SQLAlchemy ORM conversion to Pandas DataFrame

SQLAlchemy ORM conversion to Pandas DataFrame

Converting data from an SQLAlchemy ORM query to a Pandas DataFrame is a fairly straightforward process. Here's how you can do it:

1. Install necessary packages

First, make sure you have pandas and SQLAlchemy installed:

pip install pandas sqlalchemy

2. Convert SQLAlchemy query result to DataFrame

Here's a general workflow:

  1. Set up your SQLAlchemy ORM models and session.
  2. Query your database using the ORM.
  3. Convert the result to a Pandas DataFrame.

Here's a simple example:

from sqlalchemy import create_engine, Column, Integer, String, Sequence from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import sessionmaker import pandas as pd Base = declarative_base() # Define a simple ORM model class User(Base): __tablename__ = 'users' id = Column(Integer, Sequence('user_id_seq'), primary_key=True) name = Column(String(50)) age = Column(Integer) # Connect to the database and create a session DATABASE_URL = "sqlite:///./test.db" engine = create_engine(DATABASE_URL) SessionLocal = sessionmaker(bind=engine) # Assuming tables are already created # If not, use `Base.metadata.create_all(bind=engine)` to create them # Start a session session = SessionLocal() # Query the User table users = session.query(User).all() # Convert the result to a DataFrame df = pd.DataFrame([(user.id, user.name, user.age) for user in users], columns=['id', 'name', 'age']) print(df) # Close the session session.close()

In this example, we first define a simple SQLAlchemy ORM model for a users table. Then, we connect to a SQLite database, create a session, and query the User table. The resulting ORM objects are then easily converted into a Pandas DataFrame.

An even simpler alternative, especially for larger datasets, is to use the read_sql function provided by Pandas:

# Using the same session and engine setup from above sql = session.query(User).statement df = pd.read_sql(sql, session.bind) print(df)

This method can be more efficient for larger datasets, as it allows Pandas to directly read the SQL result without manually iterating through ORM objects.

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