SQLAlchemy ORM转换为Pandas DataFrame

This topic hasn’t been addressed in a while, here or elsewhere. Is there a solution converting a SQLAlchemy <Query object> to a pandas DataFrame?

Pandas has the capability to use pandas.read_sql but this requires use of raw SQL. I have two reasons for wanting to avoid it: 1) I already have everything using the ORM (a good reason in and of itself) and 2) I’m using python lists as part of the query (eg: .db.session.query(Item).filter(Item.symbol.in_(add_symbols) where Item is my model class and add_symbols is a list). This is the equivalent of SQL SELECT ... from ... WHERE ... IN.

Is anything possible?

Below should work in most cases:

df = pd.read_sql(query.statement, query.session.bind)

See pandas.read_sql documentation for more information on the parameters.

Just to make this more clear for novice pandas programmers, here is a concrete example,

pd.read_sql(session.query(Complaint).filter(Complaint.id == 2).statement,session.bind) 

Here we select a complaint from complaints table (sqlalchemy model is Complaint) with id = 2

The selected solution didn’t work for me, as I kept getting the error

AttributeError: ‘AnnotatedSelect’ object has no attribute ‘lower’

I found the following worked:

df = pd.read_sql_query(query.statement, engine)

If you want to compile a query with parameters and dialect specific arguments, use something like this:

c = query.statement.compile(query.session.bind)
df = pandas.read_sql(c.string, query.session.bind, params=c.params)

from sqlalchemy import Column, Integer, String, create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

engine = create_engine('postgresql://postgres:postgres@localhost:5432/DB', echo=False)
Base = declarative_base(bind=engine)
Session = sessionmaker(bind=engine)
session = Session()

conn = session.bind

class DailyTrendsTable(Base):

    __tablename__ = 'trends'
    __table_args__ = ({"schema": 'mf_analysis'})

    company_code = Column(DOUBLE_PRECISION, primary_key=True)
    rt_bullish_trending = Column(Integer)
    rt_bearish_trending = Column(Integer)
    rt_bullish_non_trending = Column(Integer)
    rt_bearish_non_trending = Column(Integer)
    gen_date = Column(Date, primary_key=True)

df_query = select([DailyTrendsTable])

df_data = pd.read_sql(rt_daily_query, con = conn)