SQLAlchemy 使用总结

    # coding: utf-8

    from sqlalchemy import * 
    import tushare as ts
    import pandas as pd
    from sqlalchemy.orm import sessionmaker,mapper
    from datetime import *

    engine = create_engine('mysql+pymysql://root:[email protected]/mystock?charset=utf8')

    #%%  1  hand-written SQL 方法
    result = engine.execute('select * from stock_basics where pe < %s', 2)

    # sqlalchemy推荐使用text()函数封装一下sql字符串,不同数据库, 可以使用统一的sql参数传递写法. 参数须以:号引出.
    result = engine.execute(text('select * from stock_basics where pe < :pe'), {'pe': 2})

    # 遍历result时, 得到的每一个行都是RowProxy对象, 获取字段的方法非常灵活, 下标和字段名甚至属性都行.
    # rowproxy[0] == rowproxy['id'] == rowproxy.id
    ans = result.fetchall() # 获取所有数据
    ans1 = pd.DataFrame(ans) # 将数据转成 DataFrame格式

    #  事务处理
    conn = engine.connect()
    conn.begin()
    try:
        dosomething(connection)
        conn.commit()
    except:   
        conn.rollback()  
    conn.close()  


        #%%  SQL-expressions in Python 方法
    meta = MetaData(bind=engine, reflect=True)
    table = meta.tables['stock_basics']
    result2 = list(engine.execute(table.select(table.c.pe < 2)))   # pe为stock_basics的一个列名

    #%% ORM 方法   表中要有主键
    engine.echo = True  # We want to see the SQL we're creating
    metadata = MetaData(engine)

    # The stock_basics table already exists, so no need to redefine it. Just
    # load it from the database using the "autoload" feature.
    users = Table('stock_basics', metadata, autoload=True)

    def run(stmt):
        rs = stmt.execute()
        for row in rs:
            print(row)

    # Most WHERE clauses can be constructed via normal comparisons
    s = users.select(users.c.code == '000001')
    run(s)
    s = users.select(users.c.pe < 1)  # pe为stock_basics的一个列名
    rs = s.execute().fetchall()
    ans2 = pd.DataFrame(rs)    #将结果转换成 DataFrame格式

    # Python keywords like "and", "or", and "not" can't be overloaded, so
    # SQLAlchemy uses functions instead
    s = users.select(and_(users.c.age < 40, users.c.name != 'Mary'))
    s = users.select(or_(users.c.age < 40, users.c.name != 'Mary'))
    s = users.select(not_(users.c.name == 'Susan'))

    # Or you could use &, | and ~ -- but watch out for priority!
    s = users.select((users.c.age < 40) & (users.c.name != 'Mary'))  #最好添加（），注意优先级
    s = users.select((users.c.age < 40) | (users.c.name != 'Mary'))
    s = users.select(~(users.c.name == 'Susan'))

    # There's other functions too, such as "like", "startswith", "endswith"
    s = users.select(users.c.name.startswith('M'))
    s = users.select(users.c.name.like('%a%'))
    s = users.select(users.c.name.endswith('n'))

    # The "in" and "between" operations are also available
    s = users.select(users.c.age.between(30,39))
    # Extra underscore after "in" to avoid conflict with Python keyword
    s = users.select(users.c.name.in_('Mary', 'Susan'))

    # If you want to call an SQL function, use "func"
    s = users.select(func.substr(users.c.name, 2, 1) == 'a')

    # You don't have to call select() on a table; it's got a bare form
    s = select([users], users.c.name != 'Carl')
    s = select([users.c.name, users.c.age], users.c.name != 'Carl')

    # This can be handy for things like count()
    s = select([func.count(users.c.user_id)])
    # Here's how to do count(*)
    s = select([func.count("*")], from_obj=[users])
    #%%多表联查
    #    现在存在两个表
    users = Table('users', metadata,
        Column('user_id', Integer, primary_key=True),
        Column('name', String(40)),
        Column('age', Integer),)
    users.create()
    #    emails = Table('emails', metadata,
    #        Column('email_id', Integer, primary_key=True),
    #        Column('address', String),
    #        Column('user_id', Integer, ForeignKey('users.user_id')),)
    s = select([users, emails], emails.c.user_id == users.c.user_id)
    # 查询部分列
    s = select([users.c.name, emails.c.address], emails.c.user_id == users.c.user_id)
    #基于外键的李娜和查询
    s = join(users, emails).select()
    #使用 outerjoin 查询所有用户，不论是否有邮箱
    s = outerjoin(users, emails).select()

    #%% 将数据库中的对象映射到对象中
    users = Table('users', metadata, autoload=True)
    # These are the empty classes that will become our data classes
    class User(object):
        pass

    usermapper = mapper(User, users)
    session = DBSession()
    #  查询 -----------------
    query = session.query(User)
    print(query) # 显示SQL 语句
    print(query.statement) # 同上
    for user in query: # 遍历时查询
        print(user.name)
    print(query.all()) # 返回的是一个类似列表的对象
    print(query.first().name) # 记录不存在时，first() 会返回 None
    # print(query.one().name) # 不存在，或有多行记录时会抛出异常
    print(query.filter(User.id == 2).first().name)
    print(query.get(2).name) # 以主键获取，等效于上句
    print(query.filter('id = 2').first().name) # 支持字符串
    query2 = session.query(User.name)
    print(query2.all()) # 每行是个元组
    print(query2.limit(1).all()) # 最多返回 1 条记录
    print(query2.offset(1).all()) # 从第 2 条记录开始返回
    print(query2.order_by(User.name).all())
    print(query2.order_by('name').all())
    print(query2.order_by(User.name.desc()).all())
    print(query2.order_by('name desc').all())
    print(session.query(User.id).order_by(User.name.desc(), User.id).all())
    print(query2.filter(User.id == 1).scalar()) # 如果有记录，返回第一条记录的第一个元素
    print(session.query('id').select_from(User).filter('id = 1').scalar())
    print(query2.filter(User.id > 1, User.name != 'a').scalar()) # and
    query3 = query2.filter(User.id > 1) # 多次拼接的 filter 也是 and
    query3 = query3.filter(User.name != 'a')
    print(query3.scalar())
    print(query2.filter(or_(User.id == 1, User.id == 2)).all()) # or
    print(query2.filter(User.id.in_((1, 2))).all()) # in
    query4 = session.query(User.id)
    print(query4.filter(User.name == None).scalar())
    print(query4.filter('name is null').scalar())
    print(query4.filter(not_(User.name == None)).all()) # not
    print(query4.filter(User.name != None).all())
    print(query4.count())
    print(session.query(func.count('*')).select_from(User).scalar())
    print(session.query(func.count('1')).select_from(User).scalar())
    print(session.query(func.count(User.id)).scalar())
    print(session.query(func.count('*')).filter(User.id > 0).scalar()) # filter() 中包含 User，因此不需要指定表
    print(session.query(func.count('*')).filter(User.name == 'a').limit(1).scalar() == 1) # 可以用 limit() 限制 count() 的返回数
    print(session.query(func.sum(User.id)).scalar())
    print(session.query(func.now()).scalar()) # func 后可以跟任意函数名，只要该数据库支持
    print(session.query(func.current_timestamp()).scalar())
    print(session.query(func.md5(User.name)).filter(User.id == 1).scalar())
    # 修删------
    query.filter(User.id == 1).update({User.name: 'c'})
    user = query.get(1)
    print(user.name)
    user.name = 'd'
    session.flush() # 写数据库，但并不提交
    print(query.get(1).name)
    session.delete(user)
    session.flush()
    session.rollback()  # 回滚
    query.filter(User.id == 1).delete()
    session.commit()  #提交，保存到数据库
    print query.get(1)
    session.close()  # 关闭session


#and
print 'And:'
for u in query1.filter(User.id>80,User.age>25).all():
    print 'Id:\t',u.id,'\tname:\t',u.name,'\tage:\t',u.age

#or
print 'Or:'
for u in query1.filter(or_(User.id>88,User.age>29)).all():
    print 'Id:\t',u.id,'\tname:\t',u.name,'\tage:\t',u.age

#in
print 'in:'
for u in query1.filter(User.id.in_((79,81,90))).all():
    print 'Id:\t',u.id,'\tname:\t',u.name,'\tage:\t',u.age

#not in
print 'not in:'
for u in query1.filter(User.id.notin_((81,82,88))).all():
    print 'Id:\t',u.id,'\tname:\t',u.name,'\tage:\t',u.age

#not
print 'not:'
for u in query1.filter(not_(or_(User.name=='Tom',User.name==u'小花'))).all():
    print 'Id:\t',u.id,'\tname:\t',u.name,'\tage:\t',u.age