《Python数据可视化之Matplotlib与Pyecharts》之玫瑰

11.9.2  不同职业群体的购买力分析

为了分析该企业不同职业群体的购买力情况，绘制了不同群体的销售额的玫瑰图，Python代码如下：

# -*- coding: utf-8 -*-

#声明Notebook类型，必须在引入pyecharts.charts等模块前声明

from pyecharts.globals import CurrentConfig, NotebookType

CurrentConfig.NOTEBOOK_TYPE = NotebookType.JUPYTER_LAB

from pyecharts import options as opts

from pyecharts.charts import Page, Pie

from impala.dbapi import connect

#连接Hadoop数据库

v1 = []

v2 = []

conn = connect(host='192.168.1.7', port=10000, database='sales',auth_mechanism='NOSASL',user='root')

cursor = conn.cursor()

#读取Hadoop表数据

sql_num = "SELECT occupation,ROUND(SUM(sales/10000),2) FROM customers,orders WHERE customers.cust_id=orders.cust_id and dt=2019 GROUP BY occupation"

cursor.execute(sql_num)

sh = cursor.fetchall()

for s in sh:

    v1.append(s[0])

    v2.append(s[1])

#画玫瑰图

def rosetype() -> Pie:

    c = (

        Pie()

        .add(

            "",

            [list(z) for z in zip(v1, v2)],

            radius=["30%", "75%"],

            center=["50%", "55%"],

            rosetype="radius",

            label_opts=opts.LabelOpts(is_show=False),

        )

        .set_colors(["blue", "green", "purple", "red", "silver"])      #设置颜色 

        .set_global_opts(title_opts=opts.TitleOpts(title="2019年不同职业群体的购买力分析", subtitle="2019年销售经营状况分析"),

                        legend_opts=opts.LegendOpts(orient="horizontal", pos_top="5%", pos_left="30%"),

                        toolbox_opts=opts.ToolboxOpts())

        .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))

    )

    return c

#第一次渲染时候调用load_javasrcript文件

rosetype().load_javascript()

#展示数据可视化图表

rosetype().render_notebook()

在Jupyter lab中运行上述代码，生成如图11-9所示的玫瑰图。

图11-9  玫瑰图