How to convert SQL Query result to PANDAS Data Structure?

Question

Here's the shortest code that will do the job:

from pandas import DataFrame
df = DataFrame(resoverall.fetchall())
df.columns = resoverall.keys()

You can go fancier and parse the types as in Paul's answer.

Edit: Mar. 2015

As noted below, pandas now uses SQLAlchemy to both read from (read_sql) and insert into (to_sql) a database. The following should work

import pandas as pd

df = pd.read_sql(sql, cnxn)

Previous answer: Via mikebmassey from a similar question

import pyodbc
import pandas.io.sql as psql
    
cnxn = pyodbc.connect(connection_info) 
cursor = cnxn.cursor()
sql = "SELECT * FROM TABLE"
    
df = psql.frame_query(sql, cnxn)
cnxn.close()

If you are using SQLAlchemy's ORM rather than the expression language, you might find yourself wanting to convert an object of type sqlalchemy.orm.query.Query to a Pandas data frame.

The cleanest approach is to get the generated SQL from the query's statement attribute, and then execute it with pandas's read_sql() method. E.g., starting with a Query object called query:

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

Edit 2014-09-30:

pandas now has a read_sql function. You definitely want to use that instead.

Original answer:

I can't help you with SQLAlchemy -- I always use pyodbc, MySQLdb, or psychopg2 as needed. But when doing so, a function as simple as the one below tends to suit my needs:

import decimal

import pyodbc #just corrected a typo here
import numpy as np
import pandas

cnn, cur = myConnectToDBfunction()
cmd = "SELECT * FROM myTable"
cur.execute(cmd)
dataframe = __processCursor(cur, dataframe=True)

def __processCursor(cur, dataframe=False, index=None):
    '''
    Processes a database cursor with data on it into either
    a structured numpy array or a pandas dataframe.

    input:
    cur - a pyodbc cursor that has just received data
    dataframe - bool. if false, a numpy record array is returned
                if true, return a pandas dataframe
    index - list of column(s) to use as index in a pandas dataframe
    '''
    datatypes = []
    colinfo = cur.description
    for col in colinfo:
        if col[1] == unicode:
            datatypes.append((col[0], 'U%d' % col[3]))
        elif col[1] == str:
            datatypes.append((col[0], 'S%d' % col[3]))
        elif col[1] in [float, decimal.Decimal]:
            datatypes.append((col[0], 'f4'))
        elif col[1] == datetime.datetime:
            datatypes.append((col[0], 'O4'))
        elif col[1] == int:
            datatypes.append((col[0], 'i4'))

    data = []
    for row in cur:
        data.append(tuple(row))

    array = np.array(data, dtype=datatypes)
    if dataframe:
        output = pandas.DataFrame.from_records(array)

        if index is not None:
            output = output.set_index(index)

    else:
        output = array

    return output

MySQL Connector

For those that works with the mysql connector you can use this code as a start. (Thanks to @Daniel Velkov)

Used refs:

• Querying Data Using Connector/Python
• Connecting to MYSQL with Python in 3 steps

---

import pandas as pd
import mysql.connector

# Setup MySQL connection
db = mysql.connector.connect(
    host="<IP>",              # your host, usually localhost
    user="<USER>",            # your username
    password="<PASS>",        # your password
    database="<DATABASE>"     # name of the data base
)   

# You must create a Cursor object. It will let you execute all the queries you need
cur = db.cursor()

# Use all the SQL you like
cur.execute("SELECT * FROM <TABLE>")

# Put it all to a data frame
sql_data = pd.DataFrame(cur.fetchall())
sql_data.columns = cur.column_names

# Close the session
db.close()

# Show the data
print(sql_data.head())