pandas與sqlalchemy交互實現科學計算

import pandas as pd

import numpy as np

from sqlalchemy import create_engine

#建立數據庫引擎
    engine = create_engine('mysql+pymysql://root:mysql@127.0.0.1:3306/mymac')
    sql = 'select * from student'
    #建立dataframe對象
    df = pd.read_sql_query(sql,engine)
    print(df)
    #按照年齡正序
    # select * from student order by age asc |desc
    df = df.sort_values(['age','height'],ascending=False)
    print(df)
    #取年齡最小的學生
    # select * from student order by age limit 1
    dd = df.sort_values(['age']).head(1)
    print(dd)
    #求整個班的平均年齡 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))
    #全班的平均年齡
    print(df.age.mean())
    #全班的年齡求和
    print(df.age.sum())
    #取男生的年齡求和
    print(df.loc[df['gender'] == 1].age.sum())
    #獲取指定字段
    print(df.loc[ df['gender'] == 1,['id','name']])
    #取全部年齡最小 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)
    #取單值
    print(df.loc[2,'name'])

#   將datafram寫入數據表 表名，數據引擎，數據是否入庫 （不建議使用,因為鍵的表字段類型約束等需調整）
#   需安裝openpyxl庫
    # df.to_sql('student_copy',engine,index=False)
    #到出excel文件
    # df.to_excel('student.xlsx',index=False)