Export pandas Dataframe or numpy array to MySQL - mysql

Question

I'm using python to export a large matrixs (shape around 3000 * 3000) into MySQL.
Right now I'm using MySQLdb to insert those values but it's too troublesome and too inefficient. Here is my code:
# -*- coding:utf-8 -*-
import MySQLdb
import numpy as np
import pandas as pd
import time
def feature_to_sql_format(df):
df = df.fillna(value='')
columns = list(df.columns)
index = list(df.index)
index_sort = np.reshape([[int(i)] * len(columns) for i in index], (-1)).tolist()
columns_sort = (columns * len(index))
values_sort = df.values.reshape(-1).tolist()
return str(zip(index_sort, columns_sort, values_sort))[1: -1].replace("'NULL'", 'NULL')
if __name__ == '__main__':
t1 = time.clock()
df = pd.read_csv('C:\\test.csv', header=0, index_col=0)
output_string = feature_to_sql_format(df)
sql_CreateTable = 'USE derivative_pool;DROP TABLE IF exists test1;' \
'CREATE TABLE test1(date INT NOT NULL, code VARCHAR(12) NOT NULL, value FLOAT NULL);'
sql_Insert = 'INSERT INTO test (date,code,value) VALUES ' + output_string + ';'
con = MySQLdb.connect(......)
cur = con.cursor()
cur.execute(sql_CreateTable)
cur.close()
cur = con.cursor()
cur.execute(sql_Insert)
cur.close()
con.commit()
con.close()
t2 = time.clock()
print t2 - t1
And it consumes around 274 seconds totally.
I was wondering if there is a simplier way to do this, I thought of export the matrix to csv and then use LOAD DATA INFILE to import, but it's also too complicated.
I noticed that in pandas documentation pandas dataframe has a function to_sql, and in version 0.14 you can set the 'flavor' to 'mysql', that is:
df.to_sql(con=con, name=name, flavor='mysql')
But now my pandas version is 0.19.2 and the flavor is reduced to only 'sqlite'...... And I still tried to use
df.to_sql(con=con, name=name, flavor='sqlite')
and it gives me an error.
Is there any convinient way to do this?

Answer 1

Later pandas versions support SQLalchemy connectors instead of flavor = "mysql"
First, install dependencies:
pip install mysql-connector-python-rf==2.2.2
pip install MySQL-python==1.2.5
pip install SQLAlchemy==1.1.1
Then create the engine:
from sqlalchemy import create_engine
connection_string= "mysql+mysqlconnector://root:#localhost/MyDatabase"
engine = create_engine(connection_string)
Then you can use df.to_sql(...):
df.to_sql('MyTable', engine)
Here are some things you can do in MYSQL to speed up your data load:
SET FOREIGN_KEY_CHECKS = 0;
SET UNIQUE_CHECKS = 0;
SET SESSION tx_isolation='READ-UNCOMMITTED';
SET sql_log_bin = 0;
#LOAD DATA LOCAL INFILE....
SET UNIQUE_CHECKS = 1;
SET FOREIGN_KEY_CHECKS = 1;
SET SESSION tx_isolation='READ-REPEATABLE';