python-pandas and databases like mysql

Question

The documentation for Pandas has numerous examples of best practices for working with data stored in various formats.

However, I am unable to find any good examples

Answer 1

import the module

import pandas as pd
import oursql

connect

conn=oursql.connect(host="localhost",user="me",passwd="mypassword",db="classicmodels")
sql="Select customerName, city,country from customers order by customerName,country,city"
df_mysql = pd.read_sql(sql,conn)
print df_mysql

That works just fine and using pandas.io.sql frame_works (with the deprecation warning). Database used is the sample database from mysql tutorial.

Answer 2

I prefer to create queries with SQLAlchemy, and then make a DataFrame from it. SQLAlchemy makes it easier to combine SQL conditions Pythonically if you intend to mix and match things over and over.

from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Table
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from pandas import DataFrame
import datetime

# We are connecting to an existing service
engine = create_engine('dialect://user:pwd@host:port/db', echo=False)
Session = sessionmaker(bind=engine)
session = Session()
Base = declarative_base()

# And we want to query an existing table
tablename = Table('tablename', 
    Base.metadata, 
    autoload=True, 
    autoload_with=engine, 
    schema='ownername')

# These are the "Where" parameters, but I could as easily 
# create joins and limit results
us = tablename.c.country_code.in_(['US','MX'])
dc = tablename.c.locn_name.like('%DC%')
dt = tablename.c.arr_date >= datetime.date.today() # Give me convenience or...

q = session.query(tablename).\
            filter(us & dc & dt) # That's where the magic happens!!!

def querydb(query):
    """
    Function to execute query and return DataFrame.
    """
    df = DataFrame(query.all());
    df.columns = [x['name'] for x in query.column_descriptions]
    return df

querydb(q)

Answer 3

pandas.io.sql.frame_query is deprecated. Use pandas.read_sql instead.

Answer 4

For the record, here is an example using a sqlite database:

import pandas as pd
import sqlite3

with sqlite3.connect("whatever.sqlite") as con:
    sql = "SELECT * FROM table_name"
    df = pd.read_sql_query(sql, con)
    print df.shape

Answer 5

As Wes says, io/sql's read_sql will do it, once you've gotten a database connection using a DBI compatible library. We can look at two short examples using the MySQLdb and cx_Oracle libraries to connect to Oracle and MySQL and query their data dictionaries. Here is the example for cx_Oracle:

import pandas as pd
import cx_Oracle

ora_conn = cx_Oracle.connect('your_connection_string')
df_ora = pd.read_sql('select * from user_objects', con=ora_conn)    
print 'loaded dataframe from Oracle. # Records: ', len(df_ora)
ora_conn.close()

And here is the equivalent example for MySQLdb:

import MySQLdb
mysql_cn= MySQLdb.connect(host='myhost', 
                port=3306,user='myusername', passwd='mypassword', 
                db='information_schema')
df_mysql = pd.read_sql('select * from VIEWS;', con=mysql_cn)    
print 'loaded dataframe from MySQL. records:', len(df_mysql)
mysql_cn.close()

Answer 6

MySQL example:

import MySQLdb as db
from pandas import DataFrame
from pandas.io.sql import frame_query

database = db.connect('localhost','username','password','database')
data     = frame_query("SELECT * FROM data", database)