Sqlalchemy IN clause

python

sqlalchemy

orm

sql

byAnton Shumikhin·Nov 16, 2024

Implement an IN clause using SQLAlchemy's in_() by passing a list to it. Here's an easy-to-follow and executable example:

query = session.query(MyModel).filter(MyModel.my_column.in_([1, 3, 5, 7]))

This corresponds to the following SQL:

SELECT * FROM my_model WHERE my_column IN (1, 3, 5, 7);

Don't forget to adapt MyModel and my_column to match your schema.

Dynamic queries

If your values are dynamic, combine bindparams with in_(), like a combo meal, but better:

from sqlalchemy.sql.expression import bindparam

dynamic_values = [2, 4, 6, 8]  # As dynamic as a yoga instructor
stmt = select([my_table]).where(my_table.c.column_name.in_(bindparam('vals', expanding=True)))
result = connection.execute(stmt, {'vals': dynamic_values})

Perfect for complex scenarios or when your list of values looks more like an epic novel.

ORM vs SQLAlchemy Core

While using ORM, cozy up with session.query(). If you're partying with SQLAlchemy Core, select() is your best friend:

from sqlalchemy import select

stmt = select([my_table.c.my_column]).where(my_table.c.my_column.in_([1, 3, 5, 7]))
result = connection.execute(stmt)

This is essential because the Core allows you to micromanage your SQL far better than ORM. Like being an SQL superhero.

When ORM just isn’t cutting it...

Sometimes, ORM may leave you wanting for more. Then, you can go raw SQL:

from sqlalchemy.sql import text

stmt = text("SELECT * FROM my_table WHERE my_column IN (:param1, :param2, :param3, :param4)")
result = connection.execute(stmt, param1=1, param2=3, param3=5, param4=7)

When you need to execute database-specific, complex queries ORM cannot handle, raw SQL is your broadsword.

SQLAlchemy meets pandas

Are you a pandas fan? Pull your SQL query results directly into a DataFrame with this piece of magic:

import pandas as pd
df = pd.read_sql(session.query(MyModel).filter(MyModel.my_column.in_([1, 3, 5, 7])).statement, session.bind)

Who said magic doesn't exist?

Pragmatic techniques

Organizing your models

When defining models, consider __tablename__ and a declarative base for mapping efficiency:

from sqlalchemy.ext.declarative import declarative_base

Base = declarative_base()

class MyModel(Base):
    __tablename__ = 'my_table'
    id = Column(Integer, primary_key=True)
    my_column = Column(Integer)

This ensures your model is linked properly to its table—like a dog to its leash.

Effective data retrieval

To fetch results without causing a system meltdown:

result = query.all()  # Fetch all at once, not one by one. This isn’t a queue at the bakery!

This method guarantees harmony in your system’s performance.

SQL syntax and parameters formatting

Be sure to implement correct SQL syntax and format parameters appropriately:

stmt = text("SELECT * FROM my_table WHERE my_column IN (:x, :y)")
connection.execute(stmt, x=10, y=20)

Minor plot twist: Each database has specific requirements for placeholders and data types.

Preparing for unexpected turns

Stay prepared for SQLAlchemy exceptions like DataError or OperationalError. Logging the raw SQL query can reveal plot twists for debugging:

from sqlalchemy.exc import SQLAlchemyError

try:
    result = query.all()
except SQLAlchemyError as e:
    log.error(f"Query failed: \n{str(query.statement)}\nError: {str(e)}")