一、Create engine
Database url规则： dialect+driver://username:password@host:port/database
echo: True表示cmd窗口显示出对应的SQL 脚本信息
1 from sqlalchemy import create_engine
2
3 # Database url: dialect+driver://username:password@host:port/database
4 # eq: mysql+pymysql://purk:max123@local/test
5 # 在内存中创建一个sqllite
6 # engine = create_engine('sqlite:///:memory:', echo=True)
7 # F:/test.db 如果test.db不存在，则创建一个。
8 engine = create_engine('sqlite:///F:/test.db', echo=True)
二、create mapping class
1 from sqlalchemy import Column, Integer, String, Table 2 from sqlalchemy.ext.declarative import declarative_base 3 4 Base = declarative_base() 5 6 7 class Person(Base): 8 __tablename__ = 'person' 9 10 id = Column(Integer, primary_key=True) 11 name = Column(String(50)) 12 13 class User(Base): 14 __tablename__='user' 15 16 id = Column(Integer, primary_key=True) 17 name = Column(String(50))
1 >>> Person.__table__
2 Table('person', MetaData(bind=None), Column('id', Integer(), table=<person>, primary_key=True, nullable=False), Column('name', String(length=50), table=<person>), schema=None)
3
4 >>> Person.metadata is User.metadata
5 True
6
7 >>> Person.metadata is Base.metadata
8 True
传统的mapper configuration 就不介绍了，因为不直观，而且代码量还长，下面只贴出官网的例子
1 from sqlalchemy import Table, MetaData, Column, Integer, String, ForeignKey
2 from sqlalchemy.orm import mapper
3 metadata = MetaData()
4 user = Table('user', metadata,
5 Column('id', Integer, primary_key=True),
6 Column('name', String(50)),
7 Column('fullname', String(50)),
8 Column('password', String(12))
9 )
10
11
12 class User(object):
13
14
15 def __init__(self, name, fullname, password):
16 self.name = name
17 self.fullname = fullname
18 self.password = password
19 mapper(User, user)
三、Column
1. DataType
1) Integer
id = Column(Integer)
2) String
name = Column(String(50))
3) Boolean
gender = Column(Boolean)
1 person = Person(name='purk', gender=0)
2 person1 = Person(name='purk1', gender=11)
3 person2 = Person(name='purk2', gender=-1)
4 # try:
5 # person3 = Person(name='purk3', gender='123')
6 # except Exception as e:
7 # print('boolean类型在数据库中对应的smartint或boolean类型，输入字符串是不对的')
8 db.add_all([person, person1, person2])
9 db.commit()
结果如下
1 person_1 = db.query(Person).filter(Person.name == 'purk').first() 2 person_2 = db.query(Person).filter(Person.name == 'purk1').first() 3 person_3 = db.query(Person).filter(Person.name == 'purk2').first() 4 print(person_1.gender) 5 print(person_2.gender) 6 print(person_3.gender)
查询 结果如下，满足boolean类型的一贯判断，非0即1.
4) Date -> datetime.date()
赋值可以使用python的date对象，也可以直接使用日期的字符串'2016-10-21',不过从数据库里面取出来的结果集该字段一定是python的date类型，这样在json或xml序列化的时候会有问题。
birthday = Column(Date())
5) DateTime -> datetime.datetime() 同 Date.
create_date = Column(DateTime(), default=datetime.now) default: 默认值，相当于在为给定值时赋予的默认值
modify_date = Column(DateTime(), onupdate=datetime.now) onupdate:在每次update时默认赋予的值，注，如果该字段已经被赋值，则不会再用默认值
1 birthday = Column(Date())
2 create_date = Column(DateTime(), default=datetime.now)
3 modify_date = Column(DateTime(), onupdate=datetime.now)
4
5 person = Person(name='purk', gender=True, level='123')
6 person1 = Person(name='purk1', gender=False, level=0)
7 person2 = Person(name='purk2', gender=False, level=1)
8 person3 = Person(name='purk3', gender=False, level=4)
9 person4 = Person(name='purk4', gender=-1, level='medium')
10 db.add_all([person, person1, person2, person3, person4])
11 db.commit()
12
13 db.query(Person).filter(Person.name == 'purk').update({Person.birthday: date.today()})
14 person_1 = db.query(Person).filter(Person.name == 'purk1').first()
15 person_1.birthday = '2016-10-27'
16 person_1.modify_date = '2016-10-27 15:00:05' #update时给定值
17 db.merge(person_1)
18 db.commit()
结果如下
6) Enum
level_list = ('low', 'medium', 'high')
level = Column(Enum(*level_list))
1 person = Person(name='purk', gender=True, level='123') 2 person1 = Person(name='purk1', gender=False, level=0) 3 person2 = Person(name='purk2', gender=False, level=1) 4 person3 = Person(name='purk3', gender=False, level=4) 5 person4 = Person(name='purk4', gender=-1, level='medium')
Enum的index是从1开始的，越界的或者值不在枚举列中的都保存为null了
7）Float
menoy = Column(Float())
8) Unicode
data = Column(Unicode(200)) #目前测试的是Unicode 和String是一样样的，值得注意的是这两类型赋值可以是Byte类型的。
1 name = Column(String(50)) 2 data = Column(Unicode(200)) 3 4 person = Person(name='purk撒旦法撒旦法'.encode(), gender=True, level='123', data='asdf123') 5 person1 = Person(name='purk1', gender=False, level=0, data='asdf123是打发斯蒂芬'.encode()) 6 7 person_1 = db.query(Person).filter(Person.name == 'purk1').first() 8 print(person_1.data)
结果是
主要且常用的type我总结了一下，不常用的和我没研究懂得就pass咯。。。
来源：https://www.cnblogs.com/Purk/p/5997703.html