Hi All,
Trying something out here for an analytics app here, and I'm not sure any
of this is a good idea. I'm trying to create a data model that's flexible
enough to handle a lot of different types of analysis. From plain CSVs to
time series to survey questions with complex hierarchies. My approach is to
have a pretty plain model and query as needed to construct dataframes that
will end up in pandas and sci-kit learn.
The basic idea is that there's a dataset that has variables associated with
it. Variables have values associated. I want to retain type information on
the values. If I'm reading a file and loading it, I want to store an int as
an int or a datetime as a datetime and not stuff everything into a string
and have to guess what things are based on variable metadata. That's where
I'm kind of stuck. I think I have the multi-table inheritance set up
correctly to store different types of value. I'm totally unsure how to
query without having to know the subclass. I'd like to be able to extract
values for pandas processing with something like this:
vals = Values.value.filter_by(variable_id=123456).all()
without having to know per variable which subclass to choose. I.e., I don't
want to have to do
vals = IntValue.value.filter_by(variable_id=123456).all()
The second part of my question is only relevant if this whole design isn't
hosed, which I'm fine if you tell me that it is and I need to go back to
the drawing board.
The second part is how to extract an entire dataset by ID in a way that
would be comfortable in pandas. If I were doing this in raw SQL, I'd use a
CTE to get the variables by dataset id and use a pivot to create the
data-frame-shaped table. I'm confounded by how to do this in alchemy,
especially with the polymorphic setup.
Here are my current model definitions:
from sqlalchemy import Column, Index, Integer, Text, DateTime, Float,
ForeignKey
from sqlalchemy.orm import relationship
from .meta import Base
class DataSet(Base):
__tablename__ = 'datasets'
id = Column(Integer, primary_key=True)
name = Column(Text, nullable=False)
description = Column(Text, nullable=True)
class Variable(Base):
__tablename__ = 'variables'
id = Column(Integer, primary_key=True)
dataset_id = Column(Integer, ForeignKey('datasets.id'), Nullable=False)
name = Column(Text, nullable=False)
description = Column(Text, nullable=True)
group_var_col_id = Column(Integer, ForeignKey('variables.id'), nullable=
True)
group_var_row_id = Column(Integer, ForeignKey('variables.id'), nullable=
True)
value_map = Column(Text, nullable=True) #change to JSONB when move from
SQLite to Postgres
dataset = relationship('DataSet', backref='variables')
class Value(Base):
__tablename__ = 'values'
id = Column(Integer, primary_key=True)
variable_id = Column(Integer, ForeignKey('variables.id'), Nullable=False
)
observation_id = Column(Text, nullable=False)
value_type = Column(Text, nullable=False)
variable = relationship('Variable', backref='values')
__mapper_args__ = {'polymorphic_on': value_type}
class IntValue(Value):
__tablename__ = 'int_values'
id = Column(None, ForeignKey('values.id'), primary_key=True)
value = Column(Integer, nullable=True)
__mapper_args__ = {'polymorphic_identity': 'int'}
class StringValue(Value):
__tablename__ = 'string_values'
id = Column(None, ForeignKey('values.id'), primary_key=True)
value = Column(Text, nullable=True)
__mapper_args__ = {'polymorphic_identity': 'string'}
class FloatValue(Value):
__tablename__ = 'float_values'
id = Column(None, ForeignKey('values.id'), primary_key=True)
value = Column(Float, nullable=True)
__mapper_args__ = {'polymorphic_identity': 'float'}
class DateTimeValue(Value):
__tablename__ = 'datetime_values'
id = Column(None, ForeignKey('values.id'), primary_key=True)
value = Column(DateTime, nullable=True)
__mapper_args__ = {'polymorphic_identity': 'datetime'}
Thanks in advance for any suggestions you might have!
--
SQLAlchemy -
The Python SQL Toolkit and Object Relational Mapper
http://www.sqlalchemy.org/
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