How to Visualize Procore Data in Python with pandas

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData API Driver for Python, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Procore-connected Python applications and scripts for visualizing Procore data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Procore data, execute queries, and visualize the results.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Procore data in Python. When you issue complex SQL queries from Procore, the driver pushes supported SQL operations, like filters and aggregations, directly to Procore and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Procore Data

Connecting to Procore 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.

Start by setting the Profile connection property to the location of the Procore Profile on disk (e.g. C:\profiles\Procore.apip). Next, set the ProfileSettings connection property to the connection string for Procore (see below).

Procore API Profile Settings

To authenticate to Procore, and connect to your own data or to allow other users to connect to their data, you can use the OAuth standard.

First, you will need to register an OAuth application with Procore. You can do so by logging to your Developer Account and going to Create New App. Follow all necessary steps to register your app. First you will need to create a new version of Sandbox Manifest and then promote it to Production in order to get your Production Crendentials. Your Oauth application will be assigned a client id and a client secret.

After setting the following connection properties, you are ready to connect:

• AuthScheme: Set this to OAuth.
• InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to manage the process to obtain the OAuthAccessToken.
• OAuthClientId: Set this to the client_id that is specified in you app settings.
• OAuthClientSecret: Set this to the client_secret that is specified in you app settings.
• CallbackURL: Set this to the Redirect URI that is specified in your app settings

Follow the procedure below to install the required modules and start accessing Procore 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 Procore Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Procore data.

engine = create_engine("api:///?Profile=C:\profiles\Procore.apip&Authscheme=OAuth&OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&CallbackUrl=your_callback_url&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to Procore

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

df = pandas.read_sql("SELECT Id, Name FROM Companies WHERE IsActive = 'true'", engine)

Visualize Procore Data

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

df.plot(kind="bar", x="Id", y="Name")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps and scripts with connectivity to Procore 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("api:///?Profile=C:\profiles\Procore.apip&Authscheme=OAuth&OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&CallbackUrl=your_callback_url&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Id, Name FROM Companies WHERE IsActive = 'true'", engine)

df.plot(kind="bar", x="Id", y="Name")
plt.show()