How to Visualize Factorial 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 Factorial-connected Python applications and scripts for visualizing Factorial data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Factorial data, execute queries, and visualize the results.

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

Connecting to Factorial Data

Connecting to Factorial 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.

Authentication

Factorial uses OAuth 2.0 for authentication to connect to your HR data or to allow other users to connect to their data.

Using OAuth Authentication

To connect using OAuth, follow these steps:

1. Navigate to your Factorial admin panel and create a new OAuth application.
2. Copy the Client ID and Client Secret from your application configuration.
3. Configure the following connection properties:

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

• AuthScheme: Set this to OAuth.
• OAuthClientId: Set this to your OAuth Client ID.
• OAuthClientSecret: Set this to your OAuth Client Secret.
• Scope: Set this to specify the data access permissions (default: "read write").

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

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

engine = create_engine("api:///?Profile=C:\profiles\Factorial.apip&AuthScheme=OAuth&OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&CallbackUrl=your_callback_url")

Execute SQL to Factorial

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

df = pandas.read_sql("SELECT , FROM Agreements WHERE ProcessId = '123'", engine)

Visualize Factorial Data

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

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

👁 Factorial data in a Python plot (Salesforce is shown).

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 Factorial data. Reach out to our Support Team if you have any questions.

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Full Source Code

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

engine = create_engine("api:///?Profile=C:\profiles\Factorial.apip&AuthScheme=OAuth&OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&CallbackUrl=your_callback_url")
df = pandas.read_sql("SELECT , FROM Agreements WHERE ProcessId = '123'", engine)

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