search
HomeBackend DevelopmentPython Tutorial教大家使用Python SqlAlchemy

本文实例解析Python SqlAlchemy的使用方法,分享给大家供大家参考,具体内容如下

1.初始化连接

from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
engine = create_engine('mysql://pass@localhost/test'echo=True)
DBSession = sessionmaker(bind=engine)
session = DBSession()
ret=session.execute('desc user')
print ret
# print ret.fetchall()
print ret.first()

mysql://root:pass/test
root是用户名 pass密码 test数据库
session相当于MySQLdb里面的游标
first 相当于fetchone
echo=True 会输出所有的sql

2.创建表

from sqlalchemy import Column
from sqlalchemy.types import *
from sqlalchemy.ext.declarative import declarative_base

BaseModel = declarative_base()

from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker

engine = create_engine('mysql://root:Hs2BitqLYKoruZJbT8SV@localhost/test')
DBSession = sessionmaker(bind=engine)


class User(BaseModel):
  __tablename__ = 'user1' # 表名
  user_name = Column(CHAR(30), primary_key=True)
  pwd = Column(VARCHAR(20), default='aaa', nullable=False)
  age = Column(SMALLINT(), server_default='12')
  accout = Column(INT())
  birthday = Column(TIMESTAMP())
  article = Column(TEXT())
  height = Column(FLOAT())


def init_db():
  '''
  初始化数据库
  :return:
  '''
  BaseModel.metadata.create_all(engine)


def drop_db():
  '''
  删除所有数据表
  :return:
  '''
  BaseModel.metadata.drop_all(engine)


drop_db()
init_db()

和django的 ORM一样 一旦表被创建了,修改User类不能改变数据库结构,只能用sql语句或删除表再创建来修改数据库结构
sqlalchemy.types里面有所有的数据字段类型,等于sql类型的大写
default参数是插入数据的时候,sqlalchemy自己处理的,server_default才是让mysql处理的

3.添加记录

user1=User(user_name='lujianxing',accout=1245678)
session.add(user1)
session.commit()

要commit才能起作用

4.更新记录
1).更新单条记录

query = session.query(User) 
user = query.get('lujianxing11')
print user.accout
user.accout='987'
session.flush()

2).更新多条记录

query = session.query(User)
query.filter(User.user_name=='lujianxing2').update({User.age: '15'})
query.filter(User.user_name=='lujianxing2').update({'age': '16'})
query.filter(User.pwd=='aaa').update({'age': '17'})

5.删除记录

query = session.query(User)
user = query.get('lujianxing11')
session.delete(user)
session.flush()

6.查询

query = session.query(User)
print query # 只显示sql语句,不会执行查询
print query[0] # 执行查询
print query.all() # 执行查询
print query.first() # 执行查询
for user in query: # 执行查询
  print user.user_name

如果字段的类型是数字型,查询出来的type也是数字型的,不是字符串
高级一点的查询:

# 筛选
user = query.get(1) # 根据主键获取
print query.filter(User.user_name == 2) # 只显示sql语句,不会执行查询
print query.filter(User.user_name == 'lujianxing').all() # 执行查询
print query.filter(User.user_name == 'lujianxing', User.accout == 1245678, User.age > 10).all() # 执行查询
print query.filter(User.user_name == 'lujianxing').filter(User.accout == 1245678).all()
print query.filter("user_name = 'lujianxing'").all() # 执行查询
print query.filter("user_name = 'lujianxing' and accout=1245678").all() # 执行查询
query2 = session.query(User.user_name) # 返回的结果不是User的实例,而是元组
print query2.all() # 执行查询
print query2.offset(1).limit(1).all() # 等于 limit 1,1
# 排序
print query2.order_by(User.user_name).all() 
print query2.order_by('user_name').all() 
print query2.order_by(User.user_name.desc()).all()
print query2.order_by(User.user_name, User.accout.desc()).all()
print query2.filter("user_name = 'lujianxing' and accout=1245678").count()
# 聚合查询
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()

以上就是关于Python SqlAlchemy的使用方法介绍,希望对大家的学习有所帮助。

Statement
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn
How do you append elements to a Python list?How do you append elements to a Python list?May 04, 2025 am 12:17 AM

ToappendelementstoaPythonlist,usetheappend()methodforsingleelements,extend()formultipleelements,andinsert()forspecificpositions.1)Useappend()foraddingoneelementattheend.2)Useextend()toaddmultipleelementsefficiently.3)Useinsert()toaddanelementataspeci

How do you create a Python list? Give an example.How do you create a Python list? Give an example.May 04, 2025 am 12:16 AM

TocreateaPythonlist,usesquarebrackets[]andseparateitemswithcommas.1)Listsaredynamicandcanholdmixeddatatypes.2)Useappend(),remove(),andslicingformanipulation.3)Listcomprehensionsareefficientforcreatinglists.4)Becautiouswithlistreferences;usecopy()orsl

Discuss real-world use cases where efficient storage and processing of numerical data are critical.Discuss real-world use cases where efficient storage and processing of numerical data are critical.May 04, 2025 am 12:11 AM

In the fields of finance, scientific research, medical care and AI, it is crucial to efficiently store and process numerical data. 1) In finance, using memory mapped files and NumPy libraries can significantly improve data processing speed. 2) In the field of scientific research, HDF5 files are optimized for data storage and retrieval. 3) In medical care, database optimization technologies such as indexing and partitioning improve data query performance. 4) In AI, data sharding and distributed training accelerate model training. System performance and scalability can be significantly improved by choosing the right tools and technologies and weighing trade-offs between storage and processing speeds.

How do you create a Python array? Give an example.How do you create a Python array? Give an example.May 04, 2025 am 12:10 AM

Pythonarraysarecreatedusingthearraymodule,notbuilt-inlikelists.1)Importthearraymodule.2)Specifythetypecode,e.g.,'i'forintegers.3)Initializewithvalues.Arraysofferbettermemoryefficiencyforhomogeneousdatabutlessflexibilitythanlists.

What are some alternatives to using a shebang line to specify the Python interpreter?What are some alternatives to using a shebang line to specify the Python interpreter?May 04, 2025 am 12:07 AM

In addition to the shebang line, there are many ways to specify a Python interpreter: 1. Use python commands directly from the command line; 2. Use batch files or shell scripts; 3. Use build tools such as Make or CMake; 4. Use task runners such as Invoke. Each method has its advantages and disadvantages, and it is important to choose the method that suits the needs of the project.

How does the choice between lists and arrays impact the overall performance of a Python application dealing with large datasets?How does the choice between lists and arrays impact the overall performance of a Python application dealing with large datasets?May 03, 2025 am 12:11 AM

ForhandlinglargedatasetsinPython,useNumPyarraysforbetterperformance.1)NumPyarraysarememory-efficientandfasterfornumericaloperations.2)Avoidunnecessarytypeconversions.3)Leveragevectorizationforreducedtimecomplexity.4)Managememoryusagewithefficientdata

Explain how memory is allocated for lists versus arrays in Python.Explain how memory is allocated for lists versus arrays in Python.May 03, 2025 am 12:10 AM

InPython,listsusedynamicmemoryallocationwithover-allocation,whileNumPyarraysallocatefixedmemory.1)Listsallocatemorememorythanneededinitially,resizingwhennecessary.2)NumPyarraysallocateexactmemoryforelements,offeringpredictableusagebutlessflexibility.

How do you specify the data type of elements in a Python array?How do you specify the data type of elements in a Python array?May 03, 2025 am 12:06 AM

InPython, YouCansSpectHedatatYPeyFeLeMeReModelerErnSpAnT.1) UsenPyNeRnRump.1) UsenPyNeRp.DLOATP.PLOATM64, Formor PrecisconTrolatatypes.

See all articles

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

Atom editor mac version download

Atom editor mac version download

The most popular open source editor

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

DVWA

DVWA

Damn Vulnerable Web App (DVWA) is a PHP/MySQL web application that is very vulnerable. Its main goals are to be an aid for security professionals to test their skills and tools in a legal environment, to help web developers better understand the process of securing web applications, and to help teachers/students teach/learn in a classroom environment Web application security. The goal of DVWA is to practice some of the most common web vulnerabilities through a simple and straightforward interface, with varying degrees of difficulty. Please note that this software

mPDF

mPDF

mPDF is a PHP library that can generate PDF files from UTF-8 encoded HTML. The original author, Ian Back, wrote mPDF to output PDF files "on the fly" from his website and handle different languages. It is slower than original scripts like HTML2FPDF and produces larger files when using Unicode fonts, but supports CSS styles etc. and has a lot of enhancements. Supports almost all languages, including RTL (Arabic and Hebrew) and CJK (Chinese, Japanese and Korean). Supports nested block-level elements (such as P, DIV),

PhpStorm Mac version

PhpStorm Mac version

The latest (2018.2.1) professional PHP integrated development tool