Load CSV data into MySQL in Python

Not sure what I'm missing here but this code runs without any error message, but there's nothing in the table. I'm loading a CSV values in three columns into mysql table

import csv
import MySQLdb

mydb = MySQLdb.connect(host='localhost',
    user='root',
    passwd='',
    db='mydb')
cursor = mydb.cursor()

csv_data = csv.reader(file('students.csv'))
for row in csv_data:

    cursor.execute('INSERT INTO testcsv(names, \
          classes, mark )' \
          'VALUES("%s", "%s", "%s")', 
          row)
#close the connection to the database.
cursor.close()
print "Done"

Would appreciate if someone else could have a look.

I think you have to do mydb.commit() all the insert into.

Something like this

import csv
import MySQLdb

mydb = MySQLdb.connect(host='localhost',
    user='root',
    passwd='',
    db='mydb')
cursor = mydb.cursor()

csv_data = csv.reader(file('students.csv'))
for row in csv_data:

    cursor.execute('INSERT INTO testcsv(names, \
          classes, mark )' \
          'VALUES("%s", "%s", "%s")', 
          row)
#close the connection to the database.
mydb.commit()
cursor.close()
print "Done"

If you do not have the pandas and sqlalchemy libraries, install them using pip

pip install pandas
pip install sqlalchemy

We can use pandas and sqlalchemy to directly insert into the database

import csv
import pandas as pd
from sqlalchemy import create_engine, types

engine = create_engine('mysql://root:*Enter password here*@localhost/*Enter Databse name here*') # enter your password and database names here

df = pd.read_csv("Excel_file_name.csv",sep=',',quotechar='\'',encoding='utf8') # Replace Excel_file_name with your excel sheet name
df.to_sql('Table_name',con=engine,index=False,if_exists='append') # Replace Table_name with your sql table name

The above answer seems good. But another way of doing this is adding the auto commit option along with the db connect. This automatically commits every other operations performed in the db, avoiding the use of mentioning sql.commit() every time.

 mydb = MySQLdb.connect(host='localhost',
        user='root',
        passwd='',
        db='mydb',autocommit=true)

  from __future__ import print_function
import csv
import MySQLdb

print("Enter  File  To Be Export")
conn = MySQLdb.connect(host="localhost", port=3306, user="root", passwd="", db="database")
cursor = conn.cursor()
#sql = 'CREATE DATABASE test1'
sql ='''DROP TABLE IF EXISTS `test1`; CREATE TABLE test1 (policyID int, statecode varchar(255), county varchar(255))'''
cursor.execute(sql)

with open('C:/Users/Desktop/Code/python/sample.csv') as csvfile:
    reader = csv.DictReader(csvfile, delimiter = ',')
    for row in reader:
        print(row['policyID'], row['statecode'], row['county'])
        # insert
        conn = MySQLdb.connect(host="localhost", port=3306, user="root", passwd="", db="database")
        sql_statement = "INSERT INTO test1(policyID ,statecode,county) VALUES (%s,%s,%s)"
        cur = conn.cursor()
        cur.executemany(sql_statement,[(row['policyID'], row['statecode'], row['county'])])
        conn.escape_string(sql_statement)
        conn.commit()

using pymsql if it helps

import pymysql
import csv
db = pymysql.connect("localhost","root","12345678","data" )

cursor = db.cursor()
csv_data = csv.reader(open('test.csv'))
next(csv_data)
for row in csv_data:
    cursor.execute('INSERT INTO PM(col1,col2) VALUES(%s, %s)',row)

db.commit()
cursor.close()

If it is a pandas data frame you could do:

Sending the data

csv_data.to_sql=(con=mydb, name='<the name of your table>',
  if_exists='replace', flavor='mysql')

to avoid the use of the for.

Fastest way is to use MySQL bulk loader by "load data infile" statement. It is the fastest way by far than any way you can come up with in Python. If you have to use Python, you can call statement "load data infile" from Python itself.