Alternative MySQL Querying

testing_mysql_queries.py

import json
import mysql.connector
import numpy as np
import pandas as pd
from pandas import Series, DataFrame

# p as path, c as connection
with open('/Users/path/to/database/keys/prod_db.json') as p:
    c = json.load(p)
    p.close()

query_users = """
SELECT users.id,users.attributes
FROM users
WHERE users.created_at > '2013-12-31 23:59:59'
"""

# accepts query and list of column names, returns dataframe
def get_mysql_data(q,cols):
    cnx = mysql.connector.connect(user=c['user'],password=c['password'],host=c['host'],database=c['dbname'])
    cursor = cnx.cursor()
    cursor.execute(q)
    data = cursor.fetchall()
    df = pd.DataFrame(data,columns=cols)
    cursor.close()
    cnx.close()
    return df

users = get_mysql_data(query_users,['user_id','attributes'])

@msure I saw a link to this on Stack Overflow, and just out of interest: is this faster than using the pandas read_sql_query and sqlalchemy?
As I would not expect it to be (and you gave the link on a question about speed)