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

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, Parallel

def 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 c


c = 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): 
            continue
        subfig=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 os

from pyecharts import options as opts
from pyecharts.charts import Page, TreeMap


def 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 c


c = 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 os

from pyecharts import options as opts
from pyecharts.charts import Page, Tree

def 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 c


c = 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, SymbolType


def 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 c


c = 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 SymbolType


words = [
    ("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 c


c = 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

posted @ 2022-06-19 18:36 哥们要飞阅读(298) 评论(0) 编辑收藏举报

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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

〇、目标

一、绘制平行坐标系

1、启动Python编辑工具IDLE

2、新建Python文件

3、编写Python程序

4、保存Python文件

5、执行Python代码

6、观察生成图像

二、绘制散点图矩阵

1、启动编辑器并新建文件

2、编写Python程序

3、保存并执行程序

4、观察实验结果

三、绘制TreeMap

1、启动IDLE编辑器并新建文件

2、编写Python程序

3、保存并执行代码

4、观察实验结果

四、绘制树形图

1、启动编辑器

2、编写Python程序

3、保存并执行程序

4、观察实验结果

五、绘制热力图

1、启动编辑器并新建文件

2、编写Python程序

3、保存并执行程序

4、观察实验结果

六、绘制词云

1、启动编辑器并新建文件

2、编写Python代码

3、保存并执行Python文件

4、观察实验结果

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