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Python Connector Libraries for Pipedrive Data Connectivity. Integrate Pipedrive with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

Extract, Transform, and Load Pipedrive Data in Python



The CData Python Connector for Pipedrive enables you to create ETL applications and pipelines for Pipedrive data in Python with petl.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Pipedrive and the petl framework, you can build Pipedrive-connected applications and pipelines for extracting, transforming, and loading Pipedrive data. This article shows how to connect to Pipedrive with the CData Python Connector and use petl and pandas to extract, transform, and load 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 driver 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.

After installing the CData Pipedrive Connector, follow the procedure below to install the other required modules and start accessing Pipedrive through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for Pipedrive Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.pipedrive as mod

You can now connect with a connection string. Use the connect function for the CData Pipedrive Connector to create a connection for working with Pipedrive data.

cnxn = mod.connect("AuthScheme=Basic;CompanyDomain=MyCompanyDomain;APIToken=MyAPIToken;")

Create a SQL Statement to Query Pipedrive

Use SQL to create a statement for querying Pipedrive. In this article, we read data from the Deals entity.

sql = "SELECT PersonName, UserEmail FROM Deals WHERE Value = '50000'"

Extract, Transform, and Load the Pipedrive Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Pipedrive data. In this example, we extract Pipedrive data, sort the data by the UserEmail column, and load the data into a CSV file.

Loading Pipedrive Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'UserEmail')

etl.tocsv(table2,'deals_data.csv')

In the following example, we add new rows to the Deals table.

Adding New Rows to Pipedrive

table1 = [ ['PersonName','UserEmail'], ['NewPersonName1','NewUserEmail1'], ['NewPersonName2','NewUserEmail2'], ['NewPersonName3','NewUserEmail3'] ]

etl.appenddb(table1, cnxn, 'Deals')

With the CData Python Connector for Pipedrive, you can work with Pipedrive data just like you would with any database, including direct access to data in ETL packages like petl.

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.



Full Source Code


import petl as etl
import pandas as pd
import cdata.pipedrive as mod

cnxn = mod.connect("AuthScheme=Basic;CompanyDomain=MyCompanyDomain;APIToken=MyAPIToken;")

sql = "SELECT PersonName, UserEmail FROM Deals WHERE Value = '50000'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'UserEmail')

etl.tocsv(table2,'deals_data.csv')

table3 = [ ['PersonName','UserEmail'], ['NewPersonName1','NewUserEmail1'], ['NewPersonName2','NewUserEmail2'], ['NewPersonName3','NewUserEmail3'] ]

etl.appenddb(table3, cnxn, 'Deals')