Python class to convert all tables in a database to pandas dataframes

Question

I'm trying to achieve the following. I want to create a python Class that transforms all tables in a database to pandas dataframes.

This is how I do it, which is not very generic...

class sql2df():
    def __init__(self, db, password='123',host='127.0.0.1',user='root'):
        self.db = db

        mysql_cn= MySQLdb.connect(host=host,
                        port=3306,user=user, passwd=password, 
                        db=self.db)

        self.table1 = psql.frame_query('select * from table1', mysql_cn)
        self.table2 = psql.frame_query('select * from table2', mysql_cn)
        self.table3 = psql.frame_query('select * from table3', mysql_cn)

Now I can access all tables like so:

my_db = sql2df('mydb')
my_db.table1

I want something like:

class sql2df():
    def __init__(self, db, password='123',host='127.0.0.1',user='root'):
        self.db = db

        mysql_cn= MySQLdb.connect(host=host,
                        port=3306,user=user, passwd=password, 
                        db=self.db)
        tables = (""" SELECT TABLE_NAME FROM information_schema.TABLES WHERE TABLE_SCHEMA = '%s' """ % self.db)
        <some kind of iteration that gives back all the tables in df as class attributes>

Suggestions are most welcome...

Andy Hayden · Accepted Answer · 2014-04-10 21:20:39Z

5

I would use SQLAlchemy for this:

engine = sqlalchemy.create_engine("mysql+mysqldb://root:[email protected]/%s" % db)

Note the syntax is dialect+driver://username:password@host:port/database.

def db_to_frames_dict(engine):
    meta = sqlalchemy.MetaData()
    meta.reflect(bind=engine)
    tables = meta.sorted_tables
    return {t: pd.read_sql('SELECT * FROM %s' % t.name,
                           engine.raw_connection())
                   for t in tables}
    # Note: frame_query is depreciated in favor of read_sql

This returns a dictionary, but you could equally well have these as class attributes (e.g. by updating the class dict and __getitem__)

class SQLAsDataFrames:
    def __init__(self, engine):
        self.__dict__ = db_to_frames_dict(engine)  # allows .table_name access
    def __getitem__(self, key):                    # allows [table_name] access
        return self.__dict__[key]

In pandas 0.14 the sql code has been rewritten to take engines, and IIRC there is helpers for all tables and for reading all of a table (using read_sql(table_name)).

edited Apr 10, 2014 at 21:20

answered Apr 8, 2014 at 19:32

Andy Hayden

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17 Comments

bowlby Over a year ago

Thanks! Didn't know about sqlalchemy! The problem I'm having still is how to 'dynamically' set a class attributes, eg what you refer to as 'updating the class dict'.

Andy Hayden Over a year ago

@bowlby for attributes: class A: def __init__(self, d): self.__dict__.update(d) where d is the dict above. Maybe def __getitem__(self, key): return self.__dict__[key] for good measure.

dartdog Over a year ago

+Andy Hayden think you are missing the closing ) on the select

dartdog Over a year ago

How do I call sql2df? what is the expected argument? eg sql2df(?) and then how to access the tables?

dartdog Over a year ago

if i try t=[] sql2df(t) I get global name 'sqlalchmey' is not defined

|

dartdog · Accepted Answer · 2014-04-10 21:07:17Z

Here is what I have now: Imports

 import sqlalchemy
 from sqlalchemy import create_engine
 from sqlalchemy import Table, Column,Date, Integer, String, MetaData, ForeignKey
 from sqlalchemy.ext.declarative import declarative_base
 from sqlalchemy.orm import relationship, backref
 import pandas as pd

engine = sqlalchemy.create_engine("mysql+mysqldb://root:[email protected]/%s" % 'surveytest')
def db_to_frames_dict(engine):
    meta = sqlalchemy.MetaData()
    meta.reflect(bind=engine)
    tables = meta.sorted_tables
    return {t: pd.read_sql('SELECT * FROM %s' % t.name, engine.connect())
               for t in tables}
# Note: frame_query is depreciated in favor of read_sql

Have not started to fiddle with this part yet! below:

class SQLAsDataFrames:
    def __init__(self, engine):
        self.__dict__ = db_to_frames_dict(engine)  # allows .table_name access
    def __getitem__(self, key):                    # allows [table_name] access
        return self.__dict__[key]

And the error: Which looks like at least it is trying to get the table names...

frames=db_to_frames_dict(engine)
frames

Error on sql SELECT * FROM tbl_original_survey_master
---------------------------------------------------------------------------
 AttributeError                            Traceback (most recent call last)
 <ipython-input-4-6b0006e1ce47> in <module>()
 ----> 1 frames=db_to_frames_dict(engine)
 >>>> more tracebck
 ---> 53             con.rollback()
 54         except Exception:  # pragma: no cover
 55             pass

 AttributeError: 'Connection' object has no attribute 'rollback'

Thank you for sticking with this!

as above, Connection object is apparently not a connection! stackoverflow.com/questions/20401392/… Apologies again for poor initial state of answer!

bowlby · Accepted Answer · 2014-04-12 19:44:37Z

Thanks for all the help, this is what I ended up using:

import sqlalchemy
from sqlalchemy import create_engine
from sqlalchemy import Table, Column,Date, Integer, String, MetaData, ForeignKey
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import relationship, backref
import pandas as pd

engine = sqlalchemy.create_engine("mysql+mysqldb://root:[email protected]/%s" % 'mydb')

def db_to_frames_dict(engine):
    meta = sqlalchemy.MetaData()
    meta.reflect(engine)
    tables = meta.tables.keys()
    cnx = engine.raw_connection()

    return {t: pd.read_sql('SELECT * FROM %s' % t, cnx )
               for t in tables}

class SQLAsDataFrames:
    def __init__(self, engine):
        self.__dict__ = db_to_frames_dict(engine)  # allows .table_name access
    def __getitem__(self, key):                    # allows [table_name] access
        return self.__dict__[key]

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