9,56,41,"4"],[22,104,77,119,1.09,73,48,"3"],[23,87,62,100,1,72,28,"4"],[24,168,128,172,1.49,97,56,"2"],[25,65,45,51,0.74,39,17,"4"],[26,39,24,38,0.61,47,17,"5"],[27,39,24,39,0.59,50,19,"5"],[28,93,68,96,1.05,79,29,"4"],[29,188,143,197,1.66,99,51,"2"],[30,174,131,174,1.55,108,50,"2"],[31,187,143,201,1.39,89,53,"2"]];c = (Parallel().add_schema([{"dim": 0, "name": "date"},{"dim": 1, "name": "5QI"},{"dim": 2, "name": "PM2.5"},{"dim": 3, "name": "PM10"},{"dim": 4, "name": "3O"},{"dim": 5, "name": "NO2"},{"dim": 6, "name": "SO2"},{"dim": 7, "name": "Level"}]).add("BJ", dataBJ).add("SH", dataSH).add("GZ", dataGZ).set_global_opts(title_opts=opts.TitleOpts(title="Parallel")))return cc = parallel_base()
c.render("/home/user/Desktop/parallel.html")
4、保存Python文件
在导航栏中选择File->Save，选择一个文件夹，为文件命名后保存文件。
5、执行Python代码
在菜单栏选择Run->Run Module 执行代码。
6、观察生成图像
代码执行完毕之后，会在桌面上生成一个名为parallel.html的文件，双击文件图标即可在浏览器中观察结果。实验结果如下图：
二、绘制散点图矩阵
散点图矩阵是双变量散点图在多变量情况下的拓展，展现了各个维度两两之间的数据关系。在矩阵中，每一行、每一列均代表一个维度，行与列的维度次序相同。格点中是相应行、列维度所组成的双变量散点图。其中上、下三角矩阵相互对称，可仅展示其中一个三角矩阵以节省显示空间。
1、启动编辑器并新建文件
双击桌面IDLE图标启动IDLE，并在菜单栏选择File->New File 新建Python文件。
2、编写Python程序
编写实验代码如下：
import matplotlib.pyplot as plt
rawData=[[55,9,56,0.46,18,6,"good", "BJ"],[25,11,21,0.65,34,9,"verygood", "BJ"],[56,7,63,0.3,14,5,"good", "BJ"],[33,7,29,0.33,16,6,"verygood", "BJ"],[42,24,44,0.76,40,16,"verygood", "BJ"],[82,58,90,1.77,68,33,"good", "BJ"],[74,49,77,1.46,48,27,"good", "BJ"],[78,55,80,1.29,59,29,"good", "BJ"],[267,216,280,4.8,108,64,"severe", "BJ"],[185,127,216,2.52,61,27,"middle", "BJ"],[39,19,38,0.57,31,15,"verygood", "BJ"],[41,11,40,0.43,21,7,"verygood", "BJ"],[64,38,74,1.04,46,22,"good", "BJ"],[108,79,120,1.7,75,41,"light", "BJ"],[108,63,116,1.48,44,26,"light", "BJ"],[33,6,29,0.34,13,5,"verygood", "BJ"],[94,66,110,1.54,62,31,"good", "BJ"],[186,142,192,3.88,93,79,"middle", "BJ"],[57,31,54,0.96,32,14,"good", "BJ"],[22,8,17,0.48,23,10,"verygood", "BJ"],[39,15,36,0.61,29,13,"verygood", "BJ"],[94,69,114,2.08,73,39,"good", "BJ"],[99,73,110,2.43,76,48,"good", "BJ"],[31,12,30,0.5,32,16,"verygood", "BJ"],[42,27,43,1,53,22,"verygood", "BJ"],[154,117,157,3.05,92,58,"middle", "BJ"],[234,185,230,4.09,123,69,"severe", "BJ"],[160,120,186,2.77,91,50,"middle", "BJ"],[134,96,165,2.76,83,41,"light", "BJ"],[52,24,60,1.03,50,21,"good", "BJ"],[46,5,49,0.28,10,6,"verygood", "BJ"],[26,37,27,1.163,27,13,"verygood", "GZ"],[85,62,71,1.195,60,8,"good", "GZ"],[78,38,74,1.363,37,7,"good", "GZ"],[21,21,36,0.634,40,9,"verygood", "GZ"],[41,42,46,0.915,81,13,"verygood", "GZ"],[56,52,69,1.067,92,16,"good", "GZ"],[64,30,28,0.924,51,2,"good", "GZ"],[55,48,74,1.236,75,26,"good", "GZ"],[76,85,113,1.237,114,27,"good", "GZ"],[91,81,104,1.041,56,40,"good", "GZ"],[84,39,60,0.964,25,11,"good", "GZ"],[64,51,101,0.862,58,23,"good", "GZ"],[70,69,120,1.198,65,36,"good", "GZ"],[77,105,178,2.549,64,16,"good", "GZ"],[109,68,87,0.996,74,29,"light", "GZ"],[73,68,97,0.905,51,34,"good", "GZ"],[54,27,47,0.592,53,12,"good", "GZ"],[51,61,97,0.811,65,19,"good", "GZ"],[91,71,121,1.374,43,18,"good", "GZ"],[73,102,182,2.787,44,19,"good", "GZ"],[73,50,76,0.717,31,20,"good", "GZ"],[84,94,140,2.238,68,18,"good", "GZ"],[93,77,104,1.165,53,7,"good", "GZ"],[99,130,227,3.97,55,15,"good", "GZ"],[146,84,139,1.094,40,17,"light", "GZ"],[113,108,137,1.481,48,15,"light", "GZ"],[81,48,62,1.619,26,3,"good", "GZ"],[56,48,68,1.336,37,9,"good", "GZ"],[82,92,174,3.29,0,13,"good", "GZ"],[106,116,188,3.628,101,16,"light", "GZ"],[118,50,0,1.383,76,11,"light", "GZ"],[91,45,125,0.82,34,23,"good", "SH"],[65,27,78,0.86,45,29,"good", "SH"],[83,60,84,1.09,73,27,"good", "SH"],[109,81,121,1.28,68,51,"light", "SH"],[106,77,114,1.07,55,51,"light", "SH"],[109,81,121,1.28,68,51,"light", "SH"],[106,77,114,1.07,55,51,"light", "SH"],[89,65,78,0.86,51,26,"good", "SH"],[53,33,47,0.64,50,17,"good", "SH"],[80,55,80,1.01,75,24,"good", "SH"],[117,81,124,1.03,45,24,"light", "SH"],[99,71,142,1.1,62,42,"good", "SH"],[95,69,130,1.28,74,50,"good", "SH"],[116,87,131,1.47,84,40,"light", "SH"],[108,80,121,1.3,85,37,"light", "SH"],[134,83,167,1.16,57,43,"light", "SH"],[79,43,107,1.05,59,37,"good", "SH"],[71,46,89,0.86,64,25,"good", "SH"],[97,71,113,1.17,88,31,"good", "SH"],[84,57,91,0.85,55,31,"good", "SH"],[87,63,101,0.9,56,41,"good", "SH"],[104,77,119,1.09,73,48,"light", "SH"],[87,62,100,1,72,28,"good", "SH"],[168,128,172,1.49,97,56,"middle", "SH"],[65,45,51,0.74,39,17,"good", "SH"],[39,24,38,0.61,47,17,"verygood", "SH"],[39,24,39,0.59,50,19,"verygood", "SH"],[93,68,96,1.05,79,29,"good", "SH"],[188,143,197,1.66,99,51,"middle", "SH"],[174,131,174,1.55,108,50,"middle", "SH"],[187,143,201,1.39,89,53,"middle", "SH"]];print(rawData[0:30])
print(rawData[31:62])
print(rawData[63:])
fig = plt.figure()for i in range(6):for j in range(6):if(j<=i): continuesubfig=fig.add_subplot(5,5,i*5+j)subfig.scatter([x[i] for x in rawData[0:30]],[x[j] for x in rawData[0:30]],s=5,c='red')subfig.scatter([x[i] for x in rawData[31:62]],[x[j] for x in rawData[31:62]],s=5,c='green')subfig.scatter([x[i] for x in rawData[63:]],[x[j] for x in rawData[63:]],s=5,c='blue')plt.savefig("/home/user/Desktop/scatter.png")
plt.show()
3、保存并执行程序
程序编写完成后，选择菜单栏File->Save 命名并保存文件。保存文件后，选择菜单栏Run->Run Module 执行程序。
4、观察实验结果
代码执行完毕后，会自动显示出实验结果，具体结果如下图，同时会在桌面上产生相对于的png图片
三、绘制TreeMap
TreeMap是一种基于二维空间填充的可视化方法，与传统的层次结构数据可视化方法相比，可以提高屏幕显示空间的利用率，充分利用显示空间的每一个象素，更适合对大型的层次结构数据进行可视化，例如树状的目录结构。
1、启动IDLE编辑器并新建文件
双击桌面IDLE图标启动编辑器，并选择菜单栏File->New File新建文件。
2、编写Python程序
在新建的文件窗口中编写代码如下：
import json
import osfrom pyecharts import options as opts
from pyecharts.charts import Page, TreeMapdef treemap_base() -> TreeMap:with open("/home/user/Data/disk.tree.json","r") as tree_f:data = json.load(tree_f)c = (TreeMap().add("present data", data).set_global_opts(title_opts=opts.TitleOpts(title="TreeMap")))return cc = treemap_base()
c.render("/home/user/Desktop/treemap.html")
3、保存并执行代码
选择File->Save保存程序。
保存完成后，选择Run->Run Module执行代码
4、观察实验结果
代码执行结束后，会在桌面生成treemap.html文件，双击文件即可在浏览器中观察结果。
四、绘制树形图
树形图直接将节点之间的父子关系映射到视觉元素中，采用树状结构对层次结构数据进行可视化也是一种非常有效的手段。
1、启动编辑器
新建文件双击桌面IDLE图标，并选择File->New File新建文件
2、编写Python程序
在新建的文件窗口中编辑代码如下：
import json
import osfrom pyecharts import options as opts
from pyecharts.charts import Page, Treedef tree_lr() -> Tree:with open("/home/user/Data/flare.json") as f:j = json.load(f)c = (Tree().add("", [j], collapse_interval=2).set_global_opts(title_opts=opts.TitleOpts(title="Tree")))return cc = tree_lr()
c.render("/home/user/Desktop/tree.html")
3、保存并执行程序
选择File->Save 保存文件
选择Run->Run Module执行代码。
4、观察实验结果
代码执行结束后，会在桌面上生成tree.html文件，双击图标即可在浏览器中观察结果。
五、绘制热力图
1、启动编辑器并新建文件
双击桌面IDLE图标，启动IDLE
选择菜单栏File->New File新建文件
2、编写Python程序
在新建的文件窗口中编写代码如下：
from example.commons import Faker
from pyecharts import options as opts
from pyecharts.charts import Geo
from pyecharts.globals import ChartType, SymbolTypedef geo_heatmap() -> Geo:c = (Geo().add_schema(maptype="china").add("geo",[list(z) for z in zip(Faker.provinces, Faker.values())],type_=ChartType.HEATMAP,).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(visualmap_opts=opts.VisualMapOpts(),title_opts=opts.TitleOpts(title="Geo-HeatMap"),))return cc = geo_heatmap()
c.render("/home/user/Desktop/heatmap.html")
3、保存并执行程序
选择菜单栏File->Save File保存文件
选择菜单栏Run->Run Module执行代码
4、观察实验结果
代码执行完毕后，会在桌面上生成一个名为heatmap.html的文件，双击文件图标即可在浏览器中观察实验结果
六、绘制词云
词云是一种典型的文本可视化技术。该方法把文本数据中的关键词根据出现的频率等规则进行统计，然后进行布局排列，利用颜色、大小等视觉编码其频率信息，该方法可以帮助用户对大规模文本数据有一个概览，已经被广泛用在众多的网站和博客中。
1、启动编辑器并新建文件
双击桌面IDLE图标，启动编辑器。
选择菜单栏File->New File 新建文件
2、编写Python代码
在新建的窗口中编写代码如下：
from pyecharts import options as opts
from pyecharts.charts import Page, WordCloud
from pyecharts.globals import SymbolTypewords = [("C++", 10000),("C", 6181),("Java", 4386),("Python", 4055),("JavaScript", 2467),("PHP", 2244),("XML", 1868),("Pascal", 1484),("C#", 1112),("Object-C", 865),("HasKell", 847),("Lisp", 582),("SQL", 555),("LabVIEW", 550),("Logic-based", 462),("Ada", 366),("Clojure", 360),("Ruby", 282),("Erlang", 273),("MATLAB", 265),
]def wordcloud_base() -> WordCloud:c = (WordCloud().add("", words, word_size_range=[20, 100]).set_global_opts(title_opts=opts.TitleOpts(title="WordCloud")))return cc = wordcloud_base()
c.render("/home/user/Desktop/wordcloud.html")
3、保存并执行Python文件
选择File->Save File保存文件。
文件保存结束后，选择Run->Run Module执行程序
4、观察实验结果
程序执行结束后，会在桌面生成wordcloud.html文件，双击文件图标即可在浏览器中观察结果
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