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

Use Dash to Build to Web Apps on Pipedrive Data



The CData Python Connector for Pipedrive enables you to create Python applications that use pandas and Dash to build Pipedrive-connected web apps.

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 module, and the Dash framework, you can build Pipedrive-connected web applications for Pipedrive data. This article shows how to connect to Pipedrive with the CData Connector and use pandas and Dash to build a simple web app for visualizing 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 pandas
pip install dash
pip install dash-daq

Visualize Pipedrive Data in Python

Once the required modules and frameworks are installed, we are ready to build our web 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 os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.pipedrive as mod
import plotly.graph_objs as go

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;")

Execute SQL to Pipedrive

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

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

Configure the Web App

With the query results stored in a DataFrame, we can begin configuring the web app, assigning a name, stylesheet, and title.

app_name = 'dash-pipedriveedataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'

Configure the Layout

The next step is to create a bar graph based on our Pipedrive data and configure the app layout.

trace = go.Bar(x=df.PersonName, y=df.UserEmail, name='PersonName')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='Pipedrive Deals Data', barmode='stack')
		})
], className="container")

Set the App to Run

With the connection, app, and layout configured, we are ready to run the app. The last lines of Python code follow.

if __name__ == '__main__':
    app.run_server(debug=True)

Now, use Python to run the web app and a browser to view the Pipedrive data.

python pipedrive-dash.py

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Pipedrive to start building Python apps with connectivity to Pipedrive data. Reach out to our Support Team if you have any questions.



Full Source Code

import os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.pipedrive as mod
import plotly.graph_objs as go

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

df = pd.read_sql("SELECT PersonName, UserEmail FROM Deals WHERE Value = '50000'", cnxn)
app_name = 'dash-pipedrivedataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'
trace = go.Bar(x=df.PersonName, y=df.UserEmail, name='PersonName')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='Pipedrive Deals Data', barmode='stack')
		})
], className="container")

if __name__ == '__main__':
    app.run_server(debug=True)