zoukankan      html  css  js  c++  java
  • Python利用Plotly实现对MySQL中的数据可视化

    Mysql表数据:

    demo.sql内容

    create table demo(
     id int
    ,product varchar(50)
    ,price decimal(18,2)
    ,quantity int
    ,amount decimal(18,2)
    ,orderdate datetime
    );
    insert into demo
    select 1,'AAA',15.2,5,76,'2017-09-09' union all
    select 2,'BBB',10,6,60,'2016-05-18' union all
    select 3,'CCC',21,11,231,'2014-07-11' union all
    select 4,'DDD',55,2,110,'2016-12-24' union all
    select 5,'EEE',20,4,80,'2017-02-08' union all
    select 6,'FFF',45,2,90,'2016-08-19' union all
    select 7,'GGG',33,5,165,'2017-10-11' union all
    select 8,'HHH',5,40,200,'2014-08-30' union all
    select 9,'III',3,20,60,'2015-02-25' union all
    select 10,'JJJ',10,15,150,'2015-11-02';

    实现效果如下:

    Python代码:

     1 import pymysql
     2 import pandas as pd
     3 import plotly.plotly
     4 import plotly.graph_objs as pg
     5 
     6 
     7 def bar_chart(host, port, user, passwd, dbname, charset,output_path):
     8     try:
     9         conn = pymysql.Connection(
    10             host=host,
    11             port=port,
    12             user=user,
    13             passwd=passwd,
    14             db=dbname,
    15             charset=charset
    16         )
    17         cur = conn.cursor()
    18         cur.execute("select * from demo;")
    19         # cursor对象使用MySQL查询字符串执行查询,返回一个包含多个元组的元组——每行对应一个元组
    20         rows = cur.fetchall()
    21         # print(rows)
    22 
    23         # 使用Pandas的DataFrame来处理每一行要比使用一个包含元组的元组方便
    24         # 下面的Python代码片段将所有行转化为DataFrame实例
    25         df = pd.DataFrame([[ij for ij in i] for i in rows])
    26         print(df)
    27         df.rename(columns={0: 'id', 1: 'product', 2: 'price', 3: 'quantity', 4: 'amount', 5: 'orderdate'}, inplace=True)
    28         # df = df.sort(['LifeExpectancy'], ascending=[1])
    29 
    30         date_price = pg.Bar(x=df["product"], y=df["price"], name='价格')
    31         date_quantity = pg.Bar(x=df["product"], y=df["quantity"], name='数量')
    32         date_amount = pg.Bar(x=df["product"], y=df["amount"], name='总价')
    33         data = [date_price, date_quantity, date_amount]
    34 
    35         layout = pg.Layout(barmode='group', title="各产品销售情况")
    36         fig = pg.Figure(data=data, layout=layout)
    37         plotly.offline.plot(fig, filename=output_path)
    38 
    39     finally:
    40         if conn:
    41             conn.close()
    42 
    43 
    44 if __name__ == '__main__':
    45     output_path = "C:/Users/fuqia/Desktop/bar.html"
    46     bar_chart("localhost", 3306, "root", "123456", "test", "utf8", output_path)

     输出df内容如下:

  • 相关阅读:
    【第五年-创业路】
    【工具与解决方案】从做项目中积累学习
    【原理篇】人工智能
    【原理】分布式系统
    攻克Spring
    工具篇集锦
    最好用的JQuery插件集合以及组合拳
    设计模式 之状态模式
    设计模式 之组合模式
    设计模式之 封装算法
  • 原文地址:https://www.cnblogs.com/fuqia/p/8998290.html
Copyright © 2011-2022 走看看