group_by: according to a field grouping, such as want to be grouped according to age, then count each group how many people
having: further filtering of search results, similar to where SQL statements
Ready to work
Import create_engine SQLAlchemy from, the Column, Integer, String, the Float, the Text, a ForeignKey, the DateTime, the Enum
from sqlalchemy.ext.declarative Import declarative_base
from sqlalchemy.orm Import sessionmaker, Relationship, backref
# database information
Host = '127.0.0.1'
Port = '3306'
database = 'db_to_sqlalchemy'
username = 'the root'
password = '123456'
widget database # + type connection to the database, pymysql used herein
DB_URI = f'mysql + pymysql: // { username}: {password} @ { Host}: {Port} / {Database} '
engine = create_engine (DB_URI) # creation engine
base = declarative_base (engine) # use declarative_base create a base class
the session = sessionmaker (engine) ()
class the User (base):
__tablename__ =' User '
id = Column(Integer, primary_key=True, autoincrement=True)
username = Column(String(50), nullable=False)
age = Column(Integer, default=0)
gender = Column(String(20), default='male')
Base.metadata.drop_all() # 删除所有表
Base.metadata.create_all() # 创建表
user1 = User(username='user1', age=20, gender='male')
user2 = User(username='user2', age=21, gender='female')
user3 = User(username='user3', age=20, gender='secret')
user4 = User(username='user4', age=26, gender='male')
user5 = User(username='user5', age=19, gender='female')
session.add_all([user1, user2, user3, user4, user5])
session.commit()
group_by: The data are grouped according to specific fields, such as: according to age group, the number of statistics for each group
Look at the transformation of SQL statements
The query returns data
HAVING: returns the data to the secondary filter, similar to where, as: age group, the number of statistics for each group, and then delete the selected data older than 25
Look at the transformation of SQL statements
The query returns data
