pandas与sqlalchemy交互实现科学计算

import pandas as pd

import numpy as np

from sqlalchemy import create_engine

#建立数据库引擎
    engine = create_engine('mysql+pymysql://root:mysql@127.0.0.1:3306/mymac')
    sql = 'select * from student'
    #建立dataframe对象
    df = pd.read_sql_query(sql,engine)
    print(df)
    #按照年龄正序
    # select * from student order by age asc |desc
    df = df.sort_values(['age','height'],ascending=False)
    print(df)
    #取年龄最小的学生
    # select * from student order by age limit 1
    dd = df.sort_values(['age']).head(1)
    print(dd)
    #求整个班的平均年龄 使用loc方法指定字段
    #select * from student where gender = 0 and age < (select avg(age) from student gender=0)
    avg_age = df.loc[df['gender'] == 0].age.mean()
    print(df.loc[(df['age']< avg_age) & (df['gender'] == 0)].head(1))
    #全班的平均年龄
    print(df.age.mean())
    #全班的年龄求和
    print(df.age.sum())
    #取男生的年龄求和
    print(df.loc[df['gender'] == 1].age.sum())
    #获取指定字段
    print(df.loc[ df['gender'] == 1,['id','name']])
    #取全部年龄最小 min 最大max
    print(df.age.min())
    #或者 |
    print(df.loc[(df['gender']==0) | (df['gender'] == 1)])
    #全班多少人
    print(df.id.count())
    #排除法
    print(df.loc[(df['gender'] != 0) & (df['gender'] != 1)])
    #修改操作
    df.loc[1,'gender'] = 1
    df.loc[4,'age'] = 19
    print(df)
    #取单值
    print(df.loc[2,'name'])

#   将datafram写入数据表 表名，数据引擎，数据是否入库 （不建议使用,因为键的表字段类型约束等需调整）
#   需安装openpyxl库
    # df.to_sql('student_copy',engine,index=False)
    #到出excel文件
    # df.to_excel('student.xlsx',index=False)