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

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

Connecting to Freshsales Data

Connecting to Freshsales 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 Freshsales Profile on disk (e.g. C:\profiles\Freshsales.apip). Next, set the ProfileSettings connection property to the connection string for Freshsales (see below).

Freshsales API Profile Settings

Retrieve your API Key from Profile Settings > API Settings in the top-right corner of Freshsales. Your Domain is the subdomain from your portal URL.

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

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

engine = create_engine("api:///?Profile=C:\profiles\Freshsales.apip&ProfileSettings='APIKey=your_api_key&Domain=your_subdomain'")

Execute SQL to Freshsales

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 Account WHERE IsDeleted = 'false'", engine)

Visualize Freshsales Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the Freshsales 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 Freshsales 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\Freshsales.apip&ProfileSettings='APIKey=your_api_key&Domain=your_subdomain'")
df = pandas.read_sql("SELECT Id, Name FROM Account WHERE IsDeleted = 'false'", engine)

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