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Email Python Connector
Python Connector Libraries for Email Data Connectivity. Integrate Email with popular Python tools like Pandas, SQLAlchemy, Dash & petl.
How to use SQLAlchemy ORM to access Email Data in Python
Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Email data.
The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for Email and the SQLAlchemy toolkit, you can build Email-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Email data to query, update, delete, and insert Email data.
With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Email data in Python. When you issue complex SQL queries from Email, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to Email and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).
Connecting to Email Data
Connecting to Email data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.
The User and Password properties, under the Authentication section, must be set to valid credentials. The Server must be specified to retrieve emails and the SMTPServer must be specified to send emails.
Follow the procedure below to install SQLAlchemy and start accessing Email through Python objects.
Install Required Modules
Use the pip utility to install the SQLAlchemy toolkit and SQLAlchemy ORM package:
pip install sqlalchemy
pip install sqlalchemy.orm
Be sure to import the appropriate modules:
from sqlalchemy import create_engine, String, Column
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
Model Email Data in Python
You can now connect with a connection string. Use the create_engine function to create an Engine for working with Email data.
NOTE: Users should URL encode the any connection string properties that include special characters. For more information, refer to the SQL Alchemy documentation.
engine = create_engine("email:///?User=username@gmail.com&Password=password&Server=imap.gmail.com&Port=993&SMTP Server=smtp.gmail.com&SMTP Port=465&SSL Mode=EXPLICIT&Protocol=IMAP&Mailbox=Inbox")
Declare a Mapping Class for Email Data
After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the Mailboxes table). Use the sqlalchemy.ext.declarative.declarative_base function and create a new class with some or all of the fields (columns) defined.
base = declarative_base()
class Mailboxes(base):
__tablename__ = "Mailboxes"
Mailbox = Column(String,primary_key=True)
RecentMessagesCount = Column(String)
...
Query Email Data
With the mapping class prepared, you can use a session object to query the data source. After binding the Engine to the session, provide the mapping class to the session query method.
Using the query Method
engine = create_engine("email:///?User=username@gmail.com&Password=password&Server=imap.gmail.com&Port=993&SMTP Server=smtp.gmail.com&SMTP Port=465&SSL Mode=EXPLICIT&Protocol=IMAP&Mailbox=Inbox")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Mailboxes).filter_by(Mailbox="Spam"):
print("Mailbox: ", instance.Mailbox)
print("RecentMessagesCount: ", instance.RecentMessagesCount)
print("---------")
Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.
Using the execute Method
Mailboxes_table = Mailboxes.metadata.tables["Mailboxes"]
for instance in session.execute(Mailboxes_table.select().where(Mailboxes_table.c.Mailbox == "Spam")):
print("Mailbox: ", instance.Mailbox)
print("RecentMessagesCount: ", instance.RecentMessagesCount)
print("---------")
For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.
Insert Email Data
To insert Email data, define an instance of the mapped class and add it to the active session. Call the commit function on the session to push all added instances to Email.
new_rec = Mailboxes(Mailbox="placeholder", Mailbox="Spam")
session.add(new_rec)
session.commit()
Update Email Data
To update Email data, fetch the desired record(s) with a filter query. Then, modify the values of the fields and call the commit function on the session to push the modified record to Email.
updated_rec = session.query(Mailboxes).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.Mailbox = "Spam"
session.commit()
Delete Email Data
To delete Email data, fetch the desired record(s) with a filter query. Then delete the record with the active session and call the commit function on the session to perform the delete operation on the provided records (rows).
deleted_rec = session.query(Mailboxes).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
session.delete(deleted_rec)
session.commit()
Free Trial & More Information
Download a free, 30-day trial of the CData Python Connector for Email to start building Python apps and scripts with connectivity to Email data. Reach out to our Support Team if you have any questions.
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