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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 How to Write Python Objects to a Database for use with Tableau
1. Get Some Data
Start by accessing some data. In this example we read some data as a Pandas dataframe.
Step1:  2. Put the Data in a Database
SQLAlechemy supports SQLite, Postgresql, MySQL, Oracle, MS-SQL, Firebird, Sybase and others
SQLAlchemy is the Python SQL toolkit and Object Relational Mapper that gives Python users the full power and flexibility of SQL. This example shows how a Python user can connect to a MySQL database, and write a Pandas dataframe as a table in the DB.
Step2:  Write the dataframe as a table called 'account_info'
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Python Code:
import pandas as pd
import os
s3file = 'https://dsclouddata.s3.amazonaws.com/churn.csv'
churnDF = pd.read_csv(s3file, delimiter=',')
churnDF.head(5)
Explanation: How to Write Python Objects to a Database for use with Tableau
1. Get Some Data
Start by accessing some data. In this example we read some data as a Pandas dataframe.
End of explanation
# Install MySQLdb, the interface to the popular MySQL database server for Python for use with SQLalchemy
!sudo apt-get install python-mysqldb
!pip install sqlalchemy
from sqlalchemy import create_engine
#Create these environment variables in your DataScience.com project or user profile
mySQL_Username = os.environ['BRETT_MYSQL_USERNAME']
mySQL_Password = os.environ['BRETT_MYSQL_PASSWORD']
mySQL_IP = os.environ['BRETT_MYSQL_IP']
#Create a connection to the MySQL database
engine = create_engine("mysql+mysqldb://"+mySQL_Username+":"+mySQL_Password+"@"+mySQL_IP+"/customers")
Explanation: 2. Put the Data in a Database
SQLAlechemy supports SQLite, Postgresql, MySQL, Oracle, MS-SQL, Firebird, Sybase and others
SQLAlchemy is the Python SQL toolkit and Object Relational Mapper that gives Python users the full power and flexibility of SQL. This example shows how a Python user can connect to a MySQL database, and write a Pandas dataframe as a table in the DB.
End of explanation
# Use Pandas.to_sql to write the dataframe as a database table called 'account_info'. This is a useful way to make 
# Python data available to external BI tools such as Tableau
churnDF.to_sql(con=engine, name='account_info', if_exists='replace')
Explanation: Write the dataframe as a table called 'account_info'
End of explanation
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