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

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

Connecting to Vault CRM Data

Connecting to Vault CRM 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.

You are ready to connect after specifying the following connection properties:

• Url: The host you see in the URL after you login to your account. For example: https://my-veeva-domain.veevavault.com
• User: The username you use to login to your account.
• Password: The password you use to login to your account.

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

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

engine = create_engine("vaultcrm:///?User=myuser&Password=mypassword&Server=localhost&Database=mydatabase")

Execute SQL to Vault CRM

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

df = pandas.read_sql("SELECT ProductId, ProductName FROM NorthwindProducts WHERE CategoryId = '5'", engine)

Visualize Vault CRM Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Vault CRM to start building Python apps and scripts with connectivity to Vault CRM 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("vaultcrm:///?User=myuser&Password=mypassword&Server=localhost&Database=mydatabase")
df = pandas.read_sql("SELECT ProductId, ProductName FROM NorthwindProducts WHERE CategoryId = '5'", engine)

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