Discover how a bimodal integration strategy can address the major data management challenges facing your organization today.
Get the Report →
Ready to get started?
Download a free trial of the Pipedrive Connector to get started:
Download NowLearn more:
Pipedrive Python Connector
Python Connector Libraries for Pipedrive Data Connectivity. Integrate Pipedrive with popular Python tools like Pandas, SQLAlchemy, Dash & petl.
How to use SQLAlchemy ORM to access Pipedrive Data in Python
Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Pipedrive data.
The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for Pipedrive and the SQLAlchemy toolkit, you can build Pipedrive-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Pipedrive data to query, update, delete, and insert Pipedrive data.
With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Pipedrive data in Python. When you issue complex SQL queries from Pipedrive, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to Pipedrive and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).
Connecting to Pipedrive Data
Connecting to Pipedrive 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.
Follow the procedure below to install SQLAlchemy and start accessing Pipedrive 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 Pipedrive Data in Python
You can now connect with a connection string. Use the create_engine function to create an Engine for working with Pipedrive 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("pipedrive:///?AuthScheme=Basic&CompanyDomain=MyCompanyDomain&APIToken=MyAPIToken")
Declare a Mapping Class for Pipedrive 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 Deals 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 Deals(base):
__tablename__ = "Deals"
PersonName = Column(String,primary_key=True)
UserEmail = Column(String)
...
Query Pipedrive 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("pipedrive:///?AuthScheme=Basic&CompanyDomain=MyCompanyDomain&APIToken=MyAPIToken")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Deals).filter_by(Value="50000"):
print("PersonName: ", instance.PersonName)
print("UserEmail: ", instance.UserEmail)
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
Deals_table = Deals.metadata.tables["Deals"]
for instance in session.execute(Deals_table.select().where(Deals_table.c.Value == "50000")):
print("PersonName: ", instance.PersonName)
print("UserEmail: ", instance.UserEmail)
print("---------")
For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.
Insert Pipedrive Data
To insert Pipedrive 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 Pipedrive.
new_rec = Deals(PersonName="placeholder", Value="50000")
session.add(new_rec)
session.commit()
Update Pipedrive Data
To update Pipedrive 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 Pipedrive.
updated_rec = session.query(Deals).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.Value = "50000"
session.commit()
Delete Pipedrive Data
To delete Pipedrive 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(Deals).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 Pipedrive to start building Python apps and scripts with connectivity to Pipedrive data. Reach out to our Support Team if you have any questions.
Data Connectors
ETL/ ELT Solutions
Cloud & API Connectivity
OEM & Custom Drivers