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

How to Visualize Pipedrive Data in Python with pandas

Use pandas and other modules to analyze and visualize live Pipedrive data in Python.

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, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Pipedrive-connected Python applications and scripts for visualizing Pipedrive data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Pipedrive data, execute queries, and visualize the results.

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.

Follow the procedure below to install the required modules and start accessing Pipedrive through Python objects.

Install Required Modules

Use the pip utility to install the pandas & Matplotlib modules and the SQLAlchemy toolkit:

pip install pandas
pip install matplotlib
pip install sqlalchemy

Be sure to import the module with the following:

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engine

Visualize 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.

engine = create_engine("pipedrive:///?AuthScheme=Basic&CompanyDomain=MyCompanyDomain&APIToken=MyAPIToken")

Execute SQL to Pipedrive

Use the read_sql function from pandas to execute any SQL statement and store the resultset in a DataFrame.

df = pandas.read_sql("SELECT PersonName, UserEmail FROM Deals WHERE Value = '50000'", engine)

Visualize Pipedrive Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the Pipedrive data. The show method displays the chart in a new window.

df.plot(kind="bar", x="PersonName", y="UserEmail")

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 pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("pipedrive:///?AuthScheme=Basic&CompanyDomain=MyCompanyDomain&APIToken=MyAPIToken")
df = pandas.read_sql("SELECT PersonName, UserEmail FROM Deals WHERE Value = '50000'", engine)

df.plot(kind="bar", x="PersonName", y="UserEmail")