Importing Bulk CSV Data Into MySQL Using Python

Find the Fastest Way to Import CSV Data Into MySQL Database Using Python

1. Overview

The main objective of this tutorial is to find the best method to import bulk CSV data into MySQL.

2. Prerequisites

Python 3.8.3 : Anaconda download link
MySQL : Download link

sqlalchemy : To install sqlalchemy use the command:

pip install sqlalchemy

3. Prepare or Identify Your Data

To begin, prepare or identify the CSV file that you'd like to import to MySQL database. For example, we loaded iris data from GitHub.

3.1 Import Libraries

import os
import sys
import pandas as pd
import numpy as np
from sqlalchemy import create_engine
from sqlalchemy.exc import SQLAlchemyError
import seaborn as sns
import matplotlib.pyplot as plt
import mysql.connector as msql
from mysql.connector import Error
import csv

3.2 Extract the Data

# Loading data from github
irisData = pd.read_csv('',index_col=False)
df = irisData

3.3 Create a Table in MySQL Database

To connect from Python to a MySQL database, we use sqlalchemy and mysql.connector:

Specify the connection parameters

# Note: please change your database, username & password as per your own values
conn_params_dic = {
    "host"      : "localhost",
    "database"  : "irisdb",
    "user"      : "root",
    "password"  : "sql@123"

Define function to establish connection mysql.connector

# Define a connect function for MySQL database server
def connect(conn_params_dic):
    conn = None
        print('Connecting to the MySQL...........')
        conn = msql.connect(**conn_params_dic)
        print("Connection successfully..................")

except Error as err:
    print("Error while connecting to MySQL", err)
    # set the connection to 'None' in case of error
    conn = None
return conn

Define function to establish connection using Alchemy

# Using alchemy method
connect_alchemy = "mysql+pymysql://%s:%s@%s/%s" % (

def using_alchemy():
    engine = None
        print('Connecting to the MySQL...........')
        engine = create_engine(connect_alchemy)
        print("Connection successfully..................")
    except SQLAlchemyError as e:
        print("Error while connecting to MySQL", err)
        # set the connection to 'None' in case of error
        engine = None
    return engine

Define a function to create table

def create_table(engine):
        # Dropping table iris if exists
        engine.execute("DROP TABLE IF EXISTS iris;")
        sql = '''CREATE TABLE iris(
        sepal_length DECIMAL(2,1) NOT NULL,
        sepal_width DECIMAL(2,1) NOT NULL,
        petal_length DECIMAL(2,1) NOT NULL,
        petal_width DECIMAL(2,1),
        species CHAR(11) NOT NULL
        # Creating a table
        print("iris table is created successfully...............")
    except Error as err:
        print("Error while connecting to MySQL", err)
        # set the connection to 'None' in case of error
        conn = None

4. Running Time

For measuring execution time of each method, we used timeit.

# Example
def run_method(n):
    for i in range(n):
        3 ** nfrom timeit import default_timer as timer
start_time = timer()
end_time = timer()
elapsed = end_time-start_time
print('function took {:.3f} ms'.format((elapsed)*1000.0))

5. Methods

5.1. Using one by one inserts

To establish a baseline we start with the easiest methodology, insert records one by one:

def single_inserts(engine, df, table):
    for i in df.index:
        cols  = ','.join(list(df.columns))
        vals  = [[i,col] for col in list(df.columns)]
        query = "INSERT INTO %s(%s) VALUES(%s,%s,%s,%s,'%s')" % (table, cols, vals[0], vals[1], vals[2],vals[3],vals[4])
    print("single_inserts() done")

5.2. Using execute_many()

The mysql documentation : using executemany

Execute a database operation (query or command) against all parameter tuples or mappings found in the sequence vars_list.

# Define function using cursor.executemany() to insert the dataframe
def execute_many(conn, datafrm, table):

	# Creating a list of tupples from the dataframe values
    tpls = [tuple(x) for x in datafrm.to_numpy()]

    # dataframe columns with Comma-separated
    cols = ','.join(list(datafrm.columns))

    # SQL query to execute
    sql = "INSERT INTO %s(%s) VALUES(%%s,%%s,%%s,%%s,%%s)" % (table, cols)
    cursor = conn.cursor()
        cursor.executemany(sql, tpls)
        print("Data inserted using execute_many() successfully...")
    except Error as e:
        print("Error while inserting to MySQL", e)

5.3. Using_csv_reader()

def using_csv_reader(engine, datafrm, table_name):

        # Here, change your own path to dump the temp file
        datafrm.to_csv('./Learn Python Data Access/iris_bulk.csv', index=False)
        # dataframe columns with Comma-separated
        cols = ','.join(list(datafrm.columns))
        # SQL query to execute
        sql = "INSERT INTO %s(%s) VALUES(%%s,%%s,%%s,%%s,%%s)" % (table_name, cols)
        sql = sql.format(table_name)

# Here, change your own path to read the temp file_**
        with open('./Learn Python Data Access/iris_bulk.csv') as fh:
            #sub_sample = [next(fh) for x in range(records)]
            reader = csv.reader(fh)
            next(reader)  # Skip firt line (headers)
            data = list(reader)
        engine.execute(sql, data)
        print("Data inserted using Using_csv_reader() successfully...")
    except Error as err:
        print("Error while inserting to MySQL", e)

5.4. Using to_sql() (sqlalchemy)

def using_sqlalchemy(engine,datafrm,table):
        datafrm.to_sql(table, con=engine, index=False, if_exists='append',chunksize = 1000)
        print("Data inserted using to_sql()(sqlalchemy) done successfully...")
    except SQLAlchemyError as e:
    error = str(e.__dic__['orig'])
    print("Error while inserting to MySQL", error)

6. Results

conn = connect(conn_params_dic)
engine = using_alchemy()

Define a function to compare the performance of each method

Here, we are running only three methods execute_many, using_csv_reader, and using_sqlalchemy because we know already single inserts will take more time compare to other three. So, if you want to check performance time for single inserts also then you can uncomment below code.

def compare_methods_to_insert_bulk_data(conn,engine,df):
    # Delete records from iris table
    engine.execute("delete from iris where true;")
    print("Data has been deleted from iris table..........")

    # Including single_inserts method
    #methods = [single_inserts, execute_many, using_csv_reader, using_sqlalchemy]
    # Excluding single_inserts method
    methods = [execute_many, using_csv_reader, using_sqlalchemy]

    df_performance = pd.DataFrame(index=range(len(methods)), columns=['Total_Records','Method_Name','Time ()'])k = 0
    for method in methods:
        start_time = timer()
        if method==execute_many:
            method(conn, df, 'iris')
            method(engine, df, 'iris')
        end_time = timer()[k,'Total_Records'] = len(df.index)[k,'Method_Name'] = method.__name__[k,'Time ()'] = end_time-start_time

        # Delete records for the previous method and prepare test for the next method
        engine.execute("delete from iris where true;")
        print("Data has been deleted from iris table........")
        k = k + 1

    return df_performance

Compare the performance of each method for 1000, 5000,10000, 50000,100000,1000000 records.

df = irisData
# Repeating our dataframe 6667 times to get a large test dataframe
bulk_df = pd.concat([df]*6667, ignore_index=True)
print(len(bulk_df.index))df_performance_list = []
for records in [1000,5000,10000,50000,100000,1000000]:
    print("records = %s" % records)
    df_cutoff = bulk_df[0:records]
    df_performance = compare_methods_to_insert_bulk_data(conn,engine,df_cutoff)
    df_performance_list.append(df_performance)method_performances = pd.concat(df_performance_list, axis=0).reset_index()

Visualize the performance of each method

fig, ax = plt.subplots(figsize=(15,10))
for method in method_performances['Method_Name'].unique():
    subset = method_performances[method_performances['Method_Name'] == method]
    ax.plot(subset['Total_Records'], subset['Time ()'], 'o-.', label=method, linewidth=2, markersize=10)plt.xticks([1000, 5000,10000, 50000,100000,1000000])
plt.xlabel('Number of records in dataframe',fontsize=12)
plt.ylabel('Execution Time (second)',fontsize=12)
# Change your own path
plt.savefig(".../Learn Python Data Access/all_methods.png", dpi=600)

# Closing the cursor & connection

7. Conclusion

Now, the main question is what should we use? the answer is it depends. Each method has its own advantages and disadvantages, and is suited for different circumstances. Here, we used very simple data with only five variables. Data can be complex , and system configuration can be different . As per our example, the fastest method is to use execute_many() however, using csv_reader() is also fine method to import the data.

All code for this article is available as a Jupyter Notebook on GitHub.

For PostgreSQL

Comparison of Methods for Importing bulk CSV data Into PostgreSQL Using Python.

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