【数据分析与可视化】pyecharts可视化图表讲解及实战（超详细附源码）

深碍√TFBOYSˉ_ 2023-09-30 18:48 11阅读 0赞

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## 一、pyecharts简介 ##

pyecharts是基于Echart图表的一个类库，而Echart是百度开源的一个可视化JavaScript库

pyecharts主要基于web浏览器进行显示，绘制的图形比较多，包括折线图、柱状图、饼图、漏斗图、地图、极坐标图等，代码量很少，而且很灵活，绘制出来的图形很美观

使用pyecharts时，需要安装相应的库，安装命令为： pip install pyecharts

图形绘制过程，基本上所有的图表类型都是这样绘制的

> chart\_name = Type() 初始化具体类型图表
> 
> chart\_name .add() 添加数据及配置项
> 
> chart\_name .render() 生成本地文件（
> 
> html/svg/jpeg/png/pdf/gif） chart\_name .render\_notebook 在jupyter notebook中显示

## 二、pyechart常用图表 ##

### 1 柱状图 ###

利用Bar方法可以绘制柱状图。方法及说明见表8-1。

表8-1 Bar对象的主要方法及其说明

![a886f7de42614a3c92f2c340d70e98ee.png][]

![dd95a81f584d4c3abf97fb300017dea2.png][]

from pyecharts.charts import Bar
    from pyecharts import options as opts
    %matplotlib inline
    # V1 版本开始支持链式调用
    bar = ( Bar()
           .add_xaxis(["衬衫", "毛衣", "领带", "裤子", "风衣", "高跟鞋", "袜子"])
           .add_yaxis("商家A", [114, 55, 27, 101, 125, 27, 105])
           .set_global_opts(title_opts = opts.TitleOpts(title = "某商场销售情况")) )
    bar.render_notebook()
    #bar.render() 生成html

V1版本开始支持链式调用，如果不习惯链式调用的开发者依旧可以单独调用方法

使用多个add\_yaxis可以绘制并列柱状图

![75e4ec942b3349bba94ada54b7c23e56.png][]

from pyecharts.charts import Bar
    from pyecharts import options as opts
    %matplotlib inline
    bar = Bar()
    bar.add_xaxis(["衬衫", "毛衣", "领带", "裤子", "风衣", "高跟鞋", "袜子"])
    bar.add_yaxis("商家A", [114, 55, 27, 101, 125, 27, 105])
    bar.add_yaxis("商家B", [57, 134, 137, 129, 145, 60, 49])
    bar.set_global_opts(title_opts = opts.TitleOpts(title = "货品销售情况",subtitle  =  "A和B公司"))
    bar.render_notebook()

利用bar.reversal\_axis()可以绘制水平的直方图

![9e29522df9844bba876327fc0ce25157.png][]

%matplotlib inline
    bar = Bar()
    bar.add_xaxis(["衬衫", "毛衣", "领带", "裤子", "风衣", "高跟鞋", "袜子"])
    bar.add_yaxis("商家A", [114, 55, 27, 101, 125, 27, 105])
    bar.add_yaxis("商家B", [57, 134, 137, 129, 145, 60, 49])
    bar.set_global_opts(title_opts=opts.TitleOpts(title="货品销售情况",subtitle = "A和B公司"),toolbox_opts = opts.ToolboxOpts(is_show = True))
    bar.set_series_opts(label_opts=opts.LabelOpts(position = "right"))
    bar.reversal_axis()
    bar.render_notebook()

### 2 饼图 ###

饼图常用于表现不同类别的占比情况。利用Pie方法可以绘制饼图

![ea9162d96fb841ecb05b534d98620282.png][]

from pyecharts import options as opts    
    from pyecharts.charts import Page, Pie
    L1=['教授','副教授','讲师','助教','其他']
    num  = [20,30,10,12,8]
    c = Pie()
    c.add("", [list(z) for z in zip(L1,num)])
    c.set_global_opts(title_opts=opts.TitleOpts(title="Pie-职称类别比例"))
    c.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
    c.render_notebook()

通过参数圆形饼图radius可以绘制圆形饼图

![42ca661f51594c959e62be3d6c86846a.png][]

from pyecharts import options as opts
    from pyecharts.charts import Page, Pie
    wd = ['教授','副教授','讲师','助教','其他']
    num = [20,30,10,12,8]
    c = Pie()
    c.add("",[list(z) for z in zip(wd, num)],radius = ["40%", "75%"])   
    # 圆环的粗细和大小
    c.set_global_opts( title_opts=opts.TitleOpts(title="Pie-Radius"),legend_opts=opts.LegendOpts( orient="vertical", pos_top="5%", pos_left="2%" ))
    c .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
    c.render_notebook()

可以通过rich参数设置更多属性

![65d644f7dc8c4758a96c5ac97dfd56c8.png][]

c = Pie()
    c.add("",[list(z) for z in zip(wd, num)],radius=["40%", "55%"],
           label_opts=opts.LabelOpts(position="outside",
           formatter="{a|{a}}{abg|}\n{hr|}\n{b|{b}:}{c}  {per|{d}%} ",
           background_color="#eee",border_color="#aaa",
           border_width=1,border_radius=4,
           rich={"a": {"color": "#999", "lineHeight": 22, "align": "center"},"abg": {"backgroundColor": "#e3e3e3","width": "100%", "align": "right", "height": 22,"borderRadius": [4, 4, 0, 0],}, "hr": {"borderColor": "#aaa", "width": "100%","borderWidth": 0.5, "height": 0,}, "b": {"fontSize": 16, "lineHeight": 33},"per": {"color": "#eee","backgroundColor": "#334455",                        "padding": [2, 4],"borderRadius": 2,} } ))
    c.set_global_opts(title_opts=opts.TitleOpts(title="Pie-富文本示例"))
    c.render_notebook()

玫瑰图绘制

![2260a40d3a174657914f643744600934.png][]

from pyecharts import options as opts
    from pyecharts.charts import Page, Pie
    data = [45,86,39,52,68]
    labels = ['电脑','手机','彩电','冰箱','洗衣机']
    c = Pie()
    c.add("",[list(z) for z in zip(labels, data)],radius=["40%", "55%"],center=[240,220],rosetype='radius')
    c.add("",[list(z) for z in zip(wd, num)],radius=["40%", "55%"],center=[620,220],rosetype='area')
    c.set_global_opts(title_opts=opts.TitleOpts(title="玫瑰图"))
    c.render_notebook()

### 3 漏斗图 ###

pyecharts中通过Funnel绘制漏斗图

![69886b23df2744899666c678aaf4b8a4.png][]

from pyecharts.charts import Funnel
    from pyecharts import options as opts
    %matplotlib inline
    data = [45,86,39,52,68]
    labels = ['电脑','手机','彩电','冰箱','洗衣机']
    wf = Funnel()
    wf.add('电器销量图',[list(z) for z in zip(labels, data)], is_selected= True)
    wf.render_notebook()

### 4 散点图 ###

pyecharts利用Scatter绘制散点图

![f055445c1c25436b8af98bafe8271a1d.png][]

from pyecharts import options as opts
    from pyecharts.charts import Scatter
    week = ["周一", "周二", "周三", "周四", "周五", "周六", "周日"]
    c = Scatter()
    c.add_xaxis(week)
    c.add_yaxis("商家A", [81,65,48,32,68,92,87])
    c.set_global_opts(title_opts=opts.TitleOpts(title="Scatter-一周的销售额（万元）"))
    c.render_notebook()

### 5 K线图 ###

pyecharts利用Kline绘制K线图

![7ddd871b768941b58434091cc45b465a.png][]

from pyecharts import options as opts
    from pyecharts.charts import Kline
    data = [[2320.26, 2320.26, 2287.3, 2362.94],[2300, 2291.3, 2288.26, 2308.38],[2295.35, 2346.5, 2295.35, 2345.92],--------]
    c = Kline()
    c.add_xaxis(["2019/7/{}".format(i + 1) for i in range(31)])
    c.add_yaxis("2019年7月份K线图", data)
    c.set_global_opts(yaxis_opts=opts.AxisOpts(is_scale=True),
                xaxis_opts=opts.AxisOpts(is_scale=True),
                title_opts=opts.TitleOpts(title="Kline-基本示例"),)
    c.render_notebook()

### 6 仪表盘 ###

pyecharts利用Gauge绘制仪表盘

![ac3e75511e89407da3efdfb1d107cf51.png][]

from pyecharts import options as opts
    from pyecharts.charts import Gauge, Page
    c = Gauge()
    c.add("业务指标",[("完成率", 55.5)],axisline_opts=opts.AxisLineOpts(
    linestyle_opts=opts.LineStyleOpts(color=[(0.3, "#67e0e3"),
     (0.7, "#37a2da"), (1, "#fd666d")], width=30)))
    c.set_global_opts(title_opts=opts.TitleOpts(title="Gauge-不同颜色"),
     legend_opts=opts.LegendOpts(is_show=False)) 
    c.render_notebook()

### 7 词云 ###

Pyecharts利用WordCloud绘制词云

![48d61d40b0804778b38dbd99971f8a72.png][]

from pyecharts import options as opts
    from pyecharts.charts import Page, WordCloud
    from pyecharts.globals import SymbolType
    words = [
        ("牛肉面", 7800),("黄河", 6181),
        ("《读者》杂志", 4386), ("甜胚子", 3055),
           ---------,
          ("五泉山", 2550)]
    c = WordCloud()
    c.add("", words, word_size_range=[20, 80])
    c.set_global_opts(title_opts=opts.TitleOpts(title="WordCloud-基本示例"))
    c.render_notebook()

### 8 组合图表 ###

组合图表上下布局

![dab80d0563af4866b94486df83ef00ca.png][]

from pyecharts import options as opts
    from pyecharts.charts import Bar, Grid, Line,Scatter
    A = ["小米", "三星", "华为", "苹果", "魅族", "VIVO", "OPPO"]
    CA = [100,125,87,90,78,98,118]
    B = ["草莓", "芒果", "葡萄", "雪梨", "西瓜", "柠檬", "车厘子"]
    CB = [78,95,120,102,88,108,98]
    bar = Bar()
    bar.add_xaxis(A)
    bar.add_yaxis("商家A",CA)
    bar.add_yaxis("商家B", CB)
    bar.set_global_opts(title_opts=opts.TitleOpts(title="Grid-Bar"))
    bar.render_notebook()
    line=Line()
    line.add_xaxis(B)
    line.add_yaxis("商家A", CA)
    line.add_yaxis("商家B", CB)
    line.set_global_opts(title_opts=opts.TitleOpts(title="Grid-Line", pos_top="48%"),
    legend_opts=opts.LegendOpts(pos_top="48%"))
    line.render_notebook()
    grid = Grid()
    grid.add(bar, grid_opts=opts.GridOpts(pos_bottom="60%"))
    grid.add(line, grid_opts=opts.GridOpts(pos_top="60%"))
    grid.render_notebook()

### 9 桑基图 ###

桑基图(Sankey diagram)即桑基能量分流图，也叫桑基能量平衡图。它是一种特定类型的流程图，图中延伸的分支的宽度对应数据流量的大小，通常应用于能源、材料成分、金融等数据的可视化分析。Pyecharts中利用Sankey绘制桑基图

下面对熬夜原因进行桑基图可视化

![715065ad41064c328257375e816cbfdb.png][]

### 10 平行坐标图 ###

平行坐标图（Parallel Coordinates Plot）是对于具有多个属性问题的一种可视化方法。在平行坐标图中，数据集的一行数据在平行坐标图中用一条折线表示，纵向是属性值，横向是属性类别（用索引表示）

![747859bb3af04faf8d499b8f1872ee35.png][]

图是一种抽象的数据结构，可以描述最广泛的数据之间的关系，例如交通网络、微信中的朋友关系网，在图中，最基本的单元是顶点，顶点之间的关联关系称为边，pyecharts可以利用Graph方法绘制图

### 11 地图 ###

自从 0.3.2 开始，为了缩减项目本身的体积以及维持 pyecharts 项目的轻量化运行，pyecharts 将不再自带地图 js 文件。如用户需要用到地图图表（Geo、Map），可自行安装对应的地图文件包。 Windows下通过以下的pip命令进行安装： \#安装全球国家地图，包括世界地图和 213 个国家 pip install echarts-countries-pypkg \#安装中国省级地图，包括23 个省，5 个自治区 pip install echarts-china-provinces-pypkg \#中国市级地图，包括370 个中国城市 pip install echarts-china-cities-pypkg

下面在指定地图上的城市标识某天最高温度 效果如下 点击对应城市即可显示温度

![870dd909e6d54b98911c5c45b3cff5c7.png][]

![cf99d30384d6487692da3a907e871606.png][]

from pyecharts import options as opts
    from pyecharts.charts import Map
    temperature=[30,31,27,29,18]
    loc = ['兰州市','天水市','白银市','武威市','甘南藏族自治州']
    c =  Map()
    c.add("甘肃省", [list(z) for z in zip(loc, temperature)], "甘肃",is_roam=True) 
    # is_roam是否开启鼠标缩放和平移漫游
    c .set_global_opts(title_opts=opts.TitleOpts(title="甘肃省部分城市最高气温"))
    c.render("APP类型.html")
    c.render_notebook()

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