How to convert SQLAlchemy row object to a Python dict?

You may access the internal __dict__ of a SQLAlchemy object, like the following:

for u in session.query(User).all():
    print u.__dict__

As per @zzzeek in comments:

note that this is the correct answer for modern versions of SQLAlchemy, assuming "row" is a core row object, not an ORM-mapped instance.

for row in resultproxy:
    row_as_dict = row._mapping  # SQLAlchemy 1.4 and greater
    # row_as_dict = dict(row)  # SQLAlchemy 1.3 and earlier

background on row._mapping, new as of SQLAlchemy 1.4: https://docs.sqlalchemy.org/en/stable/core/connections.html#sqlalchemy.engine.Row._mapping

In SQLAlchemy v0.8 and newer, use the inspection system.

from sqlalchemy import inspect

def object_as_dict(obj):
    return {
        c.key: getattr(obj, c.key)
        for c in inspect(obj).mapper.column_attrs
    }

user = session.query(User).first()

d = object_as_dict(user)

Note that .key is the attribute name, which can be different from the column name, e.g. in the following case:

class_ = Column('class', Text)

This method also works for column_property.

rows have an _asdict() function which gives a dict

In [8]: r1 = db.session.query(Topic.name).first()

In [9]: r1
Out[9]: (u'blah')

In [10]: r1.name
Out[10]: u'blah'

In [11]: r1._asdict()
Out[11]: {'name': u'blah'}

as @balki mentioned:

The _asdict() method can be used if you're querying a specific field because it is returned as a KeyedTuple.

In [1]: foo = db.session.query(Topic.name).first()
In [2]: foo._asdict()
Out[2]: {'name': u'blah'}

Whereas, if you do not specify a column you can use one of the other proposed methods - such as the one provided by @charlax. Note that this method is only valid for 2.7+.

In [1]: foo = db.session.query(Topic).first()
In [2]: {x.name: getattr(foo, x.name) for x in foo.__table__.columns}
Out[2]: {'name': u'blah'}

Assuming the following functions will be added to the class User the following will return all key-value pairs of all columns:

def columns_to_dict(self):
    dict_ = {}
    for key in self.__mapper__.c.keys():
        dict_[key] = getattr(self, key)
    return dict_

unlike the other answers all but only those attributes of the object are returned which are Column attributes at class level of the object. Therefore no _sa_instance_state or any other attribute SQLalchemy or you add to the object are included. Reference

EDIT: Forget to say, that this also works on inherited Columns.

hybrid_property extention

If you also want to include hybrid_property attributes the following will work:

from sqlalchemy import inspect
from sqlalchemy.ext.hybrid import hybrid_property

def publics_to_dict(self) -> {}:
    dict_ = {}
    for key in self.__mapper__.c.keys():
        if not key.startswith('_'):
            dict_[key] = getattr(self, key)

    for key, prop in inspect(self.__class__).all_orm_descriptors.items():
        if isinstance(prop, hybrid_property):
            dict_[key] = getattr(self, key)
    return dict_

I assume here that you mark Columns with an beginning _ to indicate that you want to hide them, either because you access the attribute by an hybrid_property or you simply do not want to show them. Reference

Tipp all_orm_descriptors also returns hybrid_method and AssociationProxy if you also want to include them.

Remarks to other answers

Every answer (like 1, 2 ) which based on the __dict__ attribute simply returns all attributes of the object. This could be much more attributes then you want. Like I sad this includes _sa_instance_state or any other attribute you define on this object.

Every answer (like 1, 2 ) which is based on the dict() function only works on SQLalchemy row objects returned by session.execute() not on the classes you define to work with, like the class User from the question.

The solving answer which is based on row.__table__.columns will definitely not work. row.__table__.columns contains the column names of the SQL Database. These can only be equal to the attributes name of the python object. If not you get an AttributeError. For answers (like 1, 2 ) based on class_mapper(obj.__class__).mapped_table.c it is the same.

with sqlalchemy 1.4

session.execute(select(User.id, User.username)).mappings().all()
>> [{'id': 1, 'username': 'Bob'}, {'id': 2, 'username': 'Alice'}]

Old question, but since this the first result for "sqlalchemy row to dict" in Google it deserves a better answer.

The RowProxy object that SqlAlchemy returns has the items() method: http://docs.sqlalchemy.org/en/latest/core/connections.html#sqlalchemy.engine.RowProxy.items

It simply returns a list of (key, value) tuples. So one can convert a row to dict using the following:

In Python <= 2.6:

rows = conn.execute(query)
list_of_dicts = [dict((key, value) for key, value in row.items()) for row in rows]

In Python >= 2.7:

rows = conn.execute(query)
list_of_dicts = [{key: value for (key, value) in row.items()} for row in rows]

A very simple solution is row._asdict():

data = session.query(Table).all()
[row._asdict() for row in data]

References:

• sqlalchemy.engine.Row._asdict() (v1.4)
• sqlalchemy.util.KeyedTuple._asdict() (v1.3)

Following @balki answer, since SQLAlchemy 0.8 you can use _asdict(), available for KeyedTuple objects. This renders a pretty straightforward answer to the original question. Just, change in your example the last two lines (the for loop) for this one:

for u in session.query(User).all():
   print u._asdict()

This works because in the above code u is an object of type class KeyedTuple, since .all() returns a list of KeyedTuple. Therefore it has the method _asdict(), which nicely returns u as a dictionary.

WRT the answer by @STB: AFAIK, anything that .all() returns is a list of KeypedTuple. Therefore, the above works either if you specify a column or not, as long as you are dealing with the result of .all() as applied to a Query object.

With python 3.8+, we can do this with dataclass, and the asdict method that comes with it:

from dataclasses import dataclass, asdict

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

Base = declarative_base()
engine = create_engine('sqlite:///:memory:', echo=False)


@dataclass
class User(Base):
    __tablename__ = 'users'

    id: int = Column(Integer, primary_key=True)
    name: str = Column(String)
    email = Column(String)

    def __init__(self, name):
        self.name = name
        self.email = '[email protected]'


Base.metadata.create_all(engine)

SessionMaker = sessionmaker(bind=engine)
session = SessionMaker()

user1 = User("anurag")
session.add(user1)
session.commit()

query_result = session.query(User).one()  # type: User
print(f'{query_result.id=:}, {query_result.name=:}, {query_result.email=:}')
# query_result.id=1, query_result.name=anurag, [email protected]

query_result_dict = asdict(query_result)
print(query_result_dict)
# {'id': 1, 'name': 'anurag'}

The key is to use the @dataclass decorator, and annotate each column with its type (the : str part of the name: str = Column(String) line).

Also note that since the email is not annotated, it is not included in query_result_dict.

from sqlalchemy.orm import class_mapper

def asdict(obj):
    return dict((col.name, getattr(obj, col.name))
                for col in class_mapper(obj.__class__).mapped_table.c)

Refer to Alex Brasetvik's Answer, you can use one line of code to solve the problem

row_as_dict = [dict(row) for row in resultproxy]

Under the comment section of Alex Brasetvik's Answer, zzzeek the creator of SQLAlchemy stated this is the "Correct Method" for the problem.

I've found this post because I was looking for a way to convert a SQLAlchemy row into a dict. I'm using SqlSoup... but the answer was built by myself, so, if it could helps someone here's my two cents:

a = db.execute('select * from acquisizioni_motes')
b = a.fetchall()
c = b[0]

# and now, finally...
dict(zip(c.keys(), c.values()))

You could try to do it in this way.

for u in session.query(User).all():
    print(u._asdict())

It use a built-in method in the query object that return a dictonary object of the query object.

references: https://docs.sqlalchemy.org/en/latest/orm/query.html

The expression you are iterating through evaluates to list of model objects, not rows. So the following is correct usage of them:

for u in session.query(User).all():
    print u.id, u.name

Do you realy need to convert them to dicts? Sure, there is a lot of ways, but then you don't need ORM part of SQLAlchemy:

result = session.execute(User.__table__.select())
for row in result:
    print dict(row)

Update: Take a look at sqlalchemy.orm.attributes module. It has a set of functions to work with object state, that might be useful for you, especially instance_dict().

I've just been dealing with this issue for a few minutes. The answer marked as correct doesn't respect the type of the fields. Solution comes from dictalchemy adding some interesting fetures. https://pythonhosted.org/dictalchemy/ I've just tested it and works fine.

Base = declarative_base(cls=DictableModel)

session.query(User).asdict()
{'id': 1, 'username': 'Gerald'}

session.query(User).asdict(exclude=['id'])
{'username': 'Gerald'}

class User(object):
    def to_dict(self):
        return dict([(k, getattr(self, k)) for k in self.__dict__.keys() if not k.startswith("_")])

That should work.

You can convert sqlalchemy object to dictionary like this and return it as json/dictionary.

Helper functions:

import json
from collections import OrderedDict


def asdict(self):
    result = OrderedDict()
    for key in self.__mapper__.c.keys():
        if getattr(self, key) is not None:
            result[key] = str(getattr(self, key))
        else:
            result[key] = getattr(self, key)
    return result


def to_array(all_vendors):
    v = [ ven.asdict() for ven in all_vendors ]
    return json.dumps(v) 

Driver Function:

def all_products():
    all_products = Products.query.all()
    return to_array(all_products)

Two ways:

1.

for row in session.execute(session.query(User).statement):
    print(dict(row))

2.

selected_columns = User.__table__.columns
rows = session.query(User).with_entities(*selected_columns).all()
for row in rows :
    print(row._asdict())

As OP stated, calling the dict initializer raises an exception with the message "User" object is not iterable. So the real question is how to make a SQLAlchemy Model iterable?

We'll have to implement the special methods __iter__ and __next__, but if we inherit directly from the declarative_base model, we would still run into the undesirable "_sa_instance_state" key. What's worse, is we would have to loop through __dict__.keys() for every call to __next__ because the keys() method returns a View -- an iterable that is not indexed. This would increase the time complexity by a factor of N, where N is the number of keys in __dict__. Generating the dict would cost O(N^2). We can do better.

We can implement our own Base class that implements the required special methods and stores a list of of the column names that can be accessed by index, reducing the time complexity of generating the dict to O(N). This has the added benefit that we can define the logic once and inherit from our Base class anytime we want our model class to be iterable.

class IterableBase(declarative_base()):
    __abstract__ = True

    def _init_keys(self):
        self._keys = [c.name for c in self.__table__.columns]
        self._dict = {c.name: getattr(self, c.name) for c in self.__table__.columns}

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self._init_keys()

    def __setattr__(self, name, value):
        super().__setattr__(name, value)
        if name not in ('_dict', '_keys', '_n') and '_dict' in self.__dict__:
            self._dict[name] = value

    def __iter__(self):
        self._n = 0
        return self

    def __next__(self):
        if self._n >= len(self._keys):
            raise StopIteration
        self._n += 1
        key = self._keys[self._n-1]
        return (key, self._dict[key])

Now the User class can inherit directly from our IterableBase class.

class User(IterableBase):
    __tablename__ = 'users'
    id = Column(Integer, primary_key=True)
    name = Column(String)

You can confirm that calling the dict function with a User instance as an argument returns the desired dictionary, sans "_sa_instance_state". You may have noticed the __setattr__ method that was declared in the IterableBase class. This ensures the _dict is updated when attributes are mutated or set after initialization.

def main():
    user1 = User('Bob')
    print(dict(user1))
    # outputs {'id': None, 'name': 'Bob'}
    user1.id = 42
    print(dict(user1))
    # outputs {'id': 42, 'name': 'Bob'}

if __name__ == '__main__':
    main()

SQlAlchemy 2.x supports dataclasses. Based on the example of the original documentation:

from sqlalchemy.orm import DeclarativeBase
from sqlalchemy.orm import Mapped
from sqlalchemy.orm import mapped_column
from sqlalchemy.orm import MappedAsDataclass


class Base(MappedAsDataclass, DeclarativeBase):
    """subclasses will be converted to dataclasses"""


class User(Base):
    __tablename__ = "user_account"

    id: Mapped[int] = mapped_column(init=False, primary_key=True)
    name: Mapped[str]

you can apply dataclasses.asdict to instances of User:

import dataclasses

print(dataclasses.asdict(User(id=1,name='foo')))
# {'id': 1,'name': 'foo'}

Here is how Elixir does it. The value of this solution is that it allows recursively including the dictionary representation of relations.

def to_dict(self, deep={}, exclude=[]):
    """Generate a JSON-style nested dict/list structure from an object."""
    col_prop_names = [p.key for p in self.mapper.iterate_properties \
                                  if isinstance(p, ColumnProperty)]
    data = dict([(name, getattr(self, name))
                 for name in col_prop_names if name not in exclude])
    for rname, rdeep in deep.iteritems():
        dbdata = getattr(self, rname)
        #FIXME: use attribute names (ie coltoprop) instead of column names
        fks = self.mapper.get_property(rname).remote_side
        exclude = [c.name for c in fks]
        if dbdata is None:
            data[rname] = None
        elif isinstance(dbdata, list):
            data[rname] = [o.to_dict(rdeep, exclude) for o in dbdata]
        else:
            data[rname] = dbdata.to_dict(rdeep, exclude)
    return data

With this code you can also to add to your query "filter" or "join" and this work!

query = session.query(User)
def query_to_dict(query):
        def _create_dict(r):
            return {c.get('name'): getattr(r, c.get('name')) for c in query.column_descriptions}

    return [_create_dict(r) for r in query]

For the sake of everyone and myself, here is how I use it:

def run_sql(conn_String):
  output_connection = engine.create_engine(conn_string, poolclass=NullPool).connect()
  rows = output_connection.execute('select * from db1.t1').fetchall()  
  return [dict(row) for row in rows]

To complete @Anurag Uniyal 's answer, here is a method that will recursively follow relationships:

from sqlalchemy.inspection import inspect

def to_dict(obj, with_relationships=True):
    d = {}
    for column in obj.__table__.columns:
        if with_relationships and len(column.foreign_keys) > 0:
             # Skip foreign keys
            continue
        d[column.name] = getattr(obj, column.name)

    if with_relationships:
        for relationship in inspect(type(obj)).relationships:
            val = getattr(obj, relationship.key)
            d[relationship.key] = to_dict(val) if val else None
    return d

class User(Base):
    __tablename__ = 'users'
    id = Column(Integer, primary_key=True)
    first_name = Column(TEXT)
    address_id = Column(Integer, ForeignKey('addresses.id')
    address = relationship('Address')

class Address(Base):
    __tablename__ = 'addresses'
    id = Column(Integer, primary_key=True)
    city = Column(TEXT)


user = User(first_name='Nathan', address=Address(city='Lyon'))
# Add and commit user to session to create ids

to_dict(user)
# {'id': 1, 'first_name': 'Nathan', 'address': {'city': 'Lyon'}}
to_dict(user, with_relationship=False)
# {'id': 1, 'first_name': 'Nathan', 'address_id': 1}

from copy import copy

def to_record(row):
    record = copy(row.__dict__)
    del record["_sa_instance_state"]
    return record

If not using copy, you might run into errors.

Row objects have an _asdict method that matches tuple's builtin method:

return [row._asdict() for row in result]

You can also use the _mapping property and cast it to a dict:

return [dict(row._mapping) for row in result]

I have a variation on Marco Mariani's answer, expressed as a decorator. The main difference is that it'll handle lists of entities, as well as safely ignoring some other types of return values (which is very useful when writing tests using mocks):

@decorator
def to_dict(f, *args, **kwargs):
  result = f(*args, **kwargs)
  if is_iterable(result) and not is_dict(result):
    return map(asdict, result)

  return asdict(result)

def asdict(obj):
  return dict((col.name, getattr(obj, col.name))
              for col in class_mapper(obj.__class__).mapped_table.c)

def is_dict(obj):
  return isinstance(obj, dict)

def is_iterable(obj):
  return True if getattr(obj, '__iter__', False) else False