本篇对于Python操作MySQL主要使用两种方式：
- 原生模块 pymsql
- ORM框架 SQLAchemy
一、pymysql
-
- 下载安装
pip install pymysql
- 下载安装
- 2.使用操作
------1.执行SQL
#!/usr/bin/env python
# -*- coding:utf-8 -*-
import pymysql
# 创建连接
conn = pymysql.connect(host='127.0.0.1', port=3306, user='root', passwd='123', db='t1')
# 创建游标
cursor = conn.cursor()
# 执行SQL，并返回收影响行数
effect_row = cursor.execute("update hosts set host = '1.1.1.2'")
# 执行SQL，并返回受影响行数
#effect_row = cursor.execute("update hosts set host = '1.1.1.2' where nid > %s", (1,))
# 执行SQL，并返回受影响行数
#effect_row = cursor.executemany("insert into hosts(host,color_id)values(%s,%s)", [("1.1.1.11",1),("1.1.1.11",2)])
# 提交，不然无法保存新建或者修改的数据
conn.commit()
# 关闭游标
cursor.close()
# 关闭连接
conn.close()
------2.获取新创建数据自增ID
#!/usr/bin/env python
# -*- coding:utf-8 -*-
import pymysql
conn = pymysql.connect(host='127.0.0.1', port=3306, user='root', passwd='123', db='t1')
cursor = conn.cursor()
cursor.executemany("insert into hosts(host,color_id)values(%s,%s)", [("1.1.1.11",1),("1.1.1.11",2)])
conn.commit()
cursor.close()
conn.close()
# 获取最新自增ID
new_id = cursor.lastrowid
------3.获取查询数据
#!/usr/bin/env python
# -*- coding:utf-8 -*-
import pymysql
conn = pymysql.connect(host='127.0.0.1', port=3306, user='root', passwd='123', db='t1')
cursor = conn.cursor()
cursor.execute("select * from hosts")
# 获取第一行数据
row_1 = cursor.fetchone()
# 获取前n行数据
# row_2 = cursor.fetchmany(3)
# 获取所有数据
# row_3 = cursor.fetchall()
conn.commit()
cursor.close()
conn.close()
注：在fetch数据时按照顺序进行，可以使用cursor.scroll(num,mode)来移动游标位置，如：
- cursor.scroll(1,mode='relative') # 相对当前位置移动
- cursor.scroll(2,mode='absolute') # 相对绝对位置移动
------ 4.fetch数据类型
关于默认获取的数据是元祖类型，如果想要或者字典类型的数据，即：
#!/usr/bin/env python
# -*- coding:utf-8 -*-
import pymysql
conn = pymysql.connect(host='127.0.0.1', port=3306, user='root', passwd='123', db='t1')
# 游标设置为字典类型
cursor = conn.cursor(cursor=pymysql.cursors.DictCursor)
r = cursor.execute("call p1()")
result = cursor.fetchone()
conn.commit()
cursor.close()
conn.close()
二、SQLAchemy
SQLAlchemy是Python编程语言下的一款ORM框架，该框架建立在数据库API之上，使用关系对象映射进行数据库操作，简言之便是：将对象转换成SQL，然后使用数据API执行SQL并获取执行结果
安装：pip install SQLAlchemy
image.png
SQLAlchemy本身无法操作数据库，其必须以来pymsql等第三方插件，Dialect用于和数据API进行交流，根据配置文件的不同调用不同的数据库API，从而实现对数据库的操作，如：
MySQL-Python
mysql+mysqldb://:@[:]/
pymysql
mysql+pymysql://:@/[?]
MySQL-Connector
mysql+mysqlconnector://:@[:]/
cx_Oracle
oracle+cx_oracle://user:pass@host:port/dbname[?key=value&key=value...]
更多详见：http://docs.sqlalchemy.org/en/latest/dialects/index.html
1、SQLAchemy的基本使用
- 创建表
import sqlalchemy
from sqlalchemy import create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, Integer, String
engine = create_engine("mysql+pymysql://root:123456@localhost/testdb",
encoding='utf-8', echo=True)
Base = declarative_base() #生成orm基类
class User(Base):
__tablename__ = 'user' #表名
id = Column(Integer, primary_key=True)
name = Column(String(32))
password = Column(String(64))
Base.metadata.create_all(engine) #创建表结构
除上面的创建之外，还有一种创建表的方式，虽不常用，但还是看看吧
from sqlalchemy import Table, MetaData, Column, Integer, String, ForeignKey
from sqlalchemy.orm import mapper
metadata = MetaData()
user = Table('user', metadata,
Column('id', Integer, primary_key=True),
Column('name', String(50)),
Column('fullname', String(50)),
Column('password', String(12))
)
class User(object):
def __init__(self, name, fullname, password):
self.name = name
self.fullname = fullname
self.password = password
mapper(User, user)
#the table metadata is created separately with the Table construct,
then associated with the User class via the mapper() function
事实上，我们用第一种方式创建的表就是基于第2种方式的再封装。
- 新增
from sqlalchemy.orm import sessionmaker, relationship
Session_class = sessionmaker(bind=engine)
#创建与数据库的会话session class ,注意,这里返回给session的是个class,不是实例
Session = Session_class() #生成session实例
user_obj = User(name="alex",password="123456") #生成你要创建的数据对象
print(user_obj.name,user_obj.id) #此时还没创建对象呢，不信你打印一下id发现还是None
Session.add(user_obj) #把要创建的数据对象添加到这个session里， 一会统一创建
print(user_obj.name,user_obj.id) #此时也依然还没创建
Session.commit() #现此才统一提交，创建数据
- 查询
my_user = Session.query(User).filter_by(name="alex").first()
#这样查询出来的不是直接的数据是一个对象
print(my_user)#<__main__.User object at 0x105b4ba90>
#所以再经一轮提取才能获得数据
print(my_user.id,my_user.name,my_user.password)
如果想查询出来直接是数据的话，可以通过修改类的定义来返回
def __repr__(self):
return "" % (
self.name, self.password)
- 修改
修改就是先查询出将要修改的内容，然后直接重新对其赋值，这样就能达到修改的目的。
my_user = Session.query(User).filter_by(name="alex").first()
my_user.name = "Alex Li"
Session.commit()
- 回滚
my_user = Session.query(User).filter_by(id=1).first()
my_user.name = "Jack"
fake_user = User(name='Rain', password='12345')
Session.add(fake_user)
#这时看session里有你刚添加和修改的数据
print(Session.query(User).filter(User.name.in_(['Jack','rain'])).all() )
#此时你rollback一下
Session.rollback()
#再查就发现刚才添加的数据没有了。
print(Session.query(User).filter(User.name.in_(['Jack','rain'])).all() )
# Session
# Session.commit()
- 获取所有数据
print(Session.query(User.name,User.id).all()) - 多条件查询
objs = Session.query(User).filter(User.id>0).filter(User.id<7).all()
上面2个filter的关系相当于 user.id >1 AND user.id <7 的效果 - 统计和分组
#统计
Session.query(User).filter(User.name.like("Ra%")).count()
#分组
from sqlalchemy import func
print(Session.query(func.count(User.name),User.name).group_by(User.name).all() )
#相当于原生sql为
select count(user.name) AS count_1, user.name AS user_name
FROM user GROUP BY user.name
- 外键关联
我们创建一个addresses表，跟user表关联
from sqlalchemy import Column, Integer, String, ForeignKey
from sqlalchemy.orm import relationship
class Address(Base):
__tablename__ = 'addresses'
id = Column(Integer, primary_key=True)
email_address = Column(String(32), nullable=False)
user_id = Column(Integer, ForeignKey('user.id'))
user = relationship("User", backref="addresses")
#这个nb，允许你在user表里通过backref字段反向查出所有它在addresses表里的关联项
def __repr__(self):
return "" % self.email_address
表创建好后，我们可以这样反查试试
obj = Session.query(User).first()
for i in obj.addresses: #通过user对象反查关联的addresses记录
print(i)
addr_obj = Session.query(Address).first()
print(addr_obj.user.name) #在addr_obj里直接查关联的user表
创建关联对象
obj = Session.query(User).filter(User.name=='rain').all()[0]
print(obj.addresses)
obj.addresses = [Address(email_address="[email protected]"), #添加关联对象
Address(email_address="[email protected]")]
Session.commit()
2、多外键关联
下表中，Customer表有2个字段都关联了Address表
from sqlalchemy import Integer, ForeignKey, String, Column
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import relationship
Base = declarative_base()
class Customer(Base):
__tablename__ = 'customer'
id = Column(Integer, primary_key=True)
name = Column(String)
billing_address_id = Column(Integer, ForeignKey("address.id"))
shipping_address_id = Column(Integer, ForeignKey("address.id"))
billing_address = relationship("Address")
shipping_address = relationship("Address")
class Address(Base):
__tablename__ = 'address'
id = Column(Integer, primary_key=True)
street = Column(String)
city = Column(String)
state = Column(String)
创建表结构是没有问题的，但你Address表中插入数据时会报下面的错.
sqlalchemy.exc.AmbiguousForeignKeysError: Could not determine join
condition between parent/child tables on relationship
Customer.billing_address - there are multiple foreign key
paths linking the tables. Specify the 'foreign_keys' argument,
providing a list of those columns which should be
counted as containing a foreign key reference to the parent table.
解决办法如下:
class Customer(Base):
__tablename__ = 'customer'
id = Column(Integer, primary_key=True)
name = Column(String)
billing_address_id = Column(Integer, ForeignKey("address.id"))
shipping_address_id = Column(Integer, ForeignKey("address.id"))
billing_address = relationship("Address", foreign_keys=[billing_address_id])
shipping_address = relationship("Address", foreign_keys=[shipping_address_id])
这样sqlachemy就能分清哪个外键是对应哪个字段了
3、多对多关系
现在来设计一个能描述“图书”与“作者”的关系的表结构，需求是
- 一本书可以有好几个作者一起出版
- 一个作者可以写好几本书
#一本书可以有多个作者，一个作者又可以出版多本书
from sqlalchemy import Table, Column, Integer,String,DATE, ForeignKey
from sqlalchemy.orm import relationship
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
Base = declarative_base()
book_m2m_author = Table('book_m2m_author', Base.metadata,
Column('book_id',Integer,ForeignKey('books.id')),
Column('author_id',Integer,ForeignKey('authors.id')),
)
class Book(Base):
__tablename__ = 'books'
id = Column(Integer,primary_key=True)
name = Column(String(64))
pub_date = Column(DATE)
authors = relationship('Author',secondary=book_m2m_author,backref='books')
def __repr__(self):
return self.name
class Author(Base):
__tablename__ = 'authors'
id = Column(Integer, primary_key=True)
name = Column(String(32))
def __repr__(self):
return self.name
接下来创建几本书和作者
Session_class = sessionmaker(bind=engine)
#创建与数据库的会话session class ,注意,这里返回给session的是个class,不是实例
s = Session_class() #生成session实例
b1 = Book(name="Python入门到放弃")
b2 = Book(name="精通Python72式")
b3 = Book(name="MYSQL入门到装逼")
b4 = Book(name="C#学习")
a1 = Author(name="Alex")
a2 = Author(name="Jack")
a3 = Author(name="Rain")
b1.authors = [a1,a2]
b2.authors = [a1,a2,a3]
s.add_all([b1,b2,b3,b4,a1,a2,a3])
s.commit()
此时，手动连上mysql，分别查看这3张表，你会发现，book_m2m_author中自动创建了多条纪录用来连接book和author表
mysql> select * from books;
+----+------------------+----------+
| id | name | pub_date |
+----+------------------+----------+
| 1 | Python入门到放弃 | NULL |
| 2 | 精通Python72式 | NULL |
| 3 | MYSQL入门到装逼 | NULL |
| 4 | C#学习 | NULL |
+----+------------------+----------+
4 rows in set (0.00 sec)
mysql> select * from authors;
+----+------+
| id | name |
+----+------+
| 10 | Alex |
| 11 | Jack |
| 12 | Rain |
+----+------+
3 rows in set (0.00 sec)
mysql> select * from book_m2m_author;
+---------+-----------+
| book_id | author_id |
+---------+-----------+
| 2 | 10 |
| 2 | 11 |
| 2 | 12 |
| 1 | 10 |
| 1 | 11 |
+---------+-----------+
5 rows in set (0.00 sec)
此时，我们去用orm查一下数据
print('--------通过书表查关联的作者---------')
book_obj = s.query(Book).filter_by(name="Python入门到放弃").first()
print(book_obj.name, book_obj.authors)
print('--------通过作者表查关联的书---------')
author_obj =s.query(Author).filter_by(name="Alex").first()
print(author_obj.name , author_obj.books)
s.commit()
输出如下:
--------通过书表查关联的作者---------
Python入门到放弃 [Alex, Jack]
--------通过作者表查关联的书---------
Alex [精通Python72式, Python入门到放弃]
- 多对多删除
删除数据时不用管boo_m2m_authors ， sqlalchemy会自动帮你把对应的数据删除 - 通过书删除作者
author_obj =s.query(Author).filter_by(name="Jack").first()
book_obj = s.query(Book).filter_by(name="精通Python72式").first()
book_obj.authors.remove(author_obj) #从一本书里删除一个作者
s.commit()
- 直接删除作者
删除作者时，会把这个作者跟所有书的关联关系数据也自动删除
author_obj =s.query(Author).filter_by(name="Alex").first()
# print(author_obj.name , author_obj.books)
s.delete(author_obj)
s.commit()
- 处理中文
sqlalchemy设置编码字符集一定要在数据库访问的URL上增加charset=utf8，否则数据库的连接就不是utf8的编码格式:
eng = create_engine('mysql://root:root@localhost:3306/test2?charset=utf8',echo=True)